Patterns of diversity in stream macroinvertebrate communities in a low-gradient agricultural stream (Sydenham River, Ontario, Canada) were driven by surficial geology and catchment position
Bibliographic record
Abstract
Abstract Freshwater biodiversity is increasingly at risk wherever land uses such as agriculture exert multiple stressors that degrade habitat quality. Although stream macroinvertebrates act as bioindicators for monitoring these impacts, their responses are context-specific: examining drivers of community composition is therefore important to understand the results of monitoring efforts. In a primarily agricultural landscape, 15 sites across the Sydenham River watershed, Ontario, Canada, were assessed for in-stream habitat quality and stream macroinvertebrate diversity. We predicted that community assemblage would be driven by differences in surficial geology across branch (east branch versus north branch) and catchment position (main stem versus tributary). We found that the main stem of the east branch was characterised by significantly higher proportions (P = 0.053) and abundance (P = 0.038) of Ephemeroptera–Plecoptera–Trichoptera (EPT) taxa than north branch sites were, and sites in the east and north branch tributaries were characterised by significantly lower Hilsenhoff Biotic Index (HBI) scores (P = 0.088). Redundancy analysis found that substrate size was the main driver of community composition, generating a model that described these patterns across branch and catchment position. Our findings suggest that EPT abundance and HBI scores were the variables that were most effective at revealing differences in stream communities due to agricultural impacts and that sediment size is an important driver of these patterns.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".