Variation in resource competition traits among Microcystis strains is affected by their microbiomes

Author:

Baker Dylan1,Godwin Casey M.2,Khanam Muhtamim1,Burtner Ashley M.2,Dick Gregory J.23,Denef Vincent J.1ORCID

Affiliation:

1. Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor Michigan USA

2. Cooperative Institute for Great Lakes Research, School for Environment and Sustainability University of Michigan Ann Arbor Michigan USA

3. Department of Earth and Environmental Sciences University of Michigan Ann Arbor Michigan USA

Abstract

AbstractFreshwater harmful algal blooms are often dominated by Microcystis, a phylogenetically cohesive group of cyanobacteria marked by extensive genetic and physiological diversity. We have previously shown that this genetic diversity and the presence of a microbiome of heterotrophic bacteria influences competitive interactions with eukaryotic phytoplankton. In this study, we sought to explain these observations by characterizing Monod equation parameters for resource usage (maximum growth rate μmax, half‐saturation value for growth Ks, and quota) as a function of N and P levels for four strains (NIES‐843, PCC 9701, PCC 7806 [WT], and PCC 7806 ΔmcyB) in presence and absence of a microbiome derived from Microcystis isolated from Lake Erie. Results indicated limited differences in maximum growth rates but more pronounced differences in half‐saturation values among Microcystis strains. The largest impact of the microbiome was reducing the minimal nitrogen concentration sustaining growth and reducing half saturation values, with variable results depending on the Microcystis strain. Microcystis strains also differed from each other in their N and P quotas and the extent to which microbiome presence affected them. Our data highlight the importance of the microbiome in altering Microcystis‐intrinsic traits, strain competitive hierarchies, and thus bloom dynamics. As quota, μmax, and Ks are commonly used in models for harmful algal blooms, our data suggest that model improvement may be possible by incorporating genotype dependencies of resource‐use parameters.

Publisher

Wiley

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