Extension of Pradel capture–recapture survival‐recruitment model accounting for transients

Abstract

Abstract Population growth is the sum of survival and recruitment, and knowledge of these two vital rates is crucial for understanding population dynamics. Moreover, animal populations often contain varying number of transient (i.e. nonresident) individuals that do not contribute to these rates but may bias their estimates. The widely used Pradel (1996, Biometrics, 52: 703) survival‐recruitment model for capture–mark–recapture data is only able to handle resident individuals on a fixed study area across a particular study period. Yet, numerous capture–mark–recapture data sets, from a wide range of taxa, feature transient individuals. The most widespread sources of avian demographic data, based on citizen science projects, feature both transient individuals and changes in the study area over time. We present an extension of the Pradel model that accounts for the presence of transient individuals and changes in the study area. In contrast to known extensions of the Cormack–Jolly–Seber models in which transients are modelled as a proportion of newly captured individuals, our novel approach models transient individuals as a proportion of all birds captured. In addition, we present a new simple way to visualize the interlinkage of the vital rates produced by our extended Pradel model. We demonstrate utilization of the model using capture–mark–recapture data collected by a constant‐effort mist‐netting citizen science programme in the Czech Republic, presenting demographic rates of two species with different population dynamics. To demonstrate the newly achieved ability to analyse the phenomenon of transience, we show the differences in transience and its temporal trends between wet and dry habitats. Removing the limitations of the Pradel model opens up new potential for much wider range of applications. Furthermore, our novel parametrization of transients as a proportion of all birds captured facilitates biological interpretation of the transience parameter and the study of transience as a biological phenomenon. Calculating all demographic parameters in a single model also opens up a unique possibility to take into account their correlated error distributions in follow‐up analyses. Our model can be further extended in several ways and can serve as a basic building block in a wide range of demographic analyses.