Editorial: How overfishing handicaps resilience of marine resources under climate change
Bibliographic record
Abstract
Editorial on the Research Topic How overfishing handicaps resilience of marine resources under climate changeIt is now clear to most scientists and many non-scientists that Climate Change (CC) is altering ocean physics and chemistry, thereby affecting the ecology and biology of marine life.These changes in turn impact ocean economics and the lives and livelihoods of millions of people who depend on it.Independent and IPCC (Intergovernmental Panel on Climate Change) researchers have been developing this knowledge for decades (e.g., Cheung et al., 2010;Sumaila et al., 2011;Bopp et al., 2013;Pörtner et al., 2019).In recent years, scientists and the world have come to realise that the interaction between CC and the ocean and its dependent economies are not unidirectional but bidirectional.That is, it is not just that CC impacts ocean life and related economies, but ocean economic activities also contribute to carbon emission and therefore CC.To demonstrate the latter in terms of fish and fisheries, researchers at the University of British Columbia in collaboration with the environmental NGO, Our Fish, launched a Research Topic to see if we could show that addressing overfishing is also in effect climate action.Over 40 scholars collaborated to author nine papers in this Research Topic entitled "How Overfishing Handicaps Resilience of Marine Resources Under Climate Change".We describe here the highlights of each paper with the goal of whetting the appetite of the reader to dig deeper into our findings by reading the papers in full.In the opening paper in the Research Topic, Sumaila and Tai explain how ending overfishing can increase the resilience of the ocean to CC.The authors conducted a literature review and analysis, and concluded that (i) marine fish stocks are overfished in many parts of our oceans; (ii) CC has significant consequences on ocean life; (iii) ending overfishing could make fish stocks more climate resilient; and (iv) fish and fish stocks are like people and more likely to withstand the impact of an attack (e.g., by CC) when they are in a healthy condition to start with.Ferrer et al. make the powerful point that overfishing, often caused by large, subsidised fishing fleets (Sumaila et al., 2021;Skerritt et al., 2023), is a double whammy for small-scale fisheries (SSF).First, it forces people to spend more time burning fuel to search for scarcer, Frontiers in Marine Science frontiersin.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".