High endemism at cave entrances: a case study of spiders of the genus Uthina
Bibliographic record
Abstract
Endemism, which is typically high on islands and in caves, has rarely been studied in the cave entrance ecotone. We investigated the endemism of the spider genus Uthina at cave entrances. Totally 212 spiders were sampled from 46 localities, from Seychelles across Southeast Asia to Fiji. They mostly occur at cave entrances but occasionally appear at various epigean environments. Phylogenetic analysis of DNA sequence data from COI and 28S genes suggested that Uthina was grouped into 13 well-supported clades. We used three methods, the Bayesian Poisson Tree Processes (bPTP) model, the Bayesian Phylogenetics and Phylogeography (BPP) method, and the general mixed Yule coalescent (GMYC) model, to investigate species boundaries. Both bPTP and BPP identified the 13 clades as 13 separate species, while GMYC identified 19 species. Furthermore, our results revealed high endemism at cave entrances. Of the 13 provisional species, twelve (one known and eleven new) are endemic to one or a cluster of caves, and all of them occurred only at cave entrances except for one population of one species. The only widely distributed species, U. luzonica, mostly occurred in epigean environments while three populations were found at cave entrances. Additionally, eleven new species of the genus are described.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".