Additions of landscape metrics improve predictions of occurrence of species distribution models

• Published in: Journal of forestry research Vol. 28; no. 5; pp. 963 - 974
• Main Authors: Hasui, Érica, Silva, Vinícius X., Cunha, Rogério G. T., Ramos, Flavio N., Ribeiro, Milton C., Sacramento, Mario, Coelho, Marco T. P., Pereira, Diego G. S., Ribeiro, Bruno R.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2017; Springer; Springer Nature B.V; Laboratório de Ecologia de Fragmentos Florestais(ECOFRAG), Instituto de Ciência da Natureza, Universidade Federal de Alfenas, Rua Gabriel Monteiro da Silva, 700,Alfenas, MG 37130-000, Brazil%Laboratório de Ecologia Espacial e Conserva(c)(a)o(LEEC),Departamento de Ecologia, UNESP, Rio Claro.Av.24A,1515, Rio Claro, SP 13506-900, Brazil%Esta(c)(a)o de Hidrobiologia e Piscicultura de Furnas-EHPF, Rua Lavras, 288 Bairro de Furnas,S(a)o José da Barra, MG CEP: 37947-000, Brazil%Programa de Pós-Gradua(c)(a)o em Ecologia e Evolulu(c)(a)o da Universidade Federal de Goiás, Universidade Federal de Goiás, Goi(a)nia, GO, Brazil%Departamento de Ciências Florestais, Universidade Federal de Lavras, C(a)npus Universitário, Caixa Postal 3037, Lavras,MG 37130-000, Brazil
• Subjects: biogeography; Biomedical and Life Sciences; climate; Climate models; Ecological; environmental factors; Forestry; Fragments; habitat fragmentation; habitats; Landscape; landscapes; Life Sciences; linear; linear models; model;Generalized; models;Habitat; niche; Original Paper; prediction; Predictions; Scale (ratio); Soil types; Spatial distribution; Species; structure;Maxent; suitability;Landscape; surveys; Topography; Vegetation; Habitat suitability; Ecological niche model; Landscape structure; Maxent; Generalized linear models
• ISSN: 1007-662X; 1993-0607
• DOI: 10.1007/s11676-017-0388-5

Species distribution models are used to aid our understanding of the processes driving the spatial patterns of species’ habitats. This approach has received criticism,however, largely because it neglects landscape metrics. We examined the relative impacts of landscape predictors on the accuracy of habitat models by constructing distribution models at regional scales incorporating environmental variables (climate, topography, vegetation, and soil types)and secondary species occurrence data, and using them to predict the occurrence of 36 species in 15 forest fragments where we conducted rapid surveys. We then selected six landscape predictors at the landscape scale and ran general linear models of species presence/absence with either a single scale predictor (the probabilities of occurrence of the distribution models or landscape variables) or multiple scale predictors (distribution models ? one landscape variable).Our results indicated that distribution models alone had poor predictive abilities but were improved when landscape predictors were added; the species responses were not, however,similar to the multiple scale predictors. Our study thus highlights the importance of considering landscape metrics to generate more accurate habitat suitability models.