The 81st Annual Meeting of the American Association of Physical Anthropologists (2012)

Model selection, zero-inflated models, and predictors of primate abundance in Cameroon


Department of Sociology and Anthropology, James Madison University

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Determining the ecological and anthropogenic factors that shape the abundance and distribution of wild primates is a critical component of primate conservation research. Such research is complicated, however, whenever the species under study are encountered infrequently, a characteristic of many taxa that are threatened with extinction. Typically, the resulting data sets based on surveys of such species will have a high frequency of zero counts which makes it difficult to robustly determine the predictor variables that are associated with species presence or absence. In this study, we test various statistical models using survey data that was gathered on seven species of primate in Korup National Park, Cameroon. Predictor variables include hunting signs and aspects of habitat structure and floristic composition. Our statistical models include zero-inflated models that are tailored to deal with a high frequency of zero counts. First, using a stepwise regression procedure and Akaike’s Information Criterion (AIC) corrected for small sample size, we found that models with up to five variables were most informative. Based on this analysis, we used five predictor variables to construct several regression models including Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial. Results show that a zero-inflated negative binomial model outperformed all other models. We discuss the statistical logic behind zero-inflated models and we recommend that researchers explore a variety of models when determining the factors that correlate with primate abundance. In particular, the often-used Poisson regression model can provide misleading results that can have negative implications for conserving threatened primate species

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