Trophic transfer and spatial distribution of mercury in the Gulf of St. Lawrence using northern gannets «Morus bassanus» as biological samplers
Bibliographic record
Abstract
Contaminants are ubiquitous in marine environments, but they are not evenly distributed.Organisms at varying trophic levels and habitats are exposed to different contaminant loads due to biomagnification through food webs and spatial heterogeneity.This thesis seeks to understand how mercury, a contaminant of high concern for wildlife due to its neurotoxic properties, moves through the food web up to a top predator, the northern gannet (Morus bassanus) in the Gulf of St. Lawrence, and how different foraging habitats may influence the mercury loads in their prey.Chapter 1 reviews the current literature pertaining to mercury, the use of stable isotopes in ecotoxicology and the use of seabirds as ecological indicators.Chapter 2 compares the use of two stable isotope analysis methods to evaluate the rate of mercury biomagnification in the northern gannet food web.Compound-specific stable isotope analysis of amino acids (CSIA-AA) provides a method to estimate baseline stable nitrogen isotope values of food webs.The method allows less biased estimates of trophic positions than those provided by bulk stable isotope analysis, improving estimates of contaminant biomagnification.I calculated trophic positions with published CSIA-AA equations for four species of fish and for northern gannets and examined the effect of CSIA-AA-derived trophic positions on biomagnification metrics for mercury and compared to the more traditional bulk stable isotope approach.Trophic magnification factors (TMFs) produced by CSIA-AA were lower than that produced by bulk stable isotope analysis.The bulk technique yielded one of the highest TMFs ever reported in the scientific literature.My work demonstrates discrepancies in biomagnification assessed using different approaches that may go undetected when using a single approach.Chapter 3 seeks to understand how different foraging habitats may determine the contaminant load contained in gannets' diet, and what anthropogenic and biotic factors drive mercury contamination in the Gulf of St. Lawrence.To map the chemical landscape of mercury, I collected fish regurgitations from GPS-tracked northern gannets.I assigned mercury concentrations from fish muscle to the last known gannet foraging location, classified using Hidden Markov Models.In small fish species, trophic positions calculated with CSIA-AA values were the best predictor of THg.THg in large fish species was best explained by stable carbon isotopes, indicating inshore habitats had higher THg contamination than pelagic ones.Demonstrating where contaminants accumulate more efficiently is crucial to understanding what risks wildlife are exposed to based on their habitat and feeding ecology.The thesis develops novel tools for measuring contamination in our increasingly polluted oceans.
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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 | 0.001 | 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".