Use of integrated population models for assessing density-dependence and juvenile survival in Northern Bobwhites (Colinus virginianus).

Abstract

Management of wildlife populations is most effective with a thorough understanding of the interplay among vital rates, population growth, and density-dependent feedback; however, measuring all relevant vital rates and assessing density-dependence can prove challenging. Integrated population models have been proposed as a method to address these issues, as they allow for direct modeling of density-dependent pathways and inference on parameters without direct data. We developed integrated population models from a 25-year demography dataset of Northern Bobwhites (Colinus virginianus) from southern Georgia, USA, to assess the demographic drivers of population growth rates and to estimate the strength of multiple density-dependent processes simultaneously. Furthermore, we utilize a novel approach combining breeding productivity and post-breeding abundance and age-and-sex ratio data to infer juvenile survival. Population abundance was relatively stable for the first 14 years of the study but began growing after 2012, showing that bobwhite populations may be stable or exhibit positive population growth in areas of intensive management. Variation in breeding and non-breeding survival drove changes in population growth in a few years; however, population growth rates were most affected by productivity across the entire study duration. A similar pattern was observed for density-dependence, with relatively stronger negative effects of density on productivity than on survival. Our novel modeling approach required an informative prior but was successful at updating the prior distribution for juvenile survival. Our results show that integrated population models provide an attractive and flexible method for directly modeling all relevant density-dependent processes and for combining breeding and post-breeding data to estimate juvenile survival in the absence of direct data.