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 \nreported to remove up to 90% of individuals in a population. MMEs can be especially damaging \nto population persistence for long-lived species, such as chelonians. While MMEs have been \nregarded as rare events, they are predicted to occur with increased frequency as environmental \nstochasticity associated with climate change increases. Unfortunately, a limited understanding of \nthe causes and consequences of MMEs remains. In the current thesis, I investigated the potential \ncauses of an acute MME of at-risk Blanding’s turtles (Emydoidea blandingii) at Misery Bay \nProvincial Park on Manitoulin Island, Ontario in which approximately 50% of the population \nsuccumbed to mortality, and used population viability analyses (PVAs) to examine strategies to \nrecover 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 \nconsidered natural threats, including disease, failed overwintering, and predation in the winter \nand active seasons. I determined that the most likely cause of death was a large-scale predation \nevent, which received support from several lines of evidence, including the presence of predators \nwithin the park, a failed predation attempt on a live Blanding’s turtle, and the meticulous \ndestruction of a turtle decoy stationed where carcasses were found. The recovery strategies \nexamined included nest protection, introduction of juveniles, introduction of adults, and a nest \nprotection 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 \nannual introduction of 25 two-year-old females for a period of 50 years. The information gained \nthrough my study has led to the recommendation of appropriate conservation strategies for this \npopulation, and will aid in the management of future MMEs elsewhere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".