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 asIps 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 ofAnoplophora glabripennisand other invasive species) or are still in development (e.g. e-noses and acoustic detection) are also discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".