The spatial pattern of nitrogen cycling in the Adirondack Park, New York

Soil and Tree Nutrient Status of High Elevation Mixed Red Spruce (Picea rubens Sarg.) and Broadleaf Deciduous Forests

Anthropogenic and industrial emissions have resulted in historically high levels of acidic deposition into central Appalachian forests. Despite the reduction in acidic inputs due to legislation curbing industrial emissions in the United States, continued N deposition may impact forest ecosystems. Soil and foliar samples were collected from four high elevation red spruce sites along a modeled gradient of historic N deposition. The three most abundant tree species at all sites, Acer rubrum L., Betula alleghaniensis Britt., and Picea rubens Sarg., were sampled. Bulk soil beneath the canopies of individual trees were collected from the top 15-cm and separated into organic and mineral fractions for analysis. Mehlich-III soil extracts of soil fractions and foliar digests from these trees were subjected to elemental analysis. Soil N concentrations supported the presence of a N deposition gradient: in organic horizon soil fractions, N concentrations were driven by precipitation volume and elevation; whereas in mineral soil fractions, N concentration was explained by modeled N deposition rate and elevation. In organic fractions, significant reductions in Ca, K, and P were evident as N deposition increased, whereas the Ca:Sr ratio increased. Foliar Ca, K, and Sr declined in foliage with increasing N deposition, with concomitant increases in foliar Ca:Sr ratios. Although the three species were sympatric in mixed stands at all four sites, the foliar–soil nutrient associations differed among them across the gradient, indicating differential uptake and cycling of nutrients/metals by these forest tree species.

Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (Nmass), 42–54% for carbon (Cmass), 17–58% for fiber (acid-digestible fiber, ADF), 7–44% for lignin (acid-digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M(area)), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M(area) and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than M(area) and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95 per thousand), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

Using foliar and forest floor mercury concentrations to assess spatial patterns of mercury deposition

We evaluated spatial patterns of mercury (Hg) deposition through analysis of foliage and forest floor samples from 45 sites across Adirondack Park, NY. Species-specific differences in foliar Hg were evident with the lowest concentrations found in first-year conifer needles and highest concentrations found in black cherry (Prunus serotina). For foliage and forest floor samples, latitude and longitude were negatively correlated with Hg concentrations, likely because of proximity to emission sources, while elevation was positively correlated with Hg concentrations. Elemental analysis showed moderately strong, positive correlations between Hg and nitrogen concentrations. The spatial pattern of Hg deposition across the Adirondacks is similar to patterns of other contaminants that originate largely from combustion sources such as nitrogen and sulfur. The results of this study suggest foliage can be used to assess spatial patterns of Hg deposition in small regions or areas of varied topography where current Hg deposition models are too coarse to predict deposition accurately.