Modelling the Habitat Selection of the Indian fox (Vulpes bengalensis) in and around the Great Indian Bustard

Wildlife Sanctuary, Nannaj, Maharashtra

A Thesis Submitted to

The Manipal Academy of Higher Education (Deemed University)

in partial fulfilment for the degree of

Master of Science

Wildlife Biology & Conservation

By Aditya Bhaskaran

Centre for Wildlife Studies Post-Graduate Programme in Wildlife Biology & Conservation

Centre for Wildlife Studies and

National Centre for Biological Sciences UAS-GKVK Campus, Bellary Road

Bangalore – 560 065

Contents

Acknowledgement:................................................................................................................i

Executive Summary:............................................................................................................ii

Introduction: ........................................................................................................................1

Previous Studies:................................................................................................................3

Present Study: ....................................................................................................................4

Study Area:...........................................................................................................................5

Intensive Study Area:.........................................................................................................6

Methods: ...............................................................................................................................7

Study Design:.....................................................................................................................7

Data Collection: .................................................................................................................9

Spatial Modeling:.............................................................................................................11

Results:................................................................................................................................14

Parameter Estimation Model (PEM):...............................................................................15

Habitat Selection Model (HSM): .....................................................................................16

Discussion: ..........................................................................................................................17

Conclusion: .........................................................................................................................21

References: .........................................................................................................................22

Acknowledgement

Firstly, I would like to thank my guides Dr. Shomita Mukherjee, Dr. Mohammed Irfan-Ullah

and Mr. Abi Tamim Vanak for all their support. Mr. Aditya Singh, my ‘unofficial guide’

coded and the entire modeling on Python© and helped as much as my ‘official guides’. Mr.

Abi Tamim also lent me the latest satellite imagery which was vital to the analysis. They

were extremely supportive of this work and gave it their best in terms of helping formulate,

execute and write up this work. I do not think a mere written acknowledgement like this

could do justice to thanking any of my guides.

The Maharashtra State Forest Department was very helpful. I would like to personally thank

the Principal Chief Conservator of Forests (Wildlife) Shri B. Majumdar, the Conservator of

Forests (Wildlife) Shri D. R. Parihar for being extremely supportive of this study and

extending as much help as possible.

Ashoka Trust for Research in Ecology and the Environment has been extremely helpful by

allowing me to use their GIS lab for analyzing and writing this work without which this

would not have been possible.

I would also like to thank the Centre for Wildlife Studies and the faculty of the Post Graduate

Programme for Wildlife and Conservation Biology for their support and funding.

Mr. Abhijeet Kulkarni and Bharath Chedda were very helpful with logistics and getting work

done in ‘Chaotic’ Sholapur. I would like to thank my field assistants Salim and Sarang who

aided in data collection on field. A warm thanks to my classmates Samira and Nibedita for all

the good times.

I would like to thank Terenia for always being there.

i

Executive Summary

The Indian fox (Vulpes bengalensis) is one of the three species of foxes found in India. It is

widely distributed in peninsular India, prefers semiarid to flat and undulating terrain. The

habitat of the Indian fox is rapidly degrading due to livestock grazing, agriculture and other

developmental activities. This threatens a unique assemblage of flora and fauna such as the

endangered Indian Bustard (Ardeotis nigriceps), Blackbuck (Antelope cervicapra) and Indian

wolf (Canis lupus). There is a paucity of information on the ecology and status of this

species. The Indian fox is protected under Schedule II of the Indian Wildlife (Protection) Act,

1972. The species is ‘Data Deficient’ and is rated “Least Concern” by the IUCN.

This study was conducted in the Great Indian Bustard Wildlife Sanctuary, Nannaj, Sholapur,

Maharashtra between January 2006 to May 2006. The study aimed at mapping the modeled

distribution of the Indian fox and looking at the factors that influence its habitat selection. A

Markov Chain Monte Carlo Simulation (MCMC) based Generalized Linear Model (GLM)

was used within a Bayesian framework. This was implemented using an Auto-logistic term to

account for Spatial Autocorrelation. Data was collected on parameters like disturbance,

resource base and habitat characteristics that was quantified using non-invasive techniques.

The Parameter Estimation Model (PEM) and Habitat Selection Model (HSM) indicate high

probability of occurrence of the Indian fox in grasslands and grassland-fallow complexes.

The Indian fox may be able to tolerate a certain degree of habitat disturbance, but are

sensitive to high disturbance. The output of the Habitat Selection Model has been used to

prepare a map of the derived probabilities of habitat selection of the Indian fox. Results from

such models could enable improved predictions of species distributions, emerging risks and

aid in implementation of efficient conservation strategies. This work has been written as a

manuscript and formatted keeping in mind the guidelines of the Journal of Zoology.

ii

Introduction

The Indian fox is one of three species of foxes that occur in India (Pocock 1936). It is a small

fox with body weight ranging between 2.7 and 3.2 kg (Gompper and Vanak 2006). It has

grayish coat and typical features such as an elongated muzzle, long pointed ears, bushy tail

with a black tip, pale ventral pelage with brownish to rufous legs that characterizes it of the

genus Vulpes (Menon 2003, Prater 1980, Vanak and Gompper 2006, Johnsingh and Jhala

2004).

The Indian fox is crepuscular and nocturnal in habit, although cool day time temperatures

could induce some activity (Johnsingh and Jhala 2004, Gompper and Vanak 2006). Analyses

of scat of the Indian fox from Rollapadu Wildlife Sanctuary and Kutch have suggested that

its diet consists of rodents, grasshoppers, termites, beetles, scorpions, centipedes, monitor

lizard, ground nesting birds, fruit and seed of Zizyphus mauritiana, Citrullus vulgaris,

Azadirachta indica, Mangifera indica, Syzigium cumini, Melia azedarachta, and Ficus

bengalensis (Manakadan and Rahmani 2000, Johnsingh and Jhala 2004, Vanak in review,

Vanak 2003, Prater 1980, Gompper and Vanak 2006). Of these invertebrates, grasshoppers

and termites tended to form a major part of their diet (74%) followed by rodent remains

(61%) (Vanak in review). Diet analysis from Kutch showed a similar trend of scat containing

arthropods (77%, 84%), mammals (46%, 53%), fruits (54%, 23%), reptiles (44%, 26%) and

birds (6%, 2%) in grassland and scrub habitats (Home 2005).

The Indian fox is one of the most common small carnivores in grasslands (Vanak 2005). It is

known to have a wide distribution in India covering an extensive range from the foothills of

the Himalayas and some of the Terai landscape to the southern tip of the Indian peninsula;

and from Sindh province of Pakistan to southeastern Bangladesh across the Indian

subcontinent (Prater 1980; Johnsingh 1978, Johnsingh and Jhala 2004, Gompper and Vanak

1

2006). The species is abundant in bio-geographic zones 3 (Desert), 4 (Semi-arid) and 6

(Deccan peninsula) of India which are best described as low rainfall areas with scrub, thorn

or dry deciduous forests and short grasslands (Rodgers, Panwar and Mathur 2002). It has also

been speculated that the range of the Indian fox is currently extending into the Sunderbans

with the decline of mangrove forests in Bangladesh. It is perceived to be widely distributed in

semi-arid grasslands but rarely abundant (Vanak 2003, 2005, Gompper and Vanak 2006).

The species prefers flat and undulating terrain where it is easy to dig dens and hunt, thus

being confined to open semi-arid, scrub and grassland habitats (Johnsingh and Jhala 2004,

Vanak 2005). It avoids dense wooded vegetation, thorn and dry forests (Johnsingh and Jhala

2004, Vanak 2005). The Indian fox is perceived to be widespread but is not abundant

throughout its range (Vanak 2005). Previous literature has suggested that though locally

abundant in some areas the Indian fox occurs at low densities throughout their range (0.04--

0.06 to 1.63 per km2) (Manakadan and Rahmani 2002, Vanak 2005). This may be due to a

variety of anthropogenic factors affecting their distribution. It has also been theorized that

grazing and grazing related activity may be a cause of low densities of foxes in unprotected

grasslands (Manakadan and Rahmani 2000). The species also faces the threat of poaching

from nomadic communities (Manakadan and Rahmani 2000). This is probably one of the

biggest threats due to the belief that fox flesh has medicinal properties (Johnsingh 1978). It is

rated as a species of “Least Concern” by the IUCN (Johnsingh and Jhala 2004) due to

insufficient data regarding accurate distribution status and population trends. It is legally

protected under Schedule II of the Indian Wildlife (Protection) Act, 1972 (Anon. 2002).

Although it is thought that the Indian fox is tolerant of human habitation and modification of

their habitats (Johnsingh and Jhala 2004), little research has been undertaken to ascertain the

degree of disturbance and interference that the species will actually tolerate. Degraded

2

pastures/grazing land, scrub and thorn forests are classified as ‘waste land’, which has

resulted in land being encroached upon for agriculture or taken up for social afforestation

(Anon. 2006). The other pressures on these ecosystems are from agriculture, soil extraction,

stone quarrying and a host of other ‘developmental’ activities.

Previous Studies:

The Indian fox has not been the focus of active research in the recent past. Johnsingh’s

(1978) study on the Indian fox, concentrated on ecology and behavior of the animal; and

attempted to assess threats that the Indian fox faced. Manakadan and Rahmani (2000)

conducted a preliminary assessment of the population of the Indian fox in and around the

Rollapadu Wildlife Sanctuary, Andhra Pradesh. The study also contributed insights into the

diet and behavior of the species. The most recent work on the Indian fox has been the

mapping of the distribution of the Indian fox in Karnataka and Andhra Pradesh (Vanak

2005). This also culminated in the identification of scat (Vanak and Mukherjee, in review)

and dietary analysis of the Indian fox in Rollapadu and Ranebennur Wildlife Sanctuaries in

Andhra Pradesh and Karnataka (Vanak, in review). Further work into gap analysis of the

Indian fox using ecological niche modeling has also been done (Vanak, Irfan Ullah and

Peterson in review.) The results confirmed that just over 1% of potential fox habitat is

covered by protected areas. Studies have also focused on non-invasive techniques to detect

the Indian fox (Vanak and Gompper, in press). This study attempted to test the efficacy of

camera traps and baited track stations to record presence. The resource utilization across

seasons (summer and winter) in grassland and scrub habitats has also been studied in Kutch,

Gujarat (Home, 2005). A thorough review of all the available information on the Indian fox

was done by Gompper and Vanak (2006).

3

Present Study:

Previous studies mainly focused on the ecology of the Indian fox. Not much work however

has been done to study spatial correlates of its habitat selection. This study therefore aims at:

1. Modeling and mapping habitat selection of the Indian fox across different landuses.

2. Assessing relative importance of different environmental surrogates such as

disturbance, resource base and habitat characteristics.

Monitoring carnivores is not easy as they are often cryptic and nocturnal (Wilson and

Delahay 2001). With carnivore species such as the Indian fox, methods of estimating relative

abundance work well as they can be cost effective, and demand relatively less effort and

logistics than methods such as radio tracking to obtain absolute population estimates (Wilson

and Delahay 2001). However while designing the sampling for these species, care must be

taken to strongly consider the ecology of the species, its distribution in space and time,

movement, and relationships with landscape and habitat, in order that the results can be

reliably extrapolated (Wilson and Delahay 2001). Field signs of carnivore species often

provide the only way to monitor them. Foot prints, den sites and droppings are some of the

only indirect methods by which many carnivore species can be identified (Wilson and

Delahay 2001). These signs can then be used to provide relative indices of abundance as the

frequency of these signs often reflects some measure of the number of animals present

(Wilson and Delahay 2001). This is usually used to compare abundance or monitor trends at a

particular location or locations over time (Sutherland 1996). Track plots have been previously

used in India to monitor small and medium sized carnivores such as the Jungle cat, Common

palm civet (Paradoxurus jerdoni) and the Golden jackal (Mukherjee, 1998; Mudappa 2001).

4

Generalised Linear Models are used to analyze species presence with environmental

surrogates to develop a relationship that can be extended to areas beyond the sampled

regions. Auto-logistic models are generally used to model relationships between binary

response variables and potential explanatory variables, while incorporating a term to account

for spatial auto-correlation (Zhu, Huang, Hsin-Cheng and Jungpin 2005). Auto-logistic

regression models consist of a linear regression of a response variable on an explanatory

variable and an auto regression on the responses at neighbouring location on the lattice (Zhu

et al. 2005).

These models are used when the outcome needs to be predicted at an un-sampled site given

the data from a sampled site (Zhu et al. 2005). Auto-logistic modelling also allows the use of

any other data such as GPS locations for other covariates being measured (Segurado and

Arau´jo 2004) that in turn provide opportunities to use every kind of data that may be

available.

Study Area:

The Great Indian Bustard Wildlife Sanctuary is spread over a distance of 8,496 Km2 and

encompasses the districts of Sholapur and Ahmednagar in western Maharashtra. Major towns

and settlements such as Sholapur and Ahmednagar are also included within the Sanctuary.

Very small patches of it are actually under protection. This study was conducted in two small

patches of protected grassland near the villages of Nannaj and Wadala approximately 20 km

and 25 km north of Sholapur.

The region lies in Biogeographic Zone 6 (Deccan peninsula), Province 6B (Chhota Nagpur)

in India (Rodgers et al 2002). It is part of the Deccan Trap plateau formed approximately

between 60 and 68 million years ago (Sheth, Pande and Bhutani 2001). The plateau is at an

5

average elevation of 500 m and is flanked by the Western Ghats in the west and Eastern

Ghats to the east. The soil type in this area is predominantly laterite and black volcanic,

basaltic soils. The terrain is undulating with flat topped hills and lateral slopes.

The temperatures in summer rise to 47º C and rarely drop below 10º C in winter. The average

rainfall in this region is between 608 – 635 mm, with humidity rarely exceeding 60 %.

Vegetation of the region is classified as Type 6 (Tropical Thorn Forest), Subgroup 6A/C1

(Southern Thorn Forest) (Champion and Seth 1968). Associated with this region are several

Acacia spp., thorny Mimosae spp. and Zizyphus spp. Canopy height normally ranges between

6 m to 9 m. The lower storey vegetation is xerophytic with a thin grass growth and a lot of

bare soil (Champion and Seth 1968). Some of the tree species found in this area are Acacia

catechu, A. leucophloea, Ailanthus excelsa, Azadirachta indica, Dolichandrom falcate,

Gymnosporia spinosa, Ixora arborea, Carissa carandas, C. spinarum and Euphorbia spp.

(Champion and Seth 1968). Some of the major grasses that dominate this area are Aristida

spp., Heterpogon spp., Chrysopogon spp., Cymbopogon spp. and Dicanthium spp.

Intensive Study Area:

The intensive study area is centered around a small portion of protected grasslands and

plantations within the Great Indian Bustard Wildlife Sanctuary at Nannaj (North 17° 49' 83",

East 75° 52' 05.99"). The bounding coordinates of the study area are North 17° 43' 23.16",

East 75° 45' 28.8", and North 17° 53' 43.8", East 75° 55' 9.12". To the south east of the

village of Nannaj lies the protected part of the wildlife sanctuary. Human activity in the

protected area is minimal and grazing is prohibited. Flanking the sanctuary on the northwest

and the southeast are two protected plantations of Azadirachta indica. Towards the south

west adjoining the state highway leading to Sholapur are five more protected patches of

6

grassland and plantations. The plantations here consist of a mixture of Glyceridia spp. and A.

indica. The rest of the plots are all grasslands. Another two protected plots of land lie

approximately 5 km west of Nannaj near the village of Wadala.

The study area consists of a matrix of landuse types that are protected grassland, grazing

land, ploughed land, cultivated agricultural land, fallow land and plantations. The agricultural

land and vineyards typically radiate outward from the settlements, gradually taper to fallow

lands and then merge with the grassland. Of this the fallow land and grassland form an

intricate complex, not discernable in some areas. This complex stretches right across the

study area from the south to the north. This swath of grassland and fallow complex is

crescent shaped with maximum width of approximately 4 km.

Methods:

Study Design:

The Intensive Study area was superimposed with 1x1 km grids using ArcView3.3© GIS

software. The entire grid network was oriented north. The grid size was kept consistent with

the approximate home range of the Indian fox as described by Johnsingh (1978). This was

also to minimize the effects of spatial autocorrelation (Wilson and Delahay 2001). The

centroid of each grid was used as the grid identifier out of which 29 random grids (11%) were

selected for sampling, considering minimum sampling criteria and total time and effort

required to collect data (January 20, 2006 to May 2, 2006) (Fig. 1).

7

Fig. 1: Map of study area showing study design and grid arrangement

Track plots and transects were designed, placed on the selected grids and located for ground

sampling using tools like ArcView©, MapSource© and handheld GPS unit (Garmin

GPSMap76©). Plantations occupied a considerable part of the study area and were

additionally sampled. Each sampled grid contained 8 track plots spaced 200 m from each

other, and were located on a ‘square transect’ (1600 m in length) in the centre of the grid.

Termite mounds and rodents burrows were sampled within each grid using two parallel ‘line

transects’ (300 m apart) of 1 km each oriented north-south. In addition, two ‘strip transects’

of width 2m and length 200 m were used to record the frequency of blackbuck, cattle and

goat dung in each grid. The line transects and strip transects were monitored on the final day

of sampling in each grid (Fig. 2).

8

Fig. 2: Schematic diagram showing the layout of transects and track plots within a grid.

Grid (1km x 1km)

Track plot (each spaced 200m apart)

Strip transect (2m x 200m)

Line transect (1 km)

Vegetation Quadrat

Circular plots of half meter diameter were cleared of vegetation and a layer of fine sand was

sieved over them to record the foot print of the species that visits the site. Each track plot was

baited with canid lure (Kishel’s Canine Supreme) and was checked every morning between 8

am and 11 am. The species that visited the track plot were identified by their foot prints after

which the track plots were reset for the next day’s recording. Each grid was monitored for a

period of five days to provide 40 replicates per grid. We used track plots, field signs such as

scat, dung and visible structures such as dens to monitor the presence of the Indian fox and

other species that concerned the study.

Data Collection:

Data was collected on following four aspects to model habitat suitability of the Indian fox:

9

Presence of focal species:

Track plots were used to gather visitations of focal species in each grid. On the last day of

monitoring each grid, fox scat locations were collected along the square transect. The two

transects of 1 km each were also used to search for fox dens.

Disturbance:

We used a combination of direct counts (as visibility in grasslands is high), GPS locations,

track plots and dung counts along strip transects to document the presence of dogs, livestock

and humans to record disturbance.

Resource base:

Data on visitation of rodents, hare and some species of ground dwelling birds were collected

from the track plots. GPS locations of termite mounds and rodent burrows in each sampled

grid was documented on the two line transects to estimate abundance. Sweep net counts for

grasshoppers were done at all track plot locations in the grid to estimate grasshopper density.

Habitat Characteristics:

Vegetation was sampled in a 5 m x 5 m quadrat around each track plot (Fig. 2). Grass height

(in inches) was recorded at the four corners of the quadrat and at the track plot using the

Robel Pole Technique (Robel et al 1970). Canopy cover (using standard mirror densitometer)

and substrate (classified as Rock – 1, Soil – 0 and rocky soil as – 0.5) were also recorded at

the same points. All tree and shrub species above a height of one foot were documented

within each quadrat. Grass height, canopy cover and substrate readings were averaged for the

track plot to create different indices.

10

Spatial Modelling:

Data preparation:

For the purpose of habitat selection modelling various spatial layers describing

landuse/landcover, NDVI, features of human induced disturbance (roads, dirt trails, canals,

etc.) were generated using IDRISI© Andes. Parameters describing disturbance were derived

using a standard distance operator and aggregated within each grid. All parameters in the data

sets thus produced were checked for normality using p-p plots in SPSS© Statistical Software.

Parameters falling outside the normal distributions were tested for normality using arcsine,

square root and natural log transformations and suitably transformed.

Multispectral (LISS-IV), high resolution (5.8m) satellite imagery from the Indian Remote

Sensing Satellite (IRS-P6) was used to generate a landuse/landcover map of the study area

(Fig. 3). Patterns and processes at landscape level have been found to be necessary

prerequisites for studying an organism’s relationship with its habitats (O'Neill et al. 1988,

Turner 1990, Turner and Gardner 1991, Baker and Cai 1992, McGarigal and Marks 1995).

FRAGSTATS 3.3 was used to quantify Patch Density (as a measure of fragmentation) using

a 1km diameter moving window analysis (corresponding to the grid size). This class metric

quantifies the proportional abundance of each patch type in the landscape. This is a relative

measure and important to many ecological applications (FRAGSTATS manual version 3.3).

Pooled data on the percentage of each landcover class at the grid level and fox visitations was

integrated with spatial layers for the habitat selection modelling.

11

Model Description:

The first model, termed as ‘Parameter Estimation Model’ (PEM), was used to test response of

the Indian fox to varying parameters using data on species visitations, sightings, rodent and

termite mound transects, disturbance and habitat characteristics collected at track plot level.

The second model, termed as ‘Habitat Selection Model’ (HSM), was used to predict habitat

selection of the Indian fox across the study area at grid level. Total number of fox detections

pooled at grid level (40 replicates per grid) were intersected with other spatial layers to be

used in HSM.

Fig. 3: Landcover derived from IRS-P6 (LISS-IV) Satellite Data (22 March 2006).

12

To make models robust and to minimize spatial autocorrelation we implemented spatial auto-

logistic term for the parameterizations. The PEM included the spatial auto-logistic term as a

distance weighted point process, whereas the HSM incorporated a lattice design using a

second order neighbourhood structure. Using ‘n’ as the number of replicates and ‘x’ as the

number of detections we used a binomial model specification to calculate the probability of

landcover used by foxes.

a.

Where, − r is the number of detections − n is the replicates − p is the probability

b. )...(it log 110 pnXXp ∑++⋅+−< ββ

Where, − logit is the link function = exp[x]/(1+exp[x]) − 0β is the constant − 1β is the parameter coefficient − iX is the number of parameters

We used a Bayesian Markov Chain Monte-Carlo simulation approach to estimate parameters.

We used non-informative priors with uniform distributions for all parameters, in the absence

of any prior information for Indian fox from the Indian subcontinent. We used distance-

weighted averages to implement the spatial auto-logistic term at track plot level data for

implementing the PEM. The grid size was used as the effective range to account for the

spatial autocorrelation. The distance weights and averages of the interim probabilities were

used as the spatial auto-logistic term as the micro-level data constituted point locations,

livestock counts and locations, blackbuck counts and locations, resource transects (frequency

13

of termite mounds and rodent burrows) and habitat characteristics data (canopy cover, tree

and shrub diversity).

Pooled grid data at a second-order (queen connected lattice) neighbourhood structure was

specified to simulate spatial autocorrelations for the HSM. Simulations for both (PEM and

HSM) models were iterated 10,000 times with a 1,500 burn-in period, as parameter

convergence was observed at 1,000 iteration onwards. The burn-in period was chosen by

studying the trace plots, parameter mean (weights) and variance. We constructed a total of 20

models each for PEM and HSM, trying different combinations of the parameters to achieve

acceptable AIC scores. The parameters found not contributing significantly were dropped at

each run to enhance model performance. Care was taken to always include the parameters

that covered different aspects of species’ ecology, such as disturbance, resource base and

habitat characteristics. The model with the lowest AIC score as compared to the universal

model and ecologically meaningful parameters was selected as the best model (Burnham and

Anderson, 2002). Derived probabilities of fox habitat selection from the best HSM were used

to prepare a habitat selection map for the entire study area.

Results:

Results of visitation to track plot by fox are expressed as percentages and the grid number

286 (located within the Sanctuary) showed a visitation of 45% which was the maximum

among all sampled grids. The lowest visitation (5%) were recorded on grids 182, 324, and

446 (Table 1).

14

Table 1: Visitations by fox to the track plots in different grids.

Grid Percent Visitations Grid Percent Visitations 324 5 257 20182 5 199 20446 5 360 20PP6 10 409 20300 10 267 22.5PP5 12.5 206 22.5222 12.5 PA2 22.5

2831 12.5 366 25166 12.5 UPP3 27.5346 12.5 PP1 32.5PP4 15 238 32.5188 15 306 35226 17.5 449 35PP2 17.5 286 45PA 17.5

The field data collected was specifically used to build models (PEM and HSM) and to apply

the derived understanding to an extended region to predict fox habitat.

Parameter Estimation Model (PEM):

The universal PEM (using all input parameters) had an AIC of 144. The best model selected

out of 20 model runs, achieved an AIC score of 139 and contained 8 input parameter layers as

given below:

Table 2: Parameter weights and variances from Parameter Estimation Model (PEM)

Parameter Weight Variance FOX - Probability of fox occurrence in neighbouring grids 0.90 0.91B_BUCK - Index of blackbuck visitation, herd locations and counts 0.12 0.01DIST- Index of visitation, herd location and counts on Cattle, Goats, Dogs and Humans -0.01 0.00HARE - Index of hare visitations -0.31 0.06RODENT - Index of rodent visitations and burrow locations 0.01 0.00GR_HT - Index of grass height 0.03 0.00CANOPY - Index of canopy cover -0.03 0.00SIMP – Simpson’s diversity for tree and shrub species -0.32 0.19CONST -1.77 0.24Number of Parameters 8 8Deviance 119.12 17.90

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Habitat Selection Model (HSM):

The Universal HSM (using all input parameters) had an AIC of 126. The best model selected

out of 20 model runs, achieved an AIC score of 123 and contained 7 input parameter layers as

given below:

Table 3: Parameter weights and variances from Habitat Selection Model (HSM) Parameter Weight Variance

FOX - Probability of fox occurrence in neighbouring grids 0.32 0.01AGRI - Index of the percentage of agricultural land 0.02 0.00PLOUGH - Index of the percentage of ploughed land -0.01 0.00PLANT - Index of the percentage of plantations -0.01 0.00DIST_SET - Index of distance to settlements 0.44 0.03NDVI - Normalized Difference Vegetation Index -0.29 0.03GR_FA - Index of percentage of combined grassland and fallow landcover 0.17 0.03CONST -1.02 1.54Number of Parameters 7 7Deviance 106.81 12.72

HSM results were mapped using ArcView© GIS application. The composed map (Fig. 4)

shows derived probabilities for habitat selection and fox detections in respective grids

overlaid with the features of human settlements, roads, trails and canals. The figure shows

derived probabilities for the extended un-sampled region within the study area.

16

Fig. 4: Map of HSM derived output of Indian fox distribution.

Discussion:

The PEM was used to study the relationship of fox’s presence against environmental

parameters and to determine best descriptor parameters, and also to look for reasonable

ecological justification for the demonstrated relationship that would quantitatively expound

upon the ecology of the species. The results from the best model indicates that fox’s presence

is negatively correlated with both the Simpson’s diversity index (SIMP), indicating that the

species does not prefer landscapes dominated by trees and shrub. Natural history observations

have shown Indian fox prefer open grasslands as compared to plantations and excessively

17

wooded areas (Manakadan and Rahmani 2000, Johnsingh and Jhala 2004, Gompper and

Vanak 2006).

The other most accountable resource parameter was index of hare visitation (HARE) that also

shows negative relationship. Other studies have not shown hare to be an important prey for

other co-predators like jungle cats and jackals (Mukherjee et al 2004). Hares in this region

could probably be responding to other factors.

Even though the remaining 6 parameters do not show very significant relationship, they are

however, able to pick subtle responses that can be explained with existing ecological

understanding of the species. Fox shows negative relationship with disturbance and canopy

cover, indicating its sensitivity to disturbance and preference for open areas. It also shows

positive relationship with grass height and rodents again indicating its preference for grass

dominated areas. Foxes are believed to track rodent distributions and even keep them under

check (Johnsingh and Jhala 2004) and rodents form a substantial part of the Indian fox’s diet

(Vanak in review, Home 2005). Grasshoppers could not be included in the model as there

were no captures during the sweep net sampling conducted during the period of this study, as

it was the dry season. The parameter blackbuck is included in the models solely as they are

abundant in the area and co-exist with foxes and other wild grassland fauna.

The HSM was implemented with the main objective of spatially depicting the habitat

selection of the Indian fox throughout the study area and to extend it beyond to the un-

sampled sites given the data from a sampled site. The best model used 7 parameters, out of

which the most significant relation is demonstrated by the parameter distance to settlement

(DIST_SET) again stating the point that foxes are sensitive to human disturbance (Fig. 4).

NDVI shows clear negative relationship with fox presence indicating that they may not prefer

areas under dense vegetation types (i.e. plantations etc.) this is also supported by a positive

18

relationship with grassland-fallow complexes. The reason for using a parameter that

combined both grassland and fallow agricultural land types was that in most areas, old fallow

is indistinguishable from unprotected grassland.

The remaining 3 parameters (plantations, ploughed land and agriculture) show weak

relationships with fox presence. While plantations and ploughed land show negative

relationship the agriculture shows slight positive relationship. This may again indicate that

foxes do not prefer plantations. Agricultural land probably acts as buffer between the

disturbed areas like settlements and the grassland-fallow complex, which is clearly seen by

the predicted probabilities of fox presence in the HSM map (Fig. 4).

The results imply that there is a high probability of occurrence of the Indian fox in grasslands

and grassland-fallow complexes. Most of the fallow land is normally left uncultivated for

months or years and slowly gets transformed into grass and scrub vegetation. Despite being

affected by grazing reflected in grass height, this land type may face relatively lesser

perturbation as compared to ploughed agricultural land (Plates 1 & 2).

19

Plates 1, 2 and 3: The Indian fox, Black buck and Livestock in the Landscape.

20

Conclusion:

A main problem in semi-arid grasslands is livestock grazing, which cannot be stopped as the

landscape is regularly used by communities that are known to be pastoralist in nature (Pandey

and Singh 1991). These landscapes have remained dominated by grasslands and have not

been converted into agriculture primarily because of poor soil quality and lack of water.

Conversion of these landscapes into any other landuse like agriculture, industrial and forest

plantations would certainly be detrimental to the health of these ecosystems that hold

significant populations of sensitive species adapted to these kinds of landscapes. Ironically,

grasslands and scrubs have been classified as ‘wastelands’ by the Department of Land

Resources, Ministry of Rural Development, India.

Both the models indicated that the Indian fox exists mostly in grasslands and grassland-

fallow complexes in these areas. Our results, however, suggest that the Indian fox may be

able to tolerate a certain degree of habitat disturbance. Conserving and protecting these areas

needs to be looked at policy level. Both the models provide quantitative support to our

understanding about the species. They prove to be handy tools in mapping species

distributions over larger landscapes using limited field data. The output of such models could

enable improved predictions of species distributions, emerging risks and aid in

implementation of efficient conservation strategies toward high-risk populations.

21

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