Flow velocity–ecology thresholds in Canadian rivers: A comparison of trait and taxonomy‐based approaches
Bibliographic record
Abstract
Abstract Understanding the physical and biological mechanisms contributing to flow velocity–ecology relationships is crucial for successful river management. The application of an ecological traits‐based approach offers the potential to explore mechanistic linkages between aquatic communities and a hydrological gradient. To date, however, studies focused on identifying these relationships have been limited by a lack of large‐scale, long‐term biological data. To address this gap at a scale relevant for water policy management, we employed data from a large‐scale standardised benthic monitoring program—the Canadian Aquatic Biomonitoring Network—obtained from wadeable river sites across Canada. We applied the Threshold Indicator Taxa AN alysis method to quantify the response of the macroinvertebrate community, expressed as traditional taxonomic information and also as ecological traits, along a flow velocity gradient in reference and potential reference sites. Five key findings emerged: (1) using taxa and trait modalities revealed different flow velocity thresholds, (2) trait flow velocity indicators were less variable than taxon indicators, especially for positively responding trait modalities, (3) labile and non‐labile trait modalities demonstrated highly similar patterns along the flow velocity gradient, (4) taxa from 12 different orders responded negatively to flow velocity, while only EPT taxa and some dipterans responded positively to flow velocity, and (5) traits related to mobility and ecology (e.g. climber and swimmer habits, preference of cold‐cool eurythermal water and ability to survive desiccation) tended to respond positively to flow velocity, while traits related to morphology, life history and ecology (e.g. sprawler and burrower habits, preference for warm eurythermal water and inability to survive desiccation) tended to respond negatively to flow velocity. Providing ecologically based flow management targets can improve management plans, anticipate ecosystem consequences of anthropogenic change and support the development of policies to mitigate anthropogenic flow alteration. While our taxon and trait modality flow indicators were developed for Canadian watersheds, our methods to develop flow indicators and thresholds are transferrable to other systems where long‐term biomonitoring programs are being developed, underscoring the need for long‐term biomonitoring programs to support better ecosystem 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".