Marine mammal culling programs: review of effects on predator and prey populations
Bibliographic record
Abstract
Abstract Culling is widely practised as a means to reduce predation effects of terrestrial carnivores, birds and marine mammals in many parts of the world. Of marine mammals, coastal pinniped species have usually been the target of culling programs, but dolphins and a large odontocete have also been culled. We reviewed the published literature on marine mammal culling programs to evaluate the extent of their efficacy as a fisheries management measure. Changes in species' distributions and abundance demonstrate that culling programs can be very effective at reducing predator density. Several conclusions from experimental studies of terrestrial mammals and birds may also apply to marine mammal control. Firstly, predator removal generally increases productivity and population size of target prey populations, but not always. Secondly, culling programs typically involve a large proportional reduction (>50%) in predator populations. Thirdly, the effects of culling are typically dependent on continued control, and in the absence of control the population rapidly returns to pre‐culling density. This underscores the need for predator removal to be a long‐term management strategy. Fourthly, culling predators often has non‐intuitive and unintended consequences for target species and for other predator and prey species. Marine mammal culling programs rarely have measurable objectives with respect to prey populations, and their success has not been evaluated. Culling marine mammals is controversial because of the following: (i) they are high‐profile charismatic megafauna; (ii) many populations are recovering from a period of over‐exploitation while others remain threatened or endangered; and (iii) the scientific evidence needed to justify a cull is usually highly uncertain. Marine mammal culling programs should be based on scientific analysis with stated and measurable objectives to be evaluated during planned follow‐up monitoring.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".