An investigation of the factors affecting mercury accumulation in lake trout, Salvelinus namaycush, in northern Canada
Bibliographic record
Abstract
The major aim of this thesis project was to determine the variables that most explain the elevated mercury concentrations in lake trout (Salvelinus namaycush), a predatory aquatic fish species in some lakes in northwestern Canada. High mercury concentrations in lake trout in other regions have been associated with the biological features of the fish and various chemical and physical aspects of their aquatic ecosystems. Data including lake trout age, length, weight, and stable isotope values, water chemistry, latitude, and lake and watershed area were collected, compiled and then included in statistical analyses of the factors affecting mercury concentration in the muscle of lake trout from a series of lakes from the Mackenzie River Basin (MRB) in the Northwest Territories (NT), Canada. These results are reported in Chapter 2. Fish age and lake surface area were the most important variables affecting mercury concentrations. However mercury concentration in muscle also was significantly (p < 0.05) related to: fish length, weight, and ẟ¹³C; watershed area to lake area ratio; and to total mercury concentration in zooplankton and water. These variables were run through best subsets analyses and multiple regressions in order to determine the regression equation most efficiently capable of predicting mercury concentration in lake trout in unstudied lakes in the MRB region. The resulting equation was: log Hg = 0.698 – (0.0156 × latitude) + (0.0031 × age) + (0.000535 × length) – (0.245 × log lake area) + (0.00675 × watershed area/lake area ratio), r² = 0.73 Small lakes located in the southern NT and dominated by large and/or old lake trout are most likely to have lake trout whose mean mercury concentrations exceed 0.5 μg/g; the guideline for the commercial sale of fish. Latitude may be linked to mean annual temperature (and variables such as duration of ice cover, summer water temperature) while fish age and length may be related in part to fishing pressures and growth rates on these lake populations. In chapter 3, a more in-depth study was undertaken to investigate of role of feeding and relative tropic level in the bioaccumulation of mercury in lake trout. This was accomplished by comparing MRB lake trout population characteristics with those from a series of lakes in northern Alberta and Saskatchewan (NAS). The two population groups were compared with respect to size, age, growth rates, and mercury concentrations. In addition, trophic and mercury biomagnification relationships, as inferred from stable carbon and nitrogen isotope analyses, for the two lake trout populations were compared. Lake trout from the NT exhibited significantly higher mercury concentrations than those from the NAS lakes (p < 0.001). Mercury concentrations in biota (including lake trout, forage fish, benthic invertebrates and zooplankton) were positively and significantly correlated to ẟ¹⁵N values in all lakes in both of the study areas (p < 0.001). Mercury biomagnification in the NT lakes, as estimated from the slope of ẟ¹⁵N versus mercury concentration, was lower than in the NAS lakes. Thus, mercury biomagnifies more slowly in NT lake trout, but because of their greater mean age, reaches higher values than in NAS lakes. Northwest Territory lake trout generally exhibited more negative ẟ¹³C values, indicating more pelagic feeding habits than in NAS lakes: higher mercury concentrations previously have been associated with more pelagic feeding. Finally, the relationship between mercury levels and growth rates in lake trout was investigated by comparing NAS and NT lake trout populations. These results are reported in chapter 4. Lake trout from the NT lakes grew at a slower rate (10.4 mm per year) than those from the NAS lakes (35.1 mm per year). Log mercury concentration was inversely correlated (p < 0.001) with growth rate for both lake trout populations; however, growth rate explained more of the variation in mercury level in the NT lakes than in the NAS lakes (NT, r² = 0.11, p < 0.001; NAS, r² = 0.03, p = 0.024). However, the correlation between mercury concentration and growth rate in the NAS study area improved when Reindeer Lake, possibly affected by anthropogenic inputs, was removed from the analyses (r = 0.13, p = 0.001). Therefore, lower mercury levels in lake trout are associated with higher growth rates through growth dilution. The higher mercury concentrations in NT lake trout are due not only to the old age of the fish, but to slower growth rates as well.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".