Two sides of the same coin? Rare and pest plants native to the United States and Canada
Bibliographic record
Abstract
Plant biodiversity is at risk, with as many as 10% of native species in the United States being threatened with extinction. Habitat loss has led a growing number of plant species to become rare or threatened, while the introduction or expansion of pest species has led some habitats to be dominated by relatively few, mostly nonindigenous, species. As humans continue to alter many landscapes and vegetation types, understanding how biological traits determine the location of species along a spectrum from vulnerability to pest status is critical to designing risk assessment protocols, setting conservation priorities, and developing monitoring programs. We used boosted regression trees to predict rarity (based on The Nature Conservancy global rankings) and pest status (defined as legal pest status) from data on traits for the native vascular flora of the United States and Canada including Hawaii, Puerto Rico, and the Virgin Islands (n approximately = 15,000). Categories were moderately to highly predictable (AUCpest = 0.87 on 25% holdout test set, AUCrarity = 0.80 on 25% holdout test set). Key predictors were chromosome number, ploidy, seed mass, and a suite of traits suggestive of specialist vs. generalist adaptations (e.g., facultative wetland habitat association and phenotypic variability in growth form and life history). Specifically, pests were associated with high chromosome numbers, polyploidy, and seed masses ranging from 0.1 to 100 mg, whereas rare species were associated with low chromosome numbers, low ploidy, and large (>1000 mg) seed masses. In addition, pest species were disproportionately likely to be facultatively associated with wetlands, and variable in growth form and life history, whereas rare species exhibited an opposite pattern. These results suggest that rare and pest species contrast along trait axes related to dispersal and performance in disturbed or novel habitats.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".