Bibliographic record
Abstract
Large reductions in the abundance of exploited land predators have led to significant range contractions for those species. This pattern can be formalized as the range-abundance relationship, a general macroecological pattern that has important implications for the conservation of threatened species. Here we ask whether similar responses may have occurred in highly mobile pelagic predators, specifically 13 species of tuna and billfish. We analyzed two multidecadal global data sets on the spatial distribution of catches and fishing effort targeting these species and compared these with available abundance time series from stock assessments. We calculated the effort needed to reliably detect the presence of a species and then computed observed range sizes in each decade from 1960 to 2000. Results suggest significant range contractions in 9 of the 13 species considered here (between 2% and 46% loss of observed range) and significant range expansions in two species (11-29% increase). Species that have undergone the largest declines in abundance and are of particular conservation concern tended to show the largest range contractions. These include all three species of bluefin tuna and several marlin species. In contrast, skipjack tuna, which may have increased its abundance in the Pacific, has also expanded its range size. These results mirror patterns described for many land predators, despite considerable differences in habitat, mobility, and dispersal, and imply ecological extirpation of heavily exploited species across parts of their range.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".