Improvement and spatial extension of the European Fish Index

Transcription

http://efi-plus.boku.ac.at/
Project no.: 0044096
Project acronym: EFI+
Instrument: STREP
Thematic Priority: Scientific Support to Policies (SSP) - POLICIES-1.5
D 3.4 - Report on development of new metrics for the assessment of all
European rivers including European historical diadromous fish species
distribution
Due date of deliverable: 31.12.2007
Actual submission date: 07.05.2008
Start date of project: 01.01.2007
Duration: 24 Month
Organisation name of lead contractor for this deliverable:
CEMAGREF (HYAX),
3275 Route de Cézanne, CS 40061,
13182 AIX EN PROVENCE Cedex 5, FRANCE
Final version
Project co-funded by the European Commission within the Sixth Framework Programme
(2002-2006)
Dissemination Level
PU
Public
PP
Restricted to other programme participants (including the Commission Services)
RE
Restricted to a group specified by the consortium (including the Commission Services)
CO
Confidential, only for members of the consortium (including the Commission Services)
X
Contents
Task 3.4 Diadromous species distribution
Responsible author: Gertrud Haidvogl, University of natural resources and applied life
sciences, Vienna, Austria.
Task 3.5 Central/Eastern Rivers assessment
Responsible authors: Klaus Battes and Karina Battes, Bacau University, Romania.
Task 3.6 Mediterranean Rivers assessment
Responsible authors: Teresa Ferreira and Pedro Segurado, Instituto superior de
Agronomia, Lisbon, Portugal.
Task 3.7 Large Floodplain Rivers assessment
Responsible author: Christian Wolter, Leibniz-Institute of Freshwater Ecology and
Inland Fisheries, Berlin, Germany.
Task 3.8 Low species diversity rivers assessment
Responsible author: Didier Pont, CEMAGREF Aix en Provence, France.
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Table of Contents
1.
Objective of the task...........................................................................................................................3
2.
Data .......................................................................................................................................................4
2.1.
Selection of diadromous fish to be considered......................................................................4
2.2.
Information sources for historical distribution .....................................................................5
2.2.1.
2.3.
3.
Using historical data on fish species distribution .........................................................6
Collection of data on present distribution..............................................................................7
Methods ................................................................................................................................................7
3.1.
Data collection............................................................................................................................7
4.
Results – historical distribution.........................................................................................................9
5.
Modelling of the potential distribution ..........................................................................................17
6.
References ..........................................................................................................................................18
3
1. Objective of the task
The existing EFI developed in the EU-funded FAME-project showed only very weak response
to continuum interruptions. This was true for the overall index as well as for the ten singular
metrics of the EFI with the exception of the metric for migratory species. Thus, it was one of the
aims of the EFI+ project, to improve the ability of the index to detect pressures on river
continuum by enhancing the data basis and by calculating particular metrics.
With respect to the improvement of the data basis data we integrated in EFI+ a larger number
and more precise pressure variables for river continuum. While in FAME only the connectivity
situation at the river basin scale and at the segment scale was considered in general (existence of a
barrier preventing the upstream migration of diadromous and potamodromous fish), we
integrated in EFI+ altogether seven pressure variables in the central database. One variable
accounted again for the catchment scale (existence of barrier downstream to the sea), six further
variables are related to migration barriers at the segment scale (existence of barriers up/downstream, number of barriers in the segment up-/downstream, distance to next barrier up/downstream).
Apart from the more detailed information on connectivity pressures, we aimed further in
improving the data on fish. It was a characteristic of the FAME data set that long-term impacts
on migratory fish (both, diadromous as well as potamodromous) could not have been considered
since even unimpaired or only minimally disturbed reference and calibration sites may have been
impacted already by the absence of migratory fish due to migration barriers.
In order to improve the information on fish our approach was therefore to reconstruct
“reference conditions” for the distribution of migratory fish species based on historical data. Due
to the enormous amount of work necessary for the preparation of historical data we limited our
data collection to diadromous species, even if data sources and case studies are available for some
potamodromous species from former projects performed especially in Austria, Germany and
France.
Hence, the objective of subtask “continuity disruption” in work-packages two (data collection)
and three (data analyses and modelling) was to compile information on the historical distribution
of diadromous fish species and to compute metrics which can be compared later on with the
present situation. Due to the well known incompleteness of historical information (occurrence
not registered; loss of records, log term human impacts on the occurrence of migratory fish etc.)
we will also analyse a potential distribution of fish species. This is based on models of the
historical presence of diadromous fish species as a factor of environmental characteristics. Due to
the particularities of historical data special modelling techniques have to be used (see for this the
subchapter on used methods). Further analyses will consider the existence of barriers if a species
is absent at present or any other type of pressure that may impact the occurrence of a
diadromous species.
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2. Data
2.1.
Selection of diadromous fish to be considered
In a first step we selected the fish species to be considered for the data search. The prerequisites
for species selection were a broad geographical distribution, (former) commercial interest leading
to a better chance of exact species determination and to more frequent recordings in historical
information sources (see below for the limits of historical data on fish). However, we also tried to
take into account all important European catchments. Thus we also considered diadromous
species endemic to the Danube catchment. Finally, we also agreed to select only fish species
which are obligatory diadromous. This excluded fish species which are potamodromous or even
resident fish in many catchments and undertake diadromous migrations in others. Such situations
occur e.g. for fish species of the Baltic Sea and its connected rivers (species such as Vimba
vimba). Against this later rule we kept Acipenser nudiventris, Acipenser gueldensaedti and
Acipenser naccari for data collection, since it seemed possible for these three Sturgeon species of
the Danube catchment and Adriatic Sea, respectively, to distinguish between potamodromous
and diadromous forms.
Based on these prerequisites we selected the following list of 17 species:
Family
Lampreys:
Sturgeons:
Shads:
Salmonids:
Coregonids:
Eels:
Smelts:
Flounders:
Genus/Species
River lamprey (Lampetra fluviatilis)
Sea lamprey (Petromyzon marinus)
European Atlantic sturgeon (A. sturio/A. oxyrinchus 1 )
Adriatic sturgeon (A. naccari)
Beluga (Huso huso)
Stellate sturgeon (Acipenser stellatus)
Russian sturgeon (Acipenser gueldenstädti)
Ship sturgeon (Acipenser nudiventris)
Allis shad (Alosa alosa)
Twaite shad (Alosa fallax)
Danube shad (Alosa immaculata)
Atlantic salmon (Salmo salar)
Sea trout (Salmo trutta trutta)
Coregonus sp. diadr. (houting, c. whitefish)
Eel (Anguilla Anguilla)
European smelt (Osmerus eperlanus)
Flounder (Platichthys flesus)
According to information about the distribution on native species, which was compiled for 400
European catchments during the FAME project (see Reyjol et al., 2007; trout was only
considered on species and not on sub species level, Sea Trout was not represented in this dataset)
the most frequent species is the Eel. It occurs in 316 of the 400 catchments and in 22 main river
regions and marine areas, respectively. Also the Atlantic salmon has a wide biogeographical
Latest genetic studies show that historically also A. oxyrinchus occurred in Europe; however it is not possible to
distinguish between the two species only based on written historical records; therefore the two species are mentioned
in common
1
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distribution. It is native in 155 catchments, however only in 15 main river regions or marine areas
ranging from rivers of the Bay of Biscay to the Gulf of Riga. The two Lampreys (River and Sea
Lamprey) as well as the two shad species (Alosa alosa and Alosa fallax) are less frequent in terms
of catchments, but the have a broad biogeographical distribution ranging from the Aegean and
Adriatic Sea (Alosa fallax) to the Gulf of Riga (all three species except Alosa alosa). The
European Sturgeon occurs from the Black Sea (however, rare and no longer migrations upstream
to the Danube are recorded) to the Gulf of Riga and is the only Sturgeon species which is
common over all European rivers. All other sturgeon species are endemic to the Danube
catchment and/or to the Adriatic Sea (Huso huso, A. nudiventris, A. gueldenstaedti, A. naccari,
A. stellatus). For the Danube catchment moreover an endemic diadromous shad species, the
Danube Shad (Alosa immaculata/A. pontica), was included. The main focus of the data search
was on these 14 species. Three further species were integrated, even if there was no broad and
detailed knowledge about the availability and reliability of historical information. Diadromous
Coregonids were integrated, even if it was quite unsure whether it will be possible to determine
diadromous forms from residential or potamodromous in historical written records. Moreover,
Flounder and European Smelt were selected.
2.2. Information sources for historical distribution
Data which were used to define the historical distribution of the species mentioned above can be
grouped into three different classes:
1. The Institute of Hydrobiology (BOKU Vienna), the Leibniz-Institute of Freshwater
Ecology and Inland Fisheries (IGB Berlin), CEMAGREF and the Inland Fisheries
Institute (IRS) based the data search on the collection of printed literature from the 19th
and the first decades of the 20th century. As a consequence these data are not consistent.
The long temporal period covered integrates also possible changes of species distribution
due to a change of environmental conditions (e.g. long term climate change) but also
impacts of migration barriers which started to be more numerous and severe in terms of
continuum interruption in the 2nd half of the 19th century, as well as the risk for pollution
and pressure on water quality. Also the possibility of fish stocking has to be respected,
even if stocking may have occurred also before the 19th century. CEMAGREF and IRS
limited the data search to France and Poland, while IGB and BOKU made a Europeanwide literature search and integrated information an all possible rivers to the diadromous
species database (thus covering also Switzerland and the whole Danube catchment
including Romania; as far as possible also Italy).
Data from Hungary were collected from printed literature from the first half of the 20th
century. At this time an impact of weirs seems very likely, however data and information
on the erection of dams were not compiled.
2. For Portugal and Spain the Institute of Agronomy, Lisbon and the University of Madrid
based their search for historical data on fish distribution on systematic archival primary
sources from the late 18th and first half of the 19th century. These data sources were
topographical and socioeconomic descriptions of the country, including information on
fish as well as cadastral inventories including also information on fish. Portugal
performed the data collection together with the Aberta University, Lisbon.
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3. For Finland, Sweden, United Kingdom, Lithuania and The Netherlands present literature
on the historical distribution of fish was used as main information source. Even if this
information can be considered as reliable as the basic documents it was split into an extra
data group since we do not have a complete overview on the historical basic data sources.
2.2.1. Using historical data on fish species distribution
Historical data were collected based on written documents and fish species maps. They have in
comparison with present data several particularities, which must be accounted for.
First, historical information has to be checked in terms of reliability of the data. Even, if we
decided to focus on well known and commercially exploited species, there is the problem of
misclassifications. In our search for diadromous species this can be demonstrated well e.g. for the
Danube catchment. Here the occurrence of river lamprey was recorded in several publications
from the 19th century. At this time the biological knowledge Lampreys was still very vague and
the life cycle of the species was not well investigated. Later on, for the Danube catchment
singular Lamprey species have been determined for the Danube as it is also the case for Sturgeon
and Shad species. However, Lampreys occurring natively in the Danube basin have been
described as single species only in the 20th century (Eudontomyzon danfordi Regan, 1911;
Eudontomyzon mariae (Berg, 1931); Eudontomyzon vladykovi Oliva & Zanandrea, 1959) and
they are not diadromous. Therefore, all information on the occurrence of Lamprey species in the
Danube and it´s tributaries was omitted. The same was the case for information on the
occurrence of Allis shad and Twaite Shad in the Danube. However, in such case we registered the
indicated locations and rivers section as basis for comparison with the modelled potential
distribution of the Black Sea Shad A. immaculata, since all are diadromous species and historical
information about the occurrence of Allis or Twaite Shad may have been a misclassification of
the Danube Shad. Another problem of historical written documents is that information is often
general on a genus level. This can be demonstrated for the distribution also for Sturgeon species
in the Danube catchment, for which many authors refer often only to “Stör”, which is in this
case the general term for the genus (and not the German name for A. sturio). If there was no
additional information allowing a definite determination of one of the Danube sturgeon species
(Beluga, Stellate, Ship and Russian sturgeon) such kind of information was also excluded from
the database.
While it is quite simple to identify misclassifications when it relates to a species outside of it´s
biogeographical range of distribution, it is usually more difficult to detect incorrect information
when it refers to a location within the range of a species´ distribution. In such cases expert
judgement was used to identify doubtful information and to exclude according data. This
appeared e.g. for the information on the occurrence of Allis Shad in the Rhone catchment.
Nevertheless, it can be concluded that in contrast to the use and interpretation of historical fish
data on e.g. Cyprinids the risk for misclassification for diadromous species may be lower due to
their commercial interest (was one of the prerequisites for species selection) and the generally
quite good knowledge about the species (at least in comparison to other fish). Also, the
distinction of Salmo salar and Salmo trutta is in written documents not always clear. While the
distribution of Salmon could have been reconstructed quite well also due to the existence of
precise maps, Sea Trout could have sometimes not be considered, like e.g. in France due to the
uncertainty of data.
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Finally, only very general information for species occurrence on river level without any further
local precision was not integrated but these data were registered to compare the modelled
potential distribution with such indications.
Finally, it has to be stressed that historical fish distribution data based for a large part on sources
from the 2nd half of the 19th century and even on data from the first decades of the 20th century
are not showing a “pristine” or “natural” fish community. Pressures on the occurrence of
diadromous fish species are already recorded for the later Middle Ages, e.g. as a result of large
number of weirs for energy production in many rivers. While a complete interruption of
migration may have been more exceptional at this period, river regulation works, deteriorating
water quality and overfishing became a more frequent phenomenon from the 18th and especially
from the 19th century onwards (see e.g. Hoffmann, 1996, 1999, for central Europe, Balon, 1968,
for the Beluga Sturgeon in Hungary or Lenders, 2003, for the Rhine). As a consequence the
migration distances of diadromous fish may have been already reduced in the 19th century,
although traditional weirs were often constructed in a way that they enabled fish migration, not at
least due to the importance of fisheries as commercial factor.
Another reason for possible changes in the distribution of diadromous fish in the 19th century is
stocking of species and transfer of fish between different catchments or sub-catchments via
navigation channels. This fact has to be considered e.g. for Eel or Salmon, for which written
historical source refer regularly to stocking or to the interruption of migration routes through
weirs. The advantage for our data search was that these pressures were recognized and discussed
intensely and in many cases historical sources refer to previous migration routes which enabled us
to compensate the pressures at least for a part.
Finally, we have to take into account that historical climate changes affected the distribution of
migratory fish. In order to account for these facts we will use air temperature data from the
beginning of the 20th century for modelling of the potential distribution (see Mitchell, et al.,
2004).
2.3. Collection of data on present distribution
Information on the present occurrence of the selected diadromous species was accounted for on
the level of existing sampling sites and integrated directly during national data collection. The
EFI+ partners used for this information not only the results from the sampling but also
additional data from other samples/sampling methods and commercial fisheries. For the present
distribution four modalities were possible: species is absent, species occurs at present naturally,
species occurs at present mainly due to stocking, no data/status unknown.
3. Methods
3.1.
Data collection
Historical data were collected based on written sources or fish distribution maps. Information
was registered in two different ways. Some partners (BOKU, CEMA, IGB, ISA, HU) compiled
data completely independent from existing sampling sites and the data input sheets for the central
data base. Information was registered into data files (mostly Excel). Even, if we used directly only
information which refers to a precise location (such as e.g. “Beluga Sturgeon occurs in the
Morawa river up to Rabensburg”), we recorded also information referring to the presence of a
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species in a river in general (e.g. “Salmon occurs in river Aare”) for later validation of the
potential distribution of modelled species. Precise information on locations or reaches was after
geo-referenced to national river networks. Other partners were transferring the compiled
historical information directly to the central database. Here is was possible to indicate the
historical distribution on site scale in table “Diadromous species”.
Finally, all data were integrated to a separate diadromous species database and into one common
GIS-document. For the later all data geo-referenced on national river networks or Google Earth
were adjusted to the European CCM river network in order to have a common basis for
environmental variables. The results of this step were the historical distribution maps of the
selected diadromous fish species.
As it can be seen in the historical distribution maps in chapter four there are gaps in the
distribution of fish species. This is on the one hand because not all fish species could have been
collected by all partners due to very limited time and financial resources. On the other hand it is
also well known that historical sources for the distribution of species (this is true for all species,
not only for fish) are more or less incomplete. The advantage of this data collection on
diadromous species was, that the relevant species are well known and of commercial interest.
Thus, the distribution is probably at least for those areas for which maps or comprehensive
written documents exist quite complete. Nevertheless, especially the distribution in smaller rivers
is probably underestimated.
Due to the data gaps we have to assume for the historical distribution we will in a next step
model the potential distribution of fish species. For this procedure we will develop models which
explain the presence of diadromous fish as a factor of environmental variables. The selection of
environmental variables had to be based on the availability of data available on a European scale.
As for the central database of the EFI+ project we used also for geo-referencing historical fish
species the CCM river network. It was in particular possible to extract from version 2 of the
CCM some important environmental variables, such as altitude at the upper and lower end of a
segment, slope, distance to sea, drainage area or stream order (Strahler). We further integrated
mean monthly air temperature from the period 1901-1921. These data were provided by the
Tyndall Climate Research centre (see Mitchell, et al., 2004). In order to account for regional
effects of fish species distribution, we integrated the catchment, the eco-regions after Illies &
Botosaneanue (1963) as well as the country code.
It was not possible to obtain data on natural barriers on European scale and consider them in our
data set. Thus it might appear that a potential distribution of species will be predicted for
segments upstream of natural barriers. In such cases the validation of the national data providers
is the only possibility to exclude concerned river segments after modelling and preparing maps
for the potential distribution.
In terms of modelling the potential distribution we have to take into account particularities of
historical data in comparison to present fish samples. For present sampling it can be assumed in
principle that a species is absent form a particular river section when it is not represented in a
sample (of course depending on sampling method and period). This is not at all the case for
historical data. Here, one can only say that according to historical information (and after
validation of the reliability of the data source) a species was present at a particular site. For all
other river segments, there is no knowledge whether the species was really absent (“true
9
absence”) or whether it was present but there is no historical record about this (false or pseudoabsence).
Thus we will have to work with particular modelling techniques, which are especially adapted to
the treat “presence-only” data (see below, chapter 5).
4. Results – historical distribution
An large amount of records on the historical distribution of the selected diadromous fish could
have been compiled. This was mainly possible due to additional funds and co-operations some
EFI+ partners were able to establish and organise, respectively. Depending on the possibility to
obtain further support the regional coverage of historical records differs between the countries.
Also, partners had to focus on those species for which historical data were easy to find and
analyse. A general search for all species over all European countries was done by the University
of Natural Resources and Applied Life Sciences and the Leibnitz Institute for Freshwater
Ecology, who analysed printed material mainly from the late 18th to the first half of the 20th
century. Cemagref and the Inland Fisheries Institute did a literature survey for all species for
France and Poland, respectively. This resulted in general in a good coverage of all species in these
four countries. In order to limit financial resources needed France did not compile the numerous
materials on the historical distribution of Eel. Since there is a good coverage for Eel in other
areas it was decided to use later on the modelled potential distribution after verification of
plausibility. Portugal and Spain analysed archive material resulting in a good overview especially
on Eel, and revealing also sufficient data on the two Lampreys, on Shad species, Sturgeon,
Salmon and Sea trout. Hungary, Romania and Italy provided information on the mostly endemic
diadromous fish species of the Danube catchment as well as for the Adriatic Sea. The
Netherlands and Lithuania analysed present information about the historical occurrence of all
species concerned. For Sweden, Finland and UK data search had to be limited to literature about
particular species. UK was especially focusing on Eel. Further literature on Shads as well as on
Lampreys will be used for validation of the potential distribution. Sweden and Finland focused
mainly on literature about the historical presence of Salmon and Sea Trout.
As already indicated before, literature information had to be validated in terms of reliability of
information. Even, if there are some historical sources reporting the occurrence of Eel in the
Middle and Upper Danube the species was finally excluded from the Danube catchment except
for the lowest part close to the Black Sea due to the contrasting information in historical
literature (see especially Siebold, 1863, but also Heckel & Kner, 1858, v. d. Borne, 1883,
Wittmack, 1875 for the discussion of native occurrence of Eel in the Danube).
The number of records found for the different species in historical sources and the number of
resulting segments in the CCM river network is indicated in the table 1. Most written evidence
was found for Eel, Salmon and Sea trout. The number of records is also remarkable for the two
diadromous Lamprey species, for Allis Shad as well as the Atlantic Sturgeon. A lower number but
probably still sufficient for modelling is available for Twaite Shad (well distributed in terms of
river size, see below). A surprisingly high number of records was found for Flounder (Platichthys
flesus), an estuarine species for which historically larger migrations to Rhine, Loire or Elbe are
recorded.
10
As expected the number of records is limited for the Sturgeon species of the Danube and the
Danube Shad as well. It will be tested if modelling these species separately is useful or whether
the historical distribution is already quite complete. As it is shown it table 1 the number of
segments is too low for more detailed analysis of Coregonidae and the Adriatic sturgeon. For the
Po catchment in particular further solutions to account for diadromous species will be discussed.
Table 1: Number of historical records and related CCM segments for the historical distribution of diadromous fish
species
On the following pages the historical distribution maps for different species are shown. It has to
be emphasised that these maps do not reflect the complete historical distribution but the
distribution as we have been able to identify it based on historical documents. Data gaps are for a
big part the result of the incompleteness of historical data. However, it has to be mentioned in
this respect that migration barriers and other pressures like habitat degradation and water
pollution are in some catchments and rivers already existing on a longer term and gaps may be
also a result of existing pressures. This has to be considered in later analyses, and discussed
depending on the results of tests of metrics sensitivity in WP four. To get a more complete
picture and to compensate missing historical information and possible effects of historical
pressures we will afterwards model the potential distribution of fish species as a factor of
environmental variables.
As it was already mentioned, we considered for the historical distribution only references with a
precise location or river section for the occurrence of a species. General indications that a species
occurs in a particular river were registered but they are not shown in the maps. Nevertheless,
general information will be used finally to validate modelling of the potential distribution.
11
Figure: Identified written records about the historical distribution of the European Atlantic Sturgeon
12
Figure: Identified written records on the historical distribution of Sturgeon Species in the Danube catchment
13
Figure: Identified written records on the historical distribution of the Eel
14
Figure: Identified written records on the historical distribution of the Sea Lamprey (P. marinus) and the River
Lamprey (L. fluviatilis)
15
Figure: Identified written records on the historical distribution of the Atlantic Salmon (S. salar) and the Sea trout
(S. trutta trutta);
16
Figure: Identified written records on the historical distribution of Shad species (Allis Shad A. alosa; Twaite Shad
A. fallax; Danube Shad A. immaculata);
17
Since historical information does not reflect the complete picture of the former distribution of a
fish species it is not possible to make any straight biological or ecological conclusions about the
distribution of fish in terms of environmental conditions. However some descriptive
comparisons were done, to clarify whether we will have a good representation of datasets for
modelling. As it turned out the original concern that we will have not enough information for
smaller rivers due to the fact that historical evidence is more frequent for large and medium sized
rivers was not correct. Table 2 shows the frequency of fish species in terms of stream order. For
the most frequent species in the dataset we have a good representation of smaller rivers even for
stream order 2. This concerns in particular Eel, Salmon and Sea trout. Some segments in such
river sections are also available for the two Lamprey and Shad species (n. b. that records for
Atlantic sturgeon are related to small coastal rivers).
Table 2: Number of river segments per Strahler stream order for 15 diadromous species
5. Modelling of the potential distribution
Mapping species distribution with historical data induced specific problems for the preparation of
modelling routines. Indeed, generally, we have only the information on the species presences and
the observations are spatially structured (e. g. Legendre et al. 2002, Schabenberger & Gotway
2005, Dormann et al. 2007). In the literature, we found several methods to estimate the potential
species distribution such as generalized linear model (GLM, Nelder & Wedderburn 1972,
McCullagh & Nelder 1989), generalised additive model (GAM, Hastie & Tibshirani 1989, Guisan
& Zimmermann 2000), Ecological Niche Factorial Analysis (ENFA, Hirzel et al. 2002).
The use of probabilistic models (GLM, GAM with binomial distribution) requires the
establishment of pseudo-absence to complete the datasets (e.g. Pearce & Boyce 2005, Lütolf et al.
2006) and the integration of spatial dependence which is a complex step (e.g. Dormann et al.
2007, Keitt et al. 2002, Miller et al. 2007). For example, several authors proposed some
corrections/penalization of likelihood to take into account the potential misclassification (e.g.
Lancaster & Imbens 1996, Pearce & Boyce 2005). However, all these approaches are in
experimental step and involve important statistical developments and programming.
ENFA computes uncorrelated factors that explain the major part of the ecological distribution of
the species: The first factor is the marginality factor, which describes how far the species
18
optimum is from the mean habitat in the study area. The others factors (specialisation factors) are
sorted by decreasing amount of explained variance (Hirzel et al. 2002). They describe how
specialised the species is by reference to the available range of habitat in the study area. The
routines for modelling the potential distribution of diadromous species have been established and
first tests are relatively encouraging and the necessary adaptations to networks are moderate.
ENFA does not provide true probabilities of presence, but it is possible to compute indices
based on the position of the niche defined by the analysis within the multidimensional space of
environmental variables (Calenge 2007). As a conclusion, this approach seems more adapted to
solve our question.
6. References
Balon, E. (1968): Einfluss des Fischfangs auf die Fischgemeinschaften der Donau. Arch.
Hydrobiol./Suppl. Suppl. 34, 3. 228-249
Calenge C. (2007) Exploring Habitat Selection by Wildlife with adehabitat, Journal of statistical
Software, 22(6), 1-19, URL: http://www.jstatsoft.org/v22/i06.
Dormann C. F., McPherson J. M., Araújo M. B., Bivand R., Bolliger J., Carl G., Davies R. G.,
Hirzel A., Jetz W., Kissling W. D., Kühn I., Ohlemüller R., Peres-Neto R. P., Reineking B.,
Schröder B., Schurr F. M. & Wilson R. (2007) Methods to account for spatial autocorrelation
in the analysis of species distributional data: a review, Ecography, 30, 609-628.
Graham, C.H. & Hijmans, R.J. (2006) A comparison of methods for mapping species ranges and
species richness. Global Ecology and Biogeography, 15, 578-587.
Guisan, A. & Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology.
Ecological Modelling, 135, 147-186.
Hastie, Trevor, and Tibshirani, R. (1989) Generalized Additive Models (with discussion),
Statistical Science, 1(3) , 297-318.
Heckel, J. and Kner R. (1858): Die Süßwasserfische der österreichischen Monarchie mit
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20
Project no. 044096
Project acronym
EFI+
Project title
IMPROVEMENT AND SPATIAL EXTENSION OF THE EUROPEAN FISH INDEX
Instrument: Specific Targeted Project
Thematic Priority: Integrating and strengthening the European Research Area
Central and Eastern European Rivers
Period covered: from 01.01.2007 to 31.12.2007
Date of preparation: 10.02.2008
Duration: 2 years
Project coordinator name: Dr. Stefan Schmutz
Project coordinator organization name: University of Natural Resources and Applied Life Sciences
Persons responsible for the report: Klaus Battes and Karina Battes,
Bacau University, Romania
21
TABLE OF CONTENTS:
1. General overview on the river basins included in the EFI+ project from each
country
1.1. General data concerning the catchment areas from each country
1.2. Characteristics of fish communities from the considered catchment areas
2. Main pressures in Central and Eastern European Rivers
3. General analysis of pressures
page 02
page 02
page 09
page 12
page 17
1. General overview on the river basins included in the EFI+ project from each country
1.1. General data concerning the catchment areas from each country
LITHUANIA:
Nemunas River basin covers more than 72% of Lithuanian territory. The rest ~28% are
shared between 5 river basins (Table 1). Small rivers (L – 3-10 km) predominate, covering ~ 50%
of total river length ~83% of total number. There are only 17 rivers longer than 100 km, 13 of them
are in the Nemunas River basin.
Table 1 River basins, number of rivers of different length (N) and their total length (∑L, km)
River basin
Nemunas
Coastal rivers
Venta
Lielupe
Daugava
Prieglius
Total
Area in
Lithuania
km2
46695
2132
5140
8939
1857
65
64828
L = 3-10
L = 10.1-30
L = 30.1-60
N
∑L
N
∑L
N
∑L
2377
126
365
669
105
4
3646
12169
628
1921
3585
519
20
18842
418
30
67
109
17
641
6535
466
1047
1801
229
60
3
5
15
3
10078
86
L = 60.1-100
L >100
Total
N
∑L
N
∑L
N
∑L
2490
139
195
562
141
20
2
2
4
-
1405
131
190
309
-
13
1
3
-
2548
161
445
-
3527
28
2035
17
3154
2888
161
440
1245
125
4
4418
25147
1364
3514
6702
889
20
37636
The total length of the largest Nemunas River is 937 km, and the basin area constitutes
97863.5 km2 while the Lithuanian part of the basin covers an area of 46 695.4 km2. Its drainage area
is located in the territories of Byelorussia, Lithuania, Russia (Kaliningrad region), Latvia and
Poland. The total water surface area of the Nemunas equals 157.9 km2. The Nemunas River ends up
in the Curonian Lagoon, a freshwater coastal lagoon of the southeastern Baltic Sea. The largest
tributaries in the Lithuanian part of Nemunas river basin are Neris, Nevezis, Merkys, Dubysa,
Sesupe, Jura and Minija (Table 2).
Table 2 Characteristics of the main rivers of the Nemunas river basin
River
Merkys
Neris
Dubysa
Sesupe
Jura
Nevezis
Minija
Sventoji
Zeimena
Length, km
Total
203
509.5
139
297.6
171.8
208.6
201.8
246
79.6
In Lithuania
185.2
228
139
157.5
171.8
208.6
201.8
246
79.6
Catchment area, km²
Total
In Lithuania
4415.7
3781
24942.3
13849.6
2033
2033
6104.8
4899
3994.4
3994.4
6140.5
6140.5
2942.1
2942.1
6889
6801
2792.7
2792.7
In the Nemunas river drainage area the soils are mainly represented by moraine and sandy
loams, which in some places are covered with peat formations. Loams prevail in the northwest of
the basin, covering almost completely the basins of the rivers Minija, Jura, Dubysa and Nevezis.
Sand and sandy loams cover significant areas in the upper part of the basin and in the littoral region
22
(the Jura River Basin, eastern part of the Dubysa River Basin and part of the Minija River Basin).
Sandy-mud deposits occur in river valleys and cover the whole mouth of the Nemunas. In the
southeast of the Nemunas catchment area more sandy grounds dominate, which are porous and
permeable. Here the rain and snow waters are absorbed fast. Average annual runoff modules in this
region are 7 – 9 l/s/km2. Climate is changing between maritime and continental in the Nemunas
river basin as in the whole Lithuania. The mean annual temperature is +6 – +7 0C, the average in
January being -4.9oC and in July +17oC. Lithuania is in the zone of surplus of humidity. Average
precipitation varies from 600 to 700 mm. Rainfall contains 75 % of the total precipitation.
Evaporation contains 65 % approximately, and surface runoff – about 32 %. The long-term average
annual discharge to the Curonian Lagoon by the Nemunas river is 667 m3/s which equals to 21.1
km3. The Curonian Lagoon discharge to the Baltic Sea comprises approximately 23 km3 per annum.
For most of the Lithuanian rivers big spring floods are typical while in summer and winter the flow
of rivers is significantly reduced. In the western part of the Nemunas river basin the highest water
runoff coefficients of 0.40 – 0.55 dominate, that is, about 40-50 % of annual precipitation flows
down to the rivers. Average annual runoff modules of 9 – 14 l/s/km2 are the highest in all the
country. The distribution of the river run-off throughout the year is soundly impacted by flood
events. Spring floods in the rivers of this region usually start in the middle of May and last for 25 –
40 days. During spring floods approximately 30 % of annual runoff flows down, while in summer –
10 % and in autumn-winter season – 60 %. Minimal winter runoff is approximately 3 times higher
than the summer one. The division of annual runoff in mid region of the catchment is mostly
uneven. The rivers in the mid of the river basin are subject to a less abundant runoff than in other
regions. Average annual runoff modules there amount for 4.5 – 6 l/s/km2. The general discharge of
the river contains 33-40 % of snowmelt water, 32-25 % of rain runoff, and 10-40 % of groundwater.
Most of the discharge of the river basin comes from combined surface/sub-surface runoff, including
snowmelt water (in average by 40 %) that cannot infiltrate into deeper layers because of the frozen
soil.
POLAND:
There are three main river basins in the Baltic Sea drainage area in Poland:
- The Vistula River Basin - total area 194.4, from which in Poland – 168.7 thousands km2 (56%
of Poland area), main tributaries: Skawa, Raba, Dunajec, Wisłoka, San, Wieprz, Narew and Bug,
Drweca, Przemsza, Nida, Pilica, Bzura, Brda),
- The Oder River Basin – area: 118.9 thousands km2, in Poland 106.0 thousands km2 (34%),
main tributaries: Barycz, Warta, Bobr, Nysa Klodzka, Nysa Luzycka),
- The Pomeranian Rivers - flowing directly to the Baltic Sea, area: 28.2 thousands km2 (9%),
main rivers: Rega, Parseta, Wieprza, Slupia, Pasleka).
- The remaining 1% of Poland area contains small parts of Nemunas River Basin (Baltic Sea
drainage area) in North-East Poland, few small rivers of Danube Basin and Dnister Basin (Black
Sea drainage area) in South Poland and small part of Elbe River basin (North Sea drainage area)
in West Poland.
The sampling sites (919) considered for the EFI+ project were located in 4 groups in various
regions of Poland: North Poland - Pomeranian Rivers (551 sites), East Poland - Narew R. with
tributaries (50 sites), Central Poland - middle course of the Vistula River with tributaries (111sites),
South Poland - upper Vistula R. tributaries (116 sites) and West Poland - middle Oder R. tributaries
(91 sites) (see Fig. 1)
General environmental characteristics of the sites located in particular regions are presented
in Table 3. Most sites are located in Ecoregion No 14 (Central Plains) and 16 (Eastern Plains), some
sites are also situated in Ecoregion No 10 (The Carpathians). Climatic conditions are differentiated
between regions, with privilege of Atlantic climate in North Poland, severe, continental Climate in
East Poland, some mixture of this two in Central Poland, mountaneous Sub-Carpathian climate in
South Poland and mild, Atlantic climate in West Poland. The difference in duration of vegetation
season between West and East Poland reaches up to100 days. All sites, except one, are located in
23
Baltic Sea drainage area, only the site on Strwiaz R. belongs to Black Sea drainage area. As much
as 60% of sites is located in Pomeranian Rivers catchments, 27% - in Vistula R. catchment and 13%
in Oder R. catchment.
Fig. 1 The location of the 919 sampling sites considered for the EFI+ project
Table 3 Environmental characteristics of the sites available in Polish dataset, with respect to
particular regions: North Poland, East Poland, Central Poland, South Poland and West Poland
Ecoregion
Catchment
name
Distance
from
source
km
215
14, 16
Vistula,
Baltic Sea
1-180
50
9
16
Vistula
20-284
Central
111
19
14, 16
South
91
40
10, 16
West
116
47
14
Oder
3-223
Poland
919
330
10, 14, 16
Vistula,
Baltic Sea,
Oder,
Dnister
1-988
Region
No.
of
sites
No.
of
rivers
North
551
East
Vistula,
Baltic Sea
Vistula,
Dnister
3-988
2-83
Size of
catchment
km2
3-2960
median 88
137-15300
median 2910
12-195200
median 490
4-960
median 54
8-5370
median 131
3-195200
median 117
Altitude
m a.s.l.
0-75
95-119
9-316
185-843
60-630
0-843
River slope
‰
0.1-25.0
median 1.6
0.07-1.8
median 0.2
0.1-11.0
median 0.8
0.6-60.8
median 7.0
0.2-44.1
median 2.4
0.07-60.8
median 1.7
Geological
typology organic
% sites
No.
large
river
sites
No.
oxbow
sites
0
0
0
96
19
23
0
32
4
0
0
0
0
0
0
5
51
27
Largest rivers were sampled in Eastern and Central Poland (median size of catchment 2910
and 490 km2 respectively), medium-size rivers dominated in West Poland (131 km2), while sites
located on small rivers prevailed in South and North Poland (54 and 88 km2 respectively). The
altitude gradient in whole dataset was high (0-843 m.a.s.l.), with low altitudes in North, and East
Poland (< 120 m.a.s.l.), medium - in Central Poland (up to 316 m.a.s.l.) and highest in South Poland
(up. to 843 m.a.s.l). Considerable altitude gradient was represented by sites from West Poland (60630 m.a.s.l.). Slope values were diversified from less than 0.1 ‰ to over 60 ‰. Lowest median
slope values were found in Eastern and Central Poland (0.2 and 0.8‰ respectively), while highest in South and West Poland (7.0 and 2.4‰ respectively). Despite low absolute elevation (≤ 75
m.a.s.l.) Pomeranian Rivers showed quite high slope values (median 1. 6‰, maximal value 25‰).
24
This explains some similarities between fish communities in this region and mountain ones. 96 % of
sites in Eastern Poland were located on rivers of organic geological typology, but such rivers
occurred only in this region. In Central and Eastern Poland 51 sites located on large rivers were
sampled, including 26 sites on middle and lower Vistula R., as well as 27 sites in oxbow lakes of
Vistula, Narew and Biebrza rivers.
Considerable differentiation of governing environmental factors, like: climate, drainage
basin, altitude, actual river slope and geological typology between groups of sites located in
particular regions of Poland causes need of separate analysis of each region in terms of fish
community structure and pressures assessment.
HUNGARY
The data considered for the EFI+ project were part of the results of the ECOSURV project
(Ecological Survey of Surface waters, Hungary) that was carried out in 2005. The information was
collected in 2005 from April to September and the main goals of the project for fish were:
completion of sampling manual, drafting a recommendation for the future monitoring manual,
based on field experiences; standardised sampling in 234 waterbodies; establish a fish database;
estimate the ecological status of the sampled waterbodies using the EFI, and examine the validation
of the method; statistical analysis to validate the water typology.
From the total number of 234 sampling sites included in the ECOSURV project, 193 rivers
(running water) sites were added to the EFI+ project. The sampling sites well represent the
Hungarian river types. According to Table 4, there are 8 types of waters based on cluster analysis
(Halasi-Kovács & Tóthmérész, 2006), as follows:
1. Middle-height mountainous streams: 16%
2. Lower hills streams and small rivers: 23%
3. Gravel bottomed epipotamal section of medium and large rivers of major slope: 9%
4. Sand bottomed epipotamal section of medium and large rivers of minor slope: 10%
5. Lowland small streams and brooks: 18%
6. Lowland small and medium rivers and channels: 7%
7. Lowland (metapotamal) section of large rivers: 9%
8. River Danube: 8%
Table 4 The main characteristics at the sites considered for the EFI+ in Hungary
Cat.
>150
150100
<100
Actual water velocity
(cm/s)
No. Perc.
Cat.
(pcs) (%)
21
10,9
0-5
No.
(pcs)
48
Perc.
(%)
24,9
Water discharge
(m3/s)
No. Perc.
Cat.
(pcs) (%)
99
51,3
<1
96
49,7
10
39
20,2
6-35
67
49
25,4
100
1000
>1000
29
19
7
15,0
9,9
3,6
36-75
76-100
>100
69
29
7
Altitude
Dominating substrate
stone
No.
(pcs)
3
Perc.
(%)
1,6
34,7
pebble
20
10,4
35,8
15,0
3,6
pebble/sand
sand
sand/clay
clay
organic
sediment
47
25
18
33
24,3
13,0
9,3
17,1
47
24,3
Cat.
Two sampling methods were used (Halasi-Kovács et al, 2005): wading, back pack electric
sampling equipment, for wadable running waters and sampling from boat, generate electric
sampling equipment, for larger rivers.
ROMANIA
The lower Danube catchment area includes several ecoregions, as follows: ecoregion no. 10
– The Carpathians; ecoregion no. 12 – The Pontic Plain and ecoregion no. 16 – The Eastern Plain.
25
Table 5 presents the main river courses in Romania and Bulgaria with an area of the catchment that
exceeds 8000 km2.
Table 5 Physical and geographical data concerning the rivers from the lower Danube
catchment area
Ecoregions
Ecoregion
no. 16 The
Eastern
Ecoregion
Plain
no. 12 The Pontic
Plain
Catchment
area
Length of the
catchment
(km)
Area of the
catchment
(km2)
Discharge
(mc/s)
The Prut
252.9
2839.6
The Siret
726
The Ialomita
The Arges
Altitude
Comments
Headwater
Mouth
81.6
-
2
Ukraine
Republic of
Moldavia
43933
254.0
1238
2
Romania
414
10430
38.8
2395
8
339.6
12521
64.0
2030
10
The Jiu
348
10070
94.0
1760
22
The Iskei
410
-
57.0
-
-
The Yantra
285.5
7862
42.0
1340
10
Romania
Bulgaria
The Eastern region (Ecoregion no. 16) includes the largest river basins (The Siret and The
Prut). In the South (ecoregion no. 12), besides the Olt River, all running water catchment areas do
not exceed 10000 km2. The Olt River however represents an exception, the river regularization
regions exceeding 60-70% from its catchment area.
Table 6 depicts the river types included in each ecoregion, next to information regarding the
areas, slopes, altitude, annual mean flow etc., together with the main fish species characteristic to
every region.
For the EFI+ project only sampling sites located on the Siret catchment area were
considered – a total number of 263 sites (see Fig. 2)
Fig. 2 The considered sampling sites located in the Siret catchment area, Romania
26
6
Table 6 The running water types characteristic to Romania
Parameters
Type
Symbol
Area
km2
Geology
Lithological
structure
Slope
‰
Altitude
m a.s.l.
Precipitations
mm/year
Temperature
0
C
q
l/s/km2
q95%
l/s/km2
Potential fish
fauna
10-1000
a- siliceous
b - calcareous
blocs,
boulders,
pebble
40-200
>800
700-1400
-2+8
>20
>1
Trout
Grayling
10-1000
a - siliceous
b- calcareous
boulders,
pebble
20-50
500-800
600-800
7-9
5-20
0.5-2
pebble,
boulders
3-20
500-800
600-800
7-9
5-20
1-3
sand,
pebble
0.5 - 5
200-500
500-700
8-10
3-15
0.4 -2
1-3
500-800
600-800
7-9
3-20
0.2-2
1-2
200-500
500-700
8-10
3-15
0.4 -2
sand,
pebble
5-30
200-500
500-700
8-10
2-10
0.2 - 0.8
pebble,
boulders
3-20
200-500
500-700
8-10
2-10
0.2-0.8
sand,
pebble
5-20
200-500
500-700
8-10
2-8
0.2-0.6
Chub
Undermouth
sand,
silt
<8
<200
400-500
9-11
<3
<0.3
Chub
Undermouth
Barbel
Carp
Ecoregion 10 – The Carpathians
Mountainous water course
RO01
Water course from the high plateau
regions
RO02
Water course sector from the high
plateau regions
RO03
1000-10000
RO04
1000-10000
Water course sector from
depression regions
RO05
10-1000
Water course sector with wet lands
from hilly and plateau regions.
RO06
1000-10000
Water course sector from hilly or
plateau regions
Sub-ecoregion no. 10- The Transylvanian Plateau
Water course from hilly or plateau
10-1000
regions
RO07
plateau regions
RO08
Ecoregion no. 11- The Hungarian Plain
regions
RO09
1000-10000
10-1000
10-1000
Water course from plain regions
RO10
Water course sector from plain
regions
RO11
>2000
from hilly and plateau regions.
RO12
1000-10000
from plain regions
RO13
>10000
Ecoregion no. 12- The Pontic Region
RO14
regions
10-1000
a- siliceous
b- calcareous
a- siliceous
b- calcareous
c-organic
a-siliceous
b-calcareous
c-organic
a-siliceous
c-organic
a- siliceous
b - calcareous
c - organic
a-siliceous
b- calcareous
a- siliceous
b - calcareous
c- organic
a -siliceous
b -calcareous
c- organic
a-siliceous
b- calcareous
c-organic
a-siliceous
b- calcareous
c-organic
a-siliceous
b- calcareous
c-organic
a- siliceous
b -calcareous
c- organic
sand,
pebble,
boulders
sand,
pebble,
silt
Undermouth
Chub
Trout
Grayling
Undermouth
Undermouth
Barbel
Chub
Undermouth
Undermouth
Barbel
Chub
Undermouth
Chub
Undermouth
sand,
silt,
clay
sand,
pebble,
silt
sand,
silt,
clay
<1
200-250
400-600
9-11
2-10
0.1-1
0.5 - 5
200-500
500-700
8-10
3-15
0.4 -2
<200
400-600
9-11
2-10
0.1-1
Barbel
Carp
sand,
pebble
5-20
200-500
500-700
8-10
2-5
0.2-0.4
Chub
Undermouth
<1
Undermouth
Barbel
27
Table 6 (continued)
RO15
10-2000
plateau regions
RO16
1000-10000
regions
RO17
>2000
RO18
>10000
from plain regions.
The Danube - Cazane
RO19
The low River Danube Cazane - Calarasi
RO20
The Danube - Calarasi-Isaccea
RO21
Delta Dunarii
RO22
Ecoregion 16 – The Eastern Plain
plateau regions
RO23
570.900574.850
574.000698.000
698.00780.650
805.300
a -siliceous
b- calcareous
c-organic
a-siliceous
b- calcareous
c-organic
a-siliceous
b- calcareous
c-organic
a-siliceous
b- calcareous
c-organic
calcareous
siliceous
siliceous
organic
sand, muddy
clay
<8
<200
400-600
9-11
<2
0.2
Chub
Perch
sand,
pebble
0.5 - 5
200-500
500-700
8-10
3-15
0.4 -2
Undermouth
Barbel
<1
<200
400-600
9-11
2-10
0.1-1
Barbel
Carp
<1
<200
400-600
9-11
2-10
0.1-1
Barbel
Carp
0.07
100-200
600-800
8-10
9
3
0.05
5-70
500-600
9-11
8
2
0.04
5
400-500
9-11
7
1.5
sand,
silt,
clay
sand,
silt,
clay
sand, pebble,
boulders
sand, clay,
pebble
sand, clay
sand, silt
<0.01
<5
400-500
a- siliceous
sand,
b -calcareous
5-20
200-500
500-700
pebble
c- organic
a-siliceous
sand, muddy
RO24
10-2000
<8
<200
400-600
b- calcareous
clay
c-organic
a-siliceous
sand,
plateau regions
RO25
1000-10000
b- calcareous
0.5 - 5
200-500
500-700
pebble
c-organic
a-siliceous
sand,
regions
RO26
>10000
b- calcareous
silt,
<1
<200
400-600
c-organic
clay
a-siliceous
sand,
RO27
>10000
<1
<200
400-600
b- calcareous
silt,
from plain regions.
c-organic
clay
Water courses qualitatively influenced by natural factors and temporary water courses
Water courses qualitatively
RO28
10-1000
influenced by natural factors
Non permanent mountainous water
siliceous
blocs, boulders,
RO29
10-1000
20-150
>800
700-1100
course
pebble
Non permanent water course from
boulders,
RO30
10-1000
calcareous
25-45
500-800
600-800
the high plateau regions
pebble
a-siliceous
RO31
10-1000
pebble, sand
5-30
200-500
450-550
hilly and plateau regions.
b-calcareous
a-siliceous
RO32
10-2000
sand, silt
<8
<200
400-500
plain regions
b-calcareous
Other fish species present:
* starlet, starry sturgeon, Russian sturgeon, Beluga, Pontic shad, pike, tench, roach, red eye, common bream, crucian carp, catfish, perch, pikeperch, , asp
** starry sturgeon, Russian sturgeon, Beluga, pike, roach, tench, red eye, common bream, crucian carp, catfish, perch, pikeperch, asp
10-1000
Carp
Carp*
Carp*
Carp**
Pontic shad
>11
8-10
2-5
0.2-0.4
Chub
Undermouth
9-11
<2
0.2
Chub
Undermouth
8-10
3-15
0.4 -2
9-11
2-10
0.1-1
Barbel
Undermouth
9-11
2-10
0.1-1
Barbel
Undermouth
-2+8
2-16
Undermouth
Barbel
0
7-9
5-17
0
8-10
1.5-7
0
9-11
<2
0
28
8
1.2. Characteristics of fish communities from the considered catchment areas
LITHUANIA:
Native fish and lamprey fauna consists of 50 species, however only 47 of them inhabit
Lithuanian inland water bodies at present. Three species (Atlantic sturgeon, Allis shad (historical
presence of the latter species is questionable) and Blue bream) got extinct. Native fishes and
lampreys of the Lithuanian freshwaters belong to 17 families (see ANNEX 1). The most abundant
is Cyprinidae family – 22 fish species, Cobitidae – in 4, Salmonidae, Percidae, Petromyzontidae in 3, Clupeidae, Coregonidae, Gasterosteida -, in 2, Cottidae Acipenseridae, Thymallidae,
Osmeridae, Esocidae, Angullidae, Siluridae, Gadidae, Pleuronectidae - in 1 species. Six longdistance migrating species currently spawn in Lithuanian rivers: Sea lamprey, River lamprey, Eel,
Twaite shad, Salmon and Sea trout. Vimba should also be considered as long distance migrating
species (at least – in Lithuania; migrates up to 400-450 km from the Sea). Shad and sea lamprey, on
the contrary, seem to spawn close to the sea, i.e. in the Nemunas River delta area.
There were many attempts of introduction of various non-native fish species in Lithuania.
First attempts date back to 16-17-th century, but the most intensive works of introduction took place
in 20-the century. Occasional introductions of several fish species were observed too. Overall, 18
fish species were released into rivers and lakes or reared in fish farms. Luckily, only 3 of them had
more or less acclimated to local conditions: Gibel, which is now widespread in various water
bodies, Amur sleeper – dwells in several small dystrophic lakes and pools (however, there are
records from rivers, too), and Carp. Information on the success of natural reproduction of the latter
fish species is rather controversial: natural spawning events are being observed quite often,
sometimes young-of-the-year juveniles are recorded. However, yearlings are extremely rare in the
water bodies prevented from occasional access of artificially reared carp. Natural reproduction of
Brook char was observed too, however it took place in a small brook linking two ponds of fish
farm. All attempts of introduction of this species into rivers had failed.
At present, 9 alien fish species occur in the inland water bodies of Lithuania, while others
got extinct (Table 7).
Table 7 List of introduced and acclimated fish species which are currently present in Lithuanian
inland waters (C- common, M – minor)
Present
status
C
Common Name
Latin Name
Gibel or Prussian Carp
Carassius gibelio (Bloch)
Amur sleeper
Percottus glenii (Dybowski)
M
Common Carp
Brook Char
Northern Whitefish
Grass Carp
Silver Carp
Rainbow Trout
Cyprinus carpio (L.)
Salvelinus fontinalis (Mitchill)
Coregonus peled (Gmelin)
Ctenopharyngodon idella (Valenc)
Hypophthalmichthys molitrix (Valenc)
Oncorynchus mykiss (Walbaum)
C
M
M
M
M
M
Stone moroio
Pseudorasbora parva (Schlegel)
M
Introduced
into the country
before 1852
1985 (occasional
introduction)
16-17-th cent.
1961
1960-1962
~1960-1965
1962
1885
1963 (occasional
introduction)
2007 (?;
occasional
introduction)
Comments on reproduction
reproduces naturally
reproduces naturally
Occasional natural reproduction
only 1 spawning place
no natural reproduction
reproduction was observed, but
till 1990 got extinct;
new records in 2007
POLAND:
Total number of fish species registered in Polish rivers amounted to 57, 10 of them were
alien species and 5 - diadromous ones (Table 8). In Central and North Poland 46-47 species
occurred (8 and 5 alien respectively), while in West Poland - 37 (3 alien) and in East and South
Poland - 25-28 (3 and 1 alien respectively). Species number reflects mainly heterogeneity of
sampled river habitats - greatest in Central and North Poland (rivers of different size, various
altitude and slope values etc.) and lowest in South Poland (small mountain rivers) and East Poland
29
(lowland large rivers). Alien species number was highest in Central Poland, especially in the Vistula
R., quite well connected to Baltic Sea and other catchments (like Dniapro River in Black Sea
drainage area via canals and Bug R.). Occurrence of diadromous species was strictly correlated with
connectivity of rivers with the Baltic Sea. In Pomeranian rivers all 5 registered diadromous species
(eel, European river lamprey, salmon, sea trout and vimba) were found, while in other regions - up
to 2 species (including eel originating almost exclusively from stocking) were noted. In upper-most
region of South Poland no diadromous species were found, despite historical information on
spawning grounds of many species (sturgeon, salmon, sea trout, vimba, European river lamprey)
located in this region.
Table 8 Number of fish species and specimens caught in various regions of Poland. List of alien and
diadromous species present in catches.
No. of species
No. of
fish caught
No. of
alien species
No. of diadromous
species
North
47
77881
5
5
East
28
30453
3
1
Central
46
54950
8
2
South
25
13668
1
0
West
37
44948
3
2
Poland
57
221900
10
5
Region
Alien species: Abramis sapa, Ameiurus nebulosus, Carassius gibelio, Coregonus lavaretus, Cyprinus carpio,
Neogobius fluviatilis, Neogobius gymnotrachelus, Neogobius melanostomus, Percottus glenii, Pseudorasbora
parva
Diadromous species: Anguilla anguilla, Lampetra fluviatilis, Salmo salar, Salmo trutta trutta, Vimba vimba
In Polish rivers taken altogether 4 fish species: gudgeon, brown trout, perch and roach
dominated in terms of frequency (50-46% of fishing occasions with species present), accompanied
by pike, stone loach, and three-spined stickleback (see ANNEX 2a Poland). In terms of fish
number and total fish aboundance roach was a dominant (about 20%), while gudgeon and common
minnow were co-dominants. This picture is of course not realistic - there is no existing real fish
community with such a dominance structure. As it was stated previously only separate analysis of
fish communities in particular regions of Poland can give accurate outcomes.
Rivers of North Poland (mainly small streams and upper reaches of larger rivers) were
dominated in terms of frequency by brown trout, three-spined stickleback, gudgeon and bullhead,
co-dominants were: perch, pike, roach and European brook lamprey while in terms of abundance gudgeon, bullhead and brown trout dominated, accompanied with common minnow, bleak and
roach (see ANNEX 2a Northern Poland). Dominating species represent communities of small
streams with quite steep slope, while co-dominants, like pike, perch, bleak and roach are typical of
middle-size, slowly flowing rivers. This is in accordance with characteristic of environmental
conditions described above for rivers sampled in this region.
In Eastern Poland most frequent fish species were: roach, pike, perch and white bream (>
80%), accompanied by ide, burbot, rudd, tench, bleak, bitterling, gudgeon, crucian carp, spined
loach, loach, and bream. In terms of abundance fish communities were highly dominated by roach
(>45%), accompanied by pike, white bream, perch, loach and rudd (see ANNEX 2a Eastern
Poland). Such fish community structure clearly corresponds to environmental conditions in rivers
sampled in this region (mainly large, slowly flowing rivers and their oxbows). Values of river slope
in this part of Poland ranged between 0.7 and 1.8‰ and half of sites were located in oxbows. This
explains high share of roach, white bream, rudd, pike and perch in fish communities.
Rivers of central Poland were dominated in terms of frequency by gudgeon, stone loach
perch, roach and pike (> 70%), accompanied by dace, chub, ide, bleak, burbot and three-spined
stickleback. In terms of abundance roach was a dominant (27%) with perch, bleak, gudgeon and
stone loach as co-dominants (see ANNEX 2a Central Poland). Considerable diversity of
30
ichthyofauna assemblages of this region was clearly connected with high diversity of environmental
conditions (high altitude amplitude, river slope range 1-11‰, sampling in very small streams and in
Vistula River with some oxbows). However slowly flowing larger rivers prevailed in this region
(median catchment size 490 km2, median slope 0.8 ‰), what explain domination of roach, perch
and bleak in fish communities.
South Poland is a considerably different region from all previously described ones. Submountain and mountain streams and small rivers of high slope dominated there. This correspond
well with dominance of brown trout, common minnow, eastern sculpin, stone loach and chub
(frequency > 50%), accompanied by spotted barbell and gudgeon. In terms of abundance dominated
common minnow and eastern sculpin, together with brown trout, stone loach, chub and spotted
barbell (see ANNEX 2a Southern Poland). Attention should be paid to roach, which was present
in this region, but in minor abundance and low frequency. Some similarities, like high share of
brown trout and common minnow, occurred between ichthyofauna of South and North Poland, due
to high percentage of small streams of high slope in both regions, despite great difference in
absolute altitude and climatic conditions.
West Poland differed from South Poland region mainly with different main catchment area
(Oder River instead of Vistula), lower altitudes and higher share of medium-size lowland rivers.
However small, steep streams had considerable share in sampled sites also in this region, what was
reflected in dominance of stone loach and gudgeon in terms of frequency and gudgeon, common
minnow, stone loach and brown trout in terms of abundance (see ANNEX 2a Western Poland).
Considerable share of roach (both in frequency and abundance) in this region was connected to
mentioned above share of larger, lowland stretches of rivers, but also to high human impact in this
densely populated and industrialized area.
When only large rivers (sites located in Central and Eastern Poland) are concerned, high
dominance (>75%) of roach, perch, pike, ide, bleak white bream in terms of frequency is visible
(see ANNEX 2b Large Rivers). Concerning total fish abundance roach strongly dominates in large
rivers (31%), followed by bleak, perch and gudgeon (Table 4A). In the case of oxbow sites fish
community structure is similar to that noted in stagnant water bodies, with strong dominance of
roach, pike, perch, rudd, tench, and white bream in terms of frequency. The only riverine species
with frequency higher than 75% is ide. Concerning total fish abundance roach is main dominant
(47%), followed by white bream, pike, rudd, spined loach, perch and bitterling (see ANNEX 2b
Oxbows). Considerably small share of bream in electrocatches in large rivers is caused by the fact
that bream lives mainly in mid-channel habitats, which are very difficult to sample. This is
confirmed by the results of additional net catches done in Vistula and Narew rivers, in which bream
clearly dominated.
HUNGARY:
The total number of species identified for the EFI+ project was 62 (including 1 new species
for Hungary - Neogobius gymnotrachelus) (Halasi-Kovács et al, 2005). An average number of 1-25
species were caught per site (median: 11). The number of catches (records) was 2094 pcs. and the
number of specimens 80336 pcs. An average number of 2-2754 individuals were caught per site
(median: 299). No fish length was measured. The age groups recorded were 0+; >0+.
For the complete list of fish species considered for the EFI+ project, see ANNEX 3.
ROMANIA:
For the EFI+ project only data sampled from the Siret catchment area were considered. The
Siret River basin is located in the Eastern Romania and it is the largest one from the country. The
main fish associations from the Siret catchment area depending on habitat conditions were the
following:
The mountainous regions (> 800 m a.s.l.):
- brown trout (ca. 40-60%) – leading species
- bullhead (10-20%)
31
-
minnow (<5%)
High plateau regions (500-800 m a.s.l.):
- minnow (40-60%) – leading species
- bullhead (15-20%)
- brown trout (10-15%)
- Mediterranean barbel (10-20%)
- spirlin (5-10%)
- grayling (<5%)
Medium plateau regions (400-600 m a.s.l.):
- Mediterranean barbel (20-40%)
- minnow (20-30%)
- spirlin (10-15%)
- stone loach (< 5%)
- Danube longbarbel gudgeon (< 5%)
- chub – invasive species in the area (10-20%)
Hilly regions (200-400 m a.s.l.):
- undermouth (20-40%) – replaced by the chub
- barbel (15-20%)
- gudgeon (5-10%) (G. obtusirostris)
- bleak (5-10%)
- stone loach (< 5%)
- crucian carp (invasive species)
- stone moroko (invasive species)
- Cobitis sp.
- Sabanejewia sp.
- tench (in large rivers – the Siret)
- perch (in large rivers – the Siret)
- catfish (in large rivers – the Siret)
- red eye (in large rivers – the Siret)
- common bream (in large rivers – the Siret)
- vimba (in large rivers – the Siret)
Plain regions (below 200 m a.s.l.):
- gudgeon (40%) (G. obtusirostris)
- bitterling (20%) (Rhodeus ammarus)
- chub (15-20%)
- stone moroko (10%) – invasive species
- Sabanejewia valahica (2-7%)
- Cobitis danubialis (5%)
- perch (1-2%)
- crucian carp (sub 1%)
The complete freshwater fish species list from Romania, together with the list of introduced
and invasive species are included in ANNEX 4.a and 4.b.
2. Main pressures in Central and Eastern European Rivers
LITHUANIA:
In total three main significant pressures were identified: urban waste water treatment plants
(point sources), agricultural activities (diffused sources) and the hydro-morphological changes of
water bodies (canalization and water flow regulations).
The biggest overall loads of pollution come from the agricultural sources, i.e. livestock and
application of fertilizers. Point sources of pollution, which is mainly industrial and municipal waste
water is the second source of the pollution. Non sewered and sewered inhabitants are also
32
responsible for serious loads of pollution. Therefore based on the calculation of loads, the main
following drivers for pressures can be identified:
- agriculture,
- municipal and industrial wastewater,
- diffused pollution from sewered households, without waste water treatment.
Pollution with nutrients clearly predominates, accounting for ca. 93% of total pollution,
causing rivers being at risk. Pollution with toxic substances accounts for ca 2% only.
In addition to pollution, pressures related to the morphological changes of water bodies
were identified. The biggest pressure identified is river straightening, followed by dam construction.
River straightening. In 1910-1997 more than 4.4 mln ha of land was drained in Lithuania, or
46.6% of the territory. In total, 46 thou. km of river beds were straightened, from those – 24.3 thou.
km of rivers, longer than 3 km. Only 13.4 thousands km or 29% of total length of river beds remain
natural, majority of them being large rivers. In proportional scale, >80% of river beds are
straightened in 3-10 km length rivers, ~40-50% in 10-30 km length rivers, and <20% in >30 km
length rivers. Overall length of straightened rivers which catchment size is >10 km2 in the Nemunas
River basin is ~9000 km.
Dam construction. There are more than 1150 dams constructed on rivers in Lithuania, 858
of them – in the Nemunas River basin. From those, ~50 dams are constructed for energy production
(with installed turbines), the rest – for different purposes (water reservoirs, recreation). More than
100 dams are constructed at the distance of >10 km from river source. Overall situation (with regard
to river basins, accessible for migrating fish; in green) is schematically presented in Fig. 3 below.
Fig. 3 An overview of the dams built on the main river courses in Lithuania
POLAND:
Hydromorphological pressures: The main hydromorphological pressure in Polish rivers is
undoubtedly lack of connectivity on catchment scale (Barriers catchment down) – this pressure
occurred in 84% of fishing occasions (FO) (Table 9). Lowest pressure was found in Northern (76%)
and Central (83%) Poland, while highest – in East, South and West Poland (99-100%). Only some
Pomeranian rivers, lower Vistula R. and lower Oder R. have connection to the Baltic Sea, all
tributaries of the middle and upper Vistula and Oder are impacted. In the case of Vistula R.
33
catchment the main obstacles for fish migration are Wloclawek dam on Vistula R., and for East
Poland – also Debe dam on lower Narew R. Barriers in a river segment scale (up and down) have
less impact – 16 and 9% FO in whole dataset. This impact is highest in West Poland (27 and 21%)
and lowest in East Poland (2 and 3 %).
Impoundment has considerable impact only in West Poland (78% FO) and Central Poland
(11%), while negligible in other regions (1-3%). High impact of instream habitat modification was
found for the whole dataset (34% FO), with maximal values in Central and South Poland (56-57%)
and minimal in East Poland (7%). Water abstraction has significant impact in Central and West
Poland (mainly for irrigation and fish ponds purposes), while negligible in East Poland (1%).
Hydrograph modifications are strongest in Central and West Poland (40 and 35% FO) and quite
substantial in East Poland – 29% (due to influence of Siemianowka Reservoir on upper Narew R.).
Velocity increase was found mainly in Central Poland, while riparian vegetation alterations –
mainly in South Poland (85% FO) and to less extent in West and Central Poland (Table 9).
Water quality-related pressures: Sedimentation, eutrophication and organic pollution
were highest in Central Poland, mainly due to large share of sites located on Vistula R. and its
tributaries, flowing through densely populated and industrialized regions with high share of
intensively managed arable fields in the landscape. Considerable impact of eutrophication and
sedimentation was also found in West Poland, due to the same reasons (Table 10).
Water quality index was assessed mainly at 2-ond and 3-rd class (out of 5) - 74% of total
FO number, with the exception for South Poland (31% in 1-rst class) and for Central Poland (56%
in 4-th and 5-th class). Higher impact on water quality was also stated in Central and West Poland
than in other regions, due to the same reasons as mentioned above. Lowest impact was found in
upper river stretches sampled in mountain region of South Poland - 57% FO in 1-rst and 2-ond
water quality class.
-
-
-
HUNGARY:
The main human pressures in Hungary are related to the following aspects:
Water quality:
▪ bad: 6 sites
3.1%
▪ poor: 52 sites
26.9%
▪ moderate: 86 sites
44.6%
▪ good: 46 sites
23.8%
▪ high: 3 sites
1.6%
Organic pollution (only N, P forms): mainly in the lowland rivers and streams (not easy to
clarify whether there are natural or human impact)
▪ No: 93 sites
48.2%
▪ Weak (medium): 77 sites 39.9%
▪ Strong: 23 sites
11.9%
River alteration: channelization (No: 91 sites; Intermediate: 70 sites; Straightened: 32 sites),
bed and bank fixation, transverse constructions.
Connectivity:
▪ At larger scale: for example the Iron Gate on the River Danube, what inhibit the
migration and spreading of the diadromous species.
▪ At smaller scale: the end barriers on the small running waters at the mouth what inhibit
the species exchange in the given river system.
ROMANIA
The most important pressures from the Siret catchment area were the following: hydrotechnical works – river regularizations, dam reservoirs, canalization, dikes etc.; pollution of aquatic
habitats, especially downstream the large cities (Suceava, Piatra Neamt, Bacau etc.), by means of
chemical pollutants, domestic wastes, fertilizers etc. and rubble pits located on the main river
courses
34
14
Table 9 Main hydromorphological pressures determined for sites available in Polish dataset, with respect to particular regions, % sites impacted
Region
Barriers
Barriers Barriers Impound Instream
Floodplain
Water
Main water
Hydro- Velocity
Riparian
catchment segment segment
ment
habitat
**
abstra
use
mod
increase vegetation*
down
down
up
ction
North
76
7
15
1
29
86
10
Fishponds
2
1
22
East
100
3
2
1
7
0
1
Fishponds
29
1
6
Central
83
12
27
11
56
39
24
Irrigation
40
30
31
South
100
12
8
3
30
100
8
Drinking
0
0
85
West
99
21
27
78
57
60
37
Fishponds
35
9
33
Poland
84
9
16
12
34
37
14
Fishponds
13
5
29
* “low” or “slight” impact was treated as „no impact”
** “No” and “Small” floodplain connection were treated as “impacted”, “large” and “medium” – as “not impacted”, “No data” cases (mainly due to
lack of former floodplain) were excluded .
Table 10 Main water quality-related pressures determined for sites available in Polish dataset, with respect to particular regions; % sites impacted
or % sites in particular water quality class.
Region
Sedimentation*
Water quality index
Eutrophic Organic
ation*
pollution*
1
2
3
4
5
North
East
Central
South
West
Poland
8
4
37
41
14
4
2
0
27
72
1
0
60
1
3
40
41
15
4
31
26
33
6
4
10
1
29
49
16
5
14
6
30
44
15
5
* “low” or “slight” impact was treated as „no impact”
5
8
92
23
57
24
2
0
48
3
3
8
35
The hydro-technical works on the Siret catchment area include numerous dam reservoirs
built for hydropower, flood protection, irrigation or fishponds. In the Siret River basin alone there
are ca. 20 dam reservoirs on its middle and lower course (including its major tributaries). On the
Bistrita lower course there are 7 man-made lakes and 14 hydropower plants. On the Siret river
course there are 7 lakes, downstream the Bacau locality its course being totally regularized.
Moreover, large-scale canalization works can be seen on the Bistrita River course, from Bicaz to
Bacau – about 80 km, as well as dikes on the middle and lower Barlad River course.
Hydro-technical works can be found throughout Romania: there are over 250 dam reservoirs
(see fig. 4).
Fig. 4 Overview of hydropower constructions in Romania
The pollution of aquatic habitats represents another important factor leading to major
pressures on river ecosystems. Industrial pollution represents the main source for chemical
pollutants (fertilizers, cellulose and paper factory wastes, oil products etc.). The main water courses
affected by industrial pollution are the following: The Suceava (downstream Suceava locality), the
Bistrita (downstream Piatra Neamt locality), the Siret (downstream its junction with the Suceava
and downstream Bacau locality) and the Trotus (mainly due to oil products). However, after the
year 1990, this pollution decreased with 60-80% due to the closure of numerous chemical factories.
Domestic wastes affect water courses downstream large cities like Bacau, Suceava, Piatra Neamt,
Focsani, Vaslui and Barlad, leading to eutrophication and organic pollution of the middle and lower
Siret river basin and its main tributaries. Thus, from the 10 major water courses, only the Moldova
and Putna catchment areas are not affected.
The rubble pits located on the main river courses affect river natural habitats, changing the
water course, eliminating fish refuge or breeding places. Even if this pressure is not so important
compared to the first two, the increased number of rubble pits especially in the middle and lower
river courses, strongly affect the natural habitats. Up to 5% from the length of some river segments
are severely affected.
36
3. General analysis of pressures
Nearly 65% of watercourses are potentially at risk in the Nemunas River basin in the
Lithuanian territory. From those due to non-point pollution - 37%, point sources – 12%,
morphological changes – 35%, hydropower – 1%, due to several impacts – 14%. Straightened rivers
are polluted in quite a few of cases, particularly those, flowing in the agricultural regions in the
lowlands of Nemunas River basin.
As concerns polluting substances (nutrients), good/moderate status threshold values are
more or less established already (based on status of fish communities). Hydromorphological
alterations and continuity pose much more problems, because we lack data on rivers, affected by
those pressures alone. Data on impact on fish communities is controversial, too.
Continuity. Two types of disruption in river continuity can be singled out, differing in a
spatial scale and in their impact on fish communities of the rivers of different types. Artificial
obstacles on a river basin scale. Artificial obstacle on the main river that is essential for migratory
fish to reach spawning grounds results in total disappearance of fish in the whole catchment and
sub-catchments above the obstacle. On the other hand, long distance migrating fish and lamprey
species rarely spawn in the small tributaries with catchment size less than 100 km2. Therefore basinscale artificial obstacle seems to have no impact on the status of fish communities in small rivers.
Artificial obstacles on a river scale. Artificial obstacle on a river scale prevents free fish migration
to spawning, feeding and wintering places. In this case high or even good status of fish communities
is unachievable in the river stretches above the obstacles, even in those of small streams. But, again,
this concerns only rivers (river stretches) with catchment size greater than 30-40 km2. Fish species,
sensitive to disruptions in river continuity rarely occupy <30-40 km2 catchment size stream
stretches.
Hydrology. Available data is insufficient for well based assessment of impact of decrease in
water yield on state of fish communities (the lack of sites with deviation of water yield being the
main reason of poorer ecological status). However, current data indicates >30% decrease in water
yield being considerable pressure, particularly if such decrease occurs during the low flow period.
Based on published data, water reservoirs constructed on the rivers change significantly the annual
pattern of the flow. Reservoirs diminish maximum flow quantity during the high flow periods.
However, the impact of reservoirs on minimal water yields is controversial, being dependant on
annual abundance of water. There are differences in different regions of Lithuania, too. Published
data on impact of land reclamation and regulation of river beds on deviation from natural flow
quantity and pattern of the rivers of Lithuania is also highly controversial.
Straightening of river beds. There are great differences in the status of fish communities
depending on the environmental characteristics. There are evidences that fish communities meet
good status criteria in some of highland, higher slope canalized streams in Lithuania. Fish
community status in the lowland, low slope canalized rivers is poor or even bad; community is
represented by few eurytopic species (usually roach, perch, pike), in very low densities.
Currently we have no possibility to clearly indicate potential new metrics (not covered by
previous FAME project) which incorporate Lithuanian river particularities. However, metric of fish
density (total density as well as density of separate species) seems promising for identification of
hydrological, and particularly – morphological pressure (canalization). Metrics characterizing
benthic species seem promising for assessment of low slope canalized streams (however, not valid
for higher slope ones).
Due to substantial differences between particular regions of Poland, described above,
pressure analysis should be done separately for each region (Table 5 and 6).
Rivers in North Poland were affected by continuity disruption on catchments scale (76%
FO), but to much less extent than in other regions. Also instream habitat changes and riparian
vegetation degradation were important pressures in this region, as well as floodplain water bodies
disconnection with the river. Water quality was good or moderate in this region, due to less dense
37
population, low share of intensively managed arable fields and lack of heavy industry. Main
pressures in this region were: "barriers catchment down", "Floodplain", "instream habitat" and
"riparian vegetation". Hydromorphological pressures prevailed over water quality-related ones.
In East Poland most important pressure was lack of connectivity on catchment scale (100%
FO) and hydrological modification, due to strong influence of Siemianowka reservoir on Narew R.
Water quality was moderate (2-end and 3-rd class made 99% FO, no 5-th class was found), other
pressures occurred in less than 10% FO. This region should be considered as relatively natural, with
slight human impact and sites on Biebrza and Narew rivers, located mainly in National Parks may
be treated as reference conditions for lowland, organic rivers, except for diadromous species, which
are strongly impacted by disruption of connectivity on catchment scale. Almost every river in this
region have floodplain and no impact on its connection with river channel was noted.
Rivers of Central Poland were strongly impacted by barriers on catchment scale as well as
on river segment scale. Also instream habitat was modified in 56% FO, while hydrological regime in 40%. Important pressures in this region are also floodplain disconnection, hydrological
modification, velocity increase, riparian vegetation alteration, water abstraction (mainly for
irrigation and fish ponds) and impoundment. Water quality was usually poor (56% FO in 4-th and
5-th class), strong eutrophication, sedimentation and organic pollution levels were also stated. This
results from high population density, intensive agriculture, considerable urbanization and
industrialization of this region. Vistula R. (30 sites) and some smaller rivers, like Brok R. are still
quite polluted and eutrophicated, although this situation improved much in last two decades (only
15% of FO in 5-th class of water quality).
Sub-mountain and mountain region of South Poland was affected mainly by permanent lack
of catchment scale connectivity. Considerable changes of instream habitat and strong modification
of riparian vegetation were also found. High level of disconnection of floodplain was a misleading
parameter here, because only few rivers in this region possessed former floodplain. Water quality
was the best in this region (57% FO in 1-rst and 2-cond class), only eutrophication level was a bit
higher (23 % FO). Rivers sampled in this region (mainly small streams and upper reaches of larger
rivers) are slightly impacted with water quality-related pressures, while considerable impact on
morphology and especially connectivity is visible. Nevertheless among these streams one can find
sites close to reference conditions for those kinds of rivers, with a precaution about connectivity for
diadromous species, similar to those in Eastern Poland.
West region of Poland is most populated, industrialized and modified by human activity
part of Poland. This is reflected in high values of all hydromorphological pressures listed in Table 5,
the most important of which are: connectivity on catchment and river segment scale, impoundment,
instream habitat alteration, floodplain disconnection, (> 50% FO) and also hydrological
modification, and water abstraction (mainly for fish ponds). Water quality was moderate in this
region, however 49% FO were in 3-dr and 21% - in 4-th and 5-ths class, what indicates
considerable impact, together with high eutrophication rate. Some data from this region were
however collected in mid-1990-ties, so the present state of water quality in this region may be much
better than this registered in the previous years (parallel to fish sampling). High level of pressure in
this region, including considerable impoundment may be responsible for higher share of roach in
fish communities, than in South Poland. In case of small, sub-mountain rivers increased share of
roach may indicate high hydromorphological alteration, as opposite to lowland rivers of Eastern and
Central Poland, where roach strongly dominate in almost unimpacted environments.
Main drivers found in the pressure analysis for Polish rivers were:
- barriers segment up - hydrological modification - hydropeaking and reservoir flushing
- barriers segment down - impoundment
- channelization + crossection - instream habitat modification and velocity increase
- embankment + flood protection - riparian vegetation alteration and floodplain connectivity
For general pressure analysis, presented above, only key pressures (having more complex
and direct influence on fish communities) from each chain were used, in order reduce the number of
38
variables compared. Pressures with low significance (affecting scarce percentage of sites) were also
excluded from analysis.
Conclusions and potential new metrics which incorporate the considered river
particularities in Poland:
Described above substantial differentiation of governing environmental factors, like:
climate, drainage basin, altitude, actual river slope and geological typology between groups of sites
located in particular regions of Poland causes need of separate analysis of each region in terms of
fish community structure and pressures assessment. However for new metrics development
geographical aspects are not good criteria – except for large scale regions – e.g. Mediterranean,
Western Europe, Northern Europe, and Central-Eastern Europe.
Taking into account the above analysis of Polish dataset it looks obvious, that river slope
should be considered as the most important, governing environmental parameter. The “Actual river
slope” median value is closely related to fish community structure in particular regions. Eastern and
Central Poland shows some similarities in ichthyofauna composition and dominance structure. The
median slope values in these regions are 0.2 and 0.8‰ respectively (that is below 1 ‰). Also fish
assemblages found in North and West Poland show some similarities, despite great difference in
geographical parameters, including absolute elevation. Median river slope in those regions is 1.6
and 2.4 ‰. Considerably different fish community structure was found in mountain region of South
Poland (median slope 7.0‰).
Combining the data on environmental characteristic, fish community structure and main
pressures in particular regions and taking into account available literature data one can formulate
several conclusions, leading to some metrics changes and new propositions for Central-Eastern
Europe rivers:
1) It is necessary to exclude in rivers of slope ≤ 1‰ the metrics basing on presence and abundance
of brown trout, bullhead (Cottus sp.) and other species of similar environmental requirements,
as far as these species are rare or completely not occur in such habitats also at close to natural
conditions. This change is obligatory for “Organic” rivers.
2) In rivers of slope ≤1‰) presence of roach, rudd, bleak, bream, white bream and tench should
not be treated as a result of human impact (lowering the evaluation score), but as a natural fish
community composition (as it can be seen in Eastern Poland at small pressure level). This
change is obligatory for “Organic” rivers.
3) In case of tench, crucian carp and rudd there is even possibility to develop a new metric,
applicable only for rivers of slope ≤ 1‰ with former floodplain (and especially to “organic”
rivers). This metric will base on the fact, that presence (and considerable abundance) of that
species in mid-channel habitats indicates good connectivity between the river and floodplain
water-bodies (oxbows).
4) In rivers considered (slope ≤1‰) the presence and abundance of predators - mainly pike and
perch, but in larger rivers also pikeperch, wells and asp, may serve as a new metric, indicating
good (close to natural) fish community structure.
5) In rivers of slope higher than 1‰ metrics developed in former EFI project, scoring positively
presence and abundance of brown trout, bullhead (Cottus sp.) and other species of similar
environmental requirements are applicable in Poland. However a precaution should be made,
that in Poland brown trout (and also grayling) originates in a number of rivers exclusively or
mainly from introduction and stocking, so other species of this group should have more
“weight” in this metrics calculation for Polish rivers.
6) In rivers with slope >1‰ the presence (or probably better the abundance) of roach may still be
treated as a metric indicating human pressure (especially connected with the presence of
impoundment). This can be seen in the data set from West Poland, where roach constituted
considerable part of fish communities in rivers of higher slope, but strong hydromorphological
impact.
39
As for Romania, the sampling sites located in the Siret catchment area were chosen in order
to illustrate the pressures presented above, according to three criteria: altitude, slope and habitat
nature.
According to the altitude, the sampling sites from the Siret catchment area were distributed
as follows:
- mountainous region (over 800 m a.s.l.) – 23 sites – 8.70%
- plateau region (500-800 m a.s.l.) – 91 sites – 34.5 %
- hilly region (200-500 m a.s.l.) – 89 sites – 33.7%
- plain region (under 200 m a.s.l.) – 61 sites – 23.1%
Depending on the river slope, the sampling site distribution was balanced in the plateau,
hilly and plain regions, as follows:
- mountainous region (> 40‰) – 9 sites – 3.42%
- high plateau region (20 - 40‰) – 29 sites – 10.98%
- plateau region (10-20‰) – 54 sites – 20.45%
- hilly region (5-10‰) – 85 sites – 32.2%
- plain region (0,2-5‰) – 87 sites – 32.95%
The distribution of the 263 sampling sites from the Siret catchment area according to the
riverbed nature was made as follows:
- rocks and boulders – 76 sites – 28.79%
- pebble – 141 sites – 53.41%
- sand – 18 sites – 6.82%
- clay – 29 sites – 10.98%
The sampling sites located on pebble riverbed dominated (exceeding 50%), together with
those located on boulders and rocks. This is caused by the fact that 6 out of the 10 rivers considered
flow through mountainous regions - the Eastern Carpathians.
Hydrological pressures refer to the following:
- Barriers, upstream and downstream, affect 6-10% from the habitats characteristic to the
sampling sites. The barrier impact is less severe compared to the hydrotechnical works (20 large
dam reservoirs, over 200 km of canals and dikes).
- The other pressures (hydropeaking, water abstraction, channelization, and embankment)
play a major role too, even if they only affect up to 10% from the habitats
The percentage of sited affected by these pressures was as follows:
- Barriers – upstream – 24 sites – 9.1%; downstream - 17 sites – 6.4%
- Hydropeaking – 14 sites – 5.3%
- Water abstraction – 11 sites – 4.17%
- Channelization - intermediate – 13 sites – 4.92%
- straightened – 23 sites – 8.71%
- Embankment - local – 15 sites – 5.68%
- continuous – 15 sites – 5.68%
Water quality of aquatic habitats refers to toxic substances, eutrophication, organic pollution
and organic siltation, together with a division into water quality classes.
The toxic substances affecting the habitats in the considered sites from the Siret
catchment area were relatively low (high – 3.41%, intermediate – ca. 9.5%). Over 85% from the
river courses could be considered to be clean waters from this point of view. Even if toxic
substances sources were numerous (oil products, fertilizers, cyanide products, phenols etc.), after
the year 1990 the effects became less severe due to decreases of ca. 80% of Romanian industrial
production, as depicted below:
- Toxic substances – no – 220 sites – 83.33%
- low – 10 sites – 3.79%
- intermediate – 25 sites – 9.47%
- high – 9 sites – 3.41%
40
On the other hand, the domestic wastes and polluted waters coming from agriculture and
animal husbandry caused an increase in organic substances, affecting ca. 40% from the considered
habitats, causing eutrophication of ca. 20% of the habitats, especially in the middle and lower river
courses. This phenomenon led to the accumulation of organic pollutants in sediments (in ca. 13% of
the sites). The percentage of sites affected by these pressures is presented below:
- Organic pollution - no – 150 sites – 56.82%
- weak – 101 sites – 38.26%
- strong – 13 sites – 4.92%
- Eutrophication - no – 212 sites – 80.3%
- low – 47 sites – 17.8%
- intermediate – 5 sites – 1.9%
- high – 0 sites
- Organic siltation – no – 230 sites – 87.12%
- yes – 34 sites – 12.88%
- Water quality - 1- excellent – 184 sites – 69.69%
- 2 - good – 42 sites – 15.91%
- 3 – mediocre – 27 sites – 10.23%
- 4 – poor – 11 sites – 4.17%
Thus, from this point of view, the strongest pressures in the Romanian river courses come
from hydrotechnical works, followed by pollution and not the other way around.
Romania entered a period of important political, social and economic changes. As a direct
result, the construction of new access roads and buildings increased in the past years. Since more
and more building and infrastructure material is needed, the rubble pits on the rivers throughout
Romania increased proportionally. That is why we consider that it might be an illustrative and
country-specific metric for the EFI+ project, relative to the decrease of benthic fish densities.
A general comparison between Western and Central/Eastern European rivers might reveal
the fact that while the first have no problems with pollution, the last are still characterized by good
hydromorphological conditions. However, the present report shows a different situation as concerns
the Central and Eastern European Rivers. While in Hungarian and Lithuanian rivers pollution was
identified as the main pressure (mainly organic pollution in both countries), in Poland and Romania
the main pressures were the hydromorphological ones: lack of connectivity, impoundment, water
abstraction etc. in Poland and hydrotechnical works together with other hydromorphological
pressures (including instream habitat changes due to rubble pits) in Romania.
41
21
ANNEX 1:
List of fish species according to the EFI+ project from Lithuanian freshwaters:
Fish families and species
English
Petromyzontidae
Sea lamprey
Petromyzon marinus L.
River lamprey
Lampetra fluviatilis (L.)
Brook lamprey
Lampetra planeri (Bloch)
Acipenseridae
Atlantic sturgeon
Acipenser sturio (L.)
Clupeidae
Allis shad
Alosa alosa (L.)
Latin
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Twaite shad
Salmonidae
Atlantic salmon
Salmo salar (L.)
Sea trout
Salmo trutta trutta (L.)
River trout
Salmo trutta fario (L.)
Coregonidae
Vendace
Coregonus albula (L.)
Whitefish (both, sea and lake
Coregonus lavaretus (L.)
forms)
Thymallidae
Grayling
Thymallus thymallus (L.)
Osmeridae
Smelt (both, sea and lake forms)
Osmerus eperlanus (L.)
Esocidae
Pike
Esox lucius (L.)
Anguillidae
Eel
Anguilla anguilla (L.)
Cyprinidae
Roach
Rutilus rutilus (L.)
Chub
Leuciscus cephalus (L.)
Dace
Leuciscus leuciscus (L.)
Ide
Leuciscus idus (L.)
Minnow
Phoxinus phoxinus (L.)
Lake minnow
Phoxinus percnurus
Scardinius erythrophthalmus (L.) Rudd
Asp
Aspius aspius (L.)
Moderlieschen
Leucaspius delineatus (Heck.)
Tench
Tinca tinca (L.)
Nase
Chondrostoma nasus (L.)
Gudgeon
Gobio gobio (L.)
Barbel
Barbus barbus (L.)
Bleak
Alburnus alburnus ( L.)
Schneider
Alburnoides bipunctatus (Bloch.)
Silver bream
Blicca bjoerkna (L.)
Zope or Blue Bream
Abramis ballerus (L.)
Bream
Abramis brama (L.)
Zahrte or Vimba
Vimba vimba (L.)
Ziege
Pelecus cultratus (L.)
Bitterling
Rhodeus sericeus (Bloch)
Crucian carp
Carassius carassius (L.)
Alosa fallax (Leceped)
Current status*
M
C
C
Ex
Ex;(historical presence
questionable)
C
M-C
C
C
C
C
C
C
C
C
C
C
C
C
C
M
C
C
C
C
M
C
C
C
C
C
Ex
C
C
C
C
C
42
Cobitidae
Stone loach
Spined loach
Golden loach
Pond loach
Siluridae
42 Silurus glanis (L.)
Wels
Gadidae
43 Lota lota (L.)
Burbot
Percidae
44 Gymnocephalus cernuus (L)
Ruff
45 Perca fluviatilis (L.)
Perch
46 Sander lucioperca (L.)
Zander or pikeperch
Cottidae
47 Cottus gobio (L.)
Bullhead
Pleuronectidae
48 Platichthys flesus (Duncker)
Flounder
Gasterosteidae
49 Pungitius pungitius (L.)
Ten-spined stickleback
50 Gasterosteus aculeatus (L.)
Three-spined stickleback
* : C – common species, M – minor species, Ex - extinct
38
39
40
41
Barbatula barbatula (L.)
Cobitis taenia (L.)
Sabanejewia aurata (Filippi)
Misgurnus fossilis (L.)
C
C
M
M
M-C
C
C
C
C
C
C
M-C
C
43
ANNEX 2.a:
Dominant fish species – according to frequency (% FO – fishing occasions), number of fish caught
(%N) and total fish abundance (%A) in Poland and its particular regions
Poland
Fish number (≥ 5%)
Frequency (≥ 33 %)
Species
Gobio gobio
Salmo trutta fario
Perca fluviatilis
Rutilus rutilus
Esox lucius
Barbatula barbatula
Gasterosteus aculeatus
%FO
50
49
46
46
43
39
33
Species
Rutilus rutilus
Gobio gobio
Phoxinus phoxinus
Alburnus alburnus
Barbatula barbatula
Salmo trutta fario
Perca fluviatilis
Northern Poland
Species
%FO Species
60
Salmo trutta fario
Gobio gobio
45
Salmo trutta fario
44
Gobio gobio
Phoxinus phoxinus
42
Cottus gobio
Rutilus rutilus
38
Perca fluviatilis
Cottus gobio
37
Esox lucius
Alburnus alburnus
34
Rutilus rutilus
33
Lampetra planeri
Pungitus pungitus
Eastern Poland
Species
%FO Species
96
Rutilus rutilus
Rutilus rutilus
96
Esox lucius
Blicca bjoerkna
81
Blicca bjoerkna
Esox lucius
87
Perca fluviatilis
Perca fluviatilis
77
Leuciscus idus
Scardinius erythrophthalmus
73
Lota lota
Rhodeus amarus
Scardinius erythrophthalmus 68
62
Tinca tinca
57
Alburnus alburnus
51
Rhodeus amarus
39
Gobio gobio
34
Carassius carassius
34
Cobitis taenia
34
Misgurnus fossilis
33
Abramis brama
Central Poland
Species
%FO Species
78
Gobio gobio
Rutilus rutilus
76
Barbatula barbatula
Alburnus alburnus
74
Perca fluviatilis
Perca fluviatilis
71
Rutilus rutilus
Gobio gobio
69
Esox lucius
50
Leuciscus leuciscus
%N
20
13
10
8
7
6
5
%N
14
12
11
10
7
7
7
5
%N
48
8
8
7
6
5
%N
32
22
10
8
Total fish abundance (≥ 5%)
Species
%A
19
Rutilus rutilus
14
Gobio gobio
9
Phoxinus phoxinus
6
Alburnus alburnus
6
Barbatula barbatula
6
Salmo trutta fario
5
Perca fluviatilis
Species
%A
13
Gobio gobio
12
Cottus gobio
11
Salmo trutta fario
9
9
Phoxinus phoxinus
8
Pungitus pungitus
8
Rutilus rutilus
Species
%A
46
Rutilus rutilus
8
Esox lucius
7
Blicca bjoerkna
6
Perca fluviatilis
5
Lota lota
Total fish abundances (≥ 5%)
Species
%A
27
Rutilus rutilus
19
Perca fluviatilis
18
Alburnus alburnus
12
Gobio gobio
5
Barbatula barbatula
44
Central Poland (continued)
Species
%FO Species
%N
47
Leuciscus cephalus
46
Cobitis taenia
43
Leuciscus idus
42
Alburnus alburnus
41
Lota lota
33
Southern Poland
Species
%FO Species
%N
80
35
Salmo trutta fario
Phoxinus phoxinus
67
12
Phoxinus phoxinus
Leuciscus cephalus
60
10
Cottus poecilopus
Barbatula barbatula
56
9
Barbatula barbatula
Barbus peloponesius
52
9
Leuciscus cephalus
Cottus poecilopus
47
5
Barbus peloponesius
Salmo trutta fario
38
Gobio gobio
Western Poland
Species
%FO Species
%N
86
29
Barbatula barbatula
Gobio gobio
66
19
Gobio gobio
Barbatula barbatula
56
17
Rutilus rutilus
Phoxinus phoxinus
48
10
Salmo trutta fario
Rutilus rutilus
45
8
Phoxinus phoxinus
Salmo trutta fario
5
Total fish abundances (≥ 5%)
Species
%A
Species
%A
30
Phoxinus phoxinus
25
Cottus poecilopus
8
Salmo trutta fario
7
Barbatula barbatula
7
Leuciscus cephalus
5
Barbus peloponesius
Species
%A
31
Gobio gobio
22
Phoxinus phoxinus
18
Barbatula barbatula
10
Salmo trutta fario
6
Rutilus rutilus
45
ANNEX 2.b:
Dominant fish species – according to frequency (% FO – fishing occasions), number of fish caught
(%N) and total fish abundance (%A) in large rivers and oxbows (East and Central Poland)
sampled with electrofishing:
Species
Rutilus rutilus
Perca fluviatilis
Esox lucius
Leuciscus idus
Alburnus alburnus
Blicca bjoerkna
Large rivers
%FO
99
95
91
90
81
76
%N
36
24
11
5
Oxbows
Species
Rutilus rutilus
Esox lucius
Perca fluviatilis
Tinca tinca
Blicca bjoerkna
Leuciscus idus
Species
Rutilus rutilus
Alburnus alburnus
Perca fluviatilis
Gobio gobio
%FO
100
98
91
86
84
80
77
Species
Rutilus rutilus
Blicca bjoerkna
Esox lucius
Perca fluviatilis
Rhodeus amarus
%N
46
8
7
7
7
6
Species
%A
31
Rutilus rutilus
19
Alburnus alburnus
13
Perca fluviatilis
10
Gobio gobio
Species
%A
47
Rutilus rutilus
8
Blicca bjoerkna
7
Esox lucius
5
Cobitis taenia
5
Perca fluviatilis
5
Rhodeus amarus
46
ANNEX 3:
List of fish species according to the EFI+ project from Hungarian water bodies:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Species
Eudontomyzon mariae (BERG, 1931)
Eudontomyzon danfordi (REGAN, 1911)
Acipenser ruthenus (LINNAEUS, 1758)
Anguilla anguilla (LINNAEUS, 1758)
Rutilus rutilus (LINNAEUS, 1758)
Rutilus pigus (HECKEL, 1852)
Ctenopharyngodon idella (VALENCIENNES, 1844)
Scardinius erythrophthalmus (LINNAEUS, 1758)
Leuciscus leuciscus (LINNAEUS, 1758)
Leuciscus cephalus (LINNAEUS, 1758)
Leuciscus idus (LINNAEUS, 1758)
Phoxinus phoxinus (LINNAEUS, 1758)
Aspius aspius (LINNAEUS, 1758)
Leucaspius delineatus (HECKEL, 1843)
Alburnus alburnus (LINNAEUS, 1758)
Alburnoides bipunctatus (BLOCH, 1782)
Blicca bjoerkna (LINNAEUS, 1758)
Abramis brama (LINNAEUS, 1758)
Abramis ballerus (LINNAEUS, 1758)
Abramis sapa (PALLAS, 1814)
Vimba vimba (LINNAEUS, 1758)
Pelecus cultratus (LINNAEUS, 1758)
Chondrostoma nasus (LINNAEUS, 1758)
Tinca tinca (LINNAEUS, 1758)
Barbus barbus (LINNAEUS, 1758)
Barbus peloponnesius petenyi (HECKEL, 1852)
Gobio gobio (LINNAEUS, 1758)
Gobio albipinnatus LUKASH, 1933
Gobio kessleri (DYBOWSKI, 1862)
Pseudorasbora parva (TEMMINCK & SCHLEGEL, 1842)
Rhodeus sericeus (PALLAS, 1776)
Carassius carassius (LINNAEUS, 1758)
Carassius gibelio (BLOCH, 1782)
Cyprinus carpio (LINNAEUS, 1758)
Hypophthalmichthys molitrix (VALENCIENNES, 1844)
Barbatula barbatula (LINNAEUS, 1758)
Misgurnus fossilis (LINNAEUS, 1758)
Cobitis elongatoides (BACESCU & MAIER, 1969)
Sabanejewia aurata (FILIPPI, 1865)
Silurus glanis (LINNAEUS, 1758)
Ameiurus melas (RAFINESQUE, 1820)
Ameiurus nebulosus (LESUEUR, 1819)
Salmo trutta m. fario (LINNAEUS, 1758)
Umbra krameri (WALBAUM, 1792)
Esox lucius (LINNAEUS, 1758)
Lota lota (LINNAEUS, 1758)
Gasterosteus aculeatus (LINNAEUS, 1758)
Lepomis gibbosus (LINNAEUS, 1758)
Perca fluviatilis (LINNAEUS, 1758)
Gymnocephalus cernuus (LINNAEUS, 1758)
47
51
52
53
54
55
56
57
58
59
60
61
62
Species (continued)
Gymnocephalus baloni (HOLČIK & HENSEL, 1974)
Gymnocephalus schraetser (LINNAEUS, 1758)
Sander lucioperca (LINNAEUS, 1758)
Sander volgensis (GMELIN, 1788)
Zingel zingel (LINNAEUS, 1758)
Zingel streber (SIEBOLD, 1863)
Proterorhinus marmoratus (PALLAS, 1814)
Neogobius fluviatilis (PALLAS, 1814)
Neogobius kessleri (GÜNTHER, 1861)
Neogobius melanostomus (PALLAS, 1814)
Neogobius gymnotrachelus (KESSLER, 1857)
Perccottus glenii (DYBOWSKI, 1877)
48
ANNEX 4.a:
List of Romanian freshwater fish species according to Nalbant 2003 considered
for the EFI+ project:
No.
EFI+ species name
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Abramis ballerus
Abramis brama
Abramis sapa
Acipenser gueldenstaedtii
Acipenser nudiventris
Acipenser ruthenus
Acipenser stellatus
Acipenser sturio
Alburnoides bipunctatus
Alburnus alburnus
Alosa maeotica
Anguilla anguilla
Aspius aspius
Atherina boyeri
Barbatula barbatula
Barbus barbus
Barbus petenyi
Blicca bjoerkna
Carassius carassius
Carassius gibelio
Chalcalburnus chalcoides
Chondrostoma nasus
Cobitis elongata
Cobitis elongatoides
Cobitis megaspila
Cobitis taenia
Coregonus lavaretus
Cottus gobio
Cottus poecilopus
Cyprinus carpio
Esox lucius
Eudontomyzon danfordi
Eudontomyzon mariae
Gambusia holbrooki
Gobio gobio
Gobio kesslerii
Gobio uranoscopus
Gymnocephalus baloni
Gymnocephalus cernuus
Gymnocephalus schraetser
Hucho hucho
Huso huso
Knipowitschia caucasica
Lampetra planeri
Lepomis gibbosus
Leucaspius delineatus
Leuciscus borysthenicus
Romanian synonyms
Caspialosa maeotica
Orthrias barbatulus
Cobitis danubialis
Gobio obtusirostris
Romanogobio kessleri
Rheogobio uranoscopus
Squalius borysthenicus
Species name according to
Kottelat & Freyhof 2007
Ballerus ballerus
Abramis brama
Ballerus sapa
Acipenser gueldenstaedtii
Acipenser nudiventris
Acipenser ruthenus
Acipenser stellatus
Acipenser sturio
Alburnus alburnus
Alosa maeotica
Anguilla anguilla
Aspius aspius
Atherina boyeri
Barbatula barbatula
Barbus barbus
Barbus petenyi
Blicca bjoerkna
Carassius carassius
Carassius gibelio
Alburnus chalcoides
Chondrostoma nasus
Cobitis elongata
Cobitis elongatoides
Not listed
Cobitis taenia
Coregonus lavaretus
Cottus gobio
Cottus poecilopus
Cyprinus carpio
Esox lucius
Eudontomyzon danfordi
Eudontomyzon mariae
Gambusia holbrooki
Gobio gobio
Romanogobio kesslerii
Romanogobio uranoscopus
Gymnocephalus baloni
Hucho hucho
Huso huso
Knipowitschia caucasica
Lampetra planeri
Lepomis gibbosus
Petroleuciscus borysthenicus
49
No.
EFI+ species name
Romanian synonyms
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
Leuciscus cephalus
Leuciscus idus
Leuciscus leuciscus
Liza aurata
Liza ramada
Liza saliens
Lota lota
Misgurnus fossilis
Mugil cephalus
Pelecus cultratus
Perca fluviatilis
Perccottus glenii
Phoxinus phoxinus
Proterorhinus marmoratus
Pseudorasbora parva
Pungitius platygaster
Rhodeus amarus
Romanichthys valsanicola
Rutilus heckelii
Rutilus rutilus
Sabanejewia balcanica
Sabanejewia bulgarica
Sabanejewia romanica
Salmo trutta fario
Salvelinus fontinalis
Sander lucioperca
Sander volgensis
Scardinius racovitzai
Silurus glanis
Syngnathus abaster
Thymallus thymallus
Tinca tinca
Umbra krameri
Vimba vimba
Zingel streber
Zingel zingel
Zosterisessor ophiocephalus
Squalius cephalus
Idus idus
Odontobutis glenii
Rutilus carpathorosicus
Salmo fario
Stizostedion lucioperca
Stizostedion volgensis
Vimba carinata
Species name according to
Kottelat & Freyhof 2007
Squalius cephalus
Leuciscus idus
Leuciscus leuciscus
Liza aurata
Liza ramada
Liza saliens
Lota lota
Misgurnus fossilis
Mugil cephalus
Pelecus cultratus
Perca fluviatilis
Perccottus glenii
Phoxinus phoxinus
Proterorhinus semilunaris
Pseudorasbora parva
Pungitius platygaster
Rhodeus amarus
Romanichthys valsanicola
Rutilus heckelii
Rutilus rutilus
Sabanejewia bulgarica
Sabanejewia romanica
Salmo trutta
Sander lucioperca
Sander volgensis
Scardinius racovitzai
Silurus glanis
Syngnathus abaster
Thymallus thymallus
Tinca tinca
Umbra krameri
Vimba vimba
Zingel streber
Zingel zingel
Zosterisessor ophiocephalus
50
ANNEX 4.b:
List of introduced and invasive species from Romania:
I. Fam. Salmonidae
1.
Onchorhyncus mykiss – Rainbow trout
2.
Salvelinus fontinalis – Brook trout
3.
Salvelinus namayicus – Lake trout
4.
Coregonus albula
5.
Coregonus lavaretus
II. Fam. Acipenseridae
6.
Polyodon spatula
III. Fam. Cyprinidae
7.
Ctenopharhyngodon idella – gras carp
8.
Hypophthalmychties molitrix – silver carp
9.
Aristichtyes nobilis
10.
Mylopharhyngodon piceus
11.
Pseudorasbora parva
IV. Fam. Catostomidae
12.
Ictiobus niger
13.
Ictiobus bubalus
14.
Ictiobus cyprinellus
V. Fam. Poecilidae
15.
Gambusia holbroki
VI. Fam. Siluridae
16.
Clarias sp.
VII. Fam. Ictaluridae
17.
Ictalurus nebulosus
18.
Ictalurus melas
VIII. Fam. Centrarchidae
19.
Lepomis gibbossus
IX. Fam. Odonthobutidae
20.
Percottus glenii
51
EFI + - Improvement and spatial extension of the
European Fish Index
WP 3, Subtask 7 - Mediterranean River Assessment
Periodical report – Testing new responsive metrics
Teresa Ferreira, Pedro Segurado, Paulo Pinheiro & José Maria Santos
Instituto Superior de Agronomia, Portugal
March 2008
52
Table of contents
1. Introduction..................................................................................................... 3
2. Identification of Mediterranean-type sites ....................................................... 6
2.1 Background............................................................................................... 6
2.2 The EFI+ classification.............................................................................. 7
3. Testing new metrics for mediterranean rivers............................................... 11
3.1 Methodology ........................................................................................... 11
3.1.1 Overview .......................................................................................... 11
3.1.2 Data screening ................................................................................. 11
3.1.3 Pressure indexes.............................................................................. 12
3.1.4 Selected metrics............................................................................... 15
3.1.5 Quantification of species tolerance................................................... 17
3.1.6 Testing metric responses ................................................................. 19
3.2 Results.................................................................................................... 24
3.2.1 Geographical gradients .................................................................... 24
3.2.2 Pressure analysis ............................................................................. 25
3.2.3 Testing metrics’ responses to pressure............................................ 28
4. Conclusions and recommendations ............................................................. 47
5. References ................................................................................................... 50
2
53
1. Introduction
Previously in FAME project and also in other studies, fish assemblage’ metric
responses to perturbation across Mediterranean areas were poor, and weaker than
those used at the European level, both using fish-based or spatially based models
(Pont et al. 2006; Schmutz et al. 2007). Major bottlenecks for the development of a
multimetric index in Mediterranean regions include i) the peculiar richness patterns
displayed at different space scales, ii) the naturally harsh and fluctuating, warm
climate-dependent, aquatic environment, and iii) a complex and hardly-predictable
combination of hydrological variability with human pressures, either present or inherited
throughout centuries of fluvial and landscape uses. Moreover, there are also
considerable
within-region
differences
related
to
the
micro-scale
fluctuating
environments and macroscale landscape patchiness, shaped by a complex geological
evolutionary
background.
Attempts
to
develop
local
metric
indices
for
the
Mediterranean regions dealt with these limitations (Ferreira et al., 1996; Oliveira &
Ferreira 2002), with modest degrees of success and always at small regional scales,
while taxa-based fish indices for quality assessment are virtually non-existent.
In Southern European areas, the primary freshwater fish fauna is dominated by
cyprinids and is characterized by a low number of genera and a high number of
species per genera (Doadrio, 2001). Low species richness per site, a high degree of
endemicity and basin-specific taxa assemblages, are problematic for developing biotic
indices (Miller et al. 1988; Moyle & Marchetti 1999). Often, Mediterranean fish species
have restricted distributions, and characterize a small region, a small basin or even a
group of sites. A non-spatially, modeling approach such as the one in FAME’s tried to
overcome this problem, however, the taxonomic variability of fish assemblages found in
Mediterranean systems can be so high that the number of available total and reference
sites was too reduced to apply a site-based approach in southernmost areas of Iberia
and Greece (Pont et al. 2006).
Mediterranean-type regions generally experience limited water availability during part
of the year. For 6000 years now, Man has overcome this water shortage by water
storage in reservoirs, water abstraction from ground and surface sources and water
transfers (Davies et al., 1994). While in temperate European rivers, anthropogenic
disturbance frequently focuses on water quality and physical habitat modification, and
3
54
hydrological alterations are minima, in Mediterranean ecosystems, water quality issues
are determined and amplified by the amount (or lack) of water that flows in the river
channel (sometimes represented only by sewage water…), and water quality is
superimposed by the hydrological yearly evolution. Even a small quantity of sewage
can represent a large impairment when the river flow is smaller than it should be, but
likewise in can be masked if flow is artificially increased by dam or irrigation outflows.
The water quantity-dependent nature of human pressures results in less predictable,
antagonistic or cumulative effects. These effects have been taking place for centuries,
though intensified mid-last century onwards with the up-scale of engineering expertise
and materials. As a result, it is often difficult to determine whether a site is experiencing
a natural or otherwise induced flow change situation, or to quantify such change.
Hydrological variability of Mediterranean-type regions profoundly determines the life
forms and life cycles of aquatic organisms, as well as ecological processes (Gasith &
Resh 1999). Fish fauna from these heterogeneous ecosystems must frequently survive
under alternating scenarios of too much or too little water with a few intermediate but
crucial periods of investment in recruitment and growth. Under these conditions, fishes
tend to have short life spans, rapid growth rates, high fecundity and early sexual
maturity and spawning, as well as generalist and opportunistic feeding strategies
(Granado-Lorencio 1996; Pires, Cowx & Coelho 2001; Vila-Gispert, Moreno-Amich &
Garcia-Berthou 2002). During low-flow season, biotic controls (e.g. predation,
competition) may take over assemblage responses to other pressures (Matthews &
Marsh-Matthews, 2003). The apparent tolerance of native species to naturally harsh
environments and their obvious short-term resilience may actively mask man-made
pressures, e.g. impede the distinction between a fortuitous series of natural low-flow
years and the downstream water decrease through damming. Doubtless, separating
natural and human-made pressures is a central problem in bioassessment (Fausch et
al. 1990). Finally, affinity taxa, sometimes with a recent genesis in geological terms, are
likely to have similar ecological requirements, but frequently there is a lack of evidence
for such assumption. However, metric development strongly relies on accurate guild
classification and reliable tolerance responses.
The objectives of the sub-task 3.7 Mediterranean River Assessment, which will be dealt
with along this report, included:
4
55
a) For the improvement of the database
- To identify truly Mediterranean-type sites and increase the number of fishing sites
available for data treatment;
- To increase the quality and decrease the spatial scale of the impairment drivers,
especially those related to hydrological and geo-morphological changes;
- To increase the quality of the reference conditions, through ecological data screening
b) For the improvement of metric response
- To attempt the definition of synthetic variables for different types of pressure,
including biotic pressure;
- To test key-species (either as presence or abundance) as potentially relevant metrics
responding to different types of pressures and to test individual species’ indicator value
for pressure response, taking into account the environmental background;
- To test the response to different types of pressure of widely spread, longtime
established, target exotic species, assuming that a large part of the native species are
quite tolerant to harsh physical-chemical environments and therefore poor indicators
(c.f. Ross, 1991; Kennard et al, 2005);
- To test new ecotaxa guilds for different types of pressures, and for combined effects
and response types, taking into account the environmental background;
- To study the response to pressure of length-age metrics, or size-class proportions of
the population (juveniles or adults), either for key-species of potamodromous, namely
to detect hydromorphological river alteration impacts (also in Task 3.8) and connectivity
losses (also in Task 3.6).
c) For contributing to follow-up Task 4
- Improvement of metrics used before, on the basis of the tolerance indicator’ values
obtained in this study, tolerant species and intolerant species;
- Recommendations of inclusion of Mediterranean-specific metrics and single or
combined pressures, to be used in Task 4;
-Recommendations to be incorporated in the development of the EFI model.
5
56
2. Identification of Mediterranean-type sites
2.1 Background
The task of identifying Mediterranean-type rivers at the European scale is particularly
challenging, as no unequivocal and consensual criteria are found in the literature, even
for classifying Mediterranean climate zones (Hooke, 2006). According to early
definitions, such as those of Köppen (Harding, 2006; Hooke, 2006), the Mediterranean
region corresponds to the climatic zone in which there is at least three times as much
rain in the wettest month of winter as in the driest month of summer, the latter having
less than 30mm precipitation. However, this definition has the limitation of only
considering the temporal distribution of precipitation, which is not the single factor
influencing the hydrological regimes. Other climatic parameters such as temperature
and evapotranspiration also play an important role on water availability along the year.
More recent bioclimatic classification criteria, mainly those developed by RivasMartinez (1999; 2005), take into account the annual distribution and relationships
among several climatic parameters. One of the most important parameters are the
Ombrothermic Indexes that, in broad terms, are given by the quotient between
Precipitation and Temperature, though they may express slightly different conditions
depending on how they are calculated (see Rivas-Martinez 1999 for further details on
index calculations). According to the Rivas-Martinez ombrothermic criteria, the
Mediterranean macrobioclimate is characterized by, at least, two consecutive dry
months during the summer. A month is defined as dry if the precipitation (mm) is less
than twice the temperature (centigrade degrees). Hence, if the ombrothermic bimonthly
quotient of the two driest months is higher than two, the territory is not Mediterranean.
However, if that quotient is less than two, the territory may or may not Mediterranean,
as the bimonthly deficient hydrical balance may or may not compensated with the
previous month’s precipitation. To account for this compensatory effect a table of
Summer ombrothermic compensation values was defined (see Rivas-Martinez, 1999,
for further details).
The task of defining Mediterranean regions is even more demanding when we consider
the high variability of annual precipitation among Mediterranean areas. Rainfall usually
ranges between 275 and 900 mm, but certain Mediterranean-climate regions may fall
6
57
into the category of semiarid regions, i.e., with annual precipitation ranging between
200 and 500 mm (Velasco et al., 2003).
Despite the absence of consistent and simple criteria, there are four basic
characteristics of Mediterranean climate that are most often mentioned in the literature,
namely i) low annual precipitation, ii) high precipitation seasonality, iii) mild winters and
iv) hot and dry summers (e.g. Blondel & Aronson 1999; Gasith & Resh 1999; Hooker,
2006). As a consequence, streams on this climatic region have two important features
that makes them diverge from other European rivers: i) the frequent occurrence of
extreme flood or torrential events due to the concentration of annual precipitation in few
months and ii) the occurrence of a dry period, during which the water flow is
interrupted, due to very low rainfall and high temperatures on summer months (Romero
et al., 1998; Gasith & Resh, 1999; Magalhães et al., 2002; Bonada et al., 2005;
Ferreira et al., 2007).
Among the climatic attributes typically attributed to Mediterranean regions, we
intentionally favoured those that affect more directly the extent of the dry season. In
fact, it is here assumed that these attributes are the most closely related to a key
feature of Mediterranean streams that have strong implications on bioassessment
analyses: the increased role of spatial pattern and physical characteristics of summer
refugia on structuring fish assemblages (Magalhães et al., 2002).
2.2 The EFI+ classification
In our identification of Mediterranean sites, given the great number of sites and the
extent of territory to be classified, we have used exclusively climatic information, mainly
for its availability and simplicity to process in a GIS environment.
With the purpose of express in a simple and straightforward way the probability of a
given river stretch to show Mediterranean features we based our classification on
relationships between temperature and precipitation only. Information on these two
parameters has the advantage of being easily available over a vast territory and with
adequate spatial and temporal resolutions.
We adopted a conservative criteria of mediterranity by considering the fourmonthly
estival ombrothermic index (Ios4), that is, the sum of monthly precipitation divided by
the sum of monthly mean temperature of the two driest months (July and August in
Europe) plus the two previous months (May and June):
7
58
Ios 4 =
Pp May + Pp June + Pp July + Pp August
Tp May + Tp June + Tp July + Tp August
× 10
Eq. 1
, where Ppm and Tpm are, respectively, the yearly positive precipitation (in mm, total
average precipitation of those months whose average temperature is higher than 0ºC)
and the yearly positive temperature (in tenths of degrees Celsius, sum of the monthly
average temperature of those months whose average temperature is higher than 0ºC)
on month m. The two previous months to the dry period are included because it is
assumed that summer aridity greatly depends on the rain that falls during May and
June.
Since this criteria included many regions from the Atlantic climatic zone we further use
a total annual precipitation (TAP) threshold of 1200 mm to separate Mediterranean
from temperate regions. We considered two levels of mediterranity according to the
following criteria:
Mediterranity level 1 - Ios4 < 1 AND TAP < 1200mm
Mediterranity level 2 - Ios4 < 2 AND TAP < 1200mm
As climatic variables we used 30 seconds (600 - 800 meters) resolution maps of
monthly precipitation and monthly mean temperature that are freely available in the
WORLDCLIM website (http://www.worldclim.org/).
According to the resulting map (Fig. 1) the level 1 Mediterranean zone, among the
countries of the EFI+ consortium, include most of the Iberian Peninsula, the Southern
France coastal strip and Southern Italy. The level 2 Mediterranean zone mainly
represent an extension to more continental zones. The map of figure 1 also shows
some isolated areas (e.g. in Nantes region of France, central Hungary and eastern
Romania) classified as level 2 Mediterranean zone. Sites included in those areas were
not included in the mediterranean river dataset.
8
59
Fig. 1 – EFI+ classification of Mediterranity across Europe.
According to the chosen classification, Spain is the country with the largest
Mediterranean area, followed by Italy, Portugal and France (Table 1). Spain has also
the highest total number of sites, followed by Portugal, Italy and France (Table 1, Fig.
2). However, the number of sites in Spain is not representative of the area covered by
each bioclimatic zone. For example, the level 1 mediterranean zone is clearly underrepresented in Spanish dataset.
9
60
Table 1 – Total area (106 ha) of each bioclimatic region and number of Mediterranean sites per
country.
Area
Bioclimatic zone
Italy
France
Spain
Portugal
Temperate
12.19
49.82
10.46
1.14
Mediterranean level 1
11.63
1.47
29.60
6.91
Mediterranean level2
18.11
4.96
38.86
7.76
Temperate
461
1051
1791
105
Mediterranean level 1
51
20
1092
721
Mediterranean level2
191
94
2448
818
Total
652
1145
4239
923
Number of sites
Fig. 2 – Location of Mediterranean river EFI+ sites.
10
61
3. Testing new metrics for mediterranean rivers
3.1 Methodology
3.1.1 Overview
Data analysis was essentially based on the following four questions, which basically
are alternative ways to address the same problem of quantifying the effect of pressures
on metrics:
•
What are the upper tolerance limits of taxon or ecotaxon-based metrics to each
kind of human pressure and to global disturbance?
•
After accounting for environmental variability, how much does each kind of
pressure and global disturbance contribute to explain taxon or ecotaxon-based
metrics distribution and abundance?
•
Is the absence of taxon or ecotaxon from environmentally suitable sites the
result of any kind of pressure?
•
Are different kinds of pressure and global disturbance related to a smaller or
greater expected abundance of taxon or ecotaxon at sites according to their
environmental suitability?
3.1.2 Data screening
In the present report the analysis of Mediterranean rivers was restricted to the Iberian
Peninsula, since this region corresponds to a well defined biogeographical unit and,
furthermore, contains most of the available Mediterranean sites. A subset of sites was
selected since many had missing data for some key pressure variables (Fig. 3).
Selected sites had complete information for, at least, the 25 pressure variables that are
listed on table 2. We eliminated those pressure variables that had too much missing
values (e.g. sedimentation) or were highly unbalanced (e.g. reservoir flushing). After
the site selection procedure, the resulting dataset included 2128 sites, which
represented 65% of the 3266 original sites (Fig. 4). These sites were included in 22
main river catchment systems (Fig. 5).
11
62
3.1.3 Pressure indexes
The selected single pressure variables were integrated in synthetic pressure variables
according both to pressure-type-specific combinations and a global pressure
combination. We used the scores of the first component of Principal Component
Analysis as combined variables, in order to account for colinearities among variables.
We also considered the biotic pressure, obtained from five single variables related to
the number, abundance and ecotype of exotic species. Five pressure-type-specific
combinations
were
therefore
obtained
expressing,
respectively,
problems
of
connectivity, hydrology, morphology, water quality, and biotic pressures. Two global
pressure combinations were also considered either including or excluding biotic
pressures. The selected single pressure variables, their classification scheme and the
correspondent pressure types are shown in table 2.
Fig. 3 – Number of missing data on pressure variables per site.
12
63
Fig. 4 – Selected sites for new metric evaluation.
Fig. 5 – Catchment names of selected sites
13
64
Table 2 – Selected pressure variables and classification scheme.
Pressure
type
Single pressure variables
Classification
Presence of barriers downstream in the catchment
No (1), partial (2), yes (3)
Presence of barriers upstream in the river segment
Presence of barriers downstream in the river segment
No barrier (0)
Connectivity
Number of barriers upstream or downstream in the river
1km segments <=1 (3), >=1 (4)
segment (2 separate variables)
5 km segments <=2 (3), >=2 (4)
10 km segments <=3 (3), >=3 (4)
No barrier (0)
Distance to barriers upstream or downstream (2 separate 1km segments >250 (3), <250 (4)
variables)
5 km segments >1250 (3), <1250 (4)
10 km segments >2500 (3), <2500(4)
Hydrology
Morphology
Water quality
Impoundment
No (1), weak (3), strong (5)
Hydropeaking
Water abstraction
Hydrological modifications
No (1), yes (3)
Temperature impact
No (1), yes (3)
Velocity increase
No (1), yes (3)
Channelisation
No (1), partial (3), strong (5)
Cross section
Instream habitat alterations
Riparian vegetation alteration
No (1), slight (2), partial (3), high (5)
Embankment
No (1), slight (2), partial (3), high (5)
Floodprotection
No (1), yes (3)
Toxic substances
No (1), weak (3), high (5)
Acidification
No (1), yes (3)
Eutrophication
No (1), low (3), interm. (4), extreme (5)
Organic pollution
Organic siltation
No (1), yes (3)
Water Quality Index
1 (good quality) – 5 (poor quality)
Number of exotic species
Biotic
Total abundance of exotic species
Classification with 5 levels based on
Proportion of exotic species among total fish abundance
quantiles of the first principal
Total abundance of exotic insectivorous species
component scores.
Total abundance of exotic piscivorous species
14
65
3.1.4 Selected metrics
Analyses of metric responses to pressures were based either on taxa-based metrics aiming at identifying potential sentinel species - and guild-based metrics. For the taxabased metrics we considered the 18 Iberian endemic cypriniformes, and two
widespread invasive fish species Lepomis gibbosus and Gambusia holbrooki. We used
both data on taxon presence/absence and abundance as responsive metrics, and the
analyses were restricted to each respective potential species’ geographical range. The
selected taxa, the potential distribution and the number of records are shown on table
3.
For the guild-based metrics three ecotaxa were tested: small cyprinids, large cyprinids
and salmonids. Presence/absence, proportional and abundance data were used as
response values. The cyprinid length classification is given on table 4.
For large cyprinids size class-based guilds were also considered. Since length data
was limited to a restricted number of sites, only data on Barbus bocagei, Barbus
sclateri, Pseudochondrostoma duriense and Pseudochondrostoma polilepys was used.
For the Barbus species three size-classes were considered: <100mm (juveniles), 100200 (small adults) and >200 (large adults). For the Pseudochondrostoma species only
two age classes were considered: < 120mm (juveniles) and > 120mm (adults). For
size-class-based guilds only proportional data was used as response values.
15
66
Table 3 – Species used as taxa-based metrics, total relative abundance (number per ha x 104),
number of occurrence sites (total and calibration) and potential area of distribution.
Number of
calibration
sites
Species
Total
Number
abundance of sites
Achondrostoma arcasii
19.30
155
25
Achondrostoma oligolepis
15.98
132
2
Anaecypris hispanica
Barbus bocagei
0.03
5
2
• Guadiana
39.12
408
27
• Ave, Cavado, Douro, Galaica,
Lima, Lis, Minho, Mondego, Oeste,
Sado, Tejo, Vouga
Barbus comizo
Barbus graellsii
Barbus haasi
Barbus microcephalus
Barbus sclateri
9.25
91
1
• Guadiana, Tejo
16.79
85
3
• Cantabrica, Catalana, Ebro
0.25
14
2
• Catalana, Ebro, Valenciana
4.56
51
2
• Guadiana
15.34
78
22
• Algarve, Guadalquivir, Guadiana,
Mira, Segura, Sur
Chondrostoma miegii
Cobitis calderoni
5.75
66
3
• Ebro
0.90
39
1
• Ave, Cavado, Douro, Ebro, Lima,
Minho, Tejo,
Cobitis paludica
9.07
245
23
• Algarve, Cantabrica, Catalana,
Douro, Ebro, Galaica, Guadalquivir,
Guadiana, Lis, Mira, Mondego,
Oeste, Sado, Sur, Tejo,
Valenciana, Vouga
Iberochondrostoma almacai
Iberochondrostoma lemmingii
0.53
18
7
• Algarve, Mira
1.83
54
4
• Algarve, Douro, Guadalquivir,
Guadiana, Sado, Tejo
Iberochondrostoma lusitanicum
Pseudochondrostoma duriense
2.40
28
0
• Oeste, Sado, Tejo
54.53
433
87
• Ave, Cavado, Douro, Galaica,
Lima, Minho
Pseudochondrostoma polylepis
23.79
245
20
• Algarve, Lis, Mondego, Oeste,
Sado, Segura, Tejo, Valenciana,
Vouga
Pseudochondrostoma willkommii
Squalius alburnoides
3.43
48
2
• Guadalquivir, Guadiana, Sur
33.85
308
14
• Douro, Guadalquivir, Guadiana, Lis,
Mondego, Sado, Tejo, Vouga
Squalius aradensis
Squalius carolitertii
5.94
34
15
• Algarve
27.74
336
43
• Ave, Cavado, Douro, Ebro, Galaica,
Lima, Minho, Mondego, Vouga
Squalius pyrenaicus
39.19
272
35
• Ebro, Guadalquivir, Guadiana,
Oeste, Sado, Segura, Sur, Tejo,
Valenciana
Gambusia holbrooki
11.18
112
1
• Algarve, Ave, Catalana, Cavado,
Douro, Ebro, Guadalquivir,
Guadiana, Lima, Lis, Mira,
Mondego, Oeste, Sado, Segura,
Tejo, Valenciana, Vouga"
Lepomis gibbosus
21.38
263
3
Distribution (river basins)
• Ave, Cantabrica, Catalana,
Cavado, Douro, Ebro, Galaica,
Guadiana, Lima, Lis, Minho, Tejo,
Valenciana, Vouga
• Ave, Cavado, Douro, Lima, Lis,
Mondego, Oeste, Tejo, Vouga
• Algarve, Catalana, Douro, Ebro,
Guadalquivir, Guadiana, Lis,
Minho, Mira, Mondego, Oeste,
Sado, Segura, Sur, Tejo,
Valenciana, Vouga"
16
67
Table 4 – Cyprinid guild classification according to the mean body lengths.
Species
Guild
Small
Small
Small
Barbus bocagei
Large
Barbus comizo
Large
Barbus graellsii
Large
Barbus haasi
Large
Large
Barbus sclateri
Large
Chondrostoma miegii
Large
Small
Small
Small
Large
Large
Large
Small
Squalius aradensis
Small
Small
Squalius pyrenaicus
Small
3.1.5 Quantification of species tolerance
As a first approach to assess the indicator value of species we estimated species
tolerance based on estimations of optimal conditions and niche breath with respect to
each pressure-type combination and global pressures. Two simple alternative
approaches were used: the quadratic logistic regression and the weighted averaging
approaches (e.g. ter Braak & Looman 1986; Jongman et al. 1995).
The quadratic or Gaussian logistic regression estimates a species response curve from
presence-absence data using a second-order polynomial in the environmental variable
as linear predictor. Theoretically this curve assumes a Gaussian species response
curve, i.e., a symmetric unimodal curve describing species probability of occurrence
along the environmental gradient, for which it is possible to estimate optimum condition
(or niche position) and tolerance (or niche breath) values. The quadratic logistic
function is expressed by:
17
68
⎡ p ( x) ⎤
2
log ⎢
⎥ = b0 + b1 x + b2 x
1
−
p
(
x
)
⎣
⎦
where p(x) is the probability of a species to occur as a function of x. The optimum and
tolerance can be obtained as follows:
Optimum u = − b1 (2b2 )
Tolerance t = 1
(−2b2 )
In this study we considered the upper tolerance (u + t) value (sensu ecological
literature) as an estimator of species tolerance (sensu biotic integrity assessment
literature) to pressures.
The method of weighted averaging is a simpler alternative to regression methods that
circumvents the problem of fitting a particular response curve. This method has been
long used in ecology and recently, as in the present study, it was used to quantify
species tolerance to pressures in the context of biotic integrity assessments of rivers
(Whittier et al. 2007; Welsh & Hodgson in press). The species optimum is simply
obtained by taking the average of the values of the environmental variables weighted
by species abundance, over those sites where the species is present (Jongman et al.
1995). Species tolerance is given by one standard deviation of the optimum. Optimum
and tolerance are expressed as follows:
n
Optimum u =
∑y
i =1
n
ik
∑y
i =1
xi
ik
n
Tolerance t =
∑ y (x
i =1
ik
− u)
2
i
n
∑y
i =1
ik
18
69
Here we used the bootstrap approach recommended by Whittier et al. (2007) in order
to obtain more robust estimates of upper tolerance (u + t) values, particularly of those
species collected in few sites.
The main disadvantages of the weighted averaging method is that it disregards
absences and can give misleading results if the sampling is too uneven distributed
along the environmental gradient (ter Braak & Looman, 1986). On the other hand the
quadratic logistic approach has the main disadvantage of requiring an unimodal
response for optima and tolerance to be estimated. Nevertheless, the two methods
gives similar results in case of species with low probability of occurrence and/or narrow
tolerance (ter Braak & Looman, 1986).
For comparison purposes, the upper tolerance values estimated with both approaches
were rescaled in order to vary between 1 and 10 using the expression: 10*(species
upper tolerance – minimum score) / range of values.
3.1.6 Testing metric responses
We used two distinct procedures to test the effect of different types of pressure on
biotic metrics. In a first approach we assessed the contribution of pressure variables to
the improvement of models that related each metric to natural environmental
conditions. This was accomplished by testing the inclusion each pressure-type
combination into models already fitted with variables describing natural environmental
variability. In case the pressure variables explained significantly the remaining variation
after fitting environmental models it was assumed that the metric was responsive to
pressure. Alternatively we also used a calibration method to assess the metric
response to pressure. A calibration dataset of sites with minimal human alterations was
used to fit models that related each metric to natural environmental variability. The
model was then extrapolated for the whole dataset and relationships between the
resulting regression residuals and each kind of combined pressure were tested.
Since the aim of this study was primarily exploratory we intentionally disregarded some
statistical problems during model fitting, namely problems derived from the fact that no
independent validation subset was used for model accuracy assessment. Since for
many taxon there were not enough sites to select a validation subset, models were
validated using resubstitution methods and therefore an overestimation of accuracy is
expected. Nevertheless we assume that this problem will not strongly interfere with the
19
70
aim of identifying general trends on metric responses. All statistical analyses were
performed using S-Plus version 2000 for Windows (Statistical Sciences, 1999) and
MASS library (Venables and Ripley, 1997).
Assessing the contribution of pressure variables to environmental models
The quantification of metric responses to environmental and pressure variables was
based on regression methods. Logistic regression analysis was used for modelling
presence/absence responses while linear regression analysis was used for modelling
abundance data. A metric was considered responsive if a given pressure type
contributed significantly to the model fit, after controlling for geographic and
environmental effects. The procedure included the following steps:
1. Production of synthetic geographic / environmental variables by means of a
Principal Component Analysis (PCA);
2. Adjustment of statistical models (logistic or linear models) describing metric
responses to natural environmental variables using the scores of the PCA as
independent variables, following a stepwise procedure based on Akaike
Information Criterion (AIC);
3. Assessment of the contribution of each pressure type to the model produced in
step 2, using Akaike Information Criterion, i.e., whether AIC values decreased
with the addition of each pressure variable.
In the case of taxa-based metrics, several PCA, based on the correlation matrix, were
produced for the geographical range (subset of river basins) where each species
potentially occur. For the geographic / environmental PCA three main sets of variables
were selected: pure geographical variables (longitude and latitude), variables
expressing longitudinal river gradients (distance to source, distance to sea, size of
catchment’s area, altitude and river slope) and local climatic variables (total annual
mean precipitation, annual mean temperature, mean temperature in January and mean
temperature in July). In the regression models the quadratic terms of the PCA scores
were also considered for inclusion in order to account for possible unimodal
relationships. Only those species occurring at least on 30 sites were considered for
analysis.
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71
Calibration-based approach
Regression-based
statistical
models
describing
metric
response
to
natural
environmental variability were first fitted using a subset of minimum disturbed sites –
the calibration dataset (Table 5, figure 6). Similarly to the previous approach, logistic
regression analysis was used for modelling presence/absence data while linear
regression analysis was used for modelling abundance data. For ecotaxa-based
metrics logistic regression was also used for proportional data. The significance of
quadratic terms of environmental variables was also assessed to account for possible
unimodal relationships. Only species with at least 20 presence records on the
calibration sites were considered in the analysis.
The basic procedure included the following steps:
1. Production of synthetic geographic / environmental variables by means of a
Principal Component Analysis (this step is common to the first step of the
previous described approach);
2. Selection of the calibration dataset with minimal human disturbances;
3. Adjustment of geographic / environmental models using the scores of the PCA
at calibration sites as independent variables, following a stepwise procedure
based on Akaike Information Criterion (AIC);
4. Extrapolation to the whole set of sites and extraction of residuals;
5. Correlation analysis between residuals and each pressure-type and global
pressure combinations.
The absence of a species from suitable conditions can be the result of inefficient
sampling, metapopulacional dynamics, natural barrier to dispersal and human induced
pressures. Based on this line of thought, in a first approach, presence/absence data
was used to test whether the absence from suitable sites of each taxon or ecotaxon,
according to the presence/absence calibration model, could be attributed to a given
pressure type or global pressure. For that purpose the analysis was restricted to
absence sites, i.e., using negative deviance residuals. The absolute value of such
residuals is proportional to the probability of occurrence at absence sites and it is a
measure of site suitability where the taxon is absent. If the relationship between
residuals and pressure variables is significantly negative then pressure may be
21
72
responsible for the species absence at environmentally suitable sites. However a
positive relationship does not mean that the species is responding positively to
pressure, since the analysis is based on absence sites only. We did not consider
residuals at presence sites (that is proportional to the probability of the species being
absence from presence sites) because it would be unreasonable to consider that a
species is present at an environmentally unsuitable site only because the site is
affected by a given pressure.
In a second alternative approach a calibration model was fitted using abundance data
instead. In this case the whole set of residuals (Pearson residuals) were related to
each pressure-type combination also using correlation analysis. A significant negative
relationship could mean that species were either less abundant than expected at
disturbed sites or more abundant than expected at non-disturbed sites, while a positive
relationship would mean that species were either more abundant than expected at
disturbed sites or less abundant than expected at non-disturbed sites.
Fig. 6 – Geographical (left) and environmental (right; first two components of the PCA on
environmental variables) location of calibration sites.
22
73
Table 5 – criteria used for the selection of the calibration site subset.
Pressure type
Single pressure variables
Criteria
Connectivity
Presence of barriers downstream in the river segment
No (1)
Hydrology
Impoundment
No (1)
Hydropeaking
No (1)
Morphology
Water quality
Water abstraction
No (1), weak (3)
Hydrological modifications
No (1), yes (3)
Temperature impact
No (1)
Velocity increase
No (1)
Channelisation
No (1)
Cross section
No (1)
Instream habitat alterations
No (1), partial (3)
Riparian vegetation alteration
No (1), slight (2)
Embankment
No (1), slight (2)
Floodprotection
No (1)
Toxic substances
No (1), weak (3)
Acidification
No (1), yes (3)
Eutrophication
No (1), low (3)
Organic pollution
No (1), weak (3)
Organic siltation
No (1), yes (3)
Water Quality Index
1 (good quality)- 3 (interm. quality)
23
74
3.2 Results
3.2.1 Geographical gradients
The main geographic gradients are essentially described by four components of the
PCA, which explained 84% of the variation (Table 6): i) the first component essentially
describing the temperature gradient, ii) the second and third components expressing in
different ways the longitudinal gradient, and iii) the fourth component describing mainly
the actual river slope (Table 7). In subsequent analyses the first four principal
components were retained as covariables, in order to account for the main geographic,
climatic and longitudinal gradients when testing metrics’ responses to pressures.
Table 6 – Summary table of global Principal Component Analysis describing the main
geographic/climatic gradient.
Comp. 1
Comp. 2
Comp. 3
Comp. 4
Standard deviation
1.96
1.53
1.42
1.00
Proportion of Variance
0.35
0.21
0.18
0.09
Cumulative Proportion
0.35
0.56
0.74
0.84
Table 7 - Loadings of the global Principal Component Analysis describing the main
geographic/climatic gradient.
Variables
PC 1
PC 2
PC 3
PC 4
Latitude
-0.28
-0.27
-0.37
-0.31
Longitude
-0.23
0.39
0.06
-0.20
Distance from source
0.16
0.34
-0.57
0.08
Size of catchment
0.15
0.29
-0.62
0.19
Altitude
-0.42
0.17
0.10
0.18
Actual river slope
-0.17
-0.15
0.03
0.87
Distance from sea
-0.31
0.36
0.09
-0.09
Annual Mean Precipitation
-0.01
-0.48
-0.24
0.00
Annual Mean Temperature
0.48
0.06
0.14
0.01
Mean Temperature in January
0.46
-0.14
0.05
-0.04
Mean Temperature in July
0.29
0.36
0.25
0.11
24
75
3.2.2 Pressure analysis
The relationships among variables within and between different pressure–type groups
are evidenced by the plots of PCA loadings on the first two principal components (Fig.
7) and by their geographical distribution (Fig. 8). Within the different pressure-type
groups all variables are roughly related to each other along the axis that explains most
variation. Connectivity-related pressures are mainly divided in two groups along the
second component axis: those related to barriers upstream and those related to
barriers
downstream.
Concerning
hydrological-related
pressures,
variables
impoundment, hydropeaking and hydrological modifications are the most related to
each other and with a distinct distribution from water abstraction, velocity increase and
temperature impact, according to the second component axis. Among the
morphological-related pressures, the embankment and channelisation are strongly
related to each other and, to a lesser extent, to floodprotection. Cross section, instream
habitat and riparian vegetation alterations are separated from the previous group along
the second component axis. Most pressure variables related to water quality show a
strong relationship with each other, except the variable toxic substances and, in
particular, water acidification, which are separated from the main group of variables
according to the second component axis. Biotic-related pressures are mainly divided in
two groups along the second component axis: (1) number and proportion of exotic
species, and total relative abundance of exotic piscivorous fishes; (2) total relative
abundance of exotic species and total relative abundance of exotic insectivorous
fishes. Concerning the two global pressure variable combinations there are two
important facts that should be mentioned: (1) connectivity-related variables are clearly
unrelated to the remaining pressure variables according to the first to principal
component axis; this is also evident from the comparison of the geographical
distribution of connectivity-related pressures with those of other pressure types (Fig. 8);
(2) biotic-related pressures are more associated to the main pressure variable group
than to connectivity-related pressures.
25
76
Fig. 7 – Loadings of pressure
variables of PCA ran separately
for each pressure-type and using
all pressure variables together.
26
77
Fig. 8 – Map of sites showing
the
scores
component
of
of
the
first
PCA
ran
separately for each pressuretype and using all pressure
variables together.
27
78
3.2.3 Testing metrics’ responses to pressure
3.2.3.1 Quantification of species tolerances
Although the two approaches to species upper tolerance estimation produced distinct
results, they evidenced similar general trends (Tables 8 and 9; figures 9 to 11), namely:
(1) according to both methods, large cyprinids are more often tolerant to a greater
number of pressure combined variables than small cyprinids (Tables 8 and 9); (2) only
in very few cases there are high discrepancies between the two methods (e.g. I.
duriense; Fig. 9 to 11); (3) many species were consistently ranked according to upper
tolerance values (e.g. S. aradensis, I. almacai and A. hispanica as globally intolerant
species and G. holbrookii and B. bocagei as globally tolerant species; Fig. 9 to 11).
An important difference between the results of the two approaches is that the quadratic
logistic regression estimates species’ tolerances that may go beyond the conditions
experienced by species (S. aradensis and P. duriense; Fig. 11). This is due to the fact
that this method is based on the estimation of a theoretical response curve to
pressures. In opposition, the weighted averaging method always predicts tolerances
that are included within the range of conditions experienced by species (Fig. 11).
Overall, water quality and morphological changes were the pressure-types that yielded
consistently higher number of significant tolerance values among both approaches,
while fewer and more contradictory responses were found for the remaining pressuretypes.
28
79
Table 8 – Rescaled upper species’ tolerance values according to the quadratic logistic
regression approach (empty cells correspond to non-unimodal relationships between species
presence and pressure).
Species
Small cyprinids
Squalius aradensis
Squalius pyrenaicus
Large cyprinids
Barbus bocagei
Barbus comizo
Barbus graellsii
Barbus haasi
Barbus sclateri
Chondrostoma miegii
Cobitids
Cobitis calderoni
Cobitis paludica
Exotic
Lepomis gibbosus
Gambusia holbrooki
Connect. Hydrol. Morphol.
2.86
0.80
1.84
0.03
4.65
1.53
1.14
10.00
0.01
0.00
1.03
0.70
2.04
0.06
1.87
3.43
0.32
1.82
3.87
0.69
0.37
2.56
Water
quality
4.16
3.28
4.96
4.15
5.68
4.54
Biotic
0.10
0.04
0.18
0.11
2.71
Global
Global
+
Biotic
6.47
4.86
1.66
1.85
3.00
5.04
5.21
0.00
3.37
1.30
3.87
3.08
0.48
1.04
1.96
3.24
5.47
6.12
4.49
4.99
3.01
3.56
4.34
0.11
1.87
1.35
0.41
0.47
0.00
0.59
0.15
0.41
0.16
10.00
0.38
6.29
4.08
5.83
6.43
3.94
0.86
5.71
10.00
8.18
3.77
0.00
8.49
3.66
8.36
10.00
1.99
10.00
0.19
0.80
0.90
2.74
0.00
3.72
3.17
0.60
2.20
1.19
0.46
2.76
3.27
0.71
0.53
1.81
2.14
1.48
4.44
1.60
4.86
10.00
0.00
1.00
5.21
1.73
4.58
5.01
3.28
0.00
10.00
2.15
2.86
4.60
3.95
4.47
1.98
1.79
4.03
3.75
3.22
5.24
0.41
2.10
4.63
5.88
3.41
5.83
2.48
6.20
2.70
2.91
4.61
6.72
5.71
6.99
*
*
6.53
8.86
*
*
29
80
Table 9 – Rescaled bootstrap estimates of upper species’ tolerance values according to the
weighted averaging method.
Species
Small cyprinids
Squalius aradensis
Squalius pyrenaicus
Large cyprinids
Barbus bocagei
Barbus comizo
Barbus graellsii
Barbus haasi
Barbus sclateri
Chondrostoma miegii
Cobitids
Cobitis calderoni
Cobitis paludica
Exotic
Lepomis gibbosus
Gambusia holbrooki
Water
quality
Biotic Global
Global
+
Biotic
4.57
3.45
0.17
0.00
1.74
1.30
4.26
0.07
7.36
3.94
4.73
4.85
0.94
0.00
3.04
1.96
8.81
0.63
4.46
5.37
4.73
5.70
0.15
0.00
1.62
4.90
2.98
0.45
2.09
2.35
2.74
4.88
4.49
3.53
7.80
10.00
6.55
3.03
3.05
4.52
4.69
0.72
1.65
2.54
3.41
2.08
6.58
2.00
0.68
2.50
3.38
6.00
0.21
0.09
3.99
7.46
5.46
0.17
0.00
1.97
2.41
4.73
0.83
0.66
4.85
6.85
7.18
0.20
0.00
2.26
6.06
5.41
0.36
2.11
2.86
4.23
1.04
10.00
5.94
4.20
9.28
8.59
1.01
3.09
10.00
4.34
2.16
5.58
9.48
7.05
4.53
1.04
10.00
8.52
1.22
2.52
9.78
3.05
3.66
2.69
7.04
7.43
2.88
0.00
4.88
1.27
2.70
4.16
5.06
4.28
7.93
9.85
8.89
0.00
7.38
1.93
10.00
0.44
7.31
7.57
6.90
3.52
10.00
7.89
3.61
1.08
9.79
0.99
4.59
3.48
8.39
8.25
9.91
4.90
6.67
0.85
10.00
0.86
5.73
6.09
5.99
2.60
8.65
4.15
4.14
4.58
2.33
6.86
4.23
9.92
3.74
6.19
3.88
7.75
4.10
2.92
7.87
6.88
3.57
4.36
6.54
8.85
*
*
4.97
9.07
*
*
30
81
Fig. 9 – Species fitted response curves to global pressure according to the quadratic logistic
regression approach. Upper tolerance values (vertical solid line) and the available range of
conditions at the species potential area of distribution (two vertical dashed lines representing the
1st and the 99th percentiles) are also represented.
31
82
Fig. 10 – Scatter plot of species relative abundance along the global pressure gradient. Upper
tolerance values estimated with the weighted averaging approach (vertical solid line) and the
available range of conditions at the species potential area of distribution (two vertical dashed
lines representing the 1st and the 99th percentiles) are also represented.
32
83
Squalius pyrenaicus
Barbus sclateri
Squalius aradensis
Barbus graellsii
Cobitis paludica
Barbus bocagei
Barbus haasi
Lepomis gibbosus
Gambusia holbrooki
Cobitis paludica
Barbus bocagei
Barbus haasi
Gambusia holbrooki
Chondrostoma miegii
Chondrostoma miegii
Lepomis gibbosus
Barbus graellsii
-8.00
-6.00
-4.00
-2.00
Cobitis calderoni
0.00
Cobitis calderoni
2.00
4.00
Barbus comizo
6.00
Tolerance
Range of conditions available
(1st and 99th percentiles)
10.00
b)
Barbus comizo
Squalius pyrenaicus
Barbus sclateri
8.00
Squalius aradensis
Combined global pressure (PC1 scores)
Combined global pressure (PC1 scores)
a)
15.00
Tolerance
Range of conditions available
(1st and 99th percentiles)
10.00
5.00
0.00
-5.00
-10.00
Fig. 11 – Estimates of species tolerance to global pressure according to the quadratic
logistic regression approach (a) and weighted averaging approach. Species are ordered
by increasing upper tolerance. The available range of conditions at the potential
distribution range of each species is also represented.
33
84
3.2.3.2 Taxa-based metric responses to pressure – endemic cypriniformes
The analysis of Iberian endemic cypriniformes responses to pressure using presence
and abundance data and different modelling approaches produced distinct results
(Tables 10 to 16). All analysis showed that different taxa tend to respond in many
distinct ways and strengths to the several pressure-type combinations. A common
result among the different approaches is that connectivity showed small overall effects
on species presence and abundance.
Non-calibration methods
The approaches that assessed the contribution of combined pressure variables to each
taxon occurrence, either considering species presence or abundance, produced overall
consistent results (Tables 11 and 13). However, most models explained low
percentages of variability (Tables 10 and 12) according to the R2 values, with most
models explaining less than 30% of data variability. The best models were produced for
S. aradensis, though most probably it is due to an overfitting problem since few
samples were available and the potential distribution of the species is very restricted.
Surprisingly, there were more overall significant positive (38 for both presence/absence
and abundance approaches) than negative responses (22 for the presence/absence
approach and 30 for the abundance approach) (Table 11 and 13). However, small
cyprinids showed a greater proportion of negative responses to pressure (10 out of 20
for presence/absence data and 17 out of 26 for abundance data), than large cyprinids
(11 out of 35 for presence/absence data and 13 out of 35 for abundance data). The
pressure-type combination that describes morphological alteration of rivers was the
variable that yielded more negative responses by individual species (eight negative
responses for presence/absence data and 10 out of 11 negative responses for
abundance data). S. pyrenaicus and B. sclateri were the taxa that most consistently
responded negatively to pressure-type and global pressure combinations. A. oligolepis
and I. lemmingii also showed many significant negative responses to pressure.
The strongest consistent negative relationships with pressure-type combination
variables were attained by B. sclateri on its response to overall water quality
deterioration (44% of explained variation for presence/absence data and 27%
explained variation for abundance data) and by B. comizo and S. pyrenaicus on their
response to morphological river alterations (respectively, 21% and 16% of model
34
85
explained variation for presence/absence data and 23% and 20% of model explained
variation for abundance data).
Table 10 – Deviance-based pseudo-R2 of logistic regression models (% of deviance decrease
in relation to the deviance of the pure geographic/climatic model) and contributions of pressure
variables to the explained variation (values in bold).
Species
Water
Biotic Global
quality
Global
+
Biotic
Connect.
Hydrol.
Morphol.
1.71
0.00
22.47
0.00
5.06
0.00
13.08
2.23
48.81
0.00
26.67
8.63
24.09
5.08
2.52
32.80
22.47
0.00
5.28
4.39
12.83
0.00
48.81
0.00
24.94
0.00
23.15
0.00
1.71
0.00
22.47
0.00
5.34
5.47
15.30
18.53
48.81
0.00
25.66
3.76
26.48
16.38
2.61
35.32
26.83
20.98
5.06
0.00
12.83
0.00
48.81
0.00
24.94
0.00
25.49
11.97
1.71
0.00
23.80
7.18
5.36
5.85
12.83
0.00
48.81
0.00
24.94
0.00
23.40
1.40
2.56
33.99
22.92
2.53
5.42
6.92
13.03
1.75
48.81
0.00
25.19
1.33
24.81
8.70
2.17
21.89
23.61
6.21
5.54
9.22
12.83
0.00
48.81
0.00
25.06
0.65
24.54
7.35
24.75
1.54
22.99
0.00
31.57
9.20
10.82
0.00
10.29
0.00
15.89
0.00
24.04
21.75
19.78
22.58
9.87
0.00
31.72
30.30
22.99
0.00
31.01
6.84
10.82
0.00
10.29
0.00
15.89
0.00
21.92
11.82
19.90
23.19
9.87
0.00
24.74
1.50
27.32
20.58
29.52
0.00
12.27
13.22
11.80
14.24
15.89
0.00
19.85
0.00
16.96
6.53
11.02
11.57
24.58
0.67
22.99
0.00
29.52
0.00
10.82
0.00
17.09
44.34
15.89
0.00
20.24
2.43
16.03
0.00
9.87
0.00
28.17
17.43
25.42
12.42
50.16
58.39
10.82
0.00
10.29
0.00
35.51
65.69
19.85
0.00
17.14
7.74
12.16
20.89
26.03
7.99
24.04
5.68
30.71
5.52
10.82
0.00
13.35
25.55
15.89
0.00
23.35
18.73
17.17
7.95
9.87
0.00
27.98
16.66
22.99
0.00
34.43
20.22
10.82
0.00
12.12
16.79
22.00
33.04
22.84
16.38
17.74
11.51
9.87
0.00
1.60
0.00
16.73
0.85
2.66
40.45
16.61
0.00
1.60
0.00
16.94
2.30
1.60
0.00
16.61
0.00
1.91
16.45
16.88
1.91
1.87
14.40
16.61
0.00
2.07
23.09
16.61
0.00
Small cyprinids
Squalius aradensis
Squalius pyrenaicus
Large cyprinids
Barbus bocagei
Barbus comizo
Barbus graellsii
Barbus sclateri
Chondrostoma miegii
Cobitids
Cobitis calderoni
Cobitis paludica
35
86
Table 11 - Coefficient estimates of each pressure type for each logistic regression model using
Iberian endemic cypriniformes (negative effects of pressure types are indicated in bold; darker
blue indicates stronger contributions of pressure variables to the model according to the values
of table 8; empty cells correspond to non-selected variables).
Species
Small cyprinids
Achondrostoma.arcasii
Achondrostoma.oligolepis
Iberochondrostoma.lemmingii
Squalius.alburnoides
Squalius.aradensis
Squalius.carolitertii
Squalius.pyrenaicus
Large cyprinids
Barbus.bocagei
Barbus.comizo
Barbus.graellsii
Barbus.microcephalus
Barbus.sclateri
Chondrostoma.miegii
Pseudochondrostoma.duriense
Pseudochondrostoma.polylepis
Pseudochondrostoma.willkommii
Cobitids
Cobitis.calderoni
Cobitis.paludica
Water
Biotic Global
quality
Global
+
Biotic
Connect.
Hydrol.
Morphol.
0.25
0.58
0.34
-
-0.66
-0.42
-0.97
0.81
-0.55
-0.47
-0.30
-0.30
-
0.98
0.69
-0.86
0.81
-0.65
0.61
-0.33
0.50
-1.38
-0.98
0.64
1.05
-
1.38
-0.65
0.22
0.32
0.75
0.96
0.49
-0.45
-1.43
-0.88
-0.80
-1.06
0.46
-1.11
-
0.62
0.34
0.90
0.56
0.32
0.20
1.18
-0.69
-1.29
1.24
1.11
-
1.62
1.27
0.87
1.47
1.10
-
-
1.11
-
-
-0.30
0.20
-
1.01
-
0.98
-
0.66
0.42
36
87
Table 12 - R2 of linear regression models and contributions of pressure variables to the
explained variation (values in bold).
Species
Water
Biotic Global
quality
Global
+
Biotic
Connect.
Hydrol.
Morphol.
7.39
0.00
47.55
0.00
26.39
0.00
13.22
1.86
44.48
0.00
29.55
11.37
21.86
2.03
8.59
15.16
47.55
0.00
26.39
0.00
13.01
0.00
44.48
0.00
27.48
1.94
21.51
0.00
7.39
0.00
47.55
0.00
26.39
0.00
15.06
15.69
44.48
0.00
27.66
2.79
25.40
19.50
9.34
22.62
48.15
2.39
26.39
0.00
13.01
0.00
44.48
0.00
27.10
0.00
22.34
4.72
7.39
0.00
48.11
2.22
27.71
6.49
13.01
0.00
44.48
0.00
27.10
0.00
21.51
0.00
8.77
17.02
47.55
0.00
26.39
0.00
13.01
0.00
44.48
0.00
27.73
3.13
22.43
5.22
8.26
11.43
47.55
0.00
27.03
3.26
13.01
0.00
44.48
0.00
27.50
1.99
21.73
1.29
27.41
5.38
28.82
0.00
36.14
11.54
13.42
0.00
15.14
0.00
9.16
16.25
22.26
15.73
22.09
13.96
14.39
0.00
33.75
29.86
28.82
0.00
34.03
2.96
13.42
0.00
15.14
0.00
7.78
0.00
21.13
9.93
24.09
23.16
15.36
7.37
27.14
4.07
34.41
22.82
33.36
0.00
15.07
12.70
16.70
11.03
7.78
0.00
19.44
0.00
19.61
0.00
17.55
21.03
26.48
0.77
29.27
2.14
33.36
0.00
14.52
8.78
19.54
26.56
7.78
0.00
19.44
0.00
19.61
0.00
14.39
0.00
29.67
15.30
31.39
11.51
42.32
31.79
13.42
0.00
15.14
0.00
16.16
56.20
19.44
0.00
21.54
11.15
15.59
8.97
28.24
9.18
29.55
3.45
33.36
0.00
13.42
0.00
16.96
12.64
7.78
0.00
21.48
11.81
21.53
11.11
14.39
0.00
30.18
17.32
28.82
0.00
35.78
10.15
13.42
0.00
15.14
0.00
9.65
20.98
21.44
11.58
22.44
15.71
14.39
0.00
5.08
0.00
25.25
0.00
9.81
50.82
25.25
0.00
5.08
0.00
25.25
0.00
5.08
0.00
25.62
1.90
5.67
11.05
25.25
0.00
6.63
24.69
25.25
0.00
6.86
27.37
25.25
0.00
Small cyprinids
Squalius aradensis
Squalius pyrenaicus
Large cyprinids
Barbus bocagei
Barbus comizo
Barbus graellsii
Barbus sclateri
Chondrostoma miegii
Cobitids
Cobitis calderoni
Cobitis paludica
37
88
Table 13 - Coefficient estimates of each pressure-type for each linear regression model using
log transformed relative abundance data on Iberian endemic cypriniformes (negative effects of
pressure types are indicated in bold; darker blue indicates stronger contributions of pressure
variables to the model according to the values of table 8; empty cells correspond to nonselected variables).
Species
Small cyprinids
Achondrostoma.oligolepis
Iberochondrostoma.lemmingii
Squalius.aradensis
Squalius.pyrenaicus
Large cyprinids
Barbus.bocagei
Barbus.comizo
Barbus.graellsii
Barbus.microcephalus
Barbus.sclateri
Chondrostoma.miegii
Pseudochondrostoma.willkommii
Cobitids
Cobitis.calderoni
Cobitis.paludica
0.26
-0.09
0.28
-0.92
0.30
-0.10
-0.71
0.48
0.76
-0.37
-0.81
0.22
1.31
-0.84
-1.10
Water
quality
-0.25
-0.77
-0.62
-0.69
Biotic Global
-0.26
-0.05
0.41
-0.42
-0.19
-0.38
0.29
-0.60
-0.18
-0.86
-0.12
0.39
-1.15
0.29
-0.87
0.20
0.67
0.28
1.38
1.00
-0.67
-1.13
1.49
-1.70
1.87
0.90
-0.93
1.83
2.00
0.91
0.12
0.16
-1.32
0.99
0.91
1.26
0.93
0.95
0.44
-0.45
-0.41
0.26
0.27
0.15
0.05
0.08
0.15
-0.11
Global
+
Biotic
2.29
Calibration methods
The calibration approach of assessing taxa-based metrics response to pressure
variables yielded distinct results from the previous approach (tables 14 and 15),
although fewer taxa were analysed due to sample size constraints. The models
explained reasonably well the taxa presence and abundance for the calibration dataset,
but the agreement between observed and predicted values for the non-calibrated
dataset was variable for presence data and very low for abundance data (Table 16).
38
89
According to the calibration model approach using presence/absence data the variable
integrating water quality disturbances showed significant negative relationships with
most single taxa (i.e. environmentally suitable sites where the species is absent are in
average more affected by water quality disturbances) (Table 14). Morphological and
hydrological disturbances also showed many significant negative relationships,
especially for large cyprinids. No significant negative relationships of taxa-based
metrics with connectivity disturbances were found.
The calibration model approach using abundance data yielded quite distinct results
from the approach using presence/absence data (Table 15). However, for both
methods water quality-related disturbances yielded the largest number of significant
negative correlations and connectivity disturbances yielded no significant negative
correlations with taxa-based metrics. Furthermore, one of the taxa that was found to be
more frequently negatively related to combined pressure-type variables was common
to both presence/absence and abundance approaches (P. duriense). The most evident
inconsistency
between
presence/absence
and
abundance
calibration
model
approaches was found for B. bocagei. According to the presence/absence approach
the species is negatively related to five out of seven combined pressure variables (all
except connectivity and biotic combined disturbance) but according to the abundance
approach the species is positively related to six out of seven combined pressure
variables (all except connectivity related combined disturbance). Nevertheless, this
result is not necessarily incompatible, since the two approaches try to address distinct
questions. While the presence/absence approach suggests that the species tend to be
absent from environmentally suitable but disturbed sites, the abundance approach
does not necessarily suggest that the species is more abundant than expected at more
disturbed sites: it might suggest instead that it is less abundant than expected at less
disturbed sites. Nevertheless, the positive response based on abundance data yielded
stronger correlations than the negative response based on presence/absence data.
Considering the strength of the global response estimates, and its consistency
throughout pressure types, Squalius pyrenaicus, Barbus comizo and B. sclateri could
be potentially useful as metrics. Nonetheless, as expected, different species have
distinctive responses across pressure types and methods, and though autoecologically useful, they do not favour the selection of robust metrics.
39
90
Table 14 – Pearson correlation between negative deviance residuals of logistic regression
calibration models and each pressure-type synthetic variables (significant negative correlations
are shown in bold).
Species
Small cyprinids
Squalius aradensis
Squalius pyrenaicus
Large cyprinids
Barbus bocagei
Barbus sclateri
Cobitids
Cobitis paludica
Water
quality
Biotic
Global
Global
+
Biotic
0.01
0.14
-0.10
0.07
0.05
0.01
0.16
-0.17
0.00
-0.04
0.00
-0.08
NA
0.06
0.05
-0.01
-0.24
-0.36
-0.19
-0.23
-0.02
-0.12
-0.13
0.05
-0.22
0.00
0.01
-0.15
0.01
-0.18
-0.01
-0.06
0.05
0.02
-0.27
0.16
-0.05
0.04
-0.09
-0.09
0.12
-0.13
-0.13
-0.11
-0.35
-0.09
-0.14
-0.10
-0.14
-0.42
0.05
-0.03
0.08
-0.06
0.08
-0.12
0.04
-0.18
-0.09
-0.12
0.08
-0.22
-0.06
0.04
-0.17
-0.06
-0.15
-0.26
-0.09
-0.23
Table 15 – Pearson correlation between Pearson residuals of linear regression calibration
models and each pressure-type synthetic variables (significant correlations are shown in bold;
values in blue are negative correlations and values in red are positive correlations).
Species
Small cyprinids
Squalius aradensis
Squalius pyrenaicus
Large cyprinids
Barbus bocagei
Barbus sclateri
Cobitids
Cobitis paludica
Connect.
Hydrol.
Morphol.
Water
quality
Biotic
Global
Global
+
Biotic
0.00
-0.01
0.02
0.00
0.09
0.01
0.02
0.04
0.00
0.01
0.00
0.00
0.02
-0.03
-0.13
-0.01
0.00
0.10
-0.01
-0.08
-0.02
-0.02
0.06
-0.02
-0.08
0.00
0.00
0.04
-0.02
-0.04
-0.01
-0.01
-0.02
-0.03
-0.07
-0.01
0.18
0.00
0.00
0.25
0.01
-0.18
-0.11
0.07
-0.08
0.01
-0.06
0.22
-0.17
-0.13
-0.11
0.18
-0.10
-0.67
-0.03
0.21
-0.02
-0.10
-0.09
0.24
-0.10
-0.41
-0.10
-0.03
-0.06
0.01
-0.03
-0.05
0.02
-0.04
40
91
Table 16 – Measures of calibration model adjustment (AUC – Area under the ROC) for
calibration and non-calibration sites.
Logistic regression
Linear regression
AUC
calibration
AUC
non-calibration
R2
calibration
R2
non-calibration
0.76
0.51
0.06
0.00
Barbus.bocagei
0.95
0.74
0.24
0.00
Barbus.sclateri
0.91
0.66
0.42
0.03
Cobitis.paludica
0.92
0.78
0.30
0.00
0.84
0.67
0.24
0.01
0.99
0.69
0.29
0.03
0.99
0.62
0.50
0.00
Squalius.aradensis
1.00
0.47
0.67
0.07
0.88
0.78
0.25
0.08
Squalius.pyrenaicus
0.82
0.73
0.31
0.01
Espécies
3.2.3.3 Taxa-based metrics – widespread invasive species
The assessment of the two main invasive species as responsive taxa-based metrics to
pressure yielded less surprising results than those obtained for cypriniformes.
Significant relationships with all pressure-type combinations and global pressures
yielded positive responses (Table 17). Metrics based on abundance data resulted in
less significant relationships for Lepomis gibbosus, but stronger responses for
Gambusia holbrokii, than metrics based on presence-absence data (Tables 9 and 10).
The stronger positive response to pressure-type combination by Lepomis gibbosus was
attained for hydrological alterations (16% of explained variation), while Gambusia
holbrooki showed the strongest positive response to the overall water quality
deterioration (52% of model explained variation) and to the global pressure
combination (34% of model explained variation) (Table 18).
Both L. gibbosus and G. holbrooki can be used as metrics.
41
92
Table 17 - Coefficient estimates of each pressure type for each regression model with L.
gibbosus and G. holbrooki (negative effects of pressure types are indicated in bold; darker blue
indicates stronger contributions of pressure variables to the model according to the values of
table 10; empty cells correspond to non-selected variables).
Data
Presence
Abundance
Species
Connectivity Hydrology Morphology
Water
quality
Global
L. gibbosus
0.43
0.99
-
0.72
1.25
G. holbrooki
-
0.41
0.30
0.48
0.85
L. gibbosus
-
-
-
-
-
G. holbrooki
-
74.80
40.31
188.03
193.39
Table 18 - Contribution of pressure variables to the explained variation of L. gibbosus and G.
holbrooki, according to the deviance-based pseudo-R2 (logistic regression) and the R2 (linear
regression).
Data
Presence
Abundance
Species
Connectivity Hydrology Morphology
Water
quality
Global
L. gibbosus
2.93
16.21
0.00
8.38
10.10
G. holbrooki
0.00
3.47
1.90
4.36
4.72
L. gibbosus
0.00
0.00
0.00
0.00
0.00
G. holbrooki
0.00
16.97
7.63
52.09
33.98
3.2.3.4 Ecotaxa guild-based metrics
As was found for taxa-based metrics the response of ecotaxa to the several pressuretype and global pressure combinations varied among different approaches (Tables 19
and 21). Nevertheless, ecotaxa-based metrics were globally more responsive to
disturbances than taxa-based metrics.
Non-calibration methods
The approach that assessed the contribution of each combined pressure-type and
global pressure variables to each taxon model resulted in quite similar trends, either
according to analysis based on presence/absence data or on abundance data (Table
11). The main difference was found for small cyprinids, which did not respond
significantly to any combined pressure variable when abundance data was used.
42
93
According to presence/absence data, except for the overall morphological river
alterations, small cyprinids respond positively to all combined pressure variables. Large
cyprinids showed even stronger positive responses to the same pressure-type and
global combinations, both according to presence and abundance data (Table 11 and
12). As an exception, presence data yielded a negative response of large cyprinids to
overall morphological river alterations.
According both to presence and abundance data, in opposition to cyprinids, salmonids
showed significant negative responses to all combined pressure variables, with the
exception to overall river connectivity disturbances where a positive and non-significant
response resulted, respectively, for presence and abundance data. (Table 11). The
strongest relationship with pressure variables was found for the response of large
cyprinids to biotic (44% of model explained variation) and global+biotic combined
pressure variables (47% of total variation) (Table 12).
Table 19 - Coefficient estimates of each pressure type for each regression model with the three
eco-guild metric (negative effects of pressure types are indicated in bold; darker blue indicates
stronger contributions of pressure variables to the model according to the values of table 12;
empty cells correspond to non-selected variables).
Data type
Presence
Abundance
Ecotaxon
Connect. Hydrol.
Morph.
Wqual.
Biot.
Global.
Global +
Biot.
Small
cyprinids
0.54
0.54
-
0.34
0.15
0.90
0.88
Large
cyprinids
0.68
0.99
-0.21
0.37
0.56
1.21
1.70
Salmonids
0.70
-0.23
-0.65
-1.04
-0.78
-0.95
-1.54
Small
cyprinids
-
-
-
-
-
-
-
Large
cyprinids
283.68
497.07
-
153.31
313.49
620.17
848.32
-
-510.66
-342.46
-431.08
-68.54
-888.52
-733.56
Salmonids
43
94
Table 20 - Contribution of pressure variables to the explained variation of each one of the guild
metric, according to the deviance-based pseudo-R2 (logistic regression) and the R2 (linear
regression) (Numbers in bold represent negative responses).
Data type
Presence
Abundance
Wqual.
Biot.
Global
Global +
Biot.
0.00
1.93
1.32
5.66
5.83
35.67
3.36
6.73
32.34
23.89
39.50
3.64
0.47
4.94
6.86
5.87
2.65
6.00
Small
cyprinids
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Large
cyprinids
20.08
39.47
0.00
4.80
44.01
28.84
46.62
Salmonids
0.00
12.43
7.42
8.34
0.74
14.61
11.38
Ecotaxon
Connect.
Hydrol. Morph.
Small
cyprinids
5.98
5.63
Large
cyprinids
23.51
Salmonids
Calibration methods
The approach that was based on calibration models produced results that contrasted to
those of the non-calibration method (Table 21). The results of the calibration approach
using presence/absence data estimated overall responses of metrics to pressures that
contradicted those of non-calibration methods. Models using presence/absence data
showed moderately good overall agreements, except for the extrapolation of small and
large cyprinids’ models to non-calibration sites that showed poor overall agreements
(Table 22). According to this approach models’ residuals for both small and large
cyprinids showed a negative relationship with all combined pressure variables, except
for connectivity-related disturbances. This suggest that both small and large cyprinids
tend to be absent from environmentally suitable sites due to human disturbances.
Salmonids showed an opposite response to disturbances to that found for cyprinids
and therefore their absence from environmentally suitable sites cannot be attributed to
any pressure-type.
The calibration-based method using proportional data produced similar results to those
based on presence/absence data, except for salmonids. This ecotaxa guild also
resulted negatively related to all pressure-type and global pressure combination,
except for connectivity-related disturbances that now resulted positively correlated to all
ecotaxa-based guild proportions. Models using proportional data also showed
moderately good overall agreements, except for large cyprinids and for and the
44
95
extrapolation of small cyprinids’ model to non-calibration sites, that showed poor overall
agreements (Table 22).
Calibration methods using abundances produced very poor relationships between
calibration model residuals and disturbances.
Table 21 – Pearson correlation between residuals of logistic (presence data) and linear
(abundance data) regression calibration models and each pressure-type synthetic variables
(significant negative correlations are shown in bold).
Global
Data type
Ecotaxon
Connect.
Hydrol.
Morph.
Wqual.
Biot.
Global
+
Biot.
Presence
Proportions
Abundance
Small cyprinids
0.09
-0.32
-0.33
-0.29
-0.22
-0.41
-0.44
Large cyprinids
0.04
-0.30
-0.40
-0.18
-0.12
-0.41
-0.39
Salmonids
-0.01
0.20
0.05
0.35
0.16
0.25
0.32
Small cyprinids
0.07
-0.32
-0.32
-0.32
-0.31
-0.44
-0.49
Large cyprinids
0.16
-0.13
-0.30
-0.13
-0.20
-0.33
-0.33
Salmonids
0.11
-0.27
-0.27
-0.37
-0.16
-0.42
-0.42
Small cyprinids
-0.02
-0.05
0.01
-0.03
-0.05
-0.01
-0.03
Large cyprinids
-0.02
-0.05
0.01
-0.03
-0.05
-0.01
-0.03
Salmonids
0.01
0.01
-0.01
0.01
-0.11
0.00
-0.03
Table 22 – Measures of calibration model adjustment (AUC – Area under the ROC) for
calibration and non-calibration sites.
Data type
Adjustment measure
Small cyprinids
Large cyprinids
Salmonids
AUC calibration
0.88
0.83
0.85
AUC non-calibration
0.69
0.67
0.91
R2 calibration
0.29
0.11
0.34
R2 non-calibration
0.04
0.04
0.54
R2 calibration
0.32
0.19
0.38
R2 non-calibration
0.00
0.00
0.03
Presence
Proportions
Abundance
45
96
3.2.3.5 Size-class guild-based metrics
The assessment of size-class based metric response to pressure-type and global
pressure synthetic variables were based exclusively on calibration methods using
logistic
regression
models
with
proportional
data.
This
approach
produced
contradictory results amongst the large cyprinid species considered for analysis (Table
23). For B. bocagei, residuals of calibration models for all size-class proportions
showed significant positive correlations with most combined pressure variables. The
exception was found for connectivity-related overall disturbance that showed a
significant negative correlation with models’ residuals for both the proportion of
juveniles and small adults. On the other hand, for B. sclateri, negative correlations were
found between models’ residuals and four pressure combinations (Hydrological,
Morphological and biotic-related disturbances, and global + biotic synthetic pressure)
for both the proportions of juveniles and small adults. Furthermore the negative
relationships found for B. sclateri were much stronger than the positive relationships
found for B. bocagei. Therefore, besides B. sclateri´s abundance as metric, sizeclasses of this species can also be used.
Surprisingly, for the proportion of juveniles of both Pseudochondrostoma species and
also for the total proportion of juveniles, only positive correlations between residuals
and combined pressure variables were found, although only very weak relationships
were found.
Table 23 – Pearson correlation between residuals of logistic regression calibration models using
size-class proportions and each pressure-type synthetic variables (significant correlations are
shown in bold; values in blue are negative correlations and values in values in red are positive
correlations).
Global
+
Biotic
Connect.
Hydrol.
Morphol.
Water
quality
B. bocagei juv
-0.11
0.24
0.09
0.07
0.08
0.19
0.20
B.bocagei small ad
-0.14
0.36
0.13
0.19
0.11
0.30
0.32
B.bocagei large ad
0.12
0.31
0.11
0.40
0.13
0.26
0.35
B. sclateri juv
-0.03
-0.32
0.28
-0.35
-0.44
-0.02
-0.24
B. sclateri small ad
-0.05
-0.41
0.17
-0.48
-0.65
-0.19
-0.47
B. sclateri large ad
0.10
0.42
0.22
0.47
0.26
0.45
0.48
P. duriense juv
0.20
0.18
-0.02
0.20
0.05
0.03
0.13
P. polylepis juv
0.13
0.17
-0.09
-0.07
0.07
-0.09
-0.01
Total % of juveniles
0.03
0.25
0.04
0.16
0.04
0.14
0.18
Species/length class
Biotic Global
46
97
4. Discussion and recommendations
In the present periodical report of sub-task 3.7 new metrics for a specific Mediterranean
biogeographical unit (the iberian Peninsula) were tested using several alternative
approaches, with the aim of selecting those metrics that most consistently responded
to disturbances. Metrics may respond differently to each kind of disturbance and
therefore separate analysis were carried out for different pressure-type combined
variables. A first overall impression of the results presented in this report is that
different methods basically produced variable results concerning metric responses to
pressures, and that it is difficult to draw a conclusion about which metrics should be
further selected for the biotic index development. Besides the variability displayed by
different methods, various ways to quantify the fish populations, or parts of the
population, also yielded different outputs. Nonetheless, some of the most consistent
and general negative responses to disturbance came from B. sclateri, B. comizo and S.
pyrenaicus.
However, the different approaches used in this report tried to deal with distinct
questions that can be asked in the context of biological responses to human
disturbances (see subsection 3.1.1). Therefore, a metric response detected with a
given approach is not necessarily contradicting the result of another approach; the two
approaches may simply being addressing different questions. For example, in theory it
is possible that the abundance of a species showing a higher upper tolerance value will
be more negatively responsive to disturbance than the abundance of a species
showing a lower upper tolerance.
An important feature of river fish communities in the Mediterranean region is the high
level of endemic species with restricted geographical ranges. The ecology of many of
such species is largely unknown, which poses a problem for their guild classification.
An example is the species classification into tolerance guilds, which have been typically
carried out through expert judgment. In this report we propose two possible
approaches to objectively quantify species upper tolerance to each pressure-type and
to global pressure variability. The procedure can be used to help on guild classification,
and also for creating new testable metrics, by considering for example the mean
species tolerance per site or the percentage of species with upper tolerances below or
above certain thresholds. Metrics using upper tolerance information separately to each
kind of pressure may also be considered.
47
98
A potential problem of both approaches used to quantify species tolerance is that
environmental variability may interfere with estimates. In fact, there is a geographical
coincidence between pressure and environmental variability: for example most
disturbed sites tend to be concentrated in the lower river segments. As a consequence,
species showing low estimated upper tolerance values may in fact be tolerant species
that are responding to environmental variability and not to pressure. This is why
tolerance estimates cannot be directly used as surrogates to species response to
pressures. In this report metric response to pressure was based on methods that tried
to account for environmental variability. These included non-calibration and calibration
methods. The calibration method is probably more reliable, since theoretically more
realistic metric responses to environmental variability are obtained using a calibration
dataset that includes only the most undisturbed sites. However, the non-calibration
method have the advantage of allowing the assessment of a larger number of taxabased metric responses. The use of a calibration dataset can also be limited by its
representativeness of environmental variability. If the full range of environmental
conditions are not represented in the calibration dataset then there is the problem of
predicting metrics beyond the environmental conditions at calibration sites. In fact, this
was the case with the calibration dataset used in this report: calibration sites were
found to be more frequently located in the North-western region of Iberia and in Central
Spain and, therefore, clearly did not represent the full environmental spectrum (see Fig.
6). Hence, caution is needed when interpreting metrics responses to pressure using
the calibration method, since there is the danger of spurious relationships to be found.
Another challenge of calibration methods is to discriminate whether metrics are higher
or lower than expected at less disturbed sites or higher or lower than expected at more
disturbed sites, or both. This is a relevant question because in the context of biotic river
quality assessments, the main concern is to find metrics that helps to identify the most
disturbed sites. Therefore, analyses should focus on whether the metrics has higher or
lower values than expected at the most disturbed sites.
Another potential problem that should be accounted for when interpreting results of
metric responses to pressure variables is the spatial autocorrelation among sites, i.e.,
the tendency of closer sites to show similar biotic and abiotic conditions. The effects of
spatial autocorrelation in regression and correlation are well known and include biased
coefficient estimates and inflation of statistical significance of relationships (see e.g.
Lennon 2000). Since unimpaired locations probably tend to be aggregated in space,
the calibration subset will be more susceptible of showing an even stronger spatial
48
99
autocorrelation than the original dataset, which may represent an additional problem to
calibration approaches.
Despite all the above mentioned possible problems, the results suggest that there are
potentially good ecotaxa and taxa-based indicators among the several endemic
cypriniformes and the most widespread invasive fish species, although surprisingly
some of these metrics responded positively to pressures. The ecotaxa guild-based
metrics were globally more responsive to metrics than simple taxa-based metrics. The
results also showed that the two invasive species analysed (L. gibbosus and G.
holbrookii) are also good candidate metrics, namely as positive indicators of
hydrological, water quality and global disturbances.
The most consistent result across the several approaches used was that connectivityrelated pressures showed the lowest relationship with both the taxa and ecotaxa-based
metrics considered in this study. In fact, a stronger response of large potamodromous
cyprinids with connectivity-related disturbances was expected. Nonetheless, the
stronger negative relationship with connectivity disturbances was indeed found for the
proportion of juveniles of a potamodromous species (B. bocagei), suggesting that it
may be a good metric for connectivity problems. It should be emphasized that
connectivity pressure may be underestimated due to the lack of a thorough field
inventory on the number of small and/or old barriers and their capacity to be
transposed by fish undergoing migratory movements.
Species-based metrics are limited by the geographical range of the species,
sometimes quite small. This is not the case for widespread L. gibbosus and G.
holbrooki. Also, metrics based on eco-taxa guilds are more promising because of its
wider distribution.
The methods and procedures used in this report represent just a few among many
other approximations to the problem of isolating and quantifying metric responses to
pressures in Mediterranean areas. Based on the results of this report and the
considerations made above, important challenges for further development of this issue
would be to i) using estimated tolerances to the several combined pressure variable in
metric calculations, ii) assess different methods for combining single pressure-type
variables, iii) consider additive or interaction effects among different types of pressure,
iv) test further responses of length-age-based metrics, both for other species and for
eco-taxa guilds, v) use different sets of geographical and environmental variables, vi)
develop a straightforward procedure to account for the effect of spatial autocorrelation
49
100
when testing metric responses, vii) taking into account fish home-range by using a
segment-base and not a site-based approach.
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53
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http://efi-plus.boku.ac.at/
Project no.: 0044096
Project acronym: EFI+
Instrument: STREP
Thematic Priority: Scientific Support to Policies (SSP) - POLICIES-1.5
D 3.4, Task 3.7 – Large Floodplain rivers assessment
Due date of deliverable: 31.12.2007
Actual submission date: 18.04.2008
Duration: 24 Month
Organisation name of lead contractor for this deliverable:
Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
Responsible author: Christian Wolter
Revision [draft 1]
Project co-funded by the European Commission within the Sixth Framework Programme
(2002-2006)
Dissemination Level
PU
Public
PP
Restricted to other programme participants (including the Commission Services)
RE
Restricted to a group specified by the consortium (including the Commission Services)
CO
Confidential, only for members of the consortium (including the Commission Services)
X
105
WP 3.7 Large floodplain rivers
A separate large rivers data base has been compiled at IGB containing fishing data obtained
by electric fishing as well as complementary methods like trawling, seining, fyke- and
gillnetting. Additional sampling methods should be essentially considered for large rivers to
representatively survey the mid channel section and type-specific potamal fish species. The
data base was completed in December 2007 and contains 2730 data sets covering 18 river
systems.
Pre-test
A subset of 250 river stretches was evaluated using the existing German fish-based
assessment scheme. This was used as a pre-test to elucidate the improvement of the
assessment results by incorporating data from various sources as well as from complementary
sampling gears. Both, the assessment result and the Ecological Quality Ratio (EQR) were
highly significantly related to the number of species and individuals caught.
Table 1 Significant correlation results between the Ecological Quality Ratio (EQR) and
species respectively individuals in a sample (** Correlation is significant at the 0.01 level (2tailed)).
EQR
Spearman's rho
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Sum of Squares and
Cross-products
Species Individuals
.712(**)
.601(**)
.000
.000
.699(**)
.430(**)
.000
.000
163.503 51994.758
Covariance
N
.667
246
212.224
246
0.60
EQR
0.50
0.40
0.30
0.20
0.10
0.00
1.00
1.50
2.00
2.50
3.00
3.50
Assessment value (2.51 = GES)
Fig. 1 Correlation between assessment result and Ecological Quality Ratio (EQR) according
to the German national fish-based assessment system for 250 sites (GES= good ecological
status).
In addition, there was a slight indication, that the assessment results were biased towards the
good ecological status, if the sample increased a threshold of about 10,000 individuals.
106
Therefore, the recursive segmentation procedure C&RT (Classification and Regression Trees)
was used to separate the influence of species and individuals caught, respectively on the
assessment results. At each classification level, this binary procedure divided the target
variable (assessment value) in two homogenous subgroups with increasing homogeneity in
the new subgroups, and indicated this variable which most contributed to the separation. The
procedure stopped when complete homogeneity was reached. However, the procedure was
terminated after the second segmentation level, because it becomes inefficient when the same
predictor is selected twice.
There was a strong indication for species number as main predictor for the good ecological
status, with 14 species per sample as significant threshold, whilst at a total of >20 species per
sample the average site reached the quality aim of the WFD (≥2.51 represents the GES).
Node 0
Mean
2.157
Std. Dev.
0.528
n
246
%
100.0
Predicted
2.157
Species
Improvement=0.114
<= 14
Node 1
Node 2
Mean
1.828
Std. Dev.
0.442
n
126
%
51.2
Predicted
1.828
Mean
2.503
Std. Dev.
0.367
n
120
%
48.8
Predicted
2.503
SpeciesRatio
Species
Improvement=0.032
Improvement=0.015
<= 0.215
Node 3
Mean
Std. Dev.
n
%
Predicted
> 14
1.371
0.216
29
11.8
1.371
> 0.215
Node 4
Mean
Std. Dev.
n
%
Predicted
1.965
0.398
97
39.4
1.965
<= 20
> 20
Node 5
Mean
Std. Dev.
n
%
Predicted
2.306
0.330
53
21.5
2.306
Node 6
Mean
Std. Dev.
n
%
Predicted
2.658
0.319
67
27.2
2.658
Fig. 2 Result of the C&RT segmentation of the assessment result predicted by various sample
characteristics. Significant predictors are shown. The procedure was terminated at the second
level, as species had been selected the second time.
As indicated by the C&RT segmentation results, fish species number in the sample seemed to
be a paramount impact on the result of the fish-based assessment. Even if representative
sampling is almost a prerequisite for reliable assessments, these findings might imply that it
becomes essentially to sample all present fish species even in large rivers. This would
essentially require the implementation of additional sampling gears for mid channel potamal
species in the survey design to properly evaluate large rivers.
107
Frequency of occurrence
As a first step, the frequency of occurrence (% of samples containing a certain species) has
been compared between standard electric fishing during the day and complementary fishing
gears, like seines, trawls, gill- or trap nets. This analysis aimed to characterize the
contribution of complementary gears to the species inventory obtained. It has been performed
for all rivers in the data base with sufficient numbers of samples collected with both kinds of
sampling gears.
For most of the species recorded, their frequencies in the catch significantly differed between
standard electric fishing and alternative gears (Appendix Table 1). However, the number of
species substantially more frequently recorded by additional gears ranged between 0 (Narew
River, Poland) and 8 (Ijssel River, The Netherlands) with an average of 4.43 (± 3.29 standard
deviation). In contrast, much more species have been more frequently recorded by standard
electric fishing (Appendix Table 1).
Further analyses focused on rivers with more than 100 samples in each strategy to account for
rare species by keeping the contribution of a single record below 1%. The number of species
exclusively caught by additional gears ranged between 1 (Meuse River) and 6 (Ijssel River),
whilst the number of species exclusively caught by electric fishing during the day was in
minimum 5 (Oder River) and in maximum 15 (Elbe River) (Fig. 3).
# species
16
electro
other
14
12
10
8
6
4
2
0
Elbe (811)
Ijssel (239)
Coregonus peled
Cobitis taenia
Hypophthalmichthys molitrix Carassius gibelio
Coregonus lavaretus
Salmo trutta, (Sea-)
Abramis sapa
Coregonus oxyrhynchus
Meuse (319)
Abramis sapa
Oder (339)
Coregonus maraena
Vimba vimba
Osmerus eperlanus
Rhine (624)
Osmerus eperlanus
Abramis sapa
Petromyzon marinus
Silurus glanis
Fig. 3 Number of fish species exclusively caught by either electric fishing during the day or
an alternative gear. Species caught by alternative gears only are indicated in blue.
By introducing a 1% criterion of rarity, i. e. omitting all species with less than one percent
frequency, because they only accidentally occur in a sample, the number of exclusively
recorded species substantially dropped to 0-4 in the alternative gears and to 4-10 in the
electric fishing (Fig. 4). At a 5% criterion of rarity, in two rivers one species each remained
exclusively for additional gears and 1-2 for electric fishing samples (Fig. 5)
108
# species
16
electro
other
14
12
10
8
6
4
2
0
Elbe (811)
Ij ssel (239)
Meuse (319)
Cobitis taenia
Carassius gibelio
Coregonus lavaretus
Salmo trutta, (Sea-)
Oder (339)
Coregonus maraena
Vimba vimba
Osmerus eperlanus
Rhine (624)
Osmerus eperlanus
Abramis sapa
Fig. 4 Number of fish species with frequency >1% exclusively caught by either electric
fishing during the day or an alternative gear. Species caught by alternative gears only are
indicated in blue.
# species
16
electro
other
14
12
10
8
6
4
2
0
Elbe (811)
Ij ssel (239)
Meuse (319)
Oder (339)
Coregonus maraena
Rhine (624)
Osmerus eperlanus
Fig. 5 Number of fish species with frequency >5% exclusively caught by either electro fishing
during the day or an alternative gear. Species caught by alternative gears only are indicated in
blue.
109
In total, the contribution of additional sampling gears to the species inventory was
surprisingly low compared to standard electric fishing during the day.
day electric fishing
alternative fishing methods
Elbe (316/495)
80
60
40
20
0
ns
Ijssel (117/122)
80
60
Frequency of occurrence (% samples)
40
20
0
ns
Maas (150/169)
80
60
40
20
0
Oder (122/217)
80
ns
60
40
20
ns
0
Rhine (289/335)
ns
80
60
40
20
ns
ns
i
us ma nus illa ius bus kna ena ius ing uus tilis dus nus tilis sus ilus rca nis
r
a
c
l
e
t
i
a
a
n
lle bra bur ngu asp bar oer ara
fle ru
gl
lu
rla vi
be cer uvi us
op
ba is
m
al la a ius us
bj
pe flu hys lus uci rus
fl
ox / R
sc
s
e
i
s
is am us
s
l
a
i
l
a
a
t
u
u
i
b
t
c
l
E
u
s
l
o
r
c
h
tr
u
u
sp ar licc
n
a m br
bi
Si
R
ha pe Leu eru Per tic
A
on
de
B
ng
A bur
br
B
go ep
la
m
A
eg
an
m
A
l
c
P
a
s
S
G
A
or
L
O
no
C
ym
G
0
Fig. 6 Observed differences in species records obtained by electro fishing and other gears,
respectively for the 19 most frequent species (ns= not significant, all other comparisons
significantly different at the p<0.05 level of confidence, Fisher’s exact test). Rheophilic
species are highlighted in blue. Number of samples in parentheses electric fishing / others.
110
day electric fishing
alternative fishing methods
Elbe (316/495)
80
60
40
20
0
ns
Ijssel (117/122)
80
60
Frequency of occurrence (% samples)
40
20
0
ns
Maas (150/169)
80
60
40
20
0
Oder (122/217)
80
ns
60
40
20
ns
0
Rhine (289/335)
ns
80
60
40
20
ns
ns
i
us ma nus illa ius bus kna ena ius ing uus tilis dus nus tilis sus ilus rca nis
r
a
c
l
e
t
i
a
a
n
lle bra bur ngu asp bar oer ara
fle ru
gl
lu
rla vi
be cer uvi us
op
ba is
c
m
al la a ius us
bj
pe flu hys lus uci rus
fl
ox / R
s
s
e
i
s
is am us
s
l
a
i
l
a
a
t
c
lu
E
u
s
lu
o
r
c
h
tr
ut
ui
sp arb licc
n
a m br
bi
Si
R
ha pe Leu eru Per tic
A
on
de
B
ng
A bur
br
B
go ep
la
m
A
eg
an
m
A
l
r
c
P
a
s
S
G
A
o
L
O
no
C
ym
G
0
Fig. 7 Observed differences in species records obtained by electro fishing and other gears,
respectively for the 19 most frequent species (ns= not significant, all other comparisons
significantly different at the p<0.05 level of confidence, Fisher’s exact test). Lithophilic
species are highlighted in blue, psammophilic in brown. Number of samples in parentheses
electric fishing / others.
However, among the 19 most frequently recorded species in large rivers, only six have been
classified as rheophilic, which was considered of certain indicator value for habitat structures
111
and characteristics typical for more natural river systems. Three of them occurred – if
anything – more frequently in the samples obtained by additional fishing gears (Fig. 6).
Lithophilic fish are obligate gravel spawner with benthic larvae, and thus, of highest indicator
value for hydrodynamic integrity of riverine habitats. Three lithophilic species belonged to the
most frequently caught, of them only one – the river lamprey in the Rhine River – was
predominantly caught by alternative gears. The two other lithophilic fish were more
frequently caught by electric fishing, except for barbel in the Ijssel River (Fig. 7).
The psammophilic gudgeons require sandy substrates for spawning. The have been most
frequently caught by additional gears, which was probably because of the typically high
abundance of the river gudgeon Romanogobio belingii in the mid channel sections of large
lowland rivers. However, in the data set of Netherland’s large rivers, this species was not
distinguished.
To summarize, eurytopic species were most frequent in samples from large rivers obtained by
both standard electric fishing during the day and alternative fishing gears. Most species have
been more frequently or exclusively obtained by electric fishing, while the exclusive
contribution of additional gears to the species inventory was rather limited. However, three
out of six indicator species for rheophilic conditions and one out of three for lithophilic were
more frequently contributed by additional sampling gears.
Relative abundance
The relative abundance of species has been compared between standard electric fishing during
the day and complementary fishing gears to identify species which essentially require
sampling by additional gears for their sufficient representation in the dominance structure.
Means of relative abundance in the samples have been compared for all rivers in the data base
with sufficient numbers of samples collected with both kinds of sampling gears.
In most cases, the relative abundance of those species exclusively contributed by one method
was very low (<0.1%), except the electric fishing results from the rivers Vistula and Narew
(Appendix Table 2).
Additional sampling gears performed highly significantly better for the abundance estimation
of common bream and silver bream. They also performed significantly better for catching
blue bream, whitefish, smelt or pikeperch, however, these pattern were more heterogeneous,
the species much lesser abundant in general, respectively rare or limited to very few rivers.
In two cases the comparably high abundance of tench and rude respectively resulted from the
exposition of additional gillnets in backwaters.
Corresponding to the results of the frequency analysis, most species have been recorded in
higher abundances by standard electric fishing along the banks (Appendix Table 2).
Looking at the rheophilic indicator species, gudgeons (probably only river gudgeon, which
has not been distinguished in the data from The Netherlands) and asp in the rivers Elbe and
Vistula were caught in higher abundance by alternative gears, whilst burbot and ide were
more abundant in the electric fishing samples (Fig. 8).
Correspondingly, asp was the only lithophilic indicator species with higher abundance
recorded by using additional gears (Fig. 9). In general only two lithophilic and psammophilic
species belonged to the most abundant in the large river data set analysed.
112
Relative abundance (%)
A
br
a
am
B
rk
oe
bj
na
R
ru
tilu
s
A
as
us
pi
A
rn
bu
al
us
G
r
ce
us
nu
L
u
id
s
P
via
fl u
tili
s
E
ciu
lu
S
s
lu
r
pe
cio
ca
A
an
illa
gu
G
go
o
bi
R
be
g
lin
ii
T
tin
ca
L
t
lo
O
a
er
ep
s
nu
la
Fig. 8 Observed differences in relative abundance of the most numerous species in samples
obtained by electric fishing and other gears. Rheophilic species are highlighted in blue.
113
Relative abundance (%)
A
br
a
am
B
rk
oe
bj
na
R
ru
tilu
s
A
as
us
pi
A
rn
bu
al
us
G
r
ce
us
nu
L
u
id
s
P
via
fl u
tili
s
E
ciu
lu
S
s
lu
r
pe
cio
ca
A
an
illa
gu
G
go
o
bi
R
be
g
lin
ii
T
tin
ca
L
t
lo
O
a
er
ep
s
nu
la
Fig. 9 Observed differences in relative abundance of the most numerous species in samples
obtained by electric fishing and other gears. Lithophilic species are highlighted in blue,
psammophilic in brown.
In total, large rivers were dominated by eurytopic, phyto-lithophilic species. Indicator species
with higher respectively more structurally specialized habitat requirements were comparably
rare amongst the most frequent and abundant fish. Accordingly, in large rivers there was only
a limited number of indicator species for a fish-based assessment available. Unfortunately,
alternative sampling methods did not significantly improve the amount of indicator species.
114
Further attempts have to be made to analyse the assessment power of the results obtained by
electric fishing only for the evaluation of structural deficits in large rivers.
Metrics based on presence absence or abundance thresholds of mid channel potamal species
would require a sampling effort similar to electric fishing except for common bream, silver
bream, river gudgeon, pike perch, and diadromous fish. However, the latter were so rare in
general, that it seemed most suitable to assess these species at their upstream spawning
stretches instead of the main channel migration corridor.
Floodplain assessment
The fish-based assessment of the integrity of floodplains requires the additional survey of
floodplain water bodies, permanently and/or temporarily with the main channel connected
still waters. In the data base they are typically represented as backwaters surveyed together
with the main channel sites. This type of connected backwaters represents only a part of the
typical floodplain, the frequently inundated ecotones. Older stages of succession, water bodies
naturally connected to the main channel less than 20 years on average were not represented in
the data base. A detailed comparison of floodplain water bodies in different stages of
succession has been performed at the lower Oder River. In the National Park “Lower Oder
Valley” the water bodies in the non-floodable polder have been flooded for the last time in
1947. Therefore, these water bodies represent typical floodplain waters in a stage of 60 years
of succession.
In general, highly significant differences between floodplain water bodies have been detected
at the level of functional guilds. A significant dispersion of rheophils and limnophils (both
species numbers and abundance) has been detected along the gradient of lateral connectivity.
Main channel samples contained on average 5.1 (± 1.0 standard deviation) rheophilic species,
those of frequently flooded water bodies 2.7 (± 1.7). Also the abundance of rheophilic fish
dropped significantly with the flooding frequency of the water body (Fig. 10).
Rheophilis relative abundance (%)
10
# rheophilic species
8
6
4
2
80
60
40
20
0
0
main channel
floodable
polder
non-floodable
polder
main channel
floodable
polder
non-floodable
polder
Fig. 10 Number and abundance of rheophilic fish in the main channel and both floodplain
water body types. All differences were significant at p<0.05 (ANOVA, post hoc Dunnett T3).
Functionally intact floodplains are characterized by well developed ecotone connectivity as
well as the availability of stagnant water bodies or temporarily inundated terrestrial areas.
Samples in water bodies representative for floodplain integrity will yield significantly lower
115
numbers and amounts of rheophilic fish. However, it is highly difficult to distinguish a drop in
rheophils abundance due to well developed floodplain connectivity from those due to human
impacts like flow regulation, siltation, damming etc. from the data base.
While number and abundance of eurytopic fish did not significantly differ between the
habitats, those of limnophils were inversely related to the inundation frequency, and patterned
opposite to the rheophils (Fig. 11).
Limnophilis relative abundance (%)
# limnophilic species
10
8
6
4
2
0
80
60
40
20
0
main channel
floodable
polder
non-floodable
polder
main channel
floodable
polder
non-floodable
polder
Fig. 11 Number and abundance of limnophilic fish in the main channel and both floodplain
water body types. All differences were significant at p<0.05 (ANOVA, post hoc Dunnett T3).
The amount of limnophilic and correspondingly of phytophilic fish were found to be highly
indicative for ecotone connectivity and seemed to be a suitable metric for functionally intact
floodplains. However, similar to the typical decline of rheophils in relation to lower
inundation frequencies, the correlated raise of limnophils is potentially indicative for both
floodplain integrity and human impacts. Lowered flow dynamics, e.g. by damming, might
increase limnophils in the same way like improved ecotone connectivity.
Backwaters and floodplain water bodies provide environmental conditions similar to lakes
with the ability of species exchange via permanent and/or temporary connections. Degraded
rivers, especially flow regulated and dammed, increasingly provide habitats corresponding to
stagnant water bodies, which improves their suitability for eurytopic and limnophilic fish. The
resulting fish assemblage structure may be similar to floodplain waters or backwaters. This
makes it nearly impossible to distinguish intact floodplain sites from degraded river sites just
from the data set, without knowing further site details. Similarly, the differentiation potential
of metrics for certain pressures is much lower when calibrated for both situations in the same
way.
However, normally a site will not be evaluated as a black box, so that the site characteristics
and pressures are well known. Therefore, the floodplain could be evaluated separately, to not
mix the contrary indications of the same metric in the main channel. It is suggested to
evaluate backwaters and floodplain water bodies distinct from the main river, preferably by
using metrics based on limnophilic and phytophilic fish.
116
Table 1 Comparison of fish species frequencies (%) obtained by electro fishing during the day and other gears, respectively. Significant differences
(Fisher’s exact test, p<0.05) are marked in bold coloured; total= number of samples.
Species
Total
Abramis ballerus
Abramis brama
Abramis sapa
Alburnus alburnus
Ameiurus nebulosus
Anguilla anguilla
Aspius aspius
Barbatula barbatula
Barbus barbus
Blicca bjoerkna
Carassius carassius
Carassius gibelio
Chelon labrosus
Chondrostoma nasus
Cobitis taenia
Coregonus lavaretus
Coregonus maraena
Coregonus peled
Cottus gobio
Ctenopharyngodon idella
Cyprinus carpio
Dicentrarchus labrax
Esox lucius
Gobio gobio
Hypophthalmichthys molitrix
Elbe (811)
electro
other
316
495
4.43 16.97
66.14 77.78
Ijssel (239)
electro
other
117
122
Meuse (319)
electro
other
150
169
Narew (44)
electro other
31
13
43.59
0.00
92.62
0.82
49.33
0.00
97.04
0.59
29.03
53.85
61.29
0.00
84.18
1.90
81.33
65.82
1.27
10.13
69.62
3.80
4.43
23.84
1.01
5.05
49.29
0.00
0.81
77.58
0.00
0.81
47.01
18.85
34.00
11.24
70.94
19.66
0.85
6.84
32.48
2.56
0.00
12.30
8.20
0.00
18.03
84.43
0.00
1.64
86.00
46.67
2.00
19.33
11.33
76.33
10.65
0.00
0.00
70.41
3.16
9.49
0.40
0.00
6.84
0.00
0.00
8.20
2.46
1.64
1.33
4.67
10.67
2.67
0.00
0.59
2.37
0.00
0.00
0.82
0.00
2.22
0.20
0.00
5.13
22.95
3.16
0.40
6.84
0.82
59.18
1.58
76.58
39.24
0.00
17.17
0.00
3.03
31.52
0.20
29.06
18.80
7.69
49.57
3.28
4.92
50.00
81.15
15.33
2.00
17.33
1.33
37.33
20.67
9.33
37.33
14.20
0.00
3.55
0.00
6.51
4.73
44.97
88.76
Oder (339)
electro
other
122
217
0.82 41.47
56.56 74.65
97.01
1.49
23.50
45.33
12.84
4.15
4.15
0.00
0.00
71.43
0.46
0.46
61.94
32.53
0.35
13.15
20.07
1.04
0.69
0.35
9.34
1.04
20.00
18.21
0.00
16.42
83.88
0.00
0.00
0.00
3.88
0.30
0.00
0.30
7.61
6.87
6.23
1.38
28.37
13.49
12.11
50.17
0.90
0.00
4.78
1.19
60.60
71.94
0.00
7.69
77.42
35.48
9.68
23.08
7.69
7.69
38.71
0.00
66.39
0.00
0.00
18.43
38.46
0.00
0.00
0.00
49.48
0.00
72.95
21.31
27.05
0.82
0.82
68.85
2.46
0.82
93.55
6.45
45.16
16.13
Rhine (624)
electro
other
289
335
4.10
0.92
77.05
7.38
42.62
71.31
11.98
0.46
0.46
16.59
Vistula (30)
electro other
21
9
61.90
4.76
14.29
95.24
88.89
0.00
0.00
0.00
4.76
57.14
66.67
47.62
71.43
0.00
33.33
0.00
11.11
22.22
14.29
33.33
9.52
66.67
11.11
0.00
4.76
0.00
90.48
23.81
90.48
28.57
0.00
0.00
0.00
0.00
117
Hypophthalmichthys nobilis
Lampetra fluviatilis
Leuciscus cephalus
Leuciscus idus
Leuciscus leuciscus
Lota lota
Misgurnus fossilis
Neogobius gymnotrachelus
Neogobius melanostomus
Oncorhynhus mykiss
Osmerus eperlanus
Perca fluviatilis
Perccottus glenii
Petromyzon marinus
Phoxinus phoxinus
Platichthys flesus
Pseudorasbora parva
Pungitius pungitius
Rhodeus amarus
Romanogobio belingii
Rutilus rutilus
Salmo salar
Salmo trutta, MeerSalmo trutta, BachSalvelinus fontinalis
Sander lucioperca
Silurus glanis
Thymallus thymallus
Tinca tinca
Vimba vimba
0.32
1.27
1.58
85.44
94.94
58.86
54.43
0.00
0.00
0.00
8.89
31.31
1.01
0.20
1.27
0.00
93.35
28.69
0.63
0.00
0.63
0.32
25.32
92.09
2.22
0.00
0.00
1.21
70.30
0.81
0.95
0.32
25.95
18.67
2.53
1.58
5.70
0.95
0.00
0.00
15.15
0.61
0.20
0.00
1.21
0.20
3.42
5.74
14.53
72.65
11.11
0.00
51.64
0.82
12.82
68.38
45.08
46.72
2.00
0.67
20.00
91.33
6.67
2.00
89.33
4.73
0.00
0.59
29.59
1.18
36.69
75.74
16.13
0.00
80.65
6.45
80.65
32.26
38.46
0.00
0.00
0.00
80.65
30.77
3.28
86.07
74.59
29.51
94.26
7.38
0.00
94.26
0.00
7.37
32.26
1.84
15.21
0.00
2.30
16.59
5.19
0.69
29.41
69.20
14.88
16.42
0.00
0.30
40.60
2.39
0.35
0.00
0.00
67.82
5.07
58.51
0.00
0.60
3.42
40.98
52.67
3.33
77.51
0.59
21.45
4.15
35.52
4.48
3.42
2.56
0.82
0.00
1.33
4.00
1.18
0.00
2.08
4.15
0.00
0.30
87.89
87.76
0.35
0.30
48.39
0.00
94.87
93.44
92.67
92.31
0.00
1.64
1.33
0.00
21.37
9.40
64.75
0.00
46.00
16.67
0.67
95.86
0.00
1.18
61.29
7.69
3.42
0.00
2.00
0.00
70.97
61.54
100.00
53.85
36.89
83.61
64.06
44.24
76.19
90.48
80.95
52.38
0.00
66.67
0.00
0.00
19.05
0.00
100.00
23.81
22.22
0.00
14.29
0.00
38.10
4.76
100.00
0.00
0.00
22.22
19.67
18.85
6.56
59.45
34.56
19.82
37.02
9.00
0.00
85.07
1.19
0.60
42.86
28.57
14.29
0.00
0.00
0.00
9.84
0.00
0.46
4.61
7.96
0.00
14.29
4.76
11.11
11.11
118
Table 2 Comparison of mean relative abundance (%) obtained by electric fishing during the day and other gears, respectively. Significant
differences (Students t-statistics, at least p<0.05) are marked in bold coloured.
Abramis ballerus
Abramis brama
Abramis sapa
Alburnus alburnus
Ameiurus nebulosus
Anguilla anguilla
Aspius aspius
Barbatula barbatula
Barbus barbus
Blicca bjoerkna
Carassius carassius
Carassius gibelio
Chelon labrosus
Chondrostoma nasus
Cobitis taenia
Coregonus lavaretus
Coregonus maraena
Coregonus peled
Cottus gobio
Cyprinus carpio
Esox lucius
Gobio gobio
Hypophthalmichthys nobilis
Elbe
electro
other
0.05
2.10
5.17 32.37
9.29
0.14
3.79
1.38
0.01
0.18
6.69
0.03
0.02
5.27
0.39
0.30
6.14
0.00
0.03
24.96
0.00
0.00
0.03
0.10
0.04
0.00
Ijssel
electro
other
Meuse
electro
other
Narew
electro
other
2.96
0.00
25.91
0.01
1.84
0.00
40.66
0.00
0.43
14.89
5.28
0.19
2.05
0.32
2.80
0.00
12.42
0.48
0.00
0.26
1.70
0.01
0.00
0.12
0.05
0.00
1.81
19.36
0.00
0.00
13.40
1.62
0.01
0.60
0.39
2.98
0.05
0.00
0.00
1.18
0.00
0.59
0.51
0.00
0.00
0.10
0.00
0.02
0.02
0.07
0.21
0.04
0.00
0.00
0.01
0.00
9.21
0.37
0.15
0.92
0.00
0.02
0.00
0.01
0.00
0.00
0.12
0.66
0.01
0.00
0.12
0.02
1.28
0.02
6.96
1.02
0.00
0.00
1.61
0.00
0.27
4.05
0.00
0.00
1.77
0.86
0.08
3.86
0.00
0.06
3.03
14.07
0.18
0.02
0.93
0.00
0.63
0.43
0.07
1.60
0.55
0.00
0.01
0.00
0.06
0.01
1.98
6.74
9.02
0.20
2.52
0.18
Oder
electro
other
0.01
6.55
1.23 20.74
Rhine
electro
other
4.29
0.00
40.85
0.02
5.99
1.38
7.37
0.15
8.01
1.33
1.92
0.15
0.16
0.00
0.00
7.74
0.00
0.00
0.09
0.05
0.00
0.00
33.09
0.00
0.01
0.00
3.54
0.00
8.94
1.55
0.01
0.71
1.64
0.01
0.00
0.00
0.42
0.01
1.35
0.17
0.00
1.10
13.90
0.00
0.00
0.00
0.03
0.00
0.00
0.57
0.00
0.00
0.11
0.15
0.25
0.02
0.90
0.45
0.47
3.90
0.00
0.00
0.05
0.02
6.00
6.79
15.06
0.00
0.00
0.00
0.04
0.01
3.97
0.09
1.00
5.20
0.82
0.01
0.00
2.88
Vistula
electro
other
1.13
0.00
0.03
29.57
79.60
0.00
0.00
0.00
0.03
0.40
2.06
0.22
1.43
0.00
1.09
0.00
0.38
1.19
0.05
1.92
0.02
2.13
0.29
0.00
0.00
0.00
1.38
1.15
6.89
0.08
0.00
0.00
0.00
0.00
119
Leuciscus cephalus
Leuciscus idus
Leuciscus leuciscus
Lota lota
Misgurnus fossilis
Oncorhynhus mykiss
Osmerus eperlanus
Perca fluviatilis
Perccottus glenii
Petromyzon marinus
Phoxinus phoxinus
Platichthys flesus
Pseudorasbora parva
Pungitius pungitius
Rhodeus amarus
Romanogobio belingii
Rutilus rutilus
Salmo salar
Salmo trutta, MeerSalmo trutta, BachSalvelinus fontinalis
Sander lucioperca
Silurus glanis
Thymallus thymallus
Tinca tinca
Vimba vimba
0.00
0.01
6.32
16.57
2.20
1.23
0.00
0.00
0.75
2.95
0.04
0.01
0.01
0.00
16.90
2.38
0.00
0.00
0.09
0.57
7.61
0.28
4.81
8.61
0.07
0.01
0.00
0.99
18.99
0.01
0.00
0.00
0.12
15.15
0.02
0.01
0.00
0.33
0.17
0.01
0.01
0.03
0.00
0.00
0.00
0.98
0.02
0.00
0.00
0.03
0.00
0.10
0.02
0.16
0.00
2.35
0.05
10.92
1.36
0.89
0.02
0.00
0.00
0.02
0.20
16.31
0.08
0.01
18.61
5.10
0.01
0.01
0.01
0.05
0.00
0.00
0.58
0.00
0.29
7.07
2.68
0.00
3.49
0.08
7.24
0.66
5.53
0.00
0.00
0.00
6.62
5.28
0.03
3.19
4.24
0.40
26.38
0.04
0.00
11.83
0.00
0.16
2.42
0.02
1.08
0.00
0.03
1.08
6.41
0.00
0.00
0.00
1.89
47.02
0.00
45.84
17.31
32.74
12.24
16.06
0.00
0.10
0.03
0.00
0.64
0.93
1.38
0.00
2.41
0.32
0.00
18.79
0.00
0.00
2.87
0.43
0.02
0.00
0.02
0.00
1.66
29.62
5.77
18.31
16.40
3.41
0.29
0.01
1.55
13.00
0.79
1.13
0.00
0.00
1.52
0.01
0.01
0.00
0.00
13.60
0.04
2.51
0.00
0.01
1.52
0.50
4.71
0.12
0.06
0.18
0.00
0.01
35.10
13.86
0.04
0.00
5.44
4.53
2.53
0.29
0.00
3.41
0.00
0.00
1.13
0.00
11.71
0.38
5.59
0.00
0.03
0.00
0.66
0.13
26.00
0.00
0.00
6.13
0.30
0.28
0.04
4.22
2.99
0.46
1.81
0.42
0.00
5.45
0.01
0.01
0.11
0.24
0.02
0.00
0.00
0.00
0.06
0.00
0.00
0.06
0.07
0.00
0.23
0.01
0.20
0.20
120
1
Low Species Rivers
BADY P., LOGEZ M., D. PONT & VESLOT J.
CEMAGREF. HYAX UNIT. Aix en Provence
Report on February 2008
Contents
1. Dataset definition
1.1 Calibration and reference sites datasets
1.2 Fish length data
1.3 Concllusion
2. Environmental characteristics of low species rivers
2.1 Typology of sites according to richness
2.2 Environment
2.3 Conclusion
3. Modelling river size
3.1 Motivation
3.2 Description of the variables
3.3 Modelling the width
3.4 Validation on the reference dataset
3.5 Conclusion
4. Development of new petrics based on age classes
4.1 Methodology
4.2 First results
4.3 Conclusion
References
Appendix A to L
1.
Datasets definition
121
2
1.1 Calibration and Reference Datasets
Among the total number of fishing occasions, we first only consider one fishing
occasion per site (aleatory procedure) and only sites where the following environmental
variables are fulfilled.
Environmental variables:
"Geomorph.river.type"
"Size.of.catchment"
"Flow.regime"
"Altitude"
"Geological.typology"
"temp.ann"
"temp.jan"
"temp.jul"
"Actual.river.slope"
"Water.source.type"
"Floodplain.site"
"Natural.sediment"
"Lakes.upstream"
"Sampling.strategy"
"Method"
"Fished.area"
"STRAHLER"
"PREC.AN.du”
"forest25.du"
"urban.du"
"ECOREG"
We then retain a total of 10,158 sites, distributued among the following countries.
AT
840
CH
601
DE
782
ES
1735
FI
220
FR
971
HU
165
IT
498
LT
109
NL
121
PL
866
PT
922
RO
239
SE
522
UK
1567
For these sites, the following pressure variables are in general fulfilled.
"Barriers.catchment.down", "Barriers.river.segment.up",
"Barriers.river.segment.down", "Impoundment", "Hydropeaking", "Water.abstraction",
"Hydro.mod", "Temperature.impact", "Velocity.increase", "Reservoir.flushing",
"Sedimentation", "Channelisation", "Cross.sec", "Instream.habitat", "Riparian.vegetation",
"Embankment", "Floodprotection", "Floodplain", "Toxic.substances", "Acidification",
"Water.quality.index", "Eutrophication", "Organic.pollution", "Organic.siltation",
"Navigation", "Colinear.connected.reservoir"
Sites with some gaps in the pressure variables are accepted at that step, in order to not
exclude a country from our selection:
- missing values in Switzerland for "Colinear.connected.reservoir" and
"Toxic.substances"
- missing values in United Kingdom for "Riparian vegetation"Temperature.impact",
"Velocity.increase", "Organic.siltation", "Floodplain" and "Navigation".
- in most of case, missing values
122
3
- missing values for "Water.quality.index" and "Toxic.substances" in a large part of
French sites
Reference sites
Sites considered as reference sites are sites without any local pressure, except
alteration of the flood plain (presence of flood protection works). But sites for which a barrier
is present downstream are not excluding.
We then retain 459 reference sites. Most of these reference sites are situated in Spain
and Romania
AT
1
ES
193
FI
14
FR
26
IT
53
LT
13
PL
13
RO
133
SE
13
Calibration sites
In order to calibrate our models on a more representative dataset, we selected a
calibration dataset. For these sites, the following pressures are accepted:
- no or presence of partial barrier downstream and/or upstream from the segment
- no or Weak water abstraction
- no or intermediate level for channelization
- no “Impoundment”
- no “Hydropeaking”
- no “Reservoir flushing”
- no alteration of “Cross section”
- no or weak “Sedimentation”
- no alteration of “Instream habitat”
- no or intermediate level for “Toxic substances”
- no “Acidification”
- Water quality classes 1 or 2
- no or missing values for “Velocity increase”
- no or missing values for “Colinear.connected.reservoir”
- no or only local “Embankment”
- no or slight alteration of “Riparian vegetation” (or missing values)
- no or missing values for “Temperature Impact”
- no “Hydrological alteration”
- no or low level of “Eutrophication”
- no or Weak level of “Organic pollution”
- no or missing values for “Organic siltation”
The calibration dataset considers 959 sites, without including reference sites
AT
49
DE
21
ES
372
FI
44
FR
89
HU
IT
6
LT
58
PL
20
PT
124
RO
2
SE
34
UK
112
123
4
1.2. Fish length Data
The aim of this part is to give a detailled description of the available fish length data.
These data are included in the table “Length” from our common European database.
Fish length data are described for both all sites, reference sites and calibration sites.
1.2.1 Fish length for all sites
General presentation of the table “Length”
This table contains 7 columns: Length_id (id of the line), Site_code (the id of the
sampling site), Date (the date of sampling), Species (the names of the species), Run (the
number of the run where fishes were caught), Total_length (the total length of fishes) and
Number_of_individuals (the number of fishes sharing the same length). This table contains
2,195,914 rows; each one represents the number of individuals of species Y having a length of
X millimetres, sampled at the sampling site Z at the date D. The data covers thirteen
countries: Austria (AT), Switzerland (CH), Germany (DE), Spain (ES), France (FR), Italy
(IT), Lithuania (LT), Netherlands (NL), Poland (PL), Portugal (PT), Romania (RO), Sweden
(SE) and United Kingdom (UK). No data are available for Finland. The data represent:
- 9,422 sites on the 14,221 sites occurring in the data base;
- 23,204 fishing occasions on the 29,509 total ones;
- 146 species on the 162 species sampled (omitting hybrids);
- 6,291,717 individuals on the 7,706,588 fishes caught (Table 1).
Those fishes were measured across the period 1955-2007. However, seventy percent
of individuals were measured during the last decade. The sixteen species for which we don’t
have lengths are very rare; their catches are ranging from 1 to 1,850 (it is to say less than
0.03% of the total catches).
Countries
Austria
Switzerland
Germany
Spain
France
Italy
Lithuania
Netherlands
Poland
Portugal
Romania
Sweden
United Kingdom
Number of sites
938
539
803
1,902
1,145
471
115
159
636
421
198
598
1,497
Number of Fishing
occasions
1,172
667
1,817
2,210
6,542
785
129
748
687
421
219
5,345
2,462
Number of
species
57
35
57
47
64
57
37
47
52
37
37
41
22
Number of
lengths
326,032
171,583
648,243
233,337
3,867,694
62,847
17,801
135,866
73,140
60,431
27,722
425,910
241,111
Table 1: Distribution of sites, fishing occasions, species and lengths per country.
Lengths were calculated with four methods:
- Total lengths, fishes were sized on the field, for some of them the real total
length (from the head to the end of the caudal fin) was measured and for
124
5
others it was the fork length (from the head to the beginning of the caudal fin)
which was translated in total length with an equation;
- Classes, the sizes of fishes were divided into different classes with a constant
interval between classes, and each fish was assign to a class;
- Min-Max, only the minimal and the maximal sizes of a species was reported
as a size (concern very few individuals);
- Subsample, not all lengths were measured but only a sample of the fishes
caught.
With this classification we can see that fish lengths arise from are very heterogeneous
sources. Inside the category “Classes”, there is also different type of classes. The interval
between classes can be very different: some fishing occasions exhibit only three classes with
125 mm between them (100, 225 and 350 mm); others can have interval of 5mm between
classes (70, 75, etc.). Moreover for some fishing occasions you can have a mix of both total
individual lengths and classes. For example, all fishes less than 100 mm could have been
measured precisely and all fish over 100 mm could have been assigned to a size class: 105,
110 mm…
Lengths
This table contains lengths for 146 species and 6,291,717 fishes for all runs and
5,020,994 fishes for the first run (after removing all hybrids and all individuals with
undetermined species). Considering all the runs, the number of species length available for
each species is ranging from 1 to 926,908 whereas is ranging from 1 to 721,930 considering
only the first run. For all the runs, the ten most abundant species are: minnow, brown trout,
roach, gudgeon, stone loach, bullhead, chub, bleak, perch and Atlantic salmon (Table 2). For
only the first run, the ten most abundant species are: minnow, roach, brown trout, gudgeon,
chub, stone loache, bullhead, bleak, perch and eel (Table 3).
Species names
Phoxinus phoxinus
Salmo trutta fario
Rutilus rutilus
Gobio gobio
Barbatula barbatula
Cottus gobio
Leuciscus cephalus
Alburnus alburnus
Perca fluviatilis
Salmo salar
Number of lengths
926,908
876,033
713,614
575,677
441,269
392,365
381,197
262,827
165,642
143,633
Table 2: The number of lengths available for the ten most abundant species, for all runs.
Species names
Phoxinus phoxinus
Rutilus rutilus
Salmo trutta fario
Gobio gobio
Leuciscus cephalus
Barbatula barbatula
Cottus gobio
Alburnus alburnus
Perca fluviatilis
Anguilla anguilla
Number of lengths
721,930
662,634
626,009
479,851
339,192
337,856
262,175
252,676
157,214
102,670
125
6
Table 3: The number of lengths available for the ten most abundant species, first run.
The number of lengths for each species for all runs is detailed in appendix A and in
appendix B for the first run. The five most abundant species (in number of lengths) per
country are detailed in appendix C for all runs and in appendix D for the first run.
Lengths of fishes were collected during different runs. The number of runs is ranging
from 1 to 5. The majority of lengths (79.8%) were measured during the first run. There are
also 6% of lengths for which we don’t have information concerning the run. Spain and
Sweden are the only two countries concerned buy this lake of information (Table 4). It
represented 15 and 80% respectively of the total of lengths for those countries. If we take into
account only lengths measured during the first run the percent of remaining lengths varies
greatly between countries (Table 5). In Sweden due to the lack of information 16% of the
lengths remained if we take into account only the first run.
Austria
Switzerland
Germany
Spain
France
Italy
Lithuania
Netherlands
Poland
Portugal
Romania
Sweden
United Kingdom
No Data
0
0
0
35,210
0
0
0
0
0
0
0
342,930
0
1
270,511
121,236
648,243
130,173
3,233,453
59,073
17,801
135,866
73,140
60,431
27,722
69,345
174,000
2
42,173
37,401
0
48,856
630,539
3,569
0
0
0
0
0
9,426
47,369
3
13,348
12,906
0
18,896
3,686
205
0
0
0
0
0
4,209
19,265
4
0
40
0
193
16
0
0
0
0
0
0
0
477
5
0
0
0
9
0
0
0
0
0
0
0
0
0
PT
100
RO
100
Table 4: Distribution of length between runs and country.
AT
82.97
CH
70.66
DE
ES
FR
IT
LT
NL
100
83.6
93.99
100
100
55.79
Table 5: percent of lengths measured during the first run
PL
100
SE
16.28
On other important issue for lengths is the date of fishing, especially for young of the
year and juveniles. The date reflects indirectly the amount of energy collected by fishes
during their annual growing period. We expect young fish to be bigger in a fishing occasion
occurring in the end of year than fishes caught earlier in the year.
Two-thirds of the fishing occasions were sampled between August and October,
representing also two thirds of the lengths available (both for all runs and first run, Table 6).
Months
January
February
March
April
May
June
July
August
Number of
Number of
fishing occasions lengths all runs
101
17,035
97
12,341
514
61,618
929
156,751
1,356
368,064
2,076
757,917
2,598
561,556
4,861
1,124,631
Number of
lengths run 1
15,609
11,094
58,214
141,777
321,795
633,925
420,377
805,940
126
UK
72.17
7
September
October
November
December
6,398
3,179
927
168
2,129,885
878,877
191,129
31,913
1,670,756
745,602
167,364
28,541
Table 6: Distribution of fishing occasions and lengths per months.
The distribution of fishing occasions (by the way the number of lengths) across the
year is very different between countries (Table 7 and Table 8). If we consider only August,
September and October, the proportion of available lengths varies greatly between countries.
For Italy and Portugal the number of lengths for this period only represents less than 10% of
the total lengths provided. On the contrary for Lithuania, Romania and Sweden it represents
more of 80% of the lengths provided by those countries (Table 9).
AT
CH
DE
ES
FR
IT
LT
NL
PL
PT
RO
SE
UK
January February March April May June July August September October
3
16
49
60
47
84
124 129
211
268
0
0
0
0
9
10
124 233
139
126
8
4
39
162 275 114
72
311
370
275
2
12
55
49
47
87
189 396
725
512
4
3
14
174 520 1,230 665 851
2,158
829
66
47
92
65
53
85
47
9
71
29
0
0
0
0
0
1
27
45
52
4
0
2
235
171 43
0
0
0
32
195
0
0
0
2
23
55
61
72
186
249
8
0
8
49 106
95
120
31
4
0
0
7
0
0
2
0
6
95
56
24
0
1
3
5
12
41
709 2,183
1,911
445
10
5
19
192 219 274 454 506
483
223
November December
136
45
25
1
163
24
110
26
83
11
176
45
0
0
69
1
39
0
0
0
29
0
26
9
71
6
Table 7: Distribution of fishing occasions per country across the year.
AT
CH
DE
ES
FR
IT
LT
NL
PL
PT
RO
SE
UK
January
634
0
8,157
10
1,536
5,322
0
0
0
710
0
0
666
February
5,524
0
1,076
751
686
3,949
0
45
0
0
201
7
102
March
10,652
0
3,007
4,464
5,942
4,639
0
32,310
0
148
0
42
414
April
May
June
July
19,396 7,722
18,612 39,654
0
2,932
2,346
21,820
26,554 64,695 35,036 20,879
3,441
5,046
6,181
26,419
54,773 224,780 640,627 319,887
3,767
5,837
10,338
3,356
0
0
82
2,343
22,922 12,774
0
0
42
4,729
9,009
9,396
4,956 18,366 10,468 19,947
0
130
0
411
164
499
2,001
50,983
20,736 20,554 23,217 46,461
August
36,377
63,030
156,108
47,308
555,437
868
6,515
0
12,927
5,405
10,141
184,284
46,231
September
58,757
34,089
161,296
88,688
1,531,822
5,280
8,119
6,287
14,847
431
11,338
155,641
53,290
October
87,805
34,324
114,231
41,770
481,112
1,979
742
43,884
20,217
0
2,023
30,030
20,760
November December
31,609
9,290
12,959
83
49,119
8,085
7,782
1,477
46,144
4,948
11,191
6,321
0
0
16,961
683
1,973
0
0
0
3,478
0
1,742
517
8,171
509
Table 8: Distribution of lengths per country across the year for all runs.
AT CH DE ES FR
IT
LT NL PL PT RO SE UK
56.1 76.6 66.6 76.2 66.4 12.9 86.4 36.9 65.6 9.7 84.8 86.9 49.9
Table 9: Proportion of lengths for the period August-October.
127
8
1.2.2 Fish length for reference sites
Introduction
On the 459 reference sites, fish lengths are available for 364 of them. The 364 sites are
located in eight countries: Austria, Spain, France, Italy, Lithuania, Poland, Romania and
Sweden. No data is available for Finland. The 364 sites include 460 fishing occasions, 59
species and 44,894 lengths during the period 1986-2007. Sixty percent of the lengths are
located in Spain and Romania (Table 10).
Austria
Spain
Finland
France
Italy
Lithuania
Poland
Romania
Sweden
Number of
sites
Number of sites with
lengths
Number of fishing
occasions with lengths
Number of
species
Number of
lengths
1
193
14
26
53
13
13
133
13
1
135
0
26
51
13
12
115
11
1
145
0
48
68
23
12
126
37
5
17
0
7
15
29
6
21
11
49
12,945
0
6,313
4,212
5,170
499
14,238
1,468
Table 10: Distribution of reference sites per country.
Lengths
Regarding reference sites, this table contains lengths for 59 species and 44,894 fishes
taking into account all runs, where as it contains lengths for 58 species and 37,860 individuals
for the first run. Without distinction of runs, number of length per species is ranging from 1 to
21,895, whereas for the first run it is ranging from 1 to 16,002. The ten most abundant species
are the same if we consider all runs or only the first run: brown trout, minnow, stone loach,
barbell, chub, stream bleak, bullhead, gudgeon, golden spined loach and roach (Table 11 and
12).
Species
Salmo trutta fario
Phoxinus phoxinus
Barbatula barbatula
Barbus petenyi
Leuciscus cephalus
Cottus gobio
Gobio gobio
Rutilus rutilus
Lengths
21,895
5,681
2,902
2,347
1,853
1,535
1,522
1,508
929
681
Table 11: Number of lengths for the ten most abundant species for all runs.
Species
Salmo trutta fario
Phoxinus phoxinus
Barbatula barbatula
Barbus petenyi
Leuciscus cephalus
Gobio gobio
Lengths
16,002
5,419
2,897
2,347
1,850
1,535
1,413
128
9
Cottus gobio
1,296
929
Rutilus rutilus
681
Table 12 : Number of lengths for the ten most abundant species taking the first run.
The number of lengths for each species for all runs is detailed in appendix E and in
appendix F for the first run. The five most abundant of species (in number of lengths) per
country are detailed in appendix G for all runs and in appendix H for the first run.
Lengths where measured during run 1 to run 5, but the majority (84%) were measured
during the first run. As previously seen for all sites, there is some individuals for which the
run was not filled in. They represent 2.5% of the total lengths for reference sites and are all
located in Sweden (Table 13). Except for this country, the majority of individual were
measured during the first run (Table 14).
Austria
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
No data
0
0
0
0
0
0
0
1,132
1
49
8,644
5,030
4,002
5,170
499
14,238
228
2
0
3,063
1,283
210
0
0
0
94
3
0
1,233
0
0
0
0
0
14
4
0
5
0
0
0
0
0
0
Table 13: Distribution of lengths between countries and runs.
AT
100
ES
66.77
FR
79.68
IT
95.01
LT
100
PL
100
RO
100
SE
15.53
Table 14: Ratio of length measured during the first run.
The majority of fishing occasions (72%) takes place between August and October. The
number of lengths measured during this period follows the same trend (Table 15). However,
this pattern is not followed by each country: France and Italy only have 46 and 20% of their
lengths measured during this period (Table 16, 17 and 18). If we considered only the first run
those proportions don’t change except for Sweden, where it moves from 82% to 40% (Table
18).
Months
January
February
March
April
May
June
July
August
September
October
November
December
Number of fishing Number of lengths Number of lengths
occasions
all runs
run 1
451
431
9
150
146
5
395
361
10
370
272
8
228
228
2
3,122
2,404
22
3,440
2,682
34
14,282
12,850
124
16,328
13,213
159
3,201
2,624
49
2,897
2,638
37
30
11
1
Table 15: Distribution of fishing occasions and number of lengths across the year.
129
10
Austria
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
January
0
0
0
9
0
0
0
0
February
March
April May June
July
August September
0
0
0
0
0
0
1
0
1
1
4
0
4
5
47
57
0
0
0
0
10
9
6
19
0
9
4
1
8
10
0
9
0
0
0
0
0
1
11
11
0
0
0
0
0
0
0
5
4
0
0
1
0
5
56
29
0
0
0
0
0
4
3
29
Table 16: Number of fishing occasions per country and months.
October
0
23
2
4
0
7
12
1
November December
0
0
2
1
2
0
14
0
0
0
0
0
19
0
0
0
Table 17: Number of lengths per country and months for all runs.
Austria
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
January February March
0
0
0
0
10
31
0
0
0
451
0
364
0
0
0
0
0
0
0
140
0
0
0
0
April
0
207
0
163
0
0
0
0
May
0
0
0
205
0
0
23
0
June
0
1,000
1,423
699
0
0
0
0
July
0
652
1,503
811
80
0
136
258
August
49
3,967
874
0
2,566
0
6,787
39
September October November December
0
0
0
0
50
30
5,376
1,622
478
0
1,775
260
644
0
689
186
0
0
2,524
0
0
0
157
342
1,725
0
4,651
776
0
0
1,156
15
All run
1st run
AT
100
100
ES
FR
IT
LT
84.71 46.08 20.78 98.45
86.14 49.02 20.79 98.45
PL
100
100
RO
SE
85.79 82.43
85.79 39.91
1.2.2 Fish length for calibration sites
Introduction
On the 959 calibrations sites recorded, fish lengths are available for 721 of them. The
721 sites are distributed between 10 countries: Austria, Germany, Spain, France, Italy,
Lithuania, Poland, Romania, Sweden and United Kingdom. Three countries (Finland,
Hungary and Portugal) with calibration sites don’t have lengths associated with the fishing
occasions. The 721 sites were sampled during the period 1978-2007 and correspond to 2,016
fishing occasions, 78 species and 277,470 lengths (Table 19). The number of fishing
occasions varies a lot between countries with more than one thousand one for Sweden and no
data for Finland, Hungary and Portugal. The number of species per country is ranging from 17
(Italy and Romania) to 36 (France) and the number of length is ranging from 2,164 to more
than 100,000 (Table 19). Consequently we observe a lot of variability in the number of fishing
occasions and in the number of lengths per country.
Number of sites
Austria
Germany
Spain
Finland
France
Hungary
Italy
49
21
372
44
89
6
58
Number of sites
with length
49
21
255
0
89
0
26
Number of fishing
occasion with lengths
62
27
295
0
280
0
37
Number of
species
19
18
25
0
36
0
17
Number of
lengths
9,858
6,503
33,136
0
118,515
0
3,274
130
11
Lithuania
Poland
Portugal
Romania
Sweden
United Kingdom
20
124
2
34
112
28
20
108
0
26
111
16
23
109
0
30
1,124
29
23
30
0
17
23
15
2,164
6,832
0
3,753
91,330
2,105
Table 19: Number of calibrations sites per country.
Lengths
Focusing our interest only on calibration sites we have data for 721 sites, 78 species,
277,470 individuals all runs confounded and 160,231 individuals for the first run. Without
differentiating runs, the number of length per species is ranging from 1 to 99,778 and the tens
most abundant species are: trout, minnow, Atlantic salmon, bullhead, sea trout, lake trout,
stone loach, gudgeon, eel and chub (Table 20). Considering only the first run the number of
lengths per species is ranging from 1 to 61,902 and the ten most abundant species are: brown
trout, minnow, bullhead, Atlantic salmon, stone loach, gudgeon, eel, chub, roach and an
Iberian endemic fish Pseudochondrostoma duriense (Table 21).
Species
Salmo trutta fario
Phoxinus phoxinus
Salmo salar
Cottus gobio
Salmo trutta trutta
Salmo trutta lacustris
Barbatula barbatula
Gobio gobio
Anguilla anguilla
Leuciscus cephalus
Lengths
99,778
37,773
23,868
23,638
20,643
14,149
10,249
6,978
6,110
4,265
Table 20: The ten most abundant species considering all runs.
Species
Salmo trutta fario
Phoxinus phoxinus
Cottus gobio
Salmo salar
Barbatula barbatula
Gobio gobio
Anguilla anguilla
Leuciscus cephalus
Rutilus rutilus
Lengths
61,902
29,137
11,923
11,542
7,933
5,245
4,135
3,888
3,357
2,188
Table 21: The ten most abundant species considering only the first run.
The number of lengths for each species for all runs is detailed in appendix I and in
appendix J for the first run. The five most abundant species (in number of lengths) per country
are detailed in appendix K for all runs and in appendix L for the first run.
Lengths were measured between the run 1 and 5 but mostly during the first run. Like
previously observed for reference and for all sites, a lot of information concerning the run is
131
12
missing in Sweden (Table 22). Except for this country, the majority of lengths were collected
during the first run (Table 23).
Austria
Germany
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
United Kingdom
No data
0
0
0
0
0
0
0
0
74,900
0
1
8,249
6,503
21,041
94,241
2,694
2,164
6,832
3,753
13,349
1,405
2
1,245
0
8,410
24,274
388
0
0
0
2,082
418
3
364
0
3,558
0
192
0
0
0
999
282
4
0
0
118
0
0
0
0
0
0
0
5
0
0
9
0
0
0
0
0
0
0
Table 22: distribution of lengths per run and country.
AT
83.68
DE
100
ES
FR
IT
63.5 79.52 82.28
LT
100
PL
100
RO
100
SE
UK
14.62 66.75
Table 23 : ratio of length measured during the first run.
As previously observed for both all sites and reference sites, fishing occasions and
lengths are mostly distributed between August and October. Those three months concentrate
more than three-quarters of fishing occasions and lengths of calibration sites (Table 24).
Nevertheless, this general pattern is not true for all countries (Table 25 and 26). Germany and
especially Italy have lees than fifty percent of their available lengths measured during this
period (Table 27).
Months
January
February
March
April
May
June
July
August
September
October
November
December
Number of fishing Number of lengths
occasions
all runs
115
2
587
5
426
5
3,108
16
3,468
25
16,449
65
26,572
175
93,514
744
106,960
695
20,256
235
4,922
35
1,093
14
Number of
lengths run 1
76
296
355
2,304
2,635
12,766
13,766
40,945
67,076
15,119
3,947
946
Table 24: Distribution of fishing occasions and lengths per months.
Austria
Germany
Spain
France
Italy
Lithuania
Poland
January February March April May June July August September October November December
0
0
0
0
4
6
8
3
1
9
14
17
0
0
1
4
0
0
0
2
2
2
5
11
0
2
2
7
11
1
29
12
11
72
92
56
0
1
1
0
6
36 27
2
0
64
132
11
1
1
1
4
1
8
6
7
0
3
5
0
0
0
0
0
0
1
1
0
0
8
12
1
0
0
0
1
1
8
2
5
0
10
39
43
132
13
Romania
Sweden
United Kingdom
0
0
1
1
0
0
0
0
0
0
0
0
1
1
0
0
4
1
0
98
4
15
550
11
1
387
8
9
83
4
3
1
0
0
0
0
Table 25: Distribution of fishing occasion per months and per country.
Austria
Germany
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
United
Kingdom
January
0
0
0
0
78
0
0
0
0
37
February March April
0
0
0
0
240
1,310
448
84
1,494
108
57
0
20
45
274
0
0
0
0
0
30
11
0
0
0
0
0
0
0
May
716
0
695
1,674
78
0
63
107
135
June
599
0
262
12,537
795
82
1,893
0
278
0
3
0
July August September October November December
1,118
230
116
766
2,641
3,672
0
945
649
1,308
1,153
898
6,389 6,537
1,001
328
11,479
4,419
9,783 31,114
1,383
0
58,884
2,975
690
492
0
421
381
0
47
0
0
580
1,120
335
20
81
0
699
1,844
2,202
0
766
0
1,379
277
1,213
8,058 50,059
24
0
28,368
4,408
467
651
813
134
0
0
Table 26: Distribution of lengths per months and per country considering all runs.
All runs
First run
AT
DE
ES
FR
IT
LT
PL
RO
SE
UK
71.81 51.65 67.71 78.44 24.5 94.04 69.45 76.44 90.7 75.92
72.82 51.65 71.12 79.45 20.01 94.04 69.45 76.44 95.87 67.4
1.2.3 Conclusion
The table tbllength contains a large amount of fish lengths distributed in thirteen
countries, that is to say all countries involved in this project except Finland. The repartition of
lengths per country is very unbalanced, which one country providing half of the data (France).
The period concerned spreads over fifty years but is mostly concentrated during the last
decade.
Also fish lengths are very heterogeneous, with some fishes precisely measured (total
length) and with other fishes for which we have only a coarse estimation of their lengths
(large interval between classes).
This data set contains lengths for the majority of species caught and that for both
reference sites, calibrations sites and all sites. Six species are always among the ten most
abundant species: stone loach (Barbatula barbatula), bullhead (Cottus gobio), gudgeon
(Gobio gobio), chub (Leuciscus cephalus), minnow (Phoxinus phoxinus) and brow trout
(Salmo trutta fario). However, the most abundant species differ between countries and this for
all categories of sites.
The sampling dates are spread all over the year, but they are not distributed similarly
between countries. This pattern is observed for all kind of sites. The dare will be a very
important factor to take into account in further analysis, especially if we focus on young
fishes.
The majority of individual lengths were measured during the first run and that for all
countries except Sweden. For Sweden the low proportion of lengths coming from the first run,
is not a problem of efficiency of sampling during the first run, but it’s mainly a lack of
information. Most of the time the information related to the run is missing. It concerns 3,829
133
14
fishing occasions on the 5,345 Swedish fishing occasions. 482 Spanish fishing occasions are
concerned by the same problem. In order to keep those fishing occasions and especially the
Swedish ones, we propose to determine randomly if the length of each fish was determined
during the first run or not. This will be possible because in the table tblcatch we have the
details of how many fish of a certain species was sampled during the first run. Consequently if
one hundred brown trout would have been sampled during the first run, we will assign to one
hundred brown trout lengths the run number one. With this methodology we can keep 3,760
Swedish fishing occasions but no Spanish fishing occasions.
2. Environmental characteristics of low species rivers
2.1Typology of sites according to richness
By defining arbitrary levels - say only 1; 2 to 3; and 4 or more species- reference and
calibration sites can be sorted according to their absolute richness as measured during fishing
events. This results in the following distribution:
Richness
1
2-3
4+
Figure 1. Distribution of sites by richness level.
134
15
Reference and calibration sites
Reference and calibration sites
with at least 50 fishes
Species richness
1
2-3
4+
438
542
437
179
294
351
Mapping sites by richness levels gives first information on locations of low and high
richness level sites (See figure 1). Notice for instance the great number of low richness sites in
Northern Spain and the Asturies, as well as in the Alps; and the relative lack of such sites in
Central and Eastern Europe.
2.2 Environment
To go further in the exploratory analysis of site environment according to richness,
factorial analyses were performed on a set of available variables. The following factors were
considered:
• Geomorphological factors: Geomorph.river.type, Valley.form, Floodplain.site and
Actual.valley.slope;
• Geological factors: Geological.typology, Natural.sediment, ERODI.du;
• Geographical factors: Altitude, ALT.GRADIENT, ELEV.MN.du;
• Hydrological factors: Size.of.catchment, STRAHLER, Lakes.upstream,
Water.source.type;
• Meteorological factors: precmean.ann, temp.ann, temp.jan, temp.jul, PREC.AN.du,
TEMP.AN.du;
• Percentages for soil occupation in upstream catchment: clastseddu, calcsed.du,
igneous.du, morphic.du, fluvdep.du, glacdep.du, eolian.du, organic.du;
• Percentages for deposit composition in upstream catchment: urban.du, agri.du, past.du,
fores.du, scrub.du, noveg.du, wetl.du, mwetl.du, est.du.
A Multiple Correspondence Analysis of this set of factors - grouped into interval
classes when necessary, that is for continuous factors –helps find out relevant pattern in our
calibration dataset. Sites and factor levels can be then displayed into main resulting factorial
plans. Note that too asymmetrically distributed variables were previously transformed using
usual transormations; Size of catchment was log-transformed whereas actual river slope and
percentages were squared-root-arcsine transformed.
Beyond expected gradients, notably that of temperature and that of altitude, which
correspond more or less to respectively the first and second axes, other factors prove to be
particularly discriminating (See figure 2). Among them: sand sediment, glacial water source,
nival water source and braided river type, which are all closely related to geographically
consistent site groups.
135
16
Natural.sediment.Sand
Valley.form.Gorges
0.5
0.0
-0.5
-1.0
0.5
0.0
-0.5
-1.0
clastseddu.3
igneous.du.1
Floodplain.site.Yes
Geomorph.river.type.Braided
-1.5
Natural.sediment.Sand
-1.5
Water.source.type.Glacial
Actual.river.slope.3
precmean.ann.2
temp.jan.1
TEMP.AN.du.2
Geological.typology.Calcareous
PREC.AN.du.2
temp.jan.2
ALT.GRADIENT.3
temp.ann.2
scrub.du.0
TEMP.AN.du.1
igneous.du.0
glacdep.du.3
ELEV.MN.du.0
calcsed.du.3 Geomorph.river.type.Meand
ERODI.du.1
Size.of.catchment.0
temp.ann.1
Altitude.0
Floodplain.site.No
Altitude.3
temp.jul.1
scrub.du.3
past.du.2
clastseddu.0
noveg.du.3
Valley.form.Plains
STRAHLER.1
urban.du.0
Water.source.type.Nival
fores.du.0
PREC.AN.du.0
Geomorph.river.type.Naturally.constraint.no.mob
Valley.form.V.shape
agri.du.0
Size.of.catchment.1
fluvdep.du.3
ELEV.MN.du.3
precmean.ann.1
agri.du.3
ELEV.MN.du.2
fores.du.1
ALT.GRADIENT.0
STRAHLER.2
ERODI.du.2
past.du.0
wetl.du.0
wetl.du.3
Natural.sediment.Boulder.Rock
STRAHLER.3
fluvdep.du.0
temp.jul.2
fores.du.2
Water.source.type.Pluvial
temp.jul.0
Altitude.2
STRAHLER.4
agri.du.2
noveg.du.0
ERODI.du.3
agri.du.1
calcsed.du.0
Natural.sediment.Gravel.Pebble.Cobble
temp.jul.3
glacdep.du.0
Geomorph.river.type.Sinuous
Geological.typology.Siliceous
urban.du.2
scrub.du.2
past.du.3
temp.ann.0
fores.du.3
Size.of.catchment.3
ALT.GRADIENT.2
igneous.du.2
Size.of.catchment.2
precmean.ann.0
urban.du.3
PREC.AN.du.3
ALT.GRADIENT.1
ERODI.du.0
precmean.ann.3
PREC.AN.du.1
TEMP.AN.du.0
Actual.river.slope.1Altitude.1
temp.jan.0
Valley.form.U.shape scrub.du.1
temp.ann.3ELEV.MN.du.1
temp.jan.3
STRAHLER.5.6
TEMP.AN.du.3
Comp4
TEMP.AN.du.0
ELEV.MN.du.3
Water.source.type.Nival
Altitude.3
agri.du.0
temp.jul.0
Natural.sediment.Boulder.Rock
Floodplain.site.Yes
temp.ann.0
calcsed.du.3
noveg.du.3
temp.jan.0
Valley.form.Gorges
ERODI.du.2
clastseddu.3
fores.du.3
ALT.GRADIENT.3
igneous.du.1PREC.AN.du.1
agri.du.1
Geomorph.river.type.Naturally.constraint.no.mob
STRAHLER.5.6
Geological.typology.Calcareous
scrub.du.2precmean.ann.2
temp.jan.2
ELEV.MN.du.2
precmean.ann.1
ALT.GRADIENT.2
TEMP.AN.du.2
scrub.du.1STRAHLER.3
Size.of.catchment.0
Geomorph.river.type.Braided
Valley.form.U.shapepast.du.2
urban.du.0
PREC.AN.du.2
Altitude.2
STRAHLER.4
ERODI.du.1
wetl.du.3
glacdep.du.0
fluvdep.du.0
temp.ann.2
scrub.du.3
igneous.du.0
precmean.ann.0
Valley.form.V.shape
temp.jul.3 past.du.0
wetl.du.0
Size.of.catchment.3
urban.du.2
Size.of.catchment.2
Natural.sediment.Gravel.Pebble.Cobble
Geological.typology.Siliceous
igneous.du.2
temp.jan.1
past.du.3
Floodplain.site.No
clastseddu.0
ALT.GRADIENT.1
Size.of.catchment.1
fores.du.2calcsed.du.0
STRAHLER.2
STRAHLER.1
temp.ann.1
fores.du.1
Water.source.type.Pluvial
noveg.du.0
Altitude.1
temp.jul.1
Geomorph.river.type.Sinuousfores.du.0
ERODI.du.3
glacdep.du.3
PREC.AN.du.0
PREC.AN.du.3
urban.du.3
fluvdep.du.3
agri.du.2
TEMP.AN.du.1
ELEV.MN.du.1 ERODI.du.0
precmean.ann.3
temp.jul.2
temp.ann.3
ALT.GRADIENT.0
scrub.du.0
Valley.form.Plains
temp.jan.3
TEMP.AN.du.3
ELEV.MN.du.0
Altitude.0
Geomorph.river.type.Meand
agri.du.3
1.0
Comp2
1.0
Water.source.type.Glacial
1.5
-1.0
-0.5
0.0
0.5
1.0
-1.5
-1.0
-0.5
0.0
Comp1
0.5
1.0
Comp3
d = 0.5
d = 0.5
DE
FI
RO SE
AT
IT
IT
FR
PL
FR PT
UK
LT
AT
PT
ES
SE
FI
HU
HU
UK
ES
LT
RO
PL
DE
Figure 2. Factor levels and sites grouped by country in the first two factorial plans
of a Multiple Correspondence Analysis of environment data.
Using hierarchical clustering, the distribution of sites by richness levels and by the
resulting typology site groups can be analysed further using a Factorial Correspondence
Analysis of the contingency table or by mapping sites (See figures 3 and 4). It is obvious that
the distribution of richness levels differs according to environment types. Notably, low levels
are relatively more abundant within groups 1, 6 and 7, which are mainly located in the
Asturies, whereas high levels are relatively abundant in groups 4 and 5, which are mainly
located in Central and Eastern Europe.
d = 0.5
Eigenvalues
40
Cluster Dendrogram
30
8
20
3
2-3
1
2
4
4+
10
1
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81
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73
6
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9
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53
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96
8
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45
55
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85
06
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61
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5
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6
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74
55
3
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91
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1
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8
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6
5
7
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5
7
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3
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4
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7
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61
70
12
1
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9
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3
7
2
2
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8
6
7
9
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5
7
5
2
5
8
8
4
6
0
1
9
0
4
7
3
2
8
5
8
2
6
6
4
2
6
6
4
25
0
54
3
64
7
9
3
1
1
7
2
5
3
10
0
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4
9
0
1
1
7
6
8
5
2
0
3
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8
6
5
6
4
7
2
4
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7
0
8
4
6
8
6
5
6
3
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4
7
6
6
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5
2
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6
5
4
3
3
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13
7
4
64
2
42
6
6
1
3
3
5
2
9
1
8
9
4
0
7
4
6
1
2
6
8
0
7
3
4
7
2
3
2
4
3
45
9
9
6
7
6
0
1
7
7
0
4
8
5
6
3
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1
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23
4
74
2
2
3
2
6
6
7
0
4
3
1
4
7
7
8
3
0
9
5
4
7
4
3
1
3
6
9
1
2
6
8
5
7
4
2
4
47
7
8
0
6
3
1
0
4
5
9
6
5
8
7
8
4
0
5
45
7
5
8
2
65
22
4
0
4
7
8
7
5
7
5
6
6
1
3
1
7
0
5
6
62
3
44
7
78
8
1
1
6
5
8
0
2
4
7
1
4
5
7
7
2
5
7
1
4
4
10
3
80
6
7
6
33
36
5
0
1
7
8
2
6
1
5
6
2
2
0
1
9
1
6
5
8
9
4
1
9
1
2
6
5
0
62
39
89
8
7
0
1
3
4
1
0
5
12
1
4
7
4
1
1
2
8
8
3
5
9
5
3
4
2
4
1
0
2
7
6
2
8
1
2
4
2
4
2
0
7
5
6
8
7
5
8
9
0
9
5
8
8
3
7
6
2
4
5
6
8
0
8
1
0
1
1
1
0
7
9
6
2
5
3
3
4
2
1
3
5
8
1
52
7
2
13
2
2
9
8
2
9
4
1
5
7
4
4
9
0
3
1
4
5
2
1
2
7
4
3
26
61
45
6
5
9
5
3
2
6
6
2
6
3
2
4
7
4
5
5
4
4
7
3
1
2
3
7
8
61
19
90
4
5
6
8
2
6
4
8
5
5
3
6
2
3
0
9
7
6
4
1
5
5
3
8
4
4
6
7
49
9
1
6
1
16
14
3
1
50
3
3
7
2
0
0
5
6
Figure 3. Typology of sites in terms of environment and factorial analysis of the
contingency table of resulting groups vs. richness levels
136
17
Environment
1
2
3
4
5
6
7
8
Richness
1
2-3
4+
Figure 4. Geographical distribution of sites according to richness and environment
Finally, looking at factors separately to investigate differences in environmental
features according to richness level, marked trends can be stressed.
First, notice the increase in the proportions of meanders and braided river types, of
plains and U-shaped forms, and of floodplain sites with increasing richness; and conversely,
look at decreasing river slope (See figure 5).
137
18
Geomorph river type
Actual river slope
1.0
0.6
Braided
0.8
Meand
0.6
0.5
0.4
Constraint
0.3
Sinuous
0.4
0.2
0.2
0.1
0.0
0.0
1
2-3
4+
1
2-3
Floodplain site
4+
Valley form
1.0
1.0
No
0.8
Gorges
0.8
Yes
Plains
0.6
0.6
0.4
0.4
0.2
0.2
0.0
U-shape
V-shape
0.0
1
2-3
4+
1
2-3
4+
Figure 5. Barplot and boxplot for geomorphological factors within each richness level
Geological typology
Natural sediment
1.0
1.0
Calcareous
0.8
Boulder/Rock
0.8
Siliceous
Gravel/Pebble/Co
0.6
0.6
0.4
0.4
0.2
0.2
0.0
Sand
0.0
1
2-3
4+
1
2-3
Altitude
4+
ERODI du
2000
5
1500
4
1000
3
500
2
1
0
1
2-3
4+
1
2-3
4+
Figure 6. Barplot and boxplot for geological factors and altitude within each richness level
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19
As regards geological factors, there is an increase in the proportion of calcareous types
and a marked decrease in natural sediment size, with notably a sharp increase in sand
proportions. Altitude is lower for higher richness level in average and, conversely, catchment
size and Strahler order are lower (See figures 6 and 7). Note also apparent relationships
between glacial water source and low richness, and between upstream lakes and higher
richness level, which were quite expectable.
Size of catchment
STRAHLER
10
1.0
8
0.8
6
0.6
4
0.4
1
2
3
4
5-6
2
0.2
0
0.0
1
2-3
4+
1
2-3
Water source type
4+
Lakes upstream
1.0
1.0
Glacial
0.8
No
0.8
Nival
0.6
Pluvial
Yes
0.6
0.4
0.4
0.2
0.2
0.0
0.0
1
2-3
4+
1
2-3
4+
Figure 7. Barplot and boxplot for hydrological factors within each richness level
There is a decreasing trend for richness with mean annual temperature and to a greater
extent with annual precipitations (See figure 8). As regards, soil occupation factors, low
richness levels appear to be related with high percentages of scrub or/and herbaceous
vegetation as well as no vegetation areas (See figure 10).
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20
precmean ann
temp ann
1600
15
1400
1200
10
1000
5
800
600
0
400
1
2-3
4+
1
temp jan
2-3
4+
temp jul
10
24
22
5
20
0
18
-5
16
14
-10
12
-15
10
1
2-3
4+
1
2-3
4+
Figure 8. Boxplot for meteorological factors within each richness level
2.3 Conclusion
In short, some more or less expectable relationships between environment patterns and
richness levels can be found out. As expected, higher richness levels are distributed in sites
with relatively higher catchment size, higher strahler order, lower altitude, lower slope. In
proportions, higher richness levels also mean more calcareous geology, more sand, more
floodplains and more meanders and, as expected too, more braided rivers and more sites with
upstream lakes.
Maybe more interesting is the geographical distribution of low level sites. They are
mainly located in two areas: first continental Europe mountains, and second in noncontinental Europe, that is United Kingdom and Scandinavian Europe. Conversely, there are
almost no such sites in eastern and central Europe plains.
First location in southern Europe Mountains can be quite well explained by previously
mentioned factors. Other interesting associated factors, notably glacial and nival water source,
as well as scrub or/and herbaceous vegetation might be helpful in explaining other locations.
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21
3. Modelling river size
This document contains a procedure to estimate the wetted width in function of the
drainage area, annual precipitation, temperature and elevation and water source type. We
propose two equations to compute the theoretical width.
3.1 Motivation
The aim of this study is the modelling of theoretical width based on the drainage area
and some other variables. In the river, the width is important parameter associated with
several hydraulic mechanisms. For example, the width is strongly linked to the flow discharge
(more details in Leopold et al. 1992, Knighton 1998, figure 9). The relationship between these
two variables is defined as follow:
W = a Qc
log(W ) = log(a ) + c log(Q )
where a and c are empirical coefficients. In the same way, the discharge is linked to
the drainage area (pages 6-8, Knighton 1998):
Q = b Ad
log(Q ) = log(b ) + d log( A)
where b and d are empirical coefficients. As results, we can postulate that the
relationship between width and drainage area is defined as follow:
W ≈ a' Ac '
log(W ) ≈ log(a ') + c' log( A)
where a’ and c’ are empirical coefficients. In the literature, the authors (ex. Rational
Method, http://www.lmnoeng.com/Hydrology/rational.htm, see the section References)
suggest that discharge values (Q) is proportional to the product of the Drainage Area, intensity
of rainfall and Runoff coefficient to account for surface characteristics (C). The relationship is
defined as follow:
Q = kCiA
where k is a conversion factor (empirical coefficient). This model is relatively similar
to the Mulvaney equation (Beven 2003):
Q p = CAR
where Qp, A, R and C corresponded to the hydrograph peak, the catchment area, a
maximum catchment average rainfall intensity and empirical coefficient. Under the above
hypotheses, we propose to model the theoretical width values in function of the potential flow
defined by average precipitation (mm/year) in the drainage area (km2) and the water source
type (ex. Nival, Glacial, Pluvial, …).
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22
Figure 9. Examples of relation between width, discharge and drainage area (Knighton
1998).
In our study, we complete the list of explicative variables by average temperature and
average elevation of the drainage area. These variables appear to be important to integrate the
climatic condition associated with drainage area and potentially associated with the Runoff.
3.2 Description of the variables
We use the calibration dataset to model the potential width, and reference dataset to
evaluate the quality of the model.
Precipitation: Mean annual precipitation upstream catchment (mean of primary
catchments, mm/year)
Catchment size: Area drained by segment (upstream area + primary catchment, km2)
Water source type: glacial, groundwater, pluvial, nival.
Wetted width (meter).
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23
Average temperature of the drainage area (degree Celsius)
Average elevation of the drainage area (meter)
variables
log-width
Log(P*A)
mean of Temperature of drainage area
Log-elevation
Water.source.type :
Glacial
Groundwater
Nival
Pluvial
STRAHLER :
1
2
3
4
5
6
Country.abbreviation :
AT
CH
DE
ES
FI
FR
HU
IT
LT
NL
PL
PT
RO
SE
UK
N
941
959
959
959
959
description
1.386294/1.791759/2.302585
9.942976/10.802038/12.015321
6.097255/ 8.414133/11.731278
5.462796/6.192375/6.732177
1% ( 7)
5% ( 44)
24% (228)
71% (680)
959
18% (174)
34% (323)
24% (231)
14% (131)
7% ( 71)
3% ( 29)
959
5% ( 49)
0% ( 0)
2% ( 21)
39% (372)
5% ( 44)
9% ( 89)
1% ( 6)
6% ( 58)
2% ( 20)
0% ( 0)
13% (124)
0% ( 2)
4% ( 34)
12% (112)
3% ( 28)
Table 28. Statistical description for the calibration dataset (N=959, coding for
continuous variables: 1st Qu. / Median / 3RD Qu.).
variables
log-width
Log(P*A)
mean of Temperature of
drainage area
Log-elevation
Water.source.type :
Glacial
Groundwater
Nival
Pluvial
STRAHLER :
1
N
442
459
description
1.386294/1.791759/2.302585
9.756642/10.560738/11.644547
459
459
459
5.323545/ 7.852267/11.107016
6.306282/6.769081/7.011304
1% ( 4)
2% ( 10)
11% ( 51)
86% (394)
459
18% ( 83)
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24
2
3
4
5
Country.abbreviation :
AT
CH
DE
ES
FI
FR
HU
IT
LT
NL
PL
PT
RO
SE
UK
33% (153)
31% (143)
12% ( 57)
5% ( 23)
459
0% ( 1)
0% ( 0)
0% ( 0)
42% (193)
3% ( 14)
6% ( 26)
0% ( 0)
12% ( 53)
3% ( 13)
0% ( 0)
3% ( 13)
0% ( 0)
29% (133)
3% ( 13)
0% ( 0)
Table 29. Statistical description for the reference dataset (N=959, coding for
continuous variables: 1st Qu. / Median / 3RD Qu.).
The modalities Groundwater and Glacial are relatively rare. This could be induced
some numerical problem in model fitting. We propose to agglomerate these modalities with
the modality Nival. We obtain a variable which discriminated Non-Pluvial or Pluvial status.
Precipitations in function of the water source type:
Figure 10. Precipitation in function of the water source type for calibration Dataset.
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25
Figure 11. Precipitation in function of the water source type for reference dataset.
Discordance between the two measures of the catchment size
There are some discordance between the two measures of the catchment (AREA.ctch
from CCM2 and Size.of.catchment from the database). We conserve the variable catchment of
size (completed by EFI+ partners) to limit the potential systematic error associated with the
GIS estimation. We propose to skip the observations characterized by a difference between
the two measures superior to 5%. We conserve only 191 reference sites and 318 calibration
sites.
Small, medium and large Rivers
The distributions of the sites among the Strahler order show that the number of small
rivers in the calibration and reference datasets are overrepresented (Figure 12). To regularise
the sample, we propose to weight the observations by classification based on Strahler order.
We obtain three groups: 0-2, 3-4 and 5-6 (see table 30).
Weight
1/170
1/98
1/46
1
0
21
0
2
0
77
0
3
100
0
0
4
70
0
0
5
0
0
33
6
0
0
13
Table 280. Relationship between weight and Strahler order for the calibration dataset.
The weight correspond to 1/(site number in the category).
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26
Figure 12. Distributions of sites in function of the Strahler order for reference and
calibration datasets. The term “Limited” refers to the sites characterized by difference
between the two measures of catchment size superior to 5% respectively.
Selection bias of GIS estimation of drainage is clearly shown graphically in the figure
12. After selection of sites characterized by difference between the two measures of
catchment size superior to 5% respectively, we observe that the proportion of the small river
decreases (figure 12).
Distribution by country
The distribution of calibration and reference sites is unbalanced (Figures 13 and 14).
The Spanish and Romania sites are largely dominant in reference dataset. In calibration
dataset, Spanish sites are dominant, but the sample is more representative of others countries.
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27
Figure 13. Distribution of reference sites by country.
Figure 14. Distribution of calibration sites by country.
3.3 Modelling the width
We use linear model to estimate the coefficient of the regression of width (W) on
potential flow (PA, product of the potential area and averaged annual precipitation), water
source type (S=0 for Non-pluvial and S=1 for Pluvial), average temperature (T) and average
elevation (E) of the drainage area. The model equation is given as follow:
log(W ) = d + a ⋅ log(PA) + b ⋅ log(E ) + d ⋅ T + e ⋅ S + f ⋅ log(PA) ⋅ S + ε
Where a, b, c, d, e and f correspond to the empirical coefficient and ε corresponds to
model error.
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28
Table 31. Summary statistics of the fitted linear model. The table gives coefficients,
standard errors, etc. and additionally gives "significance stars" (Signif. codes: 0 '***' ; 0.001
'**' ; 0.01 '*'; 0.05 '.'; 0.1 ' ' ).
coefficients
Estimate
Std
Error
t-
Pr(>|t|)
0.0004
<
value
(intercept)
log(P*A)
-1.78105
0.25277
0.50099
0.02585
-3.555
9.777
S.Pluvial
-3.40902
0.41630
-8.189
0.0001
<
0.0001
Log(E)
T
0.11926
0.04131
0.03806
0.01006
3.133
4.107
Log(P*A):S.pluvial
0.28295
0.03226
8.770
0.0019
<
0.0001
<
0.0001
All parameters are significantly different to zero (Table 31) and the explained variance
is equal to 0.6981% (F-statistic: 139.2 on 5 and 301 DF, p-value: < 2.2e-16 ).
Figure 15. Graphical representation associated with the regression to EFI values on
global pressure index. The first graphic corresponds to residuals in function of the fitted
values. The second shows the QQ-plot representation of standardised residuals against normal
theoretical quantiles. The third correspond to the representation of the square root of
standardized residuals against the fitted values. The fourth graphic plots the leverage against
the standardised residuals to detect the potential influent points (the red dotted line correspond
to leverage limit 3*p/n). The last graphic corresponds to the histogram of the Pearson
residuals.
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29
Figure 16. Observed and expected values from linear model and robust linear model.
In the figure 15, we observe that the proportion of outliers is relatively high and the
graphic entitled “leverage vs standardized residuals” shows that there were some potential
influent points (see Fox, 2002, chapter 6: Diagnosing problems in linear and generalized
linear models, pages 191-234). For this reason, we complete the analysis by robust regression
to evaluate deviation of the parameter estimations compared with the classical linear model.
For more detail on the robust regression, you can consult these references: Venables and
Ripley 1999, pages 167-174; MacKimmon & White 1985, Greene 2002, Zeileis 2004). In this
study, the version of the robust model is based on the random resampling procedure (Marazzi
1993). In the software R (R Development Core Team 2007), the function lmRob (package
robust) performs a robust linear regression with high breakdown point and high efficiency
regression.
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30
Figure 17. Observed log-width of reference dataset in function of the predicted values
by country. The equation of a red dotted line is y=x.
To examine performances of both models, we use the following values (see Greene
2002, Potts & Elith 2006):
RMSE (Root Mean Square Error) provides indication on the divergence between the
observed and predicted values.
Pearson correlation (CORP) and Spearman correlation (CORS) provide indication of
similarities between the ranks of the observed and predicted values.
AVER (Residuals distribution) provides indication on the goodness of fit.
Table 32. Summary statistics of the fitted robust linear model. The table gives
coefficients, standard errors, etc. and additionally gives "significance stars" (Signif. codes: 0
'***' ; 0.001 '**' ; 0.01 '*'; 0.05 '.'; 0.1 ' ' ).
coefficients
Estimate
Std
t-value
Pr(>|t|)
Error
(intercept)
-3.04708
0.69660
0.34922
0.03588
-2.05556
0.15967
0.02167
0.17199
0.58418
0.05285
0.01369
0.04643
4.37423
<
0.0001
log(P*A)
<
9.73425
-
S.Pluvial
Log(E)
T
Log(P*A):S.pluvial
3.51873
3.02091
1.58300
3.70392
0.0001
0.0005
0.0027
0.1145
0.0003
The parameters associated with the robust model are given in table 32 (R2=0.487936;
test for Bias: M-estimate= -24.07663 with p-value=1 and LS-estimate =1899.16898 with pvalue= 0). The robust regression shows that the effect of the temperature of the drainage area
is not significant (p < 0.11). This result moderates the importance of the temperature in the
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31
linear model. The significant test associated with temperature coefficient in the linear model
is probably induced by some site characterized by particular environment. However, the
expected values obtained by the both methods were relatively similar (figure 18, table 33).
We observe deviations for some sites with theoretical width superior to 15 meters. The linear
model appeared to be slightly more efficient than the robust model but the variability is more
stabilized in the robust model.
Table 33. Performance analyses of the models. The terms RMSE, AVER, CORP and
CORS correspond to Root Mean Square Error, averaged residuals, Pearson correlation and
Spearman correlation.
ESTIMATION
RMSE
AVER
CORP
CORS
Linear Model
Robust Linear
0.2283
0.7258
0.0130
0.0414
0.7760
0.7718
0.7714
0.7788
by
Model
Figure 18. Comparison between the predictive values based on classical linear model
and robust linear model. The equation of the red dotted line is y=x.
Analyses of variance on the residuals show that the effect of Strahler order is no
significant for the both models (P= 0.7142 for the linear model and P=0.1987 for the robust
linear model, table 34).
Table 34. Summary of analyses of variance. We test the effect of Strahler (river size)
on the residuals of the both models (LM=linear model; Robust LM=Robust linear model).
The table contains coefficients, degree of freedom (df) and F-value and probability (Pr(<F)).
ANOVA
df
Sum
Sq
LM
Strahler
Mean
Sq
5
F-
Pr(>F)
0.141
0.5815
value
0.703
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32
residuals
Robust LM
Strahler
residuals
301
72.772
0.242
5
301
1.838
75.163
0.368
0.250
0.1987
We don’t test the potential effect of the Flow regime on residuals, because there are
only 4 sites characterized by the modality “Summer dry” (Figure 19).
Figure 19. Regression residuals in function of the Strahler order and Flow regime. The
equation of the red dotted line is y=0.
Figure 20. Residuals of linear model in function of the Strahler order and the variable Flood
plain. The equation of the red dotted line is y=0.
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33
Figure 21. Residuals of robust linear model in function of the Strahler order and the
variable Flood plain. The equation of the red dotted line is y=0.
The both models trend to underestimate the width of the sites with Floodplain and to
overstimate the width of the sites without floodplain (figures 20 and 21). In addition, we
observe that the robust linear model is more affected by the Floodplain variable than the linear
model. This result is more in favour of the linear model estimation.
Figure 22. Residuals of robust linear model in function of the Strahler order and water
source type. The equation of the red dotted line is y=0.
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34
Figure 23. Residuals of robust linear model in function of the Strahler order and countries.
The equation of the red dotted line is y=0.
3.4 Validation on the reference dataset
To complete our study, we add a validation step based on the reference dataset. The
values of correlations and AVER are relatively similar. The third picture of the figure 24
confirms that both models converge on similar expected values. The Root Mean Square Error
(table 35) is lower for the robust linear model. The two histograms illustrate that the shape of
error distribution of the linear model tends to be slightly asymmetric.
Table 35. Performance analyses of the models. The terms RMSE, AVER, CORP and
CORS correspond to Root Mean Square Error, averaged residuals, Pearson correlation and
Spearman correlation.
ESTIMATION
by
Linear Model
Robust Linear
Model
RMSE
AVER
CORP
CORS
2.5890
-0.1924
0.7341
0.7476
1.8625
-0.1384
0.7521
0.7569
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35
Figure 24. Predictive performance of models. Histograms correspond to the
distribution of the difference between the observed and expected values of log-width. The last
graphics illustrates the comparison between the predictive values based on classical linear
model and robust linear model. The equation of the red dotted line is y=x.
Figure 25. Observed log-width of reference dataset in function of the predicted values
based on linear model by country. The equation of the red dotted line is y=x.
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36
3.5 Conclusion
To conclude, we propose two procedures to compute a theoretical width as follow:
Classical estimation
log(W ) = −1.7811 + 0.2528 ⋅ log(PA) + 0.1193 ⋅ log(E ) + 0.0413 ⋅ T − 3.4090 ⋅ S + 0.2829 ⋅ log(PA) ⋅ S
Robust estimation :
log(W ) = −3.0471 + 0.3492 ⋅ log(PA) + 0.1597 ⋅ log(E ) + 0.0217 ⋅ T − 2.0556 ⋅ S + 0.1720 ⋅ log(PA) ⋅ S
Where W, PA, S, T and E correspond to theoretical width, potential flow (PA, product
of the potential area and averaged annual precipitation), water source type (S=0 for Nonpluvial and T=S for Pluvial), Temperature of the drainage area and Elevation of the drainage
area. In practice, we suggest that the linear model can be more appropriate to estimate the
width. The performances of the both models are very similar, but the use of the linear model
is easier.
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37
4. Development of new metrics based on age classes
4.1 Methodology
Different ideas could be explore in order to take into account the age classes of fishes
in the new European fish index. We will expose in this part, only one of the possibilities.
In this work we will focus only one species: the brown trout, Salmo trutta fario. By
working on this species the main idea is to extend the detection capacities of the future index
to headwater systems, and also to low species rivers. Headwater systems very often present
particular environmental conditions due to their geographical position and to their proximity
to the source. They very often exhibit a high slope, high velocities, cold water and a high
oxygenation (suitable conditions for the occurrence of brown trout). Moreover the brown trout
is a widespread species which occur all over Europe. For all those reasons we think that
focusing on brown trout could be very interesting for the development of a metric based on
age class. Nonetheless the brown trout is also known to be very plastic and to adapt itself to a
high range of environmental conditions (i.e. this species also occur in Mediterranean streams).
In the data base we don’t have the information about age of fishes. We will have to determine
it from the lengths of fishes.
For the development of a new metrics, one possibility is to focus more specifically on
one age class: the young of the year (YOY or 0+). This fraction of the population is normally
easier to distinguish from the rest of the population than older age classes (i.e. Fig. 26).
0+
Olde
Frequencies
0
0
0
0
Lengths (mm)
Figure 26: An example of distribution of brown trout lengths for one fishing occasion.
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38
The main idea is to use this portion of the populations to create new metrics. When the
metrics will be defined we will have to test if those metrics could be related to environment
and if they can detect human pressure. It’s only at the end of those three steps that the metrics
could be integrated in an index.
This part is strongly influenced by a previous report realised by Reyjol et al. (2005).
To create a metric based on brown trout YOY, we must be able to identify those fishes
within their population. The solution proposed for this first step is to use a mixture of normal
laws to estimate the two parameters of the distribution (mean and standard deviation) of the
0+. By using this methodology we suppose that the distributions of brown trout lengths are
composed of at least two parts:
- a normal law for the YOY;
- one or more normal law(s) for the older fishes.
Consequently we have to estimate, the mean and the standard error of the distribution
of the YOY, the mean(s) and the standard error(s) of the older fish distribution(s) and the
proportions of each laws in the total distribution. Hence, for two normal laws we have to
estimate 6 parameters. Here clearly appear the two first limitations. First it’s not sure that the
distribution of lengths for the YOY follows (or always follows) a normal law and second
estimating automatically those parameters could be sometimes delicate.
For this part we will only used data from undisturbed sites.
With the mean and the standard error of the normal law of the YOY we would be able
to compute a “theoretical” maximal length than a YOY can reach (i.e. Fig. 26).
YOY
Older fishes
0.05
Theoretical maximal length = 91 mm
Densities
0.04
0.03
0.02
0.01
0
0
0
00
20
40
60
80
Lengths (mm)
00
Figure 16: A theoretical distribution of lengths, with µ = 80, σ = 5 for YOY and µ = 150, σ = 15 for
the older fishes. The proportions of each class are respectively 62.5 and 37.5 % of the total number of
lengths. In this example the “theoretical” maximal length for a 0+ would be 91 mm.
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39
Estimating the first normal law involves to have enough data of lengths for each
fishing occasion and especially data on young fishes. As a consequence we will have to select
the fishing occasions with enough data.
The next step will be to determine the number of YOY in each fishing occasion. At
this point we will only have maximal lengths for some undisturbed sites. We will need a tool
to determine also the maximal length for all sites: reference and calibration sites not used in
the first step and the disturbed sites. We propose to develop a model which links the
environment of the sites with the maximal lengths. This model will be calibrated with the
maximal lengths computed during the first step on the undisturbed sites (Fig. 27).
Theoretical maximal lengths from normal laws
Fitted values
One maximal length
Confidence interval
Environment
Figure 27: Hypothetic model relating maximal lengths and environment.
Several types of models are possible to compute, the multiple linear regression and the
partial least square regression are two examples. The purpose here is not to have an
explicative model to determine which environmental factor explain the variability of maximal
length, but to have as far as possible the best predictive model.
This step is very important because without a model relating maximal length with
environment we would not be able to determinate the maximal length of the YOY for
disturbed sites.
The next step will be to estimate for each fishing occasion the number of YOY in the
population. For that we will compute the maximal length of a YOY with the previous model
and count the number of fishes smaller than this size (Fig. 26). With this information it will be
possible to create three new metrics:
- occurrence of brown trout 0+;
- proportion of brown trout YOY in the population;
- density of brown trout YOY.
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40
It is clear that at each step of this methodology there are many limitations which can
lead to difficulties in computing the three metrics.
Once the three new metrics would be available, they will enter in a process of
selection to know if they can be relevant for the new index. The methodology is derived from
the “reference condition approach” (Bailey et al. 1998) and is divided into two parts:
- modelling the metric with environmental variable;
- test of the discriminant power of the metric.
The first part consists in estimating a model to explain the variability of the metrics by
environmental variable. It is totally possible that the metric could not be related with
environment, or that environment only explains a short amount of variance of the metric.
Without a model explaining a significant part of variance, the metric could not be integrated
in the further analysis.
The second part consists in determining if the metric is relevant to detect an
anthropogenic disturbance or not. We will compare the deviation to the model (difference
between the observed values and the fitted values, residuals), of calibrations sites and
disturbed sites. If there is no significant difference between the two distributions the metric is
considered as non-informative. We can test if the metric is relevant to detect human impact
without distinguishing the pressure, or we can test if the metric reponse to a specific pressure.
The metric can reflect an environmental gradient but not a human pressure.
4.2 First results
Estimation of the maximal lengths
In this part we decided to use both reference sites and calibration sites because we did
not expect a difference of maximal lengths between YOY living in reference or calibration
sites. It also increased the amount of data. As previously discussed we had to select fishing
occasions for which we could estimate a mixture of normal laws. The brown trout occured in
1,121 of the 1418 fishing occasions (959 calibration fishing occasions and 459 reference
fishing occasions). We decided to retain only the fishing occasions for which at least 70
brown trouts were caught. This threshold (which can be disccussed) was decided to maximize
our chance to find enough data for YOY in the fishing occasions. After this selection it
remained only 286 fishing occasions on the 1,211 ones. On the 286 fishing occasions we had
fish lengths in the table tbllength for 247 of them. The next step was to select the fishing
occasions for which we had the information on the run. In a process of standardisation we
decided to keep only lengths of fishes caught during the first run. After this selection we only
lost one fishing occasion. After having a look on length data for those 246 fishing occasions
we kept 181 of them. During this selection we did not conserve the fishing occasions with too
few lengths for small fishes (some fishing occasions only exhibit lengths for what seemed
older fish) or when the data was not appropriated to fit normal mixture law (i.e. data in classes
with high interval between each class). After those selections we finally had a data set
composed of 181 fishing occasions divided between 141 calibration fishing occasions and 40
reference fishing occasions. Those fishing occasions are distributed between nine countries
but France and Spain represents 85% of the data (Table 31). This data set is strongly
unbalanced.
Table 31: Distribution of fishing occasions per country.
AT
12
DE
5
ES
104
FR
51
IT
3
PL
1
RO
1
SE
3
UK
1
160
41
After this selection we tried to estimate the mixture of normal laws with an
Expectation-maximisation algorithm (EM). It is an iterative algorithm which criteria of
maximisatio is to maximise the likelihood or it is to say to minimise the log-likelihood. Those
estimations was computed under the software R (version 2.5.1, R Development Core Team
2007) with the fonction “normalmixEM” of the library “mixtools” (Young et al. 2007).
0.035
0.030
Densiti
0.025
.020
es
0.015
0.010
0.005
0.000
100
0
150
200
250
Lengths (mm)
Figure 28: A good estimation of the normal law for the YOY.
0.020
0.015
Densities
0.010
0.005
0.000
100
0
150
200
250
Lengths (mm)
Figure 29: Problem in the estimation of the normal law for the YOY.
First we computed for each fishing occasion a mixture of two normal laws without
entering initial values for the parameters. With omitting initial values, we wanted to let the
algorithm free of any judgment on the distributions. For some fishing occasions the
161
42
distributions estimated for the YOY seemed to estimate well the distribution of YOY whereas
for others the fit was not good (Figure 28 and 29).
For fishing occasions for which we encounter some problems we decided to use a
mixture of three normal laws instead of two. This enhanced in general the goodness of fit of
the normal law for the YOY. The maximal length was computed as a 95%quantile of the first
normal law.
We checked the distribution of fish lengths for each of the 181 fishing occasions and
we decided to left all fishing occasions for which we had a doubt. After this selection we only
kept 85 fishing occasions. We made this selection because sometimes the distribution of the
YOY was not clearly separated from the rest of the population (Fig. 30), or because the date
of sampling was too early in the year or because we were not enough self-confident in the
results. One possible solution to keep more fishing occasion and to be more self-confident
could be to ask people who have provided the data if they agree or not with the results found
by the mixture of normal laws. We can also ask them to provide us the maximal length that
they thought it occurs in the fishing occasion, without giving any information about our
results. Afetr we can compare maximal lengths estimate by an expert judgment and with the
EM algorithm.
35
Frequenci
30
25
20
15
10
5
0
50
100
150 200
Lengths
250
300
350
Figure 30: Strong overlap between distributions.
Currenlty we are trying to link the maximal lengths we computed, with environment.
We used multiple linear regressions. The best model that we have computed until now,
explain 53% of the variance. It is composed of annual temperature, squared annual
temperature, geological typology, natural logarithm of distance from source and the mean
annual precipitations. If we look at the deviation from this theoretical model we can see that
for 80% of the fishing occasions we have an absolute residuals lower than 10 millimetres.
However, the absolute deviation can reach 30 millimetres (Fig. 31). To improve our model,
we have to identify the reason why for some fishing occasions we have an “acceptable”
deviation and why for others the deviation is so important.
162
43
0.04
D
0.03
0.02
0.01
0.00
-20
-10
0
Re
10
20
30
Figure 31: Distribution of the residuals.
We can try to use different models than multiple linear regressions, like for exemple
partial least square regression.
All age classes of the population
The idea here is not to focus only on YOY but to try to take into account the whole
population. We will expose there the results from a French experiment which was done for by
Reyjol et al. (2005).
th
Leng
The authors try to estimate the proportion of each age class in the population. The
methodology they have used for the whole population is very close than the one used for the
YOYs, except for the determination of the proportions. Due to a strong overlap between
lengths distribution of older age classes, they could not used a mixture of normal laws to
estimate the boundaries between classes. To face this problem they used Von Bertalanffy
growth functions (VBGF) to estimate the maximum lengths that a fish of a certain age can
reach (Fig. 32).
Ti
me
Figure 32: an example of Von Bertalanffy growth curve.
The VBGF related the length at one age with the growth rate, the maximum possible
size and the theoretical age when length = 0:
163
44
(
Lt = L∞ 1 − e(
− K ( t − t0 ) )
)
With:
Lt: Fish length at age t;
L∞: maximum possible size;
K: rate of growth;
t0: theoretical age when length = 0.
Total lengths (mm)
They computed the theoretical maximal length for all age classes, which was used to
delimiter the age classes. They related those limit of classes to the annual mean temperature
(Fig. 33).
Temperature (°C)
Figure 33: Relationship between mean air temperature and length of different age classes. In red
the 1+ (fish in their second year), in blue the 2+ (fish in their third year) and in green the fish older than
three years (Reyjol et al. Date).
With those relationships they were able to compute the proportion of each age classes
for each fishing occasions. They defined their metrics for each age class. They modelled the
proportion of each age class with environmental variables and they compared the distribution
of the residuals between calibration sites and disturbed sites.
4.3 Conclusion
On the large amount of data present in the table tbllength only a very short part seems
available for the construction of the new metrics based on brow trout young of the year.
Moreover, this small data set is very unbalanced between countries with one country
providing half of the data. This can be a strong limitation for the analysis.
164
45
Concerning the determination of the maximal lengths of YOY by normal mixture
laws, currently we lose also half of the remaining data due to doubts on the distribution of the
YOY. Sometimes it appears very difficult to estimate which part of the distribution of fish
lengths is only composed of YOY. The process we used during this part of the methodology
did not appear to be so automatic, we had to check the results for each fishing occasion, and
sometimes we have to modify the initial parameters (i.e. three normal laws instead of two).
For some fishing occasions, as seen on the figure 5, we were sure that the estimate parameters
where not good, because the normal laws fitted, were very far from the supposed
distributions. But for others we were not sure that the fitted distribution really mismatched
with the real distribution. We decided to add a third normal law, because we observed an
other maximum in the distribution but we were not always sure that this maximum was
related to an older age class. One solution to keep more data and to validate our primary
results would be to compare our results with maximal lengths estimated by expert judgment.
This first part is crucial because without a good estimation of the threshold which
distinguishes the YOY from the older fishes we could not compute metrics precisely.
Concerning the model linking the maximal lengths and the environment, we have to
improve it but we also have some satisfactions. For 80% of the 85 fishing occasions the error
we made with a multiple linear regression was less than ten millimetres. Although it remains
20% of the data for which we have a too important errors.
Currently we only try one type of model and we just estimated a model on a priori
important factors for fish populations. We will have to try other combinations of
environmental variables, maybe with procedure of selection like stepwise, and we will have to
try others kind of statistical model.
One of the possible biases explaining the biais can be the date of sampling. After
visualisation of the distribution of lengths, it seems obvious that the date of samplinbg
influence the maximal length. Currently we don’t have any solution to correct this bias.
There are also different alternatives to the proposed methodology. We can for example
use class of size instead of class of age, and cut the distribution of lengths with fixed
threshold. We can also try to do the same work as done by Reyjol et al. with the age classes of
the whole population.
References
Beven, J. K. (2003) Rainfall – Runoff Modelling: The Primer, John Wiley & Sons, New-york,
372 pages
Knighton D. (1998) Fluvial forms & processes, Arnold Publishers, New-York, 218 pages.
Leopold, L.B., Wolman, M.G. & Miller, J.P. (1992) Fluvial processes in geomorphology
Freeman, W.H. and Co, San Fransisco, California.
Fox, J. (2002) Applied Regression, Linear Models, and Related Methods. Sage.
Greene (2002) Econometric Analysis, Prentice Hall,US, 5e International Ed, 1026 pp.
MacKinnon JG, White H (1985). Some Heteroskedasticity-Consistent Covariance Matrix
Estimators with Improved Finite Sample Properties. Journal of Econometrics, 29,
305–325.
165
46
Marazzi, A. (1993) Algorithms, routines, and S functions for robust statistics. Wadsworth &
Brooks/Cole, Pacific Grove, CA.
Potts, J. & Elith, J. (2006) Comparing species' abundance models. Ecological Modelling 199,
153-163.
Reyjol Y., J. De Bortoli & D. Pont 2005. Indice Poisson Rivière. Modélisation et sensibilité
aux perturbations de métriques basées sur la structure en âge. Etude réalisée pour
le compte du Conseil Supérieur de la Pêche. 73 pp.
R Development Core Team (2007). R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0,
URL http://www.R-project.org.
Venables W. N. & Ripley B. D. (1999) Modern Apllied Statistics with S-plus, Third edition,
Statistics and computing, Springer-Verlag, New York, pp. 501.
Young D., Elmore R., Hettmansperger T., Hunter D., Thomas H. and Xuan F. 2007. mixtools:
Tools for mixture models. R package version 0.2.0.
Zeileis A (2004). Econometric Computing with HC and HAC Covariance Matrix Estimators.
Journal of Statistical Software, 11(10), 1–17. URL
http://www.jstatsoft.org/v11/i10/.
166
47
Appendix A: Fish lengths for all species and all runs
Species names
Phoxinus phoxinus
Salmo trutta fario
Rutilus rutilus
Gobio gobio
Barbatula barbatula
Cottus gobio
Leuciscus cephalus
Alburnus alburnus
Perca fluviatilis
Salmo salar
Anguilla anguilla
Salmo trutta trutta
Leuciscus leuciscus
Barbus barbus
Rhodeus amarus
Leuciscus souffia
Thymallus thymallus
Oncorhynchus mykiss
Abramis brama
Blicca bjoerkna
Lampetra planeri
Leuciscus idus
Lepomis gibbosus
Esox lucius
Chondrostoma nasus
Barbus bocagei
Lota lota
Pungitius pungitius
Ameiurus melas
Squalius pyrenaicus
Chondrostoma toxostoma
Tinca tinca
Cobitis taenia
Barbus sclateri
Barbus meridionalis
Osmerus eperlanus
Aspius aspius
Cottus poecilopus
Lengths
926 908
876 033
713 614
575 677
441 269
392 365
381 197
262 827
165 642
143 633
123 443
113 635
107 005
102 953
80 763
60 833
60 395
52 029
49 267
45 834
45 054
44 833
42 411
36 381
34 907
33 729
29 403
24 027
22 925
19 755
19 021
17 813
16 149
15 142
14 757
12 886
12 069
9 874
9 855
9 806
8 061
7 996
7 889
7 639
6 775
6 488
6 016
5 833
Species names
Species names
Gobio lozanoi
Sander lucioperca
Cyprinus carpio
Pseudorasbora parva
Chondrostoma miegii
Salmo marmoratus
Rutilus aula
Barbus petenyi
Platichthys flesus
Carassius carassius
Romanogobio belingi
Barbus graellsii
Barbus plebejus
Gambusia affinis
Padogobius martensii
Cobitis paludica
Carassius gibelio
Silurus glanis
Micropterus salmoides
Barbus peloponnesius
Salaria fluviatilis
Barbus comizo
Petromyzon marinus
Carassius auratus
Leuciscus muticellus
Gasterosteus gymnurus
Pachychilon pictum
Gambusia holbrooki
Barbus tyberinus
Rutilus rubilio
Gobio kesslerii
Ameiurus nebulosus
Liza ramada
Hucho hucho
Knipowitschia punctatissima
Chelon labrosus
Mugil cephalus
Cobitis calderoni
Lengths
5 025
4 784
4 624
4 563
4 523
4 470
4 055
3 988
3 587
3 389
3 283
2 971
2 795
2 500
2 365
1 990
1 925
1 504
1 486
1 472
1 392
1 324
1 302
1 296
1 286
1 141
1 042
1 001
878
868
828
817
695
636
613
563
502
467
437
432
422
406
383
373
365
357
336
334
Lengths
167
48
Eudontomyzon mariae
Vimba vimba
Salmo trutta macrostigma
Abramis ballerus
Chondrostoma genei
Misgurnus fossilis
Zingel streber
Salvelinus alpinus
Australoheros facetus
Barbus caninus
Zingel zingel
Gobio albipinnatus
Barbus guiraonis
Sabanejewia aurata
Gobio uranoscopus
Sabanejewia larvata
Alosa alosa
Padogobius nigricans
Barbus haasi
Leuciscus lucumonis
Neogobius fluviatilis
Perccottus glenii
Atherina boyeri
Atherina presbyter
Salvelinus umbla
Pomatoschistus microps
Alburnus albidus
Pomatoschistus minutus
Abramis sapa
Zingel asper
Coregonus albula
Salvelinus namaycush
Knipowitschia panizzae
Pleuronectes platessa
Squalius malacitanus
Chondrostoma soetta
Umbra pygmaea
Alosa fallax
Aristichthys nobilis
Acipenser naccarii
Coregonus lavaretus
Coregonus peled
Liza aurata
317
285
279
232
229
214
195
175
168
100
93
77
62
58
55
48
46
40
36
34
31
31
30
30
30
25
23
20
19
18
15
12
12
12
8
8
5
5
5
5
4
4
3
2
1
1
1
1
1
1
168
49
Appendix B: Fish lengths for all species for the first run
Species names
Phoxinus phoxinus
Rutilus rutilus
Salmo trutta fario
Gobio gobio
Leuciscus cephalus
Barbatula barbatula
Cottus gobio
Alburnus alburnus
Perca fluviatilis
Anguilla anguilla
Leuciscus leuciscus
Barbus barbus
Salmo salar
Rhodeus amarus
Leuciscus souffia
Thymallus thymallus
Blicca bjoerkna
Abramis brama
Oncorhynchus mykiss
Leuciscus idus
Lepomis gibbosus
Lampetra planeri
Esox lucius
Chondrostoma nasus
Barbus bocagei
Lota lota
Salmo trutta trutta
Ameiurus melas
Pungitius pungitius
Tinca tinca
Squalius pyrenaicus
Cobitis taenia
Osmerus eperlanus
Aspius aspius
Barbus meridionalis
Barbus sclateri
Sander lucioperca
Lengths
721 930
662 634
626 009
479 851
339 192
337 856
262 175
252 676
157 214
102 670
96 043
88 497
71 434
68 095
58 154
49 076
45 168
44 213
43 847
37 408
36 320
33 144
29 912
27 561
25 452
22 895
18 570
17 250
16 352
14 889
14 858
14 520
14 270
12 346
10 159
9 208
9 111
8 799
8 752
7 941
7 713
6 486
6 013
5 981
5 952
5 707
5 075
4 536
Species names
Gobio lozanoi
Pseudorasbora parva
Cyprinus carpio
Rutilus aula
Salmo marmoratus
Barbus petenyi
Chondrostoma miegii
Romanogobio belingi
Carassius carassius
Platichthys flesus
Barbus plebejus
Cottus poecilopus
Barbus graellsii
Gambusia affinis
Carassius gibelio
Cobitis paludica
Silurus glanis
Barbus peloponnesius
Micropterus salmoides
Barbus comizo
Salaria fluviatilis
Carassius auratus
Gambusia holbrooki
Barbus tyberinus
Pachychilon pictum
Rutilus rubilio
Petromyzon marinus
Leuciscus muticellus
Gobio kesslerii
Ameiurus nebulosus
Liza ramada
Knipowitschia punctatissima
Hucho hucho
Mugil cephalus
Cobitis calderoni
Eudontomyzon mariae
Lengths
4 361
4 181
4 151
4 101
3 944
3 939
3 587
3 389
3 199
2 795
2 775
2 479
2 193
2 055
1 986
1 911
1 778
1 446
1 387
1 386
1 318
1 285
1 279
1 039
916
881
811
630
615
612
603
563
535
521
505
499
482
467
437
419
405
383
369
356
353
336
326
286
169
50
Species names
Chelon labrosus
Vimba vimba
Abramis ballerus
Chondrostoma genei
Misgurnus fossilis
Zingel streber
Australoheros facetus
Barbus caninus
Zingel zingel
Gobio albipinnatus
Sabanejewia aurata
Gobio uranoscopus
Salvelinus alpinus
Barbus guiraonis
Sabanejewia larvata
Alosa alosa
Padogobius nigricans
Leuciscus lucumonis
Neogobius fluviatilis
Perccottus glenii
Barbus haasi
Atherina boyeri
Atherina presbyter
Pomatoschistus microps
Alburnus albidus
Salvelinus umbla
Pomatoschistus minutus
Abramis sapa
Zingel asper
Coregonus albula
Knipowitschia panizzae
Pleuronectes platessa
Squalius malacitanus
Chondrostoma soetta
Umbra pygmaea
Alosa fallax
Aristichthys nobilis
Acipenser naccarii
Coregonus lavaretus
Coregonus peled
Liza aurata
Lengths
285
283
266
230
229
201
195
132
100
63
59
56
55
48
46
46
44
40
36
34
31
30
30
30
28
25
23
19
18
18
15
12
12
12
8
8
5
5
5
5
4
4
3
2
1
1
1
1
1
1
170
51
Appendix C: The five most abundant species per country
all runs confounded
Country
Species names
Salmo trutta fario
Oncorhynchus mykiss
Austria
Thymallus thymallus
Leuciscus cephalus
Cottus gobio
Salmo trutta fario
Cottus gobio
Switzerland Phoxinus phoxinus
Barbatula barbatula
Leuciscus cephalus
Rutilus rutilus
Perca fluviatilis
Germany Gobio gobio
Alburnus alburnus
Cottus gobio
Salmo trutta fario
Spain
Phoxinus phoxinus
Anguilla anguilla
Barbus sclateri
Phoxinus phoxinus
Gobio gobio
France
Rutilus rutilus
Barbatula barbatula
Salmo trutta fario
Salmo trutta fario
Alburnus alburnus
Italy
Leuciscus souffia
Leuciscus cephalus
Salmo marmoratus
Rutilus rutilus
Phoxinus phoxinus
Lithuania Gobio gobio
Cottus gobio
Lengths Country
109 978
42 113
38 086 Netherlands
24 745
22 766
98 587
21 423
Poland
14 134
11 149
7 001
139 408
71 512
Portugal
55 824
51 075
33 059
133 263
18 566
Romania
10 032
8 783
7 835
822 673
457 973
Sweden
407 838
373 992
362 063
21 256
5 154
United
4 411
Kingdom
4 374
4 055
4 211
1 894
1 845
1 450
1 161
Species names
Rutilus rutilus
Perca fluviatilis
Leuciscus idus
Anguilla anguilla
Osmerus eperlanus
Rutilus rutilus
Salmo trutta fario
Phoxinus phoxinus
Perca fluviatilis
Alburnus alburnus
Barbus bocagei
Squalius pyrenaicus
Phoxinus phoxinus
Leuciscus cephalus
Barbatula barbatula
Barbus petenyi
Gobio gobio
Salmo trutta trutta
Salmo salar
Salmo trutta fario
Cottus gobio
Rutilus rutilus
Salmo trutta fario
Gobio gobio
Leuciscus cephalus
Leuciscus leuciscus
Lengths
62 603
22 468
8 355
6 587
6 068
18 763
10 307
5 318
4 755
4 425
15 314
6 688
5 354
4 780
4 359
5 165
5 137
3 403
3 389
2 204
110 196
103 194
75 401
48 727
34 876
65 986
36 577
24 512
20 504
18 370
171
52
Appendix D: The five most abundant species per country
first run
Country
Species names
Salmo trutta fario
Oncorhynchus mykiss
Austria
Thymallus thymallus
Leuciscus cephalus
Cottus gobio
Salmo trutta fario
Cottus gobio
Switzerland Phoxinus phoxinus
Barbatula barbatula
Leuciscus cephalus
Rutilus rutilus
Perca fluviatilis
Germany Gobio gobio
Alburnus alburnus
Cottus gobio
Salmo trutta fario
Spain
Phoxinus phoxinus
Barbus sclateri
Phoxinus phoxinus
Rutilus rutilus
France
Gobio gobio
Salmo trutta fario
Barbatula barbatula
Salmo trutta fario
Alburnus alburnus
Italy
Leuciscus souffia
Leuciscus cephalus
Rutilus aula
Rutilus rutilus
Phoxinus phoxinus
Lithuania Gobio gobio
Cottus gobio
Lengths Country
91 610
34 061
32 857 Netherlands
21 427
15 355
77 607
10 428
Poland
8 576
5 903
4 988
139 408
71 512
Portugal
55 824
51 075
33 059
66 461
10 499
Romania
7 484
4 914
4 805
652 403
377 519
Sweden
375 742
297 669
288 622
19 216
5 148
United
4 226
Kingdom
4 218
3 944
4 211
1 894
1 845
1 450
1 161
Species names
Lengths
Rutilus rutilus
62 603
Perca fluviatilis
22 468
Leuciscus idus
8 355
Anguilla anguilla
6 587
Osmerus eperlanus
6 068
Rutilus rutilus
18 763
Salmo trutta fario
10 307
Phoxinus phoxinus
5 318
Perca fluviatilis
4 755
Alburnus alburnus
4 425
Barbus bocagei
15 314
6 688
5 354
Squalius pyrenaicus
4 780
Pseudochondrostoma duriense 4 359
Phoxinus phoxinus
5 165
Leuciscus cephalus
5 137
Barbatula barbatula
3 403
Barbus petenyi
3 389
Gobio gobio
2 204
Salmo salar
34 815
Salmo trutta trutta
11 528
Salmo trutta fario
6 168
5 181
Phoxinus phoxinus
2 599
Rutilus rutilus
48 650
Salmo trutta fario
28 370
Gobio gobio
17 019
Leuciscus cephalus
14 960
Leuciscus leuciscus
13 888
172
53
Appendix E: Fish lengths of reference sites, for all species
and all runs
Species
Salmo trutta fario
Phoxinus phoxinus
Barbatula barbatula
Barbus petenyi
Leuciscus cephalus
Cottus gobio
Gobio gobio
Rutilus rutilus
Leuciscus souffia
Salmo marmoratus
Anguilla anguilla
Salmo trutta trutta
Leuciscus leuciscus
Barbus plebejus
Salmo salar
Alburnus alburnus
Gobio kesslerii
Cottus poecilopus
Barbus barbus
Thymallus thymallus
Blicca bjoerkna
Squalius pyrenaicus
Barbus bocagei
Perca fluviatilis
Chondrostoma nasus
Pseudorasbora parva
Barbus meridionalis
Esox lucius
Petromyzon marinus
Lota lota
Carassius gibelio
Cobitis taenia
Lampetra planeri
Oncorhynchus mykiss
Lengths
21 895
5 681
2 902
2 347
1 853
1 535
1 522
1 508
929
681
446
439
393
354
320
212
208
190
146
127
124
114
110
100
100
98
75
64
53
46
40
34
33
29
23
23
20
13
12
11
10
10
10
10
8
7
6
Species
Cobitis paludica
Rhodeus amarus
Chondrostoma genei
Leuciscus idus
Tinca tinca
Abramis brama
Carassius carassius
Platichthys flesus
Pungitius pungitius
Salvelinus alpinus
Vimba vimba
Lengths
4
4
3
2
2
2
1
1
1
1
1
1
173
54
Appendix F: Fish lengths of reference sites, for all species
caught during the first run
Species
Salmo trutta fario
Phoxinus phoxinus
Barbatula barbatula
Barbus petenyi
Leuciscus cephalus
Gobio gobio
Cottus gobio
Rutilus rutilus
Salmo marmoratus
Leuciscus souffia
Salmo trutta trutta
Leuciscus leuciscus
Anguilla anguilla
Barbus plebejus
Salmo salar
Alburnus alburnus
Gobio kesslerii
Barbus barbus
Thymallus thymallus
Blicca bjoerkna
Cottus poecilopus
Perca fluviatilis
Chondrostoma nasus
Barbus bocagei
Pseudorasbora parva
Squalius pyrenaicus
Barbus meridionalis
Esox lucius
Lota lota
Carassius gibelio
Cobitis taenia
Oncorhynchus mykiss
Petromyzon marinus
Rhodeus amarus
Chondrostoma genei
Lengths
16 002
5 419
2 897
2 347
1 850
1 535
1 413
1 296
929
681
439
436
351
320
215
206
153
146
143
127
124
110
95
75
62
53
48
43
40
34
33
32
29
24
21
20
20
13
11
10
10
10
8
4
4
3
3
Species
Cobitis paludica
Leuciscus idus
Tinca tinca
Abramis brama
Carassius carassius
Lampetra planeri
Platichthys flesus
Salvelinus alpinus
Vimba vimba
Pungitius pungitius
Lengths
2
2
2
2
2
1
1
1
1
1
1
0
174
55
Appendix G: The five most abundant species per country
for reference sites, all runs
Country
Austria
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
Species
Lengths
Salmo trutta fario
21
Cottus gobio
20
Thymallus thymallus
5
Oncorhynchus mykiss
2
1
Salmo trutta fario
11 515
Anguilla anguilla
328
Gobio gobio
235
Phoxinus phoxinus
221
190
Salmo trutta fario
5 127
Cottus gobio
626
Phoxinus phoxinus
458
Anguilla anguilla
53
Barbatula barbatula
25
Salmo trutta fario
2 567
Leuciscus souffia
446
Salmo marmoratus
439
Barbus plebejus
212
Leuciscus cephalus
147
Phoxinus phoxinus
1 237
963
Rutilus rutilus
681
Cottus gobio
517
Gobio gobio
460
Salmo trutta trutta
325
Salmo trutta fario
124
33
Perca fluviatilis
9
Esox lucius
4
Phoxinus phoxinus
3 531
Barbatula barbatula
2 558
Barbus petenyi
2 347
Leuciscus cephalus
1 626
Salmo trutta fario
1 303
Salmo trutta fario
1 156
Phoxinus phoxinus
204
Cottus poecilopus
73
Lampetra planeri
9
Cottus gobio
8
175
56
Appendix H: The five most abundant species per country
for reference sites, first run
Country
Austria
Spain
France
Italy
Lithuania
Poland
Romania
Sweden
Species
Salmo trutta fario
Cottus gobio
Thymallus thymallus
Oncorhynchus mykiss
Salmo trutta fario
Anguilla anguilla
Phoxinus phoxinus
Gobio gobio
Salmo trutta fario
Cottus gobio
Phoxinus phoxinus
Anguilla anguilla
Barbatula barbatula
Salmo trutta fario
Salmo marmoratus
Leuciscus souffia
Barbus plebejus
Leuciscus cephalus
Phoxinus phoxinus
Rutilus rutilus
Cottus gobio
Gobio gobio
Salmo trutta trutta
Salmo trutta fario
Perca fluviatilis
Esox lucius
Phoxinus phoxinus
Barbatula barbatula
Barbus petenyi
Leuciscus cephalus
Salmo trutta fario
Phoxinus phoxinus
Salmo trutta fario
Cottus gobio
Lota lota
Thymallus thymallus
Lengths
21
20
5
2
1
7 813
166
145
143
140
4 192
400
360
38
20
2 381
439
436
206
144
1 237
963
681
517
460
325
124
33
9
4
3 531
2 558
2 347
1 626
1 303
116
86
8
8
6
176
57
Appendix I: Fish lengths of calibration sites, for all species
and all runs
Species
Salmo trutta fario
Phoxinus phoxinus
Salmo salar
Cottus gobio
Salmo trutta trutta
Barbatula barbatula
Gobio gobio
Anguilla anguilla
Leuciscus cephalus
Rutilus rutilus
Lampetra planeri
Thymallus thymallus
Leuciscus souffia
Lota lota
Cottus poecilopus
Barbus sclateri
Oncorhynchus mykiss
Leuciscus leuciscus
Esox lucius
Barbus meridionalis
Perca fluviatilis
Barbus barbus
Squalius pyrenaicus
Barbus petenyi
Gobio lozanoi
Barbus bocagei
Pungitius pungitius
Alburnus alburnus
Barbus plebejus
Rutilus rubilio
Salaria fluviatilis
Gobio kesslerii
Salmo marmoratus
Rhodeus amarus
Lengths
99 778
37 773
23 868
23 638
20 643
14 149
10 249
6 978
6 110
4 265
4 032
3 656
2 467
2 173
1 718
1 243
1 175
1 166
1 076
1 011
917
915
768
705
629
619
594
577
555
490
445
428
238
212
212
198
196
176
138
137
128
121
104
93
Species
Barbus tyberinus
Blicca bjoerkna
Carassius gibelio
Cobitis taenia
Pseudorasbora parva
Carassius carassius
Cobitis paludica
Cyprinus carpio
Chondrostoma nasus
Lepomis gibbosus
Tinca tinca
Platichthys flesus
Petromyzon marinus
Vimba vimba
Chelon labrosus
Hucho hucho
Leuciscus idus
Sabanejewia aurata
Carassius auratus
Abramis brama
Chondrostoma genei
Gambusia affinis
Ameiurus melas
Barbus graellsii
Eudontomyzon mariae
Mugil cephalus
Sander lucioperca
Silurus glanis
Lengths
87
73
67
47
45
43
41
40
36
33
30
27
23
18
15
11
9
7
7
7
6
5
4
4
4
4
4
4
1
1
1
1
1
1
177
58
Appendix J: Fish lengths of calibration sites, for all species
caught during the first run
Species
Salmo trutta fario
Phoxinus phoxinus
Cottus gobio
Salmo salar
Barbatula barbatula
Gobio gobio
Anguilla anguilla
Leuciscus cephalus
Rutilus rutilus
Salmo trutta trutta
Thymallus thymallus
Leuciscus souffia
Lampetra planeri
Leuciscus leuciscus
Oncorhynchus mykiss
Barbus meridionalis
Barbus sclateri
Esox lucius
Lota lota
Barbus barbus
Barbus petenyi
Perca fluviatilis
Squalius pyrenaicus
Pungitius pungitius
Alburnus alburnus
Barbus plebejus
Barbus bocagei
Rutilus rubilio
Gobio kesslerii
Gobio lozanoi
Salmo marmoratus
Salaria fluviatilis
Rhodeus amarus
Barbus tyberinus
Blicca bjoerkna
Lengths
61 902
29 137
11 923
11 542
7 933
5 245
4 135
3 888
3 357
2 188
2 019
1 951
1 551
1 289
1 080
916
864
782
754
716
705
629
611
560
445
418
405
383
321
212
207
197
181
176
167
137
134
121
118
104
102
93
87
73
Species
Carassius gibelio
Cobitis taenia
Pseudorasbora parva
Cottus poecilopus
Chondrostoma nasus
Cyprinus carpio
Cobitis paludica
Carassius carassius
Lepomis gibbosus
Tinca tinca
Platichthys flesus
Vimba vimba
Chelon labrosus
Hucho hucho
Leuciscus idus
Sabanejewia aurata
Chondrostoma genei
Petromyzon marinus
Carassius auratus
Abramis brama
Ameiurus melas
Barbus graellsii
Eudontomyzon mariae
Gambusia affinis
Mugil cephalus
Sander lucioperca
Silurus glanis
Lengths
67
45
41
36
30
28
27
27
23
20
17
17
17
14
9
7
7
7
6
4
4
4
3
2
2
1
1
1
1
1
1
1
1
1
178
59
Appendix K: The five most abundant species per country
for calibration sites, all runs
Country
Austria
Germany
Spain
France
Italy
Species
Salmo trutta fario
Cottus gobio
Thymallus thymallus
Oncorhynchus mykiss
Lota lota
Salmo trutta fario
Phoxinus phoxinus
Gobio gobio
Thymallus thymallus
Cottus gobio
Salmo trutta fario
Gobio gobio
Anguilla anguilla
Barbus sclateri
Salmo trutta fario
Phoxinus phoxinus
Cottus gobio
Barbatula barbatula
Anguilla anguilla
Salmo trutta fario
Cottus gobio
Leuciscus souffia
Barbus plebejus
Rutilus rubilio
Lengths
6 242
1 258
1 028
974
159
2 948
826
554
548
463
21 311
4 032
1 721
1 228
1 166
40 343
31 859
13 849
9 556
4 829
1 193
1 005
318
176
137
Country
Species
Gobio gobio
Cottus gobio
Lithuania Salmo trutta fario
Phoxinus phoxinus
Leuciscus leuciscus
Salmo trutta fario
Rutilus rutilus
Poland
Esox lucius
Salmo trutta trutta
Leuciscus cephalus
Romania Barbus petenyi
Gobio gobio
Phoxinus phoxinus
Salmo trutta fario
Salmo trutta trutta
Sweden Salmo salar
Cottus gobio
Salmo trutta fario
Salmo salar
United
Kingdom Leuciscus leuciscus
Phoxinus phoxinus
Rutilus rutilus
Lengths
381
358
255
253
223
2 622
1 222
621
579
443
1 467
705
445
297
259
24 116
20 098
18 759
13 937
6 635
671
317
209
195
174
179
60
Appendix L: The five most abundant species per country
for calibration sites, first run
Country
Austria
Germany
Spain
France
Italy
Species
Salmo trutta fario
Cottus gobio
Thymallus thymallus
Oncorhynchus mykiss
Lota lota
Salmo trutta fario
Phoxinus phoxinus
Gobio gobio
Thymallus thymallus
Cottus gobio
Salmo trutta fario
Gobio gobio
Barbus sclateri
Anguilla anguilla
Salmo trutta fario
Phoxinus phoxinus
Cottus gobio
Barbatula barbatula
Salmo salar
Salmo trutta fario
Cottus gobio
Leuciscus souffia
Barbus plebejus
Rutilus rubilio
Lengths
5 200
989
982
781
101
2 948
826
554
548
463
14 460
2 188
825
754
635
33 148
26 590
9 139
7 256
3 675
1 019
599
318
176
137
Country
Species
Gobio gobio
Cottus gobio
Lithuania Salmo trutta fario
Phoxinus phoxinus
Leuciscus leuciscus
Salmo trutta fario
Rutilus rutilus
Poland
Esox lucius
Salmo trutta trutta
Leuciscus cephalus
Romania Barbus petenyi
Gobio gobio
Phoxinus phoxinus
Salmo salar
Salmo trutta fario
Sweden Salmo trutta trutta
Phoxinus phoxinus
Salmo trutta fario
Salmo salar
United
Kingdom Leuciscus leuciscus
Leuciscus cephalus
Phoxinus phoxinus
Lengths
381
358
255
253
223
2 622
1 222
621
579
443
1 467
705
445
297
259
7 375
1 652
1 478
1 077
566
521
261
135
105
96
180

Improvement and spatial extension of the European Fish Index

Transcription

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