Search

Site fidelity—the tendency to return to previously visited locations—is widespread across taxa. Returns may be driven by several mechanisms, including memory, habitat selection, or chance; however, pattern‐based definitions group different generating mechanisms under the same label of 'site fidelity', often assuming memory as the main driver. We propose an operational definition of site fidelity as patterns of return that deviate from a null expectation derived from a memory‐free movement model. First, using agent‐based simulations, we show that without memory, intrinsic movement characteristics and extrinsic landscape characteristics are key determinants of return patterns and that even random movements may generate substantial probabilities of return. Second, we illustrate how to implement our framework empirically to establish ecologically meaningful, system‐specific null expectations for site fidelity. Our approach provides a conceptual and operational framework to test hypotheses on site fidelity across systems and scales. [ABSTRACT FROM AUTHOR]

Most animals live in home ranges, and memory is thought to be an important process in their formation. However, a general memory‐based model for characterising and predicting home range emergence has been lacking. Here, we use a mechanistic movement model to: (1) quantify the role of memory in the movements of a large mammal reintroduced into a novel environment, and (2) predict observed patterns of home range emergence in this experimental setting. We show that an interplay between memory and resource preferences is the primary process influencing the movements of reintroduced roe deer (Capreolus capreolus). Our memory‐based model fitted with empirical data successfully predicts the formation of home ranges, as well as emergent properties of movement and spatial revisitation observed in the reintroduced animals. These results provide a mechanistic framework for combining memory‐based movements, resource preferences, and the formation of home ranges in nature. [ABSTRACT FROM AUTHOR]

Aim: Determining the drivers of movement of different life‐history stages is crucial for understanding age‐related changes in survival rates and, for marine top predators, the link between fisheries overlap and incidental mortality (bycatch), which is driving population declines in many taxa. Here, we combine individual tracking data and a movement model to investigate the environmental drivers and conservation implications of divergent movement patterns in juveniles (fledglings) and adults of a threatened seabird, the white‐chinned petrel (Procellaria aequinoctialis). Location: South‐west Atlantic Ocean. Methods: We compare the spatial distributions and movement characteristics of juvenile, breeding and non‐breeding adult petrels, and apply a mechanistic movement model to investigate the extent to which chlorophyll a concentrations (a proxy for food resources) and ocean surface winds drive their divergent distribution patterns. We also consider the conservation implications by determining the relative overlap of each life‐history stage with fishing intensity and reported fishing effort (proxies for bycatch risk). Results: Naïve individuals fledged with similar flight capabilities (based on distances travelled, flight speeds and track sinuosity) to adults but differed in their trajectories. Comparison of simulations from the mechanistic model with real tracks showed that juvenile movements are best predicted by prevailing wind patterns, whereas adults are attracted to food resources on the Patagonian Shelf. The juveniles initially dispersed to less productive oceanic waters than those used by adults, and overlapped less with fishing activity; however, as they moved westwards towards South America, bycatch risk increased substantially. Main conclusions: The use of a mechanistic framework provided insights into the ontogeny of movement strategies within the context of learned versus innate behaviour and demonstrated that divergent movement patterns of adults and juveniles can have important implications for the conservation of threatened seabirds. [ABSTRACT FROM AUTHOR]

We present methodological advances to a recently developed framework to study sequential habitat use by animals using a visually-explicit and tree-based Sequence Analysis Method (SAM), derived from molecular biology and more recently used in time geography. Habitat use sequences are expressed as annotations obtained by intersecting GPS movement trajectories with environmental layers. Here, we develop IM-SAM, where we use the individual reference area of use as the reference spatial context. To assess IM-SAM's applicability, we investigated the sequential use of open and closed habitats across multiple European roe deer populations ranging in landscapes with contrasting structure. Starting from simulated sequences based on a mechanistic movement model, we found that different sequential patterns of habitat use were distinguished as separate, robust clusters, with less variable cluster size when habitats were present in equal proportions within the individual reference area of use. Application on real roe deer sequences showed that our approach effectively captured variation in spatio-temporal patterns of sequential habitat use, and provided evidence for important behavioral processes, such as day-night habitat alternation. By characterizing sequential habitat use patterns of animals, we may better evaluate the temporal trade-offs in animal habitat use and how they are affected by changes in landscapes. [ABSTRACT FROM AUTHOR]

Conservation translocation is a controversial, yet sometimes the only applicable, conservation tool in the face of the human-induced environmental change. One important aspect often neglected in translocation experiments is predicting the spatial expansion potential of the translocated population in the release area. In this study, we used a spatially explicit movement model to project the expansion rate of a translocated clouded Apollo ( Parnassius mnemosyne ) population. The model was parameterised with data collected from the source population inhabiting an open landscape with well-connected habitat patches. We then used these parameter estimates to simulate the spread of the translocated population in the new area, which was a forested landscape with more fragmented habitat. We tested the model predictions by comparing them to the observed expansion rate between 2000 and 2016. Additionally, we tested whether including annually varying weather conditions would increase realism in the projections. Model predictions were qualitatively correct and quantitatively most accurate for flight seasons with prevailing weather conditions similar to those of the season when the data for parameter estimation was collected. We conclude that spatially explicit movement models are potential tools to improve the planning of future conservation translocations in terms of choosing the optimal release area, as they enable the comparison of functional connectivities even between structurally different landscapes. However, our results suggest that a model fitted based on movement data from a single year may not be representative for the overall dispersal rates due to temporal variation in dispersal. [ABSTRACT FROM AUTHOR]

Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. , We use stochastic simulations of two movement models to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states from movement paths. , Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0% when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was uncorrelated with path length, but the variance in classification accuracy was inversely related to path length. , Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose ( Alces alces). , We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data. [ABSTRACT FROM AUTHOR]