Improving the predictability and interpretability of co‐occurrence modelling through feature‐based joint species distribution ensembles. Issue 1 (25th June 2022) - Powell‐Romero, Francisca; Fountain‐Jones, Nicholas M.; Norberg, Anna; Clark, Nicholas J.
Abstract: Species Distribution Models (SDMs) are vital tools for predicting species occurrences and are used in many practical tasks including conservation and biodiversity management. However, the expanding minefield of SDM methodologies makes it difficult to select the most reliable method for large co‐occurrence datasets, particularly when time constraints make designing a bespoke model challenging. To facilitate model selection for practical out‐of‐sample prediction, we consider three major challenges: (a) the difficulty of incorporating multiple functional forms for species associations; (b) the limited knowledge on how characteristics of co‐occurrence data impact model performance; and (c) whether individual model predictions could be combined to obtain optimised community predictions without the need for bespoke models. To address these gaps, we propose an ensemble method that uses descriptive features of binary co‐occurrence datasets to predict model weightings for a set of candidate SDMs. We demonstrate how this method may be applied through a simple case study that uses five independent Joint Species Distribution Models (JSDMs) and Stacked Species Distribution Models (SSDMs) to predict out‐of‐sample observations for a diversity of co‐occurrence datasets. Moreover, we introduce a novel SSDM that offers the potential to include multiple functional forms for each species while delivering robust community predictions. Our case study highlights two major findings. First, the ability for the feature‐based ensemble to offer more robust species co‐occurrence predictions compared to other candidate SDMs while providing insights into the data features that impact model performance. Second, the competitiveness of the novel SSDM method for forecasting species co‐occurrences, even when using a simple univariate generalised linear model (GLM) as the base model prior to stacking. We conclude that feature‐based ensembles can provide ecologists with a useful tool for generating species distribution predictions in a way that is reliable and informative. Moreover, the flexibility of the ensemble and the novel SSDM method both offer exciting prospects for incorporating a diversity of functional forms while prioritising out‐of‐sample prediction.