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Record W2974831507 · doi:10.1093/biosci/biz110

Comment on Havens and colleagues (2019)

2019· article· en· W2974831507 on OpenAlexaffabout
Hariet L. Hinz, Robert S. Bourchier, Urs Schaffner, Mark Schwarzländer, Aaron S. Weed

Bibliographic record

VenueBioScience · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPolitical scienceEpistemologyEnvironmental ethicsPhilosophy

Abstract

fetched live from OpenAlex

Havens and colleagues (2019) concluded that “given the uncertain efficacy and the demonstrable risks of biocontrol, its use should be less frequent, better regulated, and better monitored.” In contrast, we argue that: (1) The frequency of implementation of biocontrol should continue based on records of specificity, safety and cost-effective success. All examples of nontarget attack and impact cited by Havens and colleagues were from first-generation biocontrol programs and are not representative of current biocontrol practice (for a review, see Hinz et al. 2019). The authors have overlooked a large body of literature addressing economic impact assessments of weed biocontrol (e.g., Page and Lacey 2006, De Lange and van Wilgen 2010). Despite the “tremendous resources… invested in biological control programs.” these accounts show extremely advantageous cost: benefit ratios of up to 1:4000 (Culliney 2005). As Havens and colleagues correctly stated, “partial control of the plant populations can make other management efforts… more cost effective.” Therefore, statements such as “agents cannot be deemed successful unless population level impacts are apparent” are overly simplistic and incorrect. Some of the most successful integrated management programs against woody invaders in South Africa are based on a combination of physical removal of established trees and seed-feeding biocontrol agents (e.g., Hakea sericea; Esler et al. 2010). (2) Weed biocontrol is already well regulated. The current US review process for release of weed biocontrol agents includes a thorough consultation with stakeholders within and outside federal and tribal governments and takes at least 2–4 years. The review is focused entirely on the risks of biocontrol releases for individual species, thereby ignoring the significant risk to entire habitats of no management, and the potential benefits of biocontrol for those habitats. (3) Thorough and systematic postrelease monitoring, quantifying impact of biocontrol agents on target and nontarget species should continue to be the standard for biocontrol projects, as has been advocated previously in several papers. We agree that the study of plant demography at sites with or without the respective biocontrol agents can yield important information on success and safety (e.g., Catton et al. 2016). However, the authors’ decision to entirely exclude post-release studies lacking experimental controls ignores spatial and the extended temporal scales at which ecological systems including biocontrol operate. Controlled demographic studies by their intensive nature are typically limited to single or very few sites. As an alternative, long-term postrelease monitoring studies (longer than 10 years) over large spatial scales, even when lacking control sites, can estimate effects of biocontrol agents on weed population growth rates (e.g., Van Hezewijk et al. 2010). In addition, mechanistic modeling combined with model selection (e.g., Schooler et al. 2011, Weed and Schwarzländer 2014) provides an opportunity to simultaneously evaluate multiple hypotheses including individual and interactive effects of agent density, competition and climate to explain weed population dynamics. These approaches can provide valuable insights and should not be ignored. In summary, biocontrol should continue to be an important tool for invasive plant management, regulation should include benefit–risk analysis for all actions and inaction, and postrelease monitoring should consider all available data. Hariet L. Hinz (h.hinz@cabi.org), and Urs Schaffner are affiliated with the CABI, in Delémont, Switzerland. Robert S. Bourchier is affiliated with the Agriculture and Agri-Food Canada, in Lethbridge, Canada. Mark Schwarzländer is affiliated with the University of Idaho, in Moscow, USA. Aaron Weed is affiliated with the National Park Service, in Woodstock, USA.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.069
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0050.007
Scholarly communication0.0060.012
Open science0.0040.005
Research integrity0.0690.069
Insufficient payload (model declined to judge)0.0200.019

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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2019
Admission routes2
Has abstractyes

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