Landscape composition and configuration have scale-dependent effects on agricultural pest suppression

Yajun Zhang; Nathan L. Haan; Douglas A. Landis

doi:10.1016/j.agee.2020.107085

Landscape composition and configuration have scale-dependent effects on agricultural pest suppression

Yajun Zhang, Nathan L. Haan, Douglas A. Landis

Entomology

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

Abstract

Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250−500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000−1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.

Original language	English
Article number	107085
Journal	Agriculture, Ecosystems and Environment
Volume	302
DOIs	https://doi.org/10.1016/j.agee.2020.107085
State	Published - Oct 15 2020

Bibliographical note

Publisher Copyright:
© 2020 The Authors

Funding

We thank Timothy D. Meehan for providing the field data. This work was supported by the Great Lakes Bioenergy Research Center, the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research [Award DE-SC0018409]; by the National Science Foundation Long-term Ecological Research Program [DEB 1832042] at the Kellogg Biological Station; and by Michigan State University AgBioResearch. We thank Timothy D. Meehan for providing the field data. This work was supported by the Great Lakes Bioenergy Research Center, the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research [Award DE-SC0018409]; by the National Science Foundation Long-term Ecological Research Program [DEB 1832042] at the Kellogg Biological Station; and by Michigan State University AgBioResearch .

Funders	Funder number
Great Lakes Bioenergy Research Center
Kellogg Biological Station
Michigan State University AgBioResearch
National Science Foundation Long-term Ecological Research Program	DEB 1832042
Office of Biological and Environmental Research
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China	1832042
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China
U.S. Department of Energy Oak Ridge National Laboratory U.S. Department of Energy National Science Foundation National Energy Research Scientific Computing Center
National Science Foundation Office of International Science and Engineering
Biological and Environmental Research	DE-SC0018409
Biological and Environmental Research
Michigan State University AgBioResearch
Great Lakes Bioenergy Research Center

Keywords

Landscape composition
Landscape configuration
Multiscale
Natural enemies
Natural pest suppression
Weighted correlation network analysis (WGCNA)

ASJC Scopus subject areas

Ecology
Animal Science and Zoology
Agronomy and Crop Science

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10.1016/j.agee.2020.107085

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title = "Landscape composition and configuration have scale-dependent effects on agricultural pest suppression",

abstract = "Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250−500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000−1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.",

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year = "2020",

month = oct,

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doi = "10.1016/j.agee.2020.107085",

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AB - Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250−500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000−1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.

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Landscape composition and configuration have scale-dependent effects on agricultural pest suppression

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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