Benthic-based contributions to climate change mitigation and adaptation
Bibliographic record
Abstract
Innovative solutions to improve the condition and resilience of ecosystems are needed to address societal challenges and pave the way towards a climate-resilient future. Nature-based solutions offer the potential to protect, sustainably manage and restore natural or modified ecosystems while providing multiple other benefits for health, the economy, society and the environment. However, the implementation of nature-based solutions stems from a discourse that is almost exclusively derived from a terrestrial and urban context and assumes that risk reduction is resolved locally. We argue that this position ignores the importance of complex ecological interactions across a range of temporal and spatial scales and misses the substantive contribution from marine ecosystems, which are notably absent from most climate mitigation and adaptation strategies that extend beyond coastal disaster management. Here, we consider the potential of sediment-dwelling fauna and flora to inform and support nature-based solutions, and how the ecology of benthic environments can enhance adaptation plans. We illustrate our thesis with examples of practice that are generating, or have the potential to deliver, transformative change and discuss where further innovation might be applied. Finally, we take a reflective look at the realized and potential capacity of benthic-based solutions to contribute to adaptation plans and offer our perspectives on the suitability and shortcomings of past achievements and the prospective rewards from sensible prioritization of future research. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".