The importance of integrating theory and application when estimating survival of wildlife populations

The paradigm of population regulation is one of ecology\u27s more controversial foundations, with arguments about the role of density dependence versus independence persisting throughout much of the 20th century. With time, the intensity of opposition has diminished, and ecologists acknowledge that both lend a hand in driving populations. There is less consensus about how to properly incorporate these ideas into population models. In practice, they are routinely included in models with an additive relationship. This means that density dependence and independence are both happening, but with no interaction. Although common, this method ignores the existence of a carrying capacity (K), which is a concept at the forefront of ecological theory. K determines the number of individuals able to persist in a given area and is driven by density-independent variables. Survival is then driven by the current density in relation to K, implying a sometimes complicated interaction between density and density-independent covariates. The purpose of this project is to quantify the importance of including this interaction (as in ecological theory) in estimates of survival in a wildlife population. I simulated data under a scenario with a time-varying carrying capacity and then used three models to estimate survival. Model one assumes survival was directly affected by an environmental covariate. Model two assumes an additive relationship between density and an environmental covariate. Model three matches ecological theory and assumes that survival is determined by an interaction between density and K driven by an environmental covariate. By comparing the survival estimates of each model, I can assess how far off the predictions are when the proper model is not used. The results of this study could have important implications for wildlife management by telling us how inaccurate current survival estimates could be. Utilizing this new method of survival estimation could help improve management of wildlife populations