Characterizing habitat suitability for a central‐place forager in a dynamic marine environment

Briscoe, Dana K.; Fossette, Sabrina; Scales, Kylie L.; Hazen, Elliott L.; Bograd, Steven J.; Maxwell, Sara M.; McHuron, Elizabeth A.; Robinson, Patrick W.; Kuhn, Carey E.; Costa, Daniel P.; Crowder, Larry B.; Lewison, Rebecca L.

doi:10.1002/ece3.3827

Cited by 25 publications

(27 citation statements)

References 93 publications

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“…To our knowledge, this study provided the longest duration of telemetry data available for AFSSLs and the first winter dataset for AFSSLs in SEAK and western Alaska. Similar to previous studies of AFSSL (Loughlin et al 1998, Andrews et al 2002 and other species of lactating sea lions (Thompson et al 1998, McHuron et al 2016, Briscoe et al 2018, habitat use varied considerably within and among individual AFSSLs during this study. Overall, movements and diving behaviors of AFSSLs from SEAK resembled the seasonal distribution of predictable forage fish, whereas individuals from western Alaska utilized a variety of marine ecosystems, providing new insights into the foraging ecology of AFSSLs from those areas.…”

Section: Discussionsupporting

confidence: 88%

“…, Briscoe et al. ), habitat use varied considerably within and among individual AFSSLs during this study. Overall, movements and diving behaviors of AFSSLs from SEAK resembled the seasonal distribution of predictable forage fish, whereas individuals from western Alaska utilized a variety of marine ecosystems, providing new insights into the foraging ecology of AFSSLs from those areas.…”

Section: Discussionmentioning

confidence: 58%

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Mixing it up in Alaska: Habitat use of adult female Steller sea lions reveals a variety of foraging strategies

et al. 2020

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From 2010 to 2015, satellite transmitters were deployed on 16 adult female Steller sea lions (AFSSLs; Eumetopias jubatus) in three regions of Alaska because there is limited information regarding the habitat use of this age class during winter and populations have yet to recover in western Alaska. Two approaches were used to assess how static (distance to shore, sea lion site, and continental shelf break, presence on/off the continental shelf, and bathymetric depth and slope), dynamic (proportion of daylight, fraction of lunar illumination, chlorophyll‐a, wind speed, sea surface height, eddy kinetic energy, and sea surface temperature), and other (region, distinct population segment, and season) covariates affected the habitat use of AFSSLs. Multimodel inference was first used to examine diving behaviors (mean and maximum dive depths, dive frequency) with respect to covariates using linear mixed‐effects models, whereas single model inference was used to examine kernel density estimates (KDEs) of individual monthly utilization distributions (n = 74) in western Alaska with respect to environmental covariates using generalized additive models. Additionally, weighted coefficients from these models were examined for the population as a whole, within each individual, between regions, and across monthly scales. Comparisons of foraging behaviors of AFSSLs over time and space revealed pronounced individual variability within overall broader patterns. Response variables of most models were related to various combinations of predictor variables, but distance to shore was the most influential variable across all models. As expected with a non‐migratory central place forager, frequency of diving and KDEs were greater on the shelf and near shore, though maximum dive depths increased with distance to shore. Interaction effects (proportion of day light*on/off shelf) observed for mean dive depths suggested AFSSLs were feeding on benthic species when in shelf waters near shore, whereas they were likely feeding on vertically migrating prey species while off‐shelf. Relationships regarding diving behaviors and KDEs of AFSSLs relative to dynamic oceanographic variables were not as prominent as those observed for static environmental variables, though some signals were apparent at different scales. Overall, static environmental features likely provided more consistent sources of habitat for prey resources, thereby making them more predictable for AFSSLs.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 58%

Mixing it up in Alaska: Habitat use of adult female Steller sea lions reveals a variety of foraging strategies

et al. 2020

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“…Despite this, the choice and simulation of pseudo‐absence data can influence model outcomes (Barbet‐Massin et al., ). CRWs are regularly used for simulating pseudo‐absence tracks (Briscoe et al, ; Hazen et al, , ; Willis‐Norton et al, ); however, no simulation method is capable of fully capturing where animals were not present (Aarts, MacKenzie, McConnell, Fedak, & Matthiopoulos, ). CRWs can result in biases, particularly when animals conduct long‐distance movements.…”

Section: Discussionmentioning

confidence: 99%

Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean

Maxwell

Scales

Bograd

et al. 2019

Diversity and Distributions

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Aim Animal tracking can provide unique insights into the ecology and conservation of marine species, such as the partitioning of habitat, including differences between life history stages or sexes, and can inform fisheries stock assessments, bycatch reduction and spatial management such as dynamic management. Location Northeast Pacific Ocean. Methods We used satellite tracking data from 47 blue sharks (Prionace glauca) from the Northeast Pacific to determine movements and home range along the west coast of North America, and sex–size class (immature females, mature males) specific habitat preferences using boosted regression trees. Using a suite of static and dynamic environmental variables, we determined distribution and habitat preferences across summer and fall for each sex–size class. Results We found that there was spatial segregation between sex–size classes particularly in the summer months with immature females found largely north of 33°N, and males south of 35°N. In fall, females travelled south, resulting in an overlap in distributions south of 37°N. Sea surface temperature (SST), latitude and longitude were top predictors. However, immature females and adult males demonstrated unique habitat preferences including SST, with immature females preferring cooler temperatures (SST < 15°C) than adult males in summer, and a broader band of SST than adult males in fall. All models performed well, explaining 50%–67% of deviance, and 23%–41% of deviance when predictions were cross‐validated. Main conclusions We provide first insights into coastal movements and habitat preferences of blue sharks in the Northeast Pacific. We found that immature females undergo a seasonal southward migration in this more coastal habitat, similar to patterns observed in the North Atlantic. We also found some overlap between adult males and immature females in fall months, suggesting the importance of more coastal habitat in managing this species, particularly in determining population structure for blue shark stock assessments, and reducing blue shark bycatch.

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“…While it is well established that recording the patterns of time-activity budgets in central-place foraging can provide information on foraging traits such as habitat use and diet (e.g. Lamb, Satgé, & Jodice, 2017; Briscoe et al, 2018), fewer studies have revealed their potential for analysing keystone ontogenetic traits (e.g. Lambin & Yoccoz, 2001; Gopukumar, 2003; Benton, St Clair, & Plaistow, 2008; Fay, Barbraud, Delord, & Weimerskirch, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…However, bio-logging applications are still restricted to birds (particularly seabirds, e.g. Rishworth et al, 2014; Fay et al, 2016; Lamb et al, 2017), fishes (Gurarie et al, 2016) and mammals (Lambin & Yoccoz, 2001; Gopukumar, 2003; Gurarie, Andrews, & Laidre, 2009; Briscoe et al, 2018). Very few studies were conducted on insects (but see Degen et al, 2016 for bees), despite the recent availability of adapted miniaturized devices (Kissling, Pattemore, & Hagen, 2014; Nunes-Silva et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Measuring ontogenetic shifts in central-place foraging insects: a case study with honey bees

Réquier

Henry

Decourtye³

et al. 2020

Preprint

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13 7 INRAE, UE 1255 APIS "Abeilles paysages interactions et systèmes de culture", Abstract 27 1. Measuring time-activity budgets over the complete individual lifespan is now 28 possible for many animals with the recent advances of life-long individual 29 monitoring devices. Although analyses of changes in the patterns of time-activity 30 budgets have revealed ontogenetic shifts in birds or mammals, no such technique 31has been applied to date on insects. 32 2. We tested an automated breakpoint-based procedure to detect, assess and 33 quantify shifts in the temporal pattern of the flight activities in honey bees. We 34 assumed that the learning and foraging stages of honey bees will differ in several 35 respects, to detect the age at onset of foraging (AOF). 36 3. Using an extensive dataset covering the life-long monitoring of 2,100 individuals, 37 we compared the AOF outputs with the more conventional approaches based on 38 arbitrary thresholds. We further evaluated the robustness of the different methods 39 comparing the foraging time-activity budget allocations between the presumed 40 foragers and confirmed foragers.41 4. We revealed a clear-cut learning-foraging ontogenetic shift that differs in duration, 42 frequency, and time of occurrence of flights. Although AOF appeared to be highly 43 plastic among bees, the breakpoint-based procedure seems better able to detect it 44 than arbitrary threshold-based methods that are unable to deal with inter-individual 45 variation. 46 5. We developed the aof R-package including a broad range of examples with both 47 simulated and empirical dataset to illustrate the simplicity of use of the procedure.48 This simple procedure is generic enough to be derived from any individual life-long 49 monitoring devices recording the time-activity budgets of honey bees, and could 50 3 propose new ecological applications of bio-logging to detect ontogenetic shifts in 51 the behaviour of central-place foraging insects. 52 53

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Characterizing habitat suitability for a central‐place forager in a dynamic marine environment

Cited by 25 publications

References 93 publications

Mixing it up in Alaska: Habitat use of adult female Steller sea lions reveals a variety of foraging strategies

Mixing it up in Alaska: Habitat use of adult female Steller sea lions reveals a variety of foraging strategies

Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean

Measuring ontogenetic shifts in central-place foraging insects: a case study with honey bees

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