01904nas a2200217   4500008004100000022001400041245009800055210006900153260001300222300001600235490000700251520125400258653001701512653001201529653001901541653001201560653001501572100002201587700002201609856005501631       2007                        eng d  a0012-965800aQuasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?0 aQuasiPoisson vs negative binomial regression how should we model  cNovember  a2766–27720 v883 aQuasi-Poisson and negative binomial regression models have equal numbers of parameters, and either could be used for overdispersed count data. While they often give similar results, there can be striking differences in estimating the effects of covariates. We explain when and why such differences occur. The variance of a quasi-Poisson model is a linear function of the mean while the variance of a negative binomial model is a quadratic function of the mean. These variance relationships affect the weights in the iteratively weighted least-squares algorithm of fitting models to data. Because the variance is a function of the mean, large and small counts get weighted differently in quasi-Poisson and negative binomial regression. We provide an example using harbor seal counts from aerial surveys. These counts are affected by date, time of day, and time relative to low tide. We present results on a data set that showed a dramatic difference on estimating abundance of harbor seals when using quasi-Poisson vs. negative binomial regression. This difference is described and explained in light of the different weighting used in each regression method. A general understanding of weighting can help ecologists choose between these two methods.10adistribution10aecology10aoverdispersion10apoisson10astatistics1 aHoef, Jay, M. Ver1 aBoveng, Peter, L.  uhttp://www.biomedexperts.com/Abstract.bme/1805164502087nas a2200193   4500008004100000245003900041210003900080260001300119300001400132490000800146520159200154653001701746653001201763653001401775653001201789100002201801700001601823856005401839       2006                        eng d00aComparing species abundance models0 aComparing species abundance models  cNovember  a153–1630 v1993 aFive regression models (Poisson, negative binomial, quasi-Poisson, the hurdle model and the zero-inflated Poisson) were used to assess the relationship between the abundance of a vulnerable plant species, Leionema ralstonii, and the environment. The methods differed in their capacity to deal with common properties of ecological data. They were assessed theoretically, and their predictive performance was evaluated with correlation, calibration and error statistics calculated within a bootstrap evaluation procedure that simulated performance for independent data.The hurdle model performed best, with the highest correlations between the observed and predicted abundances. This model was also well calibrated, giving the closest agreement between observed and predicted abundances. The negative binomial was the worst performing model. It had weaker correlations than the other models and resulted in a strong, inconsistent bias in predictions. The standard Poisson model which accommodates neither zero-inflation nor over-dispersion gave accurate estimates of regional population abundance, but at the individual population level they were inconsistent and biased.The strong performance of the hurdle model, coupled with theoretical properties that suit it for these data and for the ecology of this species, suggest that it is a useful alternative to other modelling methods. The gains in performance have practical advantages where predictions are used by conservation planners to understand population dynamics or to assess the relative risks of alternative management scenarios.10adistribution10aecology10amodelling10apoisson1 aPotts, Joanne, M.1 aElith, Jane  uhttp://dx.doi.org/10.1016/j.ecolmodel.2006.05.025