In tropical ecosystems, termite mound soils constitute an important soil compartment covering around 10% of African soils. Previous studies have shown (S. Fall, S. Nazaret, J. L. Chotte, and A. Brauman, Microb. Ecol. 28: [191][192][193][194][195][196][197][198][199] 2004) that the bacterial genetic structure of the mounds of soil-feeding termites (Cubitermes niokoloensis) is different from that of their surrounding soil. The aim of this study was to characterize the specificity of bacterial communities within mounds with respect to the digestive and soil origins of the mound. We have compared the bacterial community structures of a termite mound, termite gut sections, and surrounding soil using PCR-denaturing gradient gel electrophoresis (DGGE) analysis and cloning and sequencing of PCR-amplified 16S rRNA gene fragments. DGGE analysis revealed a drastic difference between the genetic structures of the bacterial communities of the termite gut and the mound. Analysis of 266 clones, including 54 from excised bands, revealed a high level of diversity in each biota investigated. The soil-feeding termite mound was dominated by the Actinobacteria phylum, whereas the Firmicutes and Proteobacteria phyla dominate the gut sections of termites and the surrounding soil, respectively. Phylogenetic analyses revealed a distinct clustering of Actinobacteria phylotypes between the mound and the surrounding soil. The Actinobacteria clones of the termite mound were diverse, distributed among 10 distinct families, and like those in the termite gut environment lightly dominated by the Nocardioidaceae family. Our findings confirmed that the soil-feeding termite mound (C. niokoloensis) represents a specific bacterial habitat in the tropics.
This letter is intended to help potential users select the most appropriate calculator for a landscape-scale greenhouse gas (GHG) assessment of activities for agriculture and forestry. Eighteen calculators were assessed. These calculators were designed for different aims and to be used in different geographical areas and they use slightly different accounting methodologies. The classification proposed is based on the main aim of the assessment: raising awareness, reporting, project evaluation or product assessment. When the aims have been clearly formulated, the most suitable calculator can be selected from the comparison tables, taking account of the geographical area and the scope of the calculation as well as the time and skills required for the calculation. The main issues for interpreting GHG assessments are discussed, highlighting the difficulty of comparing the results obtained from different calculators, mainly owing to differences in scope, calculation methods and reporting units. A major problem is the poor accounting for land use change; the calculators are usually able to account satisfactorily for other emission sources. One of the main challenges at landscape-scale level is to produce a realistic assessment of the various production systems as the uncertainty levels are very high. The results should always give some indication of the link between GHG emissions and the productivity of the area, although no single indicator is able to encompass all the services produced by agriculture and forestry (e.g. food, goods, landscape value and revenue).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.