Increased diet breadth of little brown bats (<i>Myotis lucifugus</i>) at their northern range limit: a multimethod approach
Bibliographic record
Abstract
The distribution of small mammals is constrained by extreme environmental demands and variable food supplies that are commonly incurred at northern latitudes. Little brown bats (Myotis lucifugus (Le Conte, 1831)) are at the northwestern limits of their range in Alaska (USA), where environmental demands are higher and prey availability is more seasonal than elsewhere in their range. We hypothesized that the little brown bat in interior Alaska has adjusted to these constraints by broadening its foraging niche, relative to that of southern conspecifics. We analyzed arthropod fragments (microhistology) in guano to describe prey composition to order. We compared the efficacy of evaluating diet by microhistology with DNA analysis and stable isotope analysis on guano and hair. Bats consumed aerial prey such as Lepidoptera (moths) and Diptera (true flies and mosquitoes), as well as terrestrial arthropods including Araneae (spiders). Shifts in the proportion of aerial prey in the diet were closely linked to ordinal day. Values for δ15N in hair indicated that bats were generalists in interior Alaska, coastal Alaska, and the Yukon (Canada), but significant outliers indicated that some individuals have distinct diets. The little brown bat’s flexibility in feeding strategies likely allows this species to sustain populations in arctic and subarctic regions.
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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 | 0.001 | 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) | 0.002 | 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".