Using Multiscale Environmental and Spatial Analyses to Understand Natural and Anthropogenic Influence on Fish Communities in Four Canadian Rivers
Bibliographic record
Abstract
Science-based conservation of riverine fishes can be best targeted with specific information about spatial-ecological controls on the community, including anthropogenic stressors. Because anthropogenic stressors can originate at multiple spatial scales, we investigated the influence of natural and anthropogenic variables summarized within the reach, valley, and catchment on fish community composition along four river mainstems in Ontario, Canada. We used Redundancy Analyses (RDA) to explore models with multi- and single-scale variables on fish community composition. We used partial RDAs to differentiate the relative effects of variable types in multiscale models and to determine if spatial variables explained additional variation in fish community composition. Catchment variables accounted for the majority of explained variation in fish community composition in three of the four rivers, but instream habitat variables accounted for considerable variability in fish community composition in the two rivers that are highly fragmented by dams or naturally occurring rapids. Natural and human-derived fragmentation in rivers may reduce the influence of catchment controls, disrupt longitudinal gradients, and increase the influence of local instream habitat. Environmental variables that explained fish distribution had longitudinal or patchy spatial pattern within rivers, but spatial variables representing impediments to fish dispersal and proximity to receiving waterbodies failed to explain additional variation in fish community composition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".