Human adaptation to climate change in the context of forests: A systematic review
Bibliographic record
Abstract
We assessed how people adapt to climate change in the context of forests through a systematic review of the international empirical research literature. We found that drought, precipitation variability, extreme precipitation and flooding, and extreme heat were the climatic stressors to which responses were most frequently documented. Individuals and households received the most research attention, followed by national government, civil society, and local government. Europe and North America were the geographic foci of more research than other regions. Behavioral responses were more reported than technical and infrastructural responses and institutional responses. Within these types of responses, actors used a wide variety of practices such as replanting, altering species composition, and adopting or changing technology. Adaptation efforts in early planning and advanced implementation received some attention, but early implementation and expanding implementation were most reported. While connections between responses and risk reduction were discussed, there is limited evidence of risk reduction. Our review contributes to the scholarly and practical understanding of how people adapt to climate change in the context of forests. The review also identifies opportunities for future research on adaptation to other climatic stressors, such as wildfires and tree pests and pathogens, adaptation in other geographic areas, especially Oceania, and adaptation by actors beyond the individual and household level and through institutional adaptation efforts.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".