Soil Respiration Flux in Response to Soil Temperature, Moisture, and pH Bailey Murphy Environmental Science: Land­Air Interaction Undergraduate Oregon State University 2014 1 Acknowledgments This project was conducted in the H.J. Andrews Experimental Forest, the Oregon State University biomicrometerology lab, and the Oregon State University soil chemistry lab. All work occurred under the support and guidance of Dr. Christoph Thomas, Gesa Thomas, College of Earth, Ocean, & Atmospheric Sciences and Dr. Claire Phillips, Department of Crop & Soil Sciences. Data and facilities were provided by the HJ Andrews Experimental Forest research program, funded by the National Science Foundation's Long­Term Ecological Research Program (DEB 1440409), US Forest Service Pacific Northwest Research Station, and Oregon State University. Additional for my pay was provided by a grant to Dr. Christoph Thomas from the National Science Foundation, AGS 0955444. Huge thank you to everyone involved!! 1.0 Introduction: Variation of the Earth’s climate can have multiple causes, both natural and anthropogenic. Natural causes include mechanisms such as alterations in Milankovitch cycles, plate tectonics, and changes in ocean productivity and solubility. These changes occur on long geological timescale of thousands to millions of years, evidence of which can be found through historical records such as ice cores, corals, and other reliable proxies. In addition to natural climate forcing mechanisms, anthropogenic effects must also be considered in the discussion of climate change. Anthropogenic effects are the results of human impact on the environment, such as the burning of fossil fuels and land use change. One consequence of anthropogenic effects is increased atmospheric CO2 concentrations. CO2 is classified as a greenhouse gas, along with methane, nitrous oxide, and water vapor. A greenhouse gas is a gas that can absorb and re­radiate longwave radiation and thus can trap heat in the atmosphere, absorbing the energy released back into the atmosphere and on to Earth, reducing or preventing the loss of that heat to space. In a sense, greenhouse gases function like a blanket wrapped around the earth, resulting in a warming known as the greenhouse effect. While this effect is occurring naturally primarily through the presence of large quantities of water vapor in the atmosphere, human activities have amplified this effect by adding primarily carbon dioxide to the atmosphere, resulting in changes of the global climate at a rate beyond what is explainable by natural causes (IPCC 2013, Section 8.3). CO2 is the primary greenhouse gas contributing to the anthropogenic greenhouse effect and hence climate change (IPCC 2014, Section 2). The cycling of CO2 within earth systems is known as the carbon cycle. The rate at which this cycle proceeds is a defining part of overall CO2 flux within a given location, in this study Watershed 1 in the H.J. Andrews experimental forest. CO2 flux varies greatly across biomes because of differences in variables such as vegetation, climate, 2 landscape and soil conditions, etc. Alteration of the rate of carbon cycling in natural ecosystems due to land use and climate change also feeds back to further influence climate change, a category of effects called carbon­climate feedbacks.Due to the magnitude of these variations in the turnover of C in terrestrial ecosystems, as well as differences in experimental designs, it’s difficult to study global CO2 flux. To overcome this issue specific locations within a biome type are studied and a quantitative synthesis of results from multiple studies is done in order to upscale the findings and apply them to similar biomes across the globe. Currently research is being conducted at Watershed 1 (later referred to as WS1, Fig. 1) in the H.J. Andrews forest located in Blue River, Oregon to study the flux rate and variability of CO2 in a Pacific Northwest temperate coniferous forest setting. Figure 1: Map of H.J. Andrews Experimental Forest, showing the shape of the forest and its location relative to the state of Oregon. Located in the bottom left corner is Watershed 1, labeled with a ‘1’. 3 Within WS1, CO2 concentration is measured at 11 air intake locations near the stream inlet of WS1 and along a vertical canopy profile, and is used to calculate the change in storage term (∆S), which is one term in the carbon mass balance, defined as the net ecosystem exchange NEE (Eq 1): Equation 1: NEE = Ah + Av + Qh + Qv +∆S. Where Ah is horizontal advection, Av represents vertical advection, Qh is the horizontal flux divergence, Qv is the vertical flux divergence or more simply the vertical flux commonly evaluated using the eddy covariance technique from tower measurements, and ∆S represents the change in the storage term of the system. The gross ecosystem productivity, i.e., the photosynthetic uptake of carbon by the plants, is calculated using Equation 2: Equation 2: GEP = NEE + Re Ecosystem respiration (Re) is composed of several components and can be decomposed into the following: Equation 3: Re = Rsoil + Rstem + Rfoliage Rsoil in particular is the focus of this paper, and is the sum of both heterotrophic and autotrophic respiration. Heterotrophic respiration refers to microbes in the soil and surface litter decomposing organic matter from litterfall, root turnover, root exudation, deceased organisms, and fecal matter and oxidizing it, creating carbon dioxide. Heterotrophic respiration also includes carbon converted to CO2 through the decomposition of dead trees and coarse woody debris. Autotrophic respiration refers to the carbon that is returned to the atmosphere as CO2 through plant metabolism (Kirschbaum et al. 2001). 4 Figure 2: Graphical representation of the main terms describing CO2 flux within a forest (Kirschbaum et. al. 2001). Figure 3: Graphical representation of the global carbon cycle. Sinks and sources are in units of 1015 gC, and fluxes are in units of 1015 gC/year (averaged) (Schlesinger & Andrews 2000). 5 Soil respiration has been identified as one of the largest natural sources of CO2 to the global carbon cycle. Small alterations in the magnitude of soil respiration can have potentially large effects on concentrations of atmospheric CO2 , making the rate at which respiration occurs of great scientific interest (Martin et al. 2012; Schlesinger & Andrews 2000). This experiment addresses the relationships between the rate at which soil respires and the temperature, moisture content, and pH of the soil. Soil respiration is largely dependent on factors that influence the chemical and microbial properties of the soil, as well as the plant activity. Some of these factors include soil and air temperatures, soil moisture, and soil pH. As our climate changes, these factors have the potential to be altered considerably. Understanding the relationship between these factors and the rate of soil respiration is vital to understanding the implications of an altered climate on soil respiration, and the resulting changes and feedback mechanisms in the amount of CO2 being added back into the system. WS1 within the H.J. Andrews forest is an ideal setting for this experiment, as the contrast in soil conditions at the various survey plot locations on the North and South facing slopes of the watershed offer an opportunity for comparison of these factors. 1.1 Objectives and Hypotheses The objective of this experiment: ● To determine how soil temperature, moisture content, and pH vary across the study area and how they affect the rate of soil respiration within the landscape of Watershed 1 on both diel and seasonal timescales. The initial hypotheses of this experiment include: 1. The north face of Watershed 1 has a different average rate of soil respiration than the south face of Watershed 1 due to differences in soil temperature, moisture content, and pH. 2. A higher average soil temperature corresponds to high average soil respiration rates at Watershed 1. 3. A higher soil water content corresponds to higher average soil respiration rates at Watershed 1. 4. pH has an effect on soil respiration rates at Watershed 1. 6 2.0 Methods and Materials: 2.1 Site Location and Description The H.J. Andrews is an experimental forest that was established in 1948 and is administered by the USDA Forest Service, Oregon State University, and the Willamette National Forest. The Andrews covers an area of 15,800 acres in the drainage basin of Lookout Creek, within the western Cascade Range near Blue River, Oregon. The elevation ranges between 1,350 to 5,340 feet, and the forest is widely representative of temperate coniferous forests in the Pacific Northwest, with an abundance of Douglas fir, western hemlock, and Pacific silver fir. Watershed 1 is a steep ‘V’ shaped watershed within the Andrews, and consists of two distinctly different north and south facing slopes that have a slope grade of approximately 59.35%. Two small creeks converge and flow through the center of the watershed where the two slopes meet. Although a large portion of the Andrews is old growth forest, the growth in WS1 is relatively recent. The area was clear cut and burned in 1966 for experimental logging, and the current growth is the result of several replant attempts. Understory vegetation present in the watershed consists mostly of vine maple­Oregon grape (25%); gold­thread (25%); ans sword­fern (20%). Soils range from predominantly shallow and stony with little prolific development to moderately deep with well­developed profile features. Due to the abundance and size distribution of pore spaces percolation rates are on average greater than 12 cm/hr, meaning standard moisture moves rapidly throughout the soil horizons (Rothacher et al. 1967). Stone content is the dominant factor causing variation in soil moisture storage capacity (Dyrness 1969). In ridgetop and steep­slope positions, soils are generally loam and clay loam derived from colluvium from reddish breccias and tuffs. Stone content ranges from 35­50%, generally at a higher percentage on south­facing slopes, and depth to parent material is usually over 1m. Soils derived from greenish breccias and tuffs are widely distributed. The surface and subsurface horizons are loam to clay loam with up to 50% gravel content by volume. 7 Figure 4: This picture shows the approximate size and shape of Watershed 1. 8 Figure 5: This picture shows the locations of the five survey plots within Watershed 1. 2.2 Experimental Design Materials ● LI­COR LI­8100 long term soil respiration chamber ● LI­COR 8100­102 mobile survey chamber (10 cm) ● Type K 15 cm thermocouple sensors ● Campbell CS 107 temperature probes (Connected to TDR stations) ● T­handle thermocouple probe, 25 cm ● Decagon 10HS soil volumetric water content probes ● pH meter ● Car batteries to power stations ● Plastic storage boxes to house each battery 9 ● Hard plastic box housing auxiliary sensors attached to a wooden post, one at the center of the nested collars at each of the 5 locations ● PVC piping collars (25 10 cm diameter survey collars, one 20 cm diameter long term chamber collar) ● Portable solar panels to recharge batteries at survey stations Methods In order to obtain continuous data throughout the course of the experiment a continuous automated respiration chamber, LI­COR 8100 was deployed at one location in close proximity to the continuous eddy covariance tower next to the stream cutting through the center of the watershed. Measurements of soil CO2 efflux, soil temperature and soil moisture are taken every 15 minutes. This site also has measurements of soil temperature using thermocouples, and three Decagon 10HS probes to measure soil volumetric water content. In order to obtain an accurate representation of the soil temperature across the soil profile two thermocouples were inserted vertically down into the soil, offering a temperature integration across 25cm (T1) and 15cm (T3) . The third thermocouple (T2) was inserted horizontally at a depth of 5cm in order to measure the soil surface temperature, and a fourth probe (T4) was inserted horizontally at a depth of 15cm from the soil surface. See chart below. 10 Long Term Chamber Probe Length Depth Position T1, temperature 25 cm Integrated 0­25 cm Vertical T2, temperature 15 cm 5 cm below soil surface Horizontal T3, temperature 15 cm Integrated 0­15 cm Vertical T4, temperature 15 cm 15 cm below soil surface Horizontal V2, soil moisture 15 cm 5 cm below soil surface Horizontal V3, soil moisture 15 cm Integrated 0­15 cm Vertical V4, soil moisture 15 cm 15 cm below soil surface Horizontal Probe Length Depth Position T1, temperature 15 cm Integrated 0­25 cm Vertical T4, temperature (mobile) 15 cm 5 cm below soil surface Horizontal TA , temperature 10.4 cm Integrated 0­10.4 cm Vertical VA , soil moisture 30 cm Integrated 0­30 cm Vertical VB , soil moisture 30 cm Integrated 0­15 cm 60○ angle from the horizontal VC , soil moisture (mobile) 30 cm Integrated 0­15 cm Vertical Point Measurement Sites Tables 1 & 2: Probe descriptions for long term and survey sites. VA and VB represent the stationary volumetric water content sensors positioned around the TDR station in the center of each nested collar grouping. VC represents the mobile volumetric water content sensor inserted around each collar. In order to collect representative data for the entire watershed, spatially intense surveys consisting of point measurements of soil respiration at five selected sites positioned throughout WS1 were conducted weekly using a LI­COR 8100­102 mobile survey chamber. Two of the five sites were located on the south facing slope of the watershed, plots 103 and 427, and two sites were located on the north facing slope, plots 108 and 402. Plots 108 and 103 were both 11 positioned midslope, and plots 427 and 402 were positioned further upstream at the upper slopes of the watershed. The fifth plot was located at the confluence of the streams, where the north and south slopes meet. Each of the five plots is representative of the range of vegetation and soil composition within the watershed. Refer to table 3. Plot Characteristics 103 Positioned on lower south facing slope, steep grade, lots of vegetative ground cover (Oregon grape, fern). Wide spacing between trees, nearby trees are relatively small. Clay­like soil. 108 Positioned on mid north facing slope, near large decaying stump. Woody debris from upslope slides. Confluence Up against the stream at the base of the watershed, lots of decaying organic matter (trees, organic litter, etc.) from upper slopes, flat ground, moist rocky loosely packed soil. 427 On high upper south facing edge of the watershed, large number of small­medium sized trees, great deal of organic litter but less dense vegetative ground cover. 402 On upper edge of north facing side of the watershed, more shaded area, several decaying stumps nearby along with several larger trees. More hemlocks than other plots. Layer of carbon beneath the soil A horizon. Table 3: Plot descriptions. Each of the five sites had one central monitoring station consisting of two time domain reflectometers (TDR) to measure soil moisture, and one thermocouple to measure soil temperature connected to a Cr501 data logger (Campbell Sci, Logan, UT, USA). Both soil temperature and water content are measured continuously at the center of each of the five point locations, with the collars arranged in a nested design around the central monitoring station. Four of the monitoring stations operate via 12 volt car battery power, while the fifth station is hooked up to the existing power grid, which is supplied by a large solar panel. Each of the four battery powered stations have a small solar panel to recharge the batteries. Five soil collars made of PVC pipe each with 10 cm diameter were inserted around each monitoring station in a nested design. The utilization of the nested design with five collars allows for a more accurate representation of the soil CO2 flux at each plot, as it accounts for the effects of spatial variation within a given plot. Each collar was inserted levelly into the ground with an average offset of 2.8 cm protruding above the soil surface, giving each collar a consistent contained air volume of 880 cm3 when the chamber is in place. 12 At each station, the first point measurement was taken at a consistent predetermined start point, and the following measurements were taken at each successive collar in clockwise direction. Each individual collar measurement consisted of a pre­purge period of 45 seconds after which the chamber lowers and seals into place on top of the collar. Once in place the soil respiration measurement begins, lasting 2 minutes and 30 seconds. During the 2 minute and thirty second measurement period there is a deadband of 30 seconds. Following the respiration measurement the chamber rises and there is a post­purge period of 10 seconds. Once the post purge is complete, the chamber lowers and seals into place once more to take the second observation. After completing both observations at a collar, the chamber rises and allows for a 7 minute interval to get the equipment set up at the next collar before another measurement begins. 13 Figures 6 & 7 show the nested collar design at each plot. The TDR station is in the center of the web (not pictured) and a collar is placed at the end of each ribbon, 5 collars total per plot. This collar displays the extra decoration used to identify the start point collar. 14 Figure 8: This picture shows the soil respiration chamber used to conduct the spatially intense point surveys. The respiration chamber is attached to the LI­COR 8100 (photo taken at plot 402).
At each site a soil sample was taken of both the epipedon (A horizon) and subsurface (B) soil horizons using an auger and tested in order to determine soil pH at each plot. Each of the field fresh soil samples were sifted through a 2mm sieve to remove rocks and other large organic debris. Plots 108, 402, and the confluence samples were sifted using a 2mm sift. Samples 103 A and B and confluence B were sifted through a 4mm sift due to the amount of rocks and the clay­like nature of the soils which made them difficult to sift. 20 g of each sifted soil sample was weighed out into individual beakers, combined with 20 mL of deionized water, and mixed using a vortex mixing machine. The samples were allowed to sit for 30 minutes in order to reach equilibrium with the ambient atmosphere, and then the pH was tested using a pH meter once with just the soil and deionized water mixture, and once with 0.02N CaCl2 added to the existing mixture. The samples were allowed another 30 minutes to reach equilibrium after the addition of the 0.02N CaCl2 before the pH was tested again. The addition of calcium chloride provides Ca2+ ions to replace some of the H+ ions attached to the soil particles, forcing the hydrogen ions into 15 the solution and making their concentration in the solution closer to what would be found in the field. The pH measured in CaCl² is typically lower than the pH of the same soil measured in water due to the higher concentration of H+. The procedure gives a value similar to that for natural soil solution because the soil solution also contains dissolved Ca2+ and other ions, thus giving a more consistent and representative measure of the soil pH than testing in water alone. pH was not sampled at the long term chamber site. 2.3 Field data collection schedule
Date Time (approx.) 05/03/2014­05/04/2014 16:00­17:00, 10:00­17:00 06/21/2014 10:00­18:00 06/28/2014 11:00­18:00 07/08/2014 11:00­18:00 07/24/2014 12:00­19:00 08/01/2014 11:00­17:00 09/19/2014 12:00­20:00 Table 4: Collection date and time for point surveys. All field data is stored on the Oregon State University biomicrometeorology group’s remote server data archive. 2.4 Pre­field Lab Tests In order to test the accuracy of the point measurement soil respiration chamber, we designed a pre­field lab test consisting of a tub of sand with a coil of plastic tubing taped to the bottom. The plastic tubing was attached to a pressurized air tank of known CO2 concentration, and had numerous punctures to allow the air to flow freely throughout the sand container. Based upon calculations from the known flow rate of the CO2, we calculated the expected flux rate of CO2 through the sand, which was then compared to the flux rate measured using the portable LI­COR system. Due to the high permeability of sand, we were getting significant spikes in flux readings above areas where the tubing punctures were, and very low rates everywhere else. Considering that within a natural system flux occurs throughout the given area not just at specific point locations, we altered the set­up to better match what would be found in the field. We added an air­filter on top of the tubing, a gardening weed barrier on top of the air filter, and then re­filled 16 the bucket with sand. With the modifications we were getting flux rates of almost zero, showing that our modifications weren’t allowing any CO2 to diffuse through the sand. To remedy this we cut multiple small slits in the weed barrier. Through our adjustments we were able to reach a more accurate representation of the natural system to test the point measurement chamber. In addition to testing the survey chamber, we calibrated the thermocouples to ensure accuracy in the field (see figure 11). Figure 9: Photo of the tubing structure in the bottom of the sand filled tub, from the lab experiment designed to test the accuracy of the survey chamber in measuring CO2 flux. Red tape marks puncture locations in the tubing. 17 Figure 10: Photo of the lab experiment set­up used to test survey chamber accuracy. Figure 11: Graph of thermocouple calibrations (outliers removed), including equations used to adjust the temperature readings. 3.0 Results and Discussion 3.1 Long Term Chamber at a single location The deployment of the long term chamber was an essential part of this project, and offered a great deal of informative data about CO2 flux at one location within the watershed. The point surveys gave a more comprehensive look at spatial variation of CO2 flux, and the long term chamber (LTC) allowed for a better view of temporal variation of CO2 flux within the watershed. The LTC continuously collected data at 15 minute intervals from the start of the project in early May through mid­September 2014, and remains deployed. This continuous data log allows for a more extensive look at daily flux patterns, flux response to extreme events such as flooding, and the relationship between CO2 flux and variables such as temperature and soil moisture content. Interruptions to the data stream occur only on dates when point surveys were conducted, as the 18 Li­COR gas analyzer and control unit needed to be detached from the long term chamber (LTC) set­up to be used in measuring flux at the point sites. The LTC data shows a trend of slight increase in CO2 flux over time, some interesting spikes in flux, and then decreases in rate towards August. A better relationship than perhaps time alone is established looking at flux versus soil chamber temperature. As you can see from figure 13 ln­transformed flux increases linearly as temperature increases, indicating an exponential relationship between flux and temperature. The deviations from the linear fit can potentially be explained by other variables such as volumetric water content, changes in organic material, etc. that may also impact the rate of CO2 efflux in addition to soil temperature. Looking at figure 14 you can see the relationship between flux variations and soil chamber temperature broken up by month, with each month color coded to display contrast. Viewing the data in this way allows you to see that the anomalies in figure 13 all occurred during the month of July, suggesting that the data points could be the result of an extreme weather event. The consistent data capture allowed for the viewing of daily changes in flux, and hysteresis patterns were observed. See figure 15 below, specifically DOY 217 and 218. Figure 12: LTC CO2 flux data vs. time, with survey station plot flux data overlaid. 19 Figure 13: LTC CO2 flux vs. chamber temperature, with line of best fit. Figure 14: LTC CO2 flux vs. chamber temperature, color coded by month. 20 Figure 15: Graphs displaying example of LTC daily soil CO2 flux hysteresis, August 1­9 2014. Statistical analysis of LTC data was performed, with data from probes T1, T2, T3, T4, V2, V3 and V4 (described above in table 1) defined as explanatory variables. Chamber temperature was also added to the regression equation as an explanatory variable due to its high correlation with CO2 flux, as was discovered earlier in the project. Analysis revealed the combination of these variables explained 75% of the variation in CO2 flux. Through analysis it was shown that increases in CO2 flux at the long term site are most influenced by chamber temperature, soil temperature in the upper 5 cm (T2) and volumetric water content measured at 15 cm below the soil surface (V4). All three of these explanatory variables have P­values of <0.0001, implying statistical significance, that the three predictors are associated with the response in CO2 flux. Continued deployment of the LTC will allow for the collection of soil flux data throughout fall and winter, offering a chance to analyze changes in soil flux across multiple seasons and get a sense for long term patterns. Although the LTC provides frequent and reliable data on soil CO2 flux, it may not be representative of the overall flux in WS1 due to its position in a riparian area between two converging slopes. When comparing riparian areas to hillslopes, soil respiration is often higher in riparian zones (due to factors such as greater carbon pools, volumetric water content, organic inputs, microbial activity, etc.), and experiences peak flux earlier than upslope regions often by several days (Pacific et al., 2009). 21 3.2 Survey site results at locations throughout WS1 Results from the five plots showed relative variation in soil CO2 flux throughout WS1, with plot 427 and confluence having the highest flux rates, plots 401 and 103 having similar rates, and plot 108 initially experiencing rates close to 401 and 103, yet increasing slightly with time. This variation between plots is likely due to differences in vegetative and soil characteristics between the plots. High flux values at the confluence fall in line with observed increases in soil CO2 flux in riparian areas. The flux values observed at plot 427 were interesting to see, as it wasn’t a location where I expected to see such high flux values. Plot 427 is much higher upland in the watershed, has less dense vegetative ground cover and neither high nor low volumetric water content. However, soil respiration is often limited at high volumetric water content (VWC) due to bidirectional limitations on gas and nutrient diffusion to plants and microorganisms, and at low VWC by root and microbial stress (Pacific et al., 2009), so perhaps plot 427 was at a happy medium, leading to high rates of soil respiration. The variation in flux observed at the different plots indicates variation across the watershed as a whole, and demonstrates the importance of having multiple measurement locations within a given area, as one plot may not be representative of the entire area being studied. The CO2 flux rate increased at all plots between early May to mid June, but had only small variations in rate from mid June to early August (see figure 20). Figure 16: Display of soil CO2 flux vs. soil chamber temperature, shows flux variation between plots. 22 Within each plot soil CO2 flux rate varied somewhat between collars. This falls in line with what would be expected, each collar represents an area of slightly different slope, soil make up, microbial activity, water content and temperature (among other potential factors), so naturally a slightly different flux rate will be observed. Looking comprehensively at each collar flux value gives a better idea of the general flux at a plot, as it addresses spatial variation within each plot. Plot Mean Flux Mean Standard Deviation Mean Standard Error 103 1.82 1.01 0.137 108 2.31 1.82 0.245 402 2.11 1.24 0.174 427 9.77 3.69 0.522 CON 4.74 2.25 0.303 Table 5: Quantification of spatial variation for flux measurements. There was a measured CO2 flux rate increase at all plots between the first measurement in May and the following surveys, but beyond this initial change no discernable trend in flux existed (i.e. logarithmic, exponential, etc.). Surveys were conducted frequently to ensure accurate data capture, but there was a period of warming that occurred between data collections (soil warmed from 20℃ to 25℃) that is represented as a large gap in the flux graphs, and leads to the creation of two data clusters. 3.2.1 Effects of Soil Moisture and Temperature Soil CO2 efflux is hypothesized to be driven by the compounding interactions of several variables, including root and microbial activity, soil moisture, and soil temperature (Lloyd & Taylor 1994). Due to this relationship, we expect to see spatial variance in the explanatory variables listed above alongside the flux variation. Within WS1 there is evidence of spatial variation in soil volumetric water content (VWC) but little spatial variation in soil temperature. The VWC of a soil sample is a dimensionless ratio of two volumes, defined as Vwater/Vsample, where Vwater is the volume of water in the sample and Vsample is the total volume of dry soil + air + water in the soil sample. VWC is highest at plot 103, ranging from 0.348­0.6035 (v/v) and lowest at the confluence, ranging from 0.004­0.182 (v/v). VWC values for plots 108, 427 and 401 range in between these two extremes, as shown in figure 17 below. Variation in VWC between plots is potentially explained by differences in soil characteristics and structure such as pore space, soil 23 type (clay, sandy loam, etc.) and diffusivity coefficients, the amount of organic material present, and the amount of precipitation an area experiences. Figure 17: Displays soil CO2 flux plotted against volumetric water content, chamber temperature, and pH, organized by aspect. North facing slopes are denoted by ‘N’, south facing slopes are denoted by ‘S’, confluence is denoted by ‘C’. Soil CO2 fluxes are shown to be largely driven by temperature, and in the case of WS1 several plots have statistically demonstrated temperature as the most influential variable on soil CO2 flux (see table 6). Although the relationship between flux and temperature is an established one, there is little spatial variation in soil temperature with CO2 flux between the different plots in WS1. Plot temperature averages range from a low of 17.1℃ at plot 401 to a high of 18.1℃ at plot 103, with plots 108, 427 and confluence having around the same average temperature values, yet expressing quite a bit of variation in CO2 flux rates. This could be due in part to differences in recorded ‘extreme’ temperature values however. 24 3.2.2 Effects of pH The relationship between soil temperature and VWC is fairly well documented, and while both variables are again addressed in this project within the scope of WS1, the effects of soil pH on CO2 flux is also examined for a potential relationship. Soil pH was tested on two occasions at each of the five survey plots of both the A and B horizon soils as described under ‘methods’ above. Results from each trial were averaged to obtain a single pH value for each horizon at each plot, on two different collection dates. See table 4 for results. pH varied by plot, ranging from a pH of 4.09 to 5.74. An interesting pattern emerged between the pH values of the A and B horizons, showing a mirroring of plot 427 and confluence values at a single plot between the two horizons, see figures 18 and 19 below. This difference is potentially due to the leaching of acidic compounds into the lower soil horizon, and could indicate the importance of sampling pH at more than one soil horizon in order to obtain a representative value for pH for an area. Table 6: Results of lab pH tests (averaged between two trials) on the Watershed 1 soil samples (horizons A and B), once with deionized water and once with 0.02N CaCl2 added. Sample, collection date. pH (H2O) 5/23/14 pH (0.01N pH (H2O) CaCl2 & 8/4/14 H2O) 5/23/14 pH (0.01N CaCl2 & H2O) 8/4/14 103 A 5.93 5.18 5.79 5.31 103 B 5.67 4.66 5.61 5.21 108 A 5.64 5.38 5.93 5.74 108 B 5.62 5.47 5.68 5.02 CON A 5.63 5.39 5.60 5.42 CON B 5.41 4.73 5.31 4.73 402 A 5.36 4.95 4.50 4.09 402 B 5.09 4.45 5.22 4.58 402 Charcoal N/A N/A 5.34 4.58 427 A 6.20 5.64 5.78 5.33 427 B 5.36 4.64 5.32 4.64 25 Figure 18: Displays soil pH vs soil CO2 flux by horizon and collection date for each plot. Figure 19: Displays soil pH vs soil CO2 flux by horizon for each plot, averaged between the two collection dates. 26 3.2.3 Comparison of North and South Facing Slopes In order to determine whether a difference in CO2 flux exists between the north and south facing plots, averages were taken and individual flux values for each plot were plotted against measured chamber temperature, soil moisture, and pH, with the orientation of each plot marked in the graphs. Plots with an ‘S’ are south facing, those denoted by an ‘N’ are north facing, and a ‘C’ represents the confluence (not included in statistical analysis for this section as it is neither north nor south facing). As the temperature warms over time the highest flux values are seen at the south facing plot 427. However, analysis shows very little difference in soil chamber temperature between north and south facing slopes even as the season progresses, with an average chamber temperature for north facing slopes of 17.5℃ and 18.0℃ for south facing. The greatest variance between the two aspects is in soil volumetric water content (VWC). The highest VWC values are seen at south facing plots, with the difference becoming less pronounced over time as the watershed enters the dry summer months and soils retain less water (see fig 20). North facing slopes have an average VWC of 0.15 (v/v), compared to an average value of 0.29 (v/v) on the south face. This difference is likely due to variations in sun exposure and soil structure, as southern facing slopes (particularly plot 103) have more clay­like soils and thus are capable of retaining greater amounts of moisture. Interestingly the confluence has the lowest VWC values of all 5 five plots, and still some of the highest flux values. This could be explained by the texture and structure of soils at the confluence. Relatively rocky and loosely packed soils have greater pore space and generally absorb and retain less moisture, allowing introduced moisture to percolate into lower soil levels at a higher rate. Soil pH doesn’t appear to be distinctly acidic or basic depending upon aspect, with an average pH of 5.0 on the Northern aspect and 5.1 on the southern aspect. There does appear to be a greater range of pH on the north facing slope than on the south facing slope. With all of the above explanatory variables considered, the average response variable of CO2 flux rate is also quite different between the two slopes. North facing slopes experience an average soil CO2 flux of 2.6 μmol m­2 s­1 compared to an average of 6.4 μmol m­2 s­1 on south facing slopes. 27 Figure 20: Variables soil CO2 flux, soil chamber temp, VWC and pH plotted against day of the year to show change over time. Plots are designated by color, and aspect is designated using N (North), S (South), and C (confluence). Four models were used to quantify the impact of soil volumetric water content and chamber temperature on overall soil CO2 flux at each individual plot. Statistical analysis was performed on the models listed in table 5 below in order to determine the proportion of variance in soil CO2 flux explained by each model. Models of best fit were chosen for each plot location. Model Number Included Variables 1 Soil chamber temperature 2 Soil volumetric water content 3 Soil chamber temperature and volumetric water content 4 Soil chamber temperature, volumetric water content and temp by volumetric water content Table 7: Linear regression models used to quantify impact of VWC and temp on CO2 flux. 28 Table 8: Results from statistical analysis using the above models. Model of best fit is highlighted for each plot. Note: none of the models significantly explained flux at plot 427. Plot Model R2 Adj. R2 Temp VWC Temp*V
WC Temp p Value VWC p value Temp*
VWC p Value 103 1 0.08447 0.06719 0.02798 NA NA 0.03135 NA NA 103 2 0.13845 0.12050 NA ­1.99310 NA NA 0.00779 NA 103 3 0.14380 0.10737 ­0.00788 ­2.21353 NA 0.59014 0.01041 NA 103 4 0.17090 0.11682 ­0.09012 ­6.01251 0.190599 0.19559 0.06715 0.22644 108 1 0.23734 0.22295 0.06699 NA NA 0.00016 NA NA 108 2 0.02421 0.00388 NA ­5.90836 NA NA 0.28064 NA 108 3 0.13930 0.10268 0.05379 ­0.93717 NA 0.01569 0.86563 NA 108 4 0.14071 0.08467 0.01810 ­8.39484 0.38157 0.89140 0.76351 0.78496 401 1 0.07332 0.05550 0.030999 NA NA 0.04766 NA NA 401 2 0.18376 0.16806 NA ­8.54138 NA NA 0.00122 NA 401 3 0.18651 0.15461 0.00707 ­7.89162 NA 0.67939 0.01031 NA 401 4 0.18693 0.13814 0.02653 ­6.52940 ­0.08787 0.83024 0.47370 0.87384 427 1 0.01549 ­0.00501 0.01208 NA NA 0.38915 NA NA 427 2 0.00258 ­0.01820 NA ­0.46962 NA NA 0.72595 NA 427 3 0.01562 ­0.02627 0.01171 ­0.10935 NA 0.43417 0.93865 NA 427 4 0.02010 ­0.04381 ­0.02701 ­3.14333 0.168996 0.75412 0.64443 0.64861 Con 1 0.10767 0.09083 0.03907 NA NA 0.01446 NA NA Con 2 0.00319 ­0.01757 NA 0.74430 NA NA 0.69663 NA Con 3 0.03414 ­0.00696 0.02374 0.99104 NA 0.22588 0.60410 NA Con 4 0.03970 ­0.02293 0.01743 ­4.07637 0.24151 0.45267 0.68562 0.60836 29 4.0 Conclusions Soil CO2 flux at survey plots was most accurately explained by changes in soil volumetric water content (supports hypothesis 3), although temperature was also proven to have an influence. Hypothesis 4 can be rejected, as pH didn’t appear to have a statistically significant affect on plot respiration. The length of the experiment may have been too short to obtain significant data related to pH, as pH changes and related responses occur over a much longer time period than responses to changes in soil temperature and volumetric water content do. Soil chamber and upper 5 cm soil temperature had by far the greatest influence on soil respiration at the long term chamber, supporting hypothesis 2. VWC at a depth of 15 cm below the soil surface also had a distinct effect on respiration rate, although less pronounced than temperature. Hypothesis 1 can be accepted, as differences in average soil respiration rates were demonstrated between north and south facing slopes, although the degree of difference between controlling factors of VWC, soil temperature and pH varies. Deployment of the LTC provided excellent temporal data on CO2 flux, and made it possible to not only see clear flux patterns, but to also see changes and hysteresis patterns within the span of a day. The survey data supplemented the LTC by providing spatial flux data, demonstrating the variance in flux and controlling variables within the watershed. Through analysis of VWC, soil temperature and pH, it was found that although all three variables have some (ranging) impact on soil respiration, there are a great deal of other confounding variables involved that also affect respiration rates and were not measured and included in the statistical analysis of this study. These variables include but are not limited to vegetation type and density, diffusion coefficients, microbial communities, etc. In addition to the value this study provides through analyzing variables that relate to soil respiration rates, the data collected from this project on average rates of respiration also has the capacity to be scaled up to the degree of the entire watershed. The temperature and moisture relationship data from the plots could be used to normalize the soil respiration data to the same average temperature and moisture, and then the normalized respiration could be scaled up to the area of the watershed. This would provide an estimate for the contribution of soil respiration, which when combined with values for foliage and stem respiration (equation 3) would give an estimate for ecosystem respiration. Applying equation 1 and 2 (provided the other variables are determined) would allow for the estimation of net ecosystem exchange (NEE) within watershed 1. Quantifying the net ecosystem exchange of watershed 1 would increase understanding of NEE within a Pacific Northwest coniferous forest setting. Although developing this application was beyond the scope of my project, it would be a great opportunity for future research. 30 References Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, Kirschbaum, M.U.F., et al. "Definitions of Some Ecological Terms Commonly Used in Carbon Accounting." NEE Workshop (2001): 18­20. Print. HJ Andrews Experimental Forest. (2011). http://andrewsforest.oregonstate.edu/ Lloyd, J., and J.A. Taylor. "On the Temperature Dependence of Soil Respiration." Functional Ecology 8.3 (1994): 315­23. JSTOR. Web. 7 Apr. 2014. <http://www.jstor.org/stable/2389824>. Martin, J. G., C. Phillips, A. Schmidt, J. Irvine and B. Law. "High­frequency Analysis of the Complex Linkage Between Soil CO2 Fluxes, Photosynthesis and Environmental Variables." Tree Physiology 32 (2012): 49­64. Print. M. Wild and P.M. Zhai, 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.­K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Myhre, G., D. Shindell, F.­M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.­F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.­K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Pacific, V., B. McGlynn, D. Riveros­Iregui, H. Epstein, and D. Welsch. "Differential Soil Respiration Responses to Changing Hydrologic Regimes." Water Resources Research 45.W07201 (2009). Ricker, M., M. Stolt, and M. Zavada. "Comparison of Soil Organic Carbon Dynamics in Forested Riparian Wetlands and Adjacent Uplands." Soil Science Society of America Journal 78 (2014): 1817­827. 31 Rothacher, J., C. T. Dyrness, and R. Fredricksen. 1967. “Hydrologic and Related Characteristics of Three Small Watersheds in the Oregon Cascades.” USDA Forest Service Pacific Northwest Forest and Range Experiment Station. 54 pp. Schlesinger, William, and Jeffrey Andrews. "Soil Respiration and the Global Carbon Cycle."Biogeochemistry 48.1 (2000): 7­20. Web. 1 Sept. 2014. United States Department of Agriculture. Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Samples. Natural Resources Conservation Services, 1999. Web. <http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_051232.pdf>. 32