development of tiger habitat suitability model using geospatial tools – a case study in achanakmar...
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7/29/2019 Development of Tiger Habitat Suitability Model using Geospatial Tools A case study in Achanakmar Wildlife Sanct
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Environ Monit Assess (2009) 155:555567
DOI 10.1007/s10661-008-0455-7
Development of tiger habitat suitability model using
geospatial toolsa case study in Achankmar WildlifeSanctuary (AMWLS), Chhattisgarh India
R. Singh P. K. Joshi M. Kumar
P. P. Dash B. D. Joshi
Received: 29 October 2007 / Accepted: 26 June 2008 / Published online: 6 August 2008 Springer Science + Business Media B.V. 2008
Abstract Geospatial tools supported by ancillary
geo-database and extensive fieldwork regarding
the distribution of tiger and its prey in An-
chankmar Wildlife Sanctuary (AMWLS) were
used to build a tiger habitat suitability model.
This consists of a quantitative geographical infor-
mation system (GIS) based approach using field
parameters and spatial thematic information. The
estimates of tiger sightings, its prey sighting and
predicted distribution with the assistance of con-
textual environmental data including terrain, roadnetwork, settlement and drainage surfaces were
used to develop the model. Eight variables in the
dataset viz., forest cover type, forest cover density,
slope, aspect, altitude, and distance from road,
settlement and drainage were seen as suitable
R. SinghWildlife Institute of India, Dehradun 248 001, India
P. K. Joshi (B)TERI University, New Delhi 110 013, Indiae-mail: [email protected]
M. Kumar P. P. DashIndian Institute of Remote Sensing,Dehradun 248 001, India
B. D. JoshiGurukula Kangri University, Haridwar 249 404, India
proxies and were used as independent variables
in the analysis. Principal component analysis and
binomial multiple logistic regression were used
for statistical treatments of collected habitat pa-
rameters from field and independent variables re-
spectively. The assessment showed a strong expert
agreement between the predicted and observed
suitable areas. A combination of the generated
information and published literature was also used
while building a habitat suitability map for the
tiger. The modeling approach has taken the habi-tat preference parameters of the tiger and poten-
tial distribution of prey species into account. For
assessing the potential distribution of prey species,
independent suitability models were developed
and validated with the ground truth. It is envis-
aged that inclusion of the prey distribution proba-
bility strengthens the model when a key species is
under question. The results of the analysis indicate
that tiger occur throughout the sanctuary. The
results have been found to be an important input
as baseline information for population modelingand natural resource management in the wildlife
sanctuary. The development and application of
similar models can help in better management of
the protected areas of national interest.
Keywords Geospatial tools Habitat Model
Prey Suitability Tiger
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Introduction
Wildlife habitat planners require detailed infor-
mation pertaining to the spatial distribution and
abundance of species to understand the ecology
and develop management plan. Habitat may be
characterized by a description of the environ-mental features that are important for a species.
Such descriptions are often based on field experi-
ence and non-quantifiable human perceptions
(Burgman and Lindenmayer 1998). Such informa-
tion is also used to develop wildlife habitat models
(Pearce and Boyce 2006; Hirzel et al. 2006; Smith
et al. 2007; Braunisch et al. 2008). These mo-
dels are further used to assess habitat suitability,
identify potential risks to the species, understand
the implications of different land use practices
and to identify sites for the reintroduction of anendangered species (Stoms et al. 1992; Braunisch
et al. 2008; Drury and Candelaria 2008). One of
the widely used methods for these descriptions
is habitat suitability index (HSI) modeling. HSIs,
first developed by the United States Fish and
Wildlife Service in the early 1980s are conceptual
models based on evaluation procedure (USFWS
1980, 1996; Burgman et al. 2001). However, theseprocedures are generally linked by mathematical
functions (Fieberg and Jenkins 2005), which are
not able to compensate the individual variables in
case of presence and absence of all the variables
together (Hirzel et al. 2001; Zaniewski et al. 2002;
Marcot 2006). These could be managed by assign-
ing weights reflecting the importance of different
variables and are based on expert knowledge in
terms of combination of biology and life history
of a species and available data (Dzeroski et al.
1997, Venterink and Wassen 1997, Hackett andVamnclay 1998, Horst et al. 1998, Moltgen et al.
Fig. 1 Location of study area
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1999, Yamada et al. 2003, Drury and Candelaria
2008). The incorporation of location-specific know-
ledge using GIS enhances the wildlife habitat
suitability models (Wightmann 1995; Zhu 1999).
Along with, systematic field investigations and
data analysis techniques can always improve these
models (Mackenzie and Royle 2005).This paper explains a methodology that utilizes
GIS and extensive field survey to elicit habitat
suitability of tiger with detailed knowledge of the
study site in Achankarmar Wildlife Sanctuary
(AMWLS). First, the paper presents a brief
methodology for structured elicitation of knowl-
edge that combines both quantitative (GIS) and
qualitative information. Second, the paper ex-
plains habitat analyses. Third, this information is
synthesized and combined with other data layers
in GIS to develop the habitat suitability model fortiger. The model also takes prey distribution into
account along with the environmental variables.
The research was conducted under conditions that
are common to many management scenarios in
India where there is little documented informa-
tion on the biology of the species or its habitat is
available.
Study area
Achanakmar Wildlife Sanctuary (AMWLS) is lo-
cated 90 km west from the district Bilaspur in
Chhattisgarh state. It lies between 81.34E to
81.55E longitude and 22.24N to 22.35N lati-
tude with a geographical area of around 530 km2
(Fig. 1). The altitude of area varies between 362 to
721 m. Thirty percent of the total area is plain in
which 22 forest villages are situated. The hilly area
has good Bamboo forest. A river named Maniyaridissects the sanctuary area. It has a typical trop-
ical climate consisting mainly rainy, winter and
Fig. 2 Habitat variable collection points for prey and predator
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summer seasons. The average annual rainfall is
1,322 mm. The temperature varies from 4.2C to
40.2C. The vegetation is primarily tropical moist
deciduous forest followed by dry mixed forest
in less moist areas. The predominant vegetation
types are Sal forest, mixed forest, Bamboo breaks
and Teak plantation.
Material and methods
Spatial database development
GIS data layers for AMWLS were developed
to account for the spatial context. The en-
tire database was built at 1:50,000 scale with
Lambert Conformal Conic Projection. The an-cillary database including drainage, road net-
work, settlement, contours were generated from
topographic sheets. A hydrologically corrected
25 m resolution digital elevation model (DEM)
was created using TOPOGRID surface interpo-
lation in ArcInfo. It was then used to derive
other secondary environmental variables includ-
ing slope and aspect using Spatial Analyst module
of ArcInfo (ESRI 1999). Aspect was transformed
to a linear measure (south, southeast, southwest,
north, northeast, northwest, west and east), which
is indicator of illumination.
Habitat mapping using satellite data
The IRS-P6 LISS-III standard false color com-
posite of February 2004 was used to prepare the
habitat (forest cover types) and forest canopy den-
sity maps through on-screen visual interpretation.
Three canopy density classes viz., 1040% (open),
4070% (medium dense) and >70% (dense) and
non-forest classes could be interpreted from re-
motely sensed data. Image elements like tone,
texture, shape, size, shadow, location and associ-
ation were used for this purpose. Six forest cover
types and five non forest classes were identified
while preparing the forest type maps. Forest type
and density maps were evaluated for classification
accuracy using second set of field data.
Field data collection
In addition to the datasets described above, ex-
tensive field work was carried out for collection
of information related to indicator environmental
parameters while considering presence and ab-
sence of prey (sambar, chital and wild boar) and
Fig. 3 Procedure fordevelopment of tigerhabitat suitability model
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predator (tiger) species (Fig. 2). The pugmarks
and pellets/droppings were used as direct evidence
of animal presence. Random sampling strategy
was applied for collecting the information. In total
97 plots were laid representing 27 for tiger (preda-
tor), 50 for prey species and another 20 random
plots. The plots for the tiger were planned withthe random sampling method. For the prey species
line transects were taken along the river stream,
trails, tracks and road network. Transects were
planned to remove the biased-ness for any par-
ticular prey species. The 20 random plots are the
sites identified on the suggestion of local people,
porters, trackers and at time convenience while
carrying out the field work. The plots were circular
in nature with 10-m diameter. For each sampling
site altitude, canopy cover (%), canopy height,
total number of trees, number of lopped stems,cut stems, saplings, shrub cover (%), shrub height,
grass cover (%), grass height, distance form wa-
ter, road, village, number of dung/pellets were
recorded.
Statistical processing of habitat variables
The field data was statistically analyzed to un-
derstand the habitat use and distribution pattern
by the prey and predator. This included principalcomponent analysis (PCA) and binomial multiple
logistic regression (BMLR). Statistical Package
for the Social Sciences (SPSS) has been used
for the statistical analysis (SPSS 1988). PCA in-
volves a mathematical procedure that transforms
a number of (possibly) correlated variables into a
(smaller) number of uncorrelated variables called
principal components. The succeeding component
accounts for receding variability as possible. All
cases were first filtered on the basis of the sighting
of individual species and the PCA (correlationcoefficients, varimax rotation) was run on the
dataset. Variables showing very low loadings on
the rotated component matrices were successively
dropped to reduce the noise in the dataset. PCA
was carried out on the data collected from the field
only to justify the dependence on predator of the
prey distribution.
BMLR is a form of regression, which is used
when the dependent is a dichotomy and the
independents are of any type. It is used to predict
a dependent variable on the basis of independentsand to determine the percent of variance in the de-
pendent variable explained by the independents;
to rank the relative importance of independents;
to assess interaction effects; and to understand
the impact of covariate control variables. Eight
variables in the dataset viz., forest type, density,
slope, aspect, altitude, and distance from road,
settlement and drainage were used as suitable
proxies and were used as independents in the
analysis. Individual cases of animal sightings were
considered as Boolean and BMLR were run. Out-liers in the dataset were detected using standard
deviations of residuals greater than specified cut.
The coefficients thus obtained were then used for
subsequent raster analysis (distribution mapping).
Table 1 Areadistribution in differentforest type, forestcover/density and landuse
Area (km2) Area (%) Area (km2) Area (%)
Forest cover type Forest cover/density
Sal forest 143.38 27.04 Dense (>70%) 265.62 50.09
Sal mixed forest 96.19 18.14 Density (4070%) 142.17 26.81
Sal bamboo forest 49.70 9.37 Density (1040%) 16.99 3.20
Mixed forest 89.02 16.79
Bamboo mixed 27.04 5.10
Bamboo breaks 19.45 3.67
Land use
Teak plantation 2.13 0.40
Scrub 67.50 12.73
Agriculture 24.30 4.58
Water body 0.50 0.09
Riverbed 11.05 2.08
Total 530.26 100
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Table 2 Habitat variables for prey, random and tiger plot
Variables Prey plot Random plot Tiger plot F Sig.
Mean SE Mean SE Mean SE
Altitude 500.44 (10.88) 522.22 (26.09) 460.54 (19.93) 1.76 0.18
Canopy cover (%) 65.0 (2.69) 64.44 (2.94) 65.45 (3.66) 0.02 0.95
Canopy height (m) 10.28 (0.55) 8.78 (0.69) 11.45 (1.42) 1.71 0.19
Distance from road (km) 1.41 (0.18) 0.92 (0.30) 1.00 (0.22) 0.23 0.79
Distance from village (km) 2.68 (0.25) 2.853 (0.43) 2.77 (0.57) 0.06 0.94
Distance from water (km) 165.93 (35.52) 231.94 (63.60) 209.54 (118.14) 0.41 0.66
Forest density (%) 67.41 (1.87) 67.78 (1.29) 70.00 (1.91) 0.22 0.80
Grass cover (%) 26.02 (2.86) 31.67 (4.59) 29.09 (8.79) 0.47 0.63
Grass height (cm) 16.81 (1.26) 21.11 (2.00) 12.18 (3.18) 3.27 0.04
Number of cut stems 3.74 (1.03) 5.72 (1.16) 2.09 (0.61) 1.11 0.33
Number of dung pellets 0.04 (0.04) 0.000 (0.00) 0.09 (0.09) 0.46 0.63
Number of lopped stems 0.03 (0.03) 0.11 (0.11) 0.00 (0.00) 0.69 0.50
Number of saplings 12.43 (0.82) 14.17 (2.69) 13.73 (2.12) 0.41 0.67
Shrub cover (%) 36.67 (2.63) 43.33 (5.89) 48.18 (8.72) 1.57 0.21
Shrub height (m) 2.78 (1.27) 3.08 (2.17) 4.66 (3.53) 0.17 0.84
Total number of trees 20.74 (2.00) 30.05 (9.26) 16.45 (1.73) 1.68 0.19
The result was logit transformed [ P = {exp(a +
BX. . . )/(1 + (exp(a + BX. . . )))}] to obtain absolute
habitat occupancy map. For obtaining the habitat
preferences the output was rescaled to a range of
1 to 10 and was subjected to an exponential trans-
formation to produce the most conservative esti-
mates possible. For the prey species only aforesaid
eight parameters were used. However while
assessing for the tiger, probability distribution of
sambar, wild boar and chital were used in addition
to aforesaid eight parameters. The inclusion of
probability distribution of the prey probability
was based on the assumption that the predator
will be more in the abundance of prey. Thus total
eleven parameters were used in case of tiger.
GIS integration
The regression coefficients obtained from the
analysis were then attributed to the respective lay-
ers. The estimated log-odds image was then logit
transformed to produce the intended probability
map. The probability maps were developed for
sambar, chital and wild boar. The transformed
Table 3 PCA of varioushabitat variables/fielddata
Variables Factor scores
PC I PC II PC III PC IV
Altitude 0.254 0.222 0.594 0.495
Canopy cover (%) 0.323 0.689 0.125 0.221
Canopy height (m) 0.440 0.494 0.299 0.197
Distance from road (km) 0.333 0.058 0.091 0.274
Distance from village (km) 0.295 0.229 0.068 0.245
Distance from water (m) 0.083 0.162 0.402 0.209
Forest density (%) 0.187 0.776 0.033 0.035
Grass cover (%) 0.154 0.523 0.217 0.497
Grass height (cm) 0.029 0.085 0.264 0.734
Number of cut stems 0.840 0.073 0.142 0.100
Number of lopped stems 0.089 0.417 0.096 0.276
Number of saplings 0.415 0.289 0.486 0.056
Shrub cover (%) 0.374 0.381 0.131 0.167
Shrub height (m) 0.857 0.081 0.242 0.021
Total number of trees 0.745 0.029 0.387 0.148
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Fig. 4 Distribution of prey and predator (tiger) species
output was then equal-interval sliced into leastsuitable, moderately suitable, suitable and highly
suitable categories. The probability maps devel-
oped for the prey species, independents and the
coefficient derived from the BLMR were used to
develop a habitat suitability model for the tiger.
The procedure for development of habitat suit-
ability model is given in Fig. 3. The final model
is expressed in following form:
Y= constant+ B1
forest cover density
+ B2
forest cover type
+ B3 (distance from road)+ B4 (altitude)+ B5
aspect
+ B6 (distribution of Sambar)+ B7 (distribution of wild boar) (1)
Habitat suitability index is represented as:
P logit (Y) = log
1
1+ exp (Y)
(2)
Results
Habitat mapping using satellite data
The satellite data was transformed into thematic
forest cover type/land use map using onscreen
Table 4 Coefficient forwild boar
Variable B SE Wald df Sig. Exp(B)
Forest cover density 0.006 6 1.000
Forest cover density (1) 45.459 5,856.681 0.000 1 0.994
Forest cover density (2) 5.514 5,845.191 0.000 1 1.000 0.004
Forest cover density (3) 108
.267
6,343.308 0.000 1 0.986 0.000Forest cover density (4) 55.887 7,791.098 0.000 1 0.994 0.000
Forest cover density (5) 66.278 5,898.771 0.000 1 0.991 0.000
Forest cover density (6) 28.470 6,383.587 0.000 1 0.996 0.000
Forest cover type 0.012 5 1.000
Forest cover type(1) 27.306 1,829.081 0.000 1 0.988
Forest cover type(2) 54.864 1,964.701 0.001 1 0.978 0.000
Forest cover type(3) 29.167 3,030.025 0.000 1 0.992 0.000
Forest cover type(4) 50.820 1,866.875 0.001 1 0.978 0.000
Forest cover type(5) 177.553 2,497.604 0.005 1 0.943
Slope 12.127 106.045 0.013 1 0.909 0.000
Distance from settlement 0.021 0.259 0.006 1 0.936 0.979
Distance from road 0.059 0.637 0.009 1 0.926 1.061
Distance from drainage 0.271 6.067 0.002 1 0.964 0.763
Altitude 0.305 3.867 0.006 1 0.937 0.737
Aspect 0.007 7 1.000
Aspect(1) 80.850 1,326.429 0.004 1 0.951 0.000
Aspect(2) 5.487 409.929 0.000 1 0.989 241.507
Aspect(3) 1.634 1,012.831 0.000 1 0.999 0.195
Aspect(4) 23.881 1,290.907 0.000 1 0.985 0.000
Aspect(5) 12.681 1,891.896 0.000 1 0.995 0.000
Aspect(6) 5.641 208.614 0.001 1 0.978 281.823
Aspect(7) 32.766 1,173.318 0.001 1 0.978
Constant 163.756 5,821.031 0.001 1 0.978
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Table 5 Coefficient forsambar
Variable Coeff SE Wald df Sig Exp(B)
Forest cover type 10.540 5 1.000
Forest cover type(1) 36.634 608.538 0.318 1 0.988 0.000
Forest cover type(2) 68.705 858.236 2.755 1 0.978 0.163
Forest cover type(3) 88.770 1,196.104 9.185 1 0.992 0.002
Forest cover type(4) 31.095 936.818 2.260 1 0.978 0.235
Forest cover type(5) 25.407 3,029.847 6.787 1 0.943 0.011
Slope 4.759 50.581 4.589 1 0.909 0.988Distance from settlement 0.020 0.236 3.459 1 0.936 0.987
Altitude 0.198 4.661 7.284 1 0.936 1.000
Aspect 5.199 7 1.000
Aspect(1) 49.976 1,297.448 0.465 1 0.636 0.559
Aspect(2) 23.049 1,786.280 2.019 1 0.495 0.167
Aspect(3) 14.256 1,276.691 0.122 1 0.155
Aspect(4) 115.942 1,788.541 3.241 1 0.727 0.087
Aspect(5) 11.689 2,520.722 0.035 1 0.072 0.000
Aspect(6) 32.369 1,536.205 2.082 1 0.853 0.178
Aspect(7) 66.204 2,225.313 3.041 1 0.149 0.008
Constant 52.879 2,077.847 2.914 1 0.081 0.002
visual interpretation technique. To maintain the
consistency the visual interpretation was carried
out at 1:50,000 scale using the visual interpreta-
tion key developed after extensive ground truth
collection. The satellite data of 2004 was classified
into 11 (seven forest cover type and four land use)
classes including viz., Sal forest, Sal mixed forest,
Sal bamboo mixed forest, mixed forest, bamboo
breaks, bamboo mixed, teak plantation, agricul-
ture, scrub, water body, and river bed. Forestcover was also classified into forest cover density.
Around 265.62 km2 area of forest come under
very dense (>70%) category which is 50.09% of
the geographic area. 142.17 km2 area of forest
are dense (4070%) covering 26.81% of the total
forest. Open forest (1040%) categories occupy
16.99 km2 of forest land (3.20%) followed by
105.48 km2 of forest area that comes under non
forest (19.79%). Agriculture is mainly confined
to plain areas, which occupy around 24.30 km2
(4.58%). It is the main occupation of the peopleliving within the boundaries of this sanctuary. The
Table 6 Coefficient forchital
Variable B S.E Wald df Sig Exp(B)
Forest cover type 34.292 10.540 5 0.061
Forest cover type(1) 19.353 1.057 0.318 1 0.573 0.000
Forest cover type(2) 1.755 2.309 2.755 1 0.097 0.173
Forest cover type(3) 6.997 1.054 9.185 1 0.002 0.001
Forest cover type(4) 1.584 2.591 2.260 1 0.133 0.205
Forest cover type(5) 6.750 0.000 6.787 1 0.009 0.001
Distance from settlement 0.001 0.001 4.589 1 0.032 0.999
Distance from road 0.003 0.011 3.459 1 0.063 0.997
Altitude 0.030 7.284 1 0.007 1.030
Aspect 5.199 1 0.636
Aspect(1) 0.823 1.207 0.465 1 0.495 0.439
Aspect(2) 1.682 1.084 2.019 1 0.155 0.186
Aspect(3) 11.937 34.184 0.122 1 0.727
Aspect(4) 2.344 1.302 3.241 1 0.072 0.096
Aspect(5) 11.550 62.130 0.035 1 0.853 0.000
Aspect(6) 1.779 1.233 2.082 1 0.149 0.169
Aspect(7) 4.908 2.814 3.041 0.081 0.007
Constant 7.114 4.167 2.914 1 0.088 0.001
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Table 7 Coefficient fortiger
Variable B SE Wald df Sig Exp
Forest cover density 0.587 6 0.997
Forest cover density(1) 24.717 776.068 0.001 1 0.975 0.000
Forest cover density(2) 23.232 776.069 0.001 1 0.976 0.000
Forest cover density(3) 7.570 879.965 0.000 1 0.993 0.001
Forest cover density(4) 26.023 1,041.095 0.001 1 0.980 0.000
Forest cover density(5) 15.437 736.169 0.000 1 0.983 0.000
Forest cover density(6) 19.852 877.515 0.001 1 0.982 0.000Forest cover type 3.988 5 0.551
Forest cover type(1) 37.141 268.301 0.019 1 0.890
Forest cover type(2) 14.999 245.659 0.004 1 0.951 3,267,009.5
Forest cover type(3) 21.029 245.687 0.007 1 0.932 1,357,600,579.142
Forest cover type(4) 13.437 245.655 0.003 1 0.956 684,932.978
Forest cover type(5) 23.985 999.490 0.001 1 0.981 26,090,067,477.167
Distance from road 0.004 0.0025 3.202 1 0.074 1.004
Altitude 0.047 0.021 5.134 1 0.023 0.954
Aspect 5.884 7 0.553
Aspect(1) 2.887 2.037 1.665 1 0.197 17.933
Aspect(2) 3.909 2.012 3.775 1 0.052 49.850
Aspect(3) 19.365 180.441 0.012 1 0.915 0.000Aspect(4) 3.443 1.867 3.400 1 0.065 31.270
Aspect(5) 19.076 196.732 0.009 1 0.923 192,560,342.116
Aspect(6) 6.901 3.974 3.016 1 0.082 993.650
Aspect(7) 8.854 4.358 4.128 1 0.042 7,001.200
Probability distribution 29.977 178.670 0.028 1 0.867 0.000
of sambar
Probability distribution 29.967 223.961 0.018 1 0.894 0.000
of wild boar
Constant 25.022 736.195 0.001 1 0.973 73,633,232,277.120
area distribution in different forest type, forestcover/density and land use is given in Table 1.
Analysis of habitat variables
While analyzing the distribution of the prey
predator species based collected field data, it was
found that the sambar, chital and tiger are the
widely distributed species in the entire sanctuary
area. However, wild boar is only concentrated in
the central core part of the sanctuary. Chital is
distributed in entire area with good representationin the fringes, sambar is more towards the north-
ern part of the sanctuary and tiger moves in the
entire sanctuary freely with limited presence inextreme east, west and south part of the sanctuary.
Invariably all the species prefer to be in core area
of the sanctuary except sambar. The processed
field data (habitat variable for prey, predator and
random points) is given in Table 2. There were
no significant differences in the habitat variables
recorded for tiger, random and prey plots. The
variables which had higher value in tiger plots
as compared to random plots are canopy cover,
canopy height, forest, shrub cover, shrub height
and number of dung pellets whereas the variablewhich has higher values in prey plots as compared
to random plots is distance from road. The grass
Table 8 Logisticregression coefficientaccuracy for wild boar
Wild boar Accuracy
Predicted = 0 Predicted = 1
Observed = 0 75 1 98.7
Observed = 1 3 6 66.7
R2 = 0.86 95.3
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Table 9 Logisticregression coefficientaccuracy for sambar
Sambar Accuracy
Predicted = 0 Predicted = 1
Observed = 0 84 0 100.0
Observed = 1 1 11 91.7
R2 = 0.97 99.0
height has shown significant difference however;
assessing it through satellite data has not been
attempted. The analysis of data highlights im-
portance of other environmental parameters viz.,
slope, aspect and elevation along with forest cover
type and forest cover density.
The principle components 1, 2, 3, and 4
explained 52% of the variation as shown in
Table 3. The PC1 explained 18.38% of the vari-
ation, PC2 13.83%, PC3 10.21% and PC4 9.69%.
The PC1 had higher positive (+ve) loading onshrub height, number of cut stems, total numbers
of trees while the higher negative values were on
canopy height, distance from the road and num-
ber of samplings. Factor one explained trees and
taller shrubs. The PC2 had higher positive loading
on forest cover density, canopy cover and shrub
cover. While higher negative values of grass/herb
cover, number of lopped stems, and distance from
water. The PC3 had higher positive loading on
number of samplings, number of trees and canopy
height while higher negative values were for slope,altitude and distance from water. The PC4 had
higher positive value on grass height, herb/grass
cover and altitude while higher negative values
are for distance from road, distance from village
and distance from water. The PC1 and PC2 were
plotted to get the scatter plot of the distribution
of animals. The Fig. 4 shows the scatter plot. It is
evident form the plot that most of the recorded lo-
cations of tiger are in and around the distribution
of prey species.
BMLR was run individually for each preyspecies and then for the Tiger species. The vari-
ables along with the coefficients for wild boar,
sambar, chital and tiger are given in Tables 4,
5, 6 and 7 respectively. The accuracy of derived
coefficients for wild boar, sambar, chital and tiger
are given in Tables 8, 9, 10 and 11 respectively.
The prey species showed a wide distribution in the
entire area with a good degree of agreement. The
accuracy for logistic coefficients for wild boar is
95.3 (R2 = 0.86), sambar 99 ( R2 = 0.97), chital 81
(R2 = 0.65) and tiger 92.9 ( R2 = 0.87).
Habitat suitability model
The regression coefficients obtained from the
analysis were then attributed to the respective
layers. The estimated log-odds image was then
logit transformed to produce the intended prob-
ability map. The probability maps were devel-
oped for sambar, chital and wild boar. Sambar
has high probability of being found in the entire
area. However, chital and wild boar have selected
area of occurrence. The probability distribution ofwild boar is defined by forest cover density, forest
cover type, slope, aspect, altitude and distance
from road, settlement and drainage; for sambar
forest cover type, slope, aspect, altitude and dis-
tance from settlements; and for chital forest cover
type, aspect, altitude, and distance from road and
settlement.
Habitat suitability map of Tiger in Achanakmar
Wildlife Sanctuary is given in Fig. 5. As the
log-transform squashes the lower values and
exaggerates higher values and the fact that theclassification accuracies had been calculated at
Table 10 Logisticregression coefficientaccuracy for chital
Chital Accuracy
Predicted = 0 Predicted = 1
Observed = 0 44 8 84.6
Observed = 1 9 29 76.3
R2 = 0.65 81.1
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Table 11 Logisticregression coefficientaccuracy for tiger
Tiger Accuracy
Predicted = 0 Predicted = 1
Observed = 0 55 3 94.8
Observed = 1 3 24 88.9
R2 = 0.87 92.9
cutoff of 0.5, the output map was sliced to least-
suitable at values lower than 0.5 and suitable at
values higher than that. Among the total 530 km2
area of the sanctuary, 24.32 km2 is found highly
suitable. Most of the suitable area falls under the
core zone of the sanctuary. This is approximately
4.59% of sanctuary area. Around 180.33 km2 area
found suitable representing 34.39% of the total
sanctuary area. Highly suitable and suitable area
together account for around 206.65 km2, which is
39.98% of sanctuary. Around 282 km2
was foundmoderately suitable for tiger which covers the
buffer zone; this is 53.35% of the total sanctuary
area and approximately 7.68% area of sanctuary is
least suitable for tiger habitat. Fringes of these ar-
eas have scattered villages and agricultural fields.
The suitable sites for the tiger are dependent
on forest cover density, forest cover type, aspect
and distance from road. Among the prey species
distribution of sambar and wild boar highly affects
the distribution and suitable sites for tiger habitat.
Conclusion
The habitat model reflects what the geostatisti-
cal tools assess with regard to the environmental
Fig. 5 Habitat suitability map for tiger
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566 Environ Monit Assess (2009) 155:555567
conditions suitable for Tiger in AMWLS. The
result relies on the validity of two main assump-
tions. First, there is a assumption that the spatial
maps derived from the topographic maps are of
sufficient accuracy and scale to both describe the
size and to build the habitat model. Secondly,
the pressure from the anthropogenic sources isnot more that the presence in the AMWLS and
extraction of resources in their day-to-day life.
Evaluating the results with an independent set of
field observations of tiger distribution can validate
the HSI model and map, and also some of the
underlying assumptions.
This research has presented and evaluated a
quantitative field and GIS based technique for
eliciting knowledge about habitat suitability of
tiger in Achankmar Wildlife Sanctuary. The aim
was to identify suitable habitat for tiger to builda habitat map and assist with the management of
the species in the wildlife sanctuary. Moreover,
the reclassified habitat suitability map provides
more truthful and relevant predictions. The re-
search also sought to answer questions of habitat
suitability modeling with detailed field studies and
monitoring of the species to understand its habitat
preferences. The methodology used in this paper
could be improved by consulting experts and by
improving the GIS layers to reduce error in locat-
ing tiger sites. The GIS-based approach was im-portant as it provides experts with spatial context
in a repeatable, objective and structured frame-
work. It also simplifies data management, analy-
sis and construction of spatially explicit habitat
maps. The statistical analysis of quantitative prey
sighting data helped to identify probable area for
tiger sighting in AMWLS, and this was consistent
with the outcomes of the qualitative (sighting and
field data) assessment. All ecological and ground
level specialties are found in the sanctuary area for
protected wild animals.
Acknowledgements The authors are thankful toChattisgarh State Forest Department and Officials ofAchanakmar Wildlife Sanctuary for support in the fieldwork. The work was carried out under DOS-DBT projectentitled Biodiversity Characterization at Landscape Level,which is duly acknowledged. The authors are thankful tothe anonymous reviewers for constructive suggestions andhelping in reshaping the manuscript.
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