Characterization of Archaeal Community in Contaminated and Uncontaminated Surface Stream Sediments

Archaeal communities from mercury and uranium-contaminated freshwater stream sediments were characterized and compared to archaeal communities present in an uncontaminated stream located in the vicinity of Oak Ridge, TN, USA. The distribution of the Archaea was determined by pyrosequencing analysis of the V4 region of 16S rRNA amplified from 12 streambed surface sediments. Crenarchaeota comprised 76% of the 1,670 archaeal sequences and the remaining 24% were from Euryarchaeota. Phylogenetic analysis further classified the Crenarchaeota as a Freshwater Group, Miscellaneous Crenarchaeota group, Group I3, Rice Cluster VI and IV, Marine Group I and Marine Benthic Group B; and the Euryarchaeota into Methanomicrobiales, Methanosarcinales, Methanobacteriales, Rice Cluster III, Marine Benthic Group D, Deep Sea Hydrothermal Vent Euryarchaeota 1 and Eury 5. All groups were previously described. Both hydrogen- and acetate-dependent methanogens were found in all samples. Most of the groups (with 60% of the sequences) described in this study were not similar to any cultivated isolates, making it difficult to discern their function in the freshwater microbial community. A significant decrease in the number of sequences, as well as in the diversity of archaeal communities was found in the contaminated sites. The Marine Group I, including the ammonia oxidizer Nitrosopumilus maritimus, was the dominant group in both mercury and uranium/nitrate-contaminated sites. The uranium-contaminated site also contained a high concentration of nitrate, thus Marine Group I may play a role in nitrogen cycle

Thauera butanivorans sp. nov., a C2–C9 alkane-oxidizing bacterium previously referred to as ‘Pseudomonas butanovora’

The placement of ‘Pseudomonas butanovora’ in the genus Thauera was proposed previously, based on 16S rRNA gene sequence analysis, upon further studies of taxonomical characteristics. In this study, physiological characteristics and DNA–DNA reassociation data are presented and the transfer of ‘P. butanovora’ to the genus Thauera is proposed. The original description of the strain (strain Bu-B1211) indicated that it was capable of denitrification but not anaerobic growth. ‘P. butanovora’ is capable of anaerobic respiration and growth, utilizing nitrate as a terminal electron acceptor during the oxidation of organic acids and alcohols, but not aromatic hydrocarbons or open-chain terpenoids. The total fatty acid composition supported the assignment of strain Bu-B1211 to the Betaproteobacteria and resembled that of members of the genus Thauera. The combination of 16S rRNA gene phylogenetic evidence, physiological and taxonomical characteristics and DNA–DNA reassociation data supported the placement of ‘Pseudomonas butanovora’ Bu-B1211 in the genus Thauera as representing a novel species, for which the name Thauera butanivorans sp. nov. is proposed. The type strain is Bu-B1211T (=IAM 12574T=ATCC 43655T=DSM 2080T)

Spatial soil analysis using geostatistical analysis and map Algebra

Evaluating soil spatial variability through sampling is an important step in precision farming processes that aids farmers to make informed decisions on the spread of agricultural inputs. Manual sampling is essential in ascertaining soil physical characteristics and could be used to monitor the chemical components like macronutrient nitrogen (N), phosphorus (P), and potassium (K). This type of sampling however could be costly and time consuming in macronutrient sampling. In order to show the ability of manual sampling to capture the essence of variability in the agricultural fields with enough number of samples and therefore, helping the precision farming process, we conducted an experiment on different designs of random, systematic, stratified random, stratified systematic, and different sizes of samples. The experiment was carried out on the geostatistical surfaces (base maps) created from a set of data which belonged to a rice plantation in Malaysia. A krigged map for each of these schemes was created and compared with the N, P, and K base maps. The results showed that the systematic and stratified systematic schemes were the most accurate sampling schemes in terms of estimation of mean. However, both stratified schemes were not successful to create the standard deviation of populations. Concerning the standard error of mean when the schemes were used in linear mixed effect modeling grouped by the sample size, stratified samples could produce lower standard error (except for stratified random sample of P). In terms of reproducing the original spatial variability, only systematic sampling scheme could create better accuracy in most cases. The result also revealed that the most important property of a sampling scheme in the study area is representativeness of samples, and the number of samples does not play an important role in accuracy and map making. Therefore, the data could be equally valid for the precision farmin