Review of terrestrial temporarily conserved areas in Canada, the United States, and Mexico
Bibliographic record
Abstract
The establishment of protected areas is a cornerstone of conservation, but permanent protection could be inefficient or even impossible in some situations. We synthesized the literature on temporarily conserved areas (TCAs) across Canada, the United States, and Mexico. We used a comprehensive search string to retrieve peer-reviewed articles published from 2000 to 2021 from the Web of Science. We identified 27 relevant peer-reviewed articles that examined the potential benefits of TCAs in the study area, indicating TCA is a relatively understudied area of research in the peer-reviewed literature. The TCA studies were highly clustered; 77% of studies focused on protecting a single life stage of migratory species and 61% of studies related to temporary conservation of breeding or staging habitats for migratory birds. Ninety-three percent of studies focused on preventing human-driven threats, mainly on public lands of coastal areas, the Great Plains, and the Mississippi Valley in the central United States. Short-term and experimental studies were the dominant study types. TCAs have the potential to complement permanently protected areas and provide protection when permanent protection is difficult. Some included studies examined their conservation value, but the ecological, social, and economic outcomes of TCAs are unclear. More TCA research is needed to determine the role they could play in conservation worldwide. Embracing the concept of TCAs as conservation tool could lead to more comprehensive and consistent reporting of the outcomes of temporary area-based conservation measures. However, a global review and analysis of effectiveness of TCAs will be required if they are to play a formal role in meeting international targets for biodiversity conservation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".