Forestry in the Face of Global Change: Results of a Global Survey of Professionals
Bibliographic record
Abstract
Abstract Purpose of Review Forests support most global terrestrial biodiversity and contribute to the livelihood of billions of people, but these and other benefits are in jeopardy due to global change. This leads to questions, such as how to address the challenges of global change in forest management, given the lack of knowledge and deep uncertainty about future developments. In addition, many of the impediments to implement adaptation strategies are unknown. Recent Findings Here, we present an overview of results from a global survey of 754 forestry professionals (370 researchers and educators, 227 practicing foresters, 37 policymakers, 64 administrators, and 56 with other or unspecified roles) from 61 countries across 6 continents who were interested in global change issues. These professionals were asked about their opinion regarding three different adaptation strategies: resist , adapt , and transform . Most respondents agreed that the majority of global change factors will negatively influence the ability of forests to provide desired ecosystem services. Similarly, they agreed about major challenges when implementing adaptation strategies and specifically whether our current knowledge base is sufficient. These concerns were not limited to ecological aspects, but respondents also highlighted the need for a better appreciation of social/political and economic barriers, especially regarding transformation strategies. In addition, the response patterns, including differences due to economic status, highlight the importance of developing and evaluating adaptation strategies in a local social–ecological context. Summary Our study demonstrates a widespread perception on the part of forestry professionals around the world, especially among researchers and practitioners, that many global change factors will affect sustainable forest management negatively, resulting in the need for active silvicultural adaption. The results also suggest potential barriers to different adaptation strategies, particularly a relative lack of information and social acceptance for transform strategies. Further, this study highlights the importance of social and political factors and the need to understand the general public’s values regarding adaptation strategies as well as how the influence of public opinion is perceived by forest managers.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".