Alignment of threat, effort, and perceived success in North American conservation translocations
Bibliographic record
Abstract
The use of conservation translocations to mitigate human effects on biodiversity is increasing, but how these efforts are allocated remains unclear. Based on a comprehensive literature review and online author survey, we sought to determine the goals of translocation efforts, whether they focus on species and regions with high threat and likelihood of perceived success, and how success might be improved. We systematically searched the ISI Web of Knowledge and Academic Search Complete databases to determine the species and regions of conservation translocations and found 1863 articles on conservation translocations in the United States, Canada, Mexico, Central America, and Caribbean published from 1974 to 2013. We questioned 330 relevant authors to determine the motivation for translocations, how translocations were evaluated, and obstacles encountered. Conservation translocations in North America were geographically widespread (in 21 countries), increased in frequency over time for all animal classes (from 1 in 1974 to 84 in 2013), and included 279 different species. Reintroductions and reinforcements were more common in the United States than in Canada and Mexico, Central America, or the Caribbean, and their prevalence was correlated with the number of species at risk at national and state or provincial levels. Translocated species had a higher threat status at state and provincial levels than globally (International Union for Conservation of Nature Red List categorization), suggesting that translocations may have been motivated by regional priorities rather than global risk. Our survey of authors was consistent with these results; most translocations were requested, supported, or funded by government agencies and downlisting species at national or state or provincial levels was the main goal. Nonetheless, downlisting was the least reported measure of success, whereas survival and reproduction of translocated individuals were the most reported. Reported barriers to success included biological factors such as animal mortality and nonbiological factors, such as financial constraints, which were less often considered in the selection of release sites. Our review thus highlights discrepancies between project goals and evaluation criteria and between risk factors considered and obstacles encountered, indicating room to further optimize translocation projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".