Search

Soil microbial communities play a critical role in nutrient transformation and storage in all ecosystems. Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes. We examined spatial and seasonal variation in microbial communities based on 16S rRNA gene sequencing and phospholipid fatty acid (PLFA) composition across four biomes: a tropical broadleaf forest (Hawaii), taiga (Alaska), semiarid grassland-shrubland (Utah), and a subtropical coniferous forest (Florida). In this study, we used a team-based instructional approach leveraging the iPlant Collaborative to examine publicly available National Ecological Observatory Network (NEON) 16S gene and PLFA measurements that quantify microbial diversity, composition, and growth. Both profiling techniques revealed that microbial communities grouped strongly by ecosystem and were predominately influenced by three edaphic factors: pH, soil water content, and cation exchange capacity. Temporal variability of microbial communities differed by profiling technique; 16S-based community measurements showed significant temporal variability only in the subtropical coniferous forest communities, specifically through changes within subgroups of Acidobacteria. Conversely, PLFA-based community measurements showed seasonal shifts in taiga and tropical broadleaf forest systems. These differences may be due to the premise that 16S-based measurements are predominantly influenced by large shifts in the abiotic soil environment, while PLFA-based analyses reflect the metabolically active fraction of the microbial community, which is more sensitive to local disturbances and biotic interactions. To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature. Free, open access datasets and data sharing platforms are powerful tools for integrating research and teaching in undergraduate and graduate student classrooms. They are a valuable resource for fostering interdisciplinary collaborations, testing ecological theory, model development and validation, and generating novel hypotheses. Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.

Many microbes cooperatively secrete extracellular products that favorably modify their environment. Consistent with social evolution theory, structured habitats play a role in maintaining these traits in microbial model systems, by localizing the benefits and separating strains that invest in these products from ‘cheater’ strains that benefit without paying the cost. It is thus surprising that many unicellular, well-mixed microalgal populations invest in extracellular toxins that confer ecological benefits upon the entire population, for example, by eliminating nutrient competitors (allelopathy). Here we test the hypotheses that microalgal exotoxins are (1) exploitable public goods that benefit all cells, regardless of investment, or (2) non-exploitable private goods involved in cell-level functions. We test these hypotheses with high-toxicity (TOX+) and low-toxicity (TOX-) strains of the damaging, mixotrophic microalga Prymnesium parvum and two common competitors: green algae and diatoms. TOX+ actually benefits from dense populations of competing green algae, which can also be prey for P. parvum, yielding a relative fitness advantage over coexisting TOX-. However, with non-prey competitors (diatoms), TOX- increases in frequency over TOX+, despite benefiting from the exclusion of diatoms by TOX+. An evolutionary unstable, ecologically devastating public good may emerge from traits selected at lower levels expressed in novel environments.