Colorado State University

About: Colorado State University is a education organization based out in Fort Collins, Colorado, United States. It is known for research contribution in the topics: Population & Radar. The organization has 31430 authors who have published 69040 publications receiving 2724463 citations. The organization is also known as: CSU & Colorado Agricultural College.

The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter?

Abstract: The decomposition and transformation of above- and below-ground plant detritus (litter) is the main process by which soil organic matter (SOM) is formed. Yet, research on litter decay and SOM formation has been largely uncoupled, failing to provide an effective nexus between these two fundamental processes for carbon (C) and nitrogen (N) cycling and storage. We present the current understanding of the importance of microbial substrate use efficiency and C and N allocation in controlling the proportion of plant-derived C and N that is incorporated into SOM, and of soil matrix interactions in controlling SOM stabilization. We synthesize this understanding into the Microbial Efficiency-Matrix Stabilization (MEMS) framework. This framework leads to the hypothesis that labile plant constituents are the dominant source of microbial products, relative to input rates, because they are utilized more efficiently by microbes. These microbial products of decomposition would thus become the main precursors of stable SOM by promoting aggregation and through strong chemical bonding to the mineral soil matrix.

Microbial stress‐response physiology and its implications for ecosystem function

Abstract: Microorganisms have a variety of evolutionary adaptations and physiological acclimation mechanisms that allow them to survive and remain active in the face of environmental stress. Physiological responses to stress have costs at the organismal level that can result in altered ecosystem-level C, energy, and nutrient flows. These large-scale impacts result from direct effects on active microbes' physiology and by controlling the composition of the active microbial community. We first consider some general aspects of how microbes experience environmental stresses and how they respond to them. We then discuss the impacts of two important ecosystem-level stressors, drought and freezing, on microbial physiology and community composition. Even when microbial community response to stress is limited, the physiological costs imposed on soil microbes are large enough that they may cause large shifts in the allocation and fate of C and N. For example, for microbes to synthesize the osmolytes they need to survive a single drought episode they may consume up to 5% of total annual net primary production in grassland ecosystems, while acclimating to freezing conditions switches Arctic tundra soils from immobilizing N during the growing season to mineralizing it during the winter. We suggest that more effectively integrating microbial ecology into ecosystem ecology will require a more complete integration of microbial physiological ecology, population biology, and process ecology.

The Envelope of Mycobacteria

Abstract: Mycobacteria, members of which cause tuberculosis and leprosy, produce cell walls of unusually low permeability, which contribute to their resistance to therapeutic agents. Their cell walls contain large amounts of C60-C90 fatty acids, mycolic acids, that are covalently linked to arabinogalactan. Recent studies clarified the unusual structures of arabinogalactan as well as of extractable cell wall lipids, such as trehalose-based lipooligosaccharides, phenolic glycolipids, and glycopeptidolipids. Most of the hydrocarbon chains of these lipids assemble to produce an asymmetric bilayer of exceptional thickness. Structural considerations suggest that the fluidity is exceptionally low in the innermost part of bilayer, gradually increasing toward the outer surface. Differences in mycolic acid structure may affect the fluidity and permeability of the bilayer, and may explain the different sensitivity levels of various mycobacterial species to lipophilic inhibitors. Hydrophilic nutrients and inhibitors, in contra...

Benchmark map of forest carbon stocks in tropical regions across three continents.

Abstract: Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Bayesian Model Averaging for Linear Regression Models

Abstract: We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem involves averaging over all possible models (i.e., combinations of predictors) when making inferences about quantities of interest. This approach is often not practical. In this article we offer two alternative approaches. First, we describe an ad hoc procedure, “Occam's window,” which indicates a small set of models over which a model average can be computed. Second, we describe a Markov chain Monte Carlo approach that directly approximates the exact solution. In the presence of model uncertainty, both of these model averaging procedures provide better predictive performance than any single model that might reasonably have been selected. In the extreme case where there are many candidate predictors but ...