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Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

As one of the last remaining naturally grazed ecosystems on Earth, the Serengeti National Park is an ideal location to study the influence of migratory mammals on the structure of microbial communities and the factors that generate biogeography of soil microbes. Furthermore, volcanic inputs generate environmental gradients that may also structure microbial communities. We studied 16S rRNA amplicons in a 13-year herbivore removal experiment to examine the influence of grazing and environmental gradients on the natural distribution of soil microbes. Removal of mammalian herbivores shifted microbial community structure, with 31 taxa that were significant indicator taxa of the ungrazed treatment and three taxa that were indicators of the grazed treatment. The abundance of many taxa were correlated with soil texture, phosphorus, iron, calcium and rainfall, and the evenness of taxa within samples was also correlated with these variables. Bayesian general linear mixed effects models with single predictors of multiple, highly correlated variables of beta diversity were consistent with a significant, but weak (2%), effect of grazing, and stronger effects of phosphorus (14%). Beta diversity of microbial communities was greater in grazed than in ungrazed plots; suggesting that the impacts of grazing on community assembly of microbes results from deterministic environmental filtering caused by the influence of herbivores on plant communities and soil properties rather than stochastic dispersal via herds of large mammals. These herbivore effects are superimposed on deterministic environmental filtering by natural soil and precipitation gradients across the Serengeti.

This study explores the relationships of AM fungal abundance and diversity with biotic (host plant, ungulate grazing) and abiotic (soil properties, precipitation) factors in the Serengeti National Park, Tanzania. Soil and root samples were collected from grazed and ungrazed plots at seven sites across steep soil fertility and precipitation gradients. AM fungal abundance in the soil was estimated from the density of spores and the concentration of a fatty acid biomarker. Diversity of AM fungi in roots and soils was measured using DNA sequencing and spore identification. AM fungal abundance in soil decreased with grazing and precipitation and increased with soil phosphorus. The community composition of AM fungal DNA in roots and soils differed. Root samples had more AM fungal indicator species associated with biotic factors (host plant species and grazing), and soil samples had more indicator species associated with particular sample sites. These findings suggest that regional edaphic conditions shape the site-level species pool from which plant species actively select root-colonizing fungal assemblages modified by grazing. Combining multiple measurements of AM fungal abundance and community composition provides the most informed assessment of the structure of mycorrhizal fungal communities in natural ecosystems.

Field-based observational research is the first step in understanding the factors that predict the biogeography and community structure of soil microbes. The Serengeti National Park in Tanzania is an ideal location for this type of research because active volcanoes generate strong environmental gradients due to ash deposition and a rain shadow. Also, as one of the last remaining naturally grazed ecosystems on Earth, the Serengeti provides insights about the influence of herbivory on microbial communities. We used 16S rRNA amplicons to characterize bacterial and archaeal communities in soils from a 13-year herbivore removal experiment to study the influence of environmental factors and grazing on the natural distribution of soil microbes. We collected soil samples from seven sites, each with three naturally grazed plots and three plots that were fenced to prevent grazing by large mammalian herbivores. Soil fertility (phosphorus, nitrogen, iron, calcium, organic matter), texture, and pH were measured at each plot. Beta diversity of bacterial and archaeal communities was most strongly correlated with soil texture (R2 = 32.4%). The abundance of many operational taxonomic units (OTUs) were correlated with soil texture, and the evenness of taxa within samples increased with fine-textured soil. Removal of grazing shifted community structure, with 31 OTUs that were significant indicator taxa of the ungrazed treatment and three OTUs that were significant indicators of the grazed treatment.ImportanceOur results show that in this regional scale study, soil texture was the best environmental predictor, and grazing by large mammals also structures bacterial and archaeal communities. When large mammals are removed, as humans have been doing for millenia, there are cascading effects into the microbial world that can influence ecosystem functions like carbon and nitrogen cycles. These empirical findings from a natural tropical savannah can help inform models of the global distribution and function of soil microbes.

Social, political, and economic forces may inadvertently influence the stance of scientific literature. Scientists strive for neutral language, but this may be challenging for controversial topics like genetically modified (GM) crops. We classified peer-reviewed journal articles and found that 40% had a positive or negative stance towards GM crops. Proportion of positive and negative stance varied with publication date, authors’ country of origin, funding source, and type of genetic modification. Articles with a negative stance were more common at the beginning of the millennium. Authors from China had the highest positive:negative ratio (8:1), followed by authors from the USA (12:5) and the EU (5:7). Positive stance articles were six times more likely to be funded by private sources compared to those with a neutral or negative stance. Articles about glyphosate were more likely to be negative compared to articles about Bacillus thuringiensis. Linguistic features of articles with positive and negative stances were used to train a random forest classifier that predicts stance significantly better than random chance. This suggests the possibility of an automated tool to screen manuscripts for unintended biased language prior to publication.

© 2019 Elsevier Ltd The establishment and naturalization of non-native Russian olive (Elaeagnus angustifolia)in southwestern US riparian habitats is hypothesized to have negative implications for native flora and fauna. Despite the potential for Russian olive establishment in new riparian habitats, much of its ecology remains unclear. Arid river systems are important stopover sites and breeding grounds for birds, including some endangered species, and understanding how birds use Russian olive habitats has important implications for effective non-native species management. We compared native bird use of sites that varied in the amount of Russian olive and mixed native/non-native vegetation along the San Juan River, UT, USA. From presence/absence surveys conducted in 2016 during the breeding season, we found 1)fewer bird species and functional groups used Russian olive habitats and 2)the composition of species within Russian olive habitats was different from the composition of species in mixed native/non-native habitats. Our results suggest Russian olive may support different bird compositions during the breeding season and as Russian olive continues to naturalize, bird communities may change. Finally, we highlight the paucity of research surrounding Russian olive ecology and stress the need for rigorous studies to improve our understanding of Russian olive ecology.

The diversity of complex microbial communities can be rapidly assessed by high-throughput DNA sequencing of marker gene (e.g., 16S) PCR amplicon pools, often yielding many thousands of DNA sequences per sample. However, analysis of such community amplicon sequencing data requires multiple computational steps which affect the outcome of a final data set. Here we use mock communities to describe the effects of parameter adjustments for raw sequence quality filtering, picking operational taxonomic units (OTUs), taxonomic assignment, and OTU table filtering as implemented in the popular microbial ecology analysis package, QIIME 1.9.1. We demonstrate a workflow optimization based upon this exploration, which we also apply to environmental samples. We found that quality filtering of raw data and filtering of OTU tables had large effects on observed OTU diversity. While all taxonomy assignment programs performed with similar accuracy, an appropriate choice of similarity threshold for defining OTUs depended on the method used for OTU picking. Our “default” analysis in QIIME overestimated mock community OTU diversity by at least a factor of ten. Our optimized analysis correctly characterized mock community taxonomic composition and improved the OTU diversity estimate, reducing overestimation to a factor of about two. Though observed relative abundances of mock community member taxa were approximately correct, most were still represented by multiple OTUs. Low-frequency OTUs conspecific to constituent mock community taxa were characterized by multiple substitution and indel errors and the presence of a low-quality base call resulting in sequence truncation during quality filtering. Low-quality base calls were observed at “G” positions most of the time, and were also associated with a preceding “TTT” trinucleotide motif. Environmental diversity estimates were reduced by about 40% from 2508 to 1533 OTUs when comparing output from the default and optimized workflows. We attribute this reduction in observed diversity to the removal of erroneous sequences from the data set. Our results indicate that both strict quality filtering of raw sequencing data and careful filtering of raw OTU tables are important steps for accurately estimating microbial community diversity.