To boldly go where no volunteer has gone before: predicting volunteer activity to prioritize surveys at the landscape scale

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Filename	Format	Size	Description
ddi947-sup-0001-FigureS1-S3-TableS1-S3-AppendixS1.docx	Word document	506K	Figure S1. Distribution of model parameters showing (a) Probability of survey using logistic regression and a threatened species and protected areas hypothesis (‘conservation concern’), with important explanatory variables of (b) frequency of protected areas per grid cell, (c) number of different habitats per grid cell, (d) detection of a threatened species during the main atlas period in 1998–2002, and (e) road density, a single variable that explained a high level of deviance Figure S2. Discrimination capacity of the optimal distribution model developed for surveys between 2003 and 2007 in the south-west biodiversity hotspot, showing (a) distribution of predicted probability values associated with either surveyed (solid line) or unsurveyed (dotted line) cells, (b) a boxplot of absence vs. presence values for 2003–2007 relative to their predicted probability of being surveyed, and (c) the ROC curve (AUC = 0.829) Figure S3. Discrimination capacity of the optimal distribution model (‘conservation concern’), tested with surveys between 2008 and 2011 in the south-west biodiversity hotspot, showing (a) distribution of predicted probability values associated with either surveyed (solid line) or unsurveyed (dotted line) cells, (b) a boxplot of absence vs. presence values for 2008–2011 relative to their predicted probability of being surveyed, and (c) the ROC curve (AUC = 0.729) Table S1. Background on derivation of environmental variables for each grid cell used in the modelling process. Table S2. GLM of the importance of prior knowledge for predicting future surveys (response variable: number of surveys per grid cell 2003–2007). Table S3. Model parameters for the three accessibility models. Appendix S1. Species distribution modelling details

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