Where will the dhole survive in 2030? Predicted strongholds in mainland Southeast Asia

Dhole (Cuon alpinus) is threatened with extinction across its range due to habitat loss and prey depletion. Despite this, no previous study has investigated the distribution and threat of the species at a regional scale. This lack of knowledge continues to impede conservation planning for the species. Here we modeled suitable habitat using presence-only camera trap data for dhole and dhole prey species in mainland Southeast Asia and assessed the threat level to dhole in this region using an expert-informed Bayesian Belief Network. We integrated prior information to identify dhole habitat strongholds that could support populations over the next 50 years. Our habitat suitability model identified forest cover and prey availability as the most influential factors affecting dhole occurrence. Similarly, our threat model predicted that forest loss and prey depletion were the greatest threats, followed by local hunting, non-timber forest product collection, and domestic dog incursion into the forest. These threats require proactive resource management, strong legal protection, and cross-sector collaboration. We predicted <20% of all remaining forest cover in our study area to be suitable for dhole. We then identified 17 patches of suitable forest area as potential strongholds. Among these patches, Western Forest Complex (Thailand) was identified as the region's only primary stronghold, while Taman Negara (Malaysia), and northeastern landscape (Cambodia) were identified as secondary strongholds. Although all 17 patches met our minimum size criteria (1667 km(2)), patches smaller than 3333 km(2) may require site management either by increasing the ecological carrying capacity (i.e., prey abundance) or maintaining forest extent. Our proposed interventions for dhole would also strengthen the conservation of other co-occurring species facing similar threats. Our threat assessment technique of species with scarce information is likely replicable with other endangered species

An empirical evaluation of camera trap study design: How many, how long and when?

Abstract Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters. We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals. We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites