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Record W2564426392 · doi:10.4039/tce.2015.66

A history of biological control in Canadian forests, 1882–2014

2016· article· en· W2564426392 on OpenAlexaffabout
Chris J.K. MacQuarrie, D. Barry Lyons, M. Lukas Seehausen, Sandy M. Smith

Bibliographic record

VenueThe Canadian Entomologist · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of TorontoNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsBiological pest controlDominance (genetics)EcologyBiologyPredationBiodiversityPest controlGeographyAgroforestry

Abstract

fetched live from OpenAlex

Abstract Biological control has been an important tactic in the management of Canadian forests for over a century, but one that has had varied success. Here, we review the history of biological control programmes using vertebrate and invertebrate parasitoids and predators against insects in Canadian forests. Since roughly 1882, 41 insect species have been the target of biological control, with approximately equal numbers of both native and non-native species targeted. A total of 161 species of biological control agents have been released in Canadian forests, spanning most major orders of insects, as well as mites and mammals. Biological control has resulted in the successful suppression of nine pest species, and aided in the control of an additional six species. In this review, we outline the chronological history of major projects across Canadian forests, focussing on those that have had significant influence for the development of biological control. The historical data clearly illustrate a rise and fall in the use of biological control as a tactic for managing forest pests, from its dominance in the 1940s and 1950s to its current low level. The strategic implementation of these biological control programmes, their degree of success, and the challenges faced are discussed, along with the discipline’s shifting relationship to basic science and the environmental viewpoints surrounding its use.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.015
GPT teacher head0.203
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2016
Admission routes2
Has abstractyes

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