ouadi achim, chad: primary productivity trends in relation

PAN SAHARA WILDLIFE SURVEY
SCF PSWS Technical Report 8
ECOSYSTEM ASSESSMENT OF THE RÉSERVE DE
FAUNE DE L’OUADI RIMÉ - OUADI ACHIM, CHAD:
PRIMARY PRODUCTIVITY TRENDS IN RELATION
TO HABITAT SUITABILITY FOR REINTRODUCTION
OF SCIMITAR-HORNED ORYX.
December 2011
Terri Freemantle
i
REPORT TITLE:
Freemantle, T. 2011. Ecosystem assessment of the Réserve de Faune de l’Ouadi Rimé-Ouadi Achim,
Chad : Primary productivity trends in relation to habitat suitability for reintroduction of scimitarhorned oryx. December 2011. SCF/Pan Sahara Wildlife Survey. Technical Report No.8 December
2011, 34pp.
AUTHOR
Terri Freemantle (Institute of Zoology, Zoological Society of London).
SPONSORS AND PARTNERS
Funding and support for the work described in this report was provided by:






His Highness Sheikh Mohammed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi
Emirates Center for Wildlife Propagation (ECWP)
International Fund for Houbara Conservation (IFHC)
Sahara Conservation Fund
Institute of Zoology, Zoological Society of London
Conservation Programmes, Zoological Society of London
i
Abstract
Habitat evaluation is considered to be an essential step for assessing the potential for an area to
support a viable reintroduced population under the IUCN guidelines. This study investigates the
suitability of the Ouadi Rimé Ouadi Achim Faunal Reserve (OROAFR) in central Chad for the
reintroduction of the Scimitar-horned Oryx. The Scimitar-horned Oryx is now extinct in the wild and
the OROAFR was once part of the historic range of the species, thus providing a basis for speculative
reintroduction. Remote Sensing and GIS mapping techniques are invaluable tools for undertaking
habitat evaluations, and were used in this instance to assess trends in climatic variables and net
primary productivity (NPP) over the last 27 years. They were also used to assess current and
potential levels of anthropogenic impacts on the ecosystem. Results show that since the 1980’s
there has been a steady increase in precipitations and NPP (as derived from the Normalised
Difference Vegetation Index) and as a consequence the area is showing a ‘greening’ trend. The
spatial downscaling of this average trend however showed that the North of the protected area
exhibited a drying trend over the period 1982-2008, whist the South was associated with intense
greening. As a result the boundary between ecosystem types is currently narrowing. Human induced
impacts in the region were reported to be relatively low, based on landcover, the Human Footprint
Index, and information on population density. Altogether, these results suggest that OROAFR is a
favourable site for Oryx reintroduction.
ii
CONTENTS
Sponsors and partners .......................................................................................................... i
Abstract ................................................................................................................................. ii
Contents ................................................................................................................................ iii
1. Introduction ......................................................................................................................
2. Objectives ......................................................................................................................... 2
3. Context of Research ......................................................................................................... 3
3.1 Overview of area of interest and causes of decline for scimitar-horned oryx 4
4. Material and methods...................................................................................................... 7
4.1 Material ........................................................................................................... 7
4.2 Methods .......................................................................................................... 8
4.2.1 Pre-processing ......................................................................................... 8
4.2.2 Vegetation analysis.................................................................................. 11
4.2.3 Climate analysis ....................................................................................... 12
4.2.4 Future habitat conditions based on eHabitat ecological modelling ....... 12
5. Results
.......................................................................................................................... 12
6. Discussion ......................................................................................................................... 24
7. Conclusions ....................................................................................................................... 27
8. Acknowledgements .......................................................................................................... 27
9. References ........................................................................................................................ 28
iii
1. Introduction
The Ouadi Rimé Ouadi Achim Faunal Reserve (OROAFR), Central Chad, received Protected Area
status for ecological importance in 1969. Despite this designation, numerous factors have
contributed to the decline, and eventual extinction of the scimitar-horned oryx (Oryx dammah) in
this area. The OROAFR was once, in recent history, the stronghold for the scimitar horned oryx, and
the gradual decline in population of the species has been documented in detail (Newby 1980; East
1990; Wilson & Reader 1993). The reserve itself is of significant ecological and biodiversity
importance for a number of factors, primarily the presence of IUCN classified fauna, such as critically
endangered (CR) Dama Gazelles (Gazella dama) and vulnerable (VU) Dorcas Gazelle (Gazella dorcas)
(IUCN 2001). The area was also historically home to the critically endangered Addax (Addax
nasomaculatus), the endangered (EN) African hunting dog (Lycaon pictus) and other vulnerable
species such as Cheetah (Acinonyx jubatus). There is, however, no recent evidence for the presence
of these species in the area (TW, pers. comm.). Because of the historic large abundance of scimitarhorned oryx, insufficient protection was assigned to this species, leaving it vulnerable to
anthropogenic impacts, such as poaching (Bassett 1975; Newby 1980), and exclusion from good
quality pasture by the presence of nomadic herdsmen and their herds (Bassett 1975). The Oryx were
traditionally hunted for their horns, meat and skin. The introduction of automated weapons and
motorized vehicles increased the hunting pressure on the species, resulting in a devastating decline
in the OROAFR population (Newby 1980). At a larger scale, overhunting and habitat loss, including
competition with domestic livestock, have been reported as reasons for the extinction of many wild
populations of scimitar-horned oryx (Mallon and Kingswood 2001, Beudels et al. 2006, Morrow in
press). The scimitar-horned oryx is currently designated as Extinct in the Wild (EW) by the IUCN.
Fortunately, as a result of large scale captive breeding programs, such as those under taken by
Marwell Preservation Trust (2007), a captive population of scimitar-horned oryx has been
maintained and is on the rise; the global zoo community holds a population of approximately 1500
(Gilbert & Woodfine, 2005). From this population, it is possible to reintroduce the scimitar-horned
oryx into the wild, providing habitat quality and ecological functioning are sufficient to allow
successful translocation. The OROAFR has recently been proposed as a potential reintroduction site
(Turmine in press).
This reports aims to investigate and analyze whether habitat quality in the OROAFR is suitable for
the reintroduction of the scimitar-horned oryx into the wild, in compliance with the International
Union for Conservation of Nature (IUCN) guidelines for reintroduction. Work is undertaken as a joint
1
collaboration between the Institute of Zoology (IoZ), ZSL Conservation Program (CP) and the Sahara
Conservation Fund (SCF).
2
2. Objectives
This report aims to assess ecosystem functioning and habitat quality in the Ouadi Rimé – Ouadi
Achim Faunal Reserve (OROAFR), in order to evaluate the possibilities for restoration and
management of the reserve. The primary focus of this report is placed on the reserve’s suitability for
potential reintroduction of the scimitar-horned Oryx (Oryx dammah) and represents a contribution
to habitat assessment in line with IUCN Re-introduction Specialist Group Guidelines (Category IV).
The results also contribute to an assessment of the ability for OROAFR to sustain populations of
Addax (Addax nasomaculatus) and Dama Gazelle (Gazella dama).
This ecosystem assessment will consider factors as primary productivity dynamics, trends in climate
conditions, land use, associated human impact and population density over the last 27 years. Data
will be drawn from a variety of sources, though primarily will focus on the use of earth observation
data, and will build on existing analysis of vegetation trends in African protected areas already
assessed by the Institute of Zoology, Zoological Society of London (Pettorelli et al. 2012).
3
3. Context of research
It is no secret that the world as we know it today is facing a wide range of environmental issues. In
the face of habitat degradation, increased resource exploitation, urbanisation and development,
alongside future climate change and the associated uncertainties involving global and local scale
impacts, it has become a world wild consensus that we must preserve biodiversity and
environmental quality. The United Nations have designated 2011-2020 as the United Nations
Decade on Biodiversity to address such issues. In the face of environmental degradation threatening
to disrupt the planets status quo, more efforts are needed to monitor, assess, and manage the
world’s protected areas, to ensure that nature’s valuable assets are preserved.
The use of earth observation (EO) data and GIS can offer a low-cost method to evaluate varying
types of habitat over large spatial extents. Advances in technology and an increase in available
satellite data at more frequent temporal and spatial resolutions have enabled far more integrated
and long-term studies of ecosystem functioning to be completed. Data documenting vegetation
dynamics, phenology and amount and distribution are of extreme importance for terrestrial
ecologists as animal distributions and dynamics are heavily influenced by changes in vegetation
dynamics (Pettorelli et al. 2005).
One such EO method which has undergone substantial validation and research is the use of the
Normalized Difference Vegetation Index (NDVI) in determining ecosystems’ primary productivity.
NDVI is a ratio index calculated using the Red (0.4 to 0.7 µm) and Near-infrared (NIR) (0.7 to 1.1 µm)
portions of the electromagnetic spectrum.
The formula’s rationale is derived from the fact chlorophyll absorbs solar radiation in the
photosynthetically active radiation spectral region (PAR), in this case visible light centered at 0.4 to
0.7 µm (RED), whilst mesophyll leaf structures reflect in 0.7 to 1.1 µm (NIR) (Jensen 2006). The
resulting NDVI values therefore range between -1 and +1, whereby negative values correspond to an
absence of vegetation (Myneni et al. 1995), and positive values identify vegetation at various stages
of greenness. As a result, NDVI is often called a ‘greenness index’.
Substantial research has been undertaken to validate the use of NDVI, and to assess its direct
relationship with primary productivity, both theoretically and empirically (Asrar et al. 1984, Sellers et
al. 1992). This important link between NDVI and Net Primary Productivity (NPP) was identified by
Reed et al (1994) and as a result lends its use to a wide range of applications in the field of ecology,
4
including but not exclusive to; land cover identification, drought mapping and prediction, vegetation
quality for herbivores, agricultural mapping, ecosystem response to climate fluctuations, monitoring
movements of migratory animals and delineation of post-flood surface inundation (due to waters
much lower NDVI spectral response; Pettorelli et al. 2005).
The particular success of the NDVI in arid regions should be noted, work undertaken by Anyamba &
Tucker (2005) in the African Sahel of long-term trend analysis using AVHRR NDVI concluded that the
period 1982 – 1993 was characterized by below average NDVI and persistence of drought (signature
large scale drought 1982 – 1985) and 1994 – 2003 was marked with region-wide above average
NDVI, indicating ecosystems transition to wetter a period, discussed as being part of a gradual post
drought ‘recovery period’. Herrmann et al (2005) also successful applied the use of NDVI to habitat
functioning of the Sahel region to determine vegetation dynamical response to fluctuations in
climate, concluding that ‘contrary to assertions of widespread irreversible desertification in the
African Sahel, a recent increase in seasonal greenness over large areas of the Sahel has been
observed, interpreted as recovery from the great Sahelian drought’ through assessment of
vegetation response to changes in climate, namely precipitation rates. It should also be noted, that
Herrmann et al (2005) found an approximate 50% increase in average NDVI in areas within Mali,
Mauritania and Chad. The use of the established NDVI forms an integral part of this ecosystem
study.
3.1 Overview of Area of Interest and causes of decline in scimitar-horned Oryx
The OROAFR is located in central Chad at 15.77oN 19.00oE falling within the Sahelian eco-region of
central Africa. The designated park area covers 77950km² and local altitude ranges between 190m
to 461m (Keith & Plowes, 1997; Hartley et al. 2007). The environment is typical of that associated
with the Sahelian sub-Saharan zone and is characterized by high average temperatures,
moderate/low annual rainfall of approximately 77mm, and shrub-desert grasslands. Figure 1 outlines
historic climate conditions. The term ‘Sahel’ in Arabic literally translates as ‘shore’, and refers to the
‘sparsely vegetated fringe of sand seas’ (Newby 1980): the OROAFR is one of the only designated
protected areas which falls in the transition zone between true desert (Sahara) and Sahel.
Three major habitat types can be distinguished in the reserve; 1) Sahelian wooded grasslands, 2)
sub-desert grassland, and 3) Desert, with sub-desert grassland covering approximately two thirds of
the park area (Hartley et al. 2007). Vegetation is characterized by annual grasses (including Aristida
mutabilis, Cenchrus biflorus and Chloris prieurii), white herbs (Limeum viscosum, Blephriis linariifolia
5
and Indigofera hochstetteri) interspersed with trees including; Acacia senegal, Balanites aegyptiaca,
Boscia senegalensis and Combretum glutinosum.
Topographically speaking, the reserve maintains a relatively featureless low terrain, however the
area borders in the east, a series of massifs reaching approximately 1500m, from which arise a
network of wadis (Figure 2). These wadis significantly enhance the biological diversity of the reserve
as a result of their associated temporary pools, floodplains and inundation zones providing a source
of fresh water, additionally the presence of shallow water tables allows farmers to irrigate vegetable
gardens and date groves (Keith & Plowes 1997).
Fig. 1: Monthly climate averages for OROAFR derived from WorldClim 50 year data
6
7
4. Material & Methods
4.1 Material
NDVI data was obtained in two varying spatial resolutions from the Global Inventory Modelling and
Mapping Studies (GIMMS) Advanced Very high Resolution Radiometer (AVHRR) and ModerateResolution Imaging Spectroradiometer (MODIS) MOD13Q1 Vegetation Indices 16-Day L3 Global
250m.
The GIMMS product provides NDVI data at 8km resolution for the period 1981 – 2006 as standard
(http://www.glcf.umd.edu/data/gimms/); however data until 2008 was acquired, giving a total time
series of 27 years (1981 – 2008). The source imagery for the NDVI product was obtained from AVHRR
onboard the NOAA satellite series. The product has been corrected for calibration, view geometry,
volcanic aerosols, and other effects unrelated to vegetation change (Tucker et al. 2004, Pinzon et al.
2005). NDVI values for each pixel with the OROAFR were extracted for analysis using ArcGIS.
The MODIS Vegetation Indices product is produced by NASA and uses spectral reflectance from the
MODIS sensors onboard the Terra and Aqua satellites in Blue (469nm), Red (645nm) and NIR
(858nm) bands at 250m spatial resolution (500m blue) to provide consistent spatial and temporal
comparisons of vegetation conditions globally, through derivation of Normalised Difference
Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). MODIS NDVI data was obtained from
Land Processes Distributed Active Archive Centre (LP DAAC) from product MOD13Q1 and has been
corrected for atmospheric bi-directional reflectance, and has been masked for clouds, water,
aerosols and cloud shadows (NASA LP DAAC 2001), and the product has a temporal frequency of 16
days and is freely available from https://lpdaac.usgs.gov/get_data.
Data documenting climatic variables for precipitation and temperature were acquired from the
Global Precipitation Climatology Project (GPCP) v2.2 and Global Historical Climatology Network
(GHCN) v3 respectively.
The GPCP data consist of precipitation data from in excess of 6,000 rain gauge stations, merged with
satellite observations of precipitation from geostationary and low-orbit infrared. Passive and
microwave sensors with sounding observations. These data have been merged to give estimated
values of precipitation on a global scale in a 2.5 degree global grid on a monthly basis, and spanning
from 1979 to present day. Data are freely available and can be obtained from
http://www.gewex.org/gpcp.html.
8
GHCN v3 gridded monthly temperature anomaly data was acquired from the GHCN database
(http://www.ncdc.noaa.gov/ghcnm/) and consists of surface temperature anomaly observations
aggregated from ~7000 temperature station data (Peterson & Vose 1997).
Topographic and surface elevation data for the region was acquired from ASTER Global Elevation
Model (GDEM) product, freely distributed by the Ministry of Economy, Trade and Industry of Japan
(METI)
and
the
National
Aeronautics
and
Space
Administration
(NASA)
(http://www.gdem.aster.ersdac.or.jp/). Version 2, recently released in 2011 was selected in 30m
resolution.
Landover raster datasets were acquired from the most recent release of GLOBCOVER (2010), the
GLOBCOVER project was initiated in 2004 by the European Space Agency in order to document and
produce a land cover map using 300m data primarily obtained from the MERIS sensor onboard
ENVISAT. The newly released data were obtained for 2009-2010 from http://ionia1.esrin.esa.int/.
The composites have been composed from MERIS and corrected for cloud cover, atmospheric
effects, geo-localisation and re-mapping. The classification used is compatible with the UN Land
Cover Classification system (LCCS).
Human Footprint Index (HFI) data was acquired from the Human Footprint Index product provided
by Socio-Economic Data and Applications Centre (SEDAC) (http://sedac.ciesin.org/wildareas/). The
map identifies anthropogenic impacts on the environment rating this impact on a scale of 1 – 100,
with 1 indicating minimal impact from humans, for example, primary tropical rainforest, and 100
indicating high impact, such as urbanisation. HFI is a unit-less index. Additionally, Human Population
Density
data
was
also
acquired
from
SEDAC
(http://sedac.ciesin.org/data/sets/browse?facets=theme:population).
4.2 Methods
4.2.1 Pre-processing
Data regarding the official protected area (PA) boundary of OROAFR was obtained from World
Database of Protected Areas (http://www.wdpa.org) in vector ESRI shapefile format in Albers Equal
Area Conic Projection and Clarke 1880 Spheroid, additionally a second boundary for the PA was
obtained from the Sahara Conservation Fund. A 30km buffer was included around the reserve and
the boundaries were re-projected into WGS84 as this is a widely used geographic projection. All
geographic transformations applied throughout were performed using the North American Datum
Conversion Utility (NADCON) method in ArcGIS (see Table 1 for transformation parameters).
9
GIMMS Monthly 1981–2008 was obtained courtesy of IoZ and subset over the PA of interest.
Product contains scaled NDVI values observed every 15-days for the 27 year period. Product was
provided with AEA projection and Clarke 1880 spheroid. Final output was transformed into WGS-84.
Spatial sub-setting and re-projection of MODIS NDVI data was completed using the MODIS Reprojection Tool obtained from NASA (https://lpdaac.usgs.gov/tools/modis_reprojection_tool).
Spatial subset over area of interest (protected area) was performed using Lat/Lon coordinates UL
(18.0, 17.0) and LR (13.5, 21.3) to ease CPU loading, current data projection was kept as Sinusoidal
projection at this stage and later re-projected into WGS84 using ArcGIS. Spheroid parameters can be
seen in table 1 below,
Table 1 – Transformation parameters
SMajor
SMinor
STDPR1
STDPR2
CentMer
OriginLat
FE
FN
6378249.145
6356514.86955
-19.0
21.0
20.0
1.0
0.0
0.0
Sinusoidal
6371007.181
0.00
0.0
0.0
0.0
0.0
0.0
0.0
WGS-84
6378165.000000
6356783.286959
Clarke
1880
Where

SMajor: Semi-major axis of ellipsoid. If zero, Clarke 1866 in meters is assumed.

SMinor: If zero, a spherical form is assumed, eccentricity squared of the ellipsoid if less than
one, or if greater than one, the semi-minor axis of ellipsoid.

Sphere: Radius of reference sphere. If zero, 6370997 meters is used.

STDPAR: Latitude of the standard parallel

STDPR1: Latitude of the first standard parallel

STDPR2: Latitude of the second standard parallel

CentMer: Longitude of the central meridian

OriginLat: Latitude of the projection origin

FE: False easting in the same units as the semi-major axis

FN: False northing in the same units as the semi-major axis
The output MODIS data was supplied in HDF (Hierarchical Data Format) which is largely incompatible
with ArcGIS, therefore the input MODIS data requires conversion. A script was created to re-project
10
the data into WGS-84 and output the file as an ERDAS imagine image (extension .img) and also to
extract the raster values of the PA using the vector boundary. The output raster only covers the area
of interest. The script utilises a custom projection utilising the parameters from the input raster
MODIS data and exporting to WGS-84 by using the NADCON method.
11
4.2.2 Vegetation analysis
NDVI change analysis derived from Global Inventory Modelling and Mapping Studies (GIMMS)
8km data and MOD13Q1 Vegetation Indices 16-Day L3 Global 250m
NDVI values for OROAFR from GIMMS and MODIS were extracted using PA defined boundary in
ArcGIS and exported to text file (export XYv) for management and analysis. Raw data were
transposed and smoothed using an R script1. The script has been designed to smooth the data to
remove anomalies and pixels with consecutive negative values (indicative of non-vegetation
regions). Missing pixels will be interpolated in ArcGIS for consistency in visual display of vegetation
change distribution maps. The aim of smoothing is to remove large anomalies in the data, which
could be attributed to the occurrence of cloud cover, for example. The script works by reading the
data and identifying patterns and removing noise or other fine-scale structures and rapid
phenomena. There are numerous algorithms which can be utilised for smoothing data: the algorithm
used here was adapted from Garonna et al (2009).
The NDVI data was further analysed for correlation using an R script which extracted values and
correlation coefficients for a) the entire time series b) first period (1981 – 1994) c) second period
(1995 – 2008) and d) year’s corresponding to MODIS data observations (2001 – 2008), using
Spearman’s Rank Correlation Coefficient. This was calculated to provide spatial and temporal
comparisons of NDVI from 8km and 250m resolution data. From these time series four parameters
were calculated representative of vital indicators of ecosystem functioning; 1) annual maximum
(MAX), 2) annual minimum (MIN), 3) integrated-annual NDVI (I-NDVI) and 4) annual relative range
(RREL=[MAX-MIN]/MEAN, where MEAN is the annual mean NDVI for the OROAFR). The Integratedannual NDVI is calculated as the sum of all NDVI values over a defined period, (e.g. month, quarterly,
annual, decadal) in this instance annual periods are evaluated. The I-NDVI has been shown to be a
good indicator of vegetation productivity over chosen study period (Justice et al. 1985, Myneni et al.
1997, Boelman et al. 2003).
The resulting output correlation coefficients were added in ArcMAP as point shapefile and classified
according to their respective significance values (based on Spearman’s rank). Table 2 outlines
significance values for each dataset and sub data set for AVHRR and MODIS. Four classes were
chosen to show; 1) Significant decrease 2) non-significant decrease 3) non-significant increase and 4)
1
To be noted- GIMMS processing was successful, however the MODIS analysis could not be completed due to the large file
size of the output dataset. R has a maximum file processing limit based on internal RAM capacity, and the CPU available
was not sufficient to complete analysis on the large data.
12
significant increase. These classified values we visualised as colour coded maps of the PA. The
process above was repeated for the PA using Mann Kendall Correlation coefficient.
Table 2: Correlation coefficient significance values (Spearman’s (S) Rank and Mann Kendall (MK))
Period
Duration
0.025 (S)
0.05 (S)
0.02 (MK)
0.05 (MK)
(years)
GIMMS - All
27
±0.3822
±0.3236
0.32194
0.27066
GIMMS - First Period
14
±0.5341
±0.4593
0.47253
0.40659
GIMMS Second Period
13
±0.5549
±0.4780
0.51282
0.43590
Overlap with MODIS
8
±0.7143
±0.6190
0.71429
0.64286
4.2.3 Climate Analysis
Climatic conditions (temperature and precipitation) in the PA from 1982 to present were extracted
using GPCP and GHCN data. Average observations for the PA were extracted using ArGIS and plotted
as a function of time. A linear regression model was fitted to these data to identify any linear trends
over time. Historic climate averages were extracted from WorldClim data (Hijmans et al. 2005,
http://www.worldclim.org/).
Maps of the Ouadi Rime Ouadi Achim Faunal Reserve and surrounding area were created in ArcGIS,
outlining land cover use, Human footprint Index, Human Population Density and a Digital elevation
Model. Land cover for the region was extracted from GLOBCOVER using ArcGIS.
4.2.4 Future habitat conditions based on eHabitat ecological modelling
In addition, ecological model simulations were run based on environmental and ecological variables
determined by present conditions in the Ouadi Rimé- Ouadi Achim Faunal Reserve, and outputs
were generated based on predicted areas of habitat similarity from predictions made from HadCM3
General Circulation Model for present, 2020, 2050 and 2080. The online software was developed by
Dubois et al (2011) on behalf of the European Joint Research Council, and is freely available to access
online. The product is described as – ‘a modelling web service allowing end-users to assess the
13
likelihood to find equivalent habitats today or in the future, considering a virtual infinity of possible
ingredients defining these habitats’ (http://ehabitat.jrc.ec.europa.eu/).
5. Results
In order to support clarity of spatial findings, the majority of data have been presented in map form.
Historic average annual precipitation for the PA is 77mm with average annual temperature of
approximately 29◦ C, based on WorldClim observations (Figure 1). GPCP precipitation data was
extracted and a linear model fitted, the trend indicates a slight increase in precipitation (Figure 3: y =
0.0093year + 4.5066, R² = 0.0051) and the annual peaks in precipitation correlate well with NDVI
response. Figure 3 also shows long term trends in integrated annual NDVI for the PA and the 30km
buffer, a linear model was also fitted and indicates a small increasing trend in I-NDVI in the reserve
(y = 0.0003year + 2.8643, R² = 0.342). Additionally, Figure 3a shows trends in temperature anomalies
for the PA, a linear model shows an increasing trend in average temperature (y = 0.0023year +
0.1314, R² = 0.0384). These long term linear trends in I-NDVI were complemented with spatiotemporal vegetation change maps of the PA, which identifies spatial variation in NDVI over time.
These are illustrated in Figures 4.1 – 4.3. Table 3a-d outlines percentage pixels change over time
extract from the protected area based on the WDPA boundary.
14
Table 3a Percentage pixels change I-NDVI
Significant Increase
1982 - 2008
1982 - 1995
1996 - 2008
Non – Significant
Non – Significant
Increase
Decrease
Significant Decrease
9.214
36.338
26.784
27.664
5.000
61.287
20.077
12.689
0.0067
14.756
43.682
40.494
Table 3b Percentage pixels change Intra-annual Relative Range
Significant Increase
1982 - 2008
1982 - 1995
1996 - 2008
Non – Significant
Non – Significant
Increase
Decrease
Significant Decrease
48.126
31.075
20.691
0.108
4.969
85.513
6.990
0.000
9.389
67.913
22.691
0.0067
Table 3c Percentage pixels change Maximum NDVI
Significant Increase
1982 - 2008
1982 - 1995
1996 - 2008
Non – Significant
Non – Significant
Increase
Decrease
Significant Decrease
37.138
25.633
8.927
28.302
8.981
57.842
25.414
1.103
0.091
45.606
20.951
32.539
Table 3d Percentage pixels Minimum NDVI
Significant Increase
1982 - 2008
1982 - 1995
1996 - 2008
Non – Significant
Non – Significant
Increase
Decrease
Significant Decrease
0.509
8.465
62.127
28.899
0.000
30.998
68.439
0.563
0.000
0.847
51.014
48.079
15
16
17
18
19
In addition to the spatio-temporal change maps, classified NDVI images were processed for 2011
from MODIS 250m NDVI, taking observations of; 1) peak dry season 2) peak wet season. NDVI
classification is a good way of identifying relative vegetation distribution over an area of interest. In
this instance, classified images were created for the recent annual season (2011) in dry season
(February) and wet season (September) using Natural breaks (Jenks) classification (dates also
correspond to dates field work was undertaken by SCF/PSWS). Figures 5a and 5b provide some
information regarding seasonal changes in the spatial distribution of primary productivity (as
indexed by NDVI) across the protected area: as one can see, NDVI is lower in the north, gradually
increasing to the south in a North – South gradient. In general, average primary production is higher
in September than in February, and there is an increasing contrast in terms of primary productivity
between the north and south of the reserve, moving from February to September. One can also note
the densely vegetated wadis in the eastern part of the PA. Land use in the region, derived from
GLOBCOVER is dominated primarily by ‘Bare Areas’ and ‘Sparse Vegetation’ supporting the assumed
hypothesis that urban development is low in the region. Table 4 and Fig 6 outline Landover
composition for the OROAFR and the 30km buffer zone.
Table 4 – Land cover and use in OROAFR and 30km buffer
Land cover class
Irrigated Croplands
Rain fed Croplands
Mosaic Croplands/Vegetation
Mosaic Vegetation/Croplands
Mosaic Forest-Shrublands/Grass
Mosaic Grassland/ForestShrubland
Closed to Open Shrubland
Closed to Open Grassland
OROAFR (% cover)
Buffer (% cover)
0.00
0.00
0.00
0.00
0.09
0.00
0.03
0.15
0.02
0.07
0.53
0.00
0.00
21.89
0.00
33.01
Of the socioeconomic factors considered, Fig 7 illustrates that human impact in the region is low
(average OROAFR 8.394 and buffer 9.147) coupled with low population density (Fig 8 average
OROAFR 4.5, buffer 3.68 – note buffer has smaller area than PA)
Predictions of habitat similarity to the PA were simulated by eHabitat for the present, years 2020,
2050 and 2080 using HADCM3 climate model at 5 min resolution. The output identifies areas with a
probability of 0 – 1 of exhibiting similar conditions to present day. Fig 9a – 9d identify the results of
these simulations, where a northward migration of the ecosystem can clearly be seen.
20
1
21
2
22
3
23
Fig 9 Habitat similarity simulations based on OROAFR, generated using eHabitat
a) Present conditions
b) Simulations 2020
c) Simulations 2050
d) Simulations 2080
24
6. Discussion
Based on the assessment undertaken here, the results identify several positive trends. The climate of
the Ouadi Rimé-Ouadi Achim in the Sahel region presents favourable increases in annual
precipitation, based on GPCP observations, whilst the increase is small (Fig 3 y = 0.0093year +
4.5066, R² = 0.0051), the trends support previous studies undertaken by Anyamba & Tucker (2005)
and Herrmann et al (2005) in the Sahel region, which document a steady increase since drought
engulfed the region in 1970’s. Whilst most report that this increase can be attributed to ‘postdrought recovery’ and is instead a return to normal historic conditions, it is also feasible and
considered that these changes could be a result of the regional impact of climate change, such as
feedback from increasing precipitation.
Our results then show a general increase in net primary productivity for the whole period in the
considered protected area. Interestingly, this average increase in greenness was mainly driven by
increasing primary productivity in the south of the reserve. The northern part, on the other hand,
showed decreasing annual primary productivity over the period 1982-2008. Such opposite trends are
thus currently leading to an increased contrast in primary productivity dynamics between the north
and the south of the reserve.
What are the factors driving the reported patterns in primary productivity dynamics in the area?
Herrmann et al (2005) consider that, whilst rainfall is often considered to be the dominant causative
factor for increased greenness, there is evidence of another causative factor (apparent in the
residual error Figure 3), namely the existence of a human induced land cover change. Nemani et al
(2005) also discuss the possibility that climate driven global increases in primary productivity
between 1982 and 1999 could be the result of increased carbon dioxide improving plants
photosynthetic functioning. Other potential suggestions to the cause of vegetation greenness in
semi-arid regions are attributed to the stronger correlation between soil moisture and NPP, a
function of rainfall accumulated over time, in contrast to instantaneous precipitation (Lotsch et al.
2005; Nicholson et al. 1990; Justice et al. 1986). Proposed solutions include considerations into
increasing investment and management for soil and water conservation, as a direct response to
drought, to ensure that times of drought do not catastrophically reduce NPP (Reij et al. 2005;
Rasmussen et al. 2001).
Projected increases in temperature based on climate change impacts could adversely influence NPP
in the region, and could be considered a contributing cause of drought in the region, and reduced
rainfall would inherently result in reduced NPP due to the established relationship between NPP and
25
precipitation. A number of Sahelian countries have experienced periods of extreme drought in
recent years, including Niger, and Chad, where record temperature levels were recorded in 2010
(Masters, 2010). These droughts led to crop failure, and could indeed also result in reduction of NPP.
Additionally, the role of land degradation as a driver in reduced NPP can be considered. Zeng (2003)
reviews two major causes of Sahelian drought as driven either by anthropogenic influence
(overgrazing and woodland clearing) and large scale atmospheric circulation changes (attributed to
multi-decadal variations in global sea surface temperatures). Land degradation and its role in
‘desertification’ is an important factor to consider for preserving NPP. Soil degradation reduces
fertility, however also increases surface albedo. An increase in surface albedo leads to less surface
sunlight absorption, resulting in a reduced uptake in moisture to the atmosphere, ultimately
reducing precipitation rates (Zeng, 2003). For further discussion on the causes of Sahelian drought
see – (Landsberg 1975, Nicholson et al. 1998, Le Houerou 1995, Zeng, 2003, Dai et al. 2004).
Land use is hypothesised to have an impact on NPP, but when taken into consideration the findings
of this study, and the evident low anthropogenic pressure (as indexed here using human footprint
index, population density and land cover use), it can be hypothesised that providing conditions
remain stable, it is feasible for NPP to continue to increase as precipitation increases (assuming that
low population density indicates reduced land pressure, in turn reducing human footprint index).
Should land use change, or land degradation increase, it is possible that this could lead to
catastrophic reduction in ecosystem functioning and carrying capacity (Zeng 2003). Highlighting the
importance for sustainable land management processes and implementing management strategies
which work in unison with the environment, and the local people. By creating such a mutually
beneficial relationship, ecosystem functioning can be preserved, and perhaps even increased in the
years to come.
With the possibility of future climate change it is useful to consider any potential future changes to
ecosystem functioning. The results of future conditions generated from models of habitat similarity
based on HadCM3 GCM predictions identify a northward migration of the ecological zone (Fig 9),
based on probability of habitat similarity when considering present and future conditions. Despite
this however, uncertainty with future predictions must be considered, as is inherent with all GCMs.
Therefore these results can aid in giving a rough indication of future conditions, however are not
deemed as precedent.
When considering the implications for reintroducing the scimitar-horned Oryx, the habitat overall
seems favourable with a move towards greener and wetter conditions, and influence from humans
26
and land use pressure remaining minimal, based of a large scale analysis. Gillet (1966) and Newby
(1988) concluded that ‘wild scimitar-horned Oryx in Chad could breed continuously under favourable
climatic and nutritional conditions with a marked birth peak every 8–10 months, but that the pattern
was disrupted in years of drought’. So assuming that conditions are either returning to pre-drought
levels of stabilised NPP, or a move towards greener conditions, it is viable for the OROAFR to support
and sustain a translocated population of scimitar-horned Oryx, alongside carefully structured
management strategies, primarily aimed at maintaining ecosystem function and monitoring illegal
poaching.
In support of the findings presented here, several areas for expansion are proposed to investigate
the ecosystem functioning of the PA in more detail and on a localised scale. Most importantly, the
transitions to higher resolution NDVI data, in this instance the use of MODIS or RapidEye data, for
example, could facilitate identification of finer scale and localised trends in NPP in the region. The
impact of known borehole installation on surrounding NPP as observed by NDVI, and the presence of
surface water (through the application of the Normalised Difference Water Index – NDWI), are two
areas which would provide valuable results with regard to future management strategies
implemented in the region. Mapping migratory routes and accessing localised information on the
distribution of human populations could also aid in identifying areas most suitable for Oryx
reintroduction, ensuring minimum impact from human and socioeconomic disturbance. This work
then suggests an increasing contrast between the northern part (associated with decreasing NPP
and decreasing seasonality) and the southern part of the PA (associated with increasing NPP and
increasing seasonality): these results need to be matched with on the ground data, to highlight the
mechanistic pathways leading to such patterns. Likewise, more information is required to explore
how this increasing contrast might impact habitat selection for reintroduced animals.
27
7. Conclusions
This research concludes that overall, the NPP in the region has been shown to be increasing
throughout the study period. The availability of satellite data has enabled development in the field
of macro ecology, and allows us to gain a better understanding of the changes happening in the
world around us, on a much larger scale than has ever been possible before. With increased
availability of higher resolution data, already known and established methods in remote sensing and
spatial analysis can only be even further improved, with higher temporal, spatial and spectral
frequency data. The NDVI itself is a thoroughly established technique for determining ecosystem
functioning through delineation of NPP. The OROAFR has been shown to be exhibiting a greening
trend, and increase in precipitation support the notion to sustain the region as a key game reserve
for the continued existence of Sahelian wildlife, through integrated management strategies,
including the potential reintroduction of the scimitar-horned Oryx into its last know historic range.
8. Acknowledgements
I wish to thank Dr Nathalie Pettorelli, Dr Tim Wacher and John Newby for the opportunity to work on
this project, in particular, Nathalie for her continued support and encouragement. Additionally,
thanks are given to the Sahara Conservation Fund, for financing this project, and to the Institute of
Zoology, for hosting me throughout the research period. Personal thanks are also extended Alienor
Chauvenet, James Duffy and William Cornforth, for all their technical support and comments.
28
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