Fish assemblages in agricultural drains are resilient to habitat change caused by drain maintenance
Bibliographic record
Abstract
A better understanding of how human activities affect biodiversity can be important for effective resource management. We tested how excavation (maintenance) of agricultural drains (ditches) altered fish assemblages. Uncertainty regarding the effects of drain maintenance on fish assemblages has been a source of tension between landowners, drain superintendents, and fishery managers. Fish assemblages in eight southwestern Ontario drains were sampled repeatedly from before to 2 years after drain maintenance using a replicated before–after, control–impact (BACI) design. Relative to reference sites, we found no evidence for short- or long-term decreases in the number of species and total abundance of fishes following drain maintenance, nor any consistent change in assemblage composition, despite clear changes in physical habitat. The fish assemblages in drains were resilient to drain maintenance and did not show changes expected to concern fishery managers. Our findings provide fishery managers with the information needed to manage drain maintenance more effectively under the Fish Protection Program of the Fisheries Act and to develop drain maintenance practices that balance the needs of agriculture with the protection of fish biodiversity.
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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 | 0.001 |
| 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".