Modeling optimal responses and fitness consequences in a changing Arctic

Reimer, Jody R.; Mangel, Marc; Derocher, Andrew E.; Lewis, Mark A.

doi:10.1111/gcb.14681

Cited by 21 publications

(34 citation statements)

References 61 publications

(80 reference statements)

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“…Studies on polar bear habitat use have identified selection for intermediate to high sea ice concentrations over the shallow continental shelf, which is a biologically productive region (Durner et al 2009;Laidre et al 2018;Lone et al 2018b). Sea ice can be further categorized as stable landfast ice or active sea ice, which differ in their availability of prey (Stirling et al 1993;Pilfold et al 2016;Reimer et al 2019). Landfast ice is lower-quality foraging habitat where ringed seal pups and adults in birth lairs are hunted by polar bears (Smith and Stirling 1975;Smith 1980;Stirling et al 1993;Reimer et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Sea ice can be further categorized as stable landfast ice or active sea ice, which differ in their availability of prey (Stirling et al 1993;Pilfold et al 2016;Reimer et al 2019). Landfast ice is lower-quality foraging habitat where ringed seal pups and adults in birth lairs are hunted by polar bears (Smith and Stirling 1975;Smith 1980;Stirling et al 1993;Reimer et al 2019). In contrast, active sea ice is high-quality foraging habitat along leads between the fast ice and drifting offshore ice and is the habitat where juvenile/adult ringed seals and bearded seals are available to polar bears (Stirling and Archibald 1977;Stirling et al 1993;Amstrup et al 2000;Pilfold et al 2014;Reimer et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Landfast ice is lower-quality foraging habitat where ringed seal pups and adults in birth lairs are hunted by polar bears (Smith and Stirling 1975;Smith 1980;Stirling et al 1993;Reimer et al 2019). In contrast, active sea ice is high-quality foraging habitat along leads between the fast ice and drifting offshore ice and is the habitat where juvenile/adult ringed seals and bearded seals are available to polar bears (Stirling and Archibald 1977;Stirling et al 1993;Amstrup et al 2000;Pilfold et al 2014;Reimer et al 2019). Adult females with cubs-of-the-year (COY) select landfast ice in spring, likely to protect cubs from the threat of infanticide from adult males, whereas other age classes select active ice (Stirling et al 1993;Freitas et al 2012;Pilfold et al 2014;McCall et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Variation in habitat use of Beaufort Sea polar bears

Johnson

Derocher

2020

Polar Biol

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Habitat loss and climate change are major processes affecting biodiversity, especially in the Arctic which is experiencing rapid sea ice decline. Loss of sea ice habitat for ice-dependent species such as polar bears (Ursus maritimus) has been associated with declines in body condition, reproductive output, survival, and abundance. Monitoring habitat use can therefore provide insights into population responses to sea ice loss, especially for vulnerable demographic groups such as subadults. Here, we used resource selection functions to examine habitat selection patterns of subadult male and female (n = 21) and adult female (n = 37) polar bears in the Southern Beaufort Sea population from 2007 to 2011. We found that polar bears displayed broad similarities in seasonal habitat selection by using nearshore areas in winter/spring and ranging farther offshore into the multiyear ice in summer/autumn. However, there were differences in habitat use among age, sex, and reproductive classes. Adult females with cubs-of-the-year differed the most among classes and selected landfast ice in spring, allowing them to hunt for seal birth lairs while reducing risk of intra-specific predation. Adult females with older cubs and solitary adult females used active sea ice, which allowed them to hunt adult seals, while subadult females used a mix of active and landfast ice. Subadult males had similar selection for landfast ice as females with cubs-of-the-year, potentially as a mechanism to reduce intra-specific competition and/or kleptoparasitism. The Arctic faces continued warming and understanding variation in habitat use patterns can assist in identifying which bears are most vulnerable to loss of different sea ice habitats.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variation in habitat use of Beaufort Sea polar bears

Johnson

Derocher

2020

Polar Biol

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“…If the estimated daily salmon requirement for SRKWs (~1,204 to 1,445 fish; Ford, Wright, et al, 2010) is not being met, it seems likely that fewer SABs in times of low prey abundance reflects decreased energy reserves and/or may represent a trade‐off between foraging behavior and nutritional stress as examined in other studies [e.g., green sea turtles ( Chelonia mydas ), Heithaus et al., 2007; polar bears ( Ursus maritimus ), Reimer et al. (2019); and, the fruit fly ( Drosophila melanogaster ), Billings et al., 2019). Nevertheless, the true significance of the “fewer salmon‐fewer SABs” relationship in the SRKWs remains to be seen, creating an impetus for further behavioral research in this area.…”

Section: Discussionmentioning

confidence: 97%

Surface behaviors correlate with prey abundance and vessels in an endangered killer whale (Orcinus orca) population

2020

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Southern Resident killer whales (SRKWs) (Orcinus orca) are an endangered population in the United States and Canada, partly due to declines of their primary prey species, Chinook salmon. Prey availability influences various aspects of SRKW behavior, including distribution patterns and social structure. Yet, it is unclear to what extent a limited prey source influences the frequency of surface‐active behaviors (SABs), behaviors with important ecological implications. Here, we used long‐term datasets (1996–2019) to examine the relationships between the abundance of Chinook salmon, vessel presence, and the frequency with which SRKWs perform SABs. Salmon abundance was a significant predictor of SAB frequency, with fewer SABs performed in times of lower salmon abundance. SRKWs displayed more SABs when more whale watching vessels were present, and the whales spent a greater amount of time in the study area, performing more milling as opposed to traveling behavior, when vessel numbers were higher. Lastly, we found pod‐specific differences, such that K pod displayed significantly fewer SABs than either J or L pods. The observed relationships between SRKW behavior and both salmon abundance and vessel presence have implications for social network cohesion and foraging success. Our study adds to a growing body of literature highlighting factors affecting SRKW behavior as they experience increased threats from decreased prey availability, habitat loss, and anthropogenic disturbance, with implications for trans‐boundary management and conservation efforts.

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“…In some applications of SDP, one is interested in the temporal aspects of the optimal decisions, especially near some terminal time; these are finite time horizon problems. For example, we may expect an individual to make riskier foraging decisions near the end of a feeding season (Bull, Metcalfe, & Mangel, 1996;Reimer, Mangel, Derocher, & Lewis, 2019a). In many cases, the optimal decisions are stationary (i.e.…”

Section: Introductionmentioning

confidence: 99%

Matrix methods for stochastic dynamic programming in ecology and evolutionary biology

et al. 2019

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Organisms are constantly making tradeoffs. These tradeoffs may be behavioural (e.g. whether to focus on foraging or predator avoidance) or physiological (e.g. whether to allocate energy to reproduction or growth). Similarly, wildlife and fishery managers must make tradeoffs while striving for conservation or economic goals (e.g. costs vs. rewards). Stochastic dynamic programming (SDP) provides a powerful and flexible framework within which to explore these tradeoffs. A rich body of mathematical results on SDP exist but have received little attention in ecology and evolution. Using directed graphs – an intuitive visual model representation – we reformulated SDP models into matrix form. We synthesized relevant existing theoretical results which we then applied to two canonical SDP models in ecology and evolution. We applied these matrix methods to a simple illustrative patch choice example and an existing SDP model of parasitoid wasp behaviour. The proposed analytical matrix methods provide the same results as standard numerical methods as well as additional insights into the nature and quantity of other, nearly optimal, strategies, which we may also expect to observe in nature. The mathematical results highlighted in this work also explain qualitative aspects of model convergence. An added benefit of the proposed matrix notation is the resulting ease of implementation of Markov chain analysis (an exact solution for the realized states of an individual) rather than Monte Carlo simulations (the standard, approximate method). It also provides an independent validation method for other numerical methods, even in applications focused on short‐term, non‐stationary dynamics. These methods are useful for obtaining, interpreting, and further analysing model convergence to the optimal time‐independent (i.e. stationary) decisions predicted by an SDP model. SDP is a powerful tool both for theoretical and applied ecology, and an understanding of the mathematical structure underlying SDP models can increase our ability to apply and interpret these models.

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Modeling optimal responses and fitness consequences in a changing Arctic

Cited by 21 publications

References 61 publications

Variation in habitat use of Beaufort Sea polar bears

Variation in habitat use of Beaufort Sea polar bears

Surface behaviors correlate with prey abundance and vessels in an endangered killer whale (Orcinus orca) population

Matrix methods for stochastic dynamic programming in ecology and evolutionary biology

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