DOI: 10.5061/dryad.qv9s4mwfr
title: Dog in the matrix: Envisioning countrywide connectivity conservation for an endangered carnivore

description: Elevated rates of anthropogenic impacts on land-use regimes have pushed
 terrestrial megafauna to the brink of extinction. Consequently, it is
 critical to adopt conservation approaches that safeguard individual
 populations, while retaining connectivity among these populations.
 Conserving spatially structured populations of imperiled species at large
 scales is often complex; the past decades have therefore seen a rise in
 spatial conservation prioritization exercises aimed at shaping
 landscape-scale conservation programmes. We present a framework for
 informing nationwide connectivity conservation, linking ecological and
 administrative scales, to maximise relevance for management. We assessed
 connectivity of the endangered dhole Cuon alpinus among 155 potential
 source populations across India using a data-driven approach combined with
 graph and circuit theory. We used clustering algorithms to identify
 ecologically meaningful conservation landscapes; within each landscape, we
 identified priority source populations based on their connectedness, and
 quantified pixel-specific habitat accessibility. We superimposed
 administrative boundaries on our findings to provide conservation
 recommendations at this management-relevant scale. We first mapped
 potential dhole movement across India. Dhole populations fell within three
 primary clusters—Western and Eastern Ghats (WEG), Central Indian Landscape
 (CIL), and North-East India (NEI)—of which NEI had the highest forest
 cover, most diffuse connectivity, and lowest human density, while WEG had
 the highest protected area coverage, and overall connectedness. Within
 each conservation landscape we evaluated the relative importance of
 Protected Areas and accessibility to high-quality patches. Parts of the
 Eastern Ghats had low habitat accessibility, yet high potential for dhole
 landscape connectivity. In 114 identified administrative units of priority
 for habitat restoration, we highlight those with low accessibility, i.e.,
 areas where restoration needs to be spatially targeted for maximum
 benefits. Synthesis and applications. We make recommendations for
 spatially-informed habitat restoration to enhance dhole connectivity in
 India, highlighting the importance of improving matrix permeability where
 dhole movement is currently restricted. More broadly, the framework we
 present is useful across species and management contexts, as it combines
 spatial and administrative scales to make ecologically-informed
 assessments of high relevance to management. Synergistically integrating
 species ecology, threats, and administrative considerations in
 connectivity conservation plans can enhance success of species
 conservation programmes.
description: We reprocessed covariate
 data at a 1-km<sup>2</sup> resolution across the country (see
 Appendix S1 in Supporting Information for data descriptions and sources).
 Conductance <i>C</i>, of each 1-km<sup>2</sup>
 pixel was calculated as: logit (<i>C</i>)<i> =
 </i>β<sub>0</sub> +
 β<sub>1</sub>X<sub>1 </sub>+
 β<sub>2</sub>X<sub>2</sub> + …… +
 β<sub>n</sub>X<sub>n</sub> where, X<sub>i</sub>
 is the pixel-level covariate value for covariates described in Appendix
 S1, and β<sub>i</sub> is the corresponding coefficient taken
 from Srivathsa et al. (2020a). We additionally used four
 covariates that we deemed important for connectivity, and which were not
 incorporated in the global model of Srivathsa et al. (2020a): forest
 fragmentation, linear infrastructure, water bodies and density of built-up
 infrastructure. We assumed that forest fragmentation, calculated as ‘patch
 cohesion’ index, would influence dhole movement to the same degree as
 forest cover extent, i.e.,
 <i>β</i><sub><i>frag</i></sub><i>= </i><i>β</i><sub><i>fcov</i></sub>. To test the sensitivity of our results to this assumption, we considered two additional scenarios, where <i>β</i><sub><i>frag</i></sub><i>= </i><i>β</i><sub><i>fcov</i></sub><i>± </i><i>SE(β</i><sub><i>fcov</i></sub><i>)</i>. Linear infrastructure was assumed to have the same inference as human population density. We expected the density of built-up infrastructure (buildings), and presence of large water bodies to pose a barrier to dhole movement, and these covariates were accordingly assigned high negative conductance values. Conductance values calculated in this manner were constrained between 0 and 1; we rescaled them to 0–100 for ease of interpretation.
description: This dataset consists of a shapefile that contains information on
 covariate and conductance values for each 1 sq km  pixel across mainland
 India. Please refer to README.txt file for more
 details.