10.6084/m9.figshare.899720.v2 David Harris David Harris Predicting species composition when environmental drivers are missing figshare 2014 species distribution models statistics species model birds Community Ecology Statistics Ecology 2014-01-12 02:25:35 Poster https://figshare.com/articles/poster/Predicting_species_composition_when_major_environmental_drivers_are_unknown/899720 <p>Species distribution models provide valuable tools for marking species’ range limits and predicting individual species’ responses to environmental change.</p> <p>However, multi-species patterns like richness and co-occurrence involve correlations among species, and the processes driving these correlations are not always observable.</p> <p>In other words, some sites will be less suitable or more suitable for a group of species than expected by these models, depending on variables that have not even been measured.</p> <p>Current (nonlinear) models cannot account for these variables, which limits their utility for community ecology.</p> <p>How can we predict species composition without knowing what these unmeasured factors are or how they affect community assembly?</p> <p>I designed a stochastic neural network[4] (to be made available as an R package called mistnet). The model uses latent random variables (Z) for unobserved environmental factors. Since these variables can take on a range of values, the model can make a range of distinct predictions for a given set of environmental data.</p>