Testing species relationships and delimitation in the Amazonian hyperdominant<i>Astrocaryum</i>section<i>Huicungo</i>(Arecaceae) using chloroplast data from genome skimming
Bibliographic record
Abstract
Abstract Hyperdominant trees in Amazonia account for half of the individual trees (>10 cm dbh) in the forest, and thus play a crucial role in ecosystem dynamics. However, several of these widespread hyperdominant species may be complexes hiding cryptic diversity that can affect species richness estimates and conservation priorities. Here, we study the intraspecific variation ofAstrocaryum murumuru(Arecaceae), a keystone and hyperdominant species in Amazonia, also known asAstrocaryumsect.Huicungo, a complex of 15 understory to subcanopy palm species. Using chloroplast DNA from genome skimming (>66 kbp alignment) in a Bayesian framework, we present evidence thatA. sect.Huicungorepresents three separately evolving lineages, suggesting that the section is not a single hyperdominant species, and that the 15 morphology‐based species may be an over‐representation. Genome skimming chloroplast data did not fully resolve the species‐level phylogenetic relationships inA. sect.Huicungomostly because of gene discordance and the paraphyly of most species. Contrary to a previous nuclear‐based phylogenetic analysis, the chloroplast genomic data did not recoverA. sect.Huicungomonophyletic, but yielded monophyly in an increased number of species (six) in the complex. Interspecific phylogenetic relationships showed a geographic pattern, and the traditional morphology‐based classification was not supported. Our phylogenomic results are discussed in light of earlier phylogeographical studies using Sanger sequencing. Our findings show the utility of genome skimming data in species delimitation analyses to uncover intraspecific variation of hyperdominant species in Amazonia, the largest evergreen tropical forest.
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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 | 0.000 |
| 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".