TY - JOUR

T1 - Extension of the gambin model to multimodal species abundance distributions

AU - Matthews, Thomas J.

AU - Borregaard, Michael K.

AU - Gillespie, Colin S.

AU - Rigal, François

AU - Ugland, Karl I.

AU - Krüger, Rodrigo Ferreira

AU - Marques, Roberta

AU - Sadler, Jon P.

AU - Borges, Paulo A.V.

AU - Kubota, Yasuhiro

AU - Whittaker, Robert J.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Species abundance distributions (SADs) are one of the most widely used tools in macroecology, and it has become increasingly apparent that many empirical SADs can best be described as multimodal. However, only a few SAD models have been extended to incorporate multiple modes and no software packages are available to fit multimodal SAD models. In this study, we present an extension of the gambin SAD model to multimodal SADs. We derive the maximum likelihood equations for fitting the bimodal gambin distribution and generalize this approach to fit gambin models with any number of modes. We present these new functions, along with additional functions to aid in the analysis of multimodal SADs, within an updated r package (“gambin”; version 2.4.0) that enables the fitting, plotting and evaluating of gambin models with any number of modes. We use a mixture of simulations and empirical datasets to test our new models, including tests of the sensitivity of the model parameters to the number of individuals and the number of species in a sample. We show that the new multimodal gambin models perform well under a variety of circumstances, and that the application of these new models to empirical SAD and other macroecological (e.g., species range size distributions) datasets can provide interesting insights. The updated software package is simple to use and provides straightforward yet flexible statistical analyses of multimodality in SAD-type datasets.

AB - Species abundance distributions (SADs) are one of the most widely used tools in macroecology, and it has become increasingly apparent that many empirical SADs can best be described as multimodal. However, only a few SAD models have been extended to incorporate multiple modes and no software packages are available to fit multimodal SAD models. In this study, we present an extension of the gambin SAD model to multimodal SADs. We derive the maximum likelihood equations for fitting the bimodal gambin distribution and generalize this approach to fit gambin models with any number of modes. We present these new functions, along with additional functions to aid in the analysis of multimodal SADs, within an updated r package (“gambin”; version 2.4.0) that enables the fitting, plotting and evaluating of gambin models with any number of modes. We use a mixture of simulations and empirical datasets to test our new models, including tests of the sensitivity of the model parameters to the number of individuals and the number of species in a sample. We show that the new multimodal gambin models perform well under a variety of circumstances, and that the application of these new models to empirical SAD and other macroecological (e.g., species range size distributions) datasets can provide interesting insights. The updated software package is simple to use and provides straightforward yet flexible statistical analyses of multimodality in SAD-type datasets.

KW - compound distributions

KW - gambin

KW - horse flies

KW - multimodal species abundance distributions

U2 - 10.1111/2041-210X.13122

DO - 10.1111/2041-210X.13122

M3 - Journal article

AN - SCOPUS:85058150119

VL - 10

SP - 432

EP - 437

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

SN - 2041-210X

ER -