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Nests of the yellow meadow ant (Lasius flavus) occur at high densities in grasslands worldwide. Although many studies have shown that L. flavus nests influence soil nutrient contents, little is known about their effect on soil nutrient cycling rates. The aim of this study was to examine the role of nest-mounds inhabited by L. flavus as potential 'hot spots' for soil nutrient cycling. Six pairs of nest-mounds and control soils were selected at a grassland site at the plateau of the Alter Gleisberg (Thuringia, Central Germany). L. flavus significantly modified the soil environment within the nest. In comparison to the control soils, nest-mounds were characterized by slightly higher soil temperatures during the summer months. In addition, we found that nests were related to decreased potential C mineralization rates and increased potential net N mineralization rates. Nest-mound soil exhibited lower amounts of SOC, hot-water extractable DOC and DN, and higher concentrations of leachable DOC and DN. Moreover, ants promoted the enrichment of base cations in the nest. Differences in the soil environment between nests and control soils were possibly a result of the burrowing activity of ants, soil mixing, accumulation of aphid honeydew, and decreased plant-derived nutrient inputs into the nest-mound soil. In conclusion, L. flavus nest-mounds had a significant but element dependent effect on the soil nutrient cycling and may represent cold spots for C cycling and hot spots for N cycling. Thus, L. flavus nests increase the spatial heterogeneity of soil properties and create unique micro-sites within grassland ecosystems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]