02751nas a2200205   4500008004100000022001400041245013700055210006900192300001400261490000700275520211200282653000802394653001202402653000902414653001102423653000902434100002702443700002002470856005502490       2009                        eng d  a1600-058700aDifferences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors0 aDifferences in spatial predictions among species distribution mo  a907–9180 v323 aPrediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.10agis10amapping10amaps10amodels10asdms1 aSyphard, Alexandra, D.1 aFranklin, Janet  uhttp://dx.doi.org/10.1111/j.1600-0587.2009.05883.x01941nas a2200277   4500008004100000022001300041245010100054210006900155260001300224300001100237490000600248520114000254653001101394653001701405653001701422653001201439653001401451653000701465100002401472700002101496700001801517700003001535700002901565700002101594856004801615       2007                        eng d  a0303243400aCapturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity0 aCapturing the fugitive Applying remote sensing to terrestrial an  cFebruary  a1–200 v93 aAmongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques.10aamibio10abiodiversity10abiogeography10amapping10amigration10ars1 aLeyequien, Euridice1 aVerrelst, Jochem1 aSlot, Martijn1 aSchaepman-Strub, Gabriela1 aHeitkönig, Ignas, M. A.1 aSkidmore, Andrew  uhttp://dx.doi.org/10.1016/j.jag.2006.08.002