Not all rare species are the same: contrasting patterns of genetic diversity and population structure in two narrow-range endemic sedges
Bibliographic record
Abstract
The many spatial and temporal configurations in which species can be classified as rare may result in various genetic signatures, despite a persistent generalization that populations of rare species are genetically depauperate and highly differentiated. We assessed genetic diversity and differentiation in two narrow endemics with contrasting geographical distributions using 12 nuclear and six chloroplast microsatellite loci. Consistent with both marker types, the smaller, more isolated Lepidosperma sp. Parker Range populations were characterized by lower diversity and stronger divergence, relative to higher diversity and extensive connectivity among the geographically clustered L. sp. Mt Caudan populations. However, neither species exhibited low diversity, despite high inbreeding. Together, our results suggest that these species are naturally rare and have long persisted in this landscape while maintaining genetic diversity and tolerating considerable inbreeding. Their resilience is probably due to large population sizes and the flexibility afforded by employing sexual and asexual reproduction. Their contrasting genetic dynamics demonstrate that not all rare species share generalized patterns, even within the same rarity category. Moreover, these patterns were better predicted by fine-scale descriptors of rarity, population size and distribution, rather than the more typically used geographical range. This study highlights the complex dynamics of rare species and cautions against using broad assumptions to classify and manage rare species.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".