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SAMPLING THE SPECIES COMPOSITION OF A LANDSCAPE
Author(s) -
Plotkin Joshua B.,
Muller-Landau Helene C.
Publication year - 2002
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/0012-9658(2002)083[3344:stscoa]2.0.co;2
Subject(s) - sampling (signal processing) , ecology , relative abundance distribution , abundance (ecology) , species distribution , beta diversity , range (aeronautics) , species diversity , spatial ecology , breeding bird survey , biogeography , spatial distribution , scale (ratio) , macroecology , geography , relative species abundance , biodiversity , habitat , biology , cartography , remote sensing , computer science , filter (signal processing) , computer vision , materials science , composite material
The abundances and spatial distribution of species is central to biogeography and conservation. Several theories have been offered to explain landscape‐scale species distribution patterns. The verification of biogeographic theories, as well as conservation decisions, must be based upon empirical data gathered from necessarily restricted censuses. It is necessary, therefore, to understand the relationship between an underlying landscape‐scale pattern and the corresponding pattern it produces upon sampling small subregions. The similarity of species composition between two samples depends not only on the species composition of the underlying landscape from which the samples are drawn, but also on the underlying distribution of species abundances, the degree of conspecific spatial clustering, and sample size. In this paper, we investigate how sampling expectations change depending upon species abundance distributions and upon spatial distributions. We derive analytical results for the expected species overlap between two sampled regions under a wide range of conditions. We compare these results with data from a 50‐ha tropical forest census. These methodologies provide useful tools for assessing beta diversity, for testing macro‐ecological theory, and for designing landscape‐scale sampling schemes.