Effects of fisheries on ecosystems: just another top predator?
Bibliographic record
Abstract
Apex predators – such as pinnipeds, cetaceans, seabirds and sharks – are constrained by the sizes of prey they can consume and thus typically feed within a narrow range of trophic levels. Having co-evolved with their prey, they have influenced the behaviours, physiologies, morphologies and life-history strategies of the species they target. In contrast, humans can consume prey of any size from all trophic levels using methods that can rapidly deplete populations. On an ecological time scale, fisheries, like apex predators, can directly affect the abundance of other species by consuming or out-competing them; alternatively they can indirectly affect the abundance of non-targeted species by removing other predators. However, there is growing evidence that the effects of fisheries go well beyond those imposed by apex predators. Theory and recent observations confirm that the continued development and expansion of fisheries over the past half century has led to a decrease in the size and life spans of targeted species, with reproduction of fish occurring at earlier ages and at smaller sizes. In addition, high levels of fishing have altered the makeup of many ecosystems, depressing the average trophic level of heavily fished ecosystems and speeding up the rate of nutrient turnover within them. Inevitable consequences of fishing down the food web are increased ecosystem instability, unsustainable fisheries and an inability for the ecosystem to support healthy, abundant populations of apex predators.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".