Rapid multi-nation distribution assessment of a charismatic conservation species using open access ensemble model GIS predictions: Red panda (Ailurus fulgens) in the Hindu-Kush Himalaya region

The red panda (Ailurus fulgens) is a globally threatened species living in the multi-national Hindu-Kush Himalaya (HKH) region. It has a declining population trend due to anthropogenic pressures. Human-driven climate change is expected to have substantial impacts. However, quantitative and transparent information on the ecological niche (potential as well as realized) of this species across the vast and complex eight nations of the HKH region is lacking. Such baseline information is not only crucial for identifying new populations but also for restoring locally-extinct populations, for understanding its bio-geographical evolution, as well as for prioritizing regions and an efficient management. we compiled, and made publicly available through an institutional repository (dSPACE), the best known ‘presence only’ red panda dataset with ISO compliant metadata. This was done through the International Centre for Integrated Mountain Development (ICIMOD.org) data-platform to the Global Biodiversity Information Facility (GBIF.org). We used data mining and machine learning algorithms such as high-performance commercial Classification and Regression Trees, Random Forest, TreeNet, and Multivariate Adaptive Regression Splines implementations. We averaged all these Geographic Information System (GIS) models for the first produced ensemble model for this species in the HKH region. edictive model is the first of its kind and allows to assess the red panda distribution based on empirical open access data, latest methods and the major signals and drivers of the ecological niche. It allows to assess and fine-tune earlier habitat area estimates. Our models promote ‘best professional practices’. It can readily be used by the red panda Recovery Team, the red panda Action Plan, etc. because they are robust, transparent, publicly available, fit for use, and have a good accuracy, as judged by several independent assessment metrics (Receiver Operating Characteristics (ROC-AUC) curves, expert opinion, assessed by known absence regions, 95% confidence intervals and new field data).