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标题：Spatial prediction and distribution modelling of Acacia spp. in Eastern Burkina Faso
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作者：Salifou Traoré ; Oumar Kaboré ; Lamourdia Thiombiano 等
期刊名称：Science et changements planétaires / Sécheresse
印刷版ISSN：1147-7806
电子版ISSN：1777-5922
出版年度：2008
卷号：19
期号：4
页码：283-292
DOI：10.1684/sec.2008.0154
出版社：John Libbey Eurotext
摘要：Figures See all figures Authors Salifou Traoré , Oumar Kaboré , Lamourdia Thiombiano , Jeanne Rasolodimby-Millogo Cellule télédétection et systèmes d’information géographique Institut de l’environnement et des recherches agricoles 01 BP 476 CREAF-Kamboinsé Burkina Faso, Laboratoire de biologie et écologie végétales Université de Ouagadougou 03 BP 7021 Ouagadougou Burkina Faso, FAO Regional Office for Africa PO Box 1628 Accra Ghana Key words: cross validation, predictor, regression model, spatial coverage DOI : 10.1684/sec.2008.0154 Page(s) : 283-92 Published in: 2008 Spatial prediction is an important tool for ecosystem planning and biodiversity conservation. With regard to socio-economic and ecological issues of Acacia species for rural development of drylands (forage for livestock, fuel wood, gum production, restoration of degraded land), the distribution of four Acacia spp. (Acacia dudgeoni, Acacia gourmaensis, Acacia hockii and Acacia seyal) was modelled in a sudano-sahelian zone (eastern Burkina Faso). This modelling is based on a sampling of 175 plots fitting environmental (climate, soil) and geographic variables (local density, spatial trend) as the predictors of species occurrence. The models are selected and evaluated with cross validation methods using GRASP (Generalized Regression and Spatial Predictions). Validation showed that the models of A. gourmaensis and A. hockii are stable and adequate with high values of Spearman correlation between observed and predicted distribution by cross validation (respectively 0.72 and 0.84). The model of A. seyal shows a significant spatial autocorrelation and it spatial prediction is calibrated by two methods considering autoregressive terms. Spatial prediction using spatial coverage of predictors showed the potential distribution of Acacia spp., highlighting the areas of high and low occurrence. Such information is useful for the management and valorisation of these forest resources under climate change and growing human pressure.
关键词：cross validation; predictor; regression model; spatial coverage