Biological invasion hotspots: a trait‐based perspective reveals new sub‐continental patterns
Bibliographic record
Abstract
Invader traits (including plant growth form) may play an important, and perhaps overlooked, role in determining macroscale patterns of biological invasions and therefore warrant greater consideration in future investigations aimed at understanding these patterns. To assess this need, we used empirical data from a national‐level survey of forest in the contiguous 48 states of the USA to identify geographic hotspots of forest plant invasion for three distinct invasion characteristics: invasive species richness, trait richness (defined as the number of the five following plant growth forms represented by the invasive plants present at a given location: forbs, grasses, shrubs, trees, and vines), and species richness within each growth form. Three key findings emerged. 1) The hotspots identified encompassed from 9 to 23% of the total area of our study region, thereby revealing many forests to be not only invaded, but highly invaded. 2) Substantial spatial disagreement among hotspots of invasive species richness, invasive trait richness, and species richness of invasive plants within each growth form revealed many locations to be hotspots for invader traits, or for particular growth forms of invasive plants, rather than for invasive plants in general. 3) Despite eastern forests exhibiting higher levels of plant invasion than western forests, species richness for invasive forbs and grasses in the west were respectively greater than and equivalent to levels found in the east. Contrasting patterns between eastern and western forests in the number of invasive species detected for each growth form combined with the spatial disagreement found among hotspot types suggests trait‐based variability in invasion drivers. Our findings reveal invader traits to be an important contributor to macroscale invasion patterns.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".