Attending to spatial social–ecological sensitivities to improve trade‐off analysis in natural resource management
Bibliographic record
Abstract
Abstract Balancing trade‐offs amongst social–ecological objectives is a central aim of natural resource management. However, objectives and resources often have spatial dimensions, which are usually ignored in trade‐off analyses. We examine how simultaneously integrating social–ecological benefits and their spatial complexities can improve trade‐off analysis. We use Pacific herring (Clupea pallasii, Clupeidae)—an ecologically important forage fish with social, cultural and economic value to communities and commercial fisheries—as a case study. By combining spatial management strategy evaluation with social benefits analysis, we illustrate when policies aimed at aggregate stocks versus spatially segregated substocks of fish fail to balance trade‐offs amongst social–ecological objectives. Spatial measures (e.g. area‐based closures) may achieve some objectives but produce alternative trade‐offs that are sensitive to assumptions about fish population dynamics and social complexities. Our analyses identify policies that are inefficient (e.g. yielding economic costs without producing social or ecological gains), highlight management strategies that generate trade‐offs and indicate when costs are distributed unequally for different user groups. We also point to strategies with outcomes that are robust to spatial uncertainties and reveal research priorities by identifying which performance metrics exhibit sensitivity to spatial ecological assumptions. Collectively, our analyses demonstrate how incorporating social objectives and spatial dynamics into management strategy evaluation can reveal trade‐offs and the implications of management decisions.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".