Investigation into the cause(s) of a mass mortality of a long-lived species in a Provincial Park and an evaluation of recovery strategies.
Bibliographic record
Abstract
Mass mortality events (MMEs) are rapidly occurring and localized events, and have been reported to remove up to 90% of individuals in a population. MMEs can be especially damaging to population persistence for long-lived species, such as chelonians. While MMEs have been regarded as rare events, they are predicted to occur with increased frequency as environmental stochasticity associated with climate change increases. Unfortunately, a limited understanding of the causes and consequences of MMEs remains. In the current thesis, I investigated the potential causes of an acute MME of at-risk Blanding’s turtles (Emydoidea blandingii) at Misery Bay Provincial Park on Manitoulin Island, Ontario in which approximately 50% of the population succumbed to mortality, and used population viability analyses (PVAs) to examine strategies to recover the population. Because the park includes relatively pristine habitat in which most of the regular anthropogenic threats to turtles are absent, the hypotheses I tested to explain the mortality considered natural threats, including disease, failed overwintering, and predation in the winter and active seasons. I determined that the most likely cause of death was a large-scale predation event, which received support from several lines of evidence, including the presence of predators within the park, a failed predation attempt on a live Blanding’s turtle, and the meticulous destruction of a turtle decoy stationed where carcasses were found. The recovery strategies examined included nest protection, introduction of juveniles, introduction of adults, and a nest protection plus introduction of juvenile combination strategy. PVAs determined that the most effective recovery strategy for this population would be a combination of nest protection and the annual introduction of 25 two-year-old females for a period of 50 years. The information gained through my study has led to the recommendation of appropriate conservation strategies for this population, and will aid in the management of future MMEs elsewhere.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".