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USGS2001

Potential contribution of microbial degradation to natural attenuation of MTBE in surface water systems

The potential contribution of in situ biodegradation as a mechanism for natural attenuation of MTBE in surface water was studied. Surface water sediments from streams and lakes at 11 sites throughout the US. Microbial degradation of [U-14C] MTBE was observed in surface-water-sediment microcosms under anaerobic conditions, but the efficiency and products of anaerobic MTBE biodegradation were strongly dependent on the predominant terminal electron accepting conditions. In the presence of substantial methanogenic activity, MTBE biodegradation was nominal and involved reduction of MTBE to t-butanol (TBA). Under more oxidizing conditions, minimal accumulation of 14C-TBA and significant mineralization of [U-14C] MTBE to 14CO2 were observed. Microorganisms inhabiting the bed sediments of streams and lakes could degrade MTBE effectively under a range of anaerobic terminal electron accepting conditions. Thus, anaerobic bed sediment microbial processes also might contribute to natural attenuation of MTBE in surface water systems throughout the US. This is an abstract of a paper presented at the 222nd ACS National Meting (Chicago, IL 8/26-30/2001).

ACS Division of Environmental Chemistry, Preprints↗
USGS2024

Ten quick tips to get you started with Bayesian statistics

Bayesian statistics is a framework in which our knowledge about unknown quantities of interest (especially parameters) is updated with the information in observed data, though it can also be viewed as simply another method to fit a statistical model. It has become popular in many branches of biology. For context, five of the ten most cited papers in Web of Science with keywords 'Bayesian statistics' are related to biology (as of August 19, 2024). Bayesian statistics is particularly valuable for biology because it allows researchers to incorporate prior knowledge, handle complex systems, and work effectively with limited or messy data. However, most biologists are trained in frequentist techniques, and the learning curve to become fluent in Bayesian statistics may be perceived as too time-consuming to undertake, or the prospect of adopting an unfamiliar statistical framework can simply appear too daunting. We provide a list of 10 tips to help you get started with Bayesian statistics. You can also refer to the Glossary for definitions of the technical terms. This paper isn’t just for newcomers; even those with some experience in Bayesian methods may find it a useful roadmap to design, conduct, and publish Bayesian analyses. We’ve drawn mainly on our experience teaching and working with ecologists, but we hope these tips will be relevant to a broader audience of biologists. For those seeking to deepen their understanding, we point to more comprehensive resources that offer in-depth exploration of Bayesian statistics.

HAL Open Science↗