Modelling group dynamic animal movement

Ecology …, 2008

Animal movement has been the focus on much theoretical and empirical work in ecology over the last 25 years. By studying the causes and consequences of individual movement, ecologists have gained greater insight into the behavior of individuals and the spatial dynamics of populations at increasingly higher levels of organization. In particular, ecologists have focused on the interaction between individuals and their environment in an effort to understand future impacts from habitat loss and climate change. Tools to examine this interaction have included: fractal analysis, first passage time, Lévy flights, multi-behavioral analysis, hidden markov models, and state-space models. Concurrent with the development of movement models has been an increase in the sophistication and availability of hierarchical bayesian models. In this review we bring these two threads together by using hierarchical structures as a framework for reviewing individual models. We synthesize emerging themes in movement ecology, and propose a new hierarchical model for animal movement that builds on these emerging themes. This model moves away from traditional random walks, and instead focuses inference on how moving animals with complex behavior interact with their landscape and make choices about its suitability.

Ecology, 2004

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Journal of Animal Ecology, 2013

1. Movement is fundamental to individual and population dynamics, as it allows individuals to meet their basic requirements. Although movement patterns reflect interactions between internal and external factors, only few studies have examined the effects of these factors on movement simultaneously, and they generally focused on particular biological contexts (e.g. dispersal, foraging). 2. However, the relative importance of these factors in driving individual routine movements might reflect a species' potential flexibility to cope with landscape changes and therefore buffer their potential impact on fitness. 3. We used data from GPS collars on Scandinavian brown bears to investigate the relative role of these factors, as well as an additional factor (period of the year) on routine movements at two spatial scales (hourly and daily relocations). 4. As expected, internal factors played a major role in driving movement, compared to external factors at both scales, but its relative importance was greater at a finer scale. In particular, the interaction between reproductive status and period of the year was one of the most influential variables, females being constrained by the movement capacity of their cubs in the first periods of the year. The effect of human disturbance on movement was also greater for females with cubs than for lone females. 5. This study showed how reciprocal modulation of internal and external factors is shaping space use of brown bears. We stress that these factors should be studied simultaneously to avoid the risk of obtaining context-dependent inferences. Moreover, the study of their relative contribution is also highly relevant in the context of multiple-use landscapes, as human activities generally affect the landscape more than they affect the internal states of an individual. Species or individuals with important internal constraints should be less responsive to changes in their environment as they have less freedom from internal constraints and should thus be more sensitive to human alteration of the landscape, as shown for females with cubs in this study.

PLoS ONE, 2014

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Ecological Monographs, 2012

Recent developments in animal tracking technology have permitted the collection of detailed data on the movement paths of individuals from many species. However, analysis methods for these data have not developed at a similar pace, largely due to a lack of suitable candidate models, coupled with the technical difficulties of fitting such models to data. To facilitate a general modeling framework, we propose that complex movement paths can be conceived as a series of movement strategies among which animals transition as they are affected by changes in their internal and external environment. We synthesize previously existing and novel methodologies to develop a general suite of mechanistic models based on biased and correlated random walks that allow different behavioral states for directed (e.g., migration), exploratory (e.g., dispersal), area-restricted (e.g., foraging), and other types of movement. Using this ''toolbox'' of nested model components, multistate movement models may be custom-built for a wide variety of species and applications. As a unified state-space modeling framework, it allows the simultaneous investigation of numerous hypotheses about animal movement from imperfectly observed data, including time allocations to different movement behavior states, transitions between states, the use of memory or navigation, and strengths of attraction (or repulsion) to specific locations. The inclusion of covariate information permits further investigation of specific hypotheses related to factors driving different types of movement behavior. Using reversiblejump Markov chain Monte Carlo methods to facilitate Bayesian model selection and multimodel inference, we apply the proposed methodology to real data by adapting it to the natural history of the grey seal (Halichoerus grypus) in the North Sea. Although previous grey seal studies tended to focus on correlated movements, we found overwhelming evidence that bias toward haul-out or foraging locations better explained seal movement than did simple or correlated random walks. Posterior model probabilities also provided evidence that seals transition among directed, area-restricted, and exploratory movements associated with haulout, foraging, and other behaviors. With this intuitive framework for modeling and interpreting animal movement, we believe that the development and application of custommade movement models will become more accessible to ecologists and non-statisticians.

2020

Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental contribution to animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental contribution to movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods: We propose a data-driven analytic framework to quantify the environmental contribution to animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting the response of a simplified mo...

2014

1. The movements of individualsat almost any scaleare likely to depend on the behaviour of conspecifics. As an example, the movements of dispersing juveniles and their settling decisions may depend on the availability of mates and free territories, that is, both the presence and absence of other individuals. As another example, individuals can use the presence of conspecifics during foraging movements as an indicator of habitat quality. 2. We develop a general statistical framework for identifying and characterizing conspecific influence on movements from tracking data acquired simultaneously from a set of potentially interacting individuals. 3. We model conspecific attraction/repulsion through a functional response in which social behaviour is assumed to depend on proximity to other individuals. The model partitions variation in the functional response into a population component (common to all individuals), variation among individuals (modelled as random intercept-slope) and variation within an individual's trajectory (modelled through temporal autocorrelation). 4. We present a Bayesian approach for the estimation of the model and illustrate its use with simulated movement data generated from a number of contrasting scenarios. We then apply the method to a case study on eagle owl Bubo bubo juvenile dispersal, demonstrating that individual movements are generally influenced by the presence of conspecifics, with the level of attraction decreasing with increasing proximity to other individuals. We further show that female eagle owls are more attracted to conspecifics than males, and both males and females are more attracted to females than to males.

1. New technologies have made it possible to simultaneously, and remotely, collect time series of animal location data along with indicators of individuals' physiological condition. These data, along with animal movement models that incorporate individual physiological and behavioural states, promise to offer new insights into determinants of animal behaviour. Care must be taken, however, when attempting to infer causal relationships from biotelemetry data. The possibility of unmeasured confounders, responsible for driving both physiological measurements and animal movement, must be considered. Further, response values ðy t Þ may be predictive of future covariate values ðx tþs ; s ! 1Þ. When this occurs, the covariate process is said to be endogenous with respect to the response variable, which has implications for both choosing statistical estimation targets and also estimators of these quantities. 2. We explore models that attempt to relate x t = log(daily movement rate) to y t = log(average daily heart rate) using data collected from a black bear (Ursus americanus) population in Minnesota. The regression parameter for x t was 0Á19 and statistically different from 0 (P < 0Á001) when daily measurements were assumed to be independent, but residuals were highly autocorrelated. Assuming an autoregressive model (ar(1)) for the residuals, however, resulted in a negative slope estimate (-0Á001) that was not statistically different from 0. 3. The sensitivity of regression parameters to the assumed error structure can be explained by exploring relationships between lagged and current values of x and y and between parameters in the independence and ar(1) models. We hypothesize that an unmeasured confounder may be responsible for the behaviour of the regression parameters. In addition, measurement error associated with daily movement rates may also play a role. 4. Similar issues often arise in epidemiological, biostatistical and econometrics applications; directed acyclical graphs, representing causal pathways, are central to understanding potential problems (and their solutions) associated with modelling time-dependent covariates. In addition, we suggest that incorporating lagged responses and lagged predictors as covariates may prove useful for diagnosing when and explaining why some conclusions are sensitive to model assumptions.

International Journal of Geographical Information Science, 2013

Journal of Avian Biology, 2010

Ring re-encounter data, in particular ring recoveries, have made a large contribution to our understanding of bird movements. However, almost every study based on ring re-encounter data has struggled with the bias caused by unequal observer distribution. Re-encounter probabilities are strongly heterogeneous in space and over time. If this heterogeneity can be measured or at least controlled for, the enormous number of ring re-encounter data collected can be used effectively to answer many questions. Here, we review four different approaches to account for heterogeneity in observer distribution in spatial analyses of ring re-encounter data. The first approach is to measure re-encounter probability directly. We suggest that variation in ring re-encounter probability could be estimated by combining data whose re-encounter probabilities are close to one (radio or satellite telemetry) with data whose re-encounter probabilities are low (ring re-encounter data). The second approach is to measure the spatial variation in re-encounter probabilities using environmental covariates. It should be possible to identify powerful predictors for ring re-encounter probabilities. A third approach consists of the comparison of the actual observations with all possible observations using randomization techniques. We encourage combining such randomisations with ring re-encounter models that we discuss as a fourth approach. Ring re-encounter models are based on the comparison of groups with equal re-encounter probabilities. Together these four approaches could improve our understanding of bird movements considerably. We discuss their advantages and limitations and give directions for future research.