Todd C. McDonnell

Effects of Acidic Deposition and Soil Acidification on Sugar Maple Trees in the Adirondack Mountains, New York

We documented the effects of acidic atmospheric deposition and soil acidification on the canopy health, basal area increment, and regeneration of sugar maple (SM) trees across the Adirondack region of New York State, in the northeastern United States, where SM are plentiful but not well studied and where widespread depletion of soil calcium (Ca) has been documented. Sugar maple is a dominant canopy species in the Adirondack Mountain ecoregion, and it has a high demand for Ca. Trees in this region growing on soils with poor acid-base chemistry (low exchangeable Ca and % base saturation [BS]) that receive relatively high levels of atmospheric sulfur and nitrogen deposition exhibited a near absence of SM seedling regeneration and lower crown vigor compared with study plots with relatively high exchangeable Ca and BS and lower levels of acidic deposition. Basal area increment averaged over the 20th century was correlated (p < 0.1) with acid-base chemistry of the Oa, A, and upper B soil horizons. A lack of Adirondack SM regeneration, reduced canopy condition, and possibly decreased basal area growth over recent decades are associated with low concentrations of nutrient base cations in this region that has undergone soil Ca depletion from acidic deposition.

THE SPATIAL PATTERN OF NITROGEN CYCLING IN THE ADIRONDACK PARK, NEW YORK

Maps of canopy nitrogen obtained through analysis of high-resolution, hyperspectral, remotely sensed images now offer a powerful means to make landscape-scale to regional-scale estimates of forest N cycling and net primary production (NPP). Moreover, recent research has suggested that the spatial variability within maps of canopy N may be driven by environmental gradients in such features as historic forest disturbance, temperature, species composition, moisture, geology, and atmospheric N deposition. Using the wide variation in these six features found within the diverse forest ecosystems of the 2.5 million ha Adirondack Park, New York, USA, we examined linkages among environmental gradients and three measures of N cycling collected during the 2003 growing season: (1) field survey of canopy N, (2) field survey of soil C:N, and (3) canopy N measured through analysis of two 185 x 7.5 km Hyperion hyperspectral images. These three measures of N cycling strongly related to forest type but related poorly to all other environmental gradients. Further analysis revealed that the spatial pattern in N cycling appears to have distinct inter- and intraspecific components of variability. The interspecific component, or the proportional contribution of species functional traits to canopy biomass, explained 93% of spatial variability within the field canopy N survey and 37% of variability within the soil C:N survey. Residual analysis revealed that N deposition accounted for an additional 2% of variability in soil C:N, and N deposition and historical forest disturbance accounted for an additional 2.8% of variability in canopy N. Given our finding that 95.8% of the variability in the field canopy N survey could be attributed to variation in the physical environment, our research suggests that remotely sensed maps of canopy N may be useful not only to assess the spatial variability in N cycling and NPP, but also to unravel the relative importance of their multiple controlling factors.

Target loads of atmospheric sulfur and nitrogen deposition for protection of acid sensitive aquatic resources in the Adirondack Mountains, New York

TLs to calculate exceedances. Target load results, and associated exceedances, were extrapolated to the regional population of Adirondack lakes. About 30% of Adirondack lakes had simulated TL of sulfur deposition less than 50 meq m � 2 yr to protect lake ANC to 50 meq L � 1 . About 600 Adirondack lakes receive ambient sulfur deposition that is above this TL, in some cases by more than a factor of 2. Some critical criteria threshold values were simulated to be unobtainable in some lakes even if sulfur deposition was to be decreased to zero and held at zero until the specified endpoint year. We also summarize important lessons for the use of target loads in the management of acid-impacted aquatic ecosystems, such as those in North America, Europe, and Asia.

Regionalization of soil base cation weathering for evaluating stream water acidification in the Appalachian Mountains, USA

Estimation of base cation supply from mineral weathering (BC(w)) is useful for watershed research and management. Existing regional approaches for estimating BC(w) require generalized assumptions and availability of stream chemistry data. We developed an approach for estimating BC(w) using regionally specific empirical relationships. The dynamic model MAGIC was used to calibrate BC(w) in 92 watersheds distributed across three ecoregions. Empirical relationships between MAGIC-simulated BC(w) and watershed characteristics were developed to provide the basis for regionalization of BC(w) throughout the entire study region. BC(w) estimates extracted from MAGIC calibrations compared reasonably well with BC(w) estimated by regression based on landscape characteristics. Approximately one-third of the study region was predicted to exhibit BC(w) rates less than 100 meq/m(2)/yr. Estimates were especially low for some locations within national park and wilderness areas. The regional BC(w) results are discussed in the context of critical loads (CLs) of acidic deposition for aquatic ecosystem protection.

Comparison among model estimates of critical loads of acidic deposition using different sources and scales of input data

The critical load (CL) of acidic atmospheric deposition represents the load of acidity deposited from the atmosphere to the earths surface at which harmful acidification effects on sensitive biological receptors are thought to occur. In this study, the CL for forest soils was estimated for 27 watersheds throughout the United States using a steady-state mass balance approach based on both national and site-specific data and using different approaches for estimating base cation weathering. Results suggested that the scale and source of input data can have large effects on the calculated CL and that the most important parameter in the steady-state model used to estimate CL is base cation weathering. These results suggest that the data and approach used to estimate weathering must be robust if the calculated CL is to be useful for its intended purpose.

Machine learning and linear regression models to predict catchment‐level base cation weathering rates across the southern Appalachian Mountain region, USA

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous map of BCw within the southern Appalachian Mountain region. More than 50 initial candidate predictor variables were submitted to a variety of conventional and machine learning regression models. Predictors included aspects of the underlying geology, soils, geomorphology, climate, topographic context, and acidic deposition rates. Low BCw rates were predicted in catchments with low precipitation, siliceous lithology, low soil clay, nitrogen and organic matter contents, and relatively high levels of canopy cover in mixed deciduous and coniferous forest types. Machine learning approaches, particularly random forest modeling, significantly improved model prediction of catchment-level BCw rates over traditional linear regression, with higher model accuracy and lower error rates. Our results confirmed findings from other studies, but also identified several influential climatic predictor variables, interactions, and nonlinearities among the predictors. Results reported here will be used to support regional sulfur critical loads modeling to identify areas impacted by industrially derived atmospheric S inputs. These methods are readily adapted to other regions where accurate CL estimates are required over broad spatial extents to inform policy and management decisions.

Steady-state sulfur critical loads and exceedances for protection of aquatic ecosystems in the U.S. southern Appalachian Mountains

Atmospherically deposited sulfur (S) causes stream water acidification throughout the eastern U.S. Southern Appalachian Mountain (SAM) region. Acidification has been linked with reduced fitness and richness of aquatic species and changes to benthic communities. Maintaining acid-base chemistry that supports native biota depends largely on balancing acidic deposition with the natural resupply of base cations. Stream water acid neutralizing capacity (ANC) is maintained by base cations that mostly originate from weathering of surrounding lithologies. When ambient atmospheric S deposition exceeds the critical load (CL) an ecosystem can tolerate, stream water chemistry may become lethal to biota. This work links statistical predictions of ANC and base cation weathering for streams and watersheds of the SAM region with a steady-state model to estimate CLs and exceedances. Results showed that 20.1% of the total length of study region streams displayed ANC <100 μeq∙L(-1), a level at which effects to biota may be anticipated; most were 4th or lower order streams. Nearly one-third of the stream length within the study region exhibited CLs of S deposition <50 meq∙m(-2)∙yr(-1), which is less than the regional average S deposition of 60 meq∙m(-2)∙yr(-1). Owing to their geologic substrates, relatively high elevation, and cool and moist forested conditions, the percentage of stream length in exceedance was highest for mountain wilderness areas and in national parks, and lowest for privately owned valley bottom land. Exceedance results were summarized by 12-digit hydrologic unit code (subwatershed) for use in developing management goals and policy objectives, and for long-term monitoring.

Spatial Decision Support for Assessing Impacts of Atmospheric Sulfur Deposition on Aquatic Ecosystems in the Southern Appalachian Region

We present foundational work on the use of niche modeling to predict continuous surfaces of acid neutralizing capacity (ANC) and base cation weathering (BCw) within the southern Appalachian Mountain Region of the United States. Predicted ANC and BCw surfaces are subsequently used to estimate steady-state critical loads (CLs) of atmospheric sulfur deposition. We then present a logic-based model for assessing aquatic environmental effects of sulfur deposition throughout the region based on modeled stream ANC and CL exceedance, and demonstrate application of the logic model in a spatial decision-support system, presenting mapped model results for Great Smoky Mountain National Park. Whereas CLs were uniformly high within the Park area, degree of aquatic impact within watersheds was strongly associated with increasing elevation. The niche and spatial decision support modeling approaches are readily customized for other regions of interest.

An a priori process for selecting candidate reference lakes for a national survey

Abstract.  One of the biggest challenges when conducting a national-scale assessment of lakes, such as the 2007 US National Lake Assessment (NLA), is finding enough reference lakes to set appropriate expectations for the assessed sites. In the NLA, a random design was used to select lakes for sampling to make unbiased estimates of regional condition. However, such an approach was unlikely to yield enough minimally impacted lakes to use as reference sites, especially in disturbed regions. We developed a 3-stage process to select candidate reference lakes to augment the NLA probability sample in the northeastern USA (Northeast). Screening included a water-chemistry database filter, landuse evaluation, and analysis of aerial photographs. In the Northeast, we assembled a database of 2109 lakes >4 ha in surface area, of which 369 passed the water-chemistry screen. Of these, 220 failed the watershed landuse screen and 60 failed the aerial photograph screen, leaving a set of 89 optimal candidate reference lakes. Twenty of these lakes were sampled as potential reference lakes in the NLA. Based on a wide variety of indicators, NLA field measurements indicated that almost all (85–100%) of the chosen candidate reference lakes had least-disturbed water chemistry, although somewhat fewer had least disturbed physical habitat (74–79%) and biology (68–78%). Nevertheless, our 3-stage screening process was an efficient method for identification of good candidates for reference-lake sampling. The reference-lake selection process used in our study can be done in the office and relatively inexpensively. As such, it is very useful for large-scale regional or national studies encompassing areas too large to census. It also has the advantage of adding a level of consistency and quantification to the reference-site selection process.

Feasibility of coupled empirical and dynamic modeling to assess climate change and air pollution impacts on temperate forest vegetation of the eastern United States

Changes in climate and atmospheric nitrogen (N) deposition caused pronounced changes in soil conditions and habitat suitability for many plant species over the latter half of the previous century. Such changes are expected to continue in the future with anticipated further changing air temperature and precipitation that will likely influence the effects of N deposition. To investigate the potential long-term impacts of atmospheric N deposition on hardwood forest ecosystems in the eastern United States in the context of climate change, application of the coupled biogeochemical and vegetation community model VSD+PROPS was explored at three sites in New Hampshire, Virginia, and Tennessee. This represents the first application of VSD+PROPS to forest ecosystems in the United States. Climate change and elevated (above mid-19th century) N deposition were simulated to be important factors for determining habitat suitability. Although simulation results suggested that the suitability of these forests to support the continued presence of their characteristic understory plant species might decline by the year 2100, low data availability for building vegetation response models with PROPS resulted in uncertain results at the extremes of simulated N deposition. Future PROPS model development in the United States should focus on inclusion of additional foundational data or alternate candidate predictor variables to reduce these uncertainties.