Modelling the rate of successful search of red foxes during population control
Bibliographic record
Abstract
Context Relative abundance indices of wildlife can be scaled to give estimates of absolute abundance. Choice of scaling parameter depends on the data available and assumptions made about the relationship between the index and absolute abundance. Predation-mechanics theory suggests that a parameterisation involving the rate of successful search, s, will be useful where the area searched is unknown. An example arises during fox culling on shooting estates in Britain, where detection and cull data from gamekeepers using a spotlight and rifle are available, and can potentially be used to understand the population dynamics of the local population. Aims We aimed to develop an informative prior for s for use within a Bayesian framework to fit a fox population-dynamics model to detection data. Methods We developed a mechanistic model with a rate of successful search parameter for the gamekeeper–fox system. We established a mechanistic prior for s, using Monte Carlo simulation to combine relevant information on its component factors (detection probability, observer field of view and speed of travel). We obtained empirical estimates of s from a distance-sampling study of fox populations using similar survey methods, and used these as data in a Bayesian model to develop a mechanistic–empirical prior. We then applied this informative prior within a state–space model to estimate fox density from fox-detection rate on four estates. Key results The mechanistic–empirical prior for the rate of successful search was lognormally distributed with a median of 2.01 km2 h–1 (CV = 0.56). Underlying assumptions of the parameterisation were met. Local fox-density estimates obtained using informative priors closely reflected regional density. Conclusions A mechanistic understanding of the search process leading to fox detections by gamekeepers, and the use of Bayesian models, allowed the use of diverse sources of information to develop an informative prior for s that was useful in estimating fox density from detection data. Implications Careful use of prior knowledge within a Bayesian modelling framework can reduce uncertainty in population estimates derived from index data, and lead to improved management decisions. The mechanistic approach we have used will have parallel applications in many other contexts.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".