Using PIT tags to infer bat reproductive status and parturition date: busy nights during lactation
Bibliographic record
Abstract
Abstract Passive integrated transponders (PIT tags) can aid in the collection of important demographic data for species for which other methods, such as GPS technology, are not suitable. PIT tags can be particularly suitable to monitor small and cryptic species like bats and permit inference on their behavioral ecology. Literature for several species of bats states that females change their nightly activity patterns—going out and in of the roost only once per night during gestation compared to several times during lactation. Hence, we tested whether PIT tag detection patterns could be used to infer reproductive status and parturition date of female bats. From 2017 to 2021, we recorded detections of PIT-tagged little brown (Myotis lucifugus) and northern long-eared bats (M. septentrionalis) at the entrance of 2 maternity roosts in Québec and Newfoundland, Canada. We also used the maternity roost in Québec as a case study to further evaluate the potential of this method to link behavior and demography and investigated factors affecting parturition date. We were able to infer reproductive status for 63% to 97% of tagged individuals detected during both the gestation and the lactation periods, and parturition date for 61% to 95% of reproductive individuals, depending on the year and roost. Early spring arrival at the roost and warm spring mean temperature at night were associated with earlier parturition dates. Herein, we highlight that PIT tag systems may be useful to detect changes in activity patterns of female bats and infer individual reproductive parameters, which is on the long-term less stressful for bats and easier for researchers. We demonstrate that this approach is useful to investigate intrinsic and extrinsic factors of reproductive parameters, improving our understanding of bat population dynamics resulting in more informed population management decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".