Identifying plant mixes for multiple ecosystem service provision using ecological networks

WINDSOR, FREDRIC M.; TAVELLA, JULIA; ROTHER, DÉBORA C.; RAIMUNDO, RAFAEL L. G.; DEVOTO, MARIANO; GUIMARAES JR., PAULO R.; EVANS, DARREN M.

Palma

Simposio; V SYMPOSIUM ON ECOLOGICAL NETWORKS 2021; 2021

Managing agricultural environments in a way that maximises the provision of multiple ecosystemservices is a significant challenge. Advances in network ecology provide a way forward, providing a way to incorporate mutualistic and antagonistic interactions. Here, we present an approach to identify mixes of non-crop plant species that provide multiple ecosystem services whilst minimising disservices. Genetic algorithms were applied to the Norwood Farm ecological network ? a comprehensive dataset of antagonistic and mutualistic species-interactions on an organic farm in the United Kingdom. We aimed to show how network analyses can be used to select plants supporting a high diversity of insect pollinators and parasitoids of insect pests, but low herbivore diversity. Further to this, we wanted to understand the trade-offs in ecosystem service provision associated with conventional management practices that focus on individual ecosystem services. We show that multilayer network analyses can be used to identify mixes of plant species that maximise pollination and parasitism, whilst minimising herbivory. Trade-offs were apparent with several plant species associated with both positive and negative (herbivores) functional groups. As a result, optimal plant species mixes for individual ecosystem services were different from the multilayer mix. Our findings show that plant mixes designed solely for maximising pollination are not optimal for other ecosystem services and disservices. This avenue of predictive network ecology has the potential to enhance agricultural management, supporting high levels of biodiversity and food production by manipulating ecological networks in specific ways.