Patterns and Processes in Ectomycorrhizal Fungal Ecology
- Author(s): Glassman, Sydney
- Advisor(s): Bruns, Thomas D
- et al.
For my dissertation I focused on two key questions central to fungal diversity: the role of
dispersal in fungal community assembly, and the response of fungi to catastrophic disturbances. As we enter the Anthropocene and catastrophic disturbances are increasing in frequency, it is important to understand how fungi will respond to disturbances since they are major drivers of many ecological processes such as decomposition and forest function. To do this, I utilized large scale-sampling and ITS amplicon-based metagenomics to examine the environmental drivers of ectomycorrhizal fungal spore bank diversity across the North American continent. Two of my study plots were then burned in a catastrophic wildfire. Thus I was able to examine ectomycorrhizal fungal spore bank recovery after the fire, and I made a case for a group of fire fungi, adapted to wildfires.
To examine the role of dispersal in fungal community assembly, I used a high elevation
subalpine basin located in Yosemite National Park where isolated congeneric pine trees have established at varying distances from each other and the forest edge. Since ectomycorrhizal fungi are obligate symbionts of trees, isolated trees are islands from the perspective of the fungi. I used these well-replicated “islands” to test the theory of island biography in host-associated fungi. Moreover, because two distantly related pine species co-occurred in varying distances from each other and the forest edge, I could disentangle host from distance and other environmental factors to determine the drivers of community structure and beta-diversity in the ectomycorrhizal fungi associated with the trees. To do so, I determined fungal community composition with Illumina MiSeq sequencing of ITS amplicons, and applied generalized dissimilarity modeling (GDM), a non-linear form of matrix regression, to test whether total and ectomycorrhizal fungal (EMF)
communities were primarily structured by distance decay, host, seasonality, or edaphic
environmental filtering. I identified soil nutrient environment as a stronger predictor of EMF
community composition than host identity. I found pH and organic matter to be the strongest predictors of EMF, and total fungal composition. This result fits an emerging paradigm that soil pH is a major driver of microbial community composition.