Data from: Comparing the prediction of joint species distribution models with respect to characteristics of sampling data

Biotic interactions have been rarely included in traditional species
distribution models, wherein Joint Species Distribution Models (JSDMs)
emerge as a feasible approach to incorporate environmental factors and
interspecific interactions simultaneously, making it a powerful tool for
analyzing the structure and assembly processes of biotic communities.
However, the predictability and statistical robustness of JSDMs are
largely unknown because of the lack of research efforts for those newly
developed models. This study systematically evaluated the performances of
five JSDMs in predicting the occurrence and biomass of multiple species,
with a particular focus on diverse characteristics of sampling data,
including type of response variables, number of sampling sites, and the
number of species included in models. In general, most models yielded
satisfactory performances on fitting to observed data and on the
estimation of environmental effects; however, they showed less well
performances in evaluating species associations, and their predictability
had large variations. The JSDMs showed inconsistent performances between
the goodness-of-fit and predictability in cross-validation, and the Boral
model was relatively robust than others. The predictability of JSDMs was
less influenced by sample sizes and substantially improved by
incorporating rare species. This study contributes to an appropriate model
selection and application of JSDMs.