ABIOTIC CONDITIONS, ALGAL BIOMASS & FISH GROWTH RATES AFFECT FISH MERCURY CONCENTRATIONS IN TWO SUBARCTIC LAKES
Bibliographic record
Abstract
Kakisa Lake and Tathlina Lake, located in the Dehcho Region of the Northwest Territories, support important fisheries for the local Ka’a’gee Tu First Nation (KTFN). Recently, Walleye (Sander vitreus) of typical catch size in Tathlina Lake were found to have mercury concentrations above Health Canada’s commercial sale guideline of 0.5 ppm. Wild foods with elevated mercury concentrations can pose health risks to the humans who consume them, depending on consumption amounts and vulnerability factors such as age and pregnancy. Because wild fish can accumulate relatively high mercury levels and subsistence fishing contributes greatly to food security in northern regions, mercury-related health risks to people are greater in the north than in the south, where wild fish are not as frequently consumed. Here, I examine and compare known drivers of fish mercury concentrations in two aquatic food webs to investigate causes of between-lake variation in mercury concentrations in food fishes. I relate analyses of food web structure, fish growth, and lake physicochemistry to mercury concentrations, and attempt to determine why fish mercury concentrations differ between Kakisa Lake and Tathlina Lake. Sediment and water methylmercury availability and primary producer abundance appear to be major factors influencing bioaccumulation of mercury in the food webs of each lake. Concentrations of methylmercury in sediment and water were higher in Tathlina Lake than in Kakisa Lake, and % methylmercury (of total mercury) in these ecosystem components indicate that the net mercury methylation rate is higher in Tathlina Lake than in Kakisa Lake. Kakisa Lake also had higher concentrations of chlorophyll a, indicating relatively higher rates of primary production and possible bloom dilution of mercury, which was further confirmed by trophic biomagnification modeling; these factors appear to have bottom-up impacts on the food webs of both lakes, including other food fishes. Walleye mercury concentrations also appeared to be affected by growth rates and perhaps growth efficiency, as suggested by evaluations of growth rates. This research is part of a larger project that seeks to assess the risks and benefits of fish consumption in the Northwest Territories, especially by Indigenous communities, who rely on natural fisheries for subsistence and for whom wild foods hold significant cultural and spiritual value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".