Statistical aspects of using genetic markers for individual identification in capture-recapture studies
Bibliographic record
Abstract
The use of an animal's genotype as a mark for capture-recapture studies has become increasingly common in wildlife research. Frequently, animals are sampled in a non-invasive way such as collecting hair from rubs, shed feathers or feces. DNA is extracted from the samples and genotyped at a set of microsatellite loci to identify individuals. In this case, the animal's mark is self assigned. This leads to problems with misidentification of individuals. In addition, samples are often passively collected which causes a break down in the standard capture-recapture assumption of instantaneous sampling. This dissertation focuses on developing new ways of handling the unique circumstances associated with DNA-based capture-recapture studies. I develop a method to account for genotyping error in closed population estimates of abundance. This method is used to extent the standard likelihood-based closed population capture-recapture models, the finite mixture models and the conditional likelihood models. I extend the robust design model to properly estimate survival and abundance in the face of genotyping error and allow multiple sources of data to be brought together. A new method is developed that uses additional information in dung surveys generated from multiple detections of an individual within a sampling occasion to better estimate abundance. I present suggestions for applying DNA-based capture-recapture sampling for Canada lynx (Lynx canadensis) in Colorado. Sampling feces in summer appears to be the most effective way to sample the lynx population. Feces are easier to collect in the field than hair and are more amenable to DNA extraction. Finally, I present a review of the literature applicable to DNA-based capture-recapture studies. The focus of the review is to provide a starting point for researchers looking to broaden the scope of their DNA-based capture-recapture study beyond simple estimates of abundance.
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 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 | 0.203 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".