Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

作者：Jarnevich Catherine S^*; Talbert Marian; Morisette Jeffery; Aldridge Cameron; Brown Cynthia S; Kumar Sunil; Manier Daniel; Talbert Colin; Holcombe Tracy

来源：Ecological Modelling, 2017, 363: 48-56.

DOI：10.1016/j.ecolmodel.2017.08.017

• Species distribution modeling
• Habitat modeling
• Niche modeling
• Correlative models
• Maxent
• Boosted regression trees
• Random forest
• GLM
• Background data

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions. Published by Elsevier B.V.

• 出版日期2017-11-10