Phylogeographic analysis of long-legged bats, <i>Macrophyllum macrophyllum</i>, with notes on roosting behavior and natural history
Bibliographic record
Abstract
The long-legged bat (Macrophyllum macrophyllum) is widely distributed in the continental Neotropics, but poorly known because it is not commonly caught in mist nets. Available data suggest that this species is closely associated with water where it forages for insect prey. We compiled the first comprehensive molecular dataset assembled for the species, spanning its entire distributional range to investigate if the phylogeography of this monotypic genus is associated with the hydrographic drainage, ecosystem regions, or genetic clustering in Central and South America. To survey under sampled areas, fieldwork was conducted in the Brazilian Pantanal and Cerrado targeting the search for riverine roost sites of Macrophyllum. A literature review was also done to summarize roosting information for the species. New sequences of the mitochondrial cytochrome b gene were generated for tissue samples from Brazil and in museum collections. Phylogenetic trees were constructed using both maximum likelihood and Bayesian inference methods and a haplotype network was used to analyze population structure. Our phylogenetic results identified five geographic lineages of Macrophyllum from (1) the western Cerrado, (2) eastern Cerrado and Pantanal, (3) Guianas, (4) Amazonia, and (5) Central America. However, the haplotype network in conjunction with the genetic clustering identified four populations with the eastern Cerrado and Pantanal grouping with the Guianas and the eastern part of Amazonia. The fieldwork in the Cerrado and Pantanal along with the literature review identified that about half of the roost sites for the long-legged bats were drainage culverts. There is geographic structuring in the mitochondrial data of Macrophyllum with Central America, western Cerrado, Pantanal, Guianas, and eastern Ecuador reciprocally monophyletic and well differentiated populations. However, the under sampled eastern Amazonia is poorly resolved in relation to the other areas. The long-legged bats seem to be relatively adaptable to certain levels of human disturbance and landscape development with man-made drainage culverts commonly used as roosting sites. Increased biodiversity surveys of bats in central Brazil are needed to fill in distributional gaps, such as the lower Amazon River basin, to resolve phylogeographic patterns of Macrophyllum in South America and better understand the potential of cryptic species in this monotypic genus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".