Tree configuration and ancestral reconstruction approach
Scientific names were standardized among data sets and brought up to
date by querying species names against the International Plant Names
Index (http://www.ipni.org/), Tropicos (http://www.tropicos.org/), The
Plant List (http://www.theplantlist.org/) and the Angiosperm Phylogeny
website (http://www.mobot.org/MOBOT/research/APweb/) for a total
of 629 species for root traits and 643 species for vein density
information, with 117 species shared between datasets. We based
phylogenetic trees on a dated molecular phylogeny using seven genome
regions compared among >30000 species (Zanne et al.2014). This phylogenetic tree matched 515 species from the root database
and 492 species from the vein density database, which were thus used for
statistical analysis and ancestral reconstructions. Finally, we assigned
mycorrhizal state to all species in each phylogenetic tree for posterior
analyses of coevolution between plant traits and mycorrhizal
affiliation. Plants were grouped in five different phylogenetic clades,
representing major lineages in the seed plant phylogeny: Gymnosperms,
Magnoliids (Magnoliids + Basal Angiosperms), Monocots, Rosids (Rosids +
Saxifragales) and Asterids (Asterids + Caryophyllales + Santalales).
First, based on trait information from extant species we illustrate the
association between root traits and leaf venation in extant species
creating a bivariate correlation (Fig. 1) and calculate the variation in
root diameter, SRL and mycorrhizal affiliation among major seed plant
clades (Fig. 2). To test hypothesis A) shifts in mycorrhizal state
associated with root morphology changes were tested using the maximum
possible number of species in each category using a logistic regression
(OLR) and a phylogenetic logistic regression (PLR), using the package
corHMM (Beaulieu et al. 2013). The PLR incorporates the
phylogenetic relatedness among species to maximize the penalized
likelihood of the logistic regression (Ives & Garland 2010, Tung, Ho &
Ané 2014). Because the AM state is ancestral in seed plants, we compared
root morphological traits between AM species to ECM or NM states. Since
species classified as AMNM are found either with or without AM fungi
based on ecological context (Brundrett 2009; Maherali et al. 2016), AMNM
plants can be described as engaging in a facultative mutualism, which is
distinct from either the AM or NM states (Moora 2014). Therefore, we
also tested whether root morphology changed from the AM to the
facultative AMNM state. Finally, because plants in the AMNM state can
found in nature without mycorrhizae, we also tested whether root
morphology changed with the transition from the AM to the combined AMNM
+ NM grouping. For all cases, we interpret significant logistic
regressions as evidence that a shift in root morphology influenced the
likelihood of a switch in mycorrhizal state.
To test hypothesis B, (shifts in root trait correspond with leaf and
habit changes but independent from mycorrhizal association), we tested
whether the association between leaf and root traits were significant
even after accounting for mycorrhizal state and growth habit using
phylogenetic generalized least squares models (PGLS) ANOVA models as
implemented in the R package ape (version 5.0; Paradis et
al. 2004).
First, we tested four different evolutionary models (Brownian, Pagel,
Martins and Garland, and Orstein-Uhlenbeck) to determine the best fit
for the structural error in the model (Paradis and
Claude 2002).
Once Pagel’s structure was identified as the best fit for the data on
the response variables (Diameter, SRL and RTD), we created models with
root traits as dependent variables, and leaf vein density, growth habit
(herb, shrub and tree) and mycorrhizal state (AM, ECM, AMNM and NM) as
independent variables, and the phylogenetic variance-covariance matrix
among species as the error structure of the model (101 species for
Diameter, 94 species for RTD and SRL). We also calculate ordinary least
square (OLS) tests for the same models, in order to determine how the
phylogenetic correction influenced the results. For both models (OLS and
PGLS), we tested the trend across all seed plants and within Magnoliids,
Rosids and Asterids, but not within Monocots or Gymnosperms due to the
paucity of leaf venation data for these two groups.
To visualize changes in root and leaf traits over time, we reconstructed
ancestral states for each trait using the Zanne et al. (2014)
phylogeny for seed plants. To reconstruct ancestral node values for vein
density and root traits, we used a maximum likelihood (ML) estimation
for continuous variables based on the assumption of Brownian dispersion
of each character across the phylogeny (O’Meara et al. 2006,
Revell et al. 2012). To do this, we created separate
species-level phylogenies for each trait, and then we estimated node
means and variances using the Rohlf (2001) recursive re-rooting
algorithm as implemented in the function fastAnc in the packagephytools (Revell 2012) for R (ver 3.4.3, R Core Team 2017). For
mycorrhizal state, which is a discrete variable, we used a continuous
time Markov chain model using the function ace in ape(Paradis et al. 2004). To assign the probability that a
particular node was in one of the mycorrhizal states (AM, ECM, NM), we
calculated marginal estimations based on asymmetrical rates (“ARD”
mode in the function ace ) given the phylogeny and tip states. To
illustrate whether changes in root morphology occurred before or after
mycorrhizal state transitions, we created scatterplots of node values
for diameter and color coded accordingly with the estimated probability
of mycorrhizal stages at each node, using a threshold of 75% as the
minimum to switch mycorrhizal type from the ancestral AM to a new
category (see Table S5) .