Forest and Rangeland Soils of the United States Under Changing Conditions

This open access book synthesizes leading-edge science and management information about forest and rangeland soils of the United States. It offers ways to better understand changing conditions and their impacts on soils, and explores directions that positively affect the future of forest and rangeland soil health. This book outlines soil processes and identifies the research needed to manage forest and rangeland soils in the United States. Chapters give an overview of the state of forest and rangeland soils research in the Nation, including multi-decadal programs (chapter 1), then summarizes various human-caused and natural impacts and their effects on soil carbon, hydrology, biogeochemistry, and biological diversity (chapters 2–5). Other chapters look at the effects of changing conditions on forest soils in wetland and urban settings (chapters 6–7). Impacts include: climate change, severe wildfires, invasive species, pests and diseases, pollution, and land use change. Chapter 8 considers approaches to maintaining or regaining forest and rangeland soil health in the face of these varied impacts. Mapping, monitoring, and data sharing are discussed in chapter 9 as ways to leverage scientific and human resources to address soil health at scales from the landscape to the individual parcel (monitoring networks, data sharing Web sites, and educational soils-centered programs are tabulated in appendix B). Chapter 10 highlights opportunities for deepening our understanding of soils and for sustaining long-term ecosystem health and appendix C summarizes research needs. Nine regional summaries (appendix A) offer a more detailed look at forest and rangeland soils in the United States and its Affiliates

The Influence of Geology and Other Environmental Factors on Stream Water Chemistry and Benthic Invertebrate Assemblages

Catchment geology is known to influence water chemistry, which can significantly affect both species composition and ecosystem processes in streams. However, current predictions of how stream water chemistry varies with geology are limited in both scope and precision, and we have not adequately tested the specific mechanisms by which water chemistry influences stream biota. My dissertation research goals were to (1) develop empirical models to predict natural base-flow water chemistry from catchment geology and other environmental factors, (2) extend these predictions to nutrients to establish more realistic criteria for evaluating water quality, and (3) test the hypothesis that catchment geology significantly influences the composition of stream invertebrate assemblages by restricting weak osmoregulators from streams with low total dissolved solids (TDS). To meet goal 1, I first mapped geologic chemical and physical influences by associating rock properties with geologic map units. I then used these maps and other environmental factors as predictors of electrical conductivity (EC, a measure of TDS), acid neutralization capacity, and calcium, magnesium, and sulfate concentrations. The models explained 58 – 92% of the variance in these five constituents. Rock chemistry was the best predictor of stream water chemistry, followed by temperature, precipitation and other factors. To meet goal 2, I developed empirical models predicting naturally occurring stream total nitrogen and total phosphorus concentrations. These models explained most of the spatial variation among sites in total nitrogen and phosphorus and produced better predictions than previous models. By determining upper prediction limits that incorporated model error, I demonstrated how predictions of nutrient concentrations could be used to set site-specific nutrient criteria and accounted for natural variation among sites better than regional criteria. To meet goal 3, I experimentally manipulated (high and low) EC in both stream-side and laboratory flowthrough microcosms and measured survival, growth, and emergence of 19 invertebrate taxa. Observed variation among taxa in survival between treatments predicted taxon EC optima estimated from field observations (r² = 0.60). Taxa with the greatest differences in survival between treatments also had the highest EC optima, indicating that the inability to persist in low EC likely restricts the distributions of some taxa