Multi-stage cluster sampling for estimating average species richness at different spatial grains
A multi-stage cluster sampling is proposed for quantifying and monitoring plant species richness at multiple spatial grains over large spatial extents. An unbiased estimator of average species richness at different grains and a conservative estimator of its sampling variance are obtained in a complete design-based framework, i.e., avoiding any assumption about the ecological community under study. An application to the Nature Reserve “Lago di Montepulciano” demonstrates that the proposed strategy may accomplish practical advantages and quite satisfactory levels of accuracy.
KeywordsBiodiversity monitoring Design-based inference Horvitz-Thompson estimation Variance estimation
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