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  1. Shade, Ashley (Ed.)

    Different methods are commonly used to assign core microbiome membership, leading to methodological inconsistencies across studies. In this study, we review a set of the most commonly used core microbiome assignment methods and compare their core assignments using both simulated and empirical data.

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  2. Plant invasion has proven to be a significant driver of ecosystem change, and with the increased probability of invasion due to globalization, agricultural practices and other anthropogenic causes, it is crucial to understand its impact across multiple trophic levels. With strong linkages between above and belowground processes, the response of soil microorganisms to plant invasion is the next logical step in developing our conceptual understanding of this complex system. In our study, we utilized a meta-analytical approach to better understand the impacts of plant invasion on soil microbial diversity. We synthesized 70 independent studies with 23 unique invaders across multiple ecosystem types to search for generalizable trends in soil microbial α-diversity following invasion. When possible, soil nutrient metrics were also collected in an attempt to understand the contribution of nutrient status shifts on microbial α-diversity. Our results show plant invasion to have highly heterogenous and limited impacts on microbial α-diversity. When taken together, our study indicates soil microbial α-diversity to remain constant following invasion, contrary to the aboveground counterparts. As our results suggest a decoupling in patterns of below and aboveground diversity, future work is needed to examine the drivers of microbial diversity patterns following invasion. 
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  3. ABSTRACT As the range of bark beetles expands into new forests and woodlands, the need to understand their effects on multiple trophic levels becomes increasingly important. To date, much attention has been paid to the aboveground processes affected by bark beetle infestation, with a focus on photoautotrophs and ecosystem level processes. However, indirect effects of bark beetle on belowground processes, especially the structure and function of soil microbiota remains largely a black box. Our study examined the impacts of bark beetle-induced tree mortality on soil microbial community structure and function using high-throughput sequencing of the soil bacterial and fungal communities and measurements of extracellular enzyme activities. The results suggest bark beetle infestation affected edaphic conditions through increased soil water content, pH, electrical conductivity, and carbon/nitrogen ratio and altered bulk and rhizosphere soil microbial community structure and function. Finally, increased enzymatic activity suggests heightened microbial decomposition following bark beetle infestation. With this increase in enzymatic activity, nutrients trapped in organic substrates may become accessible to seedlings and potentially alter the trajectory of forest regeneration. Our results indicate the need for incorporation of microbial processes into ecosystem level models. IMPORTANCE Belowground impacts of bark beetle infestation have not been explored as thoroughly as their aboveground counterparts. In order to accurately model impacts of bark beetle-induced tree mortality on carbon and nutrient cycling and forest regeneration, the intricacies of soil microbial communities must be examined. In this study, we investigated the structure and function of soil bacterial and fungal communities following bark beetle infestation. Our results show bark beetle infestation to impact soil conditions, as well as soil microbial community structure and function. 
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  4. Abstract

    Quantitative literacy is necessary to keep pace with the exponentially increasing magnitude of biological data and the complexity of statistical tools. However, statistical programming can cause anxiety in new learners and educators alike. In order to produce graduates that are well‐prepared for quantitative research, overcoming the initial hurdles associated with statistical programming is a must. Often, valuable class time is dedicated to teaching introductory concepts of statistical programming, leaving instructors short on time. Here we present an introductory tutorial to statistical programming in the language R. Our tutorial is easily customizable, self‐paced, and can be used in secondary through graduate level classrooms. Student questionnaire responses suggest that perceptions towards R became generally more favorable following an introduction to the program, with an increased likelihood of returning to R. Similar results were found across multiple formats for introducing statistical programming in R and suggest a tutorial‐style introduction is as effective as a series of lectures and computer exercises for altering student perceptions towards statistical programming for graduate students. Our tutorial provides a self‐paced introduction that covers basic programming in R and offers students an opportunity to learn the basic skills that often act as a roadblock to learning and utilizing more complex quantitative tools, while reserving class time for instruction.

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