Food webs and the sustainability of indiscriminate fisheries
Bibliographic record
Abstract
Here, we introduce a novel theory for multispecies fisheries that exploit fish stocks evenly within and across trophic levels in an entire ecosystem (i.e., fishery comprises all fleets). These “indiscriminate” fisheries may be common in developing countries where fish provide the main source of dietary protein. We show that simple food web modules, motivated by empirical patterns in body size and energy flow, yield general and robust predictions about the fate of such a fishery. Specifically, high and uniform fishing mortality modifies the fish community in a manner that leads to increased productive capacity from a low-diversity assemblage of small-bodied fish with rapid population growth and turnover (the productive monoculture effect). We then argue that catches are relatively indiscriminate in the Tonlé Sap, a highly productive inland fishery in Cambodia that feeds millions, and show consistent qualitative agreement between the theory of indiscriminate fishing and this existing empirical data. As the theory suggests, this indiscriminate fishery appears to be remarkably productive at the community level in the face of high fishing mortality; however, it tends to be unsustainable at the species level as the Tonlé Sap has a much depleted species diversity under its current high fishing mortality. We end by arguing that the reduced diversity of these types of fisheries likely put them at severe risk of being heavily impacted by changing environmental conditions such as climate change and hydroelectric development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".