Data from: Multivariate species boundaries and conservation of harlequin poison frogs
Bibliographic record
Abstract
In this paper we present an iterative method for delimiting species under the general lineage concept (GLC) based on the multivariate clustering of morphological, ecological, and genetic data. Our rationale is that distinct multivariate groups correspond to evolutionarily independent metapopulation lineages because they reflect the common signal of different secondary defining properties (environmental and genetic distinctiveness, phenotypic diagnosability, etc.) that imply the existence of barriers preventing or limiting gene exchange. We applied this method to study a group of endangered poison frogs, the Oophaga histrionica complex. In our study case, we used next generation targeted amplicon sequencing to obtain a robust genetic dataset that we combined with patterns of morphological and ecological divergence. Our analyses revealed the existence of at least five different species in the histrionica complex (three of them new to science), some of them, occurring in small isolated populations outside any protected areas. The lineage delimitation proposed here has important conservation implications as it revealed that some of the Oophaga species should be considered amongst the most vulnerable of the Neotropical frogs. More broadly, our study exemplifies how multiple -amplicon and multivariate statistical techniques can be integrated to successfully identify species and their boundaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".