FOCALIZE: a decision support system to integrate reserve selection

Transcription

Journal of Conservation Planning Vol 8 (2012) 45 – 64
FOCALIZE: a decision support system to integrate reserve
selection and land use planning through the use of
complementary and supplementary criteria
Martha Fandiño-Lozano; Willem van Wyngaarden
Martha Fandiño-Lozano Ph.D
General Director of the UPRA
Ministry of Agriculture and Rural Development
Avenida Jiménez 7-65
Bogotá, Colombia
E-mail: [email protected]
Phone: +571-3341199, Ext. 501 or +571-8742129
ABSTRACT:
Willem van Wyngaarden Ph.D
Scientific Director Grupo ARCO
Centro Empresarial Potosí Of. 201
Km 19 Vía La Calera-Sopó
La Calera, Cundinamarca
Colombia
E-mail: [email protected]
Website: www.grupoarco.info
FOCALIZE is a decision support system developed to select wildlife conservation areas. Using groups
of ecosystems (chorological types) as an extended land surrogate, FOCALIZE provides a more complete habitat
than a sample of individual ecosystems allows, especially for those animals that move across land units. Targets
are based on the requirements of umbrella species and the characteristics of the ecosystems integrating each
pattern. Compactness is achieved by concentrating the selected cells around the centers of each chorological
type. Naturalness and social preferences are applied in a supplementary way; that is, the selection of each
chorological type starts in the most natural and socially preferred sites but, if the target is not achieved, the
selection continues on to fragmented or even transformed, and non-preferred areas providing information about
how much area must be recovered and where to expect social conflicts between conservation and development.
To present the results in a form suitable for decision making, we wrote two extensions of the program:
FOCALIZE_Import_Scenario to display thematic maps of the selected planning units and OT-Plan to highlight
the conflicts between conservation and economic activities, and to integrate the decisions made into a land use
plan. FOCALIZE has been used in Colombia in several planning exercises at the national and regional level. Here
the case of the Huila region is shown to illustrate how the system and its extensions operate.
Keywords:
Algorithm, coarse-filter, reserve selection, extended surrogate, non-arbitrary targets, connectivity, social
preferences, wildlife conservation, land use planning.
45
Fandiño-Lozano, Wyngaarden / Journal of Conservation Planning Vol 8 (2012) 45 - 64
INTRODUCTION
In the early 1980s, systematic conservation planning
came into being with the first reserve selection algorithm
(Kirkpatrick 1983). Kirkpatrick´s idea was to divide a whole
region into planning units containing comparable data about
the features to be considered for selection (Pressey 2002).
Worldwide, conservation areas do not include all the
habitats necessary for the persistence of every species
(Brooks et al. 2004). In Colombia, almost half of the
ecosystem types today remain excluded from protection,
and many natural parks are too small to support viable
populations of the various species (Fandiño-Lozano and
Wyngaarden 2005).
In general, a reserve selection algorithm is a sequence of
explicit steps through which selection criteria and rules are
applied. Following this definition, ranking techniques would
also be algorithms. The difference between systematic
conservation planning and ranking is that ranking involves
only a few sites, while systematic conservation planning
considers whole countries or regions. The planning units
are scrutinized one by one in a systematic search until the
minimum targets are achieved, expressed as a percentage
of the ecosystem’s extent or as the number of occurrences
of the species under consideration (Pressey et al. 2003).
The decision adopted by the Conference of the Parties to
the Convention on Biological Diversity at its tenth meeting,
which took place in Japan in 2010, stated that “by 2020, at
least 17 percent of terrestrial and inland water areas, and
10 percent of coastal and marine areas, especially areas
of particular importance for biodiversity and ecosystem
services, are conserved through effectively and equitably
managed, ecologically representative and well-connected
systems of protected areas and other effective area-based
conservation measures, and integrated into the wider
landscapes and seascapes” (CBD 2010).
As a series of fixed steps, reserve selection algorithms
can be easily programmed utilizing conservation planning
software known as decision support systems (DSS). These
tools produce in a few minutes what otherwise would
demand months or years of difficult manual calculations.
Thus, any gap or missing components in a set of
systematically selected reserves are no longer attributable
to a faulty selection technique, and can be explained by
either ineffective methods or low quality data.
For such an ambitious political goal to have a real impact
on wildlife persistence, the selection of new conservation
areas must be highly effective. Effectiveness will depend
on the validity of the selection method (Shrader-Frechette
and McCoy 1993), the comparability (Margules and
Pressey 2000), the quality of the data (Wyngaarden and
Fandiño-Lozano 2005), and their application via proper
computational techniques. How to select conservation
areas for wildlife protection has been an important issue in
conservation science for four decades in response to the
evident destruction of natural areas and species decline.
Two main types of methods have characterized reserve
selection since algorithms emerged (Noss and Cooperrider
1994): the fine filter, in which selection is done on target
species, and the coarse filter that uses land surrogates
which, when conserved, would protect many species at
a time. The fine-filter approach covers a large spectrum
of species, including taxonomic groups, threatened
organisms (Murphy and Noon 1992), endemics or rarities
(Rabinowitz 1981), migratory species (CMS 2012), or
other species for which data are available (Wright 1977,
Stoms et al. 2005). The coarse-filter approach operates
mainly on land units (Pressey 2004). Some species were
also proposed as biological surrogates, including umbrella
species, which are large area-demanding animals and
consequently suitable for calculating the minimum extent of
reserves (Wilcox 1984). Keystone species were considered
because of functional reasons (Paine 1995), and focal
species were suggested due to their sensitivity to habitat
change (Lambeck 1997).
Many reserves were declared as a result of ad hoc decision
making (Pressey 1994) or as the default result of approaches
in which only the lands useless for agriculture were assigned
to wildlife conservation (Klingebiel and Montgomery 1961,
FAO 1983). The discussion on how to undertake reserve
selection started in the 1970s with the emergence of ranking
techniques. Values were assigned to some sites based on
various selection criteria and rules (Ratcliffe 1971, Tubbs
and Blackwood 1971, Tans 1974, Tjallingii 1974, Gehlbach
1975, Wright 1977, Lesslie et al. 1988); places with higher
scores became new conservation areas.
Because only a few places were taken into consideration
for ranking, significant gaps emerged in reserve systems.
46
To start, selecting conservation areas with a coarse-filter is
strongly advised, as generating good data for the fine-filter
approach is often not feasible (Pressey 2004). Not only
must the species and their distribution be known, but the
minimum number of individuals needed to maintain viable
populations, the ecological requirements of each species,
and the carrying capacity of all the existing ecosystems
must be determined. For many countries, a complete list of
species does not exist, much less the other data mentioned.
ecosystems, and naturalness and social viability in a way
that creates room for the recovery of ecosystems and
reduces conflicts between conservation and development.
The method was refined throughout the last fifteen years,
although it officially became a DSS in 2002. We used
FOCALIZE to select priorities for biological conservation
in Colombia (Fandiño-Lozano and Wyngaarden 2003,
2005). After this work was done at a national level, some
environmental institutions became interested in performing
similar exercises at the regional level. Capacity-building
programs started in 2007 to establish FOCALIZE in various
decision-making institutions. In this article we use the Huila
case to illustrate how the decision support system operates.
We do not suggest that individual species should not have
any role in reserve selection. The fine-filter approach is
designed to complement the coarse filter (Noss 1987).
Migratory animals, highly endemic species, and those
surviving only in agricultural and urban landscapes after the
destruction of their natural habitats, may not get protected
in the selected fraction of the land surrogates used as a
coarse-filter (Fandiño-Lozano 1996, 2000, 2001, Lombard
et al. 2003). Discovering sensible ecological reasons to
determine which species need to be targeted will increase
the complementary power of the fine and the coarse filters.
THE STUDY AREA
The Huila region in Colombia is located at 2o south and
75o west (Figure 1), and covers about 2 million ha. It is a
highly diverse province, with important gaps in the system
of conservation areas. Only 35% remains covered with
natural ecosystems, and the transformation of land into
agricultural use, urban areas, and other artifacts continues
at an alarming rate of about 10,000 ha per year (FandiñoLozano and Wyngaarden 2009); this rapid change is
indicative of the situation in the Andean Region of Colombia.
Here we present FOCALIZE: a new decision support
system. This DSS uses groups of ecosystems as a land
surrogate, umbrella species as biological surrogates to
calculate minimum targets, compactness to concentrate
the selected cells around the center of each group of
Figure 1. Location of the study area.
47
selection begin? To answer these questions, the selection
criteria and the rules are put into place. In FOCALIZE, the
complementary criteria set the contents, size and shape
of the new conservation areas, while the supplementary
criteria help to find the best possible location.
The center of the Huila region forms the upper part of the
Magdalena river valley at an altitude of 400 to 1,000 m
above sea level. It has a semi-arid climate in the north and
is more humid in the south, exhibiting a relatively smooth
topography and fertile soils. Because of these favorable
conditions, irrigated agriculture and extensive livestock
production dominate the area. Towards the east, south
and west, various mountain ranges reach up to 5,750 m.
With increasing elevation, temperature drops and rainfall
increases. The upper slopes are extremely steep. The
lower slopes have an undulating topography and are used
intensively for agriculture; coffee is the main crop. As in
many other regions of Colombia, petroleum production is
an important economic activity in this region (IGAC 1995).
What to Conserve?
Many animals move through various ecosystems in search
of the various components of their habitat and cannot survive
within a single unit or ecosystem type (Jarman and Sinclair
1979, Sukumar 1989, Wiens 1992, Ims 1995, Wittemyer
et al. 2007). For this reason, representing ecosystems
in conservation areas, as is proposed generally, may
ultimately have limited effectiveness in protecting wildlife
(Noss 1983, Lombard et al. 2003, Dunning et al. 2006).
Groups of spatially related ecosystems can be expected to
be a better land surrogate because they provide the natural
connectivity between individual units. We previously called
such groups chorological types (Fandiño-Lozano 1996),
and here we propose them as land surrogates to use in the
coarse-filter approach.
The variety of physical conditions in the area, together
with its geologic history, has given rise to a large diversity
in flora and fauna. But today only the higher parts of the
mountains remain covered with large tracks of natural
vegetation, equivalent to 19.8% of the region. The rest of
the Huila only carries fragments spread over 15.2% of the
region in lower-lying areas. The region contains sections
of 6 national parks and 15 regional and local parks. Of
the 31 existing ecosystems, 11 remain excluded from the
conservation areas (Wyngaarden and Fandiño-Lozano
2008).
The term “chorological” was developed by the Dutch
ecologist Isaac Zonneveld (1989). He described the
landscape on the basis of the topological (vertical) and
chorological (horizontal) dimensions. The idea of using an
“expanded coarse-filter” has been present in the literature
on conservation planning since Noss (1987) proposed it.
However, this suggestion was never put into practice.
Problems related to water disequilibrium also affect the
Huila region. Out of 33 towns, 9 face insufficient water
supply in the dry season and flooding during the rainy
season due to increased riverflow. The natural vegetation
cover of the watersheds is partly responsible for such
disequilibrium; in some cases it is <10%. To secure
drinking water supply and reduce flooding, it has been
proposed that 28 watersheds should be protected and their
vegetation recovered (Fandiño-Lozano and Wyngaarden
2009). Priorities for water regulation can be brought
together with the wildlife reserves into a more effective
system of conservation areas.
Assessing How Much Could Serve as an
Effective Target?
Effectiveness of conservation areas is not only a matter of
features representation, but a matter of species persistence;
this has to do with habitat size (Soulé 1987). In that
sense, the commonly used arbitrary targets do not have
“conservation merit” (Svancara et al. 2005). A politically
set percentage of the remaining extent of the ecosystems
does not mean anything for the species present. It can
be too much for some and too little for others. Here we
present a way of calculating non-arbitrary targets.
THE SELECTION METHOD IMPLEMENTED IN
FOCALIZE
For a selection method to be effective, it must answer the
following questions correctly: what to conserve, how much
of each element should be conserved, and where should
For the chorological type, as extended land surrogate, the
targets should meet the appropriate size of each ecosystem
48
or minimum topological representativeness (MTR),
and should also satisfy the spatial balance between
those ecosystems integrating a pattern or chorological
representativeness (Fandiño-Lozano 1996).
same in the conservation areas as it was in the original
landscape to achieve chorological representativeness, it
is possible to express the area of one ecosystem in terms
of the area of another using such equivalencies, thus
reducing the number of unknowns to one:
The percent with which each ecosystem contributes to
the landscape pattern in the original-potential condition
probably influenced species distribution.
Thus the
relative contribution of an ecosystem to the pattern in the
conservation area, if not equal, should be as close as
possible to that in the landscape before it was transformed
into agro systems, settlements and other artifacts; values
which can be obtained from the map of the originalpotential ecosystems (Wyngaarden and Fandiño-Lozano
2005). The ideal chorological representativeness is
1, the case in which the proportions between (but not
necessarily amounts of) ecosystems are the same in the
conservation area and in the natural pattern.
(2) Equivalencies
areai = oai * area1 / oa1
areai = minimum area of ecosystem i needed
MVP = minimum viable population
oai = original area of ecosystem i
The last step is to find the value of the MTR:
(4) Minimum Topological Representativeness
MTR = area1 / oa1 * 100%
The MTR is expressed as the percentage of the original
extension of the ecosystems (Fandiño-Lozano 1996).
This percentage is shared by all the other ecosystems
integrating the same chorological type, even by those
where the umbrella species does not occur. In those
cases the carrying capacity will be zero. The minimum
area of each ecosystem will vary depending on its original
extent.
(1) General equation
MVP = cc1 * area1 + cc2 * area2 +...+ cci * areai
(3) Equation with a single unknown
area1 = MVP / (cc1 + cc2 * oa2/oa1 +...+ cci *oai/oa1)
Mathematically this principle can be written as follows:
cci = carrying capacity of ecosystem i
areai = minimum area of ecosystem i
If, for example, the original area of ecosystem i is 200 ha,
and the area of ecosystem 1 is 100 ha, then the minimum
area of ecosystem i is 2 times the minimum area of
ecosystem 1. After substituting equation 2 into equation
1 and rewriting it, we obtain the following:
Regarding the MTR, we use the most area-demanding
animals (umbrella species) merely to determine the
minimum area of each ecosystem to be selected. If
minimum viable populations (MVP) of umbrella species
are expected to survive in the selected conservation areas,
each ecosystem integrating a particular chorological type
should have the minimum area to contribute effectively
to the habitat needed according to the carrying capacity
of the ecosystems (Fandiño-Lozano and Wyngaarden
2003).
The balance between the ecosystems or chorological
representativeness is automatically obtained because
the equivalencies reduce the number of unknowns in
equation 1.
The MVP and the carrying capacities can be estimated
using ecological and biological data. The minimum areas
of the ecosystems are the unknowns to be solved in the
equation. However, the number of unknowns is equal to
the number of the ecosystems in the chorological type
(area of ecosystem 1, 2…i), which makes it impossible to
solve the equation. Nonetheless, since the contribution
of each ecosystem to the chorological type must be the
Where Should Selection Start?
Wildlife reserves should be as round as possible to
minimize the area exposed to external threat (Janzen
1986) and edge effects (Wilcox and Murphy 1985,
49
Schonewald-Cox and Bayless 1986). Compactness (Ball
and Possingham 2000) and adjacency (Nicholls and
Margules 1993) have been used in other algorithms to
join together selected units, thus reducing the number of
clusters or groups of cells.
effect in preventing excessive clusters in fragmented
landscapes.
We use social needs and preferences as supplementary
criteria as well. Social variables have been considered
in reserve selection since the 1970s either to increase
or lower the value of a site for conservation (Tans 1974,
Gehlbach 1975, Wright 1977, Bedward et al. 1992, Wessel
et al. 2000, Pressey and Taffs 2001, Reyers et al. 2002).
MARXAN, a very popular DSS today, combines social
variables into “a cost function” after expressing them in the
same currency (Ball and Possingham 2000).
FOCALIZE also uses compactness, but follows a logic that
suits the land surrogate we developed. The ecosystems
which are pulled together are those belonging to each
chorological type. This is done by selecting the planning
units as close as possible to the center of each chorological
type, which happens to be the center of the ecosystems
belonging to that pattern.
In our method, if two sites show equal contents, the one
that can be easily assigned to biological conservation is
selected. The preference given to an area may have a
positive character — for instance, the motivation to set
a new reserve to regulate water supply — or a negative
character, such as for areas where productive activities or
development projects are planned. Planning units can also
be excluded from selection in built-up areas, and existing
conservation areas can be preselected, given a positive
preference.
In addition to compactness, the selection should make
optimal use of natural remnants. In the 1970s it was
proposed that conservation areas should be selected if
they were natural or pristine (Ratcliffe 1971, Tubbs and
Blackwood 1971, Spellerberg 1981, Wright 1977, Lesslie
et al. 1988). Later, in systematic conservation planning,
the selection of new reserves became made exclusively
on natural remnants. When the targets were higher
than the current natural cover, they were “truncated to
present extant areas” (Pressey et al. 2003). Recovery
of diminished ecosystems has not been considered for
achieving minimum targets.
THE ALGORITHM AND THE SOFTWARE
FOCALIZE is a stand-alone computer program written in
Visual Basic (Wyngaarden and Fandiño-Lozano 2011). It
executes a heuristic algorithm written on the basis of the
described selection criteria and rules. It chooses from
square planning units or cells, into which the study area
has to be pre-divided. Cells can be of any size with a
maximum number of 32,000. For each chorological type
the algorithm does the following:
In our method, naturalness is used as a supplementary
selection criterion. The selection starts in the areas with
a natural cover and, if there is insufficient extent to reach
the target per ecosystem, the algorithm continues with
fragmented or totally transformed areas. This implies that
the data pertaining to the original-potential cover of the
ecosystems must be available. This selection consideration
provides the information needed for protection and also for
recovering diminished ecosystems.
However, looking for the last natural fragments may split
the chorological type into an unnecessarily high number of
clusters. To avoid such an effect, it is possible to switch to
less natural areas once a percentage of the target is reached
on the basis of natural cover. The same compacting effect
can be achieved by stopping the selection when the MTR
is reached by summing the natural, the fragmented and
the original-potential covers within the cells being selected.
These options have no major effect on the dispersion of
clusters in natural areas whereas they have a very positive
50
•
chooses those cells that contain the ecosystem with
the lowest MTR achievement in its chorological type;
•
if more than one cell is available, maximizes naturalness
by first selecting cells where the ecosystem has a
natural cover, followed by cells that have only fragments
or, if not available, a totally transformed cover;
•
if more than one cell available remains, maximizes
social benefits and minimizes social conflict by
selecting among preferred cells and, if the target
•
cannot be reached, looks for cells without preference
and ultimately for those with a negative preference;
and how they will relate to the use of the territory and to
development initiatives.
if more than one cell available still exists, maximizes
connectivity by selecting the cell closest to the center
of the chorological type and, if available, neighboring
to already selected cells.
To make this critical information available and approachable
for the non-expert, we programmed two extensions of
FOCALIZE in the public domain GIS software ILWIS
(52North 2011): “FOCALIZE_Import_Scenario” to display
thematic maps of the selected planning units, and “OTPlan” to highlight the conflicts between conservation and
economic activities and, based on the decisions made, to
produce a land use plan.
This process continues until the MTR for all ecosystems
of one chorological type is reached; then the algorithm
proceeds with the next chorological type. Finally, it
includes cells that border selected cells on at least three
sides, so that the perimeter of the selected areas is
reduced. Figure 2 illustrates a model of how the algorithm
operates.
THE DATA
The database for the Huila region consists of 16,250 cells
of 400 ha each, containing information on the quantity
of 7 chorological types, 31 ecosystems in 4 levels of
naturalness, the presence of 3 social variables and the
current conservation areas (Figure 3).
Correct interpretation of the scenarios produced by
FOCALIZE, or any DSS, requires training and in-depth
knowledge of conservation planning methods, tools and
data. However, decisions regarding new conservation
areas are ultimately made by governmental agents, who
usually are not experts in the topic and who do not have
the time to examine details. To make sound conclusions,
these decision-makers need as much clarity as possible
regarding why the proposed new reserves are needed
The ecosystems must be known as they are today
(actual) and as they were originally or would potentially
be, if recovered (original-potential). The original-potential
Figure 2. Rules to apply the selection criteria.
51
Figure 3. Data needed to run focalize.
Note: The square brackets indicate the data from which the other data are derived. The arrows show the data flow.
Urban areas are not shown in the figure because of their small size.
52
cover is needed to differentiate the chorological types and
find their centers, to calculate the targets, and to provide
the levels of naturalness.
distribution. Because they are competitors, the targets
must be summed where they are co-present.
Based on food chains, the carrying capacities of the
ecosystems were calculated. The main prey of jaguar
and puma are large- and medium-sized terrestrial
herbivores. Because herbivore density in Colombia is
unknown, we predicted the potential density using data
from comparable African ecosystems (e.g., Bell 1982,
Wyngaarden 1985, Bie 1991). Using these predictions
and the known consumption of large African carnivores
(Sinclair and Norton-Griffiths 1979), the maximum
sustainable density or carrying capacity for jaguar and
puma was estimated (Table 1, columns 5 and 6).
The mapping of the actual ecosystems was done following
standard procedures (Küchler and Zonneveld 1988).
The original-potential distribution of the ecosystems was
mapped by observing the recently modified limits on
satellite images and by spatial modeling in those cases
where it was not possible to see them anymore. Once
they were mapped, the boundary length was calculated
using GIS operations, and the obtained values were
clustered with a principle component analysis (PCA),
using CANOCO (Braak 1987). The chorological types
were mapped by joining the ecosystems that shared a
large proportion of boundaries (Fandiño-Lozano 1996,
Wyngaarden and Fandiño-Lozano 2005). Finally, the
centers of the chorological types were calculated using
GIS operations.
Equivalencies come from the ecosystems map on the basis of
their extension in the original landscape (Table 1, column 4).
With the data on equivalencies and the carrying capacity
of the umbrella species, the targets were calculated using
equations 3 and 4. The MTR varied from 15% to 100% of
the original-potential extent of the ecosystems, and from 19
to 61,302 ha (Table 1, columns 7 and 8). The data about
the existing conservation areas, and the social needs and
preferences, were independent cartographic inputs.
For the calculation of the MTR, we choose Panthera onca
and Puma concolor as umbrella species; the two largest
carnivores in Colombia. Their MVP was estimated using
Belovsky (1987): 300 individuals for jaguars and 400 for
pumas. The jaguar occurs in the more humid ecosystems
at lower altitudes, while the puma has a much wider
Chorological
types
Table 1. Data on the chorological types and
ecosystems of the Huila Region.
CT 38
CT 44
CT 45
CT 46
CT 48
CT 50
CT 51
Ecosystems
Code
Equivalence
Description
Central Cundiboyacense high plain and surroundings
MPH5
Glaciated landscape with dense tussock grasslands
MHH2
High andean low forests
MHM2
High andean shrublands and dwarf forests
MAH4
Evergreen andean forests
MSH5
Evergreen sub-andean forests
Eastern Cordillera: humid upper slopes
MPH6
MAH6
MAP3
MSH6
Eastern Cordillera: sub-humid lower slopes
MSS6
Semi-deciduous sub-andean forests
MSA2
Deciduous sub-andean open forests
MTS5
Semi-deciduous forests of the andes foothills
MTA6
Deciduous forests of the andes foothills
Tatacoa dry enclave
VTB6
Evergreen forests of the seasonal river plains
VTM1
Dense deciduous shrublands on terrace remnants
VTM2
Open deciduous shrublands and grasslands on dissected terraces
Upper part of the Magdalena river valley
MSS7
Semi-deciduous sub-andean forests
MTS8
Semi-deciduous forests of the andes foothills
VBB2
Deciduous forests of on the alluvial fans
VSB1
Deciduous forests of the low hills
VTB5
Deciduous forests of the terraces
VRB2
Riparian forests of the major floodplains
Central Cordillera: páramos and upper slopes
MNN1
Permanent ice and snow
MNN2
Glaciated landscape without vegetation
MUH4
Glaciated landscape with sparse tussock grasslands
MPH7
MHH3
High andean low forests
MHM3
High andean shrublands and dwarf forests
Central Cordillera: middle and lower slopes
MAH8
MSH9
MTH8
Evergreen forests of the andes foothills
53
Carrying capacity (nr/km 2)
Target (MTR)
% of original
extent
Panthera onca
Puma concolor
ha
1.000
0.202
0.855
1.998
0.627
0.00
0.00
0.00
0.00
0.00
0.48
0.48
0.48
0.48
0.48
11,939
2,414
10,205
23,860
7,482
100.0
100.0
100.0
100.0
100.0
1.000
596.250
588.710
3,299.830
0.00
0.00
0.00
0.00
0.48
0.48
0.48
0.48
19
11,077
10,937
61,302
18.6
18.6
18.6
18.6
1.000
0.111
1.027
0.256
0.80
0.80
0.80
0.80
1.44
1.44
1.44
1.44
27,261
3,031
28,010
6,975
17.8
17.8
17.8
17.8
1.000
1.775
1.463
0.00
0.00
0.00
1.44
0.96
0.96
6,162
10,937
9,016
100.0
100.0
100.0
1.000
1.589
0.387
0.270
1.044
0.408
0.80
0.80
0.80
0.80
0.80
1.33
1.44
1.44
1.44
1.44
1.44
2.40
13,135
20,871
5,089
3,549
13,713
5,360
16.7
16.7
16.7
16.7
16.7
16.7
1.000
0.594
3.386
36.718
151.762
30.015
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.48
0.48
0.48
0.48
376
223
1,272
13,790
56,998
11,273
82.0
82.0
82.0
82.0
82.0
82.0
1.000
2.352
0.196
0.00
0.00
0.00
0.48
0.48
0.48
23,493
55,246
4,594
15.6
15.6
15.6
RESULTS OF A SCENARIO FOR THE HUILA REGION
541,788 ha were selected as priorities for conservation,
corresponding to 28.5% of the study area.
Priorities for water regulation were given a positive
preference, and highly productive agricultural lands and
areas assigned to petroleum exploitation, a negative one.
Urban areas are always excluded because they cannot be
reallocated to set new conservation areas. Existing natural
parks were ignored during the selection. We omitted
them in this example to avoid distortions in the results.
The scenario is displayed in thematic maps and four
tables. Table 2 contains the attributes of the selected cells
and importing them into “FOCALIZE_Import_Scenario”
produces the maps in Figure 4. The other tables contain
the statistics on the chorological types (Table 3), the
ecosystems (Table 4) and the clusters obtained (Table 5).
Figure 4. Thematic maps in the scenario.
54
Table 2. The attributes of the selected cells.
Cell
….
Cell 1113
Cell 1114
Cell 1115
Cell 1119
Cell 1120
Cell 1121
Cell 1237
Cell 1238
Cell 1239
Cell 1240
Cell 1241
Cell 1242
….
Centre coordinates
North
West
903000
903000
903000
903000
903000
903000
901000
901000
901000
901000
901000
901000
940000
942000
944000
952000
954000
956000
938000
940000
942000
944000
946000
948000
Cluster
number
Chorological
type
Ecosystem
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
Cluster 1
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
CT 38
MSH5
MSH5
MSH5
MHM2
MPH5
MPH5
MSH5
MSH5
MSH5
MAH4
MAH4
MAH4
Transformation
(%)
0.0
0.0
34.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16.7
Reason
Reason 14
Reason 14
Reason 34
Reason 14
Reason 14
Reason 14
Reason 14
Reason 17
Reason 17
Reason 14
Reason 14
Reason 34
Note: The column “Reason” gives a code for the combination of criteria used to select the cell. Reason 14 means: cell without preference, with
a natural cover and adjacent to an already selected cell; reason 17 means: cell with a negative preference, with a natural cover and adjacent to
an already selected cell; and reason 34: cell without preference, with a fragmented cover and adjacent to an already selected cell.
Some chorological types ended up in one cluster and in
balance (38 and 46). Other chorological types were split
into various clusters because of the ecosystems patterns
in natural areas, the transformation of the landscape, the
positive or negative preferences, and the limits of the study
area (Table 3, Figures 4c and 4d). The possible inclusion
of the clusters of a chorological type in one reserve
depended on the situation that created the grouping of
cells. Chorological type 44 was split because of the positive
preference given to watersheds (Figure 4e). Chorological
types 45, 48 and 51 ended up in various clusters because
of the transformation of the landscape (in grey in Figure
3). Artificial clusters were produced in the case of type 50,
located at the edge of the study area: two clusters were
part of a large unit located in two regions, so the limit of
the Huila created a separation that does not exist in reality
(Figure 4c).
based on the current extent of the natural areas. Reaching
the MTR requires the recovery of 110,354 ha (Table 4,
column 13). Figures 4a and 4b identify where and for which
ecosystems such actions are needed, and the fragments
that can serve as sources for recovery.
In some cases, the achievement of the MTR was higher
than 100% of the target. This occurred when an ecosystem
was small in relation to the cell size. For example, in
ecosystem MPH6, chorological type 44, the achievement
had a value of 538% because the target was 19 ha and the
selected cell contained 100 ha (Table 4, columns 7 and 8).
Concerning shape, none of the clusters shows the ideal
circular shape as expressed in the ratio BL/iBL = 1
(Possingham et al. 2000). Perfect round shapes are rare in
nature and are seldom achieved in reserve selection; this
ideal shape has to be seen as an indicator. The majority
of the obtained values were not far from 1 (Table 5, column
5), with the exception of the two largest clusters bringing
together several chorological types.
Regarding targets, the MTR was always reached on the
basis of the original-potential cover; not on the basis of
the extant cover (Table 4, columns 11 and 12). Of the 31
ecosystems located in the Huila, 13 do not meet the target
55
Figure 5 (right side) shows how OT-Plan made use of these
results to indicate the conflict between conservation and
development. The legend indicates the conservation
areas, existing or selected, that resulted in conflict
either with highly productive agriculture (upper part) or
with petroleum (lower part). From 319,817 ha of highly
productive agricultural lands, 7.2% are in conflict with
biodiversity and water conservation. And from 94,579
ha of interest for petroleum, 22.7% were selected in the
scenario for conservation.
The relation between the selected cells and the positive
and negative preferences used to build the scenario is
shown in Figure 4e. As in the scenario we gave a positive
preference to some watersheds, the algorithm selected
58.7% of their extent to achieve the targets for biological
conservation. But as those areas did not have all the
ecosystems integrating the chorological types, 30.3% had
to be selected in areas without positive preference and
even 9.9% in areas with a negative preference.
Table 3. Chorological information of the scenario.
Chorological
type
Ecosystem
CT 46
CT 46
CT 46
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
CT 51
VTB6
VTM1
VTM2
MNN1
MNN2
MUH4
MPH7
MHM3
MHH3
MNN1
MNN2
MUH4
MPH7
MHM3
MHH3
MAH8
MSH9
MTH8
MAH8
MSH9
MTH8
MAH8
MSH9
MTH8
MAH8
MSH9
MTH8
MAH8
MSH9
MTH8
Cluster
identifier
Chorological
representativeness
2
2
2
6
6
6
6
6
6
16
16
16
16
16
16
7
7
7
11
11
11
14
14
14
15
15
15
16
16
16
56
1.00
1.00
1.00
25.04
16.46
7.94
0.06
3.06
0.44
0.09
0.47
0.84
0.96
0.99
1.02
1.23
0.98
0.00
0.00
1.16
4.07
0.00
0.37
13.61
3.53
0.00
0.00
1.69
0.78
0.00
Achievement
MTR within
cluster (%)
100.0
100.0
100.0
111.8
73.5
35.5
0.5
13.7
2.0
10.1
48.4
86.5
99.5
102.0
104.7
47.7
38.2
0.0
0.0
11.4
39.7
0.0
2.1
74.9
23.5
0.0
0.0
94.0
43.9
0.0
Ecosystem
MPH5
MHH2
MHM2
MAH4
MSH5
MPH6
MAH6
MAP3
MSH6
MSS6
MSA2
MTS5
MTA6
VTB6
VTM1
VTM2
MSS7
MTS8
VBB2
VSB1
VTB5
VRB2
MNN1
MNN2
MUH4
MPH7
MHH3
MHM3
MAH8
MSH9
MTH8
Chorological
type
CT 38
CT 38
CT 38
CT 38
CT 38
CT 44
CT 44
CT 44
CT 44
CT 45
CT 45
CT 45
CT 45
CT 46
CT 46
CT 46
CT 48
CT 48
CT 48
CT 48
CT 48
CT 48
CT 50
CT 50
CT 50
CT 50
CT 50
CT 50
CT 51
CT 51
CT 51
natural
ha
11,939
2,414
10,205
23,119
6,421
100
45,731
57,483
102,508
44,673
5,689
5,390
6,788
1,239
8,750
7,213
2,470
6,353
516
2,697
17,277
712
458
272
1,551
16,817
68,333
13,747
117,750
72,980
2,654
Current condition of the ecosystems
MTR Huila
transformed
total
transformation
ha
ha
%
ha
11,939
11,939
2,414
2,414
10,205
10,205
741
23,860
3.1
23,860
1,061
7,482
14.2
7,482
100
19
13,894
59,625
23.3
11,077
1,388
58,871
2.4
10,937
227,475
329,983
68.9
61,302
108,213
152,886
70.8
27,261
11,310
16,999
66.5
3,031
151,698
157,088
96.6
28,010
32,331
39,119
82.6
6,975
4,923
6,162
79.9
6,162
2,187
10,937
20.0
10,937
1,803
9,016
20.0
9,016
75,965
78,435
96.9
13,135
118,275
124,628
94.9
20,871
29,875
30,391
98.3
5,089
18,495
21,192
87.3
3,549
64,609
81,886
78.9
13,713
31,292
32,004
97.8
5,360
458
376
272
223
1,551
1,272
16,817
13,790
1,174
69,507
1.7
56,998
13,747
11,273
32,490
150,240
21.6
23,493
280,316
353,296
79.3
55,246
26,726
29,380
91.0
4,594
natural
ha
11,939
2,414
10,205
23,119
6,421
100
14,251
13,887
54,914
21,844
1,854
5,390
3,921
1,239
8,750
7,213
2,470
5,457
516
1,150
7,421
320
458
272
1,551
14,165
59,700
13,043
38,880
41,819
1,422
Selected area
transformed
ha
741
1,061
1,051
519
16,485
9,981
1,905
23,587
8,935
4,923
2,187
1,803
12,203
20,698
5,631
5,357
12,313
7,771
1,111
2,099
21,479
3,843
ha
11,939
2,414
10,205
23,860
7,482
100
15,302
14,406
71,399
31,825
3,759
28,977
12,856
6,162
10,937
9,016
14,673
26,155
6,147
6,507
19,734
8,091
458
272
1,551
14,165
60,811
13,043
40,979
63,298
5,265
total
Achievement in % of
target
natural
total
100.0
100.0
100.0
100.0
100.0
100.0
96.9
100.0
85.8
100.0
538.3
538.3
128.7
138.1
126.9
131.7
89.6
116.4
80.1
116.7
61.1
124.0
19.2
103.4
56.2
184.3
20.1
100.0
80.0
100.0
80.0
100.0
18.7
111.7
26.1
125.3
10.1
120.7
32.4
183.3
54.1
143.8
6.0
151.0
121.9
121.9
121.9
121.9
121.9
121.9
102.7
102.7
104.7
106.7
115.7
115.7
165.4
174.4
75.7
114.5
31.0
114.6
Minimum area to
be recovered
ha
741
1,061
6,388
5,418
1,177
22,620
3,054
4,923
2,187
1,803
10,665
15,414
4,573
2,399
6,293
5,039
13,426
3,172
Table 4. Topological information of the scenario.
57
Table 5. Characteristics of the clusters.
Cluster
identifier
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Cells
no
347
151
27
22
10
14
96
23
51
46
38
27
15
66
17
449
14
14
10
21
Area
Boundary
2
km
km
1,190.3
412
588.6
140
92.9
68
88.0
60
40.0
36
41.6
36
359.4
128
92.0
56
204.0
76
184.0
68
152.0
88
108.0
84
46.6
40
264.0
116
55.4
44
1,644.8
528
55.7
36
56.0
40
40.0
28
66.5
68
BL / iBL
km/km
3.36
1.62
1.99
1.80
1.60
1.57
1.90
1.64
1.50
1.41
2.01
2.28
1.65
2.01
1.66
3.67
1.36
1.50
1.24
2.35
Table 6. Land uses in OT-Plan.
Land-use
Biodiversity conservation
Biodiversity and water conservation
Water conservation
Petroleum exploitation
Highly productive agriculture
Other agriculture and forestry uses
Urban centers
58
ha
569,714
110,458
52,962
72,731
288,956
805,395
6,063
%
29.9
5.8
2.8
3.8
15.2
42.2
0.3
Figure 5. Procedure and results of the FOCALIZE extension OT-Plan.
59
DISCUSSION
equivalencies that relate the area of one ecosystem to that
of another, on which basis the equations could be solved.
The chorological representativeness was the critical key
to finding the minimum topological representativeness
and presents a graceful solution to the traditional lack of
biologically-sound targets.
From the total extent of the Huila region, only 2.3% shows
a conflict between conservation and economic activities.
Figure 5 and Table 6 contain the results of combining the
decisions made into a land use plan after analyzing the
conflicts. Biodiversity and water conservation prevailed
over the other uses, and petroleum predominated over
agriculture. The order could change in other scenarios.
The results obtained for the Huila region illustrate the
importance of counting with non-arbitrary targets. The
MTR varied from 15 to 100%. A general target, such as
the 17% set by the CBD was insufficient in 71% of the
ecosystems present in the Huila.
The results obtained not only present a transparent process
of reserve selection, but also for land use planning. Such
integration improves the decision-making process by
providing a more understandable mental picture about
how to combine conservation and productive activities in
a concrete territory. The low values obtained for areas
in conflict probably will support completing the system of
conservation areas because the constraints conservation
imposes on development are perceived as marginal.
In the MTR equation the concept of umbrella species
also found a place. In this way, MVP and minimum sizes
were brought together in solving the problem of targets;
two approaches which have been traditionally seen as
alternatives (Soulé 1987) instead of essential elements of
the same solution. A serious effort must take place in the
fields of genetics and ecology in order to refine the values
in the MTR equation. The MVP should be calculated using
the best available methods, and the carrying capacities of
the ecosystems must take into consideration not only food,
but all the ecological requirements as well as behavioral
issues such as territoriality.
Reducing conflicts can also be achieved in optimizing
algorithms by joining social variables into a cost function,
as MARXAN does. However, to express many variables in
one currency is very difficult when dealing with things “that
are not ready trade” (Brennan 1996, Shrader-Frechette
1984, 1987). Also, by adding up cost layers into a cost
function, the selection process can become a black box for
decision makers (Ardron et al. 2010).
The equation does not solve the uncertainty about MVP
which remains the worst methodological gap in conservation
biology. As long as doubt shrouds the subject of minimum
areas, we strongly support the recommendation of Noss et
al. (1997): “conservation plans should be conservative in
the sense of willing to err on the side of protecting too much
than protecting too little”. His principle applied here would
mean opting for the highest values of MVP and the lowest
values of carrying capacities.
We do not mean to suggest that FOCALIZE solves
everything. The effectiveness of the chorological type,
as an extended surrogate, must be evaluated. However,
FOCALIZE is indeed a very good starting point as it is
based on well-known patterns and processes. Who has not
noticed that animals move across ecosystems or that the
landscape is integrated by units delineated by boundaries?
Our positive results, though immensely useful, do not
suppose that all connectivity problems are solved. Some
contacts of the landscape continuum are ignored in order
to split it into chorological types. The contacts that are
missed can be found in Figures 3 and 4c, by comparing the
cells selected in the scenario with the original contacts of
ecosystems and of chorological types.
We were pleased whenever the chorological types ended
up complete in one cluster or, when split into many clusters,
could be included in a conservation area. Even better would
be to achieve more than one chorological type in a cluster,
even if it creates more elongated shapes increasing the
BL/IBL ratio. The importance of a round shape is a matter
of scale. Special attention should be given to small patch
features within a chorological type. The MTR obtained
may be insufficient for the species restricted to small
ecosystems. Avoiding small cell sizes might prevent this
risk. In any case, it is essential to check closely the results.
The chorological type concept did not solely become
a novel extended land surrogate. It also turned out
to be essential to calculate the MTR by providing the
60
LITERATURE CITED
The supplementary character of naturalness and social
variables is also an appealing characteristic of FOCALIZE.
We saw the benefits evidenced by the low values of land in
conflict. Regarding naturalness, in the present situation of
the world, recovering ecosystems should be a core action
in conservation planning. To start the selection in natural
areas and continue through fragmented or transformed
sites permits the achievement of the targets in any context,
and provides information for directing the effort and
allocating funds for protection as well as recovery.
Ardron, J.A., H. Possingham and C.J. Klein (eds.). 2010.
MARXAN Good Practices Handbook, Version 2. Pacific Marine
Analysis and Research Association, Vancouver.
Ball, I. and H. Possingham. 2000. MARXAN (v 1.8.2). Marine
Reserve Design using Spatially Explicit Annealing. A Manual
Prepared for the Great Barrier Reef Marine Park Authority. http://
www.ecology.uq.edu.au/index.html. Accessed July, 2007.
Bedward, M., R.L. Pressey and D. Keith. 1992. A New Approach
Restoration of ecosystems to achieve conservation targets
has been questioned by some conservation planners arguing
that “the target deficit should not be interpreted as accurate
indicator of restoration needs. Previous transformation will
have eliminated the portion of biodiversity in these broad
habitat units that is unable to survive in heavily modified
landscapes” (Pressey et al. 2003). That would be true if
all remnants were gone a long time ago. But extinction is
a process that implies time. Many species surviving today
with enough variability to expand into viable numbers will
face extinction if the habitat is insufficient – it is a matter of
time (Tilman et al. 1994).
for Selecting Fully Representative Reserve Networks: Addressing
Efficiency, Reserve Design and Land Suitability with an Iterative
Analysis. Biological Conservation 62:115-125.
Bell, R.H.V. 1982. The Effect of Soil Nutrient Availability on
Community Structure in African Ecosystems. In: B.J. Huntley
and B.H. Walker (eds.) Ecology of Tropical Savannas. Ecological
Studies 42. Springer-Verlag, Berlin. Pp. 193-216.
Belovsky,
G.E.
1987.
Extinction
Models
and
Mammalian
Persistence. In: M.E. Soulé (ed.) Viable Populations for
Conservation. Cambridge University Press, Cambridge. Pp. 35-57.
Bie, S. de. 1991. Wildlife Resources of the West African Savanna.
Wageningen Agricultural University Papers, Wageningen.
Not having data for restoration cannot lead to ignoring its
importance. Mapping the actual and the original-potential
ecosystems (Fandiño-Lozano 1996) also made this aspect
of the method possible and easy to execute.
Braak, C.J.F. ter. 1987. CANOCO - A
FORTRAN Program
for Canonical Community Ordination by [Partial] [Detrended]
[Canonical] Correspondence Analysis and Redundancy Analysis
(version 2.1). TNO, Wageningen.
To obtain FOCALIZE, contact the Research Group ARCO
through our webpage: www.grupoarco.info/eng/products.htm.
Brennan, A. 1996. Ethics, Ecology and Economics. In: N.S. Cooper
and R.C.J. Carling (eds.) Ecologists and Ethical Judgements.
Chapman & Hall, London. Pp. 13-26.
ACKNOWLEDGEMENTS
Brooks, T.M., M.I. Bakarr, T. Boucher, G.A.B. da Fonseca, C.
We thank Dr. Ramya Rajagopalan for her helpful
comments on the manuscript and the two referees for
their constructive observations that improved the paper in
several ways. Appreciation also goes to Javier González
for his assistance editing this paper.
Hilton-Taylor, J.M. Hoekstra, T. Moritz, S. Olivier, J. Parrish, R.L.
Pressey, A.S.L. Rodriques, W. Sechrest, A. Stattersfield, W.
Strahm, and S.N. Stuard. 2004. Coverage Provided by the Global
Protected-Area System: Is It Enough? Bioscience 54:1081-1091.
CDB. 2010. COP 10 Decision X/2. Strategic Plan for Biodiversity
2011-2020. http://www.cbd.int/decision/cop/?id=12268. Accessed
May, 2011.
CMS. 2012. Convention on Migratory Species. http://www.cms.int.
Accessed January, 2012.
61
Dunning, J.B., M.J. Groom and H.R. Pulliam. 2006. Species and
Jarman, P.J. and A.R.E. Sinclair. 1979. Feeding Strategy and the
Pattern of Resource Partitioning in Ungulates. In: A.R.E. Sinclair and
M. Norton-Griffiths (eds.) Serengeti. Dynamics of an Ecosystem.
The University of Chicago Press, Chicago. Pp. 130-163.
Landscape Approaches to Conservation. In: M.J. Groom, G.K.,
Meffe and C.R. Carroll (eds.) Principles of Conservation Biology.
Sinauer Associates Inc. Sunderland. Pp. 419-465.
Kirkpatrick, J. B. 1983. An Iterative Method for Establishing
Priorities for the Selection of Nature Reserves. An Example from
Tasmania. Biological Conservation 25:127-134.
Fandiño-Lozano, M. 1996. Framework for Ecological Evaluation
Oriented at the Establishment and Management of Protected
Areas. A Case Study of the Santuario de Iguaque, Colombia. ITC
Publication No. 45. ITC, Enschede.
Klingebiel, A.A and P.H. Montgomery. 1961. Land Capability
Classification. Agricultural Handbook 210. USDA. Soil
Conservation Service, Washington D.C.
Fandiño-Lozano, M. 2000. Propuesta de un nuevo sistema
de categorías de áreas de conservación para Colombia.
Departamento Nacional de Planeación, Bogotá.
Küchler, A.W. and I.S. Zonneveld. 1988. Vegetation Mapping.
Kluwer Academic Publishers, Dordrecht.
Fandiño-Lozano, M. 2001. Ecological Evaluation for Conservation.
A Way of Thought. In: D. van der Zee and I.S. Zonneveld (eds.)
Lambeck, R.J. 1997. Focal Species: A Multi-Species Umbrella for
Nature Conservation. Conservation Biology 11:849-856.
Landscape Ecology Applied in Land Evaluation, Development
and Conservation. ITC, Enschede. Pp. 195-219.
Lesslie, R.G., B.G. Mackey and K.M. Preece. 1988. A ComputerBased Method of Wilderness Evaluation. Environmental
Conservation 15:225-232.
Fandiño-Lozano, M. and W. van Wyngaarden. 2003. Rol de la
Fauna en la Selección de Áreas de Conservación Biológica.
In: R. Polanco-Ochoa (ed.) Manejo de la Fauna Silvestre en la
Lombard, A.T., R.M. Cowling, R.L. Pressey and A.G Rebelo.
2003. Effectiveness of Land Classes as Surrogates for Species
in Conservation Planning for the Cape Floristic Region. Biological
Amazonia y Latinoamérica. Fundación Natura, Bogotá. Pp. 205209.
Fandiño-Lozano, M. and W. van Wyngaarden. 2005. Prioridades
de Conservación Biológica para Colombia. Grupo ARCO, Bogotá.
Margules, C.R. and R.L. Pressey. 2000. Systematic Conservation
Planning. Nature 405:243-253.
Fandiño-Lozano, M. and W. van Wyngaarden. 2009. Área de
estudio y diagnóstico del tema forestal. In: Plan general de
Murphy, D.D. and B.R. Noon. 1992. Integrating Scientific Methods
with Habitat Conservation Planning. Reserve Design for Northern
Spotted Owls. Ecological Applications 2:3-17.
ordenación forestal del Huila. Grupo ARCO, Bogotá. Pp. 6-22.
FAO. 1983. Guidelines: Land Evaluation for Rainfed Agriculture.
FAO Soils Bulletin No. 52. FAO, Rome.
Nicholls, A.O. and C.R. Margules. 1993. An Upgraded Reserve
Selection Algorithm. Biological Conservation 64:165-169.
Gehlbach, F.R. 1975. Investigation, Evaluation and Priority
Ranking of Natural Areas. Biological Conservation 8:79-88.
IGAC.
1995.
Huila.
Características
Geográficas.
Noss, R.F. 1983. A Regional Landscape Approach to Maintain
Biodiversity. BioScience 33:700-706.
Instituto
Geográfico Agustín Codazzi, Bogotá.
Noss, R.F. 1987. From Plant Communities to Landscapes in
Conservation Inventories. A Look at the Nature Conservancy
(USA). Biological Conservation 41:11-37.
Ims, R.A. 1995. Movement Patterns Related to Spatial Structure.
In: L. Hansson, L. Fahrig and G. Merriam (eds.) Mosaic
Landscapes and Ecological Landscapes. Chapman and Hall,
Noss, R.F. and A. Cooperrider. 1994. Saving Nature’s Legacy:
Protecting and Restoring Biodiversity. Defenders of Wildlife and
Island Press, Washington D.C.
London. Pp. 85-109.
Janzen, D.H. 1986. The Eternal External Threat. In: M.E. Soulé
(ed.) Conservation Biology. The Science of Scarcity and Diversity.
Noss, R.F., M.A. O´Connell and D.D. Murphy. 1997. The Science
of Conservation Planning. Habitat Conservation Under the
Sinauer Associates Inc., Sunderland. Pp. 287-303.
62
Endangered Species Act. Island Press, Washington D.C.
Shrader-Frechette, K.S. 1987. The Real Risk of Risk-cost-benefit
Analysis. In: P.T. Durbin (ed.) Technology and Responsibility. D.
Reidel, Dordrecht. Pp. 343-357.
Paine, R.T. 1995. A Conversation on Refining the Concept of
Keystone Species. Conservation Biology 9:962-964.
Shrader-Frechette, K.S. and E.D. McCoy. 1993. Method in
Ecology. Strategies for Conservation. Cambridge University
Press, Cambridge.
Possingham, H,I. Ball and S. Andelman. 2000. Mathematical
Methods for Identifying Representative Reserve Networks.
In: S. Ferson and M. Burgman (eds.) Quantitative Methods for
Conservation Biology. Springer-Verlag, New York. Pp. 291-205.
Sinclair, A.R.E. and M. Norton-Griffiths (eds.). 1979. Serengeti.
Dynamics of an Ecosystem. The University of Chicago Press,
Chicago.
Pressey, R.L. 1994. Ad Hoc Reservations: Forward or Backward
Steps in Developing Representative Reserve Systems?
Conservation Biology 8:662-668.
Soulé, M.E. 1987. Introduction. In: M.E. Soulé (ed.) Viable
Populations for Conservation. Cambridge University Press,
Cambridge. Pp. 1-10.
Pressey, R.L. 2002. The First Reserve Selection Algorithm – A
Retrospective on Jamie Kirkpatrick´s 1983 paper. Progress in
Physical Geography 26:434-441.
Spellerberg, I.F. 1981. Ecological Evaluation for Conservation.
Edward Arnold Ltd, London.
Pressey, R.L. 2004. Conservation Planning and Biodiversity:
Assembling the Best Data for the Job. Conservation Biology
18:1677-1681.
Stoms, D.M., P.J. Comer, P.J. Crist and D.H. Grossman. 2005.
Choosing surrogates for Biodiversity Conservation in Complex
Planning Environments. Journal of Conservation Planning 1:26-39.
Pressey, R.L. and K.H. Taffs. 2001. Scheduling Conservation
Action in Production Landscapes: Priority Areas in Western
New South Wales Defined by Irreplaceability and Vulnerability to
Vegetation Loss. Biological Conservation 100:355-376.
Sukumar, R. 1989. Ecology of the Asian Elephant in Southern
India. I. Movement and Habitat Utilization Patterns. Journal of
Tropical Ecology 5:1-18.
Pressey, R.L., R.M. Cowling and M. Rouget. 2003. Formulating
Conservation Targets for Biodiversity Pattern and Process in
the Cape Floristic Region, South Africa. Biological Conservation
112:99-127.
Svancara, L.K., R. Brannon, J.M. Scott, C.R. Groves, R.F. Noss
and R.L. Pressey. 2005. Policy-driven versus Evidence-based
Conservation: A Review of Political Targets and Biological Needs.
BioScience 55:989-995.
Rabinowitz, D. 1981. Seven Forms of Rarity. In: H. Synge (ed.)
The Biological Aspects of Rare Plant Conservation. John Wiley &
Sons Ltd., London. Pp. 205-217.
Tans, W. 1974. Priority Ranking of Biotic Natural Areas. Michigan
Botanist 13:31-39.
Tilman, D., R.M. May, C.L. Lehman and M.A. Nowak. 1994.
Habitat Destruction and the Extinction Debt. Nature 371:65-66.
Ratcliffe, D.A. 1971. Criteria for the Selection of Natural Reserves.
Advancement of Science 27:294-296.
Tjallingii, S.P. 1974. Unity and Diversity in Landscape. Some
Remarks on the Use of Soils and Vegetation Maps in Landscape
Ecological Studies for Nature Conservation and Physical Planning
in the Kromme Rijn Area, The Netherlands. Landscape Planning
1:7-34.
Reyers, B., D.H.K. Fairbanks, K.J. Wessels and A.S. van
Jaarsveld. 2002. A Multicriteria Approach to Reserve Selection:
Addressing Long-term Biodiversity Maintenance. Biodiversity and
Tubbs, C.R. and J.W. Blackwood. 1971. Ecological Evaluation of
Land for Planning Purposes. Biological Conservation 3:169-172.
Schonewald-Cox, C. and J.W. Bayless. 1986. The Boundary
Model: A Geographical Analysis of Design and Conservation of
Nature Reserves. Biological Conservation 38:305-322.
Wessel, K.J., B. Reyers and A.S. van Jaarsveld. 2000.
Incorporating Land Cover Information into Regional Biodiversity
Assessments in South Africa. Animal Conservation 3:67-79.
Shrader-Frechette, K.S. 1984. Science Policy, Ethics and
Economic Methodology. D. Reidel, Dordrecht.
63
Wiens, J.A. 1992. Ecological Flows Across Landscape
Boundaries: A Conceptual Overview. In: Hansen, A.J. and F. Di
Castri (eds.) Landscape Boundaries, Consequences for Biotic
Diversity and Ecological Flows. Springer, New York. Pp. 217-235.
Wyngaarden, W. van. 1985. Elephants, Trees, Grass and
Grazers. Relationships Between Climate, Soils, Vegetation and
Large Herbivores in a Semi-arid Savannah Ecosystem (Tsavo,
Kenya). ITC Publication No. 4. ITC, Enschede.
Wilcox, B.A. 1984. In Situ Conservation of Genetic Resources:
Determinants of Minimum Area Requirements. In: J.A.
McNeely and K.R. Miller (eds.) National Parks, Conservation
and Development. Proc. World Congress on National Parks.
Washington D.C. Pp. 18-30.
Wyngaarden, W. van and M. Fandiño-Lozano. 2005. Mapping
the Actual and Original Distribution of the Ecosystems and the
Chorological Types for Conservation Planning in Colombia.
Diversity and Distributions 11:461-473.
Wyngaarden, W. van and M. Fandiño-Lozano. 2008.
Fortalecimiento de la capacidad de la CAM en la planificación
sistemática de la conservación y en la consolidación del sistema
regional de áreas protegidas. Grupo ARCO, Bogotá.
Wilcox, B.A. and D.D. Murphy. 1985. Conservation Strategy:
The Effects of Fragmentation on Extinction. American Naturalist
125:879-887.
Wittemyer, G., W.M. Getz, F. Vollrath and I. Douglas-Hamilton.
2007. Social Dominance, Seasonal Movements, and Spatial
Segregation in African Elephants: A Contribution to Conservation
Behavior. Behavioral Ecology and Sociobiology 61:1919-1931.
Wyngaarden, W. van and M. Fandiño-Lozano. 2011. FOCALIZE
1. User manual (version 1-0-8). Grupo ARCO, Bogotá.
Zonneveld, I.S. 1989. The Land Unit. A fundamental Concept in
Landscape Ecology, and Its Applications. Landscape Ecology
3:67-86
Wright, D.F. 1977. A Site Evaluation Scheme for Use in
the Assessment of Potential Nature Reserves. Biological
52North. 2011. ILWIS 3.7. ILWIS Software Community.
http://52north.org/communities/ilwis/. Accessed January, 2011.
64

FOCALIZE: a decision support system to integrate reserve selection

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