Detecting bat environmental DNA from water-filled road-ruts in upland forest
Bibliographic record
Abstract
ABSTRACT Declines in population and diversity of North American bats are rapidly occurring due to habitat loss, incidental take from various industry projects, and lethal White-nose Syndrome disease. It is critical to accurately census habitat for appropriate conservation measures, yet traditional sampling methodology, such as mist netting and acoustic recordings, can be time-intensive and biased. Instead, a passive sampling tool that does not rely on the a priori knowledge of bat roosts may provide crucial information on bat communities. In the water-limited habitats of forested uplands of the Appalachian Plateau, water-filled road-ruts are important resources for bats. Therefore, we developed an environmental DNA (eDNA) protocol to sample isolated road-ruts that may have the presence of sloughed cellular material from actively drinking bats. The detection of bat eDNA was investigated from a positive control experiment, and across 47 water samples collected in Kentucky and Ohio. Water samples were analyzed using both species-specific quantitative polymerase chain reaction (qPCR) and community metabarcoding methodologies. Using qPCR analysis, we detected eDNA from big brown bat ( Eptesicus fuscus ) and eastern red bat ( Lasiurus borealis ) from water-filled road-ruts. While the community metabarcoding approach failed to detect any bat eDNA, many non-target amphibians, birds, and mammals were identified. These results suggest eDNA found within road-ruts provides an additional detection tool for surveying biodiversity across upland forests. Additionally, the use of qPCR increased the detection of rare eDNA targets, which will be crucial for properly implementing future eDNA applications for improving bat conservation efforts across the landscape. Article impact statement Environmental DNA provides detection of bats from drinking sources offering a novel survey method for management and conservation efforts
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".