Additions of landscape metrics improve predictions of occurrence of species distribution models
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.
KeywordsEcological niche model Generalized linear models Habitat suitability Landscape structure Maxent
We thank Mainara Xavier Jordani and Diogo Borges Provete for their assistance in the field; the anonymous reviewers for critically reading the text; the Instituto Chico Mendes (ICMBio) for issuing the capture and transportation licenses (No. 10704-1; 22020-1); the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) for its financial support through the Biota Minas Program (Proc. No. APQ 03549-09); and Roy Richard Funch who revised the English translation.
- Blake JG (1983) Trophic structure of bird communities in forest patches in East-Central Illinois. Wilson Bull 95(3):416–430Google Scholar
- Crump ML, Scott JRNJ (1994) Standard techniques for inventory and monitoring: visual encounter surveys. In: Heyer WR et al (eds) Measuring and monitoring biological diversity: standard methods for amphibians. Smithsonian Institution Press, Washington, pp 84–92Google Scholar
- Drummond GM, Martins CS, Machado ABM, Sebaio FA, Antonini Y (2005) Biodiversidade em Minas Gerais: um atlas para sua conservação, 2nd edn. Fundação Biodiversitas, Belo Horizonte, p 222Google Scholar
- Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakasawa Y, Overton JMCCM, Peterson AT, Phillips SJ, Richardon K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
- Falls JB (1981) Mapping territories with playback: an accurate census method for songbirds. Stud Avian Biol 6:86–91Google Scholar
- Ferrier S, Watson G (1997) An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. Consult. Rep. NSW Natl. Parks Wildl. Serv. Dep. Environ., Sport Territ., Environ. Aust., Canberra. http://www.deh.gov.au/biodiversity/publications/technical/surrogates/
- Holdridge LR, Grenke WC, Hatheway WH, Liang T, Tosi JAJR (1971) Forest environments in tropical life zones: a pilot study. Pergamon Press, New YorkGoogle Scholar
- Loiselle BA, Jorgensen PM, Consiglio T, Jimenez I, Blake JG, Lohmann LG, Montiel OM (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcome? J Biogeogr 35:105–116Google Scholar
- Mcgarigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS v3: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 30 Mar 2015
- R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. www.R-project.org. Accessed 10 April 2012
- Rosales-Meda MM (2007) Caracterización de la población del mono aullador (Alouatta palliata palliata) em el Refugio Nacional de Vida Silvestre Isla San Lucas, Costa Rica. Neotrop Primates 14(3):122–127Google Scholar
- Sayre R, Roca E, Sedagatkish G, Young B, Keel S, Roca R, Sheppard S (2000) Nature in focus: rapid ecological assessment. Island Press, WashingtonGoogle Scholar