Statistical analyses
For each biogeographic region, we normalized the OTU table for
subsequent statistical analyses by rarefying the number of high-quality
fungal sequences to the smallest library size in that region (14 241
reads for the Argentinian Yungas, 24 812 for Borneo, and 2000 for
Panama). For the characterization of putative core communities of
pantropical montane and lowland fungi, we used a dataset rarefied to
2000 reads to estimate species overlap among geographic regions and to
evaluate and distinguish the possible effects of biogeography and
environmental variables on community composition.
In each geographic region, total fungal richness as well as OTU richness
of taxonomic classes and functional groups were compared among the three
major elevational forest types via ANOVA with Tukey’s HSD test,
implemented in R (R Development Core Team 2013). We calculated values of
proportional richness and proportional abundance of functional groups on
a per-sample basis and compared them among forest types as above. We
used quadratic regression analyses, also in R, to examine relationships
between elevation and environmental variables such as MAT, MAP, soil pH,
organic matter, total nitrogen content, and carbon/nitrogen (C/N) ratio,
as well as total P content in Argentina and Panama.
To compare community composition across elevational forest types, we
used the vegan R package (Oksanen et al . 2015) to run
Generalized Nonmetric Multidimensional Scaling (GNMDS) ordinations on
the Hellinger-transformed OTU table and a secondary matrix containing
environmental variables mentioned above. Ordinations were run separately
for functional groups as well as for all fungi in each geographic region
with the metaMDS function, which uses several random starts to
find a stable solution. Data were subjected to 999 iterations per run
with Bray-Curtis distance measure. Pearson correlation coefficient
(r ) values and statistical significance between environmental
variables and fungal community composition were calculated with theenvfit function, and vectors of variables with statistically
significant correlations were plotted in ordinations. We plotted
isolines of elevation on the GNMDS ordinations with the ordisurffunction.
Statistical tests of the equality variances via the betadisperfunction indicated no significant difference in multivariate homogeneity
of group dispersions across elevational forest types in any region. To
estimate the relative importance of forest type (categorical) and
environmental (continuous) variables as sources of variation in fungal
community composition, permutational multivariate analysis of variance
(PerMANOVA) was carried out for all fungi and each functional group with
the adonis function in vegan . To account for correlations
among environmental variables, we performed a forward selection of
parameters, including only significant environmental variables in the
final model. For each geographic region as well as for the
cross-regional comparison, we used partial Mantel test in veganto differentiate the effects of spatial distance and abiotic
environmental variables, standardized with the scale function in
R, on community structure. Finally, we carried out indicator species
analysis (Dufrêne and Legendre 1997) to quantify associations of
individual OTUs with specific elevational forest types at a pantropical
scale with the multipatt function in the indicspeciespackage in R (De Cáceres et al . 2012).
Results