Data from: How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS.
PLEASE SEE DOI: 10.1111/ddi.12548 AND https://doi.org/10.1111/geb.12357
--- The popularity of species distribution models (SDMs) and the
associated stacked species distribution models (S‐SDMs), as tools for
community ecologists, largely increased in recent years. However, while
some consensus was reached about the best methods to threshold and
evaluate individual SDMs, little agreement exists on how to best assemble
individual SDMs into communities, that is, how to build and assess S‐SDM
predictions. Here, we used published data of insects and plants collected
within the same study region to test (a) if the most established
thresholding methods to optimize single species prediction are also the
best choice for predicting species assemblage composition, or if
community‐based thresholding can be a better alternative, and (b) whether
the optimal thresholding method depends on taxa, prevalence distribution
and/or species richness. Based on a comparison of different evaluation
approaches, we provide guidelines for a robust community cross‐validation
framework, to use if spatial or temporal independent data are unavailable.
Our results showed that the selection of the “optimal” assembly strategy
mostly depends on the evaluation approach rather than taxa, prevalence
distribution, regional species pool or species richness. If evaluated with
independent data or reliable cross‐validation, community‐based
thresholding seems superior compared to single species optimisation.
However, many published studies did not evaluate community projections
with independent data, often leading to overoptimistic community
evaluation metrics based on single species optimisation. The fact that
most of the reviewed S‐SDM studies reported over‐fitted community
evaluation metrics highlights the importance of developing clear
evaluation guidelines for community models. Here, we move a first step in
this direction, providing a framework for cross‐validation at the
community level.