Tracing climate impacts using participatory systems mapping : informing adaptation for a marine food system in the Tla’amin First Nation
Bibliographic record
Abstract
Climate change is altering the physical and biogeochemical properties of the ocean, with implications for the biogeography, phenology, biodiversity and ecosystem functions of marine organisms, as well as for the human societies that depend upon them. Shifting species distributions, among various biological responses to climate change, may exacerbate ongoing challenges to food security, nutritional health and culture for many coastal indigenous First Nation communities. Developing appropriate, nuanced, and context-specific adaptation responses to climate change, however, requires an understanding of how climate-driven ecosystem changes act and interact with other non-climatic factors. Effective adaptation strategies also need to be developed in partnership with community members to identify people’s values, needs, and knowledge of local system dynamics and challenges. Through a collaborative effort with the Tla’amin (ɬəʔamɛn) Nation, this research aims to support the development of adaptation strategies by identifying the perceived mechanisms or pathways through which climate-driven ecosystem changes could affect local seafood access and consumption, and by identifying how these climate effects interact with other factors affecting local seafood availability and access to harvest. This thesis applied a participatory systems mapping approach to co-develop a conceptual model of the key dynamics in the Tla’amin traditional marine food system with Tla’amin Elders, legislators, managers, and community members with expertise in fisheries, traditional food harvest, resource management, and health. I used this model to trace climate stressor-impact pathways and construct a logic-based influence diagram (a modified “fuzzy” cognitive map (FCM)) focusing on the factors affecting food fish harvest. Climate change impacts on the consumption of traditional foods were perceived via both direct and indirect pathways, with reinforcing feedback loops brought about by reduced exposure and experience to traditional foods. Climate effects on local abundance, availability, and safety of fish and shellfish, accompanied by potential consequences for harvest restrictions, were found to compound onto existing constraints to physical and temporal access to the harvest of traditional marine foods. Understanding these multifaceted local climate impacts may help inform future identification and implementation of adaptation strategies for traditional seafood harvest in the face of climate change.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".