Comparing species distribution models constructed with different subsets of environmental predictors. Issue 1 (21st August 2014)

• Record Type: Journal Article
• Authors: Bucklin, David N.; Basille, Mathieu; Benscoter, Allison M.; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.; Speroterra, Carolina; Watling, James I.
• Editors: Thuiller, Wilfried
• Is Part Of: Diversity & distributions. Volume 21:Issue 1(2015:Jan.)
• Page Start: 23
• Page End: 35
• Publication Date: 2014-08-21
• Subjects: Bioclimate -- extreme weather -- Florida -- human influence -- land cover -- species distribution modelling; Biodiversity -- Periodicals; Biodiversity conservation -- Periodicals; 577
• DOI: 10.1111/ddi.12247
• Languages: English
• ISSNs: 1366-9516

Abstract

Abstract: Aim: To assess the usefulness of combining climate predictors with additional types of environmental predictors in species distribution models for range‐restricted species, using common correlative species distribution modelling approaches. Location: Florida, USA Methods: We used five different algorithms to create distribution models for 14 vertebrate species, using seven different predictor sets: two with bioclimate predictors only, and five 'combination' models using bioclimate predictors plus 'additional' predictors from groups representing: human influence, land cover, extreme weather or noise (spatially random data).We use a linear mixed‐model approach to analyse the effects of predictor set and algorithm on model accuracy, variable importance scores and spatial predictions. Results: Regardless of modelling algorithm, no one predictor set produced significantly more accurate models than all others, though models including human influence predictors were the only ones with significantly higher accuracy than climate‐only models. Climate predictors had consistently higher variable importance scores than additional predictors in combination models, though there was variation related to predictor type and algorithm. While spatial predictions varied moderately between predictor sets, discrepancies were significantly greater between modelling algorithms than between predictor sets. Furthermore, there were no differences in the level of agreement between binary 'presence–absence' maps and independent species range maps related to the predictor set used. Main conclusions: Our results indicate that additional predictors have relatively minor effects on the accuracy of climate‐based species distribution models and minor to moderate effects on spatial predictions. We suggest that implementing species distribution models with only climate predictors may provide an effective and efficient approach for initial assessments of environmental suitability.