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The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.

Habitat Management to Suppress Pest Populations: Progress and Prospects

Gurr, Geoff M., Wratten, Steve D., Landis, Douglas A., You, Minsheng·2017·Annual Review of Entomology

Abstract

Habitat management involving manipulation of farmland vegetation can exert direct suppressive effects on pests and promote natural enemies. Advances in theory and practical techniques have allowed habitat management to become an important subdiscipline of pest management. Improved understanding of biodiversity-ecosystem function relationships means that researchers now have a firmer theoretical foundation on which to design habitat management strategies for pest suppression in agricultural systems, including landscape-scale effects. Supporting natural enemies with shelter, nectar, alternative prey/hosts, and pollen (SNAP) has emerged as a major research topic and applied tactic with field tests and adoption often preceded by rigorous laboratory experimentation. As a result, the promise of habitat management is increasingly being realized in the form of practical worldwide implementation. Uptake is facilitated by farmer participation in research and is made more likely by the simultaneous delivery of ecosystem services other than pest suppression.

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Metadata

Authors
Gurr, G. M., Wratten, S. D., Landis, D. A., & You, M.
Venue
Annual Review of Entomology
Published
2017-01-31
Volume
62
Issue
1
Pages
91-109
ISSN
  • 0066-4170 (ISSN-L)
  • 1545-4487
OpenAlex
W2345426481
Cited by
699
References
118
FWCI
6.45
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Habitat Management to Suppress Pest Populations: Progress and Prospects. https://app.cochat.ai/papers/10.1146/annurev-ento-031616-035050
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Gurr, G. M., Wratten, S. D., Landis, D. A., & You, M. (2017). Habitat Management to Suppress Pest Populations: Progress and Prospects. Annual Review of Entomology. https://doi.org/10.1146/annurev-ento-031616-035050

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.