Must marine predators always follow scaling laws? Memory guides the foraging decisions of a pursuit-diving seabird

Regular, Paul M.; Hedd, April; Montevecchi, William A.

doi:10.1016/j.anbehav.2013.06.008

Cited by 52 publications

(48 citation statements)

References 55 publications

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“…In the marine environment, both predators and prey can be highly mobile and difficult to monitor simultaneously. When foraging, seabirds typically perform hierarchical movement patterns performing “area‐restricted search” (ARS) movements or series of dives in each foraging location, reflecting the spatial and temporal dynamics of food patches (Fauchald et al., ; Regular et al., ; Weimerskirch et al., ). As marine top predators, seabirds have been shown to provide unique insights on the status and changes of marine ecosystems (Piatt, Sydeman, & Wiese, ).…”

Section: Discussionmentioning

confidence: 99%

“…The distribution of the data used for the model is in Fig. S1 F I G U R E 5 Total time spent pursuing and catching (PCT) predicted by the time spent underwater during a foraging bout for individual razorbills (RAZO 1, RAZO 2, RAZO 3, RAZO 4 and RAZO 5, left panel) and common guillemots (COGU 1, COGU 2, COGU 3, COGU 4, right panel) Continuous lines indicate model prediction and dashed lines ±95% confidence intervals search" (ARS) movements or series of dives in each foraging location, reflecting the spatial and temporal dynamics of food patches (Fauchald et al, 2000;Regular et al, 2013;Weimerskirch et al, 2005). As marine top predators, seabirds have been shown to provide unique insights on the status and changes of marine ecosystems (Piatt, Sydeman, & Wiese, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Food resources are generally aggregated over a range of scales in hierarchical patch systems where high-density patches at small scales are nested within low-density patches at larger scales (Fauchald, Erikstad, & Skarsfjord, 2000;Regular, Hedd, & Montevecchi, 2013;Weimerskirch, Gault, & Cherel, 2005). Foragers should adjust their foraging patterns in such complex environments to maximize foraging efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior

Chimienti

Cornulier

Owen

et al. 2017

Ecology and Evolution

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Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior

Chimienti

Cornulier

Owen

et al. 2017

Ecology and Evolution

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“…Adults clearly use memory (Regular et al 2013) to guide subsequent decisions; under what conditions (i.e. changes in resource availability or habitat suitability) they might re-enter an exploratory phase as adults and refine their movement and foraging strategies is unknown.…”

Section: Discussionmentioning

confidence: 99%

Causes and consequences of individual variability and specialization in foraging and migration strategies of seabirds

Phillips¹,

Lewis²,

González‐Solís³

et al. 2017

Mar. Ecol. Prog. Ser.

139

172

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Technological advances in recent years have seen an explosion of tracking and stable isotope studies of seabirds, often involving repeated measures from the same individuals. This wealth of new information has allowed the examination of the extensive variation among and within individuals in foraging and migration strategies (movements, habitat use, feeding behaviour, trophic status, etc.) in unprecedented detail. Variation is underpinned by key life-history or state variables such as sex, age, breeding stage and residual differences among individuals (termed 'individual specialization'). This variation has major implications for our understanding of seabird ecology, because it affects the use of resources, level of intra-specific competition and niche partitioning. In addition, it determines the responses of individuals and populations to the environment and the susceptibility to major anthropogenic threats. Here we review the effects of season (breeding vs. nonbreeding periods), breeding stage, breeding status, age, sex and individual specialization on foraging and migration strategies, as well as the consequences for population dynamics and conservation.

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“…Scale-free patterns of movement suggest that some marine megafauna search for prey probabilistically without prior knowledge of prey distribution [8], but it is likely that they rely on learning and memory to some extent to move and forage efficiently [22,23]. The effects of learning and memory are often inferred from foraging site fidelity, but quantifying those effects remains challenging [16,24].…”

Section: How Does Learning and Memory Versus Innate Behaviours Influementioning

confidence: 99%

Key Questions in Marine Megafauna Movement Ecology

Hays

Ferreira

Sequeira

et al. 2016

Trends in Ecology & Evolution

393

389

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Must marine predators always follow scaling laws? Memory guides the foraging decisions of a pursuit-diving seabird

Cited by 52 publications

References 55 publications

Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior

Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior

Causes and consequences of individual variability and specialization in foraging and migration strategies of seabirds

Key Questions in Marine Megafauna Movement Ecology

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