Does aerial baiting for controlling feral cats in a heterogeneous landscape confer benefits to a threatened native meso-predator?
Bibliographic record
Abstract
Introduced mammalian predators can have devastating impacts on recipient ecosystems and disrupt native predator-prey relationships. Feral cats (Felis catus) have been implicated in the decline and extinction of many Australian native species and developing effective and affordable methods to control them is a national priority. While there has been considerable progress in the lethal control of feral cats, effective management at landscape scales has proved challenging. Justification of the allocation of resources to feral cat control programs requires demonstration of the conservation benefit baiting provides to native species susceptible to cat predation. Here, we examined the effectiveness of a landscape-scale Eradicat® baiting program to protect threatened northern quolls (Dasyurus hallucatus) from feral cat predation in a heterogeneous rocky landscape in the Pilbara region of Western Australia. We used camera traps and GPS collars fitted to feral cats to monitor changes in activity patterns of feral cats and northern quolls at a baited treatment site and unbaited reference site over four years. Feral cat populations appeared to be naturally sparse in our study area, and camera trap monitoring showed no significant effect of baiting on cat detections. However, mortality rates of collared feral cats ranged from 18-33% after baiting, indicating that the program was reducing cat numbers. Our study demonstrated that feral cat baiting had a positive effect on northern quoll populations, with evidence of range expansion at the treatment site. We suggest that the rugged rocky habitat preferred by northern quolls in the Pilbara buffered them to some extent from feral cat predation, and baiting was sufficient to demonstrate a positive effect in this relatively short-term project. A more strategic approach to feral cat management is likely to be required in the longer-term to maximise the efficacy of control programs and thereby improve the conservation outlook for susceptible threatened fauna.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".