The diet of <i><scp>M</scp>yotis lucifugus</i> across <scp>C</scp>anada: assessing foraging quality and diet variability
Bibliographic record
Abstract
Variation in prey resources influences the diet and behaviour of predators. When prey become limiting, predators may travel farther to find preferred food or adjust to existing local resources. When predators are habitat limited, local resource abundance impacts foraging success. We analysed the diet of Myotis lucifugus (little brown bats) from Nova Scotia (eastern Canada) to the Northwest Territories (north-western Canada). This distribution includes extremes of season length and temperature and encompasses colonies on rural monoculture farms, and in urban and unmodified areas. We recognized nearly 600 distinct species of prey, of which ≈30% could be identified using reference sequence libraries. We found a higher than expected use of lepidopterans, which comprised a range of dietary richness from ≈35% early in the summer to ≈55% by late summer. Diptera were the second largest prey group consumed, representing ≈45% of dietary diversity early in the summer. We observed extreme local dietary variability and variation among seasons and years. Based on the species of insects that were consumed, we observed that two locations support prey species with extremely low pollution and acidification tolerances, suggesting that these are areas without environmental contamination. We conclude that there is significant local population variability in little brown bat diet that is likely driven by seasonal and geographical changes in insect diversity, and that this prey may be a good indicator of environment quality.
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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".