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The continuously increasing concentration of atmospheric CO 2 has considerably altered ecosystem functioning. However, few studies have examined the long-term (i.e. over a decade) effect of elevated CO 2 on soil microbial communities. Using 16S rRNA gene amplicons and a GeoChip microarray, we investigated soil microbial communities from a Californian annual grassland after 14 years of experimentally elevated CO 2 (275 ppm higher than ambient). Both taxonomic and functional gene compositions of the soil microbial community were modified by elevated CO 2 . There was decrease in relative abundance for taxa with higher ribosomal RNA operon (rrn) copy number under elevated CO 2 , which is a functional trait that responds positively to resource availability in culture. In contrast, taxa with lower rrn copy number were increased by elevated CO 2 . As a consequence, the abundance-weighted average rrn copy number of significantly changed OTUs declined from 2.27 at ambient CO 2 to 2.01 at elevated CO 2 . The nitrogen (N) fixation gene nifH and the ammonium-oxidizing gene amoA significantly decreased under elevated CO 2 by 12.6% and 6.1%, respectively. Concomitantly, nitrifying enzyme activity decreased by 48.3% under elevated CO 2 , albeit this change was not significant. There was also a substantial but insignificant decrease in available soil N, with both nitrate (NO 3 - ) (-27.4%) and ammonium (NH 4 + ) (-15.4%) declining. Further, a large number of microbial genes related to carbon (C) degradation were also affected by elevated CO 2 , whereas those related to C fixation remained largely unchanged. The overall changes in microbial communities and soil N pools induced by long-term elevated CO 2 suggest constrained microbial N decomposition, thereby slowing the potential maximum growth rate of the microbial community., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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.

Understanding patterns in the distribution and abundance of functional traits across a landscape is of fundamental importance to ecology. Mapping these distributions is particularly challenging for species-rich groups with sparse trait measurement coverage, such as flowering plants, insects, and microorganisms. Here, we use likelihood-based character reconstruction to infer and analyze the spatial distribution of unmeasured traits. We apply this framework to a microbial dataset comprised of 11,732 ketosynthase alpha gene sequences extracted from 144 soil samples from three continents to document the spatial distribution of putative microbial polyketide antibiotic production. Antibiotic production is a key competitive strategy for soil microbial survival and performance. Additionally, novel antibiotic discovery is highly relevant to human health, making natural antibiotic production by soil microorganisms a major target for bioprospecting. Our comparison of trait-based biogeographical patterns to patterns based on taxonomy and phylogeny is relevant to our basic understanding of microbial biogeography as well as the pressing need for new antibiotics.