Linking economic activities to the distribution of exotic plants
Bibliographic record
Abstract
The human enterprise is flooding Earth's ecosystems with exotic species. Human population size is often correlated with species introductions, whereas more proximate mechanisms, such as economic activities, are frequently overlooked. Here we present a hypothesis that links ecology and economics to provide a causal framework for the distribution of exotic plants in the United States. We test two competing hypotheses (the population-only and population-economic models) using a national data set of exotic plants, employing a statistical framework to simultaneously model direct and indirect effects of human population and ecological and economic variables. The population-only model included direct effects of human population and ecological factors as predictors of exotics. In contrast, the population-economic model included the direct effects of economic and ecological factors and the indirect effects of human population as predictors of exotics. The explicit addition of economic activity in the population-economic model provided a better explanation for the distribution of exotics than did the population-only model. The population-economic model explained 75% of the variation in the number of exotic plants in the 50 states and provided a good description of the observed number of exotic plants in the Canadian provinces and in other nations in 85% of the cases. A specific economic activity, real estate gross state product, had the strongest positive effect on the number of exotics. The strong influence of economics on exotics demonstrates that economics matter for resolving the exotic-species problem because the underlying causes, and some of the solutions, may lie in human-economic behaviors.
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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 | 0.006 |
| 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) | 0.002 | 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".