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The gambin model provides a superior fit to species abundance distributions with a single free parameter: evidence,implementation and interpretation
Authors:Thomas J Matthews  Michael K Borregaard  Karl I Ugland  Paulo A V Borges  François Rigal  Pedro Cardoso  Robert J Whittaker
Institution:1. Conservation Biogeography and Macroecology Group, School of Geography and the Environment, Univ. of Oxford, , South Parks Road, Oxford, OX1 3QY, UK;2. Azorean Biodiversity Group (ABG, CITA‐A) and Portuguese Platform for Enhancing Ecological Research and Sustainability (PEERS), Depto de Ciências Agrárias, Univ. of the Azores, , Rua Capit?o Jo?o d'ávila, Pico da Urze, PT‐9700‐042 Angra do Heroísmo, Portugal;3. Dept of Marine Biology, Inst. of Biosciences, Univ. of Oslo, , PO Box 1066, Blindern, NO‐0316 Oslo, Norway;4. Finnish Museum of Natural History, Univ. of Helsinki, , PO Box 17, FI‐00014 Helsinki, Finland;5. Center for Macroecology, Evolution and Climate, Dept of Biology, Univ. of Copenhagen, , DK‐2100 Copenhagen ?, Denmark
Abstract:The species abundance distribution (SAD) has been a central focus of community ecology for over fifty years, and is currently the subject of widespread renewed interest. The gambin model has recently been proposed as a model that provides a superior fit to commonly preferred SAD models. It has also been argued that the model's single parameter (α) presents a potentially informative ecological diversity metric, because it summarises the shape of the SAD in a single number. Despite this potential, few empirical tests of the model have been undertaken, perhaps because the necessary methods and software for fitting the model have not existed. Here, we derive a maximum likelihood method to fit the model, and use it to undertake a comprehensive comparative analysis of the fit of the gambin model. The functions and computational code to fit the model are incorporated in a newly developed free‐to‐download R package (gambin). We test the gambin model using a variety of datasets and compare the fit of the gambin model to fits obtained using the Poisson lognormal, logseries and zero‐sum multinomial distributions. We found that gambin almost universally provided a better fit to the data and that the fit was consistent for a variety of sample grain sizes. We demonstrate how α can be used to differentiate intelligibly between community structures of Azorean arthropods sampled in different land use types. We conclude that gambin presents a flexible model capable of fitting a wide variety of observed SAD data, while providing a useful index of SAD form in its single fitted parameter. As such, gambin has wide potential applicability in the study of SADs, and ecology more generally.
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