Search

Microbial communities can potentially mediate feedbacks between global change and ecosystem function, owing to their sensitivity to environmental change and their control over critical biogeochemical processes. Numerous ecosystem models have been developed to predict global change effects, but most do not consider microbial mechanisms in detail. In this idea paper, we examine the extent to which incorporation of microbial ecology into ecosystem models improves predictions of carbon (C) dynamics under warming, changes in precipitation regime, and anthropogenic nitrogen (N) enrichment. We focus on three cases in which this approach might be especially valuable: temporal dynamics in microbial responses to environmental change, variation in ecological function within microbial communities, and N effects on microbial activity. Four microbially-based models have addressed these scenarios. In each case, predictions of the microbial-based models differ—sometimes substantially—from comparable conventional models. However, validation and parameterization of model performance is challenging. We recommend that the development of microbial-based models must occur in conjunction with the development of theoretical frameworks that predict the temporal responses of microbial communities, the phylogenetic distribution of microbial functions, and the response of microbes to N enrichment.

One critical tool for creating an environmentally literate citizenry is to reach students other than those in environmental-related majors in the form of the university-level large introductory environmental studies course. In this study, we relate student academic background to initial and final environmental awareness results. We found that students from a wide range of backgrounds begin with similar motivation for taking the class and uniformly low environmental knowledge. Our results indicate that a low-credit, engaging introductory environmental studies class can strongly impact student awareness independent of diverse learner academic backgrounds. For example, results suggest that students taking the course primarily to meet science breadth requirements and students with a strong previous science background had equivalent initial awareness and course outcomes. The lack of a correlation with background suggests that students are not learning about environmental studies in other classes and reinforces the importance of investing in nonmajors-focused environmental education. Additionally, our findings support the importance of affective learning gains, such as attitude changes, which we term "learning to care".

A proliferation of data gathered to predict a critically important, urgent and social-policy related question often leads to debate and divergent model results. This classic feature of complex systems is currently being evidenced in assessing a potentially serious feedback response of soil respiration with increased temperatures due to global climate change. Here we apply soft systems modelling (SSM) for a detailed analysis of the 'soft' aspects of the topic. Supported by a literature review, we conclude that that varied perspectives on the system's dynamics and its web of controlling factors have led to seemingly conflicting results. We present a simplified hierarchy organizing the comprehensive universe of interacting controls across scales. This model is implemented as a relational database, including relationships between factors such as nesting and feedbacks. We conclude that although this model is currently limited to pairwise interactions, it provides a useful tool to assess potential interactions and factors of interest. Copyright © 2009 John Wiley & Sons, Ltd. Keywords soft systems modelling; complexity; global climate change; modelling; soil carbon, INTRODUCTION With many current environmental challenges, a web of confounding factors acting at different scales complicates the coherent synthesis of experimental results. The relationship between temperature and soil carbon decomposition [...]

We performed two experiments to assess the methodological variability of microbial community-level physiological profiles (CLPP, using the BiOLOG assay) in soil from a California annual grassland ecosystem. In a study to assess the impact of sample preparation, we found that bacteria adhered to soil surfaces are numerically dominant and have CLPPs indistinguishable from that of intact soil. Studies that allow soil particles to settle prior to dilution or plating may not accurately reflect the substrate utilization pattern of whole soil. In a hierarchical ANOVA, we found that nearly all of the methodological variability in the CLPP assay comes from soil replicates rather than plate replicates. Many laboratories replicate at the level of the CLPP plate. Our results indicate that to best represent a given soil sample, it is important to replicate soil subsamples, rather than CLPP plates.