A multi‐method hierarchical modeling approach to quantifying bias in occupancy from noninvasive genetic tagging studies
Bibliographic record
Abstract
ABSTRACT Detecting ecological change in mammalian communities requires large volumes of data collected across large landscapes. Noninvasive genetic tagging (NGT) can provide such data and is a common method of estimating species occupancy, distribution, and abundance. Methods to correct errors in genetic analysis exist, but estimating and accounting for detection error in NGT studies—specifically, detection error during hair trapping—has received less attention. If detectability varies with an ecological characteristic that is instead attributed to occupancy, detection error may lead to spurious conclusions. We demonstrate how multi‐method occupancy models informed by camera‐trapping data can quantify error, partition sources of variability, and estimate occupancy from NGT studies. We surveyed marten ( Martes americana ), fisher ( Pekania pennanti ), and wolverine ( Gulo gulo ) occurrence via hair trapping and camera trapping for 3 repeated monthly surveys at 66 sites in the Rocky Mountains of Alberta, Canada. Camera traps photographed the hair traps and surrounding sampling site. We used multi‐method occupancy models to estimate site occupancy, probability of detection, and conditional occupancy at a hair trap. We tested the predictions that detection error in NGT studies could be induced by temporal variability and behavioral variability arising from the presence of heterospecifics. Models indicated that NGT via hair trapping consistently underestimated occupancy of marten, fisher, and wolverine compared to cameras. The magnitude of bias varied, but at worst underestimated occupancy by half. The presence of a heterospecific affected a species' detection at hair traps. Wolverine presence increased marten detection, whereas marten decreased fisher detection at NGT traps. For all species, detection error was reduced through successive monthly surveys, although heterogeneity induced by other mustelids remained. Detection error from multiple sources can bias NGT occupancy estimates, with implications for population estimation, ecological inference, species' legal status assessments, and conservation decisions. We recommend that NGT studies quantify and correct for detection error using independent survey methods and multi‐method occupancy models, to improve the ability to answer ecological questions and make conservation decisions in the face of ecological change. © 2014 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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".