Rap~d and simultaneous changes in temperature, precipitation and the atmospheric concentration of CO, are predicted to occur over the next century. Simple, well-validated models of ecosystem function are required to predict the effects of these changes. This paper describes an improved version of a forest carbon and water balance model (PnET-11) and the application of the model to predict stand-and regional-level effects of changes in temperature, precipitation and atmospheric CO2 conceniraiion. PnET-ii is d s u~~p i e , y e~~e l d i i~e d , l~lu~lii~iy ii~~ie-btep ~nociel of water and carbon "vlances (gross and net) driven by nitrogen availability as expressed through foliar N concentration. Improvements from the orig~nal model include a complete carbon balance and improvements in the prediction of canopy phenology, as well as in the computation of canopy structure and photosynthesis. The model was parameterized and run for 4 forestkite con~binations and validated against available data for water yield, gross and net carbon exchange and biomass production. The validation exercise suggests that the determination of actual water availability to stands and the occurrence or non-occurrence of soilbased water stress are critical to accurate modeling of forest net primary production (NPP) and net ecosystem production (NEP). The model was then run for the entire NewEngland/New York (USA) region using a 1 km resolution geographic information system. Predicted long-term NEP ranged from -85 to +275 g C m-2 yr" for the 4 forest/site combinations, and from -150 to 350 g C m-' yr-' for the region, with a regional average of 76 g C m-2 yr-l A con~bination of increased temperature (+6OC), decreased precipitation (-15%) and increased water use efficiency (2x, due to doubling of CO,) resulted generally in increases in NPP and decreases in water yield over the region.

No abstract

No abstract

A review of the literature revealed that a variety of methods are currently used for fitting net assimilation of CO2-chloroplastic CO2 concentration (A-Cc) curves, resulting in considerable differences in estimating the A-Cc parameters [including maximum ribulose 1·5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (Vcmax), potential light saturated electron transport rate (Jmax), leaf dark respiration in the light (Rd), mesophyll conductance (gm) and triose-phosphate utilization (TPU)]. In this paper, we examined the impacts of fitting methods on the estimations of Vcmax, Jmax, TPU, Rd and gm using grid search and non-linear fitting techniques. Our results suggested that the fitting methods significantly affected the predictions of Rubisco-limited (Ac), ribulose 1,5-bisphosphate-limited (Aj) and TPU-limited (Ap) curves and leaf photosynthesis velocities because of the inconsistent estimate of Vcmax, Jmax, TPU, Rd and gm, but they barely influenced the Jmax : Vcmax, Vcmax : Rd and Jmax : TPU ratio. In terms of fitting accuracy, simplicity of fitting procedures and sample size requirement, we recommend to combine grid search and non-linear techniques to directly and simultaneously fit Vcmax, Jmax, TPU, Rd and gm with the whole A-Cc curve in contrast to the conventional method, which fits Vcmax, Rd or gm first and then solves for Vcmax, Jmax and/or TPU with Vcmax, Rd and/or gm held as constants.

We describe relationships between pH, specific conductance, calcium, magnesium, chloride, sulfate, nitrogen, and phosphorus and land‐use patterns in the Mullica River basin, a major New Jersey Pinelands watershed, and determine the thresholds at which significant changes in water quality occur. Nonpoint sources are the main contributors of pollutants to surface waters in the basin. Using multiple regression and water‐quality data for 25 stream sites, we determine the percentage of variation in the water‐quality data explained by urban land and upland agriculture and evaluate whether the proximity of these land uses influences water‐quality/land‐use relationships. We use a second, independently collected water‐quality dataset to validate the statistical models. The multiple‐regression results indicate that water‐quality degradation in the study area is associated with basin‐wide upland land uses, which are generally good predictors of water‐quality conditions, and that both urban land and upland agriculture must be included in models to more fully describe the relationship between watershed disturbance and water quality. Including the proximity of land uses did not improve the relationship between land use and water quality. Ten‐percent altered‐land cover in a basin represents the threshold at which a significant deviation from reference‐site water‐quality conditions occurs in the Mullica River basin.

No abstract

No abstract

The purpose of this study was to map the areal extent and density of submerged aquatic vegetation, principally the seagrasses, Zostera marina and Ruppia maritima, as part of ongoing monitoring for the Barnegat Bay, New Jersey National Estuary Program. We examine the utility of multiscale image segmentation/object-oriented image classification using the eCognition software to map seagrass across our 36,000 ha study area. The multi-scale image segmentation/ object oriented classification approach closely mirrored our conceptual model of the spatial structure of the seagrass habitats and successfully extracted the features of ecological interest. The agreement between the mapped results and the original field reference was 68 percent (Kappa ϭ 56.5 percent) for the four category map and 83 percent (Kappa ϭ 63.1 percent) for the presence/absence map; the agreement between the mapped results and the independent reference data was 71 percent (Kappa ϭ 43.0 percent) for a simple presence/absence map. While the aerial digital camera imagery employed in this study had the advantage of flexible acquisition, suitable image scale, fast processing return time, and comparatively low cost, it had inconsistent radiometric response from image to image. This inconsistency made it difficult to develop a rule-based classification that was universally applicable across the 14 individual image mosaics. However, within the individual scene mosaics, using the eCognition software in a "manual classification" mode provided a flexible and time effective approach to mapping seagrass habitats.