Two lenses for exploring relationships between seabirds and fishers: Unveiling reciprocal contributions
Bibliographic record
Abstract
Abstract Reciprocal relationships between fishers and marine life have been documented in Indigenous fishing contexts, but there are few case studies that describe the mechanisms of such relationships, and even fewer that explore other contexts, such as that of artisanal fishers in Latin American countries. We studied the artisanal hake fishery in the sub‐Antarctic channels of Chilean Patagonia, a global hotspot for albatross and petrel diversity. We aimed to uncover nature's contributions to hake fishers and, reciprocally, the potential fishers' contributions to marine life with a particular emphasis on seabirds. We adopted a bifocal observational strategy. From a human perspective, ethnographic methods (e.g., semi‐structured interviews) are analysed with Nature's Contributions to People and reciprocal contribution frameworks. From the seabirds' view, we assessed offal consumption through experimental and observational methods. We randomly threw offal items into the sea and observed the seabird responses (whether they consumed offal). Hake fishers' relationships with the marine environment are multidimensional, particularly with seabirds. Based on fishers' perceptions, we identified four key contributions of seabirds to humans: they serve as value indicators of fishing distribution and concentration areas, offer companionship and recreation during fishing activities, enhance scenic emotionality through the presence of albatrosses and assist in the function of sea cleaning. In reciprocal contributions, artisanal fishers viewed hake offal as a beneficial food source for the seabirds, especially the liver. Fishers described that fishing in the right way can reduce bycatch and effort. The fishers' main contribution to seabirds is through offering them the offal of hake catches. We observed that seabirds consumed hake liver 99% of the time, while they consumed stomach less frequently (24%). We identified that southern giant petrels and black‐browed albatrosses consumed more liver, while kelp gulls ate more stomach. The liver comprises 51.6% fat, essential for high trophic level marine predators such as black‐browed albatrosses. Adopting reciprocal contributions and NCP served as a catalyst for understanding fishers' positive actions but also is a promoter to research multiple views of nature–human relationships in fishing settings. Values of nature, like reciprocity, could enrich ecosystem‐based management strategies. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".