Assessing noninvasive hair and fecal sampling for monitoring the distribution and abundance of river otter
Bibliographic record
Abstract
Abstract Monitoring the distribution and abundance of populations is an important component of efforts to meet management or conservation goals. Although the objectives for such studies are easy to define, cost‐effective, precise, and accurate estimates are often elusive. We tested the efficacy and compared the cost‐effectiveness of methods for estimating the number and recording the distribution of river otter ( Lontra canadensis ). We genotyped otter hair sampled using two noninvasive instruments and compared those results with a hypothetical study design based on DNA extracted from fecal matter. Patterns of distribution generated from DNA collected at latrine sites were then compared to observations of otter collected using VHF radiotelemetry. We achieved a high probability of genotyping river otter with a small number of hairs (i.e., 59.0 % probability of producing a genotype with 1 guard hair and >5 under hair samples) collected using wire body snares and knaplock hair snags. Body snares were more effective at collecting otter hair, but there was relatively little additional cost to using both sampling instruments. Genotyped hair resulted in a high multi‐year recapture rate (61.9 %). Hair collection and genotyping was the most cost‐effective method for monitoring populations of river otter ($168.50 US/datum) followed by radiotelemetry ($264.50 US/datum), and the extraction of DNA from fecal matter ($266.00 US/datum). However, the noninvasive techniques did not represent the full distribution and fine‐scale movements of otter, as observed using radiotelemetry. There has been much recent reporting of the efficacy of fecal matter as a source of DNA for conducting mark–recapture population estimates for mesocarnivores. Our data suggested that collecting DNA in hair may be a more cost‐effective and efficient approach.
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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 | 0.001 | 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 | 0.000 | 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".