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). Therefore, the objectives were threefold to: (1) identify soil-environmental variables relevant to ANPP, (2) assess the sensitivity of ESEP to the number of input variables and DEA settings, and (3) assess local management relations with ESEP. The ANPP rates were calculated for pine forests in the southeastern U.S. where 10 management types were used. This was followed by an all-relevant variable selection technique based on 696 variables that cover biotic, pedogenic, climatic, geological, and topographical factors. Five minimal-optimal variable selection techniques were further applied to create five parsimonious sets that contain a different number of variables used as DEA inputs. After setting ANPP as the output variable, two DEA orientations (input/output) and six RTS were applied to compute ESEP. The variable selection methods succeeded in objectively identifying the major factors relevant to ANPP variation. The site index showed the highest correlation with ANPP (r = 0.39), while various precipitation factors were negatively correlated (r = - 0.15~ - 0.29, p < 0.01). Parsimonious ESEP models observed a decrease in score variances as the number of input variables increased. Various RTS produced similar scores across orientations. Of the DEA settings, an output orientation with decreasing RTS produced the most progressive ESEP with large variation. Results also suggested that macro- and micronutrient fertilization is the best combination of management strategies to achieve high ESEP. This holistic benchmark approach can be applied to other ecological functions in diverse regions.

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. We applied this methodology to the two dominant pine species in the southeastern U.S., loblolly pine (Pinus taeda L.) and slash pine (Pinus elliotti). Specifically, we replaced unrealistic parameter values related to tree growth with physically meaningful parameters derived from publicly available remote-sensing products, field measurements, published literature, and expert knowledge. Outputs of the default and re-parameterized models were compared at four pine plantation sites across a range of management, soil, and climate conditions. Results were validated against MODIS-derived leaf area index (LAI) and evapotranspiration (ET), as well as field observations of total biomass. The re-parameterized model outperformed the default model in simulating LAI, biomass accumulation, and ET at all sites. The two parametrizations also resulted in substantially different mean annual water budgets for all sites, with reductions in water yield ranging from 13 to 45% under the new parameterization, highlighting the importance of properly parameterizing forest dynamics in watershed models. Importantly, our re-parameterization methodology does not require alteration to the SWAT code, allowing it to be readily adapted and applied in ongoing and future watershed modeling studies.

The uncharacterized gene EVE contributes to vessel element dimensions in Populus.

The radiation of angiosperms led to the emergence of the vast majority of today's plant species and all our major food crops. Their extraordinary diversification occurred in conjunction with the evolution of a more efficient vascular system for the transport of water, composed of vessel elements. The physical dimensions of these water-conducting specialized cells have played a critical role in angiosperm evolution; they determine resistance to water flow, influence photosynthesis rate, and contribute to plant stature. However, the genetic factors that determine their dimensions are unclear. Here we show that a previously uncharacterized gene, ENLARGED VESSEL ELEMENT (EVE), contributes to the dimensions of vessel elements in Populus, impacting hydraulic conductivity. Our data suggest that EVE is localized in the plasma membrane and is involved in potassium uptake of differentiating xylem cells during vessel development. In plants, EVE first emerged in streptophyte algae, but expanded dramatically among vessel-containing angiosperms. The phylogeny, structure and composition of EVE indicates that it may have been involved in an ancient horizontal gene-transfer event.

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. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m-2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. RESULTS: The results show that LiDAR pulse density of 5 pulses m-2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m-2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. CONCLUSION: LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m-2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

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 8.5. These represent scenarios in which total radiative forcing stabilizes before 2100 (RCP 4.5) or continues increasing throughout the century (RCP 8.5). Thirty-six sites evenly distributed across the native range of the species were used in the analysis. These sites represent a range in current mean annual temperature (14.9-21.6°C) and precipitation (1,120-1,680 mm/year). The site index of each site, which is a measure of growth potential, was varied to represent different levels of management. The 3-PG model predicted that aboveground biomass growth and net primary productivity will increase by 10%-40% in many parts of the region in the future. At cooler sites, the relative growth increase was greater than at warmer sites. By running the model with the baseline [CO2 ] or the anticipated elevated [CO2 ], the effect of CO2 on growth was separated from that of other climate factors. The growth increase at warmer sites was due almost entirely to elevated [CO2 ]. The growth increase at cooler sites was due to a combination of elevated [CO2 ] and increased air temperature. Low site index stands had a greater relative increase in growth under the climate change scenarios than those with a high site index. Water use increased in proportion to increases in leaf area and productivity but precipitation was still adequate, based on the downscaled GCM climate projections. We conclude that an increase in productivity can be expected for a large majority of the planted loblolly pine stands in the southeastern United States during this century.

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. Ecosystem C stock (all pools including soil C) and aboveground live tree C increased nonlinearly with stand age and the modeled asymptotic maxima were 168 Mg C/ha and 80 Mg C/ha, respectively. Accumulation of ecosystem C with stand age was driven mainly by increases in aboveground live tree C, which ranged from <1 Mg C/ha to 74 Mg C/ha and comprised <1% to 39% of ecosystem C. Live root C (sum of below-stump C, ground penetrating radar measurement of lateral root C, and live fine root C) increased with stand age and represented 4-22% of ecosystem C. Soil C was related to site index, but not to stand age, and made up 39-92% of ecosystem C. Live understory C, forest floor C, downed dead wood C, and standing dead wood C were small fractions of ecosystem C in these frequently burned stands. Stand age and site index accounted for 76% of the variation in ecosystem C among stands. The mean root-to-shoot ratio calculated as the average across all stands (excluding the grass-stage stand) was 0.54 (standard deviation of 0.19) and higher than reports for other conifers. Long-term accumulation of live tree C, combined with the larger role of belowground accumulation of lateral root C than in other forest types, indicates a role of longleaf pine forests in providing disturbance-resistant C storage that can balance the more rapid C accumulation and C removal associated with more intensively managed forests. Although other managed southern pine systems sequester more C over the short-term, we suggest that longleaf pine forests can play a meaningful role in regional forest C management.

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). Irrigation also changed soil water extraction patterns. In the rain-fed control plots, 73% of the average daily transpiration was extracted from the upper 0.75 m of the soil profile. Under irrigated conditions, 92% of transpired water was extracted from the upper 0.75 m of soil, with 79% of transpired water coming from the upper 0.35 m of the profile; only 10% of total transpiration in this treatment was extracted from the soil below 1 m. There was an irrigation x seed source interaction in the response of canopy conductance to water vapor (G(C)) to vapor pressure deficit (D). Under water-limited conditions, trees from the South Carolina seed source (SC) had stronger stomatal control than trees from the Florida seed source (FL), but this difference was not present when water was not limiting. The transpiration-induced water potential gradient from roots to shoots (DeltaPsi) was relatively constant across treatments (P = 0.52) and seed sources (P = 0.72), averaging 0.75 MPa. This reflects strong stomatal control that maintains relatively constant DeltaPsi but at the same time allows leaf water potential (Psi(l)) to fluctuate dramatically in synchrony with soil water potential (Psi(s)). The two seed sources evaluated also showed differences in foliar N and delta(13)C, possibly reflecting differences in adaptation to ambient humidity and water availability regimes in their respective ranges. These differences among seed sources under different water availability scenarios may be informative to natural resource managers and breeders as they design tree improvement and genetic deployment programs for future climate scenarios. For example, the increased stomatal control of SC under decreased soil moisture availability may make this taxon a more conservative deployment choice than FL under future, drier climate scenarios but perhaps at the risk of lower productivity.