Non Technical Summary
Understanding how biological systems respond and feedback to a changing climate is one of the most pressing issues of our time. The USDA vision statement includes the goal to "preserve and conserve USA natural resources through restored forests, improved watersheds, and healthy private working lands." Furthermore, extreme weather events, like hurricanes and droughts, influence the full range of greenhouse gas (GHG) fluxes from soils. This is important because changes in the emissions of these GHG will impact the global warming potential of terrestrial ecosystems. Thus, in order to address global pressing issues and country level environmental/agricultural management needs, there is a need to better understand how climate variability influence key ecosystem processes in natural, managed and urban ecosystems including their feedbacks to a changing climate. We will combine in situ measurements in an ex-urban forest (protected as a State Park) and a climate change experiment under controlled scenarios (at the University of Delaware). We will measure solid, aqueous and gas phases of C and N to understand their interactions with the potential of GHG emissions from soils. We will use state-of-the-art insturmentation to countinuously (hourly resolution) measure the major GHGs such as carbon dioxide, methane and nitrous oxide. The target audience will be: a) the scientific community by publishing scientific manuscripts; b) local managers by sharing results through the Extension center of the University of Delaware, and talking directly with the managers at the experimental study site (Fairhill State Park); and c) the general public through news release through UDaily (the news from the University of Delaware). The overall goal of this proposal is to understand how weather variability (especially extreme events in dry or wet years) influences the key ecosystem processes of nutrients and soil GHG fluxes in ex-urban forests. The effect of extreme events on ecosystem processes is becoming critical because during the last decade, different regions of the USA, including the Mid-Atlantic region, have experience extreme weather including droughts and hurricane. An unaccounted damage of these extreme events is that they can influence substantially the annual sums of C and N fluxes from landscapes, and therefore influencing the feedbacks of these ecosystems to local and regional climate. These unaccounted effects of extreme events are usually not considered in ecological assessments as they are "invisible effects", but could account for >$900 ha-1 due to an increase in GHG emissions from soils. Because of the extension of ex-urban forests and their potential social importance, there is a need to better understand how they will respond and feedback in a changing climate in order to inform appropriate management strategies.
Animal Health Component
Research Effort Categories
Goals / Objectives
Extreme weather events influence the full range of greenhouse gas (GHG) fluxes from soils and thus impact the global warming potential of terrestrial ecosystems. Furthermore, topographic gradients could influence biogeochemical hotspots of GHG fluxes and thresholds in response to weather extremes. Our goal is to understand how weather variability (especially extreme events in dry or wet years) influences the key ecosystem processes of nutrient and soil GHG fluxes in ex-urban forests, which are on the fringes of urban areas throughout the U.S. We will combine in situ field measurements across topographic gradients and a climate change experiment under different scenarios. We will couple the solid, aqueous and gas phases of C and N to understand their interactions with the potential of GHG emissions from soils. We hypothesize that extreme weather events will impact annual C and N budgets, especially at landscape hotspots, in nutrient-enriched ex-urban forest ecosystems. The objectives of this proposal are to: 1) Characterize the spatial-temporal patterns and controls of the fluxes of greenhouse gases (GHG; [i.e., CO2, N2O, CH4]) from soils at selected landscape positions across a forested (i.e., ex-urban forest) watershed. 2) Determine the amounts and compositions of C and N in soil and aqueous phases and their coupled relationship with the measured soil GHG fluxes. 3) Perform a three-year greenhouse climate change experiment to quantify the influence of changes in precipitation amount and variability. 4) Identify landscape hotspots and potential thresholds to provide quantitative information about ecosystem vulnerability as a response of weather variability and climate change.
Field measurements The goal for these activities will be to provide information to test H1 and H2. To test H1 we will select four hillslope transects within the 12 ha watershed at the study site during fall 2013. At each transect we will select three locations representing the bottom, middle and top of the soil catena. At each location we will install three GHG monitoring nodes (GHG-MN) to measure monthly soil moisture, soil temperature, CO2, CH4 and N2O fluxes. At the same locations we will measure soil and aqueous chemistry for C and N. Soil samples will be collected from O, A and B soil horizons once a year. Soil aqueous samples will be collected using suction lysimeters. At each topographic location, lysimeters will be inserted vertically to a depth of 15 cm into the soil A horizon. A suction of 60 centibars will applied prior to the day of sample recovery. Lysimeter sampling will occur once a month irrespective of weather conditions. All water samples will be recovered within 24 hours of a precipitation event and will be filtered using 0.7 µm glass fiber filter and stored in amber glass bottles at 4oC prior to analyses. The collected water and soil samples will be analyzed for the various constituents described in the paragraphs below. In addition, we will measure monthly soil moisture, soil temperature, CO2, CH4 and N2O fluxes at 50 GHG-MN randomly located across the watershed to capture spatial variability. All these measurements will continue as scheduled between January 2014 and December 2017. During these 3-years of measurements we expect to capture a large range of weather variability and potentially some extreme events such as droughts or hurricanes as has been experienced in the last 3 years. Therefore, we will be able to determine the autocorrelation, variance and skewness of all these variables according to their topographic location to identify potential thresholds. Climate change experiments We will start a climate change experiment based on manipulation of precipitation but with uniform temperature across the treatments. These experiments will represent control long-term precipitation patterns (1200 mm/year) and extreme drought (25% precipitation reduction) and wet (25% precipitation increase) years to test H3. These changes represent potential drought and wet events and will be combined with a change in the distribution of precipitation across a year based on the report of the U.S. Global Change Research Program for the expectation of the Northwest U.S.The experiments will be conducted in a greenhouse at the University of Delaware for 3 consecutive years. Soil GHG monitoring nodes At the four transects and the 50 random locations across the watershed we will install soil GHG-MN. Each node will consist on a 20-cm diameter PVC ring permanently installed at 3 cm depth into the soil. A total of 92 GHG-MN will be installed across the watershed (50 random and 42 in catena transects). All nodes will be measured monthly for soil moisture, soil temperature, CO2, CH4 and N2O fluxes using a LI-8100 analyzer (Licor, Lincoln, NE) equipped with a theta probe, a soil bar with a thermocouple, a 20-cm survey chamber (LI-8100-103, Licor, Lincoln, NE) and a port for gas extraction within the gas outflow of the analyzer. We will perform 4 minutes measurements to calculate CO2 efflux using the Li-8100, and collect 10 ml gas samples every minute (using the port for gas extraction and a syringe). All gas samples will be injected in evacuated airtight glass vials and stored in ice for transportation to the University of Delaware. Each gas sample will be analyzed for CH4 and N2O using a Picarro CH4+N2O analyzer (G2208-CRDS Analyzer, Picarro, Santa Clara, CA). Once the CH4 and N2O concentrations are determined we will calculate the flux using a simple linear regression approach to calculate the slope of change in concentrations and therefore the soil efflux. Soil GHG-flux-automated system The GHG-AF will consist on a LI-8100 analyzer unit (Licor, Lincoln, NE) connected to a Picarro CH4+N2O analyzer (G2208-CRDS Analyzer, Picarro, Santa Clara, CA) and a multiplexer (LI-8150, Licor, Lincoln, NE) controlling nine autochambers (Li-8100-104, Licor, Lincoln, NE). This GHG-AF will be hosted at the greenhouse facility at the University of Delaware where there is continuous electrical power securing continuous high-data-quality. Each autochamber (Li-8100-104, 20 cm in diameter) will be installed over a 20-cm diameter PVC ring permanently installed at 3 cm depth into the soil. Furthermore, each autochamber will be connected to a soil temperature and a soil moisture theta probe.Each autochamber will be activated at hourly intervals and onboard loggers of the Picarro G2208-CRDS and the LI-8100 analyzers will store data from all variables. Soil GHG concentration will be analyzed using the LI-8100 software, which allow us to continuously calculate fluxes for each autochamber and reduces dramatically the post-processing time. Additionally, cross validations will be done using an in-house Matlab program to calculate soil GHG fluxes. Solid phase chemistry for C and N Composite samples of soils will be collected at all the three topographic locations for the four transects. Soils samples will be collected from O (forest floor), A and B soil horizons using soil samplers and augers. Soil samples will be analyzed for - pH, NH4+, Ca2+, Na+, Mg2+, K+, NO3-, Total N, and Total C at the University of Delaware Soil and Water Laboratory using standard methods. Aqueous phase chemistry for C and N Soil aqueous samples will be collected using suction lysimeters (1900 series, Soil Moisture Corp). At each topographic location (for all four transects), lysimeters (4.8 cm diameter, 30cm long, and 500 mL) will be inserted vertically to a depth of 15 cm into the soil A horizon. A suction of 60 centibars will applied prior to the day of sample recovery. Lysimeter sampling will occur once a month irrespective of weather conditions. All water samples will be recovered within 24 hours of a precipitation event and will be filtered using 0.7 µm glass fiber filter and stored in amber glass bottles at 4oC prior to analyses. Water chemistry analyses will include - pH, cations (NH4+, Ca2+, Na+, Mg2+, K+), anions (NO3-), total dissolved N (TDN), DOC and DON (by difference between TDN and the inorganic N species) and will be conducted at the University of Delaware Soils Laboratory. Means by which results will be analyzed Data will be analyzed using parametric statistics (e.g., ANOVA), dimension reduction approaches (e.g., Principal Component Analysis, PCA), and time-series analysis (e.g., wavelet analysis). Specifically, field measurements data and comparison of mesocosm treatments will be analyzed (but not limited to) with ANOVA and PCA techniques. Furthermore, the mesocosm experiment will produce hourly resolution data for three treatments in three different 1-year long experiments for three species of GHGs. This unprecedented information has the possibility to be analyzed using wavelet analysis.Briefly, wavelet analysis is a time-series analysis, which has been widely used for climate research and for research on soil GHG fluxes. Wavelet analysis performs the estimation of the spectral characteristics of a time series as a function of time, and decomposes de original signal into subsignals that have relevant information about the time-scale of the mechanisms that could regulate the response of the original signal (i.e., soil GHG fluxes).In addition we will combine the information of two time series (e.g., CO2 and CH4) and look at the temporal correlation between them using wavelet coherence analysis.