Successional trend
A two-step approach was used to determine the processes of endophyte
community assembly and to evaluate whether these fungal communities
followed the deterministic successional trend observed for their woody
tree hosts (Jamison-Daniels et al. , 2021).
First, we assessed whether the amount of variation in community
composition of our actual endophyte assemblages, explained by a number
of predictor variables, was greater than (implying deterministic
processes), less than (implying deterministic processes) or not
different (implying stochastic processes) to what could be expected in
randomly generated communities (Dini-Andreote et al. , 2015).
Initially, a canonical correspondence analysis (CCA) was conducted to
test which factors, namely host identity, spatial distance between
sampled trees, abiotic conditions (maximum and minimum temperature,
temperature standard deviation, light intensity, soil moisture measured
per BC) and biotic factors (tree basal area per BC, tree height and
distance to forest edge), best explained fungal community composition.
Weighted linear regression was performed on the constraining variables
(Ter Braak, 1986). To incorporate the effect of spatial distance on
community composition, a weighted principal coordinates of neighbourhood
matrix (PCNM) analysis (created using the geographic coordinates of
every sampled tree) was performed. This analysis transforms coordinates
to a rectangular distance matrix that is acceptable for constrained
ordination techniques like CCA (Borcard and Legendre, 2002; Legendre and
Borcard, 2008). Backward and forward stepwise permutation tests, for
1000 permutations, were used to determine the best fitting model, based
on the model with the lowest AIC score (Venables and Ripley, 2002). Rare
species contribute heavily to the chi-squared distance used to plot site
and species scores in CCA analysis (Legendre and Legendre, 2012).
Therefore, to reduce the spurious effects of rare species within the
CCA, an eigenvalue decomposition approach was used to determine in how
many samples a particular ASV must have occurred for it to be retained
when performing the final ordination (Legendre and Legendre, 2012). When
a considerable drop in inertia for one of the first five eigenvalues is
detected, it indicates in how many samples an ASV must have occurred to
be retained for the final CCA analysis (Supporting Information Figure
S2) (Legendre and Legendre, 2012). The eigenvalue decomposition detected
a drop in the inertia for the third eigenvalue after dropping ASVs that
occurred in less than 20 samples (Supporting Information Figure S2).
Therefore, all ASVs (n= 4905) that occurred in 20 samples or less were
removed before the final CCA analysis (N= 411) and the randomisations.
The amount of variation explained by the best model with the retained
predictor variables (host identity, light intensity and four spatial
eigenvectors (spatial eigenvector 1, spatial eigenvector 2, spatial
eigenvector 13 and spatial eigenvector 17)) was calculated. Then, 10 000
randomly assembled abundance-weighted community data matrices were
constructed based on the true community identity, richness and abundance
(Greve et al. , 2008). The randomly assembled matrices conserved
species richness per sample as observed in the true community, and set
the probability of species being selected proportional to ASV read
abundance (Gotelli and Graves, 1996; Gotelli, 2000). For each of the 10
000 randomly assembled communities, a new CCA was performed with the
predictors from the best CCA model (see above). The 2.5% and 97.5%
quantiles of the percentage variation explained by the CCAs were
calculated for the 10 000 random communities, and it was assessed
whether the percentage variation explained by the CCA of the true
community was larger than (> 97.5% quantile), smaller than
(< 2.5% quantile) or not significantly different (between
2.5% and 97.5% quantiles) to the percentage variation explained by the
randomly generated communities using a z-test (Greve et al. ,
2008).
Because CCA randomisation analyses provided evidence for deterministic
community assembly (see Results), a second analysis was conducted to
test whether changes in community composition could be explained by BC
size, as BC size increases with tree succession (Jamison-Danielset al. , 2021). A significant directional change in endophyte
species composition with BC size would indicate deterministic
succession, while no predictable change in composition with BC size
would suggest stochastic succession (Dini-Andreote et al. , 2015).
We tested this for the fungal communities extracted from each of the
three host species using two different pair-wise similarity indices
(Morisita and Raup-Crick indices). The Morisita index is weighted
towards assessing similarity in common taxa, while the Raup-Crick index
gives more weight to co-occurring rare taxa (Morisita, 1962; Raup and
Crick, 1979). Pair-wise similarity in fungal community composition was
calculated between all possible combinations of each of the smallest ¼
of the BCs and each of the largest ¾ of the BCs for each host species
(following Jamison-Daniels et al. , 2021), to establish if there
was directional change in community composition as BCs increase in size.
For each of the largest ¾ BCs, similarity values with the smallest ¼ of
the BCs were averaged. Generalised linear mixed effects models (GLMMs)
were used to model the effect of BC area (of the largest ¾ BCs) on
foliar fungal endophyte community similarity for both Raup-Crick and
Morista similarity indices. BC identity of the largest ¾ BCs was
included as a random effect in the model (McCulloch, 1997). Since the
similarity values for both the indices are scaled between 0-1, models
were fitted using a binomial distribution and logit link function (Zuuret al. , 2009). If the foliar fungal community similarity between
the largest and the smallest BCs decreased or increased significantly
with BC area of the largest BCs, it was interpreted as an indication of
deterministic succession, while no relationship was taken as an
indication of communities being ecologically neutral and primarily
governed by stochastic processes that structure community composition
(Hubbell, 2001; Dini-Andreote et al. , 2015; Jamison-Danielset al. , 2021).