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      Eco-evolutionary agriculture: Host-pathogen dynamics in crop rotations

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      PLoS Computational Biology
      Public Library of Science

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          Abstract

          Since its origins, thousands of years ago, agriculture has been challenged by the presence of evolving plant pathogens. Temporal rotations of host and non-host crops have helped farmers to control epidemics among other utilities, but further efforts for strategy assessment are needed. Here, we present a methodology for developing crop rotation strategies optimal for control of pathogens informed by numerical simulations of eco-evolutionary dynamics in one field. This approach can integrate agronomic criteria used in crop rotations—soil quality and cash yield—and the analysis of pathogen evolution in systems where hosts are artificially selected. Our analysis shows which rotation patterns perform better in maximising crop yield when an unspecified infection occurs, with yield being dependent on both soil quality and the strength of the epidemic. Importantly, the use of non-host crops, which both improve soil quality and control the epidemic results in similar rational rotation strategies for diverse agronomic and infection conditions. We test the repeatability of the best rotation patterns over multiple decades, an essential end-user goal. Our results provide sustainable strategies for optimal resource investment for increased food production and lead to further insights into the minimisation of pesticide use in a society demanding ever more efficient agriculture.

          Author summary

          The invention of agriculture is a major evolutionary transition in the social evolution of the human race. Transforming the lifestyle from nomadic to sedentary, agriculture provided humankind with the stability necessary to make rapid advancements. However, agriculture, as we know it, is now in danger. While agriculture is a grand artificial selection experiment, it is in a constant battle with the brute force of natural selection, generating highly infectious plant pathogens. Traditional techniques such as slash-burn techniques are not sustainable for feeding the ever-increasing population. Crop rotation, on the other hand, has been developed over thousands of years as a sustainable method. We provide a computational model of how crop rotations can be used to tackle pathogen infection and what properties of rotation patterns make them sustainable in the long run. We hope that this study, together with other sustainable methods such as minimal pesticide use and biocontrol, can make agriculture more efficient.

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          Most cited references36

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          Eco-evolutionary dynamics.

          Evolutionary ecologists and population biologists have recently considered that ecological and evolutionary changes are intimately linked and can occur on the same time-scale. Recent theoretical developments have shown how the feedback between ecological and evolutionary dynamics can be linked, and there are now empirical demonstrations showing that ecological change can lead to rapid evolutionary change. We also have evidence that microevolutionary change can leave an ecological signature. We are at a stage where the integration of ecology and evolution is a necessary step towards major advances in our understanding of the processes that shape and maintain biodiversity. This special feature about 'eco-evolutionary dynamics' brings together biologists from empirical and theoretical backgrounds to bridge the gap between ecology and evolution and provide a series of contributions aimed at quantifying the interactions between these fundamental processes.
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            Evolution on ecological time-scales

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              Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security.

              Agricultural ecosystems are composed of genetically depauperate populations of crop plants grown at a high density and over large spatial scales, with the regional composition of crop species changing little from year to year. These environments are highly conducive for the emergence and dissemination of pathogens. The uniform host populations facilitate the specialization of pathogens to particular crop cultivars and allow the build-up of large population sizes. Population genetic and genomic studies have shed light on the evolutionary mechanisms underlying speciation processes, adaptive evolution and long-distance dispersal of highly damaging pathogens in agro-ecosystems. These studies document the speed with which pathogens evolve to overcome crop resistance genes and pesticides. They also show that crop pathogens can be disseminated very quickly across and among continents through human activities. In this review, we discuss how the peculiar architecture of agro-ecosystems facilitates pathogen emergence, evolution and dispersal. We present four example pathosystems that illustrate both pathogen specialization and pathogen speciation, including different time frames for emergence and different mechanisms underlying the emergence process. Lastly, we argue for a re-design of agro-ecosystems that embraces the concept of dynamic diversity to improve their resilience to pathogens. This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                January 2020
                16 January 2020
                : 16
                : 1
                : e1007546
                Affiliations
                [001] Research Group for Theoretical Models of Eco-evolutionary Dynamics, Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany
                University of California Irvine, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-8796-6136
                http://orcid.org/0000-0002-5749-3665
                Article
                PCOMPBIOL-D-19-01297
                10.1371/journal.pcbi.1007546
                6964815
                31945057
                cd796b3c-2994-4485-ae2d-6d4c2668fdb7
                © 2020 Bargués-Ribera, Gokhale

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 August 2019
                : 14 November 2019
                Page count
                Figures: 5, Tables: 3, Pages: 17
                Funding
                Funded by: Max-Planck-Gesellschaft (DE)
                The authors thank the generous funding from the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Agriculture
                Crop Science
                Crops
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Ecology and Environmental Sciences
                Soil Science
                Agricultural Soil Science
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Biology and Life Sciences
                Plant Science
                Plant Pathology
                Plant Pathogens
                Biology and Life Sciences
                Agriculture
                Agricultural Methods
                Biology and Life Sciences
                Evolutionary Biology
                Biology and Life Sciences
                Agriculture
                Biology and Life Sciences
                Agriculture
                Agrochemicals
                Pesticides
                Biology and Life Sciences
                Agriculture
                Pest Control
                Pesticides
                Custom metadata
                The Python core codes, describing the model are available on Github at https://github.com/tecoevo/agriculture.

                Quantitative & Systems biology
                Quantitative & Systems biology

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