Passive sampling approaches and seining revealed limited changes in the fish community of Point Pelee National Park over two decades
Bibliographic record
Abstract
Point Pelee National Park is a biodiversity hotspot in Canada. Freshwater fish sampling has been conducted sporadically in the open-water wetland ponds over the last two decades to support different objectives. We used multi-gear sampling data from 2002, 2003, 2019, and 2021 and multivariate methods to evaluate differences in fish community composition in Point Pelee National Park over time. Three data formats were considered: raw counts, catch-per-unit effort, and multi-gear mean standardized abundance, with strong ranked-based correlations observed in these measures among years (τ > 0.94). A total of 35,486 fishes encompassing 40 species were captured, with Bluegill (Lepomis macrochirus) and Warmouth (Lepomis gulosus) among the most frequently captured species. Limited differences in composition within and among ponds were observed; mean Bluegill multi-gear mean standardized abundance was reduced by 0.49 between 2003 and 2019 in East Cranberry Pond. Across ponds, Bluntnose Minnow (Pimephales notatus) had a relatively high multi-gear mean standardized abundance in 2002 (0.02 ± 0.16 SD) and 2003 (0.01 ± 0.06 SD) but was not captured in 2019 or 2021. Six species were captured in 2002/2003 but not in 2019/2021, and vice versa. The invasive Tubenose Goby (Proterorhinus semilunaris) was detected for the first time in Point Pelee National Park in 2021. The underlying cause of the compositional differences is uncertain but is at least in part the result of intermittent connectivity of wetland habitat with Lake Erie after a recent breach of the barrier beach along with sampling-induced variation. Overall, results confirm the lack of major community shifts through time, provide insight into the spatial and temporal variability of species-specific abundance using passive gears, and inform the design of future monitoring programs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".