A Traits-based assessment of aquaculture’s sustainable development potential at the food-climate-biodiversity nexus
Bibliographic record
Abstract
Aquaculture is an important component of ocean-based food systems. Production of aquatic species is simultaneously impacted by and contributes to climate change and biodiversity loss through complex interlinkages between ecosystems and our food systems. Thus, assessing aquaculture’s sustainability needs to account for the nexus of food security, climate change and biodiversity (FCB). Social-ecological traits-based approaches, which use physical, ecological and socio-cultural characteristics to understand interactions in complex systems, could help overcome challenges in navigating the FCB nexus. This study investigates aquaculture’s role in achieving FCB goals by combining biological traits of farmed species with aquaculture production statistics and stakeholders’ knowledge on the importance of these traits in achieving FCB targets. Firstly, we developed a trait-based analytical framework to quantify the potential of major farmed aquatic species to contribute to FCB goals based on their biological and ecological characteristics, and assessed temporal trends in aquaculture’s FCB potential. We find that trade-offs between the food, climate and biodiversity dimensions have resulted in an overall decline in aquaculture’s FCB potential since 1980. While mean scores across 54 highly farmed aquaculture species are low, algae and molluscs tend to perform well across FCB indices. As aquaculture has intensified, production has increasingly favored a handful of species with low potential and more trade-offs in achieving the different sustainability goals, lowering aquaculture’s FCB potential. Future aquaculture can reverse such unfavorable trends by shifting to farm species with greater FCB potential and synergies. For example, molluscs have disproportionately contributed to aquaculture’s FCB potential but are under-represented in global production. Moreover, seaweed farming has received large interest because of its high FCB potential and synergies. We thus examined stakeholder perceptions of traits associated with food, climate and biodiversity using a case study in British Columbia’s nascent seaweed aquaculture sector in order to investigate the influence of traits on FCB in a local context and understand potential social-ecological challenges related to sustainable aquaculture development. Stakeholder perceptions of seaweed traits underscore the complexity in achieving FCB objectives synergistically through developing a new seaweed farming industry. Overall, our results highlight the large scope and opportunities to improve the contributions of aquaculture to FCB goals by selecting farmed species with traits that can better support such goals and that match stakeholders’ socio-ecological preferences. This traits-based approach can help identify opportunities and barriers for aquaculture transitions to develop equitable pathways toward the sustainable development of aquaculture across nuanced regional contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".