Taking animal tracking to new depths: synthesizing horizontal–vertical movement relationships for four marine predators

Bestley, Sophie; Jonsen, Ian D.; Hindell, Mark A.; Harcourt, Robert; Gales, Nicholas J.

doi:10.1890/14-0469.1

Cited by 81 publications

(105 citation statements)

References 61 publications

(74 reference statements)

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“…Swimming and diving are energetically costly; thus, air-breathing marine species are central-place foragers, where they must locate and capture prey in a 3-dimensional environment while returning back to the surface to meet their oxygen demands (Bestley et al 2015). At all locations, the variation of dive depth for ringed seals was lower when resident than when traveling, suggesting the active pursuit and handling of prey at particular depths.…”

Section: Resident Behaviour and Divingmentioning

confidence: 97%

Influence of sea ice phenology on the movement ecology of ringed seals across their latitudinal range

Yurkowski¹,

Semeniuk²,

Harwood³

et al. 2016

Mar. Ecol. Prog. Ser.

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Environmental variation influences resource distribution, thereby affecting animal movement and foraging decisions. Climate change is altering environmental processes worldwide, but particularly in the Arctic, where changes in the phenology of sea ice have been redistributing resources across space and time. How polar marine predators such as ringed seals Pusa hispida hispida, whose ecology is tightly tied to sea ice, respond to different sea ice dynamics across large spatial scales is generally unknown. Here, behavioural states (resident and traveling) were estimated using state−space models on adult (n = 45) and subadult (n = 85) ringed seal satellite telemetry tracks from 6 Arctic locations. Tagged ringed seals spanned a wide latitudinal (56.54° to 75.58°N) and sea ice phenological range from short (1 to 2 mo) to longer (6 mo) ice-free periods. We assessed the influences of age class and several intra-and inter-annual environmental variables on ringed seal movement ecology. Both adults and subadults spent most of the icefree season in a resident state (93 and 77%, respectively). A latitudinal gradient was characterised, where longer ice-free seasons and less inter-annual variability in sea ice phenology at lower latitudes were related to ringed seals spending more time in a resident state than their conspecifics at higher latitudes (90 versus 58%, respectively), where the ice-free season was shorter and sea ice phenology between years was less synchronous. Ringed seals are responding to latitudinal differences in sea ice phenology which affect prey distribution, suggesting plasticity in their foraging decisions and spatiotemporal differences in prey distribution across the rapidly changing Arctic.

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Section: Resident Behaviour and Divingmentioning

confidence: 97%

Influence of sea ice phenology on the movement ecology of ringed seals across their latitudinal range

Yurkowski¹,

Semeniuk²,

Harwood³

et al. 2016

Mar. Ecol. Prog. Ser.

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“…2010) and more complex state‐space models have started to integrate one‐dimensional diving traces with two‐dimensional horizontal movement tracks (Bestley et al. 2015), highlighting the importance and the difficulty of combining multiple dimensions and variables when animals move and forage in more than two dimensions. Here, we have demonstrated that unsupervised learning algorithms can provide an important additional tool for analysing accelerometer data.…”

Section: Discussionmentioning

confidence: 99%

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Chimienti

Cornulier

Owen

et al. 2016

Ecology and Evolution

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The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.

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“…However, rarely do these approaches account for the full suite of data available through modern satellite telemetry devices (Bestley et al , 2015. A common approach for discerning different behavioral states from satellite telemetry data is to use a switching state-space model (SSSM; Jonsen et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…SSSM identifies a switch between two behaviors: transiting, which is characterized by movement at high rate and low turn angle; and area restricted search, which is characterized by localized movement of low rate and high turn angle (Bailey et al 2009). However, using SSSM based on horizontal metrics alone is not universally applicable to all species, as some species may forage while exhibiting wandering movements (Hays et al 2006), and residential movements do not always correspond to foraging (Bestley et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the computational complexities of expanding state-space models (SSM) to incorporate more variables beyond turn angle and rate, limited work has been done to include additional metrics available through telemetry systems , but see Bestley et al 2013Bestley et al , 2015. SSM requires a predetermined scale to be incorporated into the model to identify the specific behaviors (Jonsen et al 2005).…”

Section: Introductionmentioning

confidence: 99%

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Changepoint analysis: a new approach for revealing animal movements and behaviors from satellite telemetry data

et al. 2015

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Abstract. While telemetry is an invaluable tool for tracking animal movement patterns, the data generated by this technique is often challenging to interpret. Here, we addressed this issue by developing a novel method, based on changepoint analysis, which incorporated both the horizontal and vertical movement metrics and compared this output to that from a switching state-space model (SSSM) that categorized behavior based on horizontal movement metrics. We deployed 20 satellite transmitters on postnesting loggerhead turtles at Rethymno, Crete, Greece between 2010 and 2011 to monitor their at-sea behavior. We used both models to identify behavioral changes, such as the switches from migration to foraging, and from foraging to overwintering. The satellite-tracked turtles exhibited three discrete migratory strategies, with 9 turtles migrating southwards to the coast of northern Africa, 6 turtles migrating northwards into the Aegean Sea, and 4 turtles remaining resident in the waters of Crete. The SSSM readily identified the switch from transiting to ARS behavior in most animals, but the CPA model was able to distinguish multiple modes and more subtle shifts in behavior corresponding with shifts from migration to foraging to overwintering behaviors. We have shown that by incorporating vertical movement metrics into the analysis of telemetry data, previously hidden shifts in behavior can be revealed. The resulting increase in ability to discern complex behavioral patterns of animals remotely will likely yield better management and conservation decisions for a wide array of organisms.

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Taking animal tracking to new depths: synthesizing horizontal–vertical movement relationships for four marine predators

Cited by 81 publications

References 61 publications

Influence of sea ice phenology on the movement ecology of ringed seals across their latitudinal range

Influence of sea ice phenology on the movement ecology of ringed seals across their latitudinal range

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Changepoint analysis: a new approach for revealing animal movements and behaviors from satellite telemetry data

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