Investigations into aerial shooting approaches to achieve rapid removal of feral and pest animals
Bibliographic record
Abstract
Context Invasive animals continue to have a significant impact on the Australian landscape despite continual control efforts. Aerial shooting from helicopters is the most common tool used to control these animals at the landscape scale. However, most aerial shooting programs are generally applied ad hoc, prioritise effort based on assumed spatial distribution and are based primarily on visual sightings of the target species. Aims To determine whether a systematic search approach using thermal technology could improve removal rates during invasive animal shooting campaigns to maximise management outcomes. Methods We evaluated the impact of following-up two standard habitat-informed aerial shooting programs (one with thermal and one without) with a program that utilised a systematic search pattern utilising thermal equipment. We assessed the effectiveness of each program in reducing invasive animal populations and provide a cost comparison of each technique. Key results Neither habitat-informed approach achieved management level reductions for any species targeted. The systematic approach reduced pig populations at both sites by >95%; a similar result was not achieved for goats or deer. Multi-species aerial shooting programs may decrease overall program effectiveness and non-participating landholders can provide areas of refuge to allow for rapid reinvasion. During both systematic search programs, 26% and 10% of animals were found outside of the effective search areas of the visual and thermal habitat-informed programs respectively. Conclusions The systematic-with-thermal search approach can result in rapid local removal of pigs when it follows a habitat-informed approach. The systematic approach resulted in detections of target animals in areas the crew did not expect to find them. Targeting multiple species in a program appears to reduce program effectiveness. Implications Our results demonstrated that following a habitat-informed aerial shooting program with a systematic-with-thermal program can result in a rapid removal of >95% of feral pigs. For habitat-informed programs the addition of a thermographer can improve program outcomes. Multi-species aerial shooting programs may result in decreased effectiveness overall, although further research on the functional response in multi-species programs is required before any conclusions can be drawn.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".