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

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From: Journal of Forestry Research(Vol. 28, Issue 5)
Publisher: Springer
Document Type: Report
Length: 407 words

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To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1007/s11676-017-0388-5 Byline: Erica Hasui (1), Vinicius X. Silva (1), Rogerio G. T. Cunha (1), Flavio N. Ramos (1), Milton C. Ribeiro (2), Mario Sacramento (1,5), Marco T. P. Coelho (1,3), Diego G. S. Pereira (4), Bruno R. Ribeiro (1,3) Keywords: Ecological niche model; Generalized linear models; Habitat suitability; Landscape structure; Maxent Abstract: 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. Author Affiliation: (1) Laboratorio de Ecologia de Fragmentos Florestais (ECOFRAG), Instituto de Ciencia da Natureza, Universidade Federal de Alfenas, Rua Gabriel Monteiro da Silva, 700, Alfenas, MG, 37130-000, Brazil (2) Laboratorio de Ecologia Espacial e Conservacao (LEEC), Departamento de Ecologia, UNESP, Rio Claro. Av. 24A, 1515, Rio Claro, SP, 13506-900, Brazil (3) Programa de Pos-Graduacao em Ecologia e Evolucao da Universidade Federal de Goias, Universidade Federal de Goias, Goiania, GO, Brazil (4) Departamento de Ciencias Florestais, Universidade Federal de Lavras, Campus Universitario, Caixa Postal 3037, Lavras, MG, 37130-000, Brazil (5) Estacao de Hidrobiologia e Piscicultura de Furnas -- EHPF, Rua Lavras, 288 Bairro de Furnas, Sao Jose da Barra, MG, CEP: 37947-000, Brazil Article History: Registration Date: 23/03/2017 Received Date: 25/08/2016 Accepted Date: 27/09/2016 Online Date: 01/04/2017 Article note: Project funding: This work was supported by the Biota Minas Program (Proc. No. APQ 03549- 09) and FAPEMIG (Proc. No. PCE-00106-12). The online version is available at http://www.springerlink.com Corresponding editor: Hu Yanbo

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Gale Document Number: GALE|A501169730