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Habitat loss, habitat degradation and poaching threaten the survival of large mammals in Southeast Asia. Studies on these threats tend to focus on small spatial scales (i.e. a protected area), precluding region-wide species assessments that can inform conservation management. Using existing camera trap data, we constructed occupancy models to understand patterns of habitat use as well as predict the distribution of sun bears Helarctos malayanus across Sabah, Malaysian Borneo. We found that bear distribution was related to above-ground carbon density and human settlement density, characteristics that describe the quality of bear habitat and a potential threat of poaching, respectively. Only half of sun bear distribution in Sabah falls within protected areas. Outside of protected areas, we predicted the reduction of sun bear distribution under simulated future conventional selective logging (forest degradation) and industrial tree plantation expansion (forest loss) scenarios. In the scenario involving forest degradation, sun bear distribution across Sabah only decreased by ~ 4%, supporting existing evidence that sun bears are resilient to selective logging impacts. Forest loss, however, had a larger impact, reducing sun bear distribution by ~ 11% in the scenario involving high forest loss. We recommend a focus on long term monitoring of sun bear habitat suitability trends, especially outside protected areas, along with strong anti-poaching efforts. Our study demonstrates the utility of pooling existing camera trap data to understand region-wide species distributions that could assist in setting conservation priorities.

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF.