Related Experiment Video
Updated: Jan 23, 2026

Photorealistic Learned Landscapes for Augmented Reality
Published on: June 27, 2025
Effectiveness of augmentative biological control depends on landscape context
Ricardo Perez-Alvarez1, Brian A Nault2, Katja Poveda3
1Department of Entomology, Cornell University, Ithaca, New York, 14853, USA. mrp245@cornell.edu.
Abstract:
Biological pest control by natural enemies is an important component of sustainable crop production. Among biological control approaches, natural enemy augmentation is an effective alternative when naturally occurring enemies are not sufficiently abundant or effective. However, it remains unknown whether the effectiveness of augmentative biocontrol varies along gradients of landscape composition, and how the interactions with resident enemies may modulate the collective impact on pest suppression. By combining field and lab experiments, we evaluated how landscape composition influenced the effectiveness of predator augmentation, and the consequences on pest abundance, plant damage, and crop biomass. We show for the first time that the effectiveness of predator augmentation is landscape-dependent. In complex landscapes, with less cropland area, predator augmentation increased predation rates, reduced pest abundance and plant damage, and increased crop biomass. By contrast, predator releases in simple landscapes had a negative effect on pest control, increasing plant damage and reducing crop biomass. Results from the lab experiment further suggested that landscape simplification can lead to greater interference among predators, causing a decrease in predator foraging efficiency. Our results indicate that landscape composition influence the effectiveness of augmentative biocontrol by modulating interactions between the introduced predators and the local enemy community.
Related Concept Videos
Biological Effects of Radiation
Biological Methods for Microbial Control
Self Within Cultural Contexts
What is Conservation Biology?
Impact of Social Context on Individuals
Frequency-dependent Selection

