Measuring Coevolutionary Dynamics in Species-Rich Communities
Authors: Alex R. Hall, Ben Ashby, Jordi Bascompte, Kayla C. King
Journal: Trends in Ecology & Evolution (2020)

Coevolutionary interactions between species can display various types of temporal dynamics that impact on key properties such as biodiversity and infectious disease in different ways.Recent theory and observations of natural communities reveal how coevolution plays out in complex networks of species.Experimental research has tended to focus on pairwise coevolution, but there has been a recent surge in experiments with microorganisms in species-rich communities.We discuss challenges/approaches for quantifying coevolutionary processes in experimental species-rich communities, as well as new theoretical avenues, with the aim of helping biologists to untangle coevolving interactions in complex ecological networks. Identifying different types of coevolutionary dynamics is important for understanding biodiversity and infectious disease. Past work has often focused on pairs of interacting species, but observations of extant communities suggest that coevolution in nature occurs in networks of antagonism and mutualism. We discuss challenges for measuring coevolutionary dynamics in species-rich communities, and we suggest ways that established approaches used for two-species interactions can be applied. We propose ways that such data can be complemented by genomic information and linked back to extant communities via network structure, and we suggest avenues for new theoretical work to strengthen these connections. Quantifying coevolution in species-rich communities has several potential benefits, such as identifying coevolutionary units within networks and uncovering coevolutionary interactions among pathogens of humans, livestock, and crops. Identifying different types of coevolutionary dynamics is important for understanding biodiversity and infectious disease. Past work has often focused on pairs of interacting species, but observations of extant communities suggest that coevolution in nature occurs in networks of antagonism and mutualism. We discuss challenges for measuring coevolutionary dynamics in species-rich communities, and we suggest ways that established approaches used for two-species interactions can be applied. We propose ways that such data can be complemented by genomic information and linked back to extant communities via network structure, and we suggest avenues for new theoretical work to strengthen these connections. Quantifying coevolution in species-rich communities has several potential benefits, such as identifying coevolutionary units within networks and uncovering coevolutionary interactions among pathogens of humans, livestock, and crops. Coevolution (see Glossary) results from reciprocal selection imposed on each other by interacting species. Coevolution can result from antagonistic interactions, as in predator–prey or host–parasite interactions, or from mutualistic interactions, as in plant–pollinator interactions. Because it is a major driver of evolutionary change, coevolution is central to explaining the evolutionary maintenance of sex [1.Hamilton W.D. et al.Sexual reproduction as an adaptation to resist parasites.Proc. Natl. Acad. Sci. U. S. A. 1990; 87: 3566-3573Crossref PubMed Scopus (937) Google Scholar], evolutionary diversification [2.Paterson S. et al.Antagonistic coevolution accelerates molecular evolution.Nature. 2010; 464: 275-278Crossref PubMed Scopus (354) Google Scholar,3.Thompson J.N. et al.Diversification through multitrait evolution in a coevolving interaction.Proc. Natl. Acad. Sci. U. S. 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These studies have revealed fundamental insights into how selection imposed by interspecific interactions acts in diverse communities, which species are likely to be coevolving, and which traits are involved. However, it has remained challenging to characterize and quantify temporal coevolutionary dynamics (e.g., separating directional vs. fluctuating selection) in species-rich, natural communities directly (by experimental comparison between ancestral and evolved populations, rather than by observation of extant communities). There are practical challenges associated with doing so. For example, the timescale over which coevolution occurs is often – although not always – longer than a realistic experiment or field study. The main practical obstacles to observing and quantifying coevolutionary dynamics have been circumvented in a large number of experimental studies by using simplified microbial communities. Such work has almost exclusively focused on two-species communities (e.g., [8.Buckling A. Rainey P.B. Antagonistic coevolution between a bacterium and a bacteriophage.Proc. R. Soc. B. 2002; 269: 931-936Crossref PubMed Scopus (453) Google Scholar,42.Mizoguchi K. et al.Coevolution of bacteriophage PP01 and Escherichia coli O157:H7 in continuous culture.Appl. Environ. Microbiol. 2003; 69: 170-176Crossref PubMed Scopus (139) Google Scholar]). These communities, such as a bacterium coevolving with a viral parasite (bacteriophage), have two key advantages for studying coevolutionary dynamics. First, they coevolve sufficiently rapidly that the dynamics can be observed in real time. Second, the effects of changing genotype frequencies within each species on the fitness effects of interspecific interactions can be directly measured. Samples of organisms such as bacteria and viruses can be frozen at various timepoints and later thawed and incubated in different combinations in controlled conditions. This permits a time-shift approach to measuring coevolutionary dynamics, where selection on interspecific interactions is inferred by measuring how changes in the genotypic composition of each species (across sampled timepoints) influence fitness effects resulting from interspecific interactions [43.Gaba S. Ebert D. Time-shift experiments as a tool to study antagonistic coevolution.Trends Ecol. Evol. 2009; 24: 226-232Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar]. Such data have been used extensively for pairwise coevolutionary interactions, including real-time coevolution experiments [8.Buckling A. Rainey P.B. Antagonistic coevolution between a bacterium and a bacteriophage.Proc. R. Soc. 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Ecol. 2017; 26: 1778-1789Crossref PubMed Scopus (27) Google Scholar] and analysis of resurrected samples of dormant hosts and microparasites from nature [16.Decaestecker E. et al.Host–parasite 'Red Queen' dynamics archived in pond sediment.Nature. 2007; 450: 870-873Crossref PubMed Scopus (468) Google Scholar]. It is unclear whether the same approach can be extended to more species-rich communities. We ask here whether tools used to infer pairwise coevolutionary dynamics can be adapted or extended to communities of more than two species. We focus initially on experimental designs (e.g., [8.Buckling A. Rainey P.B. Antagonistic coevolution between a bacterium and a bacteriophage.Proc. R. Soc. B. 2002; 269: 931-936Crossref PubMed Scopus (453) Google Scholar,16.Decaestecker E. et al.Host–parasite 'Red Queen' dynamics archived in pond sediment.Nature. 2007; 450: 870-873Crossref PubMed Scopus (468) Google Scholar,47.Kashiwagi A. Yomo T. Ongoing phenotypic and genomic changes in experimental coevolution of RNA bacteriophage Qβ and Escherichia coli.PLoS Genet. 2011; 7e1002188Crossref PubMed Scopus (40) Google Scholar]) and quantitative interpretation (e.g., [43.Gaba S. Ebert D. Time-shift experiments as a tool to study antagonistic coevolution.Trends Ecol. Evol. 2009; 24: 226-232Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar,48.Blanquart F. Gandon S. Time-shift experiments and patterns of adaptation across time and space.Ecol. Lett. 2013; 16: 31-38Crossref PubMed Scopus (39) Google Scholar, 49.Gandon S. et al.Host–parasite coevolution and patterns of adaptation across time and space.J. Evol. Biol. 2008; 21: 1861-1866Crossref PubMed Scopus (164) Google Scholar, 50.Dybdahl M.F. et al.Identifying the molecular basis of host–parasite coevolution: merging models and mechanisms.Am. Nat. 2014; 184: 1-13Crossref PubMed Scopus (46) Google Scholar, 51.Brockhurst M.A. Koskella B. Experimental coevolution of species interactions.Trends Ecol. Evol. 2013; 28: 367-375Abstract Full Text Full Text PDF PubMed Scopus (137) Google Scholar]) employing interspecific interaction matrices that describe variation of interspecific interactions as a function of time from the perspective of component species, such as time-shift analyses. We use examples from antagonistic host–parasite coevolution because coevolutionary dynamics have often been measured in this context, and this is where some important applications arise. We then suggest ways that such data can be strengthened by genomic analysis, connected back to community-level properties, and driven forward by new theoretical work (e.g., [35.Guimarães P.R. et al.Indirect effects drive coevolution in mutualistic networks.Nature. 2017; 550: 511-514Crossref PubMed Scopus (140) Google Scholar,52.Guimarães P.R. et al.Evolution and coevolution in mutualistic networks.Ecol. Lett. 2011; 14: 877-885Crossref PubMed Scopus (215) Google Scholar]). Progress here would be beneficial because it would allow us to describe and quantify coevolutionary dynamics (e.g., distinguish between directional and fluctuating selection) in more realistic communities than in typical real-time coevolution experiments. This is a necessary step toward connecting the large, but separate, bodies of knowledge on (i) coevolutionary dynamics in simplified experimental communities, and (ii) the structure of diverse communities that are assumed to result from coevolution, but where the dynamics have not been observed directly. Methodologically, there are implications for designing real-time coevolution experiments using model systems that increasingly incorporate additional species (e.g., [46.Ford S.A. et al.Co-evolutionary dynamics between a defensive microbe and a pathogen driven by fluctuating selection.Mol. 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Coevolution among pathogens infecting the same hosts in agricultural or medical contexts potentially influences important properties such as virulence [56.Tollenaere C. et al.Evolutionary and epidemiological implications of multiple infection in plants.Trends Plant Sci. 2016; 21: 80-90Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar, 57.Graham A.L. Ecological rules governing helminth microparasite coinfection.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 566-570Crossref PubMed Scopus (273) Google Scholar, 58.Griffiths E.C. et al.The nature and consequences of coinfection in humans.J. Infect. 2011; 63: 200-206Abstract Full Text Full Text PDF PubMed Scopus (142) Google Scholar]. Coevolution could also influence the community-level properties of microorganisms in applications such as bioremediation [59.van der Gast C.J. et al.Temporal dynamics and degradation activity of an bacterial inoculum for treating waste metal-working fluid.Environ. Microbiol. 2004; 6: 254-263Crossref PubMed Scopus (56) Google Scholar,60.Piccardi P. et al.Toxicity drives facilitation between 4 bacterial species.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 15979-15984Crossref PubMed Scopus (76) Google Scholar] and cheese making [61.Wolfe B.E. et al.Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity.Cell. 2014; 158: 422-433Abstract Full Text Full Text PDF PubMed Scopus (359) Google Scholar], with potential downstream effects for product quality or yield. Most experimental observations of two-species coevolutionary dynamics involve measuring reciprocal fitness effects in combinations of population samples taken from different times or places. The basic principle here is that we can quantify coevolution by measuring how changes in genetic composition within the component species (across sampled timepoints; often compiled into an interspecific interaction matrix) influence the fitness effects of interspecific interactions (Box 1 and Figure 1).Box 1Estimating Interspecific Interaction Matrices for Pairwise CoevolutionFirst, we collect population samples of each species at multiple timepoints, for example, by cryo-freezing them during an experiment and later thawing them, or resurrecting dormant lifecycle stages from the past. For a given pair of population samples (one from each species), we can assay fitness for each sample (one of the species) on its own and in the presence of the other sample (the other species). We can then compile these data into an interaction matrix that gives some measure of the interspecific fitness effects (e.g., the effect of species 1 on species 2 fitness) across different combinations of species-1 time and species-2 time (see Figure 1B in main text). We can also make a corresponding matrix giving the interspecific fitness effects in the other direction (the effect of species 2 on species 1 fitness) at the same combinations of timepoints. In practice, reciprocal interspecific fitness effects are often approximated for both species simultaneously from a single fitness-related measure such as pathogen infectivity or host resistance (e.g., via plaque assays in bacteria–bacteriophage systems), and for multiple clones or genotypes within each population sample. If these interspecific fitness effects result in adaptive changes in genotype frequencies within both species (i.e., coevolution), we expect the average interspecific fitness effects themselves to change over time as a result. We can test this by analyzing how they vary as a function of the timepoint combinations across the interaction matrix (see Figure 1C,D in main text). Interaction matrices can also be compiled using samples taken from multiple locations, rather than timepoints, to analyze other properties such as local adaptation (higher average interspecific fitness effects in sympatric compared to allopatric combinations of samples). First, we collect population samples of each species at multiple timepoints, for example, by cryo-freezing them during an experiment and later thawing them, or resurrecting dormant lifecycle stages from the past. For a given pair of population samples (one from each species), we can assay fitness for each sample (one of the species) on its own and in the presence of the other sample (the other species). We can then compile these data into an interaction matrix that gives some measure of the interspecific fitness effects (e.g., the effect of species 1 on species 2 fitness) across different combinations of species-1 time and species-2 time (see Figure 1B in main text). We can also make a corresponding matrix giving the interspecific fitness effects in the other direction (the effect of species 2 on species 1 fitness) at the same combinations of timepoints. In practice, reciprocal interspecific fitness effects are often approximated for both species simultaneously from a single fitness-related measure such as pathogen infectivity or host resistance (e.g., via plaque assays in bacteria–bacteriophage systems), and for multiple clones or genotypes within each population sample. If these interspecific fitness effects result in adaptive changes in genotype frequencies within both species (i.e., coevolution), we expect the average interspecific fitness effects themselves to change over time as a result. We can test this by analyzing how they vary as a function of the timepoint combinations across the interaction matrix (see Figure 1C,D in main text). Interaction matrices can also be compiled using samples taken from multiple locations, rather than timepoints, to analyze other properties such as local adaptation (higher average interspecific fitness effects in sympatric compared to allopatric combinations of samples). Time-shift analysis and related approaches using interaction matrices have identified whether pairs of species in natural or experimental communities are coevolving (e.g., myxoma virus and rabbits in Australia [62.Best S.M. Kerr P.J. Coevolution of host and virus: the pathogenesis of virulent and attenuated strains of myxoma virus in resistant and susceptible European rabbits.Virology. 2000; 267: 36-48Crossref PubMed Scopus (113) Google Scholar]) and what type of dynamics are in play (e.g., bacteria and bacteriophages in vitro [17.Gómez P. Buckling A. Bacteria–phage antagonistic coevolution in soil.Science. 2011; 332: 106-109Crossref PubMed Scopus (301) Google Scholar,45.Betts A. et al.Contrasted coevolutionary dynamics between a bacterial pathogen and its bacteriophages.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 11109-11114Crossref PubMed Scopus (69) Google Scholar]). For example, the dynamics in Figure 1 are consistent with directional selection for increasing host resistance and parasite infectivity ('arms-race' dynamics). Each species is better-adapted to samples of the other species from the past than the future. By contrast, fluctuating selection could result in hosts or parasites being better adapted to contemporary ('present') samples of the other species than to those from other timepoints ('past' and 'future') [16.Decaestecker E. et al.Host–parasite 'Red Queen' dynamics archived in pond sediment.Nature. 2007; 450: 870-873Crossref PubMed Scopus (468) Google Scholar,17.Gómez P. Buckling A. Bacteria–phage antagonistic coevolution in soil.Science. 2011; 332: 106-109Crossref PubMed Scopus (301) Google Scholar]. Fluctuating selection can also result in fluctuations in average interspecific fitness effects [12.Sasaki A. Host–parasite coevolution in a multilocus gene-for-gene system.Proc. R. Soc. B. 2000; 267: 2183-2188Crossref PubMed Scopus (193) Google Scholar,13.Ashby B. Boots M. Multi-mode fluctuating selection in host–parasite coevolution.Ecol. Lett. 2017; 20: 357-365Crossref PubMed Scopus (28) Google Scholar], which are better inferred from changes of mean interspecific fitness effects across time from the perspective of each species separately [63.Lopez Pascua L. et al.Higher resources decrease fluctuating selection during host–parasite coevolution.Ecol. Lett. 2014; 17: 1380-1388Crossref PubMed Scopus (42) Google Scholar], rather than from time-shifts. Such experiments and analyses have been extensively reviewed for pairwise coevolution [43.Gaba S. Ebert D. Time-shift experiments as a tool to study antagonistic coevolution.Trends Ecol. Evol. 2009; 24: 226-232Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar,48.Blanquart F. Gandon S. Time-shift experiments and patterns of adaptation across time and space.Ecol. Lett. 2013; 16: 31-38Crossref PubMed Scopus (39) Google Scholar, 49.Gandon S. et al.Host–parasite coevolution and patterns of adaptation across time and space.J. Evol. Biol. 2008; 21: 1861-1866Crossref PubMed Scopus (164) Google Scholar, 50.Dybdahl M.F. et al.Identifying the molecular basis of host–parasite