Catching invasives with curiosity: the importance of passive biosecurity surveillance systems for invasive forest pest detection
Bibliographic record
Abstract
First detections of nonnative insect species are often made by curious members of the public rather than by specialists or trained professionals. Passive surveillance is a crucial component of national biosecurity surveillance, highlighted by early detection case studies of several prominent nonnative arthropod pests (e.g., Asian longhorned beetle [ALB], Jorō spider, spotted lanternfly). These examples demonstrate that curiosity and the recognition of novelty in the natural world, along with enabling technology and systems, are a critical part of early detection and effective invasive species management. This is particularly impactful when dealing with conspicuous pests or for new and emerging nonnative species that have yet to be detected in a new location. Data from historical and recent accounts of first detections of ALB incursions and other invasive forest pests underscore the need to invest in passive surveillance reporting systems and fully integrate public observations into existing surveillance frameworks. New automated approaches streamline the assessment of public observations and can generate pest alerts to initiate a formal regulatory assessment. Biodiversity monitoring platforms, such as iNaturalist, provide a focal point for community engagement and aggregate verified public observations. Empowering proactive reporting of biological novelty provides needed support for early detection of invasive species. Embracing the public as active members of the surveillance community can be cost effective and lead to the greatest gains in the proactive management of invasive species around the world.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".