Examining the Health and Energetic Impacts of Climate-Induced Prey Shifts on Beluga Whales Using Community-Based Research
Bibliographic record
Abstract
The loss of seA ice due to climAte chAnge is altering the structure of Arctic marine ecosystems. Sea ice plays a critical role in the life histories of many Arctic marine mammals that use ice as foraging habitat, a platform for raising young, and protection from predators (Moore and Huntington, 2008). Declines in sea ice are predicted to bring challenges to ice-associated and ice-obligate marine mammals, such as alterations in prey abundance and migration routes, as well as increases in human activities. As long-lived species with low reproductive rates, Arctic marine mammals may not be able to adapt to the rapid pace of environmental change. Whether climate change will have negative impacts on sea-ice associated marine mammals is uncertain because there is insufficient baseline information on their diet, population dynamics, and physical condition (Burek et al., 2008). Beluga whales (Delphinapterus leucas) are the most abundant Arctic odontocetes with a circumpolar distribution and are a potential indicator species for the effects of climate change. The eastern Beaufort Sea (EBS) beluga population is one of Canada’s largest, with an estimated 39 258 individuals (Harwood and Smith, 2002; Laidre, 2007). During the spring, the EBS population migrates from the Bering Strait to summer and reproduce in the Mackenzie Delta estuary and eastern Beaufort Sea. The beluga whale is a sea-ice-associated marine mammal, and the EBS population selects habitat on the basis of sea ice concentrations: nursing females with young calves select coastal habitat, large males inhabit ice-covered areas, and medium-sized males and females with older calves use mixed ice-covered habitat (Loseto et al., 2006). Since EBS belugas use marine, estuarine, coastal, and deep-water habitats, they are considered a sentinel species for changes in the Mackenzie Delta and the overall health of the Beaufort Sea ecosystem. Some of the predicted effects of climate change are shifts in the distribution, abundance, and composition of prey communities (Litzow et al., 2006). The “junk food hypothesis” attributes declines in productivity of marine mammals to a switch from highto low-quality prey (gross energy density) and has been linked to nutritional stress and the decline of Arctic marine mammal populations, such as the Alaskan stellar sea lions (Eumetopias jubatus) (Rosen and Trites, 2000). Arctic cod (Boreogadus saida) has been identified as a major prey species for the EBS beluga population (Loseto et al., 2009) and is an important lipid-rich prey to several marine mammal and seabird species (Welch et al., 1993; Harter et al., 2013). Since it is a sea-ice-associated fish, Arctic cod is predicted to reduce Examining the Health and Energetic Impacts of Climate-Induced Prey Shifts on
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".