Species on the menu of a generalist predator, the eastern red bat (<i>Lasiurus borealis</i>): using a molecular approach to detect arthropod prey
Bibliographic record
Abstract
One of the most difficult interactions to observe in nature is the relationship between a predator and its prey. When direct observations are impossible, we rely on morphological classification of prey remains, although this is particularly challenging among generalist predators whose faeces contain mixed and degraded prey fragments. In this investigation, we used a polymerase chain reaction and sequence-based technique to identify prey fragments in the guano of the generalist insectivore, the eastern red bat (Lasiurus borealis), and evaluate several hypotheses about prey selection and prey defences. The interaction between bats and insects is of significant evolutionary interest because of the adaptive nature of insect hearing against echolocation. However, measuring the successes of predator tactics or particular prey defences is limited because we cannot normally identify these digested prey fragments beyond order or family. Using a molecular approach, we recovered sequences from 89% of the fragments tested, and through comparison to a reference database of sequences, we were able to identify 127 different species of prey. Our results indicate that despite the robust jaws of L. borealis, most prey taxa were softer-bodied Lepidoptera. Surprisingly, more than 60% of the prey species were tympanate, with ears thought to afford protection against these echolocating bats. Moths of the family Arctiidae, which employ multiple defensive strategies, were not detected as a significant dietary component. Our results provide an unprecedented level of detail for the study of predator-prey relationships in bats and demonstrate the advantages which molecular tools can provide in investigations of complex ecological systems and food-web relationships.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".