Self-organized spatial pattern and the top-down control of herbivores

Modern agriculture owes much of its success to the application of basic scientific and technological advances. Those tools remain important, but they do not fully address one of farming’s biggest challenges: farms are living ecosystems, where insects, plants, microbes, weather, and management practices all interact in complex ways. This research emphasizes new ecological approaches, sometimes referred to as “complex systems.” Using computer models, laboratory studies, field experiments, and artificial intelligence, the results from this research will help farmers manage pests more effectively. Agricultural crops face serious pest threats, and pesticides are expensive. Instead of relying only on chemical controls, this project explores how farmers can use ecological knowledge to strengthen natural pest control and respond more quickly when new pests emerge. This is a form of biotechnology—using a deep understanding of ecological systems to develop practical tools for agriculture. By combining ecological science with AI, we aim to turn complex systems research into useful guidance for farmers, policymakers, and agricultural organizations. The research will train the next generation of scientists in cutting edge modeling and AI techniques. The ultimate goal is to support farms and farmers in reducing pest damage and promoting more resilient and profitable agricultural systems. Spatial patterns may emerge from the dynamic interplay of interacting elements, in this case, the community of ants on coffee agroecosystems. Like other spatial patterns, self-organized ones may create contingencies within which other ecosystem elements must function. This research seeks to understand the spatial pattern formation of ants, one of the most important biological control elements, on coffee farms in Puerto Rico, and how these patterns influence predatory control of two of the main pests of coffee. Our intent is to leverage ecological science to develop biotechnology that draws on the latest ideas in ecology (e.g., chaos, spatial self-organization, intransitive dynamics, higher-order effects) and is amenable to the evolving utility of AI applications. Our work has shown that ant spatial pattern is a complex system involving intransitive competition and predator-mediated coexistence. The emergent mosaic structure of ants creates spatial patches in which elements of biological control (insect parasitoids and some of the ants themselves) function. These elements are part of a complex system that operates at various levels of complexity (e.g., self-organized spatial patterns, contingent predator responses, trait-mediated effects) and across different spatial scales (individual coffee plants, whole farms, rural landscapes). We will continue pursuing the more theoretical aspects that the research has already inspired, along with experimental verification of some of its implications, to inform the practical issue of pest control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2547105 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Ivette Perfecto | Institution: Regents of the University of Michigan - Ann Arbor, ANN ARBOR, MI | Award Amount: $1,439,997 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2547105 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2547105.html