Describing ecosystem contexts with single‐species models: a theoretical synthesis for fisheries
Bibliographic record
Abstract
Abstract Fished populations exist within complex ecosystems but are typically assessed using single‐species models. It is often lamented that stock assessments rarely account for other ecosystem components explicitly, but in most fisheries there are clear difficulties in implementing data‐intensive ecosystem‐based assessment approaches. Addressing these competing challenges requires prioritizing investments in expanded assessment frameworks. To provide high‐level conceptual guidance to such prioritization, here we use general analytical theory to identify (i) characteristics of fish stocks that tend to facilitate or inhibit the precision and accuracy of reference points from single‐species assessments, (ii) characteristics of ecosystem components that introduce the greatest bias/imprecision into single‐species reference points and (iii) warning signs within single‐species frameworks that important ecosystem components may not be adequately accounted for. We synthesize and expand on theories from various branches of applied mathematics addressing analogous questions. Our theory suggests that (i) slow population dynamics (relative to the dynamics of other ecosystem components) and a wide range of abundance observations promote precision and accuracy of single‐species reference points; (ii) ecosystem components that strongly influence the focal stock's growth, and change on similar timescales as the focal stock's abundance, introduce the greatest bias/imprecision to single‐species reference points; and (iii) signs of potential challenges for single‐species assessment include fast population dynamics, ‘hydra effects’ (i.e. abundance and fishing pressure simultaneously increase), and recently detected extinctions, invasions or regime shifts in closely connected ecosystem components. Our results generalize to other levels of abstraction and provide strategic insights complementing tactical simulation approaches such as management strategy evaluation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".