Comparison of methods to detect rare and cryptic species: a case study using the red fox (Vulpes vulpes)
Bibliographic record
Abstract
Choosing the appropriate method to detect and monitor wildlife species is difficult if the species is rare or cryptic in appearance or behaviour. We evaluated the effectiveness of the following four methods for detecting red foxes (Vulpes vulpes) on the basis of equivalent person hours in a rural landscape in temperate Australia: camera traps, hair traps (using morphology and DNA from hair follicles), scats from bait stations (using DNA derived from the scats) and spotlighting. We also evaluated whether individual foxes could be identified using remote collection of their tissues. Genetic analysis of hair samples was the least efficient method of detection among the methods employed because of the paucity of samples obtained and the lack of follicles on sampled hairs. Scat detection was somewhat more efficient. Scats were deposited at 17% of bait stations and 80% of scats were amplified with a fox-specific marker, although only 31% of confirmed fox scats could be fully genotyped at all six microsatellite loci. Camera trapping and spotlighting were the most efficient methods of detecting fox presence in the landscape. Spotlighting success varied seasonally, with fox detections peaking in autumn (80% of spotlighting transects) and being lowest in winter (29% of transects). Cameras detected foxes at 51% of stations; however, there was limited seasonality in detection, and success rates varied with camera design. Log-linear models confirmed these trends. Our results showed that the appropriate technique for detecting foxes varies depending on the time of the year. It is suggested that wildlife managers should consider both seasonal effects and species biology when attempting to detect rare or elusive species.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".