CO2 exchange of a temperate fen during the conversion from moderately rewetting to flooding

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 118 (2013): 940–950, doi:10.1002/jgrg.20069.Year-round flooding provides a common land management practice to reestablish the natural carbon dioxide (CO2) sink function of drained peatlands. Here we present eddy covariance measurements of net CO2 exchange from a temperate fen during three consecutive growing seasons (May–October) that span a period of conversion from moderately rewetting to flooding. When we started our measurements in 2009, the hydrological conditions were representative for the preceding 20 years with a mean growing season water level (MWGL) of 0 cm but considerably lower water levels in summer. Flooding began in 2010 with an MWGL of 36 cm above the surface. The fen was a net CO2 sink throughout all growing seasons (2009: −333.3 ± 12.3, 2010: −294.1 ± 8.4, 2011: −352.4 ± 5.1 g C m−2), but magnitudes of canopy photosynthesis (CP) and ecosystem respiration (Reco) differed distinctively. Rates of CP and Reco were high before flooding, dropped by 46% and 61%, respectively, in 2010, but increased again during the beginning of growing season 2011 until the water level started to rise further due to strong rainfalls during June and July. We assume that flooding decreases not only the CO2 release due to inhibited Reco under anaerobic conditions but also CO2 sequestration rates are constricted due to decreased CP. We conclude that rewetting might act as a disturbance for a plant community that has adapted to drier conditions after decades of drainage. However, if the recent species are still abundant, a rise in CP and autotrophic Reco can be expected after plants have developed plastic response strategies to wetter conditions.F.K. was supported by a scholarship of the Federal State of Mecklenburg-Western Pomerania and by the German Research Foundation (DFG). The German Academic Exchange Service (DAAD) funded the collaboration with I.F.2013-12-2

Unraveling the Importance of Polyphenols for Microbial Carbon Mineralization in Rewetted Riparian Peatlands

There have been widespread attempts to rewet peatlands in Europe and elsewhere in the world to restore their unique biodiversity as well as their important function as nutrient and carbon sinks. However, changes in hydrological regime and therefore oxygen availability likely alter the abundance of enzyme-inhibiting polyphenolic compounds, which have been suggested as a “latch” preventing large amounts of carbon from being released into the atmosphere by microbial mineralization. In recent years, a variety of factors have been identified that appear to weaken that latch including not only oxygen, but also pH. In minerotrophic fens, it is unknown if long-term peat mineralization during decades of drainage and intense agricultural use causes an enrichment or a decline of enzyme-inhibiting polyphenols. To address this, we collected peat samples and fresh roots of dominating plants (i.e., the peat parent material) from the upper 20 cm peat layer in 5 rewetted and 6 natural fens and quantified total phenolic content as well as hydrolysable and condensed tannins. Polyphenols from less decomposed peat and living roots served partly as an internal standard for polyphenol analysis and to run enzyme inhibition tests. As hypothesized, we found the polyphenol content in highly decomposed peat to be eight times lower than in less decomposed peat, while condensed tannin content was 50 times lower in highly degraded peat. In addition, plant tissue polyphenol contents differed strongly between peat-forming plant species, with the highest amount found in roots of Carex appropinquata at 450 mg g−1 dry mass, and lowest in Sphagnum spp. at 39 mg g−1 dry mass: a 10-fold difference. Despite large and clear differences in peat and porewater chemistry between natural and rewetted sites, enzyme activities determined with Fluorescein diacetate (FDA) hydrolysis and peat degradation were not significantly correlated, indicating no simple linear relationship between polyphenol content and microbial activity. Still, samples with low contents of polyphenols and condensed tannins showed the highest microbial activities as measured with FDA

Long-Term Rewetting of Three Formerly Drained Peatlands Drives Congruent Compositional Changes in Pro- and Eukaryotic Soil Microbiomes through Environmental Filtering

Drained peatlands are significant sources of the greenhouse gas (GHG) carbon dioxide.Rewetting is a proven strategy used to protect carbon stocks; however, it can lead to increasedemissions of the potent GHG methane. The response to rewetting of soil microbiomes as drivers ofthese processes is poorly understood, as are the biotic and abiotic factors that control communitycomposition. We analyzed the pro- and eukaryotic microbiomes of three contrasting pairs ofminerotrophic fens subject to decade-long drainage and subsequent long-term rewetting. Abiotic soilproperties including moisture, dissolved organic matter, methane fluxes, and ecosystem respirationrates were also determined. The composition of the microbiomes was fen-type-specific, but allrewetted sites showed higher abundances of anaerobic taxa compared to drained sites. Based onmulti-variate statistics and network analyses, we identified soil moisture as a major driver ofcommunity composition. Furthermore, salinity drove the separation between coastal and freshwaterfen communities. Methanogens were more than 10-fold more abundant in rewetted than in drainedsites, while their abundance was lowest in the coastal fen, likely due to competition with sulfatereducers. The microbiome compositions were reflected in methane fluxes from the sites. Our resultsshed light on the factors that structure fen microbiomes via environmental filtering

Introduction of a guideline for measurements of greenhouse gas fluxes from soils using non-steady-state chambers

Method Soils represent a major global source and sink of greenhouse gases (GHGs). Many studies of GHG fluxes between soil, plant and atmosphere rely on chamber measurements. Different chamber techniques have been developed over the last decades, each characterised by different requirements and limitations. In this manuscript, we focus on the non-steady-state technique which is widely used for manual measurements but also in automatic systems. Although the measurement method appears very simple, experience gained over the years shows that there are many details which have to be taken into account to obtain reliable measurement results. Aim This manuscript aims to share lessons learnt and pass on experiences in order to assist the reader with possible questions or unexpected challenges, ranging from the planning of the design of studies and chambers to the practical handling of the chambers and the quality assurance of the gas and data analysis. This concise introduction refers to a more extensive Best Practice Guideline initiated by the Working Group Soil Gases (AG Bodengase) of the German Soil Science Society (Deutsche Bodenkundliche Gesellschaft). The intention was to collect and aggregate the expertise of different working groups in the research field. As a compendium, this Best Practice Guideline is intended to help both beginners and experts to meet the practical and theoretical challenges of measuring soil gas fluxes with non-steady-state chamber systems and to improve the quality of the individual flux measurements and thus entire GHG studies by reducing sources of uncertainty and error

From Understanding to Sustainable Use of Peatlands: The WETSCAPES Approach

Of all terrestrial ecosystems, peatlands store carbon most effectively in long-term scales of millennia. However, many peatlands have been drained for peat extraction or agricultural use. This converts peatlands from sinks to sources of carbon, causing approx. 5% of the anthropogenic greenhouse effect and additional negative effects on other ecosystem services. Rewetting peatlands can mitigate climate change and may be combined with management in the form of paludiculture. Rewetted peatlands, however, do not equal their pristine ancestors and their ecological functioning is not understood. This holds true especially for groundwater-fed fens. Their functioning results from manifold interactions and can only be understood following an integrative approach of many relevant fields of science, which we merge in the interdisciplinary project WETSCAPES. Here, we address interactions among water transport and chemistry, primary production, peat formation, matter transformation and transport, microbial community, and greenhouse gas exchange using state of the art methods. We record data on six study sites spread across three common fen types (Alder forest, percolation fen, and coastal fen), each in drained and rewetted states. First results revealed that indicators reflecting more long-term effects like vegetation and soil chemistry showed a stronger differentiation between drained and rewetted states than variables with a more immediate reaction to environmental change, like greenhouse gas (GHG) emissions. Variations in microbial community composition explained differences in soil chemical data as well as vegetation composition and GHG exchange. We show the importance of developing an integrative understanding of managed fen peatlands and their ecosystem functioning.

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe