Ecosystem-Scale Biodiversity in Fisheries Life Cycle Impact Assessment
Bibliographic record
Abstract
The impact of fisheries was recently operationalised in Life Cycle Assessment, accounting for effects on individual exploited species at both regional and global scales. Now, this emergent pathway is expanded to the ecosystem scale by incorporating additional indirect impacts associated with fishing through the integration of ecosystem dynamics. The methodology combines a novel coupling of dynamic ecosystem modeling and species sensitivity distribution curves to quantify fisheries effects on marine biodiversity, using an adaptation of the USEtox effect factor modeling that assesses community-scale ecotoxicity impacts. Innovative elements include the incorporation of indirect effects through a fished community via predator–prey interactions, and a bidirectional threshold interval that captures both potential population depletion and expansion dynamics resulting from exploitation. A proof of concept is presented in the Adriatic Sea, deriving novel midpoint characterization factors for 26 exploited functional groups, with impacts reported in potentially affected fraction (PAF) units. Impact values are spread over 6 orders of magnitude (1.58× 10 –7 PAF small/medium rays −2.16 × 10 –1 PAF small pelagic fish), validating the operability of the approach. Methodological choices and assumptions introduced by the novel approach are discussed. The original combination of modeling tools employed in this characterization approach contributes to the progressive enhancement of the fisheries impact pathway and how biodiversity loss is considered in life cycle impact assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".