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Ecology
Published in Yeqiao Wang, Terrestrial Ecosystems and Biodiversity, 2020
Since elucidation of the range of possible functional responses of plants is not possible with the use of model organisms, such as those typically used in plant physiology, functional ecology arises as an essentially comparative science. Ideally, functional ecology deals with traits measured on a large number of species in order to minimize the influence of the peculiarities of the autecology of each species. Two main approaches have been followed to find general ecological patterns in nature: 1) screening, that is, the design of bioassays for a trait or a set of traits measured simultaneously on a large number of species, as in the classic study by Grime and Hunt[8] of the relative growth rate of 132 species of British flora, and 2) empiricism, or the search for quantitative relationships between measurable dependent and independent variables (e.g., correlations among pairs of traits or traits and environments) producing quantitative models using traits and not species, as in the general revision of leaf traits by Reich et al.[4] Models using traits are more general than those based on species and can be more easily transferred to different floras.[1] The question arises as to which trait must be measured. A possible answer can be obtained by analyzing the basic functions that organisms perform (i.e., resource acquisition, the ability to tolerate environmental extremes, and the ability to compete with neighbors) and then either looking for traits which measure these functions or carrying out direct bioassays of them. The most common limitations of this kind of study are the difficulties in finding unambiguous linkages of a trait to a specific function, and the so-called phylogenetic constraints that are due to the fact that phylogenetic proximity among species can influence their functional similarities.
Microflora communities which can convert digested sludge to biogas
Published in Environmental Technology, 2022
Ayaka Kon, Shunsuke Omata, Yuhei Hayakawa, Nobuhiro Aburai, Katsuhiko Fujii
A phylogenetic tree based on the amplicon DNA sequences was constructed, as shown in Figure 4. For eubacteria, species from the genera Clostridium (the amplicons C1–C4, C6, and C7) and Enterobacter (E1–E5, E7–E9) were dominant in all DEBYS and DABYE (Figures 2 and 3). However, the amplicons C6 and C7 representing members of the Clostridiaceae family formed a phylogenetically independent gene cluster, suggesting that they represent eubacteria that are members of an unestablished genus. Amplicons representing Pseudomonas species (P1 and P2) were detected only in the riverbank sediments (Figures 2 and 4(a)). In contrast to what we detected with DABYS, the DABYE contained Fonticella (Clostridiaceae family, C5), Ruminoclostridium (Hungateiclostridiceae family, H1) and Acinetobacter (Moraxellaceae family, M1) species (Figures 3 and 4(a)).
Molecular characterization of biosurfactant producing Bacillus cereus strain DRDU1 for its potential application in bioremediation and further EOR studies
Published in Petroleum Science and Technology, 2018
The amplicon was sequenced and was analyzed in NCBI microbial BLAST tool for homology search. Based on maximum identity scores obtained, the aligned sequences from top ten sequences were retrieved and aligned using multiple alignment software program ClustalW™. The phylogenetic tree was constructed using MEGA6 software by Maximum Parsimony method (Saitou and Nei 1987) with 1000 bootstrap value to see their molecular relatedness. A phylogenetic tree was generated both at nucleotide and amino acid level to see the molecular similarity of the amplified sequence with other reported such genes. The hypothetical amino acid sequence was also generated from the amplicon with the help of ExPasyTM online software.
Insights into the catalytic mechanism of ligninolytic peroxidase and laccase in lignin degradation
Published in Bioremediation Journal, 2022
Pankaj Bhatt, Meena Tiwari, Prasoon Parmarick, Kalpana Bhatt, Saurabh Gangola, Muhammad Adnan, Yashpal Singh, Muhammad Bilal, Shakeel Ahmed, Shaohua Chen
Phylogenetic relationship among the protein sequences retrieved from the protein data bank and NCBI respectively were analyzed. The in silico tool MEGA.5.0 version was used for the analysis and formation of a phylogenetic tree. Clustal-W tool was used for the alignment of all protein sequences. Aligned protein sequences were used for phylogenetic tree analysis. The parameters were not changed/default or already set parameters used for phylogenetic study. We used the neighbour-joining (N.J.) method for the tree analysis.