Building tools to model the effects of ocean acidification and how it scales from physiology to fisheries
Bibliographic record
Abstract
Ocean acidification is a direct consequence of elevated atmospheric carbon dioxide caused by anthropogenic fossil fuel burning and is one of multiple climate-related stressors in marine environments. Understanding of how these stressors will interact to affect marine life and fisheries is limited. In this thesis, I used integrated modelling approaches to scale the effects of biophysical drivers from physiology to population dynamics and fisheries. I focused on ocean acidification and how it interacts with other main drivers such as temperature and oxygen. I used a dynamic bioclimatic envelope model (DBEM) to project the effects of global environmental change on fisheries under two contrasting scenarios of climate change—the low optimistic climate change scenario in line with the 2015 Paris Agreement to limit global warming to 1.5˚ C, and the high climate change scenario on par with our current ‘business-as-usual’ trajectory. First, I developed an ex-vessel fish price database and explored methods using various ocean acidification assumptions. Ex-vessel fish prices are essential for fisheries economic analyses, while model development of ocean acidification effects are important to better understand the uncertainties surrounding acidification and the sensitivity of the model to these uncertainties. These tools and methods were then used to project the impacts of ocean acidification, in the context of climate change, on global invertebrate fisheries—the species group most sensitive to acidification. My results showed that areas with greater acidification have greater negative responses to climate change, e.g. polar regions. However, ocean warming will likely be a greater driver in species distributions and may overshadow direct effects of acidification. While greater climate change will generally have negative consequences on fisheries, Arctic regions may see increased fisheries catch potential as species shift poleward. Canada’s Arctic remains one of the most pristine marine regions left in the world and climate-driven increases in fisheries potential will have major implications for biodiversity and local indigenous reliance on marine resources. In the face of global environmental change, my thesis provides databases, modelling approaches, scenario development, and assessments of global change necessary for adaptation and mitigation of climate-related effects on marine fisheries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".