Modelling the range expansion of the Tiger mosquito in a Mediterranean Island accounting for imperfect detection
Bibliographic record
Abstract
BACKGROUNDS: (Diptera; Culicidae) is a highly invasive mosquito species and a competent vector of several arboviral diseases that have spread rapidly throughout the world. Prevalence and patterns of dispersal of the mosquito are of central importance for an effective control of the species. We used site-occupancy models accounting for false negative detections to estimate the prevalence, the turnover, the movement pattern and the growth rate in the number of sites occupied by the mosquito in 17 localities throughout Mallorca Island. RESULTS: Site-occupancy probability increased from 0.35 in the 2012, year of first reported observation of the species, to 0.89 in 2015. Despite a steady increase in mosquito presence, the extinction probability was generally high indicating a high turnover in the occupied sites. We considered two site-dependent covariates, namely the distance from the point of first observation and the estimated yearly occupancy rate in the neighborhood, as predicted by diffusion models. Results suggested that mosquito distribution during the first year was consistent with what predicted by simple diffusion models, but was not consistent with the diffusion model in subsequent years when it was similar to those expected from leapfrog dispersal events. CONCLUSIONS: in Mallorca followed two distinct phases, an early one consistent with diffusion movements and a second consistent with long distance, 'leapfrog', movements. The colonization of the island was fast, with ~90% of the sites estimated to be occupied 3 years after the colonization. The fast spread was likely to have occurred through vectors related to human mobility such as cars or other vehicles. Surveillance and management actions near the introduction point would only be effective during the early steps of the colonization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".