Online Supplementary Material
Model systems in ecology: dissecting the endophyte–grass literature
Kari Saikkonen1, Päivi Lehtonen2, Marjo Helander2, Julia Koricheva3 and Stanley H. Faeth4
1
MTT Agrifood Research Finland, Plant Production Research, Karilantie 2 A, FI-50600 Mikkeli, Finland
2
Section of Ecology, Department of Biology, University of Turku, 20014 Turku, Finland
3
School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK
4
School of Life Sciences, Arizona State University, Tempe, AZ 85287-4501, USA
Corresponding author: Saikkonen, K. (kari.saikkonen@mtt.fi).
Survey of grass endophyte literature
We used a dataset of 164 titles comprising 146 primary publications, 16 congress proceedings and
2 unpublished studies on grass endophytes published or conducted between 1982–2004. We
compiled the reference database from narrative reviews of the topic, keyword searches in the Web
of Science, reference sections of published papers and information gathered by networking with
our colleagues over the past 15 years.
To be included in the statistical synthesis of original data analyses (i.e. the meta-analysis), a
study had to provide results as either (i) means, some measure of variance, and sample sizes of
endophyte-free control and endophyte-infected groups in numerical or graphical form, (ii)
correlation coefficients, (iii) two × two contingency tables, or (iv) F-, chi-square-, or t tests
statistics and df. Only 56% of the titles presented sufficient data for the meta-analysis. Thus, we
used two approaches to examine trends in grass endophyte literature in this study: (i) qualitative
vote-counting-based approaches in all primary studies to assess the direction of endophyte effects
on host plants and (ii) meta-analysis of studies that provided sufficient data to allow assessment
of the magnitude of the endophyte effect on the host plant. In both approaches, we classified the
studies in three categories: studies focusing on endophyte-mediated plant competition, plant
performance or plant resistance to herbivores. In vote-counting, the outcome of each study was
classified as either positive (endophytes benefit host plant), neutral (non-significant effect) or
negative (endophytes decrease host plant performance, resistance to herbivores and/or
competitive ability).
We conducted the analyses in three steps. First, to examine temporal changes and the
importance of individual publications on temporal trends in the endophyte literature, we carried
out cumulative meta-analyses (using means of effect sizes from the primary publication)
separately for the studies focusing on endophyte-mediated plant competition, plant performance
or plant resistance to herbivores. In cumulative meta-analysis, studies are successively added to
the analysis in a predetermined (in our case chronological) order, and the summary statistics are
recalculated at each step [S1,S2]. Second, we examined sources of variation (including plant
species, plant genotype and nutrient availability in soils) to assess the effects of endophytes on the
host plants. Third, we examined possible publication bias in the endophyte literature associated
with the quality of the journal, which was interpreted as the impact factor of the journal [obtained
from the Journal Citation Reports of the Institute for Scientific Information (ISI)].
Meta-analysis
Meta-analyses were conducted using the MetaWin, v. 2.0 statistical software [S3]. We used
Fisher’s z-transformed correlation coefficients as a measure of effect size in analyses [S3] allowing
us to combine the data from the primary studies presented in different forms. Another metric of
effect size, Hedges’ d, which represents standardized mean difference [S4], was calculated if the
means, sample sizes and standard deviations for both endophyte-infected (experimental group)
and endophyte-free (control group) plants were available. Hedges’ d was then converted into
correlation coefficient (r) by the function
(
r = d2 d2 +4
)
[S5, S6], and then r was converted into Fisher’s z-transformed correlation coefficients using
MetaWin Statistical Calculator [S3]. When the statistical summary information of the primary
study was insufficient for calculating Hedges’ d, Fisher’s z-transformed correlation coefficients
were calculated from summary statistics (e.g. F- or X2-statistics) if available using the MetaWin
Statistical Calculator [S3]. The bias-corrected 95% bootstrap confidence intervals were generated
from 4999 iterations. A relationship was considered significant if the confidence interval did not
include zero. To test whether the effects of explanatory variables differ from each other, within
and between-group heterogeneity was examined using a chi-square test statistic, Q. Because
different response variables might indicate opposite effects on the plant (e.g. higher growth rate
and mortality of herbivores, indicating higher and lower susceptibility of the host plant to the
herbivores respectively), the sign of the effect size was adjusted so that positive effect sizes
indicated benefits from the endophyte to the host plant compared with the endophyte-free host
plant in terms of enhanced growth, reproduction, competitive abilities or resistance to herbivores.
Primary publications commonly included several separate experiments (e.g. bioassays with
different species and/or parallel experiments conducted in laboratory, greenhouse or common
garden), which we considered as independent observations (see Table 1 in the main text). Some of
the studies included variables that can be classified into two or all three of the categories (e.g.
plant growth can indicate the performance or competitive ability of a plant). However, in these
cases, the ambiguous variable was classified according to the focus of the study. When several
estimates of effect size were available from a study, we pooled the data and calculated an overall
effect for the study to avoid pseudoreplication. If responses were measured multiple times during
the experiment, the last measurement was selected.
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The dataset of the 164 titles used in the meta-analysis
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