Please come again: attractive bait augments recapture rates of capture-naïve snowshoe hares
Bibliographic record
Abstract
Abstract Context Capture–recapture sampling is one of the most commonly used methods for monitoring population demographics and is needed in a wide variety of studies where repeat sampling of individuals is desired. Although studies employing capture–recapture methods often assume unbiased sampling, it is well established that inherent capture biases can occur with these methods, including those related to baits. Reducing sources of sampling bias and augmenting recapture reliability is necessary for capture-dependent studies. However, few studies have examined the efficacy of baits on individuals with variable capture experience. Aims To investigate the use of an attractant-augmented bait in enhancing capture–recapture probabilities for snowshoe hares (Lepus americanus). Methods To examine the efficacy of different attractant-augmented bait types, a variety of baits were created, with bait preference tested on a captive hare. Because a strawberry jam-based bait was preferentially consumed (in comparison with other tested baits), the effectiveness of this attractant in enhancing capture–recapture rates was subsequently examined in wild hares, using paired live-trapping field trials (n = 6 trials). Results Live-trapping trials showed that although overall hare capture rates were not affected by the use of a jam-based bait, recaptures were 33.1% higher in capture-naïve individuals exposed to our attractant. This was not the case for hares with prior capture experience; such hares had an equal likelihood of being recaptured regardless of the bait type used. Conclusions The tested attractant improved recapture rates of capture-naïve hares. Implications Studies relying on high recapture rates should use methods that maximise recapture rates wherever possible, including the use of baits that may augment recaptures in capture-naïve animals.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".