Embracing biodiversity: multispecies population genomics of leafless Bossiaea species shows novel taxa, population dynamics and conservation strategies
Bibliographic record
Abstract
Leafless Bossiaea species are a unique group of plants endemic to Australia that present intriguing challenges in taxonomy and conservation because of their morphological simplicity and often small, disjunct populations. We employed whole genome reduced representation sequencing (DArTseq) to enhance our understanding of the population dynamics, diversity and taxonomy of leafless Bossiaea species. Our dataset comprised 52,539 single-nucleotide polymorphisms across 283 samples from 7 leafless Bossiaea species, including 6 listed threatened species. We examined population structure, phylogenetic relationships, kinship and gene flow within and among populations. On the basis of our population-genomic analyses, we propose recognition of the novel taxon Bossiaea vombata subsp. orientalis and a change in status from species (B. milesiae) to subspecies for B. fragrans subsp. milesiae. Additionally, we show extensive clonal reproduction across species and limited gene flow at distances of >1 km, shedding light on the challenges faced by these species. We advocate a coordinated approach to conservation, focusing on restoring self-sustaining populations and leveraging genetic rescue strategies. By addressing the population dynamics of multiple species simultaneously in taxonomically challenging lineages, we can make informed choices to safeguard biodiversity and evolutionary potential.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".