Oxygen (O2) is the quintessential electron acceptor and therefore drives biogeochemical cycling on Earth. Its availability within soil pores strongly modulates soil oxidation-reduction (redox) potential, thereby controlling which energy yielding soil biogeochemical reactions proceed (Hefting et al., 2004; Silver, et al., 1999). For example, decreases in soil O2 reduce redox potential, causing facultative and obligate anaerobic microorganisms to shift their energy-yielding respiration processes to utilize alternative electron acceptors. Oxygen availability therefore impacts the capacity of soils to transform nutrients, such as carbon (C) and nitrogen (N) via processes like heterotrophic aerobic respiration and denitrification, respectively (Liptzin et al., 2011). For example, the transformation of nitrate (NO3−), a soluble form of N that is limiting to primary productivity in freshwater and marine ecosystems, to gaseous forms of N (N2O and N2) via denitrification, is a major N removal pathway (Hefting, 2003). This process can reduce N loading to water bodies, but it will not proceed if O2 is abundant. Oxygen availability therefore controls denitrification rates and efficiency (Groffman et al., 1988; Bouwman et al., 2013), playing a critical role in creating conducive environments for soil N removal. As such, soil O2 regulates N2O emissions and N mobility (Groffman et al., 1988), highlighting the importance of accurately characterizing shifts in soil O2.
Our ability to predict soil O2 concentrations across spatial and temporal gradients is limited, however. This is a result of the complex network of biotic and abiotic soil factors, as well as climatic conditions, that interact to modulate soil O2 dynamics and create widespread spatial and temporal soil O2 variability (Silver et al., 1999). Soil O2 levels are regulated by the diffusion of O2 into and displacement of O2 out of soil pores by water (physical processes), and the consumption of O2 via soil respiration (a biological process, i.e., aerobic microbial, plant root, and faunal respiration) (Moyano et al., 2013; Neira et al., 2015; Ponnamperuma, 1972). Because O2 has a very low solubility in water (Moldrup et al., 2000), the presence of water inhibits O2 diffusion from the atmosphere to soil pores (Skopp et al., 1985). Thus, the combined effects of inhibited O2 diffusion and displacement, and soil respiration typically result in O2 depletion if reaeration of soil pores is prevented (Neira et al., 2015; Ponnamperuma, 1972). Furthermore, variability in soil O2 is difficult to manually monitor in-situ (i.e., using handheld soil probes or gas chromatography), and the collection of high spatial and temporal resolution O2 data is costly, as it requires soil probes and data logging capabilities.
The challenges associated with measuring gaseous O2 in soils have led to the use of soil moisture as a proxy measurement for O2 under the assumption that soil moisture is inversely proportional to O2 concentration (Heinen, 2005; Ridolfi et al., 2003; Rubol et al., 2013). This assumption has been implemented in many simplified process-based denitrification sub-models embedded in N-cycling and ecosystem models (i.e., those that do not account for microbial processes or gaseous diffusion). Some of these models utilize bivariate nonlinear power functions that are modeled after an inverse relationship between O2 and soil moisture to predict O2 depletion based solely on water-filled pore space. Examples include the NEMIS model (Hénault & Germon, 2000), the LEACHMN model (Sogbedji et al., 2001), and the SHETRAN model (Birkinshaw & Ewen, 2000). Simplified process-based denitrification models that exclude direct O2 measurements have been found to exhibit high sensitivity to formulations that represent soil moisture (Hénault & Germon, 2000), which highlights that the relationship between these variables must be validated and defined using empirical data.
Indeed, due to seasonal shifts in the mechanisms that control soil O2 depletion (Silver et al., 1999), the use of soil moisture as a proxy measurement for O2 could result in inaccurate O2 estimations. For example, soil water inputs (precipitation or groundwater) vary seasonally and are modulated by hydraulic conductivity. Water demand (i.e., vegetation water uptake) also fluctuates seasonally and varies by plant species (Ewe et al., 2007). Furthermore, O2 depletion by plant and microbial respiration is primarily controlled by soil temperature and soil water content and thus exhibits seasonal fluctuations (Kang et al., 2003; Lavigne et al., 2004; Chen et al., 2010).
Because riparian zones are located at the interface of terrestrial and aquatic ecosystems, they can function as hot spots for anaerobic biogeochemical soil processes (Vidon et al., 2010) and are therefore ideal study systems for soil O2 dynamics. Due to their unique position on the landscape, riparian soils experience frequent hydrologic changes that alter soil moisture content, which can modify O2 availability. Changes in soil moisture are triggered by hydrologic fluctuations, and the magnitude of these shifts depend on site-specific riparian zone characteristics, such as topography, proximity to surface and groundwater flows, the size and depth of the upland aquifer, and soil hydraulic properties. Riparian zones can also experience seasonal hydrologic fluctuations resulting from changes in connectivity with the upland aquifer (Vidon & Hill, 2004) and variability in water inputs due to seasonal precipitation patterns. Furthermore, site-specific dominant vegetation types have unique water requirements, which could impact the physical soil wetting process. The diverse potential combinations of soil O2 drivers suggest that the response of soil O2 to soil moisture fluctuations is a result of multi-variate interactions that are highly dependent on site-specific soil conditions, seasonal fluctuations in environmental conditions, and ecosystem water and O2 demands.
Recent advancements in sensor technologies facilitate the simultaneous collection of multiple soil parameters at high temporal resolution, including O2 and its relevant controls (e.g., soil moisture, soil temperature, redox, CO2, precipitation). This enables us to comprehensively assess potential drivers of soil moisture variability and the related O2 response. While high-frequency data for multiple parameters is advantageous for ecosystem monitoring, it requires the use of tools that are specifically suited to analyze multivariate and nonlinear data. The Kohonen unsupervised self-organizing map (SOM), a type of artificial neural network, is a powerful clustering tool that can reliably analyze such multivariate and nonlinear data (Rivera et al., 2015), making it an ideal approach for detecting patterns in large environmental datasets. The SOM can overcome limitations of traditional statistical methods, as it can tolerate outliers, non-normally distributed, non-continuous data, and multicollinearity (Kundu et al., 2013; Merdun, 2011).
The SOM maps multivariate data to a two-dimensional map/lattice, where similar data points are situated in close proximity. In contrast to other, more traditional clustering algorithms, (e.g., k-means), the SOM approach enables visualization of variables that drive clustering, and thus, is a potentially powerful statistical tool for leveraging the capacity of high frequency sensor networks to monitor physical and biogeochemical parameters. SOMs have been successfully applied to resolve spatial and temporal heterogeneity in complex systems within large soil and water quality databases (Obach et al., 2001; Wu et al., 2008; Xiaoyong et al., 2019), as well as to classify sediment (Alvarez-guerra et al., 2008) and soil types (Tissari et al., 2007). Additionally, the SOM approach has been utilized to address questions concerning water resources and hydrology, such as rainfall-runoff relationships (Lin & Chen, 2006), precipitation dynamics (Kalteh et al., 2008), and links between physical soil properties and hydrologic soil processes (Merdun, 2011). However, to our knowledge, these tools have not yet been used to detect patterns in high frequency soil sensor time series.
We applied SOMs to test our overarching hypothesis that shifts in riparian soil O2 levels are predictively driven by combinations of key environmental controls, such as ecosystem water delivery, and water and O2 demand. We hypothesize that, while these controls are ubiquitous, the way they interact to impact soil O2 will be modulated by site-specific characteristics and seasonal variability in their relative importance. To test our hypotheses, we used the SOM to identify key drivers of variability in riparian soil O2 dynamics and combinations of conditions that lead to low and high O2 levels. We clustered high frequency soil and meteorological data collected over three years from a poorly drained wetland position within two riparian sites located in northeastern Vermont, USA. We studied two riparian soil environments with contrasting site characteristics (e.g., adjacent land use, vegetative cover, site elevation), allowing us to test our hypotheses in two different riparian lowland settings.