Fishing down then up the food web of an invaded lake
Bibliographic record
Abstract
Analysis of commercial catches reveals a serial depletion of some oceanic fish stocks over time, resulting in fisheries focusing on increasingly smaller species closer to the base of the food chain. This effect, described as fishing down the marine food web, is observed when the trophic level of the catch declines over time, raising concerns about the ecosystem impacts of fishing. Freshwater systems also experience harvest, yet do not appear to commonly show the same fishing down response perhaps because time series are too short to witness early depletions, fishing is often recreational, or other factors like stocking and invasive species influence patterns. Here we make use of extensive catch records from Lake Simcoe dating back to the 1860s, to examine if fishing down effects are observed in this highly exploited Canadian inland lake. We measured 2 commonly used indicators from catch data, mean trophic level (MTL) and fishing-in-balance (FiB), and compared trends between a historical period dominated by commercial fishing and a contemporary period when commercial fishing ceased and recreational fishing effort increased. We found a striking difference between the 2 time periods, with MTL (and to some extent FiB) declining during commercial fishing but increasing during recreational fishing. However, indicators either increased or decreased due to invasive species and increased due to stocking. We show that while declining MTL can occur in a freshwater lake, the trajectory can be altered by a switch to recreational fishing, as well as stocking and invasive species.
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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 | 0.001 | 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".