Movement ecology, diet, and contaminants: Establishing benchmarks for monitoring Arctic seabirds and their habitats in the face of environmental change
Bibliographic record
Abstract
Birds have long been used as ecological indicators of environmental change. Seabirds, with their high trophic position, accessibility at central breeding locations, and ability to integrate signals across space and time, are particularly useful for monitoring changes in marine environments, especially in remote regions where in-depth ecological research can be logistically challenging. Indeed, long-term monitoring of seabirds has provided important insights on many pressing environmental threats, such as changes in ocean productivity, prey species distribution, and trends in pollution. However, knowledge on the ecology, movement, and threats to many seabird species remains limited, particularly in remote regions such as the Arctic. In this thesis, I examine how seabirds can be used as indicators of environmental change in the Arctic using multiple approaches, filling key knowledge gaps on the ecology of multiple Arctic-breeding seabirds. First, I examined the migratory movements of an Arctic-breeding generalist, the herring gull (Larus smithsoniansus), and found that these birds exhibit high inter- and intra-individual variation in migration routes from the Canadian Arctic, but largely overwinter in the same area. This suggests that this population of herring gulls may be more susceptible to climate change impacts in their overwintering locations than during migration. Second, I expanded upon this work by examining individual repeatability in migration and overwintering strategies of another Arctic-breeding generalist species, the glaucous gull (Larus hyperboreus). Here, I found the first evidence of diverging migration in this species and show that wintering areas may shift due to changes in sea ice concentrations. These results indicate that this population may be flexible, at least in the short-term, to changes in climate throughout migration. Third, I used stomach content and stable isotope analyses to assess the diet of another Arctic-breeding gull, the black-legged kittiwake (Rissa tridactyla). Here, I found that kittiwakes in the Canadian Arctic rely heavily on Arctic cod (Boreogadus saida), but that stomach content analyses may underestimate the number of soft-tissue organisms in the diet, such as invertebrates, which has implications for understanding how this species will be impacted by changes in food web structure. Next, I used a combination of new data and a review of historical work to assess plastic pollution ingestion in four Arctic-breeding seabird species. Here, I showed that plastic ingestion differs across species, regions, and time, but surface-feeding species consistently ingest more plastic than pursuit-diving species, emphasizing the importance of the northern fulmar (Fulmarus glacialis) as a monitoring tool for plastic ingestion in Canada and the Arctic. Finally, I reviewed how avian movements play a role in the source, transport, and fate of contaminants, and found that many studies did not consider tissue and contaminant turnover rates, tracking device resolution, and/or statistical power. Using this information, I provided key recommendations for future research in this field. Collectively, this research contributes to the understanding of the ecology of Arctic-breeding seabirds and can act as a benchmark for monitoring change in these species and the ecosystems they inhabit, in turn informing conservation, management, and policy in a rapidly changing climate
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".