Natural and anthropogenic factors affecting the feeding ecology of a top marine predator, the Magellanic penguin
Bibliographic record
Abstract
Understanding how top predators respond to natural and anthropogenically induced changes in their environment is a major conservation challenge especially in marine environments. We used a multidisciplinary approach to explore the mechanisms through which a typical central‐place forager, the Magellanic penguin (Spheniscus magellanicus) from the Chubut province of Argentina, responds to variations in oceanic conditions and prey resources. We combined habitat and species distribution modeling with isotopic dietary reconstructions based on blood δ13C and δ15N values to quantify the role of bathymetry, sea‐surface temperature and chlorophyll‐a concentration, abundance of conspecifics, and extent of fisheries activities in explaining the foraging and feeding ecology of individuals breeding at different colonies. The at‐sea distribution of penguins was tightly coupled with the spatial distribution of their staple prey species, anchovies (Engraulis anchoita), especially in areas over the continental shelf (>200 m depth), with relatively warm water (from 16° to 21°C), and moderate abundances of conspecifics (from 50 to 250 individuals). Competition with conspecifics and human fisheries were also identified as important factors explaining penguin diet with decreasing relative contributions of anchovies with increasing abundance of conspecifics and fishing activity. Our multifactorial approach allowed us to simultaneously explore different physical, biological and anthropogenic features likely affecting marine resource availability, and, consequently, driving the feeding and foraging ecology of this central‐place forager. Our approach can be extended to a large suite of central‐place foragers, thus providing important advances in the way we investigate how to effectively conserve and manage these species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".