Int J Health Geogr - Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry.

Tópicos

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Resumo

CKGROUND: Ecological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers.METHODS: We assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions.RESULTS: We found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions).CONCLUSION: Our results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.

Resumo Limpo

ckground ecolog nich model integr known site occurr speci phenomena data environment variat across landscap infer environment space potenti inhabit ie ecolog nich generat predict map potenti distribut geograph space key input process includ raster data layer character spatial variat environment paramet veget indic remot sens satellit imageri extent ecolog nich model reflect realworld distribut depend number factor obvious concern qualiti content environment data layersmethod assess ecolog nich model predict hn avian flu presenc quantit within among four geograph region base model incorpor two mean summar three veget indic deriv modi satellit evalu model predict abil use partial roc analysi glm anova compar perform among indic regionsresult found correl veget indic high contain inform overlap broad neither type veget index use method summari affect model perform signific howev degre model predict transfer ie project onto landscap condit repres landscap train impact predict strength great withinregion model predict far outperform model project among regionsconclus result provid first quantit test appropri use differ remot sens data set ecolog nich model applic test result decis best index product mean summar indic emphas need care evalu product use model eg match tempor dimens spatial resolut optimum perform instead simpl relianc larg number data layer

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