Bias correction and uncertainty characterization of Dead-Reckoned paths of marine mammals

Liu, Yang; Battaile, Brian C.; Trites, Andrew W.; Zidek, James V.

doi:10.1186/s40317-015-0080-5

Cited by 16 publications

(22 citation statements)

References 22 publications

(23 reference statements)

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“…Assuming a constant swimming speed of 1 m s −1 (Wiley et al 2011), the position of the whale was dead-reckoned 5 times s −1 , starting at the GPS location of the tag deploy ment. The resolution of each resulting pseudotrack was then down-sampled to 1 location s −1 , and its spatial accuracy was improved by geo-referencing the pseudotrack to the GPS positions of the whale recorded at the surface using the R package 'Bayesian Animal Tracker' (Liu et al 2015). During this process, the number of triangulated positions approximated by the tag-based whale locations was maximized (Liu et al 2015), thereby reducing the effect of inaccurate, outlying triangulated positions.…”

Section: Whale Behavior Data Analysismentioning

confidence: 99%

Hierarchical foraging movement of humpback whales relative to the structure of their prey

Kirchner¹,

Wiley²,

Hazen³

et al. 2018

Mar. Ecol. Prog. Ser.

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Movement within and between prey patches can influence the fitness of a predator, and understanding such foraging decisions is an important topic in ecology. Most research has found sustained foraging in dense prey patches but has focused on the movement of raptorial predators that feed on single prey items, or suspension-feeders foraging on comparatively immobile zooplankton. The goal of this study was to investigate the fine-scale movement of a suspension-feeding marine vertebrate species while foraging for mobile prey. Using animal-borne tags and surface observations, we analyzed the movement of foraging humpback whales Megaptera novaeangliae within and among acoustically detected patches of sand lance Ammodytes spp. in the water column in the southern Gulf of Maine, USA. Analyzing data from 9 whales tagged between 2008 and 2012, we found hierarchical whale foraging movements that paralleled a complex, hierarchically structured prey landscape. For 7 out of 9 whales, feeding bout scales corresponded to prey patch scales. For 6 out of 9 whales, movement between sequential feeding events was not significantly different from distances between neighboring prey schools. Targeting neighboring schools during sequential feeding events, as opposed to sustained foraging in profitable patches, may increase foraging success in marine suspension-feeders targeting mobile prey, which confirms findings from many other marine predator taxa feeding on mobile prey species. Our study presents novel evidence for the high behavioral plasticity of an intermittent suspensionfeeder targeting mobile prey, adapting its movement to the behavior of its prey and the structure of its prey field.

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Section: Whale Behavior Data Analysismentioning

confidence: 99%

Hierarchical foraging movement of humpback whales relative to the structure of their prey

Kirchner¹,

Wiley²,

Hazen³

et al. 2018

Mar. Ecol. Prog. Ser.

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“…Heading can then be calculated by using the magnetometer axes in the ground plane. Extensive detail on the process is provided by numerous authors including Bidder et al, (2015) and Koo, Sung, and Lee, (2009), and there is an R package (TrackReconstruction; Battaile, 2015) that undertakes this and a detailed code provided by Liu, Battaile, Trites, and Zidek (2015). The full process of disentangling heading from pitch and roll replies on spherical trigonometry and is detailed, for example, in Benhamou (2018).…”

Section: Methodsmentioning

confidence: 99%

An “orientation sphere” visualization for examining animal head movements

Wilson

Williams

Holton

et al. 2020

Ecology and Evolution

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1. Animal behavior is elicited, in part, in response to external conditions, but understanding how animals perceive the environment and make the decisions that bring about these behavioral responses is challenging.2. Animal heads often move during specific behaviors and, additionally, typically have sensory systems (notably vision, smell, and hearing) sampling in defined arcs (normally to the front of their heads). As such, head-mounted electronic sensors consisting of accelerometers and magnetometers, which can be used to determine the movement and directionality of animal heads (where head "movement" is defined here as changes in heading [azimuth] and/or pitch [elevation angle]), can potentially provide information both on behaviors in general and also clarify which parts of the environment the animals might be prioritizing ("environmental framing").3. We propose a new approach to visualize the data of such head-mounted tags that combines the instantaneous outputs of head heading and pitch in a single intuitive spherical plot. This sphere has magnetic heading denoted by "longitude" position and head pitch by "latitude" on this "orientation sphere" (O-sphere). 4. We construct the O-sphere for the head rotations of a number of vertebrates with contrasting body shape and ecology (oryx, sheep, tortoises, and turtles), illustrating various behaviors, including foraging, walking, and environmental scanning. We also propose correcting head orientations for body orientations to highlight specific heading-independent head rotation, and propose the derivation of O-sphere-metrics, such as angular speed across the sphere. This should help identify the functions of various head behaviors. 5. Visualizations of the O-sphere provide an intuitive representation of animal be-

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“…In this step we made use of the R package "TrackReconstruction" (https://cran.r-project.org/web/packages/ TrackReconstruction/index.html). After that, we corrected the bias of those estimation using the data from the GPS (Liu, Y. et al, 2015). This correction was made using the R package "BayesianAnimalTracker" (https://cran.r-project.org/web/packages/ BayesianAnimalTracker/index.html).…”

Section: Real Data Examplementioning

confidence: 99%

Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals

Ruiz-Suarez

Leos‐Barajas

Alvarez-Castro

et al. 2020

PeerJ

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The study of animal movement is challenging because it is a process modulated by many factors acting at different spatial and temporal scales. Several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals.To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. The movement process is then observed at regular time intervals.As the likelihood function of this state-space model turned out to be complex to calculate yet simulating data is straightforward, we conduct inference using a few variations of Approximate Bayesian Computation (ABC). We explore the applicability of these methods as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. We demonstrate the application of this model to a real trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices.Our results suggest that accurate estimates can be obtained when the observations are less than 5 times the average time between changes in movement direction.The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols, and that sometimes high-frequency data may not be necessary to have good estimates of certain movement processes.

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Bias correction and uncertainty characterization of Dead-Reckoned paths of marine mammals

Cited by 16 publications

References 22 publications

Hierarchical foraging movement of humpback whales relative to the structure of their prey

Hierarchical foraging movement of humpback whales relative to the structure of their prey

An “orientation sphere” visualization for examining animal head movements

Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals

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