Poleward Bound: Adapting to climate-driven species redistribution
Bibliographic record
Abstract
One of the most pronounced effects of climate change on the world's oceans is the (generally) poleward movement of species and fishery stocks in response to increasing water temperatures. In some regions, such redistributions are already having diverse impacts on marine socioecological systems, including profoundly altering ecosystem structure and function, challenging domestic and international fisheries, and impacting on human communities. Such effects are expected to become increasingly widespread as waters continue to warm and species ranges continue to shift. Actions taken over the coming decade (2021-2030) can help us manage and adapt to species redistributions and minimise negative impacts on ecosystems and human communities, achieving a more sustainable future in the face of ecosystem change. We describe key drivers related to climate-driven species redistributions that are likely to have a high impact and influence on whether a sustainable future is reached by 2030. We posit two different futures-a 'business as usual' future and a technically achievable and more sustainable future, aligned with the Sustainable Development Goals. We then identify concrete actions that provide a pathway towards the more sustainable 2030 and that acknowledge and include Indigenous perspectives. Achieving this sustainable future will depend on improved monitoring and detection, and on adaptive, cooperative management to proactively respond to the challenge of species redistribution. We synthesise examples of such actions as the basis of a strategic approach to tackle this global-scale challenge for the benefit of humanity and ecosystems.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".