Mangrove forests under climate change in a 2°C world
Bibliographic record
Abstract
Abstract The world's nations are committed to keeping global temperature rises to less than 2°C to avoid the worst impacts of climate change. Such a target is crucial for mangrove forests, because they are located primarily in tropical and subtropical regions that are expected to see large changes in climatic conditions; their intertidal location and sensitivity to changes in environmental conditions means that mangroves are expected to be on the front line of climate change impacts. We conceptualize what a 2°C world might look like for mangroves, and in particular the potential negative and positive responses of the mangrove ecosystem to anticipated changes in future atmospheric CO 2 concentrations, temperature, sea level, cyclone activity, storminess and changes in the frequency, and magnitude of climatic oscillations. We also assess the spatial distribution of such stressors, their relative contributions to mangrove ecosystem dynamics, and discuss the challenges in attributing mangrove ecosystem dynamics to climate change versus other global change stressors. Such knowledge can help future‐proof conservation and restoration activities, improve the Intergovernmental Panel on Climate Change's confidence level ascribed to climate change impacts on mangrove forests, and highlight the key temperature thresholds beyond which the future of the world's mangroves is less certain. This article is categorized under: Climate, Ecology, and Conservation > Modeling Species and Community Interactions Climate, Ecology, and Conservation > Observed Ecological Changes
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".