1. 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.
2. 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.
3. 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.
4. 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.

We reprocessed covariate data at a 1-km² resolution across the country (see Appendix S1 in Supporting Information for data descriptions and sources). Conductance C, of each 1-km² pixel was calculated as: logit (C) = β₀ + β₁X₁ + β₂X₂ + …… + β_nX_n

where, X_i is the pixel-level covariate value for covariates described in Appendix S1, and β_i 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., β_frag= β_fcov. To test the sensitivity of our results to this assumption, we considered two additional scenarios, where β_frag= β_fcov± SE(β_fcov). 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.

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.