Time outweighs the effect of host developmental stage on microbial community composition.

Thousands of microbial taxa in the soil form symbioses with host plants, and due to their contribution to plant performance, these microbes are often considered an extension of the host genome. Given microbial effects on host performance, it is important to understand factors that govern microbial community assembly. Host developmental stage could affect rhizosphere microbial diversity while, alternatively, microbial assemblages could change simply as a consequence of time and the opportunity for microbial succession. Previous studies suggest that rhizosphere microbial assemblages shift across plant developmental stages, but time since germination is confounded with developmental stage. We asked how elapsed time and potential microbial succession relative to host development affected microbial diversity in the rhizosphere using monogenic flowering-time mutants of Arabidopsis thaliana. Under our experimental design, different developmental stages were present among host genotypes after the same amount of time following germination, e.g. at 76 days following germination some host genotypes were flowering while others were fruiting or senescing. We found that elapsed time was a strong predictor of microbial diversity whereas there were few differences among developmental stages. Our results support the idea that time and, likely, microbial succession more strongly affect microbial community assembly than host developmental stage.

Modified Methods for Loading of High-Throughput DNA Extraction Plates Reduce Potential for Contamination.

High-throughput DNA sequencing techniques have contributed substantially to advances in our understanding of relationships among microbial communities, host characteristics, and broader ecosystem functions. With this rapid increase in breadth and depth of sequencing capabilities have come methods to extract, amplify, analyze, and interpret environmental DNA successfully with maximum efficiency. Unfortunately, performing DNA extractions quickly can come at the cost of increasing the risk of contamination among samples. In particular, high-throughput extractions that are based on samples contained in a 96-well plate offer a relatively quick method, compared to single-tube extractions, but also increase opportunities for well-to-well cross-contamination. To minimize the risk of cross-contamination among samples, while retaining the benefits of high-throughput extraction techniques, we developed a new method for loading environmental samples into 96-well plates. We used pierceable PCR sealing films to cover each plate while loading samples and added samples first to PCR tubes before moving them into wells; together, these practices reduce the risk of sample drift and unintended double loading of wells. The method outlined in this paper provides researchers with an approach to maximize available high-throughput extraction techniques while reducing the risk of cross-contamination inherent to 96-well plates. We provide a detailed step by step outline of how to move from sample collection to DNA extraction while minimizing the risk of unwanted cross-contamination.

Discrepancies in occupancy and abundance approaches to identifying and protecting habitat for an at-risk species.

Predicting how environmental factors affect the distribution of species is a fundamental goal of conservation biology. Conservation biologists rely on species distribution and abundance models to identify key habitat characteristics for species. Occupancy modeling is frequently promoted as a practical alternative to use of abundance in identifying habitat quality. While occupancy and abundance are potentially governed by different limiting factors operating at different scales, few studies have directly compared predictive models for these approaches in the same system. We evaluated how much occupancy and abundance are driven by the same environmental factors for a species of conservation concern, the greater short-horned lizard (Phrynosoma hernandesi). Occupancy was most strongly dictated by precipitation, temperature, and density of ant mounds. While these factors were also in the best-supported predictive models for lizard abundance, the magnitude of the effects varied, with the sign of the effect changing for temperature and precipitation. These discrepancies show that while occupancy modeling can be an efficient approach for conservation planning, predictors of occupancy probability should not automatically be equated with predictors of population abundance. Understanding the differences in factors that control occupancy versus abundance can help us to identify habitat requirements and mitigate the loss of threatened species.