Patterns and Determinants of Habitat Occupancy by the Asian Elephant in the Western Ghats of Karnataka, India

<div><p>Understanding species distribution patterns has direct ramifications for the conservation of endangered species, such as the Asian elephant <i>Elephas maximus</i>. However, reliable assessment of elephant distribution is handicapped by factors such as the large spatial scales of field studies, survey expertise required, the paucity of analytical approaches that explicitly account for confounding observation processes such as imperfect and variable detectability, unequal sampling probability and spatial dependence among animal detections. We addressed these problems by carrying out ‘detection—non-detection’ surveys of elephant signs across a <i>c</i>. 38,000-km² landscape in the Western Ghats of Karnataka, India. We analyzed the resulting sign encounter data using a recently developed modeling approach that explicitly addresses variable detectability across space and spatially dependent non-closure of occupancy, across sampling replicates. We estimated overall occupancy, a parameter useful to monitoring elephant populations, and examined key ecological and anthropogenic drivers of elephant presence. Our results showed elephants occupied 13,483 km² (<i>SE</i> = 847 km²) corresponding to 64% of the available 21,167 km² of elephant habitat in the study landscape, a useful baseline to monitor future changes. Replicate-level detection probability ranged between 0.56 and 0.88, and ignoring it would have underestimated elephant distribution by 2116 km² or 16%. We found that anthropogenic factors predominated over natural habitat attributes in determining elephant occupancy, underscoring the conservation need to regulate them. Human disturbances affected elephant habitat occupancy as well as site-level detectability. Rainfall is not an important limiting factor in this relatively humid bioclimate. Finally, we discuss cost-effective monitoring of Asian elephant populations and the specific spatial scales at which different population parameters can be estimated. We emphasize the need to model the observation and sampling processes that often obscure the ecological process of interest, in this case relationship between elephants to their habitat.</p></div

Study area map of the Western Ghats landscape in Karnataka.

<p>Study area and survey design used for the landscape scale habitat occupancy of dholes in the Western Ghats, Karnataka State, India (2006–2007). The map shows overall forest cover, protected wildlife reserves with superimposition of 188 km²-grid-array. Inset: location of the study area in India.</p

Model comparisons to identify ecological and anthropogenic habitat covariates influencing dhole distribution Ψ<i>_L</i> at landscape-scale (Western Ghats, India, 2006–2007) from spatially replicated sign surveys during 2006–2007.

<p>*K = number of parameters.</p

Assessing Patterns of Human-Wildlife Conflicts and Compensation around a Central Indian Protected Area

<div><p>Mitigating crop and livestock loss to wildlife and improving compensation distribution are important for conservation efforts in landscapes where people and wildlife co-occur outside protected areas. The lack of rigorously collected spatial data poses a challenge to management efforts to minimize loss and mitigate conflicts. We surveyed 735 households from 347 villages in a 5154 km² area surrounding Kanha Tiger Reserve in India. We modeled self-reported household crop and livestock loss as a function of agricultural, demographic and environmental factors, and mitigation measures. We also modeled self-reported compensation received by households as a function of demographic factors, conflict type, reporting to authorities, and wildlife species involved. Seventy-three percent of households reported crop loss and 33% livestock loss in the previous year, but less than 8% reported human injury or death. Crop loss was associated with greater number of cropping months per year and proximity to the park. Livestock loss was associated with grazing animals inside the park and proximity to the park. Among mitigation measures only use of protective physical structures were associated with reduced livestock loss. Compensation distribution was more likely for tiger related incidents, and households reporting loss and located in the buffer. Average estimated probability of crop loss was 0.93 and livestock loss was 0.60 for surveyed households. Estimated crop and livestock loss and compensation distribution were higher for households located inside the buffer. Our approach modeled conflict data to aid managers in identifying potential conflict hotspots, influential factors, and spatially maps risk probability of crop and livestock loss. This approach could help focus allocation of conservation efforts and funds directed at conflict prevention and mitigation where high densities of people and wildlife co-occur.</p> </div

Estimates of β coefficient values for different individual habitat covariates hypothesized to influence dhole habitat-use Ψ<i>_s</i> at reserve scale (Bandipur Tiger Reserve, India, 2012).

<p>These include model-averaged β estimates with unconditional standard errors.</p

Results of comparisons to select models for estimating probability of detecting dhole signs <i>p_t</i> on 1-km long spatial replicates used in the field survey conducted at landscape scale in the Western Ghats landscape, India (2006–2007), under the constant global model for dhole occurrence [<i>Ψ_L</i> (<i>chital+sambar+livestock)</i>].

<p>*K = number of parameters.</p

Comparison between the standard occupancy model [28] and the model using spatial replicates with potential auto-correlation of sign detections among them [54] used to generate estimates of dhole habitat occupancy at landscape scale (Western Ghats, India, 2006–2007) and habitat-use at reserve scale (Bandipur Tiger Reserve, India, 2012).

<p>*K =  number of parameters.</p

Study area map of Bandipur Tiger Reserve.

<p>Study area and survey design for Bandipur Tiger Reserve, India (2012) showing protected area boundary, forest road sign-survey routes and 13-km²-grid array. Inset: location of the study area and adjoining protected areas.</p

Model selection results: Covariate effects in determining detectability p^t on 1-km-long spatial replicates, based on the Hines et al.

<p>(2010) modeling approach. No. of sites = 205. Please see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133233#pone.0133233.t001" target="_blank">Table 1</a> for descriptions of covariates.</p

Estimates of dhole habitat occupancy at landscape-scale (Western Ghats, India, 2006–2007) and habitat-use at reserve-scale (Bandipur) generated using spatially replicated sign surveys under the model [54] incorporating spatial auto-correlation of sign detections.

<p>Please see Methods section for parameter descriptions.</p><p>Footnote: Here the parameters Ψ and <i>p_t</i> are estimated only for the basic model with no covariates. Final estimates of these parameters were derived from covariate models.</p