Termites are undoubtedly amongst the most important soil macroinvertebrate decomposers in semi-arid environments in India. However, in this specific type of environment, the influence of termite foraging activity on soil functioning remains unexplored. Therefore, this study examines the link between the quality of litter and the functional impact of termite feeding preferences on soil properties and soil hydraulic conductivity in a deciduous forest in southern India. Different organic resources (elephant dung: "ED", elephant grass: "EG", acacia leaves: "AL" and layers of cardboard: "CB") were applied on repacked soil cores. ED appeared to be the most attractive resource to , leading to a larger amount of soil sheeting (i.e., the soil used by termites for covering the litter they consume), more numerous and larger holes in the ground and a lower soil bulk density. As a consequence, ED increased the soil hydraulic conductivity (4-fold) compared with the control soil. Thus, this study highlights that the more prefers a substrate, the more this species impacts soil dynamics and water infiltration in the soil. This study also shows that ED can be used as an efficient substrate for accelerating the infiltration of water in southern-Indian soils, mainly through the production of galleries that are open on the soil surface, offering new perspectives on termite management in this environment.
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http://dx.doi.org/10.3390/insects10010004 | DOI Listing |
BMC Genomics
January 2025
Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, 43210, USA.
Background: Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA. With a total of 621 soybean germplasm, we used three genome-wide association study (GWAS)-based approaches to investigate the genomic regions controlling the AA content and profile in soybean.
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January 2025
Xi'an Botanical Garden of Shaanxi Province, Institute of Botany of Shaanxi Province, Xi'an, 710061, Shaanxi, China.
Bacteria, fungi, archaea, and viruses are reflective organisms that indicate soil health. Investigating the impact of crude oil pollution on the community structure and interactions among bacteria, fungi, archaea, and viruses in Calamagrostis epigejos soil can provide theoretical support for remediating crude oil pollution in Calamagrostis epigejos ecosystems. In this study, Calamagrostis epigejos was selected as the research subject and subjected to different levels of crude oil addition (0 kg/hm, 10 kg/hm, 40 kg/hm).
View Article and Find Full Text PDFNat Microbiol
January 2025
Department of Chemistry, Indiana University, Bloomington, IN, USA.
To overtake competitors, microbes produce and secrete secondary metabolites that kill neighbouring cells and sequester nutrients. This metabolite-mediated competition probably evolved in complex microbial communities in the presence of viral pathogens. We therefore hypothesized that microbes secrete natural products that make competitors sensitive to phage infection.
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January 2025
Department of Ecology and Silviculture, Faculty of Forestry, University of Agriculture in Krakow, 29 Listopada 46 Str, Krakow, 31-425, Poland.
Tree species through aboveground biomass and roots are a key factors influencing the quality and quantity of soil organic matter. Our study aimed to determine the stability of soil organic matter in Luvisols under the influence of five different tree species. The study areas were located 25 km north of Krakow, in southern Poland.
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January 2025
Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, Milano, 20156, Italy.
This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data.
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