Mass Mortality in a Community of Headstarted (Emydoidea blandingii) and Naturally Occurring (Chrysemys picta marginata) Freshwater Turtles in Protected Urban Wetlands
Bibliographic record
Abstract
Turtles experience numerous threats and high mortality in urban areas. Mass-mortality events (MMEs) are localized, sudden events resulting in a catastrophic increase in mortality rate. Turtles are susceptible to MMEs because their long generation times do not permit density-dependent compensation. We retrospectively investigated an MME in 2020 that affected two species, headstarted Blanding’s Turtles (Emydoidea blandingii) and naturally occurring Midland Painted Turtles (Chrysemys picta marginata) in Rouge National Urban Park (RNUP) in Ontario, Canada, where we monitored the freshwater turtle community from 2014 to 2022. In 2020, 48 juvenile headstarted Blanding’s Turtle and 57 Midland Painted Turtle carcasses were found, most (75%) of which were females. The turtles were likely depredated by Raccoons (Procyon lotor) or American Minks (Neogale vison). Documented mortalities were highest in release sites for headstarted turtles, possibly due to increased monitoring efforts at those sites. Conservation initiatives such as headstarting are useful to recover turtle populations, but MMEs may disrupt and delay population stabilization, especially in areas where other threats (e.g., subsidized predators, road mortality) are prevalent. We emphasize the importance of long-term monitoring of turtle populations, especially after conservation interventions, to detect challenges that affect their persistence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".