A multi-predator analysis: comparing trophic niche dimensions and mercury concentrations among four sympatric piscivores of boreal Lakes
Bibliographic record
Abstract
Aquatic apex predators, like all predators, are an intrinsic part of a healthy ecosystem. They help stabilize food webs, as well as regulate and support strong biodiversity. In addition to being ecologically important, many predatory fish species are also of high socio-economic and cultural importance. Unfortunately, at the top of the trophic pyramid, apex predators are also at greater risk of accumulating harmful contaminants, such as mercury (Hg). With reports of rising Hg in boreal predatory fish species, the objective of this study was to compare and contrast the trophic ecologies and Hg concentrations of four sympatric piscivores of 27 boreal lakes across Ontario. In Chapter 1, trophic relationships among sympatric burbot (Lota lota), lake trout (Salvelinus namaycush), northern pike (Esox lucius) and walleye (Sander vitreus) were investigated by using stable isotopes ratios of nitrogen (δ15N) and carbon (δ13C) to calculate metrics of trophic niche dimensions (position, size and shape) and trophic interaction. How each metric responded to varying environmental conditions was also explored. The trophic range utilized by all four species was similar, and the differences in trophic niche positions and dimensions observed were greatest when comparing species along a nearshore to offshore gradient. Overall, different environmental conditions had varying effects at different scales (i.e., population, paired-species, community); however, lake mean depth had the strongest and most consistent positive effect on niche dimensions and the dispersion of species within isotopic space. Deeper, clearer, less productive lakes (i.e., greater Secchi depth) supported greater niche segregation among these four species, while shoreline complexity had a negative effect on community trophic dispersion. iv In Chapter 2, the relative importance of food web position (δ15N and δ13C) and somatic growth rate (LGR) in explaining differences in muscle total Hg concentrations ([THg]) among the same four predatory fish species was explored. Ecosystem differences accounted for 44% of the total observed variability in [THg], and species differences accounted for 15%, of which approximately half could be attributed to differences in trophic positions and growth rates. Relative to δ13C and LGR, δ15N was the best predictor of [THg] among sympatric predators, but the best model included both δ15N and LGR. My thesis highlights how top predators in boreal lakes share trophic space and how their trophic interactions are modified by different lake habitat features. Identifying trophic variability among co-habiting top predators could help us better understand differences in [THg] among the important sympatric piscivores we rely on.
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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 | 0.001 |
| 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".