Identifying realistic recovery targets and conservation actions for tigers in a human dominated landscape using spatially-explicit densities of wild prey and their determinants

Aim Setting realistic population targets and identifying actions for site and landscape-level recovery plans are critical for achieving the global target of doubling wild tiger numbers by 2022. Here, we estimate the spatially explicit densities of wild ungulate prey across a gradient of disturbances in two disjunct tiger habitat blocks (THBs) covering 5212 km2, to evaluate landscape-wide conditions for tigers and identify opportunities and specific actions for recovery. Location Western Terai Arc Landscape, India. Methods Data generated from 96 line transects in 15 systematically selected geographical cells (166.5 km2) were used to estimate spatially explicit densities of six wild ungulate prey species at a fine scale (1 km2). Employing distance-based density surface models, we derived species-specific estimates within three major forest land management categories (inviolate protected areas (PA), PAs with settlements and multiple-use forests). By scaling estimated prey densities using an established relationship, we predicted the carrying capacity for tigers within each THB. Results Species-specific responses of the six wild ungulates to natural-habitat and anthropogenic covariates indicated the need for targeted prey recovery strategies. Inviolate PAs supported the highest prey densities compared with PAs with settlements and multiple-use forests, and specifically benefited the principal tiger prey species (chital Axis axis and sambar Rusa unicolor). The estimated mean prey density of 35.16 (±5.67) individuals per km2 can potentially support 82 (62–106) and 299 (225–377) tigers across THB I and THB II, which currently support 2 (2–7) and 225 (199–256) tigers, respectively. This suggests a potential c. 68% increase in population size given existing prey abundances. Finally, while THB I represents a potential tiger recovery site given adequate prey, PAs where resettlement of pastoralists is underway represent potential prey recovery sites in THB II. Main conclusions This systematic approach of setting realistic population targets and prioritizing spatially explicit recovery strategies should aid in developing effective landscape conservation plans towards achieving global tiger conservation targets

Household Perceptions and Patterns of Crop Loss by Wild Pigs in North India

Loss to cultivated crops by wild pigs (Sus scrofa) is widespread and can jeopardize low-income farmers. In India, although there is lot of political interest in the problem, efforts to understand the patterns, correlates, and underlying reasons for wild pig conflict continue to be minimal. We quantified loss of wheat (Triticum aestivum) to wild pigs and assessed the spatial patterns of damage in a forest settlement of Van Gujjar (Haridwar, India), which is a dairy-based pastoralist community. We chose a 4-km2 cultivated area comprising 400 farmlands (each measuring 0.8 ha and belonging to a family) and assessed crop damage by wild pigs through field surveys during the harvest season. We interviewed 159 respondents who manage 219 of the total 400 farmlands in the study area to compare actual crop loss with perceived losses. Wild pigs damaged 2.29 tonnes (2,290 kg) of wheat, which was about 2.6% of the potential yield in the study area. A total of 39 farmlands (9.5%), managed by 28 respondents, suffered losses during the survey period at an average loss of about 58.8 kg (SD ± 89.5, range = 0.7–388 kg). During interviews, 81 respondents managing 155 farmlands (70.7%) reported having suffered wild pig-related crop loss during the survey period. They also perceived losing about 23.4% of the potential yield of wheat due to wild pigs. The perceived losses were much higher than actual losses. Actual losses measured through field surveys underscore the dichotomy between actual and perceived crop loss due to wild pigs. About 81% of recorded wild pig-related damage to wheat occurred within 200 m from the forest edge. The crop protection measures aimed at stopping wild pigs from entering the fields were mostly reactive. Although overall crop losses due to wild pigs seem low at the settlement level, for affected individual families, the losses were financially significant. Such recurrent crop losses can cause families to go into debt, trigger animosity toward conservation, and lead to retaliation measures, which may be indiscriminate and have the potential to affect other endangered mammals in conservation priority landscapes. Because crop losses by wild pigs are severe along the narrow band of fields along the edge of the forest, channeling monetary benefits through insurance-based compensation schemes can help assuage losses to farmers. Further, because crop damage by wild pigs is seasonal, experimenting with mobile fences that can be dismantled and packed away after use would be beneficial

Relationship between occupancy probability (y-axis) and explanatory variables across THB I and THB II.

<p>(a) wild prey index, (b) disturbance index, (c) proportional habitat and (d) principal prey index. Dashed lines represent 95% confidence intervals.</p

NESTING ECOLOGY OF THE NEAR THREATENED COLONIAL WATERBIRD BLACK-HEADED IBIS THRESKIORNIS MELANOCEPHALUS IN BHITARKANIKA MANGROVES, ODISHA

Volume: 109Start Page: 101End Page: 11

Influence of Connectivity, Wild Prey and Disturbance on Occupancy of Tigers in the Human-Dominated Western Terai Arc Landscape

<div><p>Occupying only 7% of their historical range and confined to forested habitats interspersed in a matrix of human dominated landscapes, tigers (<em>Panthera tigris</em>) typify the problems faced by most large carnivores worldwide. With heads of governments of tiger range countries pledging to reverse the extinction process and setting a goal of doubling wild tiger numbers by 2022, achieving this target would require identifying existing breeding cores, potential breeding habitats and opportunities for dispersal. The Terai Arc Landscape (TAL) represents one region which has recently witnessed recovery of tiger populations following conservation efforts. In this study, we develop a spatially explicit tiger occupancy model with survey data from 2009–10 based on <em>a priori</em> knowledge of tiger biology and specific issues plaguing the western TAL (6,979 km²), which occurs in two disjunct units (Tiger Habitat Blocks; THBs). Although the overall occupancy of tigers was 0.588 (SE 0.071), our results clearly indicate that loss in functionality of a regional corridor has resulted in tigers now occupying 17.58% of the available habitat in THB I in comparison to 88.5% in THB II. The current patterns of occupancy were best explained by models incorporating the interactive effect of habitat blocks (AIC <em>w</em> = 0.883) on wild prey availability (AIC <em>w</em> = 0.742) and anthropogenic disturbances (AIC <em>w</em> = 0.143). Our analysis has helped identify areas of high tiger occupancy both within and outside existing protected areas, which highlights the need for a unified control of the landscape under a single conservation unit with the primary focus of managing tigers and associated wildlife. Finally, in the light of global conservation targets and recent legislations in India, our study assumes significance as we identify opportunities to secure (e.g. THB II) and increase (e.g. THB I) tiger populations in the landscape.</p> </div

Summary of population sizes estimated from camera-trapping studies conducted in the western TAL.

a<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040105#pone.0040105.s004" target="_blank">Text S1</a> for details.</p>b<p>Jhala et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040105#pone.0040105-Jhala1" target="_blank">[24]</a>.</p

Probability of detecting tiger signs in THB I and THB II as a function of substrate type.

<p>Error bars represent one standard error.</p

Effect of covariates^a on occupancy ().

<p>Note: Model rankings are based on Akaike’s Information Criterion (AIC).</p>a<p>Covariates used to model detection probability were Block (B), Wild prey index (WildP), Principal prey index (PrincipP), Disturbance (Dist) and proportional habitat per cell (Hab).</p>b<p>In all models the probability of detection () was modelled as ‘B + Substrate’ based on model selection results presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040105#pone-0040105-t001" target="_blank">Table 1</a>. Segment-level occupancy parameters ( and ) were modelled on ‘B’ (Block). ‘×’ denotes covariates were modelled as an interaction.</p

Potential tiger habitat in the western Terai Arc Landscape.

<p>Framed within 57 grid cells (166 km²) spanning the area between river Yamuna and river Gola are the two Tiger Habitat Blocks (THB’s). Also indicated are the administrative units highlighting the protected areas of Rajaji National Park (RNP) and Corbett Tiger Reserve (CTR), the Chilla-Motichur corridor along the river Ganga and the major towns/cities in the area.</p