Practitioner views on the determinants of tropical forest restoration longevity
Bibliographic record
Abstract
Ensuring the long‐term persistence of tropical forest restoration projects is vital to maintaining carbon stocks, biodiversity, and other benefits of restored ecosystems. But our understanding of the factors that determine restoration longevity—the age that a restored ecosystem attains before being converted to another land use—is limited, and derived primarily from studies based on remote sensing or observations at a single site over time. In this article, we apply a new approach by surveying restoration practitioners from across the tropics on the factors that they perceive to influence restoration longevity. Through an online survey (including categorical and open‐ended questions) we asked practitioners about the ecological and social characteristics of their restoration projects, and their views on what factors contribute to project longevity. We summarized the information on project characteristics, and conducted thematic analysis and coding of the longevity drivers discussed by respondents. A total of 29 respondents from 15 tropical countries completed our survey, with the majority of projects occurring on previously pastured lands in wet and lowland tropical forests. Practitioners discussed social factors more than twice as frequently as ecological factors. The most frequently cited social factor key to restoration longevity was engagement with multiple stakeholders, followed by long‐term funding, the need for innovative project design, as well as effective and inspirational leadership. Overall, the voices of practitioners underscore the critical need to address local social context in order to achieve long‐term forest recovery.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".