Dispersal of Invasive Forest Insects via Recreational Firewood: A Quantitative Analysis
Bibliographic record
Abstract
Recreational travel is a recognized vector for the spread of invasive species in North America. However, there has been little quantitative analysis of the risks posed by such travel and the associated transport of firewood. In this study, we analyzed the risk of forest insect spread with firewood and estimated related dispersal parameters for application in geographically explicit invasion models. Our primary data source was the U.S. National Recreation Reservation Service database, which records camper reservations at > 2,500 locations nationwide. For > 7 million individual reservations made between 2004 and 2009 (including visits from Canada), we calculated the distance between visitor home address and campground location. We constructed an empirical dispersal kernel (i.e., the probability distribution of the travel distances) from these "origin-destination" data, and then fitted the data with various theoretical distributions. We found the data to be strongly leptokurtic (fat-tailed) and fairly well fit by the unbounded Johnson and lognormal distributions. Most campers ( approximately 53%) traveled <100 km, but approximately 10% traveled > 500 km (and as far as 5,500 km). Additionally, we examined the impact of geographic region, specific destinations (major national parks), and specific origin locations (major cities) on the shape of the dispersal kernel, and found that mixture distributions (i.e., theoretical distribution functions composed of multiple univariate distributions) may fit better in some circumstances. Although only a limited amount of all transported firewood is likely to be infested by forest insects, this still represents a considerable increase in dispersal potential beyond the insects' natural spread capabilities.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.004 | 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 teacher head, 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".