Homogenization of freshwater lakes: Recent compositional shifts in fish communities are explained by gamefish movement and not climate change
Bibliographic record
Abstract
Globally, lake fish communities are being subjected to a range of scale-dependent anthropogenic pressures, from climate change to eutrophication, and from overexploitation to species introductions. As a consequence, the composition of these communities is being reshuffled, in most cases leading to a surge in taxonomic similarity at the regional scale termed homogenization. The drivers of homogenization remain unclear, which may be a reflection of interactions between various environmental changes. In this study, we investigate two potential drivers of the recent changes in the composition of freshwater fish communities: recreational fishing and climate change. Our results, derived from 524 lakes of Ontario, Canada sampled in two periods (1965-1982 and 2008-2012), demonstrate that the main contributors to homogenization are the dispersal of gamefish species, most of which are large predators. Alternative explanations relating to lake habitat (e.g., area, phosphorus) or variations in climate have limited explanatory power. Our analysis suggests that human-assisted migration is the primary driver of the observed compositional shifts, homogenizing freshwater fish community among Ontario lakes and generating food webs dominated by gamefish 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 | 0.001 |
| 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".