Differentially biased sampling strategies reveal the non-stationarity of species distribution models for Indian small felids

Abstract

Species Distribution Models (SDMs) have been used extensively to understand species-habitat relationships and design conservation strategies. The ability to train these models using a wide variety of datasets and modelling algorithms has led to their wide applicability across systems. However, the ease of modelling also leads to their use as off-the-shelf models without a detailed investigation of the data and their suitable end-use application. The effect of various modelling parameters on inferences has been explored, however, their interaction with training data type is limited. We used country-wide data for four sympatric Indian small cat species to understand the sensitivity of SDMs to data types, sampling extents and their interaction. Our results reveal the non-stationarity of models with varying modelling parameters. The extent of the training dataset had major implications on the inferences and interacted strongly with the type of dataset used. The divergent distribution of the target species revealed that the effect of sampling extent was more pronounced for species that have limited distribution within the predictive extent. Lastly, our results highlight the significance of sampled environmental space in explaining the non-stationarity of the model outputs.