Microbe-mediated local adaptation to limestone barrens in northern New York

Background/Question/Methods

Plant-associated microbes are essential in determining a plant’s tolerance to environmental conditions, thus they likely facilitate plant local adaptation. We use a combination of observational and experimental approaches to explore the role of microbes in plant local adaptation to limestone barrens (also called alvars) found in New York State. Alvars are characterized by thin soils that overlay dolostone or limestone bedrock; consequently, they have shallow rooting depths, high pH, and are especially drought prone. During the summer of 2014, we collected samples of a common plant, Hypericum perforatum, which is found growing on alvars and in the surrounding habitat. We analyzed tagged-amplicons derived from the fungal ITS1 region to describe fungal communities found in and on Hypericum roots collected from the two habitat types. We also conducted a greenhouse experiment to test if plant fitness components (germination, biomass, and flower bud number) are maximized by their “home” soil communities. 

Results/Conclusions

Field measurements confirm that old-field soils are four times deeper than alvar soils. Likely as a result of soil depth differences, Hypericum plants growing in old-fields are 60% taller and produce 70% more flower buds. Preliminary greenhouse work suggests that microbes from alvars enhance germination for both alvar and old-field seeds. This finding suggests that facilitation by microbial communities may be more common on alvars than in old-fields, and occurs regardless of plant genotype.  Preliminary explorations of sequencing data using non-metric multidimensional scaling (NMDS) indicate that fungal communities from old-fields are distinct from those found on alvars. Understanding the involvement of microbes in plant local adaptation is important for understanding basic questions in ecology, such as what determines patterns of plant distribution and abundance, but also for broader applied topics, such as predicting plant responses to human-mediated changes to the environment.