Targeted efforts are more effective than combined approaches for sampling two rare carnivores
Bibliographic record
Abstract
Abstract Verifying the abundance and distribution of species of conservation concern is necessary for land management agencies to determine potential impacts of management actions and for monitoring long‐term population trends. In the Rocky Mountains of the United States, Canada lynx ( Lynx canadensis ) and wolverine ( Gulo gulo ) are currently species of management importance for federal land management agencies. Optimal winter methods for detecting the 2 species differ in that wolverines are generally detected using bait stations and lynx are most efficiently detected through snow‐track encounters. There has been interest in value‐added approaches such as observing track encounters while traveling to and from bait stations, to improve multispecies detection probabilities. To estimate the value of adding a track survey to bait station travel (referred to as en route surveys) compared to a stand‐alone snow track survey, we conducted both types of surveys in an area where bait stations were located in western Montana, known as the Southwestern Crown of the Continent, from 2013–2016. We collected genetic data (backtracking to genetic material once a track was encountered) and recorded the distance surveyed from both types of track surveys. Our results showed that stand‐alone track surveys were more efficient for detecting lynx than en route surveys in 2015 and 2016 and that there was no difference in track survey efficacy for wolverines across all survey years. In addition, for wolverine, both types of track surveys detected only 3 additional individuals not identified from bait stations (33 individuals total), suggesting that bait stations were the more effective method to detect wolverines. The opposite was true for lynx, with only 5 of 39 individuals identified during the study detected only from bait stations and not by track surveys (4 males and 1 female). In addition, distance surveyed during track surveys was a significant predictor of detection for both species. Our results suggest that ecology and behavior should be considered when designing noninvasive surveys for multiple target species and that complimentary and concurrent, but separate, efforts are likely more efficient for detecting species with differences in ecology and behavior.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".