Anthropogenic‐based regional‐scale factors most consistently explain plot‐level exotic diversity in grasslands
Bibliographic record
Abstract
Abstract Aim Evidence linking the accumulation of exotic species to the suppression of native diversity is equivocal, often relying on data from studies that have used different methods. Plot‐level studies often attribute inverse relationships between native and exotic diversity to competition, but regional abiotic filters, including anthropogenic influences, can produce similar patterns. We seek to test these alternatives using identical scale‐dependent sampling protocols in multiple grasslands on two continents. Location Thirty‐two grassland sites in N orth A merica and A ustralia. Methods We use multiscale observational data, collected identically in grain and extent at each site, to test the association of local and regional factors with the plot‐level richness and abundance of native and exotic plants. Sites captured environmental and anthropogenic gradients including land‐use intensity, human population density, light and soil resources, climate and elevation. Site selection occurred independently of exotic diversity, meaning that the numbers of exotic species varied randomly thereby reducing potential biases if only highly invaded sites were chosen. Results Regional factors associated directly or indirectly with human activity had the strongest associations with plot‐level diversity. These regional drivers had divergent effects: urban‐based economic activity was associated with high exotic : native diversity ratios; climate‐ and landscape‐based indicators of lower human population density were associated with low exotic : native ratios. Negative correlations between plot‐level native and exotic diversity, a potential signature of competitive interactions, were not prevalent; this result did not change along gradients of productivity or heterogeneity. Main conclusion We show that plot‐level diversity of native and exotic plants are more consistently associated with regional‐scale factors relating to urbanization and climate suitability than measures indicative of competition. These findings clarify the long‐standing difficulty in resolving drivers of exotic diversity using single‐factor mechanisms, suggesting that multiple interacting anthropogenic‐based processes best explain the accumulation of exotic diversity in modern landscapes.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".