Limited Range-Filling Among Endemic Forest Herbs of Eastern North America and Its Implications for Conservation With Climate Change
Bibliographic record
Abstract
Biodiversity hotspots host a high diversity of narrowly distributed endemic species, which are increasingly threatened by climate change. In eastern North America, the highest concentration of plant diversity and endemism occurs in the Southern Appalachian Mountains (SAM). It has been hypothesized that this region served as a refugium during Pleistocene glacial cycles and that postglacial migration northward was dispersal limited. We tested this hypothesis using species distribution models for eight forest herb species. We also quantified the extent to which the geography of suitable habitat shifted away from the current range with climate change. We developed species distribution models for four forest herb species endemic to the SAM and four that co-occur in the same SAM habitats but have broader ranges. For widespread species, we built models using (1) all occurrences and (2) only those that overlap the SAM hotspot in order to evaluate the extent of Hutchinsonian shortfalls and the potential for models to predict suitable habitat beyond the SAM. We evaluated the extent to which predicted climatically suitable areas are projected to shift away from their current ranges under future climate change. We detected unoccupied but suitable habitat in regions up to 1,100 km north of the endemic species’ ranges. Endemic ranges are disjunct from suitable northern areas due to a ∼100–150 km gap of unsuitable habitat. Under future climate change, models predicted severe reductions in suitable habitat within current endemic ranges. For non-endemic species, we found similar overall patterns and gap of unsuitability in the same geographic location. Our results suggest a history of dispersal limitation following the last glacial maximum along with an environmental barrier to northward migration. Conservation of endemic species would likely require intervention and assisted migration to suitable habitat in northern New England and Canada.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".