CISO : Species distribution modelling Conditioned on Incomplete Species Observations

Abstract Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked in traditional models. While some methods, such as joint species distribution models (JSDMs), partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co‐occurrence data. In practice, species observations are often sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning‐based method for species distribution modelling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species for plants and birds. Furthermore, we show that combining and conditioning on observations from multiple datasets can improve the prediction of species occurrences in scenarios with sufficient co‐occurrences between datasets to train CISO effectively. Our results show that CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.