Monitoring and Surveillance of Forest Insects
Bibliographic record
Abstract
Abstract Monitoring of insect populations is widely used in forest entomology in the context of biodiversity studies, as an aspect of pest management, and for the detection and surveillance of non-native invasive species. In particular, monitoring is undertaken to obtain information on the presence or abundance of particular species, to study their phenology (e.g. the time of oviposition or flight periods), to predict pest population size, spread and damage, or to determine if pest management activities are required. A wide variety of methods are being used for these purposes including physical surveys, the use of insect traps, molecular methods, as well as aerial surveys and remote sensing. This chapter focusses on some of the more important methods to provide an overview of the objectives and applications of monitoring and surveillance of forest insects. The principles of each method and common uses are explained and illustrated with case studies on prominent forest insects including the pine processionary moth ( Thaumetopoea pityocampa ), the Sirex wood wasp ( Sirex noctilio ), spongy moth ( Lymantria dispar ), bark beetles such as Ips typographus , and the brown spruce longhorn beetle ( Tetropium fuscum ). The chapter also explores statistical considerations and issues such as imperfect relationships between trap catch and the local population size of target species. Niche methods that are not widely used but have strengths in some situations (e.g. detector dogs for detection of Anoplophora glabripennis and other invasive species) or are still in development (e.g. e-noses and acoustic detection) are also discussed.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".