DOI: 10.5061/dryad.nf925ps
title: Data from: How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

description: 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.
description: Forest plant
 distribution and environmental data in the western Swiss
 AlpsPlant species
 (presence-absence records) and environmental data for each of the 3076
 forest plots. The Scherrer et al. (2018, Methods in Ecology and Evolution)
 paper uses the associated Dryas dataset of plant species distribution data
 and environmental predictors to compare a number of thresholding methods
 for stacked species distribution models (S-SDMs) and how to best evaluate
 them with an appropriate cross-validation framework. Subsets of these data
 have also been used in previous works by the same research group: Scherrer
 et al. (2017, Diversity and Distributions) and others (see full
 publication list under http://www.unil.ch/ecospat).forest_speciesEnvironment.csvButterfly distribution and environmental data in the western Swiss AlpsButterfly species (presence-absence records) and environmental data for each of the 192 plots. The Scherrer et al. (2018, Methods in Ecology and Evolution) paper uses the associated Dryas dataset of plant species distribution data and environmental predictors to compare a number of thresholding methods for stacked species distribution models (S-SDMs) and how to best evaluate them with an appropriate cross-validation framework. Subsets of these data have also been used in previous works by the same research group: Pellisier et al. (2012, Ecography), D’Amen et al. (2015, Global Ecology and Biogeography) and others (see full publication list under http://www.unil.ch/ecospat).butterflies_speciesEnvironment.csvGrasshopper distribution and environmental data in the western Swiss AlpsGrasshopper species (presence-absence records) and environmental data for each of the 202 plots. The Scherrer et al. (2018, Methods in Ecology and Evolution) paper uses the associated Dryas dataset of plant species distribution data and environmental predictors to compare a number of thresholding methods for stacked species distribution models (S-SDMs) and how to best evaluate them with an appropriate cross-validation framework. Subsets of these data have also been used in previous works by the same research group: Pradervand et al. (2013, Bulletin de la Société Vaudoises des Sciences Naturelles), D’Amen et al. (2015, Global Ecology and Biogeography) and others (see full publication list under http://www.unil.ch/ecospat).grasshoppers_speciesEnvironment.csv