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1 December 2005 Coupling Principal Component Analysis and GIS to map deer habitats
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Abstract

We aimed to define at a relevant scale the spatial pattern of major vegetation types available to deer in order to qkf1trdiuuucharacterise habitat quality variations within our population area. We analysed data on the timber stand and the shrub layer collected in 1993 in the 2,614 ha Chizé reserve in western France. Multidimensional analyses (Principal Component Analysis and biplot) and a Geographic Information System (GIS) were used to extract most of the variation in vegetation data collected at the 4-ha resolution. At the timber stand level, two vegetation types occurred within the reserve: an oak Quercus sp. stand in the north, and a beech Fagus sylvatica stand in the south. This classification accounted for 29.6% of the total variability of the timber stand data base. At the shrub layer scale, three vegetation types were distinguished: hornbeam Carpinus betulus dominated coppices in the northeast part of the oak stand, maple Acer sp. dominated coppices in the northwest part of the oak stand, and no shrub layer in the beech stand in the south. This classification accounted for 32% of the total variability of the shrub layer data base. The coupled use of multivariate analysis and GIS allowed us to assess classification of forest habitats and appears promising for use in wildlife management and research purposes. This simple and robust tool allows users to account for site variability, and can provide satisfactory spatial representations of habitat potential at multiple scales.

Nathalie Pettorelli, Stephane Dray, and Daniel Maillard "Coupling Principal Component Analysis and GIS to map deer habitats," Wildlife Biology 11(4), 363-370, (1 December 2005). https://doi.org/10.2981/0909-6396(2005)11[363:CPCAAG]2.0.CO;2
Published: 1 December 2005
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