Jacqueline L. Frair

Building the bridge between animal movement and population dynamics

While the mechanistic links between animal movement and population dynamics are ecologically obvious, it is much less clear when knowledge of animal movement is a prerequisite for understanding and predicting population dynamics. GPS and other technologies enable detailed tracking of animal location concurrently with acquisition of landscape data and information on individual physiology. These tools can be used to refine our understanding of the mechanistic links between behaviour and individual condition through ‘spatially informed’ movement models where time allocation to different behaviours affects individual survival and reproduction. For some species, socially informed models that address the movements and average fitness of differently sized groups and how they are affected by fission–fusion processes at relevant temporal scales are required. Furthermore, as most animals revisit some places and avoid others based on their previous experiences, we foresee the incorporation of long-term memory and intention in movement models. The way animals move has important consequences for the degree of mixing that we expect to find both within a population and between individuals of different species. The mixing rate dictates the level of detail required by models to capture the influence of heterogeneity and the dynamics of intra- and interspecific interaction.

Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data

Global positioning system (GPS) technologies collect unprecedented volumes of animal location data, providing ever greater insight into animal behaviour. Despite a certain degree of inherent imprecision and bias in GPS locations, little synthesis regarding the predominant causes of these errors, their implications for ecological analysis or solutions exists. Terrestrial deployments report 37 per cent or less non-random data loss and location precision 30 m or less on average, with canopy closure having the predominant effect, and animal behaviour interacting with local habitat conditions to affect errors in unpredictable ways. Home-range estimates appear generally robust to contemporary levels of location imprecision and bias, whereas movement paths and inferences of habitat selection may readily become misleading. There is a critical need for greater understanding of the additive or compounding effects of location imprecision, fix-rate bias, and, in the case of resource selection, map error on ecological insights. Technological advances will help, but at present analysts have a suite of ad hoc statistical corrections and modelling approaches available—tools that vary greatly in analytical complexity and utility. The success of these solutions depends critically on understanding the error-inducing mechanisms, and the biggest gap in our current understanding involves species-specific behavioural effects on GPS performance.

The interpretation of habitat preference metrics under use–availability designs

Models of habitat preference are widely used to quantify animal–habitat relationships, to describe and predict differential space use by animals, and to identify habitat that is important to an animal (i.e. that is assumed to influence fitness). Quantifying habitat preference involves the statistical comparison of samples of habitat use and availability. Preference is therefore contingent upon both of these samples. The inferences that can be made from use versus availability designs are influenced by subjectivity in defining what is available to the animal, the problem of quantifying the accessibility of available resources and the framework in which preference is modelled. Here, we describe these issues, document the conditional nature of preference and establish the limits of inferences that can be drawn from these analyses. We argue that preference is not interpretable as reflecting the intrinsic behavioural motivations of the animal, that estimates of preference are not directly comparable among different samples of availability and that preference is not necessarily correlated with the value of habitat to the animal. We also suggest that preference is context-dependent and that functional responses in preference resulting from changing availability are expected. We conclude by describing advances in analytical methods that begin to resolve these issues.

Correlation and studies of habitat selection: problem, red herring or opportunity?

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.

Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes

Abstract To maximize success, reintroduction programs generally select predator-free release areas having high habitat quality. Past studies provide little insight into recovery efforts where multiple, potentially novel, mortality hazards occur. The ability of translocated animals to cope with novel environments can be affected by both pre- and postrelease experiences with habitat and mortality risks. We experimentally released elk (Cervus elaphus) having different background experiences into an area where predators and hunters were prevalent and habitat quality varied. Using a competing risks approach, we predicted the postrelease survival of individuals and their fidelity to release areas as a function of animal source and postrelease encounters with forage resources and areas used by wolves (Canis lupus) or humans. Mortality patterns were consistent with prerelease exposure to mortality risks but not habitat differences among source areas. Wolf predation, poaching, and legal Native hunting were equivalent in magnitude and accounted for the majority of elk mortalities. Familiarity with either wolves or hunters prior to release yielded first-year survival rates 1.9–2.2 times greater than observed for animals naïve to both risks. These 2 primary sources of mortality traded off temporally as well as spatially given the proximity of roads, which wolves avoided. The prevalence of forage resources in release areas increased fidelity to release sites but coincided with higher mortality risk during the critical first year, potentially setting an ecological trap for animals naïve to local risks. Translocated individuals largely mediated their respective vulnerabilities over time, showing second-year survival rates equivalent to resident elk. In addition to using source populations that are able to adjust to mortality risks in release areas, spatial and temporal variation in mortality risks might be exploited when planning releases to increase the success of translocations into risky landscapes.

Adaptive models for large herbivore movements in heterogeneous landscapes

AbstractIt is usually assumed that landscape heterogeneity influences animal movements, but understanding of such processes is limited. Understanding the effects of landscape heterogeneity on the movements of large herbivores such as North American elk is considered very important for their management. Most simulation studies on movements of large herbivores use predetermined behavioral rules based on empirical observations, or simply on what seems reasonable for animals to do. Here we did not impose movement rules but instead we considered that animals had higher fitness (hence better performance) when they managed to avoid predators, and when they acquired important fat reserves before winter. Individual decision-making was modeled with neural networks that received as input those variables suspected to be important in determining movement efficiency. Energetic gains and losses were tracked based on known physiological characteristics of ruminants. A genetic algorithm was used to improve the overall performance of the decision processes in different landscapes and ultimately to select certain movement behaviors. We found more variability in movement patterns in heterogeneous landscapes. Emergent properties of movement paths were concentration of activities in well-defined areas and an alternation between small, localized movement with larger, exploratory movements. Even though our simulated individuals moved shorter distances that actual elk, we found similarities in several aspects of their movement patterns such as in the distributions of distance moved and turning angles, and a tendency to return to previously visited areas.

Building a mechanistic understanding of predation with GPS-based movement data

Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities along paths to a kill, we can quantify the ‘attack success’ parameter in functional response models directly. Problems remain in incorporating the behavioural complexity derived from GPS movement paths into functional response models, particularly in multi-prey systems, but we believe that exploring the details of GPS movement data has put us on the right path.

Identifying Movement States From Location Data Using Cluster Analysis

Abstract Animal movement studies regularly use movement states (e.g., slow and fast) derived from remotely sensed locations to make inferences about strategies of resource use. However, the number of movement state categories used is often arbitrary and rarely inferred from the data. Identifying groups with similar movement characteristics is a statistical problem. We present a framework based on k-means clustering and gap statistic for evaluating the number of movement states without making a priori assumptions about the number of clusters. This allowed us to distinguish 4 movement states using turning angle and step length derived from Global Positioning System locations and head movements derived from tip switches in a neck collar of free-ranging elk (Cervus elaphus) in west central Alberta, Canada. Based on movement characteristics and on the linkage between each state and landscape features, we were able to identify inter-patch movements, intra-patch foraging, rest, and inter-patch foraging movements. Linking behavior to environment (e.g., state-dependent habitat use) can inform decisions on landscape management for wildlife.