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Patterns and Predictors of Soil Organic Carbon Storage Across a Continental-Scale Network

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Patterns and Predictors of Soil Organic Carbon Storage Across a Continental-Scale Network Biogeochemistry https://doi.org/10.1007/s10533-020-00745-9 (0123456789().,-volV)( 0123456789().,-volV) Patterns and predictors of soil organic carbon storage across a continental-scale network L. E. Nave . M. Bowman . A. Gallo . J. A. Hatten . K. A. Heckman . L. Matosziuk . A. R. Possinger . M. SanClements . J. Sanderman . B. D. Strahm . T. L. Weiglein . C. W. Swanston Received: 20 May 2020 / Accepted: 7 December 2020 Ó The Author(s) 2021 Abstract The rarity of rapid campaigns to charac- the other sampled dozens of pedons across the terize soils across scales limits opportunities to landscape at each site. We demonstrate some consis- investigate variation in soil carbon stocks (SOC) tencies between these distinct designs, while also storage simultaneously at large and small scales, with revealing that within-site replication reveals patterns and without site-level replication. We used data from and predictors of SOC stocks not detectable with non- two complementary campaigns at 40 sites in the replicated designs. Both designs demonstrate that United States across the National Ecological Obser- SOC stocks of whole soil profiles vary across conti- vatory Network (NEON), in which one campaign nental-scale climate gradients. However, broad cli- sampled profiles from closely co-located intensive mate patterns may mask the importance of localized plots and physically composited similar horizons, and variation in soil physicochemical properties, as cap- tured by within-site sampling, especially for SOC stocks of discrete genetic horizons. Within-site repli- To be submitted to Biogeochemistry (special issue ‘‘Multi- scale controls on soil organic matter: leveraging networks, cation also reveals examples in which expectations synthesis, and long-term studies’’). based on readily explained continental-scale patterns do not hold. For example, even wide-ranging drainage Responsible Editor: William R. Wieder. class sequences within landscapes do not duplicate the clear differences in profile SOC stocks across drainage Supplementary Information The online version contains classes at the continental scale, and physicochemical supplementary material available at https://doi.org/10.1007/ s10533-020-00745-9. L. E. Nave (&) A. Gallo Á J. A. Hatten Á L. Matosziuk Biological Station, University of Michigan, Pellston, Forest Engineering, Resources and Management, Oregon MI 49769, USA State University, Corvallis, OR 97331, USA e-mail: [email protected] K. A. Heckman Á C. W. Swanston L. E. Nave Northern Research Station, USDA-Forest Service, Department of Ecology and Evolutionary Biology, Houghton, MI 9931, USA University of Michigan, Ann Arbor, MI 48109, USA A. R. Possinger Á B. D. Strahm Á T. L. Weiglein M. Bowman Á M. SanClements Department of Forest Resources and Environmental Environmental Studies Program, University of Colorado Conservation, Virginia Tech, Blacksburg, Boulder, Boulder, CO 80301, USA VA 24061, USA 123 Biogeochemistry factors associated with increasing B horizon SOC part of the spectrum of spatial scales (e.g., Davidson stocks at continental scales frequently do not follow 1995; Goidts et al. 2009; Huang et al. 2017; Minasny the same patterns within landscapes. Because infer- et al. 2013; Mishra et al. 2010; Patton et al. 2019a; ences from SOC studies are a product of their context Paustian et al. 1997; Schimel et al. 1994; Thompson (where, when, how), this study provides context—in and Kolka 2005; Wynn et al. 2006). The ability to terms of SOC stocks and the factors that influence make inferences across the full range of spatial scales them—for others assessing soils and the C cycle at from global to landscape has largely derived from NEON sites. reviews of this literature, such as Wiesmeier et al. (2019, and references therein), and from multi-site Keywords Soil carbon stocks Á Pedogenesis Á inventories or large data syntheses that have used Climate Á Land use Á Parent material Á National extensively (but not intensively) distributed observa- ecological observatory network tions to assess patterns at regional or larger scales (e.g., Doetterl et al. 2016; Cotrufo et al. 2019). In the context of these approaches to addressing SOC stocks and predictors as a function of scale, large networks Introduction that allow for investigation of patterns across and within sites have much to add, especially when such Most of the factors related to spatial variation in soil networks are sampled expeditiously and according to organic carbon (SOC) stocks have been known for common protocols. some time, as has the reality that their relative Studies that explore processes of SOC and soil influences vary across scales (Wiesmeier et al. organic matter (SOM) stabilization report mechanisms 2019). From molecular structures and particle sizes that may relate to patterns of SOC storage. This factors at pore to ped scales (Sollins et al. 2006; von literature has particularly focused on soil physico- Lutzow et al. 2006), to topography and moisture at chemical and biogeochemical mechanisms promoting pedon to landscape scales (Doetterl et al. 2016; SOC stability, such as physical protection, mineral or Adhikari et al. 2020), to climate and vegetation at metal association, and molecular complexity (Crow regional to global scales (Jobbagy and Jackson 2000; et al. 2007; Kallenbach et al. 2016; Kramer and Post et al. 1982), a large body of research readily Chadwick 2018; Mao et al. 2000; Preston and Schmidt explains why SOC varies so remarkably from place to 2006; Sollins et al. 2006; Six et al. 2002; von Lutzow place. The strongest studies also acknowledge that et al. 2006). These and other studies have specifically soils are dynamic through time, unique at sites and pointed to extractable metals (e.g., Fe, Al), exchange- scales that cannot be captured by even strong gener- able base cations (e.g., Ca, Mg), and soil fine fraction alizations, and therefore conclude that further inves- contents (especially clays) as having controlling tigations into factors influencing SOC storage will influence over the stability of SOC (Chen et al. continue to refine our understanding of how much is 2019; Heckman et al. 2018a, b; Lawrence et al. 2015; present, where, and why. Rasmussen et al. 2018). If these processes and In the body of research exploring patterns and mechanisms that confer SOM persistence also result predictors of variation in SOC storage, a vast number in larger quantities of SOC being present at a point in of studies have reported factors influencing SOC for time, they may provide a foundation for hypotheses addressing physicochemical predictors of variation in M. SanClements SOC stocks at varying scales. INSTAAR University of Colorado Boulder, Boulder, The literature on pedogenesis and soil taxonomy CO 80303, USA also offers a strong foundation for assessments of SOC M. SanClements patterns and predictors at distinct scales. Genetic soil National Ecological Observatory Network, Boulder, taxonomy uses morphologic and physicochemical CO 80301, USA properties to infer processes of soil formation, many of which involve gains, losses, transfers and transfor- J. Sanderman Woods Hole Research Center, Falmouth, mations of materials which are dominantly comprised MA 02540, USA of, or critically affected by C (Marbut 1921; Simonson 123 Biogeochemistry 1959). In light of the many interacting processes and Ecological Observatory Network (NEON); as such it factors in soils, all of which vary continuously, genetic is intended to provide context for studies at and across soil taxonomy provides a structure for managing this NEON sites, and to test hypotheses related to SOC complexity, creating categorical groups that reflect stocks and their variation as influenced by scale and fundamental differences between soils. This categor- study design. Data for testing these hypotheses derive ical system is also hierarchical, integrating broad from two complementary campaigns, in which one distal (e.g., climatic) and local proximal factors (e.g., sampled profiles from closely adjacent intensive plots physicochemical properties) at steadily increasing and physically composited similar horizons, and the resolution, to describe soils ultimately as unique other sampled dozens of pedons across the landscape bodies, which often differ in SOC stocks (Wills et al. at each site. Owing to the differing levels of replication 2013). As soils are inherently multi-factor, these distal of these two campaigns, they afford opportunities to and proximal factors are not completely independent assess SOC stocks across the entire network and of each other. Nonetheless, this pedogenic framework within sites, i.e., at continental and landscape scales. provides structure for a conceptual model (Fig. 1) that Our (6) hypotheses, enumerated below, are informed can be applied at any number of scales. In this model, by literature reporting predictors of SOC storage distal and proximal controls mediate each other, with across scales, and by the SOM stabilization literature, distal controls dominant in extreme climates (frozen, though it is important to note that they address SOC saturated, or arid conditions) and proximal controls stocks in terms of patterns, not stabilization as a becoming more important in the mesic, temperate mechanistic process. (1) Regarding whole soil pro- middle, and within landscapes where climatic varia- files, we hypothesized that the two designs reveal the tion is narrower. same continental-scale patterns and sources of varia- The present study is based upon this conceptual tion in terms
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