Integrated step selection analysis: bridging the gap between resource selection and animal movement

Avgar, Tal; Potts, Jonathan R.; Lewis, Mark A.; Boyce, Mark S.

doi:10.1111/2041-210x.12528

Cited by 417 publications

(847 citation statements)

References 49 publications

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“…This improved location frequency will allow us to estimate differences in resource utilization throughout the day and at a finer behavioral scale. GPS and VHF tracking are also not mutually exclusive: supplementing highly accurate and unbiased visual locations (obtained via homing to the VHF signal) with frequent GPS locations can add valuable information to our understanding of Burmese python spatial ecology, and the resulting dataset could be used to reveal fine-scale behavior by pythons, such as habitat selection by pythons along their movement paths [50,51]. There is strong potential in this approach if we associate these steps with not just habitat variables, but also meteorological variables like temperature or barometric pressure, temporal variables like season of the year or time of the day, and variables describing the internal state of the python, such as body temperature or time since last meal.…”

Section: Gps Advantagesmentioning

confidence: 99%

Evaluating GPS biologging technology for studying spatial ecology of large constricting snakes

et al. 2018

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Background: GPS telemetry has revolutionized the study of animal spatial ecology in the last two decades. Until recently, it has mainly been deployed on large mammals and birds, but the technology is rapidly becoming miniaturized, and applications in diverse taxa are becoming possible. Large constricting snakes are top predators in their ecosystems, and accordingly they are often a management priority, whether their populations are threatened or invasive. Fine-scale GPS tracking datasets could greatly improve our ability to understand and manage these snakes, but the ability of this new technology to deliver high-quality data in this system is unproven. In order to evaluate GPS technology in large constrictors, we GPS-tagged 13 Burmese pythons (Python bivittatus) in Everglades National Park and deployed an additional 7 GPS tags on stationary platforms to evaluate habitat-driven biases in GPS locations. Both python and test platform GPS tags were programmed to attempt a GPS fix every 90 min.Results: While overall fix rates for the tagged pythons were low (18.1%), we were still able to obtain an average of 14.5 locations/animal/week, a large improvement over once-weekly VHF tracking. We found overall accuracy and precision to be very good (mean accuracy = 7.3 m, mean precision = 12.9 m), but a very few imprecise locations were still recorded (0.2% of locations with precision > 1.0 km). We found that dense vegetation did decrease fix rate, but we concluded that the low observed fix rate was also due to python microhabitat selection underground or underwater. Half of our recovered pythons were either missing their tag or the tag had malfunctioned, resulting in no data being recovered.Conclusions: GPS biologging technology is a promising tool for obtaining frequent, accurate, and precise locations of large constricting snakes. We recommend future studies couple GPS telemetry with frequent VHF locations in order to reduce bias and limit the impact of catastrophic failures on data collection, and we recommend improvements to GPS tag design to lessen the frequency of these failures.

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Section: Gps Advantagesmentioning

confidence: 99%

Evaluating GPS biologging technology for studying spatial ecology of large constricting snakes

et al. 2018

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“…These latter questions can then be examined by existing point-to-point techniques, such as step selection analysis (Avgar et al, 2016;Fortin et al, 2005), conditional entropy (Riotte-Lambert, Benhamou, & Chamaillé-Jammes, 2016), sequence analysis methods (De Groeve et al, 2016), or optimal foraging theory (Pyke, 1984). and the environment (e.g., distance from C to A, effort or risk of moving from C to A and so forth).…”

Section: Results From Cattle Datamentioning

confidence: 99%

“…Our algorithm breaks a long data stream down into a simple Markov-process description of movement (similar to a "semantic trajectory" from movement analytics Demšar et al (2015)), which has the potential to be analyzed using existing point-to-point techniques such as optimal foraging theory (Pyke, 1984) or step selection analysis (Avgar, Potts, Lewis, & Boyce, 2016;Fortin et al, 2005;Merkle, Fortin, & Morales, 2014). Here, we aim to describe an animal track as a sequence of "sites of interest," which are areas where the animal spends a disproportionately long time, together with movements between these sites.…”

Section: Introductionmentioning

confidence: 99%

Making sense of ultrahigh‐resolution movement data: A new algorithm for inferring sites of interest

Munden

Börger

Wilson

et al. 2018

Ecology and Evolution

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Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high‐resolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting high‐resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte‐Lambert (2012). Adapting it for use with high‐resolution data. The data’s resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm’s speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of “high confidence” paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, high‐resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point‐to‐point analysis techniques such as optimal foraging theory. It is encoded into an package called , so should serve as a valuable tool for making sense of these increasingly large data streams.

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“…Thus, we could expect that confidence intervals for those coefficients might not overlap zero even though in reality there is no significant effect, i.e., a type I error. Variance inflation methods exist, such as sandwich estimators or Newey-West estimators, which can be used to fix this problem [40,41], or modeling approaches can include autocorrelation structures [42]. Thus autocorrelation remains a consideration when building effective models of landscape-organisms interactions, but seldom is it likely to be a major constraint.…”

Section: Autocorrelationmentioning

confidence: 99%

“…Movement and selection response to a habitat feature, (e.g., human disturbance) can be modeled together, corresponding to the reality of an organism responding to their environment [25,42]. The researcher invariably must attempt to think like the study animal; for example, does the animal perceive a clearcut itself (PMM) or does the animal perceive the cover afforded by the dense vegetation associated with the clearcut that could be measured on a gradient?…”

Section: Choices Made When Building Modelsmentioning

confidence: 99%

Defining Landscapes and Scales to Model Landscape–Organism Interactions

Boyce

Mallory

Morehouse

et al. 2017

Curr Landscape Ecol Rep

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Purpose of Review We review and provide comment on issues of scale in ecological studies in the context of two paradigms used to define landscapes: the patch-mosaic and gradient models. Our intent is to offer guidance for structuring habitat-selection models with examples of how scale, autocorrelation, measurement error, and choice of patch-mosaic or gradient models, analysis methods, and covariates by the researcher can influence inferences regarding landscapeorganism interactions. Recent Findings Methods that allow the organism or data to define the grain and extent of scale of the study offer promise by reducing subjectivity in choices of scale. Ultimately, we recommend that the ecological phenomenon of interest should shape the selection of models defining landscape-organism interaction; however, the choice of model remains with the researcher and is dependent on the research question and the availability of data. Clearly, both the patch-mosaic and gradient models can provide reasonable frameworks for study, and multiple scales that draw from both paradigms often may be most appropriate. Summary Scale has been identified as a crucial feature of landscape ecology, yet scale as a paradigm has offered little direction for ecologists. Likewise, debate contrasting gradient models and patch-mosaic models offers few new insights on how ecologists might decide on an appropriate scale for analysis of organism distribution or habitat selection. Various ecological processes influence organisms at different scales and modeling approaches need to be able to accommodate multiple scales simultaneously, which may vary by landscape structure and movement ecology. The continuum of scales and combinations of both gradient and patch-mosaic landscapes provides the necessary array of structures that can be used to construct combinations of landscape covariates that coincide with the ecology of the organism across scales.

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Integrated step selection analysis: bridging the gap between resource selection and animal movement

Cited by 417 publications

References 49 publications

Evaluating GPS biologging technology for studying spatial ecology of large constricting snakes

Evaluating GPS biologging technology for studying spatial ecology of large constricting snakes

Making sense of ultrahigh‐resolution movement data: A new algorithm for inferring sites of interest

Defining Landscapes and Scales to Model Landscape–Organism Interactions

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