Analyzing population growth curves

Assessing animal population growth curves is an essential feature of field studies in ecology and wildlife management. We used five models to assess population growth rates with a number of sets of population growth rate data. A ‘generalized’ logistic curve provides a better model than do four other popular models. Use of difference equations for fitting was checked by a comparison of that method and direct fitting of the analytical (integrated) solution for three of the models. Fits to field data indicate that estimates of the asymptote, K, from the ‘generalized logistic ’ and the ordinary logistic agree well enough to support use of estimates of K from the ordinary logistic on data that cannot be satisfactorily fitted with the generalized logistic. Akaike’s information criterion is widely used, often with a small sample version AICc. Our study of five models indicated a bias in the AICc criterion, so we recommend checking results with estimates of variance about regression for fitted models. Fitting growth curves provides a valuable supplement to, and check on computer models of populations. There is a long history of counting wild animals, and counts have been widely utilized in wildlife management. Popula-tion models have become popular in recent times, and many models utilize counts with sex, age and kill data. The present study seeks to extract information on populatio