Considerations for the Design of a SARA-listed Freshwater Fish Monitoring Program in Point Pelee National Park
Bibliographic record
Abstract
Four fish species listed under the Species at Risk Act (SARA) occupy ponds within Point Pelee National Park (PPNP; Lake Chubsucker Erimyzon sucetta, Spotted Gar Lepisosteus oculatus, Grass Pickerel Esox americanus vermiculatus, and Warmouth Lepomis gulosus) but the status of these populations is poorly understood. Here, considerations for the design of a long-term monitoring program for SARA-listed freshwater fishes in PPNP are presented, informed by prior sampling efforts. Based on a randomized resampling with replacement approach using mini-fyke net capture data from 2019, Warmouth had the highest capture probability among SARA-listed species while Lake Chubsucker had the lowest. Warmouth was the only species captured in sufficient abundance to allow future trend evaluation. The power to detect changes in adult Warmouth abundance increased with the number of sites sampled and surveys performed, the magnitude of change, and the magnitude of site-level variance explained by the model. At least 10 years of annual sampling is recommended for monitoring, where 125 or more mini-fyke nets are set across Lake, East Cranberry, and West Cranberry ponds. This would provide 78.20% (95% confidence interval = 74.32–81.74%) and 67.80% (63.51–71.88%) power for detecting a 30% decrease and increase in abundance, respectively, at the lowest level of random site-level variance tested, and would provide ~80% probability of capturing Grass Pickerel, Lake Chubsucker, and Spotted Gar. Overall, the content of this report provides quantitative guidance on sampling effort requirements for the development and implementation of a fish species monitoring program in PPNP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".