A Multitracer Approach to Quantifying Resource Utilization Strategies in Lake Trout Populations in Lake Huron
Bibliographic record
Abstract
Lake ecosystems are threatened by an array of stressors. An understanding of how food webs and bioaccumulation dynamics respond to these challenges requires the quantification of energy flow. We present a combined, multitracer approach using both polychlorinated biphenyls (PCBs) and stable isotopes to trace energy flow, and to quantify how lake trout feeding strategies have adapted to changes in food web structure in 3 basins of Lake Huron (ON, Canada). This combined tracer approach allows the quantification of dietary proportions (using stable isotopes), which are then integrated using a novel PCB tracer approach that employs knowledge of PCB bioaccumulation pathways, to estimate consumption and quantify energy flow between age cohorts of individual fish across Lake Huron. We observed basin-specific differences in ultimate energy sources for lake trout, with Georgian Bay lake trout deriving almost 70% of their energy from benthic resources compared with 16 and 33% for Main Basin and North Channel lake trout, respectively. These differences in resource utilization are further magnified when they are contrasted with age. The dependency on pelagic energy sources in the Main Basin and North Channel suggests that these populations will be the most negatively affected by the ongoing trophic collapse in Lake Huron. Our study demonstrates the utility of a multitracer approach to quantify the consequences of food web adaptations to changes in aquatic ecosystems. Environ Toxicol Chem 2019;38:1245-1255. © 2019 SETAC.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".