Effects of low-level culling of feral cats in open populations: a case study from the forests of southern Tasmania
Bibliographic record
Abstract
Context Feral cats (Felis catus) threaten biodiversity in many parts of the world, including Australia. Low-level culling is often used to reduce their impact, but in open cat populations the effectiveness of culling is uncertain. This is partly because options for assessing this management action have been restricted to estimating cat activity rather than abundance. Aims We measured the response, including relative abundance, of feral cats to a 13-month pulse of low-level culling in two open sites in southern Tasmania. Methods To do this we used remote cameras and our analysis included identification of individual feral cats. We compared estimates of relative abundance obtained via capture–mark–recapture and minimum numbers known to be alive, and estimates of activity obtained using probability of detection and general index methods, pre- and post-culling. We also compared trends in cat activity and abundance over the same time period at two further sites where culling was not conducted. Key results Contrary to expectation, the relative abundance and activity of feral cats increased in the cull-sites, even though the numbers of cats captured per unit effort during the culling period declined. Increases in minimum numbers of cats known to be alive ranged from 75% to 211% during the culling period, compared with pre- and post-cull estimates, and probably occurred due to influxes of new individuals after dominant resident cats were removed. Conclusions Our results showed that low-level ad hoc culling of feral cats can have unwanted and unexpected outcomes, and confirmed the importance of monitoring if such management actions are implemented. Implications If culling is used to reduce cat impacts in open populations, it should be as part of a multi-faceted approach and may need to be strategic, systematic and ongoing if it is to be effective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".