Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks 

Windsor et al, 2021

1. Managing agricultural environments in a way that maximises the provision of multiple ecosystem services is a significant challenge in the development of sustainable and secure food systems. Advances in network ecology provide a way forward, particularly in arable landscapes, as they incorporate mutualistic and antagonistic interactions associated with crop production.
2. Here, we present an approach to identify mixes of non-crop plant species that provide multiple ecosystem services while 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 diversity of herbivores. 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.
3. We show that multilayer network analyses can be used to identify mixes of plant species that maximise the species richness of pollinators and parasitoids (natural enemies of insect pests), while minimising the species richness of herbivores.
4. Trade-offs between ecosystem processes were apparent with several plant species associated with a high species richness of both positive (pollinators and parasitoids) and negative (herbivores) functional taxonomic groups. As a result, optimal plant species mixes for individual ecosystem services were different from the mix simultaneously maximising pollination and parasitism of pest insects, while minimising herbivory.
5. Synthesis and applications. Plant mixes designed solely for maximising pollinator species richness are not optimal for the provision of other ecosystem services and disservices (e.g. parasitism of insect pests and herbivory). The method presented here will allow for the design of management strategies that facilitate the provision of multiple ecosystem services. To this end, we provide a protocol for practitioners to develop their own plant mixes suitable for farm-scale management. 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.

A conceptual representation of the genetic algorithm approach. (a) For either bipartite or multilayer networks, N initial groups of k plant species are randomly selected. (b) The plant mixes are ranked based on the optimiser function (which here is species richness but could be any property of the network of plants and interacting taxa). Individual (f) or compound (fm) optimiser functions are used depending on the scenarios but see the red text for two examples. (c) The plant species mixes that have low values of the optimiser (i.e. low species richness) are removed from the pool of potential mixes. Plant species in the remaining mixes are recombined (d) into new mixes to replace the networks that are removed during the selection stage. In (e), random plant species not already in the mix are added to the plant mixes. Finally, the new plant mixes which have been altered from the initial mix defined in (a) are then taken through the entire process again (b–e). This continues until an optimal plant mix is identified, or until the maximum number of iterations is reached. 

https://doi.org/10.1111/1365-2664.14007

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