Graminoids vary in functional traits, carbon dioxide and methane fluxes in a restored peatland: implications for modeling carbon storage

Abstract

One metric of peatland restoration success is the re-establishment of a carbon sink, yet considerable uncertainty remains around the timescale of carbon sink trajectories. Conditions post-restoration may promote the establishment of vascular plants such as graminoids, often at greater density than would be found in undisturbed peatlands, with consequences for carbon storage. Although graminoid species are often considered as a single plant functional type (PFT) in land-atmosphere models, our understanding of functional variation among graminoid species is limited, particularly in a restoration context. We used a traits-based approach to evaluate graminoid functional variation and to assess whether different graminoid species should be considered a single PFT or multiple types. We tested hypotheses that greenhouse gas fluxes (CO 2 , CH 4 ) would vary due to differences in plant traits among five graminoid species in a restored peatland in central Alberta, Canada. We further hypothesized that species would form two functionally distinct groupings based on taxonomy (grass, sedge). Differences in gas fluxes among species were primarily driven by variation in leaf physiology related to photosynthetic efficiency and resource-use, and secondarily by plant size. Multivariate analyses did not reveal distinct functional groupings based on taxonomy or environmental preferences. Rather, we identified functional groups defined by continuous plant traits and carbon fluxes that are consistent with ecological strategies related to differences in growth rate, resource-acquisition, and leaf economics. These functional groups displayed larger carbon storage potential than currently-applied graminoid PFTs. Existing PFT designations in peatland models may be more appropriate for pristine or high-latitude systems than those under restoration. Although replacing PFTs with continuous plant traits remains a challenge in peatlands, traits related to leaf physiology and growth rate strategies offer a promising avenue for future applications.

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Title
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Graminoids vary in functional traits, carbon dioxide and methane fluxes in a restored peatland:
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implications for modeling carbon storage
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Authors
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Ellie M. Goud+*1, Sabrina Touchette+1, Ian B. Strachan2, Maria Strack1
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1Department of Geography and Environmental Management, University of Waterloo
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2Department of Natural Resource Sciences, McGill University
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+Co-first author
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*Corresponding author: Ellie M. Goud. Department of Geography and Environmental
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Management, 200 University Avenue, Waterloo, ON N2L 3G1, Canada. egoud@uwaterloo.ca.
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Abstract
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One metric of peatland restoration success is the re-establishment of a carbon sink, yet
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considerable uncertainty remains around the timescale of carbon sink trajectories. Conditions
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post-restoration may promote the establishment of vascular plants such as graminoids, often at
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greater density than would be found in undisturbed peatlands, with consequences for carbon
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storage. Although graminoid species are often considered as a single plant functional type (PFT)
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in land-atmosphere models, our understanding of functional variation among graminoid species is
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limited, particularly in a restoration context. We used a traits-based approach to evaluate
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graminoid functional variation and to assess whether different graminoid species should be
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considered a single PFT or multiple types. We tested hypotheses that greenhouse gas fluxes
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(CO2, CH4) would vary due to differences in plant traits among five graminoid species in a
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restored peatland in central Alberta, Canada. We further hypothesized that species would form
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two functionally distinct groupings based on taxonomy (grass, sedge). Differences in gas fluxes
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among species were primarily driven by variation in leaf physiology related to photosynthetic
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efficiency and resource-use, and secondarily by plant size. Multivariate analyses did not reveal
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distinct functional groupings based on taxonomy or environmental preferences. Rather, we
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identified functional groups defined by continuous plant traits and carbon fluxes that are
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consistent with ecological strategies related to differences in growth rate, resource-acquisition,
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and leaf economics. These functional groups displayed larger carbon storage potential than
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currently-applied graminoid PFTs. Existing PFT designations in peatland models may be more
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appropriate for pristine or high-latitude systems than those under restoration. Although replacing
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PFTs with continuous plant traits remains a challenge in peatlands, traits related to leaf
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physiology and growth rate strategies offer a promising avenue for future applications.
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Keywords: land-atmosphere models, leaf nitrogen, leaf stable isotope, light-use efficiency,
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peatland restoration, plant functional type, plant height, plant traits
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Introduction
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Peatlands are globally important carbon sinks, storing approximately one-third of the world’s
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terrestrial soil carbon with an estimated stock over 600 Gt (Loisel et al., 2021; Loisel et al.,
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2014; Yu et al., 2010). In Canada, peatlands cover an estimated 120 million ha of land surface,
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the second largest cover of peatlands in the world (Vitt, 2013). Canada is a major producer and
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exporter of peat for horticultural uses, producing about 1.3 million metric tons of peat per year.
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Approximately 34,000 ha of peatlands have been drained for peat harvesting, with approximately
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58% actively in use (Environment Canada 2020). Considerable carbon is released during the
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harvesting process primarily in the form of greenhouse gases carbon dioxide (CO2) and methane
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(CH4). Post-harvested peatlands may remain a net source of carbon in the absence of restoration
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efforts (Cleary et al., 2005; Nugent et al., 2019). To offset the carbon cost of peat harvesting and
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help mitigate potential feedbacks to climate change, post-harvested peatlands are restored with
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the goal of returning them to a persistent carbon sink (Chimner et al., 2017; González &
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Rochefort, 2014).
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Despite notable successes, considerable uncertainty remains around post-restoration carbon flux
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trajectories, in part due to a limited number of long-term studies (González & Rochefort, 2014).
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To complement long-term studies, process-based models are used to describe the environmental
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controls on carbon dynamics and predict future carbon accumulation (Frolking et al., 2010). A
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key component of land-atmosphere models is vegetation, as plants directly control carbon cycling
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through photosynthesis, respiration, decomposition, and CH4 release. To simplify model
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structure, vegetation are represented as ‘plant functional types’ (PFTs), with different species
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assigned to a PFT based on assumed similarity in function, environmental tolerance, or
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contribution to peat growth (Frolking et al., 2010; Heinemeyer et al., 2010; Wu & Blodau, 2013).
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While the goal of PFT is to represent species with similar function in a modeling context, generic
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PFT labels such as ‘moss’, ‘shrub’ and ‘graminoid’ are now commonly used in the peatland
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ecological literature, often forming the basis of experimental treatments (Goud et al., 2017;
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Robroek et al., 2016; Rupp et al., 2019). While a range of species are assigned to a single PFT,
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current peatland PFT designations are based on only a few studies predominantly from pristine,
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high-latitude peatlands (e.g., Frolking et al. 2010; Laine et al. 2012; Tuittila et al. 2012). Given
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that different species within a PFT may vary substantially in carbon accumulation and
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greenhouse gas emissions based on local environmental conditions and disturbances (Goud et al.,
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2017; Lai et al., 2014), some PFT designations may not be relevant for managed and restored
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peatlands.
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In a restoration context, differences in post-disturbance conditions (e.g., soil compaction and
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moisture availability) substantially alter plant community composition relative to undisturbed
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sites, especially in the first few years, in large part because of the early establishment of
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graminoid species (González & Rochefort, 2019; Graf et al., 2008). Graminoids are grass-like
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vascular plants, including grasses (family Poaceae), sedges (Cyperaceae), rushes (Juncaceae),
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arrow-grasses (Juncaginaceae), and quillworts (Isoetes). Graminoids often have much larger rates
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of CO2 and especially CH4 release relative to other peatland plants (Goud et al., 2018; Lai et al.,
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2014; Maria Strack et al., 2017). Consequently, restored peatlands with considerable graminoid
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cover have different rates of carbon storage compared to natural counterparts (Strack et al.,
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2016). Specifically, due to the greater radiative forcing of CH4 over CO2, extensive graminoid
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cover that potentially suppresses subsequent peatland successional stages could compromise a
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restored peatland’s ability to be a net carbon sink over time (Frolking et al., 2006; Strack et al.,
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2006). As such, there is considerable interest in accurately modeling carbon exchange in
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graminoid-dominated peatlands.
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Despite the importance of graminoids to peatland carbon cycling, especially for restored
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peatlands, our understanding of graminoid functional variation is limited. While rates of CO2 and
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CH4 fluxes vary in predictable ways with hydrology and peat temperature (Lai et al., 2014; Strack
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et al., 2016), biotic drivers of carbon cycling within graminoids are not well established. This
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knowledge gap is further reflected in the inconsistent representation of graminoids in current
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ecological studies and land-atmosphere models, where all graminoids are either categorized as a
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single PFT (Chaudhary et al., 2017; Wania et al., 2009; Wu & Blodau, 2013) or subdivided based
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on taxonomy (i.e., ‘grass’, ‘sedge’, ‘rush’) (Frolking et al., 2010; Heinemeyer et al., 2010) or
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environmental preferences (i.e., ‘minerotrophic’, ‘ombrotrophic’) (Frolking et al., 2010; Quillet
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et al., 2015; Tuittila et al., 2012). In other graminoid-dominated ecosystems such as grasslands
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and marshes, variation in carbon storage has been linked to plant traits such as height (Klumpp &
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Soussana, 2009; Radabaugh et al., 2017) and leaf nitrogen content (Long et al., 2019; Tripathee
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& Schäfer, 2015). Although there are fewer trait-based studies in peatlands, recent work
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demonstrates carbon cycling among divergent plant groups (i.e., moss, shrub, graminoid) can be
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predicted by functional traits such as leaf area (Goud et al., 2017; Korrensalo et al., 2016) and
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nitrogen content (Girard et al., 2020). Applying plant functional traits to identify mechanistic
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drivers of greenhouse gas fluxes within graminoids has the potential to advance our
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understanding of post-restoration ecological dynamics in general, and to better represent
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graminoid contributions to carbon fluxes in land-atmosphere models (VanBodegom et al., 2011).
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Here, we used a traits-based approach to evaluate graminoid functional variation in a restored
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peatland in central Alberta, Canada. Our objectives were two-fold: 1) determine functional
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differences among graminoid species to better understand anatomical and physiological
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mechanisms underlying variation in CO2 and CH4 fluxes; 2) assess whether graminoid species
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should be considered a single PFT or multiple based on taxonomy or environmental preferences.
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We hypothesized that CO2 and CH4 fluxes would vary among species due to differences in traits
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related to plant size and leaf physiology. We further hypothesized that species’ functional
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groupings would be more related to taxonomy than environmental preferences. To test these
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hypotheses, we measured gas fluxes and six functional traits in five dominant graminoid species
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that vary in morphology, taxonomy, and environmental preferences. We measured plant traits
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used in PFT classifications (i.e., above-ground biomass, leaf nitrogen content, photosynthetic
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quantum yield) and traits that relate to plant size and resource acquisition (i.e., plant height, leaf
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carbon and nitrogen stable isotope ratios).
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Methods
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Study site and experimental setup
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Measurements were made in a restored ombrotrophic bog located 17 km southeast of Entwistle,
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Alberta, Canada (53°27’26”N, 114°53’04”W). The climate is cool-continental, with a normal
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(1981-2010) mean annual air temperature of 3.5 °C and total annual precipitation of 551 mm.
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Peak growing season (June-August) mean temperature and total precipitation are 15.5 °C and 225
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mm, respectively (Environment Canada 2020). Peat was harvested from 2000 - 2012 and
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subsequently restored using the moss-layer transfer technique (González & Rochefort 2014) in
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the autumn and winter of 2012. We focused our measurements on five target species: American
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Slough Grass (Beckmannia syzigachne (Steud.) Fernald), Bluejoint (Calamagrostis canadensis
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(Michx.) P. Beauv.), Silvery Sedge (Carex canescens L.), Tussock Cottongrass (Eriophorum
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vaginatum L.), and Woolgrass (Scirpus cyperinus (L.) Kunth). All are obligate wetland species
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with C3 metabolism and aerenchyma tissue (Ball et al., 2002; Clark & Kellogg, 2007).
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Beckmannia and Calamagrostis are grasses (Poaceae) while Carex, Eriophorum and Scirpus are
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sedges (Cyperaceae). Beckmannia syzigachne is an annual, the other four are perennials.
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Eriophorum vaginatum is typically associated with ombrotrophic wetlands (e.g., bogs), while the
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remaining four species occur in minerotrophic wetlands (e.g., fens, marshes, swamps) (Gleason
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& Cronquist, 1991). In May 2016, we established 20 square plots (0.60 m x 0.60 cm) for gas flux
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measurements centered around a target species (n = 4 plots per species). Grooved aluminum
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collars were permanently installed to allow for repeated gas flux measurements in the same
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location to minimize disturbance to the peat and vegetation. We additionally established 50
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square plots (0.25 m x 0.25 m) for destructive plant sampling centered around a target species (n
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= 10 plots per species).
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Carbon dioxide and methane flux measurements
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Gas fluxes were measured at the whole-plant level using chambers, which included multiple plant
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individuals and soil heterotrophic respiration and CH4 fluxes. We measured net ecosystem CO2
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production (NEP, μmol CO2 m-2 s-1), ecosystem respiration (ER, μmol CO2 m-2 s-1), gross
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primary productivity (GPP, μmol CO2 m-2 s-1) and CH4 (mg m-2 d-1) fluxes every second week
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from May to September 2016. To measure NEP, we used the closed dynamic chamber method
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(Alm et al., 2007) using a transparent acrylic chamber (60 cm × 60 cm × 30 cm) equipped with a
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cooling system. Two battery-operated fans circulated headspace air within the chamber, blowing
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past a copper coil containing cold water circulating from a cooler with ice to ensure minimal
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heating during the measurements. For plants taller than 60 cm, an acrylic extension of 60 cm high
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with two additional battery-operated fans was added under the chamber to prevent damage to the
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stem of the plants. A thermocouple thermometer and a photosynthetically active radiation (PAR,
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μmol quanta m-2 s-1) sensor recorded environmental conditions within the chamber during
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measurements. CO2 concentrations were determined using a portable infrared gas analyzer
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(IRGA; PPsystems EGM-4, Massachusetts, USA) and the change in CO2 concentration over a
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two-minute period was determined in situ every 15 seconds. The linear rate of CO2 concentration
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increase was used to calculate the flux. To achieve various PAR levels for estimating light
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response curves, we measured ambient light conditions at the time of measurement and used
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mesh covers to partially shade the chambers. We measured ER by covering the chamber with an
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opaque tarp to achieve PAR = 0. The difference between NEP and ER was used to calculate GPP.
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We adopt the ecological sign convention that positive NEP values indicate uptake of CO2 by the
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ecosystem while negative values indicate a release of CO2 to the atmosphere.
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We measured CH4 fluxes using the closed static chamber method (Alm et al., 2007) with opaque
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chambers (60 cm × 60 cm × 30 cm) equipped with fans for air circulation, a thermocouple, and
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an acrylic extension of 60 cm high with two additional battery-operated fans to accommodate
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plants taller than 60 cm. Chambers were placed on sampling collars for 35 minutes, and four air
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samples were taken at 5, 15, 25, and 35 minutes into a 20 mL syringe and then transferred to 12
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mL pre-evacuated vials (Exetainers, Labco Ltd., UK) to measure the change in concentration of
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CH4 inside the chamber head space. CH4 concentrations were determined in the laboratory with a
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gas chromatograph (GC-2014 Gas Chromatograph, Shimadzu Scientific Instruments, Kyoto,
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Japan) with a flame ionization detector. The CH4 flux was determined as the slope of the linear
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change in concentration versus time over the 35-minute sampling period. Before determining the
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CH4 flux, data was quality controlled where measurements with concentrations at 5 minutes that
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were higher than 5 ppm followed by a decline in concentration, or erratic concentration changes
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likely associated with ebullition events were removed from the data set. Cases where
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concentration was less than 5 ppm and did not change more than the precision of the gas
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chromatograph (10%) were assigned a flux value of zero; zero fluxes were retained.
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Fluxes without a significant regression correlation coefficient (R2 > 0.90 at p < 0.05) were
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rejected, with a rejection rate of < 3% and < 9% for CO2 and CH4, respectively. To obtain peak
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growing season fluxes, we averaged CO2 and CH4 flux data from June - July. We retained NEP
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and GPP data during the peak growing season from measurements at PAR > 1000 μmol m-2 s-1
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for inter-specific comparisons. With each flux measurement, water table depth below the peat
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surface was measured from a standpipe next to the collars, and soil temperature (°C) was
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measured at 2, 5, 10, 15 and 20 cm below the peat surface with a portable thermocouple probe
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(Digi-Sense Type-K, Oakton Instruments, IL, USA).
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Vegetation sampling and plant traits
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In August 2016, we measured maximum plant height (m) in all plots (n = 14 per species). In each
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destructive-sampling plot, aboveground plant material was clipped and separated between living
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biomass of the target species and other non-target plant material. Target species biomass was
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further separated into stem and leaf material. Leaf and stem samples were oven-dried for 48
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hours at 60 °C in a mechanical convection oven (Heratherm OMS100, Thermo Scientific,
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Massachusetts, USA).
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Leaf nitrogen percent element (N) and carbon and nitrogen stable isotope ratios (δ13C, δ15N) were
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measured using a continuous flow isotope ratio mass spectrometer (Thermo Scientific Delta Plus
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XL) coupled to an elemental analyzer (Costech ECS 4010). Isotope ratios are expressed as δ
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values (per mil):
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δ13C, δ15N = (Rsample/Rstandard – 1) x 1000 (‰),
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where Rsample and Rstandard are the ratios of heavy to light isotope of the sample relative to the
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international standards for C and N, Vienna-Pee-Dee Belemnite and atmospheric nitrogen gas
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(N2), respectively. Samples were analysed at the University of Waterloo Environmental Isotope
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Laboratory, Waterloo, ON.
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To calculate plot-level quantum efficiency (φ), we fitted light response curves to NEP data
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obtained across all PAR value for each species using the ‘light.response’ function in the R
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Bigleaf package (Knauer et al., 2018). The curve is described by a rectangular hyperbola:
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NEP = φ * PAR/(1 − (PAR/PARref) + φ * PAR /GPPref) − ER,
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where NEP is net ecosystem production, φ is the initial slope of the curve (quantum efficiency,
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μmol CO2 m-2 s-1)/(μmol quanta m-2 s-1), GPPref is the GPP at a reference light-saturating PAR of
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2000 μmol quanta m-2 s-1, and ER is dark respiration.
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Statistical analyses
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We compared variation among variables using one-way analysis of variance (ANOVA) with
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repeated measures for gas fluxes and without repeated measures for plant traits. We calculated
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Pearson correlation coefficients among NEP, GPP, ER and CH4 (log-transformed) with above-
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ground biomass, plant height, leaf carbon and nitrogen stable isotope compositions (δ13C, δ15N),
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leaf N content, and leaf quantum efficiency (φ). For the plots without flux data (i.e., destructive
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sampling plots), we used the species’ means values calculated from the gas flux plots. We used
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cluster analysis and principal components analysis to identify functional groups based on
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variation in species’ descriptors (i.e., traits, average peak growing season CO2 and CH4 fluxes).
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We performed Ward clustering using the ‘agnes’ function in the ‘cluster’ R package on a distance
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matrix computed from a Pearson correlation matrix among descriptors (Maechler et al., 2017).
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Ward clustering is a hierarchical agglomerative clustering method that considers all species as
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being initially separate from each other, and proceeds by successively grouping species into
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larger and larger clusters until they are all are encompassed into a single cluster (Legendre &
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Legendre, 2012). We used concordance analysis based on Kendall’s coefficient of concordance
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(W) to identify how many clusters are, in fact, significantly distinct and which plots are
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significantly contributing to each cluster using the function ‘kendall.global’ in the ‘vegan’ R
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package, with 999 permutations. Then, a posteriori tests of the contribution of individual plots to
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the concordance of their cluster were computed using the function ‘kendall.post’. We performed
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principal components analysis using the ‘rda’ function in the ‘vegan’ R package (Oksanen et al.,
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2019) to identify species assemblages and to compare PCA groupings with those identified from
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the cluster analysis. PCA preserves the Euclidean distance among descriptors and assumes linear
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relationships, allowing us to further assess the contribution of descriptors to variation among
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species.
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Results
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Site conditions
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Climate during 2016 was comparable to long-term normals, with a mean annual temperature of
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5.5 ºC and total annual precipitation of 523.5 mm. Peak growing season (June-August) conditions
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were similar in mean temperature (16 ºC) and received slightly more precipitation (278 mm)
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compared to the long-term normal (225 mm). Peat water table position and temperature from 2-
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20 cm did not vary among plots (all p > 0.05).
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Variation in gas fluxes and plant traits among species
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Species varied in NEP (F = 26.6, p < 0.0001), GPP (F = 15.7, p < 0.0001), ER (F = 6.4, p =
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0.0002), and CH4 fluxes (F = 9.1, p < 0.0001). Scirpus had the largest NEP, GPP and CH4 fluxes,
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followed by Eriophorum (Figure 1 a-b, d). Eriophorum had the largest ER (Figure 1 c).
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Beckmannia, Calamagrostis, and Carex had similar NEP, GPP, ER and CH4 fluxes (Figure 1 a-
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d). Plant traits also varied among species. Scirpus had the largest above-ground biomass,
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followed by Eriophorum. Beckmannia, Calamagrostis, and Carex had similar biomass (Figure 2
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a). Scirpus was also the tallest species, followed by Beckmannia. Calamagrostis, Carex, and
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Eriophorum that were of similar height (Figure 2 b). Scirpus was the most light-use efficient
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(largest φ), followed by Eriophorum (Figure 2 c). Beckmannia had the largest leaf N,
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Calamagrostis had the smallest, while Carex, Scirpus and Eriophorum had similar, intermediate
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leaf N (Figure 1 d). Calamagrostis had the most negative (relatively depleted) δ13C, while Carex
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had the least negative (relatively enriched) δ13C (Figure 2 e). Beckmannia, Calamagrostis, and
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Carex had the most positive (relatively enriched) δ15N, while Scirpus and Eriophorum had the
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most negative (relatively depleted) δ15N (Figure 2 f).
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Plant anatomical and physiological drivers of gas fluxes
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Biomass and φ positively correlated with NEP, GPP and CH4 (Table 1). Height positively
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correlated with NEP, GPP and ER. δ13C negatively correlated with NEP and GEP, and positively
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correlated with ER. δ15N negatively correlated with NEP, GPP, and CH4. Leaf N did not correlate
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with any gas fluxes. The strongest predictors of NEP and GPP were δ15N and φ, followed by
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δ13C, biomass, and plant height. The strongest predictors of ER were δ13C and plant height. δ15N
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was the strongest predictor of CH4, followed by φ and biomass (Table 1).
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Graminoid functional groupings
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Cluster analysis segregated the 70 plots into five distinct clusters that each contained a mixture of
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2-3 different species. Three clusters contained all Scirpus and Eriophorum plots plus a single
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Beckmannia and Carex, and two clusters contained only Beckmannia, Calamagrostis and Carex
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(Figure 3 a). All five clusters and their associated plots were significant based on Global Kendall
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tests (0.44 < W < 0.54, p < 0.001) and a posteriori Kendall tests (0.97 < W < 0.99, p < 0.001).
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Plots were distributed along three PC axes that together explained 81% of the variation. The first
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PC axis (50%) was primarily associated with variation in NEP, GPP, δ15N, and φ. The second PC
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axis (18%) was primarily associated with variation in leaf N, plant height, ER, and biomass. The
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third PC axis (13%) was primarily associated with variation in δ13C and CH4 (Table 2). As in the
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cluster analysis, Beckmannia and Carex completely overlapped in the PCA biplot, and
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Calamagrostis overlapped with them along PC2. Scirpus and Eriophorum overlapped along PC1
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and PC2. Scirpus, Calamagrostis and Eriophorum were somewhat more differentiated along PC3
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(Figure 3 b-c).
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Species did not clearly group together by plant family or environmental preference in cluster or
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principal components analyses (Figure 3). However, in both analyses Scirpus and Eriophorum
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formed one functional grouping while Beckmannia, Calamagrostis and Carex formed a second.
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These two groups differed in PC1 (F = 214.2, p < 0.0001) and PC3 (F = 8.341, p = 0.0052) and
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associated variables: NEP (F = 207, p < 0.0001), GEP (F = 124.5, p < 0.0001), δ15N (F = 1734, p
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< 0.0001), and φ (F = 91.81, p < 0.0001) for PC1 and CH4 (F = 129.4, p < 0.0001) for PC3 (Table
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2). Although the two groups did not differ in PC2 (F = 0.434, p = 0.512) or associated variables
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of ER (F = 0.367, p = 0.547), leaf N (F = 0.037, p = 0.848), or plant height (F = 3.296, p =
307
0.0739), they differed in aboveground biomass (F = 32.67, p < 0.0001). The group with Scirpus
308
and Eriophorum had larger rates of photosynthetic CO2 exchange and CH4 emissions, larger
309
aboveground biomass, and leaves that were more light-use efficient (larger φ) and depleted in
310
δ15N.
311
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Table 1: Pearson correlation coefficients between gas fluxes and plant traits among five
312
graminoid species in a restored peatland. Dependent variables are net ecosystem CO2 production
313
(NEP), gross primary productivity (GPP), ecosystem respiration (ER), and methane (CH4, log-
314
transformed) fluxes. Independent variables are above-ground biomass, plant height, quantum
315
efficiency (φ), leaf carbon isotope composition (δ13C), and leaf nitrogen isotope composition
316
(δ15N). Data are from plot means (n = 14 per species).
317
Pearson correlation coefficient
Trait
NEP
GPP
ER
CH4
Biomass
0.58***
0.47***
0.11
0.39***
Height
0.40***
0.24*
0.26*
-0.03
N
-0.02
-0.1
0.17
0.05
δ13C
-0.45***
-0.54***
0.31**
-0.07
δ15N
-0.92***
-0.85***
0.08
-0.76***
φ
0.82***
0.80***
-0.14
0.50***
*p < 0.05 **p< 0.01 ***p < 0.0001
318
Table 2: Eigenvector loadings for the first three principal components analysis (PCA) axes. Trait
319
loadings describe the strength of associations (% variance explained) between the distribution of
320
five graminoid species and ten plant descriptors: net ecosystem CO2 production (NEP), gross
321
primary productivity (GPP), ecosystem respiration (ER), methane (CH4), above-ground biomass,
322
plant height, quantum efficiency (φ), leaf carbon isotope composition (δ13C), and leaf nitrogen
323
isotope composition (δ15N). Bold text indicates the corresponding PC axis for each variable. Data
324
are from plot means (n = 14 per species).
325
Variable
PC1
PC2
PC3
Biomass
0.29
-0.33
-0.03
ER
-0.10
-0.49
-0.16
GPP
0.42
0.13
0.14
Height
0.18
-0.53
-0.37
δ13C
-0.27
-0.16
0.56
Leaf N
-0.07
-0.54
0.36
δ15N
-0.42
0.06
-0.14
Methane
0.32
0.06
0.54
NEP
0.42
-0.11
0.09
φ
0.41
0.15
-0.24
326
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9
327
Figure 1: Variation in peak growing season (a) net ecosystem CO2 production (NEP), (b) gross
328
primary productivity (GPP), (c) ecosystem respiration (ER), and (d) methane emissions (CH4)
329
among five graminoid species in a restored peatland. BECK = Beckmannia syzigachne, CALA =
330
Calamagrostis canadensis, CARE = Carex canescens, SCIR = Scirpus cyperinus, ERIO =
331
Eriophorum vaginatum. ‘Grass’ refers to species in the Poaceae family, ‘Minero sedge’ and
332
‘Ombro sedge’ refers to species in the Cyperaceae family from minerotrophic and ombrotrophic
333
environments, respectively. Species that share the same letters are statistically indistinguishable
334
based on Tukey post-hoc tests. Data are from plot means (n = 14 per species).
335
336
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10
337
Figure 2: Variation in (a) above-ground biomass (g), (b) plant height (m), (c) quantum efficiency
338
(φ), (d) leaf nitrogen (N,), (e) leaf carbon stable isotope ratio (δ13C), and (f) leaf nitrogen stable
339
isotope ratio (δ15N) among five graminoid species in a restored peatland. BECK = Beckmannia
340
syzigachne, CALA = Calamagrostis canadensis, CARE = Carex canescens, SCIR = Scirpus
341
cyperinus, ERIO = Eriophorum vaginatum. ‘Grass’ refers to species in the Poaceae family,
342
‘Minero sedge’ and ‘Ombro sedge’ refers to species in the Cyperaceae family from minerotrophic
343
and ombrotrophic environments, respectively. Species that share the same letters are statistically
344
indistinguishable based on Tukey post-hoc tests. Data are from plot means (n = 14 per species).
345
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11
346
347
Figure 3: Functional variation among five graminoid species (n = 14 per species) based on net
348
ecosystem CO2 production (NEP), gross primary productivity (GPP), ecosystem respiration (ER),
349
methane (CH4,), above-ground biomass, plant height, quantum efficiency (φ), leaf nitrogen (N),
350
leaf carbon and nitrogen stable isotope ratios (δ13C, δ15N). Groups were identified using (A)
351
Ward’s agglomerative clustering and (B-C) principal components analysis (PCA). Significant
352
clusters are indicated at the nodes (filled circles). PCA biplots show the strength of associations
353
(% variance) between plant traits and species distributions along (B) PC1 (50%) and PC2 (18%);
354
and (C) PC1 and PC3 (13%).
355
356
357
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12
Discussion
358
359
We assessed functional variation among five peatland graminoid species in order to better
360
understand functional diversity in this important plant group. We tested hypotheses that CO2 and
361
CH4 fluxes would vary due to differences in traits related to plant size and leaf physiology.
362
Testing these hypotheses in a restored site presented a unique condition, as early ecosystem
363
establishment provided a situation where the five species were growing in close association on
364
similar substrate and hydrological conditions enabling more direct comparison among species. In
365
support of our hypotheses, species differentially varied in CO2 and CH4 fluxes, with the sedge
366
Scirpus cyperinus displaying the largest CO2 and CH4 fluxes followed by Eriophorum
367
vaginatum. Scirpus cyperinus is common to freshwater wetlands, minerotrophic fens, and
368
disturbed/agricultural areas in eastern North America from Georgia to Labrador (Ball et al., 2002)
369
and can spontaneously re-establish (i.e., without active restoration techniques) in post-harvested
370
peatlands (Cobbaert et al., 2004; Mahmood & Strack, 2011). Eriophorum vaginatum is abundant
371
across northern North America in minerotrophic and ombrotrophic peatlands (e.g., fens and bogs)
372
(Ball et al., 2002) and can also spontaneously re-establish on bare peat (Marinier et al., 2004).
373
Moreover, E. vaginatum acts as a companion species to facilitate the growth and establishment of
374
other peat-forming species, especially Sphagnum mosses, during early stages of restoration and
375
peatland succession (Marinier et al., 2004; Tuittila et al., 2000). Beckmannia syzigachne,
376
Calamagrostis canadensis, and Carex canescens had similar gas exchange rates (Figure 1). These
377
three species are widely distributed across North American wetlands (Ball et al., 2002; Clark &
378
Kellogg, 2007) and are less able to colonize on bare peat without restoration techniques (e.g.,
379
introducing donor material from natural areas, rewetting, straw mulch) (Cobbaert et al., 2004;
380
González & Rochefort, 2019).
381
382
Plant anatomical and physiological drivers of gas fluxes
383
Differences in gas fluxes among species were driven by variation in plant size and leaf
384
physiological traits related to resource-use. Specifically, plants with larger gas flux rates had
385
accumulated more above-ground biomass, were more light-use efficient (larger φ) and had
386
relatively depleted leaf nitrogen stable isotope composition (δ15N). In fact, the strongest overall
387
predictor of CO2 and CH4 fluxes was leaf δ15N. Variation in leaf δ15N is due to soil processes that
388
influence patterns of soil δ15N such as mineralization, immobilization and the relative abundance
389
of different soil N sources (e.g., organic, mineral). When growing in similar soil conditions, foliar
390
δ15N can reflect differences in plant N uptake strategies (Goud & Sparks, 2018). For example,
391
depleted (i.e., more negative) leaf δ15N can indicate root uptake of soil N sources that are more
392
depleted in δ15N, such as nitrate (NO3−) relative to ammonium (NH4+), or N from deep within the
393
soil profile (Craine et al., 2015). In this study, plants with relatively depleted foliar δ15N had
394
larger CO2 and CH4 fluxes. These were predominantly Scirpus and Eriophorum, which were also
395
the largest plants overall in terms of aboveground biomass and height. Depleted foliar δ15N in
396
these species may reflect deeper roots that can tap into unexploited pools of soil nutrients and
397
water, conferring growth advantages (Albano et al., 2021). Additionally, deeper soils and rooting
398
zones have the potential for larger CH4 production; as such, the positive correlation between
399
foliar δ15N and CH4 fluxes may be reflecting more plant-mediated release of CH4 from deeper in
400
the soil profile than from smaller shallow-rooted species (Noyce et al., 2014; Strack et al., 2017).
401
402
Leaf N varied among species but did not correlate with either CO2 or CH4 fluxes. Many factors
403
influence leaf N, including variation in soil N, leaf lifespan, life history, and rates of plant
404
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13
resource acquisition. Although leaf N can correlate with photosynthetic rates and biomass
405
accumulation (McJannet et al., 1995; Walker et al., 2014; Wright et al., 2004), this is not always
406
the case as much of a leaf’s N can be allocated to non-photosynthetic proteins as well as
407
structural and defensive compounds (Ghimire et al., 2017). Additionally, many plants accumulate
408
leaf N under high-irradiance or water-limited environments to economize water use during
409
photosynthesis (Schrodt et al., 2015; Wright et al., 2003). Here, all three sedges had similar leaf
410
N while the largest and smallest N contents were in grasses Beckmannia syzigachne and
411
Calamagrostis canadensis, respectively. This interspecific variation is likely due to differences in
412
leaf lifespan and phenology, rather than instantaneous carbon fluxes. For example, Beckmannia
413
syzigachne is an annual, which can have larger leaf N relative to co-occurring or closely related
414
perennial species (Garnier & Vancaeyzeele, 1994).
415
416
CO2 and CH4 fluxes were also strongly predicted by quantum yield (φ) such that gas fluxes
417
increased with increasing φ, following similar patterns as biomass and foliar δ15N. As a measure
418
of photosynthetic light-use efficiency, φ reflects the efficiency with which absorbed light is
419
ultimately converted into fixed carbon (Monteith, 1977). Given that photosynthesis depends on
420
the quantity and quality of light absorption, it has long been recognized that more light-use
421
efficient plants can achieve larger rates of CO2 gas exchange, growth, and resource acquisition
422
(Körner, 1982), and our results are no exception. Interestingly, φ also positively correlated with
423
CH4 fluxes. It is likely that the larger CO2 exchange associated with more light-use efficient
424
plants provides more photosynthetic root exudates that stimulate belowground CH4 production
425
(Lai et al., 2014; Ström et al., 2005).
426
427
In addition to light absorption, leaf-level carbon gain is driven by the average difference in leaf
428
internal and air external CO2 concentrations (ci/ca). Leaf carbon isotope composition (δ13C) is
429
proportional to ci/ca, providing an integrated measure of the balance between metabolic demand
430
and supply of CO2 via diffusion through the leaf boundary layer and stomata. In general, more
431
depleted (negative) δ13C values indicate faster carbon metabolism or abundant CO2 supply (e.g.,
432
high stomatal conductance) (Farquhar et al., 1989; Goud et al., 2019). As expected, δ13C
433
correlated with CO2 exchange, although this relationship was mainly driven by the smallest GPP
434
and most enriched δ13C in Carex canescens. When variation in foliar δ13C is driven by stomatal
435
conductance, enriched δ13C indicates stomatal closure which co-limits CO2 and water vapor
436
diffusion. The combination of enriched δ13C, low φ, and small size in Carex canescens could
437
indicate an ecological strategy to conserve water resources at the expense of carbon gain (Angert
438
et al., 2009; Goud et al., 2019). On the other hand, Scirpus had the largest CO2 and CH4 flux rates
439
and was also the largest, most light-use efficient, and had relatively fast carbon metabolism
440
(largest φ, depleted δ13C), indicating a strategy to maximize carbon gain across a range of light
441
and water availabilities.
442
443
Positive relationships between plant size and carbon fluxes have been reported for peatland and
444
tundra graminoid species due to variation in aboveground biomass (Kao-Kniffin et al., 2010;
445
Laine et al., 2012) and leaf area (Goud et al., 2017; Street et al., 2007). Variation in plant size
446
among co-occurring plant species can be related to light competition. For example, sedges in
447
riparian fens displayed strong competition for light based on height and aboveground biomass
448
(Kotowski et al., 2006). In support of this, the sedges in this study varied widely from each other
449
in gas flux rates and traits (except leaf N). Although grasses differed in height, they had similar
450
aboveground biomass, likely reflecting differences in leaf area (Goud et al., 2017).
451
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14
452
Graminoid functional groupings
453
We hypothesized that plant functional variation would be more related to taxonomy than
454
environmental preferences, with grasses and sedges forming distinct groups. Although we
455
identified discrete groupings, they were not defined by taxonomy or environment. Indeed, based
456
on multivariate analyses, no single species could be completely differentiated from neighboring
457
species. Rather, we identified two functional groups defined by continuous plant traits and carbon
458
fluxes. One group had larger rates of photosynthetic CO2 exchange and CH4 emissions, larger
459
aboveground biomass, leaves that were depleted in δ15N and more light-use efficient, while the
460
second group displayed the opposite suite of traits. These trait combinations are consistent with
461
established ecological strategies related to growth rate and leaf economics, representing plants
462
with either a strategy to grow quickly and invest in resource capture or to prioritize structural
463
investment and resource conservation (Dìaz et al., 2015; Goud et al., 2019; Wright et al., 2004).
464
465
Currently, one of the most widely used peatland carbon models is the Holocene Peatland Model
466
(HPM; Frolking et al., 2010) and its derivatives (e.g., Tuittila et al., 2012; Quillet et al., 2015).
467
HPM distinguishes between grasses and sedges, and further subdivides sedges into minerotrophic
468
and ombrotrophic plant functional types. NPP values defined for these graminoid PFTs are
469
considerably smaller than what we report here. For example, by converting fluxes into common
470
units, grasses in HPM (e.g., Calamagrostis stricta) are defined by NPP ranging from 210 – 850
471
gC m-2 yr-1 compared to a range of 1600 – 1800 gC m-2 yr-1 found here and in recent ecological
472
studies (Rupp et al., 2019). Similarly, NPP prescribed for minerotrophic (e.g., Carex canescens)
473
and ombrotrophic (e.g., Eriophorum vaginatum) PFTs in HPM are far smaller than what we
474
observed: 210 – 1130 gC m-2 yr-1 and 30 – 190 gC m-2 yr-1, respectively, compared to 1350 –
475
4100 gC m-2 yr-1 and 2950 gC m-2 yr-1. Other peatland carbon models that use a single graminoid
476
PFT (e.g., Chaudhary et al., 2017; Wania et al., 2009; Wu & Blodau 2013) also report NPP
477
values much smaller than reported here (e.g., 90 – 150 gC m-2 yr-1).
478
479
Values for productivity and other functional attributes of PFTs are based on average
480
environmental conditions of a defined site, rather than the total range of ecophysiological
481
possibilities. For example, graminoids classified as ‘ombrotrophic’ are assigned smaller NPP
482
values than ‘minerotrophic’, due to environmental constraints of ombrotrophic conditions (e.g.,
483
acidic, anoxic soils) despite their potential for a wide range of productivities across different
484
peatland types. Indeed, the sedge Eriophorum vaginatum is usually classified as ombrotrophic
485
despite its prevalence in minerotrophic peatlands, including early-successional and restored sites
486
such as in this study. This does not mean that subdividing the graminoid PFT based on discrete
487
environmental affinities is invalid. Rather, it’s possible that the range of productivity and plant
488
function is more related to site-specific conditions rather than inherent properties of species and
489
plant groups per se (Messier et al., 2010, 2016). In other words, intraspecific trait variability or
490
phenotypic plasticity driven by local environmental conditions may have large impacts on how
491
we define and apply PFTs (Adler et al., 2018; Henn et al., 2018; Westerband et al., 2021). A
492
promising area of future research would be to evaluate drivers of intraspecific trait variability
493
within functional types to constrain PFT delimitations.
494
495
Applications for empirical studies and modeling
496
We did not find strong evidence to support grouping different graminoid species into one plant
497
functional type. We also did not definitively find support for grouping species by taxonomy or
498
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15
reported environmental preferences. We do, however, think that characterising graminoids into
499
functional groups based on morphological and physiological attributes, such as plant size and
500
physiological efficiency that represent identifiable plant strategies is a promising way forward
501
and may be less subject to biases introduced by PFTs (Dìaz et al., 2015). In addition,
502
characterizing species more by their site-specific environmental conditions than assumed habitat
503
preferences may account for intraspecific variation and improve functional designations in both
504
empirical and modeling studies (Violle et al., 2012).
505
506
One limitation of this study is the limited number of species. Although the five species in this
507
study differed considerably from existing graminoid PFT designations in terms of functional
508
variation and magnitude of carbon fluxes, we acknowledge that they represent only a subset of
509
possible species and environmental conditions across the diversity of peatland graminoids. Future
510
studies are warranted that consider additional grass and sedge species and other graminoids such
511
as rushes (e.g., Juncus), which are prevalent in peatlands but vastly under-represented in
512
modeling and empirical studies. Synthesizing existing and new field data with publicly available
513
trait databases (e.g., TRY) could provide critical insights to improve the representation of
514
graminoids in ecological studies and modeling efforts.
515
516
Improved models that include peatlands are needed to couple regional and global Earth system
517
models to accurately describe vegetation change and accompanying feedbacks to climate change.
518
Moreover, modifying current models for natural peatlands to account for restored peatlands is
519
crucial to evaluate current and future carbon storage during peatland management (Wania et al.,
520
2009). Although there are efforts to incorporate peatlands into such models, the
521
oversimplification of vegetation remains a key challenge. Given the limitations of discrete PFTs,
522
an emerging approach is to apply continuous plant traits either to inform and update PFT
523
classifications or to replace PFTs with traits altogether (VanBodegom et al., 2011). Indeed,
524
replacing PFTs with continuous traits is the direction that many systems are moving towards;
525
however, this is a particular challenge for peatlands. Trait-based models are currently designed
526
for vascular plants with stomata, but a major contributor to peatland vegetative biomass and
527
carbon exchange lies in bryophytes, especially Sphagnum peat mosses, that lack vascular tissue
528
and stomata (Laine et al., 2012). Established trait protocols and trait databases for bryophytes are
529
still in the beginning stages, but we are hopeful that their development will allow for the
530
identification and application of traits common to bryophytes and vascular plants to better
531
represent vegetation in empirical studies and modeling of current and future carbon storage.
532
533
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16
Acknowledgements
534
We are grateful for field assistance from Anoop Deol, Daniel Luckhurst-Cartier and Scott
535
Macdonald. Sun Gro Horticulture provided site access and logistical support. This work was
536
funded by an NSERC Canada Research Chair to MS and an NSERC Collaborative Research and
537
Development Grant (grant number: 437463) supported by the Canadian Sphagnum Peat Moss
538
Association and its members to MS and IBS. The authors state that they have no conflicts of
539
interest to declare.
540
541
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