Crustacean zooplankton communities as indicators of game fish occurrence and abundance in Québec lakes
Bibliographic record
Abstract
• Walleye, brook trout and lake trout occurrence and abundance examined in 94 lakes. • Fish occurrence was predicted by zooplankton taxa with ∼73 % of correct predictions. • Of all three fish species, walleye abundance was the best predicted by zooplankton. • Cladoceran biomass emerged as the strongest zooplankton indicator of fish abundance. • Environment and fish community composition variables remained better predictors. Recreational inland fisheries play a vital role in the economy and culture of Canada. However, human activities and climate change are significant threats to lakes that sustain such fisheries, bolstering the need to maintain ecosystem quality while sustaining fisheries through approaches such as ecosystem-based management. Despite the importance of zooplankton communities for fish diet, very few freshwater management plans have integrated information from this lower trophic level. Here, we used a dataset including game fish abundance, crustacean zooplankton community composition and associated habitat variables in 94 north temperate lakes across the province of Quebec, Canada. Our study aimed to uncover whether zooplankton taxonomic and functional community properties were related to walleye, brook trout, and lake trout occurrence and abundance. Our analyses revealed that both taxonomic and functional zooplankton composition were significant predictors of focal fish species occurrence, albeit less so than environmental or fish community composition variables. When examining the importance of 30 different zooplankton community indices for target fish species abundance, structural equation modeling revealed that zooplankton was more important for walleye than for the two other fish species. Overall, this research improves our understanding of zooplankton-fish interactions and how these shape north temperate lake communities, with a perspective of improving fisheries conservation and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".