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Rates of biodiversity loss in Southeast Asia are among the highest in the world, and the Indo-Burma and South-Central China Biodiversity Hotspots rank among the world's most threatened. Developing robust multi-species conservation models is critical for stemming biodiversity loss both here and globally. We used a large and geographically extensive remote-camera survey and multi-scale, multivariate optimization species distribution modelling to investigate the factors driving biodiversity across these two adjoining biodiversity hotspots. Four major findings emerged from the work. (i) We identified clear spatial patterns of species richness, with two main biodiverse centres in the Thai-Malay Peninsula and in the mountainous region of Southwest China. (ii) Carnivores in particular, and large ungulates to a lesser degree, were the strongest indicators of species richness. (iii) Climate had the largest effect on biodiversity, followed by protected status and human footprint. (iv) Gap analysis between the biodiversity model and the current system of protected areas revealed that the majority of areas supporting the highest predicted biodiversity are not protected. Our results highlighted several key locations that should be prioritized for expanding the protected area network to maximize conservation effectiveness. We demonstrated the importance of switching from single-species to multi-species approaches to highlight areas of high priority for biodiversity conservation. In addition, since these areas mostly occur over multiple countries, we also advocate for a paradigmatic focus on transboundary conservation planning. [ABSTRACT FROM AUTHOR] more...

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. • Several algorithms have been developed to produce species distribution models. • Random forest has often been shown to outperform generalised linear model. • Choice of algorithm drives strong differences between spatial predictions. • Different algorithms define also differences in spatial scales' models optimisation. • Neither generalised linear model nor random forest is always better than the other. [ABSTRACT FROM AUTHOR] more...