DOI: 10.5061/dryad.dfn2z3594
title: Meta-analysis of the effects of abiotic factors on plant microbes

description: The abiotic environment exerts strong effects on plant-associated
 microbes, shaping their interactions with plants and resulting ecosystem
 processes. However, these abiotic effects on plant-microbe interactions
 are often highly specific and contingent on the abiotic driver or
 microbial group, requiring synthesis work describing general patterns and
 from this generate hypotheses and guide mechanistic work. To address this,
 we conducted a meta-analysis of the effects of climate change-related
 abiotic factors, namely warming, drought, and eCO2, on plant-associated
 microbes distinguishing by microbial taxonomic or biological group
 (bacteria, fungi or virus) and the plant part where microbes are found or
 associated with (phyllosphere or rhizosphere). We found abiotic
 driver-specific patterns, whereby drought significantly reduced microbial
 abundance, whereas warming and eCO2 had no significant effects. In
 addition, these abiotic effects were contingent on the microbial taxonomic
 group, with fungi being negatively affected by drought but positively
 affected by warming (eCO2 enrichment had no effect), whereas bacteria and
 viruses were not significantly affected by any factor. Likewise,
 rhizopheric microbes were negatively affected by drought but positively
 affected by warming (eCO2 enrichment had no effect), whereas phyllospheric
 microbes were not significantly affected by any factor. Collectively,
 these findings point to important implications for global change research
 by highlighting contrasting effects of climate change-related abiotic
 drivers on plant-associated microbes and the contingency of such effects
 on microbe life histories and the nature of their interactions with
 plants.
description: <strong><em>Data
 collection</em></strong> We carried out an
 extensive literature search in Scopus database in May 2022 using a
 combination of the following keywords: ((plant OR tree OR shrub) AND
 (drought OR warming OR co2 OR flooding OR wind OR salt OR salin OR
 deposit) AND (microb OR bacter OR fung OR virus OR protist OR alga OR
 nematod OR mycorrhiz)). We retained only articles, book chapters, reviews,
 theses, dissertations and abstracts published in English. To further limit
 the search to relevant papers, we filtered outputs to consider only the
 following research areas: Agricultural and Biological science,
 Biochemistry, Genetics and Molecular Biology, Environmental Science,
 Immunology and Microbiology. This search spanned published work from 1967
 to 2022. In addition, we also surveyed the references in review articles
 on climate change and interactions between plants and microbes and
 included any studies that were missed in our Scopus search. In total, our
 initial search yielded 5450 papers. To be included in
 our analysis, studies had to meet the following criteria: (a) provide a
 measure of plant-associated microbial abundance (e.g., amount, frequency,
 disease intensity, transmission rate, virus load) in the phyllosphere or
 rhizosphere of plants growing under experimental manipulation of climate
 change-related abiotic conditions (eCO<sub>2</sub>, warming,
 drought, etc.), and (b) report treatment level means (abiotic manipulation
 vs unmanipulated control), variability (i.e., variance, standard error or
 standard deviation), and the sample size in either the text, figures,
 tables or appendices. When needed, we extracted data from figures
 following digitalization using WebPlotDigitizer software. We excluded
 studies that applied two or more different abiotic manipulations together
 on the same plants. After applying these criteria, the resulting dataset
 consisted of 513 case studies from 96 studies (out of the original 5450)
 from the primary literature published between 1975 and 2021 in 47
 scientific journals. Study cases represented data points, i.e., treatment
 vs. control comparisons, drawn from a single primary study, where a single
 study may have one or more study cases. The occurrence of more than one
 study case in a given study took place when more than one response was
 measured and/or more than one abiotic treatment was tested (against a
 control), in which case the number of study cases in a given study equaled
 the number of responses by the number of treatment level vs. control
 comparisons. We used different approaches to account for both sources of
 non-independence in our analyses and assessed the robustness of our
 conclusions to the inclusion of multiple study cases per primary
 study. For each study case, we compiled the following
 moderators: plant species and growth form (herbaceous or woody),
 experimental conditions (field or controlled, i.e., greenhouse or
 laboratory), climate change-related abiotic factors (warming, drought,
 eCO<sub>2</sub>), microbial taxonomical group (i.e., bacteria,
 fungus, or virus), and the plant part where microbes were found
 (phyllosphere or rhizosphere).
description: # Abiotic factors and plant microbes
 [https://doi.org/10.5061/dryad.dfn2z3594](https://doi.org/10.5061/dryad.dfn2z3594) In this study, we conducted a meta-analysis testing the effects of climate change-related abiotic factors on plant-associated microbes. To this end, we analyzed studies involving experimental manipulations of climate change-related abiotic factors (e.g., warming, drought, and eCO2) and measuring abundance of microbes (e.g., virus, bacteria, fungus) in the phyllosphere or rhizosphere. We aimed at: (1) Describing the overall magnitude and direction of effects of abiotic factors on plant-associated microbes (2) Testing whether such abiotic effects were contingent on the type of microbe, namely bacteria, fungus or virus, and plant part where microbes are found, namely the phyllosphere or rhizosphere. In doing so, this study furthers our understanding of climate change-related abiotic forcing on plant–associated microbes and its implications for ecosystem responses to global change threats. ## Description of the data and file structure To be included in our analysis, studies had to meet the following criteria: (a) provide a measure of plant-associated microbial abundance (e.g., amount, frequency, disease intensity, transmission rate, virus load) in the phyllosphere or rhizosphere of plants growing under experimental manipulation of climate change-related abiotic conditions (eCO2, warming, drought, etc.), and (b) report treatment level means (abiotic manipulation vs unmanipulated control), variability (i.e., variance, standard error or standard deviation), and the sample size in either the text, figures, tables or appendices. When needed, we extracted data from figures following digitalization using WebPlotDigitizer software. We excluded studies that applied two or more different abiotic manipulations together on the same plants. After applying these criteria, the resulting dataset consisted of 513 case studies from 96 studies (out of the original 5450) from the primary literature published between 1975 and 2021 in 47 scientific journals. | Case | Study cases represented data points, i.e. island vs. mainland comparisons, drawn from a single primary study, where a single study may have one or more study cases | | :----------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Study | Studies retained in our search  | | Authors | name of the authors | | Year | year of publiaction | | Journal | name of the journal | | Journal.IF | impact factor of the journal | | Source.of.data | Figure or table where data was extracted | | Plant.species | name of the plant species | | Plant.type | plant growth form (herbaceous vs woody species) | | Microorganism.species | name of the microorganism | | Group.of.microorganism | microorganism group (virus, bacteria, fungus) | | Type.of.microorganism | Effect on the plant (beneficial vs harmful) | | Zone | Plant part where microbes were found (phyllosphere or rhizosphere) | | Setting | Experimental conditions (field or controlled, i.e., greenhouse or laboratory) | | Treatment | Climate change-related abiotic factors (warming, drought, eCO2) | | Experiment.duration.days | days of duration of the experiment | | Response.type | Measure of plant-associated microbial abundance (e.g., amount, frequency, disease intensity, transmission rate, virus load)  | | M.control | Mean of the unmanipulated control treatment | | SE.control | Standard error of the unmanipulated control treatment | | N.control | Sample size of the unmanipulated control treatment | | M.treatment | Mean of the abiotically-manipulated  treatment | | SE.treatment | Standard error of the abiotically-manipulated treatment | | N.treatment | Sample size of the abiotically-manipulated  treatment | "NA" means empty values. ## Code/Software For each study case, we estimated effect sizes using Hedges’ d metric and a confidence interval using the “metafor” package 3.8-1 version in R 2022.07.2. Hedges’ *d* is calculated as the standardized mean difference between plants subjected to the abiotic manipulation and control (unmanipulated) plants, such that negative values indicate that microbial abundance had lower mean values on abiotically-manipulated plants compared to control plants, whereas positive values indicate the inverse. We first estimated the grand mean effect size and 95% confidence interval (CI) across all studies to assess whether there was an overall effect of abiotic factors on microbial abundance. This grand effect size does not separate the effects of different types of climate change-related abiotic factors. Rather, the purpose of this analysis was to estimate the degree of consistency among studies by means of the between-studies heterogeneity (τ² and associated Q statistics), an important overall estimator for our analysis. High heterogeneity can be accounted for by using explanatory variables (referred to as “moderators” in meta-analysis literature). Total heterogeneity is split into among-group heterogeneity (i.e., among abiotic factors) and within-group heterogeneity (i.e., variance of effect sizes within moderator level). The τ² and associated Q statistics for heterogeneity aim at determining whether among-group heterogeneity is large enough as compared to within-group heterogeneity to conclude on the significant effect of the moderator tested. Because τ² is dependent on sample size, we also calculated *I*2 value which is a standardized estimate of total heterogeneity ranging from 0 and 1. We next evaluated the effects of the type of climate change-related abiotic factor (eCO2, warming, and drought) on the abundance of plant-associated microbes by estimating mean effect sizes and 95% CIs for each abiotic factor and running models with the type of abiotic factor as a moderator. Then, we tested whether effects of the type of climate change-related abiotic factor on the abundance of plant-associated microbes were contingent on the microbial taxonomic group (bacteria, fungus or virus), and the plant part with which microbes were associated (phyllosphere or rhizosphere). For this, we ran models including as moderators: the type of climate change-related abiotic factor, one of the above-mentioned factors (microbial taxonomic group or plant region of colonization), and the two-way interaction between the type of abiotic factor and the microbe grouping factor. We note that there was not enough replication to test for the three-way interaction between abiotic forces, microbial group, and plant region of colonization. We reported results from the omnibus test (i.e., overall effect of all moderators) as well as from the coefficient parameter estimate and associated confidence interval. In all the above models, we performed multi-level error meta-analyses with the *rma.mv* function of the R package metafor v. 2.0-0 , and included the primary study and study case nested within primary study as random factors in order to account for non-independence among multiple effect sizes drawn from a single primary study. Multiple comparisons of abiotically-manipulated plants with the same control plant were accounted for by computing the variance-covariance matrix among all effect sizes. We considered an effect size as significant if its 95% confidence interval did not overlap with zero. To ensure that our findings were robust, we conducted a sensitivity analysis in which we sequentially removed one primary study at a time. This analysis was aimed at testing whether the main result could have emerged from the inclusion of any particularly influential study, for instance one providing a large number of study cases. For each of the 95 runs, corresponding to removing each of the 95 primary studies included in the main analysis, we checked that model parameter estimates for each treatment (abiotic manipulated vs unmanipulated control plants) were comparable, regardless of whether each study was later included or not in the analyses. This analysis indicated that our findings were robust and unbiased by non-independence among effect sizes. In addition, we used several approaches to verify that our results were not affected by publication bias: (1) inspection of funnel plots, (2) exploration of the relationship between effect-sizes and journal impact factor, and (3) cumulative meta-analysis. These analyses indicated that our findings were robust to selective reporting and dissemination bias.