Fuzzy cognitive mapping as a tool to assess the relative cumulative effects of environmental stressors on an Arctic seabird population to identify conservation action and research priorities
Bibliographic record
Abstract
Abstract In the Arctic, chemical contaminants, shipping, oil pollution, plastic pollution, changing habitats in relation to climate change and fisheries have been identified as environmental stressors to seabirds such as Fulmarus glacialis (northern fulmar; qaqulluk; ᖃᖁᓪᓗᖅ), but rarely have these stressors been considered within a cumulative effects framework in this species which is currently showing a declining populations trend. As a novel tool to understand cumulative effects within a conservation context, we applied a fuzzy cognitive mapping (FCM) approach that allows experts to arrange key factors and their interrelationships, organizing their understanding of the components of a complex issue into a graphical representation; a ‘cognitive map’. This process was grounded in local environment concerns as documented in several Nunavut‐specific reports and discussions, and worked with western‐trained seabird experts with knowledge of northern fulmar populations to assess the inter‐related environmental threats to fulmars as a way to combine these stressors in a cumulative effects framework and identify conservation actions and knowledge gaps. We found strong agreement that the main stressors affecting northern fulmar populations in Canada include pollution (11% total influence (TI)), shipping activities (16% TI), hunting and fishing (18% TI) and mining/oil and gas exploitation activities (22% TI). The indirect influence of threats on northern fulmar population size (57% TI) exceeded the total direct influence (43% TI), emphasizing the value of cognitive mapping in cumulative effects assessment for a more holistic understanding of interacting stressors. Participants expressed substantial uncertainty regarding the strong relationships leading from the concepts, commercial fishing activity in the BBDS and the North Atlantic fisheries activity, indicating that these potential stressors require more research. Similarly, uncertainty was expressed about the potential effects of zodiac traffic, ship strikes of northern fulmar, number of oil spills and magnitude of oil spills on northern fulmar. By characterizing individual factors as manageable or not, we determined that stressors are largely manageable with enforcement of existing policies (58% TI)—importantly, fishing activities were both highly influential on fulmars and deemed manageable, which will inform ongoing co‐management planning in the region.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".