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Improving Understanding of Soil Organic Matter Dynamics by Triangulating Theories, Measurements, and Models Biogeochemistry (2018) 140:1–13 https://doi.org/10.1007/s10533-018-0478-2 (0123456789().,-volV)(0123456789().,-volV) SYNTHESIS AND EMERGING IDEAS Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models Joseph C. Blankinship . Asmeret Asefaw Berhe . Susan E. Crow . Jennifer L. Druhan . Katherine A. Heckman . Marco Keiluweit . Corey R. Lawrence . Erika Marı´n-Spiotta . Alain F. Plante . Craig Rasmussen . Christina Scha¨del . Joshua P. Schimel . Carlos A. Sierra . Aaron Thompson . Rota Wagai . William R. Wieder Received: 26 July 2017 / Accepted: 23 July 2018 / Published online: 30 July 2018 Ó Springer Nature Switzerland AG 2018 Abstract Soil organic matter (SOM) turnover currently exists between the emergent understanding increasingly is conceptualized as a tension between of SOM dynamics and our ability to improve terres- accessibility to microorganisms and protection from trial biogeochemical projections that rely on the decomposition via physical and chemical association existing models. In this opinion paper, we portray with minerals in emerging soil biogeochemical theory. the SOM paradigm as a triangle composed of three Yet, these components are missing from the original nodes: conceptual theory, analytical measurement, mathematical models of belowground carbon dynam- and numerical models. In successful approaches, we ics and remain underrepresented in more recent contend that the nodes are connected—models capture compartmental models that separate SOM into dis- the essential features of dominant theories while crete pools with differing turnover times. Thus, a gap measurement tools generate data adequate to param- eterize and evaluate the models—and balanced— Responsible Editor: Karsten Kalbitz. J. C. Blankinship (&) Á C. Rasmussen M. Keiluweit Department of Soil, Water, and Environmental Science, School of Earth and Sustainability, Stockbridge School, University of Arizona, Tucson, AZ 85721, USA University of Massachusetts, Amherst, MA 01003, USA e-mail: [email protected] C. R. Lawrence A. A. Berhe U.S. Geological Survey, Denver, CO 80225, USA Life and Environmental Sciences Unit, University of California Merced, Merced, CA 95348, USA E. Marı´n-Spiotta Department of Geography, University of Wisconsin at S. E. Crow Madison, Madison, WI 53706, USA Department of Natural Resources and Environmental Management, University of Hawaii Manoa, Honolulu, A. F. Plante HI 96822, USA Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA 19104, USA J. L. Druhan Department of Geology, University of Illinois Urbana C. Scha¨del Champaign, Champaign, IL 61820, USA Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA K. A. Heckman USDA Forest Service, Northern Research Station, Houghton, MI 49931, USA 123 2 Biogeochemistry (2018) 140:1–13 models can inspire new theories via emergent behav- response to environmental change requires a robust iors, pushing empiricists to devise new measurements. approach for evaluating SOM dynamics, an approach Many exciting advances recently pushed the bound- that integrates: (a) emerging conceptual understand- aries on one or more nodes. However, newly inte- ing, or theory, (b) quantitative measurements, and grated triangles have yet to coalesce. We conclude that (c) mathematical models. Accomplishing this integra- our ability to incorporate mechanisms of microbial tion essentially creates a paradigm. According to the decomposition and physicochemical protection into Oxford English Dictionary, a paradigm is ‘‘a concep- predictions of SOM change is limited by current tual or methodological model underlying the theories disconnections and imbalances among theory, mea- and practices of a science…a generally accepted surement, and modeling. Opportunities to reintegrate worldview.’’ In our view, a scientific paradigm can be the three components of the SOM paradigm exist by represented as a triangle composed of three nodes: carefully considering their linkages and feedbacks at conceptual theory, analytical measurement, and specific scales of observation. numerical models (Fig. 1a). The first paradigm of SOM dynamics evolved early Keywords Biogeochemical models Á Carbon in the history of soil science and included the stabilization Á Decomposition Á Global carbon cycle Á humic/fulvic/humin extraction approach that defined Soil organic matter SOM in terms of solubility and chemical recalcitrance (Waksman 1927) and a simple one-pool model was developed to describe and predict SOM changes (Salter and Green 1933). By the 1980s, other ideas Introduction emerged to explain the stability and turnover of SOM. Researchers integrated incubation studies, radiocar- Understanding soil organic matter (SOM) dynamics is bon dating, aggregate separation, and other methods to essential to predicting the size of the soil carbon develop the idea of compartmentalizing soil carbon (C) reservoir and its contributions to soil function, into pools with different turnover times (Paul 1984). global C fluxes, and climate change mitigation. Even with 1980s computing power, scientists were Resolving uncertainties about the Earth system’s able to incorporate these conceptual pools into sim- ulation models that were able to effectively describe J. P. Schimel observed large-scale patterns of SOM dynamics (van Earth Research Institute and Department of Ecology, Veen and Paul 1981; Parton et al. 1987) and an early Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA SOM paradigm formed. The current dominant paradigm continues to con- C. A. Sierra ceptualize soil as discrete C pools with differing Max Planck Institute for Biogeochemistry, 07745 Jena, turnover times (theory). Carbon pools and turnover Germany can be parameterized from experiments or measure- A. Thompson ments that include: respiration time courses during Department of Crop and Soil Science & Odum School of laboratory incubations, isolation of SOM pools by Ecology, University of Georgia, Athens, GA 30602, USA physical properties (such as size or density), and R. Wagai radiocarbon analyses (measurement). Flow between National Agriculture and Food Research Organization, pools is governed by first-order kinetics driven by pool Institute for Agro-Environmental Sciences, Tsukuba, size and modified by environmental conditions Ibaraki 305-8604, Japan (model). This assumption provides the basis for soil W. R. Wieder profile-scale models such as CENTURY (Parton et al. Institute of Arctic and Alpine Research, University of 1987) and RothC (Jenkinson and Rayner 1977), which Colorado, Boulder, CO 80309, USA have been used widely to explore SOM dynamics and form the core soil biogeochemical component of Earth W. R. Wieder Climate and Global Dynamics Laboratory, National system models (Todd-Brown et al. 2013). Center for Atmospheric Research, Boulder, CO 80307, This approach is robust and has stood the test of USA time because all three elements (theory, measurement, 123 Biogeochemistry (2018) 140:1–13 3 Fig. 1 A representation of existing and emerging approaches to expands the SOM paradigm triangle as understanding of the evaluating soil organic matter (SOM) dynamics. The existing controls on SOM dynamics grows. However, if expansion at a approach is robust because all three nodes—theory, measure- node outpaces integration of linkages within the triangle, then ment, and modeling—form strong bidirectional linkages and are cracks form causing a lack of applicability and adaptability to well balanced (a). Recent and ongoing innovation at each node changing environmental conditions (b) and model) are adaptable, linked and, simply, it New work has advanced individual, or pairs of, worked. This paradigm persists because it captures a nodes of the SOM triangle. But, there has been little general principle of SOM dynamics, that SOM is focus on evaluating how these developments can be heterogenous and comprises carbon pools that decom- integrated to form a new, cohesive, stable paradigm pose at different rates. With multiple pools and (Don et al. 2013). In agreement, Bradford et al. (2016) turnover times, most observed changes in carbon concluded that, ‘‘the major modeling uncertainty is mass over time can be simulated, but often by associated with representing common and outdated calibrating the model with different pool sizes and ideas about soil C turnover,’’ and suggested that decay constants unvalidated by measurement. The confidence in model predictions of SOM is diminished empirical models are simple enough to be adapted to because assumptions underlying SOM formation and different theories about SOM stability, distribution stabilization in climate models often conflict with our among pools, and factors influencing destabilization. emerging understanding. The existing paradigm Yet, this paradigm also has been criticized for its worked well because concept, measurement, and simplicity, i.e., it is oversimplified and excludes many model formed strong connections that reinforced each systems that are not ‘‘typical.’’ Finally, the existing other. It is difficult, however, to evolve to new paradigm is not well suited for dealing with rapidly theories, measurement approaches, and numerical changing environmental conditions such as those models within an existing paradigm—the whole developing with climate change. To address the triangle must be evaluated as a unit. We argue that limitations inherent to first-order models, many
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