Holistic aboveground ecological productivity efficiency modeling using data envelopment analysis in the southeastern U.S.

Aboveground net primary productivity (ANPP) of an ecosystem is among the most important metrics of valued ecosystem services. Measuring the efficiency scores of ecological production (ESEP) based on ANPP using relevant variables is valuable for identifying inefficient sites. The efficiency scores computed by the Data Envelopment Analysis (DEA) may be influenced by the number of input variables incorporated into the models and two DEA settings-orientations and returns-to-scales (RTSs).

Improving the representation of forests in hydrological models.

Forests play a critical role in the hydrologic cycle, impacting the surface and groundwater dynamics of watersheds through transpiration, interception, shading, and modification of the atmospheric boundary layer. It is therefore critical that forest dynamics are adequately represented in watershed models, such as the widely applied Soil and Water Assessment Tool (SWAT). SWAT's default parameterization generally produces unrealistic forest growth predictions, which we address here through an improved representation of forest dynamics using species-specific re-parameterizations.

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data.

Background: LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data.

Pinus taeda forest growth predictions in the 21st century vary with site mean annual temperature and site quality.

Climate projections from 20 downscaled global climate models (GCMs) were used with the 3-PG model to predict the future productivity and water use of planted loblolly pine (Pinus taeda) growing across the southeastern United States. Predictions were made using Representative Concentration Pathways (RCP) 4.5 and 8.

Ecosystem carbon density and allocation across a chronosequence of longleaf pine forests.

Forests can partially offset greenhouse gas emissions and contribute to climate change mitigation, mainly through increases in live biomass. We quantified carbon (C) density in 20 managed longleaf pine (Pinus palustris Mill.) forests ranging in age from 5 to 118 years located across the southeastern United States and estimated above- and belowground C trajectories.

Water availability and genetic effects on water relations of loblolly pine (Pinus taeda) stands.

The effect of water availability on water relations of 11-year-old loblolly pine stands was studied over two growing seasons in material from two contrasting seed sources. Increasing soil water availability via irrigation increased transpiration rate, and maximum daily transpiration rate on irrigated plots was similar for both seasons, reaching values of 4.3 mm day(-)(1).