1. Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers
- Author
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Andre M. Boustany, Vincent Ridoux, Karin A. Forney, Beth Gardner, Helen Bailey, Elizabeth A. Becker, Shay Viehman, Laura Mannocci, Patrick N. Halpin, Steven L. H. Teo, Arliss J. Winship, Jesse Cleary, Elliott L. Hazen, Matthew J. Oliver, Jerry Moxley, Daniel C. Dunn, Jason R. Hartog, Megan C. Ferguson, Jason J. Roberts, Charles T. Perretti, Steven J. Bograd, Daniel M. Palacios, and Brian P. Kinlan
- Subjects
0106 biological sciences ,Ecology ,010604 marine biology & hydrobiology ,Ephemeral key ,Temporal resolution ,Species distribution ,Mesoscale meteorology ,Environmental science ,Ecosystem ,Scale (map) ,010603 evolutionary biology ,01 natural sciences ,Ecology, Evolution, Behavior and Systematics - Abstract
While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time-scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine-scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.
- Published
- 2017