Impact of Forest Harvesting on Trophic Structure of Eastern Canadian Boreal Shield Lakes: Insights from Stable Isotope Analyses
Bibliographic record
Abstract
Perturbations on ecosystems can have profound immediate effects and can, accordingly, greatly alter the natural community. Land-use such as forestry activities in the Canadian Boreal region have increased in the last decades, raising concerns about their potential impact on aquatic ecosystems. The objective of this study was to evaluate the impact of forest harvesting on trophic structure in eastern Canadian Boreal Shield lakes. We measured carbon and nitrogen stable isotopes values for aquatic primary producers, terrestrial detritus, benthic macroinvertebrates, zooplankton and brook trout (Salvelinus fontinalis) over a three-year period in eight eastern Boreal Shield lakes. Four lakes were studied before, one and two years after forest harvesting (perturbed lakes) and compared with four undisturbed reference lakes (unperturbed lakes) sampled at the same time. Stable isotope mixing models showed leaf-litter to be the main food source for benthic primary consumers in both perturbed and unperturbed lakes, suggesting no logging impact on allochthonous subsidies to the littoral food web. Brook trout derived their food mainly from benthic predatory macroinvertebrates in unperturbed lakes. However, in perturbed lakes one year after harvesting, zooplankton appeared to be the main contributor to brook trout diet. This change in brook trout diet was mitigated two years after harvesting. Size-related diet shift were also observed for brook trout, indicating a diet shift related to size. Our study suggests that carbon from terrestrial habitat may be a significant contribution to the food web of oligotrophic Canadian Boreal Shield lakes. Forest harvesting did not have an impact on the diet of benthic primary consumers. On the other hand, brook trout diet composition was affected by logging with greater zooplankton contribution in perturbed lakes, possibly induced by darker-colored environment in these lakes one year after logging.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".