Climate-based ensemble predictions and uncertainty metrics (TAI, TSD) for plant species in Victoria, southeast Australia (38) and the Himalayan Kingdom of Bhutan (12)

This collection includes each of the climate variables (across multiple source datasets), presence and absence records, associated plant species distributions in Victoria (n=38) and Bhutan (n=12), and ensemble metrics generated as part of the manuscript titled 'Predicting plant species distributions using climate-based model ensembles with corresponding measures of congruence and uncertainty' (Stewart et al. 2022). \n\nEnsembles are calculated using modelled predictions of species distributions that have been fitted using alternative source climate datasets (n = 10). Each contributing surface represents an ensemble of 7 algorithms (artificial neural networks, boosted regression trees, random forests, generalised additive models, multivariate adaptive regression splines, classification tree analysis, and flexible discriminant analysis). The ensemble metrics presented include the Threshold Agreement Index (TAI; describes the degree to which model predictions agree with respect to an optimal classification threshold) and the Threshold-scaled Standard Deviation (TSD; the standard deviation across all predictions in the ensemble, penalised in regions where contributing models agree with respect to an optimal classification threshold). These metrics describe the congruence and uncertainty associated with the selection of alternative climate datasets; however, could be applied to other applications (e.g. niche overlap under climate change, alternative algorthms, etc.). These metrics are each calculated, per-pixel, using 10 contributing prediction surfaces. \n\n\n\nLineage: Please refer to Stewart et al. (2022) for detailed descriptions of how these data were generated. A tutorial, with R code, for producing the ensemble metrics described is provided as part of the files for this collection.