The Effects of Diet and Predation on Nesting Herring Gulls (Larus argentatus) in Pukaskwa National Park
Bibliographic record
Abstract
Herring gulls (Larus argentatus) are used as ecological indicators of the coastal Lake Superior ecosystem in Pukaskwa National Park.Their populations have declined by 70% over the last 40 years, suggesting changes in the park ecosystem.Previous studies highlighted declining prey abundance as a possible contributing factor to population declines.Populations of avian predators that prey on herring gulls have simultaneously increased which could be a significant factor impacting gull population trends.Here, I assessed herring gull diets via stable isotope (nitrogen, carbon) and fatty acid indicators to investigate how diet may influence population trends through effects on physiological (stress-associated hormones), reproductive (egg size), and behavioural (nest attentiveness) endpoints.I additionally investigated the degree to which predation is affecting herring gull reproductive success by examining gull nest attentiveness during the day and night.Gulls utilizing anthropogenic food sources exhibited reduced levels of stress-associated hormones, increased egg size, and increased day-time nest attentiveness.Anthropogenic food sources are likely buffering the impacts of declines in aquatic food availability; however, gull populations are still declining.Use of camera traps to monitor herring gull nests revealed that gulls were significantly more attentive to their nests during the day compared to the night.Of the nine nests monitored with camera traps, five produced chicks.However, at three of these nests chicks were predated shortly after hatch.Nocturnal predation by great horned owls accounted for these predation events which were the most significant predation-related factor affecting herring gull nest success.Predation of chicks at night and of eggs during the day (by conspecifics and corvids) is likely contributing to declines in PNP herring gull populations.Understanding factors contributing to population trends in ecological indicator species is critical for species management and for identifying stressors that are likely
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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) | 0.001 | 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".