A small dasyurid predator (Sminthopsis virginiae) rapidly learns to avoid a toxic invader
Bibliographic record
Abstract
Context Invasive species are a leading cause of extinctions, yet predicting their ecological impacts poses a formidable challenge for conservation biologists. When native predators are naïve to invaders, they may lack appropriate behaviours to deal with the invader. In northern Australia, the invasion of the highly toxic cane toad (Rhinella marina) has caused serious population declines of reptilian and mammalian predators that are ill equipped to deal with toad toxins. Cane toads recently invaded the Kimberley region of Western Australia, where they potentially threaten several species of small dasyurid predators. Aims We investigated whether red-cheeked dunnarts (Sminthopsis virginiae) attack cane toads, and if so, whether individuals subsequently learn to avoid toads as prey. Methods We quantified feeding and learning behaviours in toad-naïve red-cheeked dunnarts from the north Kimberley in Western Australia. Key results All toad-naïve dunnarts attacked toads during their first encounter. Most dunnarts bit the toad on the snout, killed it by biting the cranium, and consumed the toad snout-first, thereby initially avoiding the toad’s parotoid glands. Most dunnarts partially consumed toads before discarding them, and only one animal showed visible signs of toad poisoning. All dunnarts rapidly learnt to avoid toads as prey after one or two encounters. Predators rejected toads as prey for the duration of the study (22 days), suggesting long-term retention of the knowledge that toads are noxious. Conclusions Our results show that red-cheeked dunnarts rapidly learn to avoid cane toads as prey. Implications Our study was limited by small sample sizes, but our results suggest that small dasyurids can adapt to the cane toad invasion via taste aversion learning.
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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 | 0.000 |
| 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".