Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil - Plot 1 metagenome
Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil Plot 36 metagenome
Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil Plot 30 metagenome
Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil - Plot 6 metagenome
Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil Plot 36 MoBio metagenome
Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil Plot 6 MoBio metagenome
Project description:Tropical forest soil microbial communities from Panama analyzed to predict greenhouse gas emissions - Panama Soil Plot 30 MoBio metagenome
Project description:Global warming substantially changes precipitation patterns in the Tibetan plateau, with projection of increased precipitation in southern and northern Tibet but decreased precipitation in the center. Understanding mechanisms of such changes in greenhouse gas emissions is of vital importance in predicting ecosystem feedbacks to climate changes. Nonetheless, it has been hampered by limited knowledge in soil microbial communities, one of the major drivers of greenhouse gas emission. Here, we report a field experiment simulating drying and wetting conditions in the Tibetan grassland. Our field site is located at the Haibei Alpine Grassland Ecosystem Research Station in the northeast of Tibet Plateau, China, and we employed GeoChip 5.0 180K to analyze microbial responses. 18 samples were collected from 3 plots in Haibei Station, with 6 replicates in each plot
Project description:Global warming substantially changes precipitation patterns in the Tibetan plateau, with projection of increased precipitation in southern and northern Tibet but decreased precipitation in the center. Understanding mechanisms of such changes in greenhouse gas emissions is of vital importance in predicting ecosystem feedbacks to climate changes. Nonetheless, it has been hampered by limited knowledge in soil microbial communities, one of the major drivers of greenhouse gas emission. Here, we report a field experiment simulating drying and wetting conditions in the Tibetan grassland. Our field site is located at the Haibei Alpine Grassland Ecosystem Research Station in the northeast of Tibet Plateau, China, and we employed GeoChip 5.0 180K to analyze microbial responses.
Project description:The dataset comprises the most abundant and largest (by stem diameter) tree species in the Barro Colorado Island 50-ha forest dynamics plot in Panama, as well as all local species in 7 of the most species-rich genera in the plot: Eugenia (Myrtaceae), Inga (Moraceae), Miconia/Clidemia (Melastomataceae), Ocotea/Nectandra (Lauraceae), Piper (Piperaceae), Protium (Burseraceae), Psychotria/Palicourea (Rubiaceae). Leaf samples were extracted with 90:10 methanol:water pH 5 and analyzed using methods described in Sedio et al. 2017 Applications in Plant Sciences (doi:10.1002/aps3.1033).
