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Identifying and understanding ecological drivers that influence wildlife populations is challenging but critical for conservation. This typically requires integrating long‐term data on both the population and potential drivers within statistical models that are suitable for analysing these complex relationships. State‐space models offer one method for integrating such data. Once implemented within a Bayesian framework, these analyses can control for multifactorial influences on populations, allowing one to extract otherwise undetectable correlations between the environment and the underlying, inferred demography.In the Moray Firth, Scotland, harbour seals have been counted annually for 30 years (1988–2018). A Bayesian state‐space model was used to explore whether patterns in vital rates were correlated to changes in prey abundance, inter‐specific competition (grey seal abundance), environmental variables [the North Atlantic Oscillation (NAO) and sea‐surface temperature], or level of biotoxins (saxitoxin and domoic acid) in the Moray Firth waters.The credible interval of the posterior distributions of three of these covariate coefficients (sandeel proxy, NAO and grey seal abundance) suggested that there was a relationship between those covariates and vital rates. Both the sandeel proxy and NAO showed a positive correlation with fecundity, whereas grey seal abundance had a negative impact on pup survival.This work demonstrates how an integrated state‐space modelling approach can bring together diverse data sets and point to important interactions with prey, and with other predators in the system. This suggests that the wider‐scale management of UK harbour seal populations with their contrasting temporal trends needs to account for variation in the marine ecosystem at appropriate spatial scales, in line with current policy concerning spatial planning in the marine environment. [ABSTRACT FROM AUTHOR]

The need to understand the impacts of land management for conservation, agriculture and disease prevention are driving demand for new predictive ecology approaches that can reliably forecast future changes in population size. Currently, although the link between habitat composition and animal population dynamics is undisputed, its function has not been quantified in a way that enables accurate prediction of population change in nature. Here, using 12 house sparrow colonies as a proof-of-concept, we apply recent theoretical advances to predict population growth or decline from detailed data on habitat composition and habitat selection. We show, for the first time, that statistical population models using derived covariates constructed from parametric descriptions of habitat composition and habitat selection can explain an impressive 92% of observed population variation. More importantly, they provide excellent predictive power under cross-validation, anticipating 81% of variability in population change. These models may be embedded in readily available generalized linear modelling frameworks, allowing their rapid application to field systems. Furthermore, we use optimization on our sample of sparrow colonies to demonstrate how such models, linking populations to their habitats, permit the design of practical and environmentally sound habitat manipulations for managing populations.