Evaluating the efficacy of sample collection approaches and DNA metabarcoding for identifying the diversity of plants utilized by nectivorous bats
Bibliographic record
Abstract
In this study, we evaluated the efficacy of sample collection approaches and DNA metabarcoding to identify plants utilized by nectivorous bats. Samples included guano collected from beneath bat roosts and pollen-swabs from bat fur, both of which were subjected to DNA metabarcoding and visual identification of pollen (microscopy) to measure plant diversity. Our objectives were to determine whether DNA metabarcoding could detect likely food plants of nectivorous bats, whether sample types would produce different estimates of plant diversity, and to compare results of DNA metabarcoding to visual identification. Visual identification found that 99% of pollen was from Agave, which is thought to be the bats' main food source. The dominant taxon found by metabarcoding was also Agavoideae, but a broader diversity of plant species was also detected, many of which are likely "by-catch" from the broader environment. Metabarcoding outcomes differed between sample types, likely because pollen-swabs measured the plant species visited by bats and guano samples measured all items consumed in the bat's diet, even those that were not pollen or nectar. Overall, metabarcoding is a powerful, high-throughput tool to understand bat ecology and species interactions, but careful analysis of results is necessary to derive accurate ecological conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".