Trees planted under a global restoration pledge have mixed futures under climate change
Bibliographic record
Abstract
Nations worldwide have committed to restoring millions of hectares of forest as a strategy to mitigate climate change with many other co‐benefits. Paradoxically, the suitability of climatic conditions for the trees being planted at the restoration sites is changing, which may reduce the long‐term viability of these projects. We assessed the potential future viability of trees planted as part of Ecuador's National Reforestation Plan from 2014 to 2017, committed under the global Bonn Challenge. We selected the 10 most frequently planted tree species (all native) in 1237 restoration sites in northwest Ecuador. We modeled the species' climatic suitability at the restoration sites under Representative Concentration Pathways (RCP) 4.5 and 8.5 using 14 individual general circulation models for the 2030s, 2050s, and 2070s. We combined the 14 suitability models to create continuous climatic suitability models for each species. We then assessed the species suitability against three modeling thresholds simultaneously. Seven of the species showed high and medium climatic viability, all of these species were native to the region of planting. Three species showed low climatic viability; two of these are native to the country but not to the region being restored. Our results also show how using continuous rather than discrete modeling can affect viability assessments. Our study suggests the importance of including climate niche modeling in restoration projects; however, choosing to plant species that are within their native distribution range may be a good strategy in tropical regions if climate niche projections are not available.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.038 | 0.001 |
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; both teacher heads agree on what is shown here.
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".