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      Is Host Filtering the Main Driver of Phylosymbiosis across the Tree of Life?

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          Abstract

          Phylosymbiosis is a pattern defined as the tendency of closely related species to host microbiota whose compositions resemble each other more than host species drawn at random from the same tree. Understanding the mechanisms behind phylosymbiosis is important because it can shed light on rules governing the assembly of host-associated microbiotas and, potentially, their coevolutionary dynamics with hosts. For example, is phylosymbiosis a result of coevolution, or can it be generated by simple ecological filtering processes? Beyond qualitative theoretical models, quantitative theoretical expectations can provide new insights. For example, deviations from a simple baseline of ecological filtering may be used to test more-complex hypotheses (e.g., coevolution). Here, we use simulations to provide evidence that simple host-related ecological filtering can readily generate phylosymbiosis, and we contrast these predictions with real-world data. We find that while phylosymbiosis is widespread in nature, phylosymbiosis patterns are compatible with a simple ecological model in the majority of taxa. Internal compartments of hosts, such as the animal gut, often display stronger phylosymbiosis than expected from a purely ecological filtering process, suggesting that other mechanisms are also involved.

          ABSTRACT

          Host-associated microbiota composition can be conserved over evolutionary time scales. Indeed, closely related species often host similar microbiota; i.e., the composition of their microbiota harbors a phylogenetic signal, a pattern sometimes referred to as “phylosymbiosis.” Elucidating the origins of this pattern is important to better understand microbiota ecology and evolution. However, this is hampered by our lack of theoretical expectations and a comprehensive overview of phylosymbiosis prevalence in nature. Here, we use simulations to provide a simple expectation for when we should expect this pattern to occur and then review the literature to document the prevalence and strength of phylosymbiosis across the host tree of life. We demonstrate that phylosymbiosis can readily emerge from a simple ecological filtering process, whereby a given host trait (e.g., gut pH) that varies with host phylogeny (i.e., harbors a phylogenetic signal) filters preadapted microbes. We found marked differences between methods used to detect phylosymbiosis, so we proposed a series of practical recommendations based on using multiple best-performing approaches. Importantly, we found that, while the prevalence of phylosymbiosis is mixed in nature, it appears to be stronger for microbiotas living in internal host compartments (e.g., the gut) than those living in external compartments (e.g., the rhizosphere). We show that phylosymbiosis can theoretically emerge without any intimate, long-term coevolutionary mechanisms and that most phylosymbiosis patterns observed in nature are compatible with a simple ecological process. Deviations from baseline ecological expectations might be used to further explore more complex hypotheses, such as codiversification.

          IMPORTANCE Phylosymbiosis is a pattern defined as the tendency of closely related species to host microbiota whose compositions resemble each other more than host species drawn at random from the same tree. Understanding the mechanisms behind phylosymbiosis is important because it can shed light on rules governing the assembly of host-associated microbiotas and, potentially, their coevolutionary dynamics with hosts. For example, is phylosymbiosis a result of coevolution, or can it be generated by simple ecological filtering processes? Beyond qualitative theoretical models, quantitative theoretical expectations can provide new insights. For example, deviations from a simple baseline of ecological filtering may be used to test more-complex hypotheses (e.g., coevolution). Here, we use simulations to provide evidence that simple host-related ecological filtering can readily generate phylosymbiosis, and we contrast these predictions with real-world data. We find that while phylosymbiosis is widespread in nature, phylosymbiosis patterns are compatible with a simple ecological model in the majority of taxa. Internal compartments of hosts, such as the animal gut, often display stronger phylosymbiosis than expected from a purely ecological filtering process, suggesting that other mechanisms are also involved.

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          Testing for phylogenetic signal in comparative data: behavioral traits are more labile.

          The primary rationale for the use of phylogenetically based statistical methods is that phylogenetic signal, the tendency for related species to resemble each other, is ubiquitous. Whether this assertion is true for a given trait in a given lineage is an empirical question, but general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuous-valued characters that can be implemented with either phylogenetically independent contrasts or generalized least-squares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal alpha = 0.05 and good power (0.8) for simulated datasets with 20 or more species. Second, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two biologically motivated branch-length transformations, one based on the Ornstein-Uhlenbeck (OU) model of stabilizing selection, the other based on a new model in which character evolution can accelerate or decelerate (ACDC) in rate (e.g., as may occur during or after an adaptive radiation). Maximum likelihood estimation of the OU (d) and ACDC (g) parameters can serve as tests for phylogenetic signal because an estimate of d or g near zero implies that a phylogeny with little hierarchical structure (a star) offers a good fit to the data. Transformations that improve the fit of a tree to comparative data will increase power to detect phylogenetic signal and may also be preferable for further comparative analyses, such as of correlated character evolution. Application of the methods to data from the literature revealed that, for trees with 20 or more species, 92% of traits exhibited significant phylogenetic signal (randomization test), including behavioral and ecological ones that are thought to be relatively evolutionarily malleable (e.g., highly adaptive) and/or subject to relatively strong environmental (nongenetic) effects or high levels of measurement error. Irrespective of sample size, most traits (but not body size, on average) showed less signal than expected given the topology, branch lengths, and a Brownian motion model of evolution (i.e., K was less than one), which may be attributed to adaptation and/or measurement error in the broad sense (including errors in estimates of phenotypes, branch lengths, and topology). Analysis of variance of log K for all 121 traits (from 35 trees) indicated that behavioral traits exhibit lower signal than body size, morphological, life-history, or physiological traits. In addition, physiological traits (corrected for body size) showed less signal than did body size itself. For trees with 20 or more species, the estimated OU (25% of traits) and/or ACDC (40%) transformation parameter differed significantly from both zero and unity, indicating that a hierarchical tree with less (or occasionally more) structure than the original better fit the data and so could be preferred for comparative analyses.
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            geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees.

            Phylogenetic comparative methods are essential for addressing evolutionary hypotheses with interspecific data. The scale and scope of such data have increased dramatically in the past few years. Many existing approaches are either computationally infeasible or inappropriate for data of this size. To address both of these problems, we present geiger v2.0, a complete overhaul of the popular R package geiger. We have reimplemented existing methods with more efficient algorithms and have developed several new approaches for accomodating heterogeneous models and data types.
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              Early bursts of body size and shape evolution are rare in comparative data.

              George Gaylord Simpson famously postulated that much of life's diversity originated as adaptive radiations-more or less simultaneous divergences of numerous lines from a single ancestral adaptive type. However, identifying adaptive radiations has proven difficult due to a lack of broad-scale comparative datasets. Here, we use phylogenetic comparative data on body size and shape in a diversity of animal clades to test a key model of adaptive radiation, in which initially rapid morphological evolution is followed by relative stasis. We compared the fit of this model to both single selective peak and random walk models. We found little support for the early-burst model of adaptive radiation, whereas both other models, particularly that of selective peaks, were commonly supported. In addition, we found that the net rate of morphological evolution varied inversely with clade age. The youngest clades appear to evolve most rapidly because long-term change typically does not attain the amount of divergence predicted from rates measured over short time scales. Across our entire analysis, the dominant pattern was one of constraints shaping evolution continually through time rather than rapid evolution followed by stasis. We suggest that the classical model of adaptive radiation, where morphological evolution is initially rapid and slows through time, may be rare in comparative data.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msys
                msys
                mSystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                23 October 2018
                Sep-Oct 2018
                : 3
                : 5
                : e00097-18
                Affiliations
                [a ]Department of Botany, University of British Columbia, Vancouver, British Columbia, Canada
                [b ]Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
                [c ]Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada
                [d ]Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
                [e ]Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
                University of California, Riverside
                Author notes
                Address correspondence to Florent Mazel, flo.mazel@ 123456gmail.com .

                M.G. and L.W.P. are cosenior authors.

                Citation Mazel F, Davis KM, Loudon A, Kwong WK, Groussin M, Parfrey LW. 2018. Is host filtering the main driver of phylosymbiosis across the tree of life? mSystems 3:e00097-18. https://doi.org/10.1128/mSystems.00097-18.

                Author information
                https://orcid.org/0000-0003-0572-9901
                https://orcid.org/0000-0002-0942-7217
                Article
                mSystems00097-18
                10.1128/mSystems.00097-18
                6208643
                30417109
                6c0a6125-30f8-4942-9036-845885682856
                Copyright © 2018 Mazel et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 12 June 2018
                : 19 September 2018
                Page count
                supplementary-material: 10, Figures: 5, Tables: 0, Equations: 0, References: 56, Pages: 15, Words: 11954
                Categories
                Research Article
                Ecological and Evolutionary Science
                Custom metadata
                September/October 2018

                endosphere,gut microbiome,holobiont,macroevolution,plant microbiome,rhizosphere

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