Searching for ecology in species distribution models in the Himalayas

Modelling species across vast distributions in remote, high mountain regions like the Himalayas remains a challenging task. Challenges include, first and foremost, large-scale sampling of species occurrences and acquisition of sufficient high quality, fine-scale environmental parameters. We compiled a review of 157 Himalayan studies published between 2010 and 2021, aiming at identifying their main modelling objective in relation to the conceptualization of their methodological framework, evaluating origin of species occurrence data, taxonomic groups, spatial and temporal scale, selection of predictor variables and applied modelling algorithms. The majority of the analysed studies (40%) attempted to answer questions about potential range changes under future or past climatic conditions. The most studied organisms were trees (27%), followed by mammals (22%), herbaceous plants (20%), and birds (9%). For almost all studies we noted that a critical investigation and evaluation of input parameters and their ability to account for the species ecological requirements were neglected. Over 87% of all studies used WORLDCLIM climate data as predictor variables, while around 50% of these studies solely relied on WORLDCLIM climate data. Climate data from other sources were incorporated in only 7% and an additional 6% solely used remotely sensed predictors. Only around 2% of all studies attempted to compare the influence of different climate data sources on model performance. By far, Maxent was the most used modelling algorithm with 66%, followed by ensemble approaches (16%), whereas statistical modelling techniques lagged far behind (9%). Surprisingly, we found in 37% of the studies no interpretation on the relationship between the species and the predictor variables, while 27% of all studies included brief information, and 36% provided an elaborate, detailed interpretation on species ecological needs reflected in the final model. With this review we highlight the necessity to identify and reduce biases and uncertainty associated with species' occurrence records and environmental data a priori. Since flawed input parameters produce misleading models without ecological causality, their implementation may have detrimental consequences when the best possible adaptation to future climatic conditions is at stake.