SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution models

Charles J. Marsh, Yoni Gavish, Mathias Kuemmerlen, Stefan Stoll, Peter Haase and William E. Kunin

Ecological Modelling, 2023, vol. 475, issue C

Abstract: Species distribution models (SDMs) are key tools in biodiversity and conservation, but assessing their reliability in unsampled locations is difficult, especially where there are sampling biases. We present a spatially-explicit sensitivity analysis for SDMs – SDM profiling – which assesses the leverage that unsampled locations have on the overall model by exploring the interaction between the effect on the variable response curves and the prevalence of the affected environmental conditions. The method adds a ‘pseudo-presence’ and ‘pseudo-absence’ to unsampled locations, re-running the SDM for each, and measuring the difference between the probability surfaces of the original and new SDMs. When the standardised difference values are plotted against each other (a ‘profile plot’), each point's location can be summarized by four leverage measures, calculated as the distances to each corner. We explore several applications: visualization of model certainty; identification of optimal new sampling locations and redundant existing locations; and flagging potentially erroneous occurrence records.

Keywords: Active learning; Conservation; Ecological niche models; Model evaluation; Monitoring; Uncertainty (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:475:y:2023:i:c:s030438002200271x

DOI: 10.1016/j.ecolmodel.2022.110170

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