Testing the utility of species distribution modelling using Random Forests for a species in decline. (25th March 2020)

• Record Type: Journal Article
• Authors: Burns, Phoebe A.; Clemann, Nick; White, Matt
• Is Part Of: Austral ecology. Volume 45:Number 6(2020)
• Page Start: 706
• Page End: 716
• Publication Date: 2020-03-25
• Subjects: threatened species -- species distribution modelling -- model testing -- ground truth -- sensitivity and specificity; Ecology -- Southern Hemisphere -- Periodicals; Ecology -- Australia -- Periodicals; 557
• DOI: 10.1111/aec.12884
• Languages: English
• ISSNs: 1442-9985

Abstract

Abstract: Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species' ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse ( Pseudomys novaehollandiae ) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species' probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.