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Summary 1. The impact of hen harriers Circus cyaneus on red grouse Lagopus lagopus scoticus populations has received much attention. However, little has been done to model the population dynamics of the hen harrier alone. Such a model is needed to help inform the differing aims of conserving harriers and managing grouse moors, which serves as a reflection of human-wildlife conflicts around the globe. 2. On Langholm estate in Scotland, intensive studies have resulted in harrier numbers being known without error. We fit a Bayesian population model to these data, using a super-population model to permit inference in the presence of demographic and environmental stochasticity and in the absence of observation error. 3. Hen harriers have a straightforward life history. After fledging, juveniles show little natal site fidelity, often dispersing long distances into breeding areas rich in their preferred prey, the field vole Microtus agrestis and meadow pipit Anthus pratensis. Therefore, any increase in a local population is largely because of recruitment into the area as opposed to fledging success. Once birds have settled in an area, harriers are generally site faithful, with year-to-year survival depending, in part, on the density of meadow pipits. 4. Our model suggests that temporal patterns in harrier numbers on managed grouse moors, in the absence of illegal persecution, are influenced by vole numbers, whereas meadow pipit density appears to have a limited effect. 5. Synthesis and applications. Our modelling approach is a useful way to infer population processes, and the effects of the environment on these processes, for populations censused without error. When used to predict future harrier numbers under alternate management scenarios, our model indicates that harrier numbers on Langholm estate, Scotland, could be reduced without any direct human intervention if the estate can be managed in a way that reduces vole populations. In contrast, there appears little to gain from managing meadow pipit densities. If these conclusions apply to other harrier populations, then management to reduce vole numbers, while maintaining grouse densities, may help alleviate the conflict between conservationists and managers of grouse moors. [ABSTRACT FROM AUTHOR]

Management and conservation of populations of animals requires information on where they are, why they are there, and where else they could be. These objectives are typically approached by collecting data on the animals' use of space, relating these positional data to prevailing environmental conditions and employing the resulting statistical models to predict usage at other geographical regions. Technical advances in wildlife telemetry have accomplished manifold increases in the amount and quality of available data, creating the need for a statistical framework that can use them to make population-level inferences for habitat preference and space-use. This has been slow-in-coming because wildlife telemetry data are spatio-temporally autocorrelated, often unbalanced, presence-only observations of behaviourally complex animals, responding to a multitude of cross-correlated environmental variables. We review the evolution of regression models for the analysis of space-use and habitat preference and outline the essential features of a framework that emerges naturally from these foundations. This allows us to derive a relationship between usage of points in geographical space and preference of habitats in environmental space. Within this framework, we discuss eight challenges, inherent in the spatial analysis of telemetry data and, for each, we propose solutions that can work in tandem. Specifically, we propose a logistic, mixed-effects approach that uses generalized additive transformations of the environmental covariates and is fitted to a response data-set comprising the telemetry and simulated observations, under a case-control design. We apply this framework to a non-trivial case-study using satellite-tagged grey seals Halichoerus grypus from the east coast of Scotland. We perform model selection by cross-validation and confront our final model's predictions with telemetry data from the same, as well as different, geographical regions. We conclude that, despite the complex behaviour of the study species, flexible empirical models can capture the environmental relationships that shape population distributions. [ABSTRACT FROM AUTHOR]