Fishers' Local Ecological Knowledge Reveals Complex Food Web Dynamics With Rapidly Warming Waters
Bibliographic record
Abstract
ABSTRACT Local ecological knowledge (LEK) can provide insight into ecosystem change, particularly in dynamic ecological conditions, such as those driven by climate change. In New England lobster fisheries, warming waters have the potential to disrupt food webs, as range‐shifting species introduce novel ecological interactions. Here we use interviews with lobster fishers in Maine and Massachusetts to understand lobster fishers' LEK of dynamic food webs, taking a mental modelling approach to construct LEK food web models under rapidly warming waters. We find that fishers are observing a remarkable range of ecological interactions across habitats, collectively reporting knowledge of > 35 species that interact trophically with lobster across larval, juvenile, and adult life stages, ranging from terrestrial species like mink ( Neovison vison ) to deep sea species like redfish ( Sebastes fasciatus ). Our LEK food webs demonstrate perceptions of warming water altering species' abundances and interactions, with an overall negative impact on lobster fisheries. Fishers also report knowledge of complex interactions, including predation, competition, and habitat loss mediated by warming waters and changing species' abundances. Finally, we identify and categorise three main pathways that contribute to fishers' LEK, including observation, word of mouth, and inference. Our findings demonstrate that active fishers have complex understandings of food web interactions in dynamic ecosystems that are changing rapidly. With management unable to keep pace with climate‐driven change, fishers' LEK is an invaluable source of knowledge, whose use could improve the ability to understand the diverse impacts of warming waters on coastal ecosystems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".