Invasive leafminers on woody plants: a global review of pathways, impact, and management
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
Leafminers are a taxonomically diverse group of endophagous insects. A number of them are pests in forestry, horticulture and agriculture, and some of them have become important invasive species. Here, we discuss the characteristics of invasive leafminers of woody plants. We first present 12 cases of invasive leaf-mining species belonging to four different insect orders. For each of them, we briefly describe their invasion, including pathways of introduction, their impact and management methods and their ecology. We then discuss various aspects of these invasions. Leafminers are introduced to new continents and spread through various pathways such as horticultural trade and accidental transport of adults and pre-imaginal stages in containers and vehicles. They may also spread long distances with air currents. A few species have serious economic impacts as orchard pests, such as the citrus leafminer, Phyllocnistis citrella, or as pests of ornamental plants, such as the horse-chestnut leafminer, Cameraria ohridella. The ecological impact of these species should be better studied, especially those killing native trees, such as the birch leaf-mining weevil, Orchestes fagi, in Canada. Compared to other insect groups, invasive leafminers are usually recruited by a range of native parasitoids, which may or may not succeed in controlling the invasive species. Biological control by introduction of parasitoids from the native range has often been successful to control invasive leafminers. The review ends by short discussions on taxonomic issues and on the use of leafminers as models to study invasion ecology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it