dynamicSDM: An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution

Dobson, Rachel; Challinor, Andy J.; Cheke, Robert A.; Jennings, Stewart; Willis, Stephen G.; Dallimer, Martin

Abstract

1. Species distribution models (SDM) are widely applied to understand changing species geographical distribution and abundance patterns. However, existing SDM tools are inherently static and inadequate for modelling species distributions that are driven by dynamic environmental conditions. 2. dynamicSDM provides novel tools that explicitly consider the temporal dimension at key SDM stages, including functions for: (a) Cleaning and filtering species occurrence records by spatial and temporal qualities; (b) Generating pseudo-absence records through space and time; (c) Extracting spatiotemporally buffered explanatory variables; (d) Fitting SDMs whilst accounting for temporal biases and autocorrelation and (e) Projecting intra- and inter- annual geographical distributions and abundances at high spatiotemporal resolution. 3. Package functions have been designed to be: flexible for targeting specific study species; compatible with other SDM tools; and, by utilising Google Earth Engine and Google Drive, to have low computing power and storage needs. We illustrate dynamicSDM functions with an example of a nomadic bird in southern Africa, the red-billed quelea Quelea quelea. 4. As dynamicSDM functions are flexible and easily applied, we suggest that these tools could be readily applied to other taxa and systems globally.

Citation

Dobson, R., Challinor, A. J., Cheke, R. A., Jennings, S., Willis, S. G., & Dallimer, M. (2023). dynamicSDM: An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution. Methods in Ecology and Evolution, 14(5), 1190-1199. https://doi.org/10.1111/2041-210x.14101

• Journal Article Type: Article
• Acceptance Date: Mar 10, 2023
• Online Publication Date: Mar 26, 2023
• Publication Date: 2023-05
• Deposit Date: Mar 28, 2023
• Publicly Available Date: May 31, 2023
• Journal: Methods in Ecology and Evolution
• Electronic ISSN: 2041-210X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 5
• Pages: 1190-1199
• DOI: https://doi.org/10.1111/2041-210x.14101
• Public URL: https://durham-repository.worktribe.com/output/1178307