A0474
Title: Comparison of joint species distribution models for percent cover data
Authors: Pekka Korhonen - University of Jyvaskyla (Finland) [presenting]
Francis Hui - The Australian National University (Australia)
Sara Taskinen - University of Jyvaskyla (Finland)
Jenni Niku - University of Jyvaskyla (Finland)
Bert van der Veen - Norwegian University of Science and Technology (Norway)
Abstract: Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade due to their capacity to answer a wide range of questions at both the species- and the community level. The family of generalized linear latent variable models, in particular, has proven popular for building JSDMs, being able to handle many response types, including presence-absence data, biomass, overdispersed and/or zero-inflated counts. Latent variable models are extended to handle per cent cover data, with vegetation, sessile invertebrate, and macroalgal cover data representing the prime examples of such data arising in community ecology. Sparsity is a commonly encountered challenge with per cent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, i.e., have 0\% or 100\% cover, respectively, rendering the use of beta distribution inadequate. Two JSDMs are proposed suitable for per cent cover data, namely a hurdle beta model and an ordered beta model. The two proposed approaches are compared to a beta distribution for shifted responses, transformed presence-absence data, and an ordinal model for per cent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological per cent cover data.
