Assessing long-term environmental trends in Ontario lakes that support Lake Trout (Salvelinus namaycush)
Bibliographic record
Abstract
Oxygenated habitats for Lake Trout (Salvelinus namaycush) are projected to deteriorate with climate warming. However, a paucity of long-term limnological monitoring data impedes the ability to determine if concentrations of end-of-summer volume weighted hypolimnetic oxygen (VWHO), a management-relevant metric of deepwater oxygen conditions, have changed and if so, what environmental variables are driving these changes. This research uses limnological and paleolimnological records to investigate long-term trends in VWHO and variables that influence VWHO in Ontario Lake Trout lakes across multiple spatial and temporal scales. An investigation of ~40 years of biweekly temperature-oxygen profiles collected from six lakes in south-central Ontario tracked recent (post-2010) increases in end-of-summer VWHO and in the amount of optimal Lake Trout habitat. These changes were driven by increases in both hypolimnetic volume and hypolimnetic oxygen concentrations. Using paleolimnology, the temporal scope of this thesis was extended to analyze environmental conditions in three Lake Trout lakes over the past 100–150 years. These data suggested primary production has increased independent of lake nutrient concentrations – a significant finding as current management strategies primarily rely on limiting phosphorus loading to prevent hypolimnetic oxygen depletion. To expand the spatial scope, sediment cores from four additional study lakes (seven lakes in total) were investigated. This included an analysis of sedimentary records from two basins within the same lake, which demonstrated the individual sensitivities of unique basins to localized stressors. Paleolimnological inferences from all seven study lakes indicated that most lakes have increased with respect to thermal stability, lakewater total organic carbon (TOC) concentrations, and whole-lake primary production over the past century. Meanwhile, only two of the study lakes appear to have undergone nutrient enrichment. These findings are significant as they emphasize the need to incorporate the combined influence of warming and rising TOC concentrations (in addition to limiting nutrient loading) into management strategies for coldwater fish habitat. Collectively, this research provides insight on temporal and spatial trends of environmental variables that can impact oxygenated habitat for Lake Trout. The observed VWHO increases (in both limnological and paleolimnological data) emphasize the complex influence of multiple environmental drivers on coldwater fish habitat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".