Freshwater fish functional and taxonomic diversity above and below Niagara Falls
Bibliographic record
Abstract
Abstract The Niagara River, which connects two Great Lakes (Erie and Ontario) and forms a border between Canada and the United States, has experienced decades of abiotic and biotic disturbance as well as long-term restoration efforts. Given the iconic riverscape and importance as a binational fisheries resource, a biodiversity assessment of the mainstem Niagara River fish assemblage is overdue. Here, fish assemblage and habitat data from a standardized boat electrofishing program of the Niagara River were combined with species trait data related to substrate associations, diet preferences, reproductive strategies, and body size to quantify biodiversity patterns among river sections (sites above and below Niagara Falls), seasons (spring, summer, fall), and years (2015–2017). Sixty-five species were captured representing a variety of trait combinations. Significant differences in functional dispersion and divergence (i.e., functional diversity) were observed between river sections, seasons, and (or) years. The fish community captured in the lower river in spring 2015 had both the highest average functional dispersion (2.08 ± 0.32 SD) and divergence (0.88 ± 0.04 SD) compared to the other seasonal sampling efforts, but relatively few fishes were captured (n = 686). Although non-native fishes represented a small portion of the catch over the 3 years (8.6% of catch), the seasonal presence (spring and fall) of mostly introduced large-bodied salmonids expanded functional trait space in the lower river during these periods. The importance of rare species on functional diversity metrics suggests further insight on local species detection probabilities is needed to understand if differences in functional diversity reflect ecological patterns or are driven by sampling design.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".