2018
DOI: 10.1111/oik.05579
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Environmental heterogeneity amplifies behavioural response to a temporal cycle

Abstract: Resource acquisition is integral to maximise fitness, however in many ecosystems this requires adaptation to resource abundance and distributions that seldom stay constant. For predators, prey availability can vary at fine spatial and temporal scales as a result of changes in the physical environment, and therefore selection should favour individuals that can adapt their foraging behaviour accordingly. The tidal cycle is a short, yet predictable, temporal cycle, which can influence prey availability at tempora… Show more

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Cited by 18 publications
(33 citation statements)
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“…We used the Viterbi algorithm to estimate the most likely sequence of states to have generated the observed movement patterns (Zucchini, MacDonald, & Langrock, 2016). A three-state model was supported by model selection using AIC, and the three states and their interpretation are consistent with other kittiwake-tracking studies (Chivers et al, 2012;Trevail et al, 2019). Consecutive sequences of foraging points were aggregated into foraging sites and were represented by a single pair of central coordinates (Appendix S5).…”
Section: Gps Trackingmentioning
confidence: 55%
See 1 more Smart Citation
“…We used the Viterbi algorithm to estimate the most likely sequence of states to have generated the observed movement patterns (Zucchini, MacDonald, & Langrock, 2016). A three-state model was supported by model selection using AIC, and the three states and their interpretation are consistent with other kittiwake-tracking studies (Chivers et al, 2012;Trevail et al, 2019). Consecutive sequences of foraging points were aggregated into foraging sites and were represented by a single pair of central coordinates (Appendix S5).…”
Section: Gps Trackingmentioning
confidence: 55%
“…into an observed sequence of discrete behavioural states. Hidden Markov models were fitted using the moveHMM function from the movehmm package (Michelot, Langrock, & Patterson, 2016), which we provided with starting parameters informed by previous work using HMMs to describe kittiwake foraging behaviour (Trevail et al, 2019;Appendix S5). Based on the distributions of step lengths between GPS points (described by a gamma distribution) and turning angles (described by a von Mises distribution) between consecutive GPS points, HMMs classified each point as one of three behavioural states: foraging, resting or travelling.…”
Section: Gps Trackingmentioning
confidence: 99%
“…body mass at Skomer, Rathlin and Puffin Island: 327.9 ± 5.1 from Trevail et al . [9]). Full details of tracking procedures can be found in the first publications of the data: Wakefield et al .…”
Section: Methodsmentioning
confidence: 99%
“…However, not all heterogeneous environments are equal [911], as high-prey locations vary in distribution, predictability and numbers of competing individuals. Studies often present these ‘prey hotspots’ as beneficial resource patches [1214]; however, the optimality of foraging strategies in response to resource heterogeneity may be constrained by both the nature of resource heterogeneity [15,16] and the behaviour of other foragers [17,18].…”
Section: Introductionmentioning
confidence: 99%
“…Given the numerous constraints imposed on Antarctic petrels during the breeding season (e.g. their internal state and that of their mate) and the highly dynamic nature of their foraging habitat (Fauchald et al, 2017), a flexible behaviour might be necessary to cope with a constantly changing, unpredictable environment (Dall, Houston, & McNamara, 2004;Dehnhard et al, 2020;Trevail et al, 2019). There is evidence that Antarctic petrels do not exhibit individual specialization with regard to habitat or environmental conditions while foraging (Dehnhard et al, 2020).…”
Section: Individual Variation In Foraging Tacticsmentioning
confidence: 99%