Recovery of zooplankton assemblages in mountain lakes from the effects of introduced sport fish
Bibliographic record
Abstract
Impacts of introduced fish on zooplankton assemblages of lakes may persist for decades following fish removal. We tested this hypothesis by comparing zooplankton assemblages from four categories of lakes located in western Canadian mountain parks including lakes without and with fish that differed in their fish community complexity and fish-stocking history. Zooplankton species richness was greatest in lakes with a complex community of fish and least in pristine fishless lakes. Canonical correspondence analysis showed that taxonomic shifts in zooplankton assemblages could be attributed to differences in fish-stocking history between the study lakes. In fishless lakes, larger copepods (Eucyclops agilis, Diaptomus leptopus), cladocerans (Diaphanosoma, large Daphnia), and chaoborids were abundant, whereas in the presence of fish, small crustaceans were more common and chaoborids were relatively rare. Once introduced trout were absent from lakes, recovery trajectories for zooplankton showed a general taxonomic shift towards assemblages characteristic of fishless lakes that had never been fish stocked. Based on separation between previously stocked fishless lakes and naturally fishless mountain lakes in ordination space (chi-squared distance), taxonomic recovery by zooplankton assemblages from the influence of introduced salmonids may require an average of 19 years.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".