DOI: 10.5061/dryad.8sf7m0ch5
title: Data from: How to evaluate community predictions without thresholding?

description: Stacked species distribution models (S-SDM) provide a tool to make spatial
 predictions about communities by first modelling individual species and
 then stacking the modelled predictions to form assemblages. The evaluation
 of the predictive performance is usually based on a comparison of the
 observed and predicted community properties (e.g., species richness,
 composition). However, the most available and widely used evaluation
 metrics require the thresholding of single species’ predicted
 probabilities of occurrence to obtain binary outcomes (i.e.,
 presence/absence). This binarisation can introduce unnecessary bias and
 error. Herein, we present and demonstrate the use of several groups of new
 or rarely used evaluation approaches and metrics for both species richness
 and community composition that do not require thresholding but instead
 directly compare the predicted probabilities of occurrences of species to
 the presence/absence observations in the assemblages. Community AUC, which
 is based on traditional AUC, measures the ability of a model to
 differentiate between species presences or absences at a given site
 according to their predicted probabilities of occurrence. Summing the
 probabilities gives the expected species richness and allows the
 estimation of the probability that the observed species richness is not
 different from the expected species richness based on the species’
 probabilities of occurrence. The traditional Sørensen and Jaccard
 similarity indices (which are based on presences/absences) were adapted to
 maxSørensen and maxJaccard and to probSørensen and probJaccard (which use
 probabilities directly). A further approach (improvement over null models)
 compared the predictions based on S-SDMs with the expectations from the
 null models to estimate the improvement in both species richness and
 composition predictions. Additionally, all metrics can be described
 against the environmental conditions of sites (e.g., elevation) to
 highlight the abilities of models to detect the variation in the strength
 of the community assembly processes in different environments. These
 metrics offer an unbiased view of the performance of community predictions
 compared to metrics that requiring thresholding. As such, they allow more
 straightforward comparisons of model performance among studies (i.e., they
 are not influenced by any subjective thresholding decisions).
description: For methods on data collection please see. Scherrer et al.
 (2019).  How to evaluate community predictions without thresholding?
 Methods in Ecology and Evolution, in press. In the
 publications there are the needed references to original publications
 describing the collection of the field data used in the case
 studies.
description: This dryad submission contains four folders.
 Envrionmental data, species data, virtual species data and
 scripts error simulation. Each of this foldes has its own readme
 file. Please note that most of the functions used to
 run the simulations are included in the R package ecospat. https://github.com/ecospat/ecospat