DOI: 10.5061/dryad.4qrfj6qg7
title: Differences in pathogen resistance between diploid and polyploid plants: a systematic review and meta-analysis

description: Polyploidy, the state of having more than two full sets of chromosomes,
 has been hypothesized to provide several evolutionary advantages to
 flowering plants, including increased ability to resist pathogens and
 parasites. However, studies comparing pathogen resistance in conspecific
 and congeneric diploids and polyploids have produced mixed results. While
 the supposed relationship between polyploidy and pathogen resistance has
 been commented on in several narrative reviews, it has never been
 subjected to a systematic meta-analysis. We examined the effect of
 polyploidy on pathogen resistance by synthesizing 214 effect sizes from
 128 studies. We find that, overall, there is no consistent effect of
 polyploidy on pathogen resistance. Subgroup analyses suggest that
 polyploids perform significantly better than diploids only in resisting
 hemibiotrophic pathogens, and autopolyploids tend show greater resistance
 than allopolyploids. This is surprising given the fact that polyploids
 possess extra allele copies of R-gene alleles that provide resistance to
 biotrophic pathogens, and this pattern may indicate that signaling
 cascades needed to elicit hypersensitive responses are disrupted by
 polyploidy. Disruption is supported by the observation that, across all
 pathogens, autopolyploids show significantly greater resistance compared
 to diploids, whereas allopolyploids do not. This is corroborated by the
 observation that synthetic autopolyploids perform significantly better
 than their allopolyploid and established counterparts. Regarding pathogen
 type, diploids show greater resistance than polyploids to pathogens that
 are fungi or nematodes. Analyses of publication bias indicate little to no
 bias, and analyses of heterogeneity indicate that phylogeny explains
 almost none of the observed heterogeneity. These results underscore the
 importance of not only systematic review but also the strong degree to
 which the effects of polyploidy depend on ecological context.
description: <strong>Literature search.</strong> We searched for
 relevant publications using Google Scholar, using individual searches for
 studies comparing diploids and various cytotypes. These searches returned
 a total of 1,602 publications, which, after pruning for papers that met
 our criteria, produced 73 studies able to be analyzed. These were combined
 with 55 papers found outside our systematic search, for a grand total of
 128 publications with 214 individual effect sizes.
 <strong>Data extraction.</strong> For each effect
 size, we recorded the following information: mean, standard deviation,
 sample size, and for both the diploid and polyploid groups in each effect
 size: authors, Linnean family, ploidy
 level of the polyploid, whether or not the diploids under study were
 hybrids, whether the polyploids under study were autopolyploids or
 allopolyploids, whether the species under study were wild or cultivated,
 whether the polyploids under study were synthetic or established, the type
 of pathogen with which plants were infected, and the effect direction
 (i.e., whether a higher value indicates greater or lesser pathogen
 resistance).
 <strong>Analyses.</strong> Effect sizes were
 calculated using standardized mean difference with Hedges's
 <em>g</em>, employing the R package escalc in R versions 4.0.3
 and 4.2.1. We analyzed the data using multi-level meta-analytic models constructed with
 the <em>rma</em> and <em>rma.mv</em> functions
 from the package metafor. We included four random effects in the
 <em>rma.mv</em> model: average infection
 time in days before disease incidence was calculated, the between-study
 effect, the within-study effect, and a phylo variable calculated using a
 family-level phylogeny of angiosperms. We examined the statistical
 heterogeneity explained by each random effect using the
 I<sup>2</sup> statistic, and we tested for publication bias
 using funnel plot analysis, Egger's regression, and fail-safe
 numbers.
description: R is required to run the code in
 Hagen_Mason_2023_code.R The data in
 HagenandMason2023_data.xlsx can be opened in Microsoft Excel or an
 open-source alternative like LibreOffice Calc The
 bibliography of papers included in the meta-analysis,
 included_papers_biblio.docx, can be opened in Microsoft Word or an
 open-source alternative like LibreOffice Writer