An empirical analysis of the consequences of zebra mussel invasions on fisheries in inland, freshwater lakes in Southern Ontario
Bibliographic record
Abstract
With increasing human use of North American inland lakes for recreational purposes, there is a greater probability of the incidental transfer and introduction of aquatic invasive species within them. This raises concern over the potential ecological and economic impacts that invasive species could have in these highly valued freshwater ecosystems. In this study, we focus our interests on 63 inland lakes distributed across the Southern Ontario landscape in order to evaluate whether concerns are warranted over potential adverse impacts of a well-known aquatic invasive species (the zebra mussel (Dreissena polymorpha)) on native fish assemblages and targeted game species (i.e., walleye (Sander vitreus), smallmouth bass (Micropterus dolomieu), lake trout (Salvelinus namaycush), and northern pike (Esox lucius)). Through the analysis of a comprehensive and multivariate dataset comprising lake morphometric, water chemistry, fish assemblage, fish metric, and zooplankton haul data, we examined the potential consequences of zebra mussel invasions in inland lakes. It was found that condition, growth, and relative abundance of some of the most highly valued game fish in inland lakes significantly varies among lakes with and without zebra mussel, though the nature of such effects differed for each of the fish species examined. In addition, it was shown that the species composition (i.e., identity and relative abundance) of active, large-bodied fish assemblages within a lake can indicate zebra mussel presence/absence with a high degree of certainty. Finally, while correlations between zebra mussel presence and the richness, abundance, growth, and condition of certain fish species were found, the results of this study indicate that the potential consequences of this well-known aquatic invader to fisheries in inland lakes may be less dramatic than those reported within the Great Lakes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".