Gap Analysis of Indian Fox Conservation Using Ecological Niche Modelling

2008·3.3 MB·

by  Abi Tamim Vanak

Journal ofthe Bombay Natural HistorySociety, 105(1),Jan-Apr2008 49-54
GAPANALYSIS OF INDIAN FOX CONSERVATION USING ECOLOGICALNICHE MODELLING
AbiTamim Vanak1 Mohammed Irfan-Ullah2andA.TownsendPeterson3
,
'WildlifeConservationSociety-lndiaProgram,GraduateResearchAssistant,DepartmentofFisheriesandWildlifeSciences,
UniversityofMissouri,Columbia,MO65211,USA, Email: [email protected]
2AshokaTrustforResearchinEcologyandtheEnvironment(ATREE),Bengaluru 560024, Karnataka,India.
PresentAddress: RMSIPvtLtd,A7Sec 16.Noida201 301,UttarPradesh,India.
Email: [email protected]
NaturalHistoryMuseumandBiodiversityResearchCenter,UniversityofKansas,Lawrence,Kansas66045,USA.
Email: [email protected]
We used ecological niche modelling to predict the geographic distribution ofthe Indian Fox, acanid endemic to the
Indian subcontinent. This little known canid, while not yet endangered, is threatened due to rapid habitat loss and
poaching throughout its range. We analysed 58 known occurrence locations from survey data collected from three
states in peninsular India using the software Desktop GARP. We created an ecological niche model for India using
vegetation and topographic data and furtherrefined it by including 18 additional bioclimatic data sets. Basedon the
ecologicalnichemodellingresults,agapanalysisofprotectionofferedtopotentialFoxhabitatintwostatesofsouthern
India was conducted by overlaying existing protected areaboundaries on the refined distribution and calculating the
extent ofprotection. Our analysis showed that the Indian Fox habitat consists primarily of low elevation semi-arid
grassland, scrubandthornforests,whichrankamongthemostvulnerable in Indiaowingtoconversiontoagriculture,
industryandurbanareas.Thegapanalysisshowedthatalittleover 1%ofpredictedFoxdistributioniscoveredbythe
protectedareanetwork.Theunderrepresentationofthesehabitatsisdeleterious notonly totheIndianFoxbutalsoto
arange ofotherspecies, such as the endangeredGreat Indian Bustard, Indian Grey Wolfand Blackbuck.
Key words: Indian Fox, Vulpesbengalensis, ecological niche modelling, gap analysis, distribution, protected areas
INTRODUCTION
terrain,tallgrasslandsandtruedesert(Prater 1980;Johnsingh
and Jhala 2004). Recent surveys indicate that though the
The Indian Fox Vulpes bengalensis, a canid endemic species is widespread, it is not common through most ofits
to the Indian subcontinent, is widespread, ranging from the rangeandisencounteredathighestfrequencies inprotected
foothills ofthe Himalaya in the north to the southern tip of semi-arid shortgrasslands anddry scrub areas(Vanak 2003,
the Indian peninsula, and from Sindh Province of Pakistan 2005; Vanak and Gompper 2007). Although these habitats
easttoBangladesh(JohnsinghandJhala2004;Gompperand rank among the most endangered in India (Rahmani 1989)
Vanak 2006). Even though this species is believed to be and are subject to constant human encroachment, they are
common (Johnsingh and Jhala 2004), little is known about rarely the focus ofconservation attention. Moreover, these
its ecology or the details of its geographic distribution, or habitats have been categorised as wastelands by various land
populationstatus.TheIUCNCanidSpecialistGroupclasses management agencies and are subject to intense pressure to
thisspeciesas ‘LeastConcern,’ (JohnsinghandJhala2004); betransformedintoagriculturalandpastorallandscapes(http:/
itislistedunderScheduleIIoftheIndianWildlife(Protection) /dolr.nic.in/wasteland.htm accessedon January 28, 2008).
Act,1972 (as amended up to 2002), which prohibits hunting In the southern Indian states, Indian Fox habitats are
ofthis species (Anonymous 2002). Despite this protection, decreasing, and the continued survival of the species is
Indian Fox populations are declining owing to habitat loss seriously threatened (Johnsingh and Jhala 2004). Despite a
from conversion to intensive agriculture, industry and reported presence in some protected areas (PAs), most
developmentprojects (Johnsingh and Jhala2004). populations of this species remain outside the PA network
TheIndianFoxisfoundinsemi-arid,flatorundulating (Vanak2005). Inthe absence ofsystematicproactiveefforts
terrain in biogeographic zones 3, 4 and 6 ofIndia (Rodgers for its conservation, this species might suffer a substantial
etal. 2000), which are typically drier biomes characterised reduction in potential habitat, affecting its future survival.
bylowrainfall,scrub,thorn,ordrydeciduousforestsorshort Wethusdevelopedagapanalysisoftheprotectioncurrently
grasslands (Manakadan and Rahmani 2000; Rodgers et al. affordedtoFoxdistributionareasinthesouthernIndianstates
2000; Vanak 2005). Indian Foxes avoid dense forest, steep ofKarnatakaandAndhra Pradesh.
GAPANALYSIS OF INDIAN FOX CONSERVATION
Gapanalysisisaproactiveapproachtoplanningtimely overamongrules)tomaximisepredictability.Thepredictive
action for species conservation that focuses on evaluating accuracy is then evaluated based on 1,250 pointsresampled
the degree to which native species are represented in PAs. with replacement from the intrinsic testing data and
SpeciesnotadequatelyrepresentedintheexistingPAnetwork 1,250 points sampled randomly from the study region as a
constitute ‘gaps’ in theconservationprogram. Gapanalysisis whole to represent pseudo-absences. GARP is designed to
intendedtopreventadditionalspeciesfrombecomingthreatened work based on presence-only data; missing information is
orendangered,andinthissenseisproactive,ratherthanreactive included in the modelling via sampling of pseudo-absence
(Scott etal. 1993; Flatheretal. 1997; Davisetal. 1998). points from the set ofpixels where the species has not been
Mapping the distribution of a species exhaustively detected (Stockwell and Peters 1999). The change in
throughon-groundsurveyswouldbeprohibitivelyexpensive predictive accuracy from one iteration to the next is used to
andtime-consuming,ifnotsimplyimpossible.Thealternative evaluatewhetheraparticularruleshouldbeincorporatedinto
used here is that occurrence data available from regions the model, and the algorithm runs either 1,000 iterations or
sampledindetailcanbeusedtoreconstructthespecies’overall until convergence.
distributions using Ecological Niche Modelling (ENM) Weused58uniquepointoccurrencesoftheIndianFox
(Peterson and Kluza 2003; Peterson 2005). The ecological sampled from the states of Karnataka, Andhra Pradesh and
niche of a species can be defined as the set of ecological Maharashtra, in 2003 and 2005 (Vanak 2003, 2005) (Fig. 1)
conditions within which it is able to maintain populations foranalysis.Weused ‘monthly’compositesofthemaximum
withoutimmigration(Grinnell 1917;HoltandGaines 1992). NormalisedDifferenceVegetationIndex(NDVI)imagesfrom
Several approaches have been used to approximate species’ the Advanced Very High Resolution Radiometer (AVHRR)
ecologicalniches(Austinetal. 1990;WalkerandCocks 1991; satellite (Eidenshink and Faundeen 1994) for 2003, as well
Scott et al. 1996; Scott et al. 2002); of these, one that has aselevation,slope,aspectandCompoundTopographicIndex
seenconsiderable testing is the GeneticAlgorithm forRule- (CTI)fromtheHydro-IKdataset(USGS 2001)andaglobal
set Prediction (GARP), which includes several inferential landcover coverage (Hansen et al. 1998, 2000). All
approaches in an iterative, evolutionary computing environmental datasets were resampled to pixels of about
environment (Stockwell and Peters 1999). All modelling in 1 km x 1 km foranalysis.
this study was carried out on a desktop implementation of
the GARP algorithm (Stockwell and Noble 1992; http:// Table 1: Bioclimaticvariablesusedforrefiningthedistribution
ofthe Indian Foxinthetwosouthernstates
www.lifemapper.org/desktopgarp).
Sr. No. PredictorVariables Source
MATERIALAND METHODS 1. Meanmonthlyprecipitation WorldClim
2. Meanmonthlytemperature WorldClim
3. Maximummonthlytemperature WorldClim
Ecological Niche Modelling (ENM) has been used in 4. Minimummonthlytemperature WorldClim
numerous applications and subjected to various tests, based 5. Annualmeantemperature WorldClim
on diverse analytical approaches (Miller 1994; Csuti 1996; 6. Meandiurnal range WorldClim
(meanofmonthlymaxtemp-mintemp)
Tuckeretal. 1997;Gottfriedetal. 1999;Maneletal. 1999a,b). 7. Isothermality WorldClim
The particular approach to modelling species’ ecological (Febprecipitation/Julyprecipitation)* 100
niches and predicting geographic distributions used here 8. Temperatureseasonality WorldClim
(standarddeviation*100)
(summarised below) is described in detail elsewhere 9. Maxtemperatureofwarmestmonth WorldClim
(Stockwell and Peters 1999; Peterson etal. 2002). Previous 10. Mintemperatureofcoldestmonth WorldClim
tests ofthe predictive powerofthis modellingtechnique for 11. Temperatureannualrange WorldClim
12. Meantemperatureofwettestquarter WorldClim
diverse phenomena in various regions have been recorded 13. Meantemperatureofdriestquarter WorldClim
elsewhere (Peterson 2001; Peterson etal. 1999; Peterson et 14. Meantemperatureofwarmestquarter WorldClim
al. 2002; Peterson andVieglais 2001;Anderson etal. 2002, 15. Meantemperatureofcoldestquarter WorldClim
16. Annualprecipitation WorldClim
2003; Stockwell and Peterson 2002). 17. Precipitationofwettestmonth WorldClim
GARP works in an iterative process ofrule selection, 18. Precipitationofdriestmonth WorldClim
evaluation, testing and incorporation or rejection: first, a 19. Precipitationseasonality WorldClim
(coefficientofvariation)
method is chosen from a set of possibilities (e.g. logistic 20. Precipitationofwettestquarter WorldClim
regression, bioclimatic rules), and is then applied to the 21. Precipitationofdriestquarter WorldClim
trainingdata, and arule is developed; rules mayevolveby a 22. Precipitationofwarmestquarter WorldClim
23. Precipitationofcoldestquarter WorldClim
number of means (e.g. truncation, point changes, crossing-
50 J. Bombay Nat. Hist. Soc., 105 (1), Jan-Apr 2008
GAPANALYSIS OF INDIAN FOX CONSERVATION
estimates, following recent recommendations (Anderson
etal. 2003). First, 20 models with 0% omission errors were
chosen,andofthese 10modelswithinthecentral50%ofthe
commission values were selected as the best models.
To provide an independent validation of model
performance, we randomly created four independent
replicates of40 point locations each from the original data
set(n=58).Theremaining 18pointsofeachofthesereplicates
were set aside for an independent test of the predictive
accuracyforeachreplicate.Coincidencebetweenindependent
testing points and model predictions for each replicate was
usedasameasureofmodelpredictiveability. Binomialtests
were used to compare the observed predictive success with
that expected underrandom (null) models ofno association
between predictions and test points. The test results are in
the formofa ‘ramp’ ofmodel agreementfrom0 (all models
predict absence of 18 validation points) to 10 (all models
predictpresenceof18validationpoints).Therefore,foreach
replicate, wecalculatedbinomial probabilities ateachofthe
Fig. 1: PredicteddistributionofIndian Fox Vulpesbengalensisin 10 predictive levels (Anderson etal. 2003).
Indiabasedon NDVIandtopographicvariables To characterise modelled distributions further, based
Note:The Modelover-predictsdistribution in someareas, ontheexistingknowledgeofthespecies’ habitatpreferences,
suchasnorth east Indiaand Kashmir, wherethe we overlaid the predicted Fox distribution on a global
speciesisnot reportedtooccur
landcover data set (Hansen et al. 1998, 2000) for all of
An arbitrary setof 100 model runs was developed for mainlandIndia andcalculatedproportionsoflandcovertypes
each analysis. In general, 25% (c. 14 points) of available within the predicted distribution.
occurrences were used for rule development (training data) Since the potential distribution of the Indian Fox is
and 25% (c. 14 points) for rule selection and refinement limited in southern India and is decreasing, given growing
(intrinsictestingdata), andtheremaining50% (c. 29points) urbanisation,changeinlandusepatternsandhuman-induced
point locations were set aside for an independent test and disturbance (JohnsinghandJhala2004),wedevelopedagap
filterofbestqualitymodels(extrinsictestingdata).Tochoose analysisforthespecieswithrespecttothePAnetworkwithin
the best models from among the 100 replicates, we filtered thestatesofAndhraPradeshandKarnataka.WeobtainedPA
models on the basis of omission and commission error boundaries for the two states and updated/corrected them
usingtopographic maps.Werepeatedthe modelling process
by restricting ourselves only to the geographic limits of
Table2: PredicteddistributionofIndian Fox Vulpesbengalensis
acrosslandcovertypesin India Karnataka and Andhra Pradesh to limit overprediction. We
additionallyincorporated 19 ‘bioclimatic’ variables(Hijmans
Landcover Location Landcover Foxdistributional et al. 2004) in the analysis to improve the algorithm’s
type equivalent typeswithin rangeacross
Foxdistributional eachland resolving power and to obtained a refined estimate of the
range(%) covertype% Fox distribution within the two states. We then overlaid
existingPAboundariestodeterminegapsintheprotectionof
Water Water 0.2 2
Woodland Deciduous 0.3 1 Indian Fox habitat. The reason we have done this for only
forest these two states is that this is where the majority ofthe data
Wooded Open scrub 50.0 33 comes from, and this allows us to better represent Fox
gCrlaossseldand fTorroepsitcalthorn 23.6 62 distribution withinasmallergeographicarea.Webelievethe
shrubland Forest coarser analysis at a larger scale allows us to delimit the
Open Grassland 23.7 29 broader distribution of the species at a countrywide level,
shrubland/
grassland whiletherefinedanalysisallowsustoovercometheinherent
Cropland Cropland 2.1 3 over-prediction ofGARPdistribution forgapanalysis atthe
Bare-ground Bare-ground 0.2 1 state level.
J. Bombay Nat. Hist. Soc., 105 (1), Jan-Apr 2008 51
GAPANALYSIS OF INDIAN FOX CONSERVATION
Fig. 2: PredicteddistributionofIndian Fox Vulpesbengatensisin Indiabasedon NDVI andtopographicvariables
RESULTS ofthe species was furtherrefined to4,70,951 sq. km (9%) of
the total areaofthe states (Table 3, Fig. 2).
We used model stacking to combine the results ofthe Overlayingtheprotectedareanetworkofthetwostates
10 best models for predicting the distribution of the Indian ontherefineddistributionmaprevealedthatonly sevenPAs
Fox. Independentvalidationofthepredictivesuccessofthese
Table3:AreastatisticsofIndian Fox Vulpesbengatensis
modelsrangedfrom 11 to 18correctlypredictedoccurrences distribution inthestatesofKarnatakaandAndhra Pradesh, India
out of the 18 testing points for each of the four replicates.
Binomial probabilities at each ofthe 10 predictive levels in Area % of % of %of
(sq. km) refined generic state
all cases (4 replicate tests x 10 predictive levels eac«h) were distribution distribution geographic
significantlybetterthanrandom(binomialtests,allP 0.05). area
This success in predicting independent tests of occurrence
Karnataka (Geographicarea-1,92,493sq. km)
data gave confidence in the model accuracy; as aresult, we
used all available points to develop final models (Fig. 1). Totalgeneric 1,11,403 58
Furtherexplorationofthesefinalmodelsillustratedthe distribution
Total refined 21,324 19 11
species’potentialdistributioninecological dimensions.The
distribution
Indian Fox occupies an elevation range of 100-900 m with Totalarea (2 PAs) 92.13 0.43 0.08 0.05
low rainfall (500-1,000 mm) and moderate annual mean protected
temperatures (25-30 °C). Peak NDVI values of the post- Andhra Pradesh(Geographicarea-2,78,458sq. km)
monsoonseason(0.5-0.8)correspondcloselytoareasholding
wooded grasslands, scrub and thorn forest systems. Cross- Totalgeneric 1,04,270 37
distribution
tabulating the predicted distribution with landcover data
Totalrefined 21,833 21 8
showed that ‘open scrub forest' (50%) and ‘grassland and distribution
tropicalthornforest’ (47%)werethedominantrepresentative Totalarea(5 PAs) 495.51 2.27 0.48 0.18
protected
landcover types within the species’ distributional area
Combinedtotal 587.64 1.36 0.27 0.12
(Table 2). Focusing within Andhra Pradesh and Karnataka areaprotectedfor
andaddinginthebioclimaticvariables,thepredicteddistribution bothstates
52 J. Bombay Nat. Hist. Soc., 105 (1), Jan-Apr 2008
1 .
GAPANALYSIS OF INDIAN FOX CONSERVATION
coincided with some part ofthe predicted range, protecting These habitats also rank among the least represented in the
approximately 588 sq. km (c. 1%) of the species’ potential IndianPAnetwork(Rodgersetal.2000).Adeardemonstration
distribution. of this under-representation is that only a little over 1% of
the species’ distribution potential in the two states is within
DISCUSSION the PAsystem.
ItisthereforeclearthatkeyhabitatsfortheIndianFox
The predictedrange ofthe Indian Fox developed here are inadequately represented in the PAnetwork ofthe states
agreeswellwithcurrentknowledgeofthespecies’ distribution of Karnataka and Andhra Pradesh. A greater representation
(Johnsingh and Jhala 2004). It excludes regions such as the ofdry-landbiomesinthePAnetworkwouldbepositive, not
Himalaya, thedesertsofRajasthan andthehill rangesofthe only for the Indian Fox but also for other obligate dry
WesternandEasternGhats,fromwherethespecieshasnever grassland species such as the endangered Indian Bustard
been reported (Gompper and Vanak 2006) (Fig. 1). Despite Ardeotis nigriceps, Indian Grey Wolf Canis lupuspallipes,
the geographically limited sampling (limited areas in three andBlackbuckAntilopecervicapra.Wesuggestthatthiskind
states from peninsular India), the model performed well in ofpredictivedistributionalmodellingbeusedbyconservation
capturing the species’ ecological niche, as well as its plannerstoidentifycrucialhabitatsfortheprotectionofthese
geographicdistribution,acrossamuchbroaderregion.Studies endangeredspecies.
elsewherehavedemonstratedasimilarpredictiveperformance
ofthe GARP algorithm based on small numbers oftraining ACKNOWLEDGEMENTS
locations(Peterson2001;Andersonetal. 2003; Petersonand
Kluza 2003). The first author was supported by a Wildlife
Ouroriginalmodels(NDVIandtopography predicted Conservation Society-India Program Small Grant, and
)
about46%areaofKarnatakaandAndhraPradeshassuitable logistic support was provided by the Centre for Wildlife
(Table 3) while theanalysis using anadditional 19 variables Studies, Bangalore. The second author acknowledges the
permittedustorefinedistributionalestimatesforthisspecies, support of the Ministry of Environment and Forests,
reducing it to 20% of the total extent of the states as the GovernmentofIndia,andtheAndhraPradeshandKarnataka
potentially suitable range. This analysis confirmed that low state forest departments for permits. The Ford Foundation
elevation grasslands, open scrub forest and tropical thorn and the Indo-US Science and Technology Forum provided
forest constitute the bulk ofthe distribution ofthis species. support.
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