DOI: 10.5061/dryad.8sb8v
title: Data from: Minimum required number of specimen records to develop accurate species distribution models

description: Species Distribution Models (SDMs) are widely used to predict the
 occurrence of species. Because SDMs generally use presence-only data,
 validation of the predicted distribution and assessing model accuracy is
 challenging. Model performance depends on both sample size and species’
 prevalence, being the fraction of the study area occupied by the species.
 Here, we present a novel method using simulated species to identify the
 minimum number of records required to generate accurate SDMs for taxa of
 different pre-defined prevalence classes. We quantified model performance
 as a function of sample size and prevalence and found model performance to
 increase with increasing sample size under constant prevalence, and to
 decrease with increasing prevalence under constant sample size. The Area
 Under the Curve (AUC) is commonly used as a measure of model performance.
 However, when applied to presence-only data it is prevalence-dependent and
 hence not an accurate performance index. Testing the AUC of an SDM for
 significant deviation from random performance provides a good alternative.
 We assessed the minimum number of records required to obtain good model
 performance for species of different prevalence classes in a virtual study
 area and in a real African study area. The lower limit depends on the
 species’ prevalence with absolute minimum sample sizes as low as 3 for
 narrow-ranged and 13 for widespread species for our virtual study area
 which represents an ideal, balanced, orthogonal world. The lower limit of
 3, however, is flawed by statistical artefacts related to modelling
 species with a prevalence below 0.1. In our African study area lower
 limits are higher, ranging from 14 for narrow-ranged to 25 for widespread
 species. We advocate identifying the minimum sample size for any species
 distribution modelling by applying the novel method presented here, which
 is applicable to any taxonomic clade or group, study area or climate
 scenario.
description: vanProosdij_etal_2015_Ecography_A4_Datapreparation_scriptThis R script is used to prepare environmental data including climate, soil and altitudinal data. By specifying the study area, data layers are cropped accordingly and this script is applicable to any study area and climate change scenario.vanProosdij_etal_2015_A4_Datapreparation.rvanProosdij_etal_2015_Ecography_A5_species_presence_scriptThis R script is used to define simulated species. It can be applied to a virtual study area but also to any real study area.vanProosdij_etal_2015_A5_species_presence.rvanProosdij_etal_2015_Ecography_A6_Virtualstudyarea_scriptThis R script is used to run the analysis and identify the minimum number of records required to produce accurate Species Distribution Models. It is applicable to any taxonomic clade or group, study area or climate change scenario.vanProosdij_etal_2015_A6_Virtualstudyarea.rvanProosdij_etal_2015_Ecography_A7_Africanstudyarea_scriptThis R script is used to run the analysis and identify the minimum number of records that is required to produce accurate Species Distribution Models. It is applicable to any taxonomic clade or group, study area or climate change scenario.vanProosdij_etal_2015_A7_Africanstudyarea.rvanProosdij_etal_2015_Ecography_A8_nullmodel_scriptThis R script generates null models that are used to evaluate Species Distribution Models. The script is applicable to any taxonomic clade or group, study area or climate change scenario.vanProosdij_etal_2015_A8_nullmodel.rvanProosdij_etal_2015_Ecography_A9_Plotting_scriptThis R script is used to plot the results. It is a general plotting function that is applicable to any taxonomi clade or group, study area or climate chance scenario.vanProosdij_etal_2015_A9_Plotting.rvanProosdij_et_al_2015_Ecography_A3_ReadMeThis manual provides a brief explanation of the Appendices A4-A9. It describes the input, output and data fromat requirements of each R script in the Appendices.vanProosdij_et_al_2015_Supplementary_Material_Appendix3.docx