Correlated velocity models as a fundamental unit of animal movement: synthesis and applications

Gurarie, Eliezer; Fleming, Christen H.; Fagan, William F.; Laidre, Kristin L.; Hernández-Pliego, Jesús; Ovaskainen, Otso

doi:10.1186/s40462-017-0103-3

Cited by 64 publications

(93 citation statements)

References 48 publications

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“…Animal behavior and physiology, however, changes across temporal and spatial scales, and attraction or avoidance of different habitats is unquestionably dynamic. Although this is widely recognized (Thurfjell et al, 2014; Hooten et al, 2014; Avgar et al, 2016; Gurarie et al, 2017), behavioral changes are rarely accounted for in any habitat selection analyses, including SSFs, due to the additional complexity models require to capture the non-stationary condition.…”

Section: Introductionmentioning

confidence: 99%

Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds

Eisaguirre

Booms

Barger

et al. 2019

Preprint

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Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality. Here, we used satellite telemetry combined with step-selection functions (SSFs) designed to explicitly incorporate the energy landscape to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle Aquila chrysaetos, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014-2017. Fitted SSFs indicated eagles had a strong general preference for south-facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. SSFs also revealed that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower-paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions. Regardless of the behavioral mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have been previously documented to affect the ecology of terrestrial species (e.g., large mammals) by modifying individuals' movement patterns; our work shows these effects on movement extend to avian taxa.

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Section: Introductionmentioning

confidence: 99%

Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds

Eisaguirre

Booms

Barger

et al. 2019

Preprint

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“…In increasing order of complexity, these include a pure random walk (Wiener process; e.g. see (McClintock et al 2014)), a linear-and angular-velocity-biased random walk (Ornstein-Uhlenbeck process, (Gurarie et al 2017)), and location salient random walk (e.g., walks in a force field, (Magdziarz et al 2012); or movement dependent on the local landscape, (Harris and Blackwell 2013)), where the location itself could be a function of time (e.g., the centroid of a moving group-see (Langrock et al 2014)). The second is to use rule-based simulations of movement over rectangularly or hexangonally rasterized landscapes without the benefit of a compact underlying differential equation structure (Getz et al 2015, del Mar Delgado et al 2018 Our path simulation algorithm of the movement of individuals over landscapes is an outgrowth of Brownian motion (Pozdnyakov et al 2014), correlated random walks (Kareiva and Shigesada 1983) and, even Levy walks (Benhamou 2007) simulation methods.…”

Section: Figurementioning

confidence: 99%

Simulation and Analysis of Animal Movement Paths using Numerus Model Builder

Getz

Vissat

Salter³

2019

Preprint

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Animal movement paths are represented by point-location time series called relocation data. How well such paths can be simulated, when the rules governing movement depend on the internal state of individuals and environmental factors (both local and, when memory is involved, global) remains an open question. To answer this, we formulate and test models able to capture the essential statistics of multiphase versions of such paths (viz., movement-phase-specific step-length and turning-angle means, variances, auto-correlation, and cross correlation values), as well as broad scale movement patterns. The latter may include patchy coverage of the landscape, as well as the existence of home-range boundaries and gravitational centers-ofmovement (e.g., centered around nests). Here we present a Numerus Model Builder implementation of two kinds of models: a high-frequency, multi-mode, biased, correlated random walk designed to simulate real movement data at a scale that permits simulation and identification of path segments that range from minutes to days; and a model that uses statistics extracted from relocation data-either empirical or simulated-to construct movement modes and phases at subhourly to daily scales. We evaluate how well our derived statistical movement model captures patterns produced by our more detailed simulation model as a way to evaluate how well derived statistical movement models may capture patterns occurring in empirical data.

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“…If we interpolate the trajectory it is not clear how we would obtain the corresponding Y i measurements at these interpolated locations, other than using a method such as kriging, which may dilute any preferential effect that was present in the original data. Another option may be to use thinning (Gurarie et al, 2017), however we wanted to avoid this in our application in this paper, due to the limited temporal resolution of the data to which we have access. We term this model the "preferential correlated random walk" (PCRW) model and assume that the sampling locations X(t 1 ), .…”

Section: A Preferential Movement Modelmentioning

confidence: 99%

“…Parameter Estimates. Note that because of the way the data was generated (by subsampling trajectories created on a relatively fine time scale), the estimated movement parameters may not correspond to their nominal values used to create the data (Gurarie et al, 2017). Hence, we report here results for the estimates of the parameters of the spatial process.…”

Section: Relationship Between the Movement Model And Likelihood Integmentioning

confidence: 99%

Modelling ocean temperatures from bio-probes under preferential sampling

Dinsdale¹,

Salibián‐Barrera²

2019

Ann. Appl. Stat.

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In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes), which are often used to study the animals' behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), and measurements are taken from sensors mounted on Elephant Seals in the Southern Indian ocean. We show that standard geostatistical models may not be reliable for this type of data, due to the possibility that the regions visited by the animals may depend on the SST. This phenomenon is know in the literature as preferential sampling, and, if ignored, it may affect the resulting spatial predictions and parameter estimates. Research on this topic has been mostly restricted to stationary sampling locations such as monitoring sites. The main contribution of this manuscript is to extend this methodology to observations obtained by devices that move through the region of interest, as is the case with the tagged seals. More specifically, we propose a flexible framework for inference on preferentially sampled fields, where the process that generates the sampling locations is stochastic and moving over time through a 2dimensional space. Our simulation studies confirm that predictions obtained from the preferential sampling model are more reliable when this phenomenon is present, and they compare very well to the standard ones when there is no preferential sampling. Finally, we note that the conclusions of our analysis of the SST data can change considerably when we incorporate preferential sampling in the model.

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Correlated velocity models as a fundamental unit of animal movement: synthesis and applications

Cited by 64 publications

References 48 publications

Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds

Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds

Simulation and Analysis of Animal Movement Paths using Numerus Model Builder

Modelling ocean temperatures from bio-probes under preferential sampling

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