A bioassessment of the impact of livestock restriction on benthic macroinvertebrate communities in the Grand River watershed in Ontario, Canada
Bibliographic record
Abstract
Livestock exclusion from streams is a best management practice applied in attempts to improve water quality in the Grand River watershed in Ontario, Canada. Because of the lack of resources, minimal biomonitoring is conducted to assess the impacts of the fencing on water quality. The purpose of this study was to fill in the gaps by 1) determining if fence length and age of fence influenced the water quality within fenced areas, and 2) compare current water quality conditions of fenced locations to historical data. Benthic macroinvertebrates were used as an indicator of water quality, and a suite of biological indices (taxa richness, abundance of Ephemeroptera, Plecoptera, Trichoptera, Oligochaeta and Chironomidae, Shannon Wiener Index, Simpson’s Index, and Hilsenhoff’s Family Biotic Index) were used to compare upstream, midstream, and downstream locations of fences with varying ages and lengths, using ANCOVA and Kruksal-Wallist tests. Invertebrate samples collected in 2014 were compared to invertebrate samples collected in 2007 using paired t-tests. There were minimal statistical significances when comparing invertebrate samples between fenced areas of different ages and lengths, and minimal differences between the 2007 and 2014 data. The lack of significant differences suggests that livestock exclusion may not be facilitating passive restoration. However, upstream pollutant inputs may be masking the impacts livestock exclusion has on water quality, as a result of cumulative effects. Other best management practices and strategies may need to be implemented in order to have measureable improvements to water quality.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".