Docherty Kathryn M, Borton Hannah M, Espinosa Noelle, Gebhardt Martha, Gil-Loaiza Juliana, Gutknecht Jessica L M, Maes Patrick W, Mott Brendon M, Parnell John Jacob, Purdy Gayle, Rodrigues Pedro A P, Stanish Lee F, Walser Olivia N, Gallery Rachel E
Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan, United States of America.
School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.
PLoS One. 2015 Nov 4;10(11):e0135352. doi: 10.1371/journal.pone.0135352. eCollection 2015.
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.
土壤微生物群落在所有生态系统的养分转化和储存中发挥着关键作用。量化土壤微生物遗传和功能变异在季节和长期时间尺度上对生态系统内部和跨生态系统的生物和非生物变化的响应,将有助于我们理解气候变化对这些过程的影响。我们基于16S rRNA基因测序和磷脂脂肪酸(PLFA)组成,研究了四个生物群落中微生物群落的空间和季节变化:热带阔叶林(夏威夷)、泰加林(阿拉斯加)、半干旱草原-灌丛(犹他州)和亚热带针叶林(佛罗里达州)。在本研究中,我们采用基于团队的教学方法,利用iPlant协作平台来研究公开可用的国家生态观测站网络(NEON)的16S基因和PLFA测量数据,这些数据量化了微生物多样性、组成和生长情况。两种分析技术均表明,微生物群落按生态系统强烈分组,且主要受三个土壤因素影响:pH值、土壤含水量和阳离子交换能力。微生物群落的时间变异性因分析技术而异;基于16S的群落测量仅在亚热带针叶林群落中显示出显著的时间变异性,特别是通过酸杆菌亚群内的变化。相反,基于PLFA的群落测量在泰加林和热带阔叶林系统中显示出季节性变化。这些差异可能是由于基于16S的测量主要受非生物土壤环境的大幅变化影响,而基于PLFA的分析反映了微生物群落的代谢活性部分,这对局部干扰和生物相互作用更敏感。为了解决土壤微生物群落对样品储存温度响应的技术问题,我们比较了储存在-80°C和-20°C的土壤中基于16S的群落结构,发现基于储存温度的群落组成没有显著差异。免费、开放获取的数据集和数据共享平台是本科和研究生课堂整合研究与教学的强大工具。它们是促进跨学科合作、检验生态理论、模型开发与验证以及提出新假设的宝贵资源。通过基于项目的学习在大学课堂中进行大数据集的数据分析和解释培训,可改善学生的学习体验,并使他们在整个职业生涯中都能利用这些重要资源。