A novel statistical method for classifying habitat generalists and specialists

Authors

  • Robin L. Chazdon,

    Corresponding author
    1. Department of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Storrs, Connecticut 06269 USA
    2. Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark
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  • Anne Chao,

    1. Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan 30043
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  • Robert K. Colwell,

    1. Department of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Storrs, Connecticut 06269 USA
    2. Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark
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  • Shang-Yi Lin,

    1. Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan 30043
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  • Natalia Norden,

    1. Department of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Storrs, Connecticut 06269 USA
    2. Departamento de Ciencias Biológicas, Universidad de los Andes, Cra 1 #18A-10, Bogotá, Colombia
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  • Susan G. Letcher,

    1. Organization for Tropical Studies, Apartado Postal 676-2050, San Pedro de Montes de Oca, Costa Rica
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  • David B. Clark,

    1. Department of Biology, University of Missouri–St. Louis, One University Boulevard, St. Louis, Missouri 63121-4400 USA
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  • Bryan Finegan,

    1. Production and Conservation in Forests Program, Tropical Agricultural Centre for Research and Higher Education (CATIE), Apartado 93-7170, Turrialba, Costa Rica
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  • J. Pablo Arroyo

    1. Department of Geography, McGill University, 805 Sherbrook Street West, Montreal, Quebec H3A 2K6 Canada
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Abstract

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.

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