Phylogeography, population genetics and distribution modelling reveal vulnerability of <i>Scirpus longii </i>(Cyperaceae) and the Atlantic Coastal Plain Flora to climate change
Bibliographic record
Abstract
A proactive approach to conservation must be predictive, anticipating how habitats will change and which species are likely to decline or prosper. We use composite species distribution modelling to identify suitable habitats for 18 members of the North American Atlantic Coastal Plain Flora (ACPF) since the Last Glacial Maximum and project these into the future. We then use Scirpus longii (Cyperaceae), a globally imperiled ACPF sedge with many of the characteristics of extinction vulnerability, as a case study. We integrate phylogeographical and population genetic analyses and species distribution modelling to develop a broad view of its current condition and prognosis for conservation. We use genotyping-by-sequencing to characterize the genomes of 142 S. longii individuals from 20 populations distributed throughout its range (New Jersey to Nova Scotia). We measure the distribution of genetic diversity in the species and reconstruct its phylogeographical history using the snapp and rase models. Extant populations of S. longii originated from a single refugium south of the Laurentide ice sheet around 25 ka. The genetic diversity of S. longii is exceedingly low, populations exhibit little genetic structure and the species is slightly inbred. Projected climate scenarios indicate that nearly half of extant populations of S. longii will be exposed to unsuitable climate by 2070. Similar changes in suitable habitat will occur for many other northern ACPF species-centres of diversity will shift northward and Nova Scotia may become the last refuges for those species not extinguished.
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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 | 0.000 | 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".