Headstarting turtles to larger body sizes for multiple years increases survivorship but with diminishing returns
Bibliographic record
Abstract
Abstract Headstarting is a conservation tool that assumes raising turtles in protected ex situ environments to larger body sizes, then releasing them back into the wild, increases their survivorship compared to wild, non‐headstarted turtles. Our goal was to quantitatively test this fundamental assumption by comparing somatic growth and survivorship among 3 age classes of headstarted juvenile wood turtles ( Glyptemys insculpta ) monitored for 3 years (2016–2018) in Ontario, Canada. Our age classes were turtles headstarted for 2 years ( n = 15), turtles headstarted for 1 year ( n = 30), and turtles incubated and hatched ex situ , then released (i.e., no headstarting; hatchlings, n = 30). Both age classes of headstarted turtles were radio‐tracked for 1 year after release. We released hatchlings in August and radio‐tracked them for 1 month. All cohorts exhibited positive somatic growth after release. One‐month post‐release survival of hatchlings was 70%. Turtles headstarted for 2 years had slightly higher 1‐year post‐release survivorship (67%) than turtles headstarted for 1 year (47%), but there was little to no evidence for a difference ( P = 0.17). Modeling these survivorship results with real‐life constraints of only 100 hatchlings available for headstarting each year (biological constraint from source population), and a 100‐turtle capacity in our headstarting facility (economic constraint), projected similar population growth in 2‐year and 1‐year headstarting programs. When removing the 100‐turtle facility limit, the 2‐year program projected higher population growth but required space for 200 turtles in the headstarting facility, which may not be feasible for many programs. Our results support the fundamental assumption that headstarting increases survivorship, but we observed diminishing returns if headstarting was increased for longer than 1 year. Given the growing number of turtle headstarting projects globally, our study provides data that can aid in establishing cost‐ and resource‐effective best practices.
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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 | 0.000 |
| 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".