The regional species richness and genetic diversity of<scp>A</scp>rctic vegetation reflect both past glaciations and current climate
Bibliographic record
Abstract
Abstract Aim TheArctic has experienced marked climatic differences between glacial and interglacial periods and is now subject to a rapidly warming climate. Knowledge of the effects of historical processes on current patterns of diversity may aid predictions of the responses of vegetation to future climate change. We aim to test whether plant species and genetic diversity patterns are correlated with time since deglaciation at regional and local scales. We also investigate whether species richness is correlated with genetic diversity in vascular plants. Location Circumarctic. Methods We investigated species richness of the vascular plant flora of 21 floristic provinces and examined local species richness in 6215 vegetation plots distributed across theArctic. We assessed levels of genetic diversity inferred from amplified fragment length polymorphism variation across populations of 23 commonArctic species. Correlations between diversity measures and landscape age (time since deglaciation) as well as variables characterizing current climate were analysed using spatially explicit simultaneous autoregressive models. Results Regional species richness of vascular plants and genetic diversity were correlated with each other, and both showed a positive relationship with landscape age. Plot species richness showed differing responses for vascular plants, bryophytes and lichens. At this finer scale, the richness of vascular plants was not significantly related to landscape age, which had a small effect size compared to the models of bryophyte and lichen richness. Main conclusion Our study suggests that imprints of past glaciations inArctic vegetation diversity patterns at the regional scale are still detectable today. SinceArctic vegetation is still limited by post‐glacial migration lag, it will most probably also exhibit lags in response to current and future climate change. Our results also suggest that local species richness at the plot scale is more determined by local habitat factors.
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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 | 0.001 | 0.000 |
| 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".