Spatial networks reveal how forest cover decreases the spread of agricultural pests

Rother, Débora C., Cosmo, Leandro G., Tavella, Julia, Windsor, Fredric M. ORCID: https://orcid.org/0000-0001-5030-3470, Devoto, Mariano, Evans, Darren M. and Guimarães Jr., Paulo R. 2025. Spatial networks reveal how forest cover decreases the spread of agricultural pests. Perspectives in Ecology and Conservation 10.1016/j.pecon.2025.03.006

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Abstract

Landscape homogenization, caused by monocultures, can provide optimal conditions for the spread of crop pests. Increasing habitat heterogeneity and complexity within landscapes could slow pest spread. A next step in understanding the role of habitat heterogeneity in affecting pest spread is to understand how landscape features directly and indirectly affect spatial infestation patterns. We developed a spatial network approach to explore how landscape complexity, generated by forest patch cover, affects the pest spread in agricultural landscapes. As a studied system, we used information on the spatial distribution of traps and dispersal distance of the sugarcane borer Diatraea saccharalis (Lepidoptera: Crambidae) from a sugarcane agro-ecosystem in Brazil. Network analysis reveals that modeling pest spread was an outcome of both direct and indirect pathways connecting sugarcane fields. Therefore, using only information about the direct and indirect pathways of the spatial network and the initial focus of infestation, we were able to predict with nearly 80% accuracy the most susceptible sites to pest spread in the simulated landscape. By adjusting parameters such as pest mobility, and interaction with landscape features, our model can simulate different agricultural systems and pest behaviors, showing that forest cover can be used to control pest occurrence and that direct and indirect pathways in spatial networks can be used as a predictive tool to manage the pest spread in agricultural landscapes.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Biosciences
ISSN:	2530-0644
Date of First Compliant Deposit:	16 April 2025
Date of Acceptance:	24 March 2025
Last Modified:	23 Apr 2025 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/177739

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