Foraging behaviour of a continental shelf marine predator, the grey seal (Halichoerus grypus), is associated with in situ, subsurface oceanographic conditions
Bibliographic record
Abstract
Abstract Background The heterogeneous oceanographic conditions of continental shelf ecosystems result in a three-dimensionally patchy distribution of prey available to upper-trophic level predators. The association of bio-physical conditions with movement patterns of large marine predators has been demonstrated in diverse taxa. However, obtaining subsurface data that are spatio-temporally relevant to the decisions made by benthically-foraging species can be challenging. Methods Between 2009 and 2015, grey seals were captured on Sable Island, Nova Scotia, Canada during summer and fall and instrumented with high-resolution archival GPS tags. These tags recorded location data as well as depth (m), temperature (°C), and light level measurements during dives, until animals returned to the haulout site to breed. Hidden Markov models were used to predict apparent foraging along movement tracks for 79 individuals (59 females, 20 males) every 3 h. In situ measurements were used to estimate chlorophyll-aconcentration (mg m− 3) and temperature within the upper-water column (50 m) and temperature and depth at the bottom of dives. As chlorophyll-acould only be estimated from 10:00 to 14:00 AST for dive depths ≥50 m, we formulated two generalized linear mixed-effects models to test the association of predicted grey seal behavioural states with oceanographic conditions and phytoplankton biomass: the first representing conditions of the upper-water column likely to influence primary productivity, and a second model including environmental conditions encountered by grey seals at the bottom of dives, when seals were more likely to be foraging. Results Predicted grey seal behavioural states were associated with fine-scale chlorophyll-aconcentrations and other environmental conditions they encountered across the continental shelf. In the Water Column Model, season had no influence on the probability of observing apparent foraging, but chlorophyll-a, upper-water column temperature, and sex did, with females having a greater probability of foraging than males. In the Bottom Conditions Model, again season had no influence on the probability of apparent foraging, but females were over twice as likely as males to be foraging. Conclusions The results of this study highlight the value of in situ measurements of oceanographic properties that can be collected at high temporal resolution by animal-borne data loggers. These data provide insight into how inferred behavioural decisions made by large marine predators, such as the grey seal, may be influenced by fine-scale oceanographic conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".