Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem.

Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment.

Coastal upland forests are facing widespread mortality as sea-level rise accelerates and precipitation and storm regimes change. The loss of coastal forests has significant implications for the coastal carbon cycle; yet, predicting mortality likelihood is difficult due to our limited understanding of disturbance impacts on coastal forests. The manipulative, ecosystem-scale Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA.

Controls and variability of soil respiration temperature sensitivity across China.

Understanding the temperature sensitivity (Q) of soil respiration is critical for benchmarking the potential intensity of regional and global terrestrial soil carbon fluxes-climate feedbacks. Although field observations have demonstrated the strong spatial heterogeneity of Q, a significant knowledge gap still exists regarding to the factors driving spatial and temporal variabilities of Q at regional scales. Therefore, we used a machine learning approach to predict Q from 1994 to 2016 with a spatial resolution of 1 km across China from 515 field observations at 5 cm soil depth using climate, soil and vegetation variables.

Latitudinal shifts of soil microbial biomass seasonality.

Soil microbes ultimately drive the mineralization of soil organic carbon and thus ecosystem functions. We compiled a dataset of the seasonality of microbial biomass carbon (MBC) and developed a semi-mechanistic model to map monthly MBC across the globe. MBC exhibits an equatorially symmetric seasonality between the Northern and Southern Hemispheres.