Predators and genetic fitness: key threatening factors for the conservation of a bettong species
Bibliographic record
Abstract
Globally, many wildlife species are declining and an increasing number are threatened by extinction or are extinct. Active management is generally required to mitigate these trends and population viability analysis (PVA) enables different scenarios to be evaluated and informs management decisions. Based on population parameters obtained from a threatened bettong, the woylie (Bettongia penicillata ogilbyi), we developed and validated a PVA model. We identified the demographic and genetic responses to different threatening factors and developed a general framework that would facilitate similar work in other bettong species. The two main threatening processes are predation by introduced animals and its interaction with reduced fitness (e.g. due to inbreeding depression or a disease). Although predation alone can drive a decline in certain circumstances (e.g. when predation success is independent from prey population density), synergistically, predation and reduced fitness can be particularly relevant, especially for small populations. The minimum viable population size was estimated at 1000–2000 individuals. In addition, the models identified that research into age-specific mortality rates and predation rates by introduced animals should be the focus of future work. The PVA model created here provides a basis to investigate threatening processes and management strategies in woylie populations and other extant bettong species, given the ecological and physiological similarities among these threatened species.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".