Flexible characterization of animal movement pattern using net squared displacement and a latent state model

Bastille‐Rousseau, Guillaume; Potts, Jonathan R.; Yackulic, Charles B.; Frair, Jacqueline L.; Ellington, E. Hance; Blake, Stephen

doi:10.1186/s40462-016-0080-y

Cited by 55 publications

(62 citation statements)

References 31 publications

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“…; Bastille‐Rousseau et al . ). The agreement between the NSD analysis and the MRSA was generally high, with the important exception of correlated data leading to a higher rate of false positive identifications for the NSD.…”

Section: Discussionmentioning

confidence: 97%

A framework for modelling range shifts and migrations: asking when, whither, whether and will it return

Gurarie

Cagnacci

Peters

et al. 2017

Journal of Animal Ecology

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Many animals undertake movements that are longer scaled and more directed than their typical home ranging behaviour. These movements include seasonal migrations (e.g. between breeding and feeding grounds), natal dispersal, nomadic range shifts and responses to local environmental disruptions. While various heuristic tools exist for identifying range shifts and migrations, none explicitly model the movement of the animals within a statistical framework that facilitates quantitative comparisons. We present the mechanistic range shift analysis (MRSA), a method to estimate a suite of range shift parameters: times of initiation, duration of transitions, centroids and areas of respective ranges. The method can take the autocorrelation and irregular sampling that is characteristic of much movement data into account. The mechanistic parameters suggest an intuitive measure, the range shift index, for the extent of a range shift. The likelihood based estimation further allows for statistical tests of several relevant hypotheses, including a range shift test, a stopover test and a site fidelity test. The analysis tools are provided in an R package (marcher). We applied the MRSA to a population of GPS tracked roe deer (Capreolus capreolus) in the Italian Alps between 2005 and 2008. With respect to seasonal migration, this population is extremely variable and difficult to classify. Using the MRSA, we were able to quantify the behaviours across the population and among individuals across years, identifying extents, durations and locations of seasonal range shifts, including cases that would have been ambiguous to detect using existing tools. The strongest patterns were differences across years: many animals simply did not perform a seasonal migration to wintering grounds during the mild winter of 2006-2007, even though some of these same animals did move extensively in other, harsher winters. For seasonal migrants, however, site fidelity across years was extremely high, even after skipping an entire seasonal migration. These results suggest that for roe deer behavioural plasticity and tactical responses to immediate environmental cues are reflected in the decision of whether rather than where to migrate. The MRSA also revealed a trade-off between the probability of migrating and the size of a home range.

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Section: Discussionmentioning

confidence: 97%

A framework for modelling range shifts and migrations: asking when, whither, whether and will it return

Gurarie

Cagnacci

Peters

et al. 2017

Journal of Animal Ecology

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“…The identification or classification of species' general movement strategies has been a goal of movement ecology method development and analysis for some time (Bastille‐Rousseau et al. ). Simulated movement associated with different strategies (e.g., sedentary, nomadic, migratory, and multi‐patches) showed distinct clusters based on their graph‐level metrics (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…As such, our approach is unlikely to become an alternative to existing approaches that classify movement strategies, such as the ones based on net squared displacement (Bastille‐Rousseau et al. , Cagnacci et al. , Spitz et al.…”

Section: Discussionmentioning

confidence: 99%

Applying network theory to animal movements to identify properties of landscape space use

Bastille‐Rousseau

Douglas‐Hamilton

Blake

et al. 2018

Ecological Applications

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Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node-level (local) metrics and graph-level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node-level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter-patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph-level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large-scale data sets efficiently, providing a framework for conservationists to analyze movement data. Functions created for the analyses are available within the R package moveNT.

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“…Spatiotemporal gradients, which characterize expanding populations as individuals radiate from an introduction locality, last until an equilibrium state has been reached across the available region (Bled et al 2013). Dynamic occupancy models are emerging using these techniques (Bastille-Rousseau et al 2016). Dynamic occupancy models are emerging using these techniques (Bastille-Rousseau et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Dispersal and population state of an endangered island lizard following a conservation translocation

Angeli

Lundgren

Pollock

et al. 2018

Ecological Applications

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Population size is widely used as a unit of ecological analysis, yet to estimate population size requires accounting for observed and latent heterogeneity influencing dispersion of individuals across landscapes. In newly established populations, such as when animals are translocated for conservation, dispersal and availability of resources influence patterns of abundance. We developed a process to estimate population size using N-mixture models and spatial models for newly established and dispersing populations. We used our approach to estimate the population size of critically endangered St. Croix ground lizards (Ameiva polops) five years after translocation of 57 individuals to Buck Island, an offshore island of St. Croix, United States Virgin Islands. Estimates of population size incorporated abiotic variables, dispersal limits, and operative environmental temperature available to the lizards to account for low species detection. Operative environmental temperature and distance from the translocation site were always important in fitting the N-mixture model indicating effects of dispersal and species biology on estimates of population size. We found that the population is increasing its range across the island by 5-10% every six months. We spatially interpolated site-specific abundance from the N-mixture model to the entire island, and we estimated 1,473 (95% CI, 940-1,802) St. Croix ground lizards on Buck Island in 2013 corresponding to survey results. This represents a 26-fold increase since the translocation. We predicted the future dispersal of the lizards to all habitats on Buck Island, with the potential for the population to increase by another five times in the future. Incorporating biologically relevant covariates as explicit parameters in population models can improve predictions of population size and the future spread of species introduced to new localities.

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Flexible characterization of animal movement pattern using net squared displacement and a latent state model

Cited by 55 publications

References 31 publications

A framework for modelling range shifts and migrations: asking when, whither, whether and will it return

A framework for modelling range shifts and migrations: asking when, whither, whether and will it return

Applying network theory to animal movements to identify properties of landscape space use

Dispersal and population state of an endangered island lizard following a conservation translocation

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