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dc.contributor.authorLin, Yu-Pinen_US
dc.contributor.authorYeh, Ming-Shengen_US
dc.contributor.authorDeng, Dong-Poen_US
dc.contributor.authorWang, Yung-Chiehen_US
dc.date.accessioned2014-12-08T15:12:33Z-
dc.date.available2014-12-08T15:12:33Z-
dc.date.issued2008-03-01en_US
dc.identifier.issn1466-822Xen_US
dc.identifier.urihttp://dx.doi.org/10.1111/j.1466-8238.2007.00352.xen_US
dc.identifier.urihttp://hdl.handle.net/11536/9633-
dc.description.abstractAim To evaluate geostatistical approaches, namely kriging, co-kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km(2) basin. Location Taiwan. Methods Kriging, co-kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co-kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co-kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro-scale and the macro-scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro-scale. Co-kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small-scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co-kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.en_US
dc.language.isoen_USen_US
dc.subjectbird diversityen_US
dc.subjectco-krigingen_US
dc.subjectgenetic algorithmen_US
dc.subjectkrigingen_US
dc.subjectoptimal additional samplingen_US
dc.subjectsimulated annealing simulationen_US
dc.subjectspatial patternen_US
dc.subjectvariogramen_US
dc.titleGeostatistical approaches and optimal additional sampling schemes for spatial patterns and future sampling of bird diversityen_US
dc.typeArticleen_US
dc.identifier.doi10.1111/j.1466-8238.2007.00352.xen_US
dc.identifier.journalGLOBAL ECOLOGY AND BIOGEOGRAPHYen_US
dc.citation.volume17en_US
dc.citation.issue2en_US
dc.citation.spage175en_US
dc.citation.epage188en_US
dc.contributor.department土木工程學系zh_TW
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.identifier.wosnumberWOS:000252966000004-
dc.citation.woscount22-
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