Mountains host significantly more data deficient and threatened bat species than lowlands
Bibliographic record
Abstract
Abstract Mountains harbour one third of the world’s biodiversity and much of it is under increasing anthropogenic pressure. Yet, global assessments of the occurrence, and threat status of most mountain taxa, especially elusive ones are lacking, thereby hindering conservation and research prioritisation. In this study, we synthesise the distribution and conservation status of bats, a species rich taxon on mountains. By using data on geographical and elevational distribution ranges from the International Union for Conservation of Nature (IUCN), we examined bat species richness on mountains, species that predominantly occur in mountains (‘mountain dwelling species’), and those restricted to upper montane and alpine regions within mountains (‘highland dwelling species’). We also used published trait datasets to investigate the traits that are associated with mountain dwelling in bats. Globally, we identified 148 mountain dwelling and 46 highland dwelling bat species. Bat diversity is highest in the Northern Andes and Guiana Highlands. The mountain dwelling nature of bat species was found to be significantly associated with biogeographic realm. Importantly, our results show that mountain dwelling species are proportionately more data deficient than species that predominantly occur in lowlands. Additionally, highland dwelling species are proportionately more threatened than lowland species. Our results highlight a significant dearth of knowledge on mountain dwelling bat species. We conclude that more research is needed for bats specialised on mountain ecosystems. Our results draw attention towards improving the knowledge and protection of bat species that occur predominantly at high elevations across the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".