The Debate About Bait: A Red Herring in Wildlife Research
Bibliographic record
Abstract
ABSTRACT The use of bait (or attractants) to lure animals to a sampling site is common in wildlife research and important for optimizing species detection rates. The effect of bait on animal movement and space‐use, however, is contested, fueled by concerns bait may affect animal movement and increase residency time. If founded, bait may bias parameter estimates from density, species distribution, resource selection, or behavioral models, produce spurious ecological inferences, and skew resulting management recommendations. To test whether animal movement varies with proximity to bait, we used high‐resolution global positioning system telemetry data of 10 fishers ( Pekania pennanti ), temporally paired with 64 baited wildlife camera traps, to quantify the effect of bait on individual and population movement metrics. Although bait appeared to have a significant correlative effect on 1‐hour movement segments, landscape characteristics had an effect 1.7 times greater, where the proportion of mixed forest and cultivation explained the majority of variability in animal movements. We contend that maximizing probability of detection and controlling or modeling local‐scale landscape variability that could affect the probability of detection is a more important consideration in wildlife research than the effect of bait, which is eclipsed by differences incurred by natural habitat heterogeneity. Failing to maximize the probability of detection may obscure the modest bias potentially presented by the use of bait, or attractants, on ecological inference. © 2019 The Wildlife Society
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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 | 0.005 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".