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      A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming

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          Abstract

          Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93–0.99), precision (0.80–0.94), sensitivity (0.82–0.94), and specificity (0.95–0.98) on simulated communities, which are 10–160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and ‘drift’ (59%). Interestingly, warming decreases ‘drift’ over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology.

          Abstract

          Studies of microbial community assembly mechanisms typically use metrics for turnover within the whole community. Here, the authors develop an alternative approach based on turnover within lineages and dissect mechanistic change in grassland bacterial assembly under experimental warming.

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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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            Stochastic Community Assembly: Does It Matter in Microbial Ecology?

            Understanding the mechanisms controlling community diversity, functions, succession, and biogeography is a central, but poorly understood, topic in ecology, particularly in microbial ecology. Although stochastic processes are believed to play nonnegligible roles in shaping community structure, their importance relative to deterministic processes is hotly debated. The importance of ecological stochasticity in shaping microbial community structure is far less appreciated. Some of the main reasons for such heavy debates are the difficulty in defining stochasticity and the diverse methods used for delineating stochasticity. Here, we provide a critical review and synthesis of data from the most recent studies on stochastic community assembly in microbial ecology. We then describe both stochastic and deterministic components embedded in various ecological processes, including selection, dispersal, diversification, and drift. We also describe different approaches for inferring stochasticity from observational diversity patterns and highlight experimental approaches for delineating ecological stochasticity in microbial communities. In addition, we highlight research challenges, gaps, and future directions for microbial community assembly research.
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              Patterns and processes of microbial community assembly.

              Recent research has expanded our understanding of microbial community assembly. However, the field of community ecology is inaccessible to many microbial ecologists because of inconsistent and often confusing terminology as well as unnecessarily polarizing debates. Thus, we review recent literature on microbial community assembly, using the framework of Vellend (Q. Rev. Biol. 85:183-206, 2010) in an effort to synthesize and unify these contributions. We begin by discussing patterns in microbial biogeography and then describe four basic processes (diversification, dispersal, selection, and drift) that contribute to community assembly. We also discuss different combinations of these processes and where and when they may be most important for shaping microbial communities. The spatial and temporal scales of microbial community assembly are also discussed in relation to assembly processes. Throughout this review paper, we highlight differences between microbes and macroorganisms and generate hypotheses describing how these differences may be important for community assembly. We end by discussing the implications of microbial assembly processes for ecosystem function and biodiversity.
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                Author and article information

                Contributors
                jzhou@ou.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 September 2020
                18 September 2020
                2020
                : 11
                : 4717
                Affiliations
                [1 ]GRID grid.266900.b, ISNI 0000 0004 0447 0018, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, , University of Oklahoma, ; Norman, OK 73019 USA
                [2 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, , Tsinghua University, ; 100084 Beijing, China
                [3 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Environmental Science Policy and Management, , University of California, ; Berkeley, CA 94720 USA
                [4 ]GRID grid.216417.7, ISNI 0000 0001 0379 7164, School of Minerals Processing and Bioengineering, , Central South University, ; 410083 Changsha, Hunan China
                [5 ]GRID grid.184769.5, ISNI 0000 0001 2231 4551, Environmental Genomics and Systems Biology, , Lawrence Berkeley National Laboratory, ; Berkeley, CA 94710 USA
                [6 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Bioengineering, , University of California, ; Berkeley, CA 94720 USA
                [7 ]GRID grid.184769.5, ISNI 0000 0001 2231 4551, Earth and Environmental Sciences, , Lawrence Berkeley National Laboratory, ; Berkeley, CA 94704 USA
                [8 ]GRID grid.266900.b, ISNI 0000 0004 0447 0018, School of Civil Engineering and Environmental Sciences, , University of Oklahoma, ; Norman, OK 73019 USA
                Author information
                http://orcid.org/0000-0002-3368-5988
                http://orcid.org/0000-0003-0017-3908
                http://orcid.org/0000-0002-6649-5072
                http://orcid.org/0000-0002-4309-6140
                http://orcid.org/0000-0001-8274-6196
                http://orcid.org/0000-0003-2014-0564
                Article
                18560
                10.1038/s41467-020-18560-z
                7501310
                32948774
                2414ff22-b240-41be-a292-977948b8635c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 January 2020
                : 26 August 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100006206, DOE | SC | Biological and Environmental Research (BER);
                Award ID: DE-SC0016247
                Award ID: DE-SC0020163
                Award ID: DE-SC0010715
                Award ID: DE-AC02-05CH11231
                Award ID: DE-SC0014079
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: EF-1065844
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100003187, National Sleep Foundation (NSF);
                Award ID: EF-2025558
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                grassland ecology,microbial communities,community ecology,microbial ecology
                Uncategorized
                grassland ecology, microbial communities, community ecology, microbial ecology

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