文章基本信息

标题：Modeling of Species Geographic Distribution for Assessing Present Needs for the Ecological Networks
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作者：T. Doko ; F A. Kooiman ; A.G. Toxopeus 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B4
页码：267-276
出版社：Copernicus Publications
摘要：In Japan, attention is currently focused on designing ecological networks for wildlife animals. However there is an obvious lack of the species spatial information. This study aims (a) to acquire the potential spatial distribution of Asiatic black bear and Japanese se- row to identify core areas, and (b) to propose a methodology for assessing needs for ecological networks. 1836 species' point records and 14 potential predictors were prepared in a GIS environment, split into a train and a test dataset. Screening predictors by statisti- cal analysis, we modeled species geographic distribution by three algorithms: GARP, MaxEnt, and GLMs in Kanagawa and Shizu- oka Prefectures. Based on the most accurate maps, assessed by Kappa statistics, population was estimated based on population den- sity and area of habitat patch. For bear, MaxEnt performed best with the predictor variables: altitude, distance to paths and stone steps, distance to wide roads, and vegetation cover types. GARP failed to predict presence in Fuji. Its best GLM equation was log(p/(1-p))=(-1.486e+01)+(7.335e-04)*distance to paths and stone steps+(9.470e-03)*altitude. For serow's distribution, GARP per- formed best with altitude, slope, distance to highways, distance to general roads, distance to paths and stone steps, distance to rivers, and NDVI. Its best GLM equation was log(p/(1-p))=-5.91785430+0.04024136*slope+0.26478759*square root of altitude. The esti- mated numbers of individuals for bear was 5~9 in Mt. Ashitaka, 51~102 in Fuji-Tanzawa, 160~320 in South Alps, 4~8 in Mt. Ke- nashi, 4~8 in Izu Peninsula, and 6~11 in Hakone; for serow, < 1581 were estimated in Fuji-Tanzawa, and < 537 in other areas. For bear MaxEnt and for serow GARP are the best algorithms, but GLM has good transferability. There is a need for ecological net- works in Fuji-Tanzawa for bear, but not for serow
关键词：Ecology; Environment; GIS; Modelling; Algorithms; Landscape; Method; Proposal