Economic costs of biological invasions within North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Invasive species can have severe impacts on ecosystems, economies, and human health. Though the economic impacts of invasions provide important foundations for management and policy, up-to-date syntheses of these impacts are lacking. To produce the most comprehensive estimate of invasive species costs within North America (including the Greater Antilles) to date, we synthesized economic impact data from the recently published InvaCost database. Here, we report that invasions have cost the North American economy at least US$ 1.26 trillion between 1960 and 2017. Economic costs have climbed over recent decades, averaging US$ 2 billion per year in the early 1960s to over US$ 26 billion per year in the 2010s. Of the countries within North America, the United States (US) had the highest recorded costs, even after controlling for research effort within each country ($5.81 billion per cost source in the US). Of the taxa and habitats that could be classified in our database, invasive vertebrates were associated with the greatest costs, with terrestrial habitats incurring the highest monetary impacts. In particular, invasive species cumulatively (from 1960–2017) cost the agriculture and forestry sectors US$ 527.07 billion and US$ 34.93 billion, respectively. Reporting issues (e.g., data quality or taxonomic granularity) prevented us from synthesizing data from all available studies. Furthermore, very few of the known invasive species in North America had reported economic costs. Therefore, while the costs to the North American economy are massive, our US$ 1.26 trillion estimate is likely very conservative. Accordingly, expanded and more rigorous economic cost reports are necessary to provide more comprehensive invasion impact estimates, and then support data-based management decisions and actions towards species invasions.
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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) | 0.007 | 0.001 |
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 it