Optimizing release strategies: a stepping‐stone approach to reintroduction
Bibliographic record
Abstract
Abstract Evaluation of alternative management strategies enables informed decisions to accelerate species recovery. For reintroductions, post‐release survival to reproductive age is a key parameter influencing population growth. Here, we trial a ‘stepping‐stone’ method to maximize the success of captive‐bred animals when the availability of more suitable wild‐born release candidates is limited. Our approach makes use of relatively safe and established wild populations to prepare captive‐bred individuals for eventual translocation to a final release destination, thus building resilience through establishment of multiple populations over time. We developed a novel multievent model integrating encounter history and biotelemetry data to evaluate reintroduction strategies for the critically endangered Vancouver Island marmot Marmota vancouverensis . We compared post‐release survival of 176 individuals (52 wild‐born, 47 captive‐bred marmots released directly to destinations, and 77 captive‐bred marmots released with a stepping‐stone approach). Survival estimates to prime breeding‐age ( PBA ), were then used to quantify expected success of potential release strategies. Our analysis indicates that post‐release survival varies by source population and release method, as well as age, season, year, and years since release. Conditional on an objective of maximizing survival to PBA , our results suggest that using wild‐born marmots for translocations as often as possible, and stepping‐stone captive‐bred marmots prior to final release, will result in the best outcomes. There was a 0.86 probability that survival to PBA was greater for captive‐bred marmots released as yearlings using a stepping‐stone approach (survival to PBA mode = 0.13, 95% CRI = 0.05–0.30) than for captive‐bred animals that were directly released to destination sites as one‐year‐olds (survival to PBA mode = 0.04, 95% CRI = 0.01–0.24). Consequently, the stepping‐stone approach yields much higher population establishment or growth potential than previous release strategies that used captive‐bred marmots. Optimizing the combination of release candidates, sites and timing can thereby increase the effectiveness of reintroductions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".