Plant Soil https://doi.org/10.1007/s11104-017-3506-4
REGULAR ARTICLE
Using greenhouse gas fluxes to define soil functional types Sandra Petrakis & Josep Barba & Ben Bond-Lamberty & Rodrigo Vargas
Received: 21 July 2017 / Accepted: 17 November 2017 # Springer International Publishing AG, part of Springer Nature 2017
Abstract Aims Soils provide key ecosystem services and directly control ecosystem functions; thus, there is a need to define the reference state of soil functionality. Most common functional classifications are vegetation-centered, such as plant functional types (PFTs), and neglect soil characteristics and processes. We propose Soil Functional Types (SFTs) as a conceptual approach to represent and describe the functionality of soils based on characteristics of their greenhouse gas (GHG) flux dynamics. Methods We used automated measurements of CO2, CH4 and N2O soil fluxes in a forested area to define SFTs as surface areas with similar GHG dynamics. We performed mixed effects models, and independent cluster analyses of environmental variables and SFT classifications. Results Unique groupings based on SFTs, but not environmental variables, supported the hypothesis that SFTs provide additional insights on the spatial variability of soil functionality beyond information represented by commonly measured soil parameters (e.g., soil moisture, soil temperature, litter biomass). Responsible Editor: Feike A. Dijkstra. S. Petrakis : J. Barba : R. Vargas (*) Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA e-mail:
[email protected] B. Bond-Lamberty Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD 20740, USA
Conclusions This approach could complement vegetation-based functional classifications to better represent the broad range of ecosystem functions. A global application of the proposed SFT framework will only be possible if there is a community-wide effort to share data and create a global database of GHG emissions from soils. Keywords Automated measurements . Ecosystem function . Greenhouse gases . Soil functionality Abbreviations PFTs plant functional types SFTs soil functional types GHG greenhouse gas EFTs ecosystem functional types DFTs decomposition functional types GWP global warming potential DNERR Delaware National Estuarine Research Reserve VWC volumetric water content QA/QC quality assurance and quality control
Introduction Studying ecosystem functionality is important to improve our understanding of ecosystem responses to changing biological and physical conditions. Ecosystem functions are attributes related to the performance of ecosystems that are the consequence of one or multiple ecosystem processes (Lovett et al. 2006), and this
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definition explicitly relates the concept of ecosystem processes to ecosystem functions (Pettorelli et al. 2017). Soils play a critical role in ecosystem functions such as organic matter decomposition, water balance or nutrient dynamics, but to date there is no consensus on how to define and classify soil functionality (BondLamberty et al. 2016). Different plant-based functional classifications have been successfully used for decades to monitor local-to-global changes of ecosystem processes (Díaz et al. 2002). These classifications allow change in ecosystem functions to be predicted from projected modifications in ecosystem components, such as species composition or plant phenology, in response to environmental changes. Plant Functional Types (PFTs), one of the oldest and most widely used functional classifications (Chapin et al. 1996; Shugart and Woodward 1997), describe groups of plants with similar effects on ecosystem processes and responses to environmental conditions, but at the same time has limitations (Wright et al. 2006; Lavorel et al. 2007). Another classification is Ecosystem Functional Types (EFTs), a time-varying land surface classification based on remote sensing vegetation indexes that are used to represent the spatial variability of ecosystem functional properties (Paruelo et al. 2001; AlcarazSegura et al. 2013; Lee et al. 2013). However, these functional classifications of plants and ecosystems are vegetation-centered and neglect soil characteristics and processes. Soils directly control many ecosystem functions and provide key ecosystem services (McBratney et al. 2014; Amundson et al. 2015). Soils are of ecological importance as they store nearly 1500 Pg of soil organic carbon and 136 Pg of total nitrogen within the first meter from the surface (Oertel et al. 2016). Furthermore, of the total annual emissions of major greenhouse gases (GHGs), soils contribute 35% of CO2, 47% of CH4 and 53% of N2O (IPCC 2007, 2014). There has been a pioneer effort to classify soils into Decomposition Functional Types (DFT; Bond-Lamberty et al. 2016) based on heterotrophic respiration. This functional classification was proposed to capture differences in heterotrophic respiration metabolism and flux dynamics, and could allow the scientific community (i.e., modelers and experimentalists) to group and compare plant and soil characteristics across space and time. Undoubtedly, there is a pressing need to define the reference state of soil functionality to improve soil
security (McBratney et al. 2014). This challenge is critical because not all soils are equal in terms of functionality; soil processes could respond differently to forcing factors (e.g., changes in temperature and soil moisture) even among soils with similar biophysical characteristics. We recognize that defining a reference state of soil functionality is difficult and even controversial, but approaches such as DFTs (Bond-Lamberty et al. 2016) move the discussion forward to increase our knowledge of soil functionality across time and space. We propose that there is a need to define Soil Functional Types (SFTs) as a way to classify soil functionality that could complement vegetationbased functional classifications to better represent the broad range of ecosystem functions. In essence, SFTs should be based on key variables that characterize soil functioning, but to date there is no consensus about which are those key variables. In light of this gap, we introduce a framework to define SFTs derived from information about soil GHG fluxes (i.e., CO2, CH4 and N2O). The function of soils as sinks or sources of GHG fluxes provides information necessary to understand the effects of global environmental change, including land use change and weather variability (Cramer et al. 2001; IPCC 2013). Furthermore, the combined information of multiple GHG fluxes gives a more complete accounting of the global warming potential (GWP) of soil emissions (IPCC 2014) than simply considering heterotrophically-derived CO2. The proposed SFT framework is conceptually based on the development of EFTs categories (Paruelo et al. 2001; Alcaraz-Segura et al. 2013), but defines soil functional categories derived from rankings of GHG fluxes (i.e., CO2, CH4 and N2O; three independent variables). Here, we used high temporal frequency measurements of three GHGs within a forested site to test the tractability of this approach. We hypothesize that the derived SFTs provide insights on the spatial variability of soil functionality beyond the information represented by commonly measured soil parameters (e.g., soil moisture, soil temperature, litter biomass). We also provide a discussion on the applicability and challenges of this SFT approach at different spatial scales. This manuscript aims to encourage discussion about defining the reference state of soil functionality, and inspire the debate about defining ecosystem functionality beyond a vegetation-centric approach.
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Materials and methods
Calculation of gas fluxes
Study site
Using Soil Flux Pro Software (v4.0; Li-COR, Lincoln, Nebraska) we calculated fluxes of CO2, CH4, and N2O from the raw data collected by the Picarro G2508. First, the software applied linear and exponential equations (to maximize the fit between change in gas concentrations and time) and calculates GHG fluxes for each chamber measurement. Then, a sequential post-processing quality assurance and quality control (QA/QC) protocol was applied based on the R2 values for the three GHGs measurements comparable to Savage et al. (2014). Measurements were used for further analyses if CO2 fluxes had an R2 > 0.90; if this value was achieved we assumed that there was good gas mixing inside the chamber and that there were no mechanical errors. For CH4 and N2O fluxes we used measurements if they had R2 > 0.60 and 0.50, respectively. When CH4 and N2O fluxes were close to zero (i.e., 8 kg m−2 CO2-eq for both scenarios. The
information represented by commonly measured soil parameters. All analyses were carried out using R 3.3.1. (R Foundation for Statistical Computing, Vienna). The mixed-effects models were performed using the R package nlme V.3.1 (Pinheiro et al. 2009) and the cluster analyses were performed with the vegan package V.2.4 (Oksanen et al. 2007).
Fig. 2 Time series of hourly values for volumetric water content (VWC) of soils (a); hourly values for soil temperature (b) for each chamber (n = 9); and daily cumulative rainfall data (c)
Plant Soil Fig. 3 Time series of mean hourly fluxes of carbon dioxide (CO2; a), methane (CH4; b), and nitrous oxide (N2O; c). Different lines represent data from each of the 9 chambers/locations
GWP of the most common SFTs ranged from nearly 7.5 kg m−2 CO2-eq for Aa2 to nearly 6.2 kg m−2 CO2-eq for Bb1. The lowest GWP was represented by the SFT Bb2 with nearly 5.5 kg m−2 CO2-eq. The cluster analysis provided a way to compare location (i.e., chambers) classification based on environmental variables or SFTs. The analysis of the environmental data did not provide clear groups among the chambers (Fig. 5a). In contrast, the cluster analysis of SFTs split the chambers in two main clusters separated by higher/lower CO2 and CH4 emissions, and within each branch the SFTs were divided by higher/lower N2O fluxes (Fig. 5b).
Discussion Our results show significant differences among measurements points (i.e., chambers) for CO2 and CH4 but not for N2O. It is known that soils have high spatial physical and biological heterogeneity even at short distances (Klironomos et al. 1999; Barba et al. 2013), but the degree of spatial functional variability is still unclear. Previous research has identified high spatial variability in soil CO2 fluxes within small areas (i.e., half hectare; Rodeghiero and Cescatti 2008), which lead to areas of high metabolic activity (i.e., high CO2 fluxes) referred to as hotspots (Leon et al. 2014). The fine-scale spatial
Fig. 4 Mean CO2, CH4 and N2O fluxes for the duration of the experiment at each one of the chambers (i.e., locations)
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Fig. 5 Hierarchical cluster analysis calculated with Euclidean distances performed with (a) environmental data (soil temperature, VWC, litterfall), and with (b) SFT, derived from soil GHG emissions (CO2, CH4 and N2O)
variability of CH4 and N2O fluxes is less understood, but there is evidence that differences in soil moisture, available nitrogen and organic matter influence the spatial heterogeneity of these fluxes (Parkin 1987; Davidson et al. 1998; Jenerette et al. 2008). The lack of significant differences on N2O fluxes may be related to the low magnitude of these fluxes at our study site. Furthermore, our results suggest a spatial correlation for soil carbon dynamics (i.e., CO2 and CH4 fluxes) that is also partially coupled with nitrogen dynamics (i.e., N2O fluxes) underlying the spatial functionality of soils. This is supported by the available categories of SFTs at our study site, where soil CO2 fluxes were always positively associated with CH4 fluxes, and in seven to nine chambers, N2O was negatively associated with CO2 and CH4 fluxes. We found that spatial functional variability was represented by differences in SFTs among our measurement locations (i.e., chambers). Notably, the spatial clustering of SFTs was different than the clustering of environmental variables, despite that these variables are commonly used to describe the spatial and temporal variability of soil GHG fluxes. Others have recognized that such environmental variables alone do not fully represent the variability of GHG fluxes (Hashimoto et al. 2015) as factors such as microbial diversity (Allison 2012) and biogeochemical parameters (e.g., redox state) are relevant for determining variation of soil GHG fluxes (Smith et al. 2003; van Hees et al. 2005; Hall et al. 2013; Gutekunst et al. 2017). The categories of SFTs were directly related to the magnitude of GWP. Categories represented by high GHG fluxes (e.g., Aa1) resulted in the highest GWP and consequently categories with low GHG fluxes (e.g., Bb2) with the lowest GWP (Table 1). Some of the locations were strong CH4 sinks (e.g., Chamber 9) but these hotspots were not that strong to substantially
reduce the GWP of soils likely enhanced by higher N2O emissions –as in category Bb1. A previous study in the region demonstrated how soil GHG fluxes rapidly respond to changes in precipitation and ultimately influence the GWP driven mainly by changes in N2O emissions in forest soils (Petrakis et al. 2017). Combining information on SFTs and GWP provides complementary information of soil functionality as different SFTs may respond differently to forcing factors, even among soils with similar biophysical characteristics, and therefore have similar (or different) GWP. Table 1 The overall carbon dioxide equivalents (CO2-eq) for each chamber based on 20- and 100- year global warming potential values, and corresponding Soil Functional Types (SFT). Each CO2-eq is calculated from the average of all available fluxes of CO2, CH4, and N2O measured by each chamber during the experiment. Each SFT is an alphanumeric code describing the amounts of GHGs measured by each chamber in relation to the other chambers: Capital letters indicate higher (A) or lower (B) CO2 fluxes; Lower case letters indicate higher (a) or lower (b) CH4 fluxes; Numbers indicate higher (1) or lower (2) N2O fluxes. We highlight that the absolute values for Bhigher^ or Blower^ fluxes are dependent on the classification approach (see Fig. 1) and the variability and range of the time series of the soil GHG fluxes Location / chamber
20 year CO2-eq (g m−2)
100 year CO2-eq (g m−2)
SFT
1
6608.1
6673.8
Bb1
2
7925.3
7969.0
Aa2
3
7616.6
7661.0
Aa2
4
8171.2
8219.2
Aa1
5
7194.2
7243.8
Aa2
6
7469.6
7521.6
Aa2
7
5518.8
5594.0
Bb2
8
6521.1
6600.7
Bb1
9
5452.2
5531.5
Bb1
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This study demonstrates that the approach to categorize SFTs based on GHG fluxes from soils is applicable at small spatial scales (within 500 m2), brings attention to the importance of the spatial variability of soil functionality, and provides an alternative way to describe the spatial heterogeneity of soil processes. For a small dataset, a simplified classification of 8 SFTs is sufficient, straightforward to calculate, and conceptually easy to interpret. However, seasonal to annual sums of multiple GHGs are available across thousands of sites (e.g., Bond-Lamberty and Thomson 2010), and as datasets grow a modification of the proposed approach with 27 or 64 SFTs categories can be applied to increase Bresolution^ of SFTs across the world (Fig. 1). Theoretically, SFTs can be defined, mapped and studied at multiple spatial scales depending on the research question or application goal (Fig. 6). Additionally, SFTs could be a useful tool to assess temporal changes in soil functionality if multiple years of data are available, as it is done to study the temporal variability of EFTs. Using 8 categories of SFT results in higher spatial dependence at local scales, but it may not be enough for regional to global scales. This implies that SFTs obtained in different studies at different scales are not directly comparable and that emergent properties of SFTs are evident across the hierarchy of scales, where multiple SFTs within a smaller scale (e.g., local) are comprised by only one SFTs at a larger scale (e.g., regional). This is because at the local scale (as in this study) we may observe larger spatial dependence and high heterogeneity of SFTs. These SFTs may be influenced by fine-scale effects of soil biophysical processes such as microtopography, fine root Fig. 6 Conceptual diagram of how classification of Soil Functional Types (SFTs) would apply across different spatial scales
dynamics, changes in soil moisture and temperature that ultimately influence biogeochemical cycles of the measured GHGs. At the regional scale SFTs may be influenced by ecosystem-to-landscape scale effects of topography and vegetation types, and at the global scale SFTs may be influenced by larger gradients of vegetation and climate. Like all classification approaches, SFTs have limitations. For example, measuring multiple annual GHG fluxes could be logistically difficult and expensive when considering personnel and instrumentation/sample analyses costs. However, the major limitation could be that the defined categories of SFTs are not directly comparable among studies because the selection criteria are based on the available GHG measurements within the dataset and the spatial scale of interest (Fig. 1, 6). SFTs provide a relative comparison of soil functioning within a given study area but not an absolute classification to directly compare across different studies or scales. Thus, SFTs are comparable in time within a particular study site/region but not in space across regions. Site-specific SFTs can be locally defined, but global SFT categories will require global information on multiple GHGs at a wide variety of sites. The scientific community has put forth great effort compiling the global Soil Respiration Database (Bond-Lamberty and Thomson 2010), but there is a need to expand this information to multiple GHG fluxes to more broadly assess soil function (Kim et al. 2012). Our approach is a first step to provide a framework to define SFTs, but we recognize that a community effort is necessary to harmonize any global classification for the functionality of soils.
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In conclusion, this study aims to promote discussion about new ways to identify, classify, and address functionality of soils. Further coordination and exploration are needed to define soil functionality and determine the best descriptive global systematic categories of SFTs; a global application of the proposed SFT framework will only be possible if there is a community-wide effort to share data and create a global database of GHG emissions from soils. While our framework for SFT is based on measurements of GHG fluxes, we recognize that it is not the only way to describe the functionality of soils; other soil properties or ecological processes be used. The proposed framework can be applied at any spatial scale, although this approach was tested at one site. Applying this approach at a global scale requires investment in instrumentation, data availability for the three major GHGs, and a standardization of measurements of biogeochemical processes. We finish by sharing research questions related to SFTs to be explored by the broader scientific community. Our study shows that there could be several SFTs within a similar soil type, but can several soil types have the same soil functionality (i.e., classified within a similar SFT)? Are some SFTs more resilient to changes in biophysical conditions of the soils? Are SFTs stable across years/decades, or are they dependent on biogeochemical forcing variables? How do SFTs relate to hotmoments and hot-spots of GHG emissions from soils? Are there better ways to classify and define baselines of soil functionality? Acknowledgements Funding was provided by the United States Department of Agriculture-Agriculture and Food Research Initiative (AFRI) Grant 2013-02758, and State of Delaware’s Federal Research and Development Matching Grant Program. We are grateful for the support of Delaware National Estuarine Research Reserve for access to the site and support to maintain this experiment. Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.kq7h7.
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