Moulting matters: the importance of understanding moulting cycles in bats when using fur for endogenous marker analysis
Bibliographic record
Abstract
Endogenous markers are a valuable indicator of individual animal ecology, but data interpretation requires a detailed understanding of the timing of tissue formation. Fur is commonly used in bat research using endogenous markers, but the moulting cycles of most bat species are not well documented. In this review, we (i) describe methods of investigating bat moulting; (ii) summarize the current literature on bat moulting cycles, highlighting broad trends; (iii) discuss knowledge gaps; and (iv) make recommendations for optimal fur sampling protocols. Three characteristics may indicate moulting in bats: changing skin pigmentation; visible fur growth and colour changes; and endogenous markers. Most studies reported new fur growth once annually during summer–fall, although there were exceptions. The timing of new fur growth varies among species, sexes, and age classes. Individuals commonly experience asynchronous new fur growth, with dorsal fur growth occurring before ventral. Specific moult progressions vary among species. Knowledge gaps include moulting cycles in tropical species and in subadult and yearling bats; migration during new fur growth; and the timing of fur growth compared with shedding. We recommend that fur samples taken dorsally from adult males are the most likely to be representative of the bat’s site of summer residency.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".