Non-native vascular flora of alpine areas in the White Mountains, New Hampshire, USA
Bibliographic record
Abstract
Arctic-alpine vegetation in the eastern United States is unique to northern New England and New York and is disjunct from similar areas in eastern Canada. We present the first study of the non-native flora in the region, specifically focusing on New Hampshire’s White Mountains. By combining literature and herbaria searches, field surveys, and a seventeen-year evaluation at an alpine hut in a hypothesis-driven framework, we document the composition, chronology, and persistence of non-native plant species establishments, regress richness in relation to elevation and disturbed area, and evaluate similarities to nineteen other alpine floras globally. Our results indicate that the White Mountains support one of the most species-rich non-native alpine floras known in the world, with 58 species detected at thirty-one sites since 1874, comprising 19 percent of 300 species documented in New Hampshire’s 22 km2 of alpine tundra. There is a negative relationship between non-native alpine plant richness and elevation along a mountain road on Mt. Washington. Moreover, elevation predicts richness per unit area in proximity to clusters of built structures in alpine areas. The compositions, geographic origins, and dominant ruderal strategies of non-native species are similar to those of Arctic and other alpine non-native floras globally. Sørenson similarity index and nonmetric multidimensional scaling ordination of twenty alpine regions reveals the White Mountains have highest similarity with widely separated mountain regions in Australia, Hawaii, the Rocky Mountains, the Andes, Southern Africa, and Iceland, driven by shared species of Eurasian origin. We report the unexpected discovery of Plagiobothrys hispidulus, a borage native to western North America not previously reported from New Hampshire. These findings have important implications for managers of alpine areas in eastern North America and may facilitate the early detection, monitoring, and control of non-native species, minimizing their establishment and spread.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".