title: Scale effects in species distribution models: implications for conservation planning under climate change.
abstract: Predictions of future species' ranges under climate change are needed for conservation planning, for which species distribution models (SDMs) are widely used. However, global climate model-based (GCM) output grids can bias the area identified as suitable when these are used as SDM predictor variables, because GCM outputs, typically at least 50x50 km, are biologically coarse. We tested the assumption that species ranges can be equally well portrayed in SDMs operating on base data of different grid sizes by comparing SDM performance statistics and area selected by four SDMs run at seven grid sizes, for nine species of contrasting range size. Area selected was disproportionately larger for SDMs run on larger grid sizes, indicating a cut-off point above which model results were less reliable. Up to 2.89 times more species range area was selected by SDMs operating on grids above 50x50 km, compared to SDMs operating at 1 km2. Spatial congruence between areas selected as range also diverged as grid size increased, particularly for species with ranges between 20000 and 90000 km2. These results indicate the need for caution when using such data to plan future protected areas, because an overly large predicted range could lead to inappropriate reserve location selection.
authors.affiliation: University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, Seoul 130-743, Korea. dharmascw@uos.ac.kr
authors.forename: Changwan
authors.initials: C
authors.lastname: Seo
authors.forename: James H
authors.initials: JH
authors.lastname: Thorne
authors.forename: Lee
authors.initials: L
authors.lastname: Hannah
authors.forename: Wilfried
authors.initials: W
authors.lastname: Thuiller
mesh_terms: D044822:Biodiversity; D002140:California; D003247:Conservation of Natural Resources; D017752:Greenhouse Effect; D016014:Linear Models; D008954:Models, Biological; D016571:Neural Networks, Computer; D011156:Population Density; D014197:Trees