Underexplored and growing economic costs of invasive alien trees
Bibliographic record
Abstract
The high ecological impacts of many invasive alien trees have been well documented. However, to date, we lacked synthesis of their economic impacts, hampering management actions. Here, we summarize the cost records of invasive trees to (I) identify invasive trees with cost information and their geographic locations, (II) investigate the types of costs recorded and sectors impacted by invasive trees and (III) analyze the relationships between categories of uses of invasive trees and the invasion costs attributed to these uses. We found reliable cost records only for 72 invasive trees, accumulating a reported total cost of $19.2 billion between 1960 and 2020. Agriculture was the sector with the highest cost records due to invasive trees. Most costs were incurred as resource damages and losses ($3.5 billion). Close attention to the ornamental sector is important for reducing the economic impact of invasive trees, since most invasive trees with cost records were introduced for that use. Despite massive reported costs of invasive trees, there remain large knowledge gaps on most invasive trees, sectors, and geographic scales, indicating that the real cost is severely underestimated. This highlights the need for further concerted and widely-distributed research efforts regarding the economic impact of invasive trees.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".