Nature‐based framework for sustainable afforestation in global drylands under changing climate
Bibliographic record
Abstract
Drylands cover more than 40% of Earth's land surface and occur at the margin of forest distributions due to the limited availability of water for tree growth. Recent elevated temperature and low precipitation have driven greater forest declines and pulses of tree mortality on dryland sites compared to humid sites, particularly in temperate Eurasia and North America. Afforestation of dryland areas has been widely implemented and is expected to increase in many drylands globally to enhance carbon sequestration and benefits to the human environment, but the interplay of sometimes conflicting afforestation outcomes has not been formally evaluated yet. Most previous studies point to conflicts between additional forest area and water consumption, in particular water yield and soil conservation/desalinization in drylands, but were generally confined to local and regional scales. Our global synthesis demonstrates that additional tree cover can amplify water consumption through a nonlinear increase in evapotranspiration-depending on tree species, age, and structure-which will be further intensified by future climate change. In this review we identify substantial knowledge gaps in addressing the dryland afforestation dilemma, where there are trade-offs with planted forests between increased availability of some resources and benefits to human habitats versus the depletion of other resources that are required for sustainable development of drylands. Here we propose a method of addressing comprehensive vegetation carrying capacity, based on regulating the distribution and structure of forest plantations to better deal with these trade-offs in forest multifunctionality. We also recommend new priority research topics for dryland afforestation, including: responses and feedbacks of dryland forests to climate change; shifts in the ratio of ecosystem ET to tree cover; assessing the role of scale of afforestation in influencing the trade-offs of dryland afforestation; and comprehensive modeling of the multifunctionality of dryland forests, including both ecophysiological and socioeconomic aspects, under a changing climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".