Temperate coastal wetland near-surface carbon storage: Spatial patterns and variability

Journal Pre-proof Temperate coastal wetland near-surface carbon storage: Spatial patterns and variability Christopher J. Owers, Kerrylee Rogers, Debashish Mazumder, Colin D. Woodroffe PII:

S0272-7714(19)30889-3

DOI:

https://doi.org/10.1016/j.ecss.2020.106584

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YECSS 106584

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Estuarine, Coastal and Shelf Science

Received Date: 17 September 2019 Revised Date:

9 December 2019

Accepted Date: 6 January 2020

Please cite this article as: Owers, C.J., Rogers, K., Mazumder, D., Woodroffe, C.D., Temperate coastal wetland near-surface carbon storage: Spatial patterns and variability, Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/j.ecss.2020.106584. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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Temperate coastal wetland near-surface carbon storage: spatial patterns and variability

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Christopher J. Owers1,2*, Kerrylee Rogers2, Debashish Mazumder2,3 and Colin D. Woodroffe2

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Author Statement

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Christopher Owers: Conceptualization, Methodology, Validation, Formal analysis, Investigation,

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Writing - Original Draft, Writing - Review & Editing, Visualization

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Kerrylee Rogers: Conceptualization, Methodology, Validation, Resources, Writing - Review &

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Editing, Supervision, Funding acquisition

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Debashish Mazumder: Methodology, Validation, Resources, Writing - Review & Editing,

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Supervision, Funding acquisition

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Colin Woodroffe: Conceptualization, Validation, Writing - Review & Editing, Supervision, Funding

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acquisition

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Temperate coastal wetland near-surface carbon storage: spatial patterns and variability

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Christopher J. Owers1,2*, Kerrylee Rogers2, Debashish Mazumder2,3 and Colin D. Woodroffe2

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1

Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, United

Kingdom 2

School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia

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Australian Nuclear Science and Technology Organisation (ANSTO), Sydney, Australia

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corresponding author: [email protected]

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Keywords: mangrove; saltmarsh; carbon storage; vegetation structure; landscape position; carbon

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stable isotopes

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Paper type: Original Research Article

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Manuscript highlights

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Carbon storage variation with a wetland corresponds to variation between sites

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Assessments of carbon storage should recognise vegetation structure

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Previous environmental conditions may be identified using stable carbon isotopes

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Abstract

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Carbon mitigation services provided by coastal wetlands are not spatially homogeneous, nevertheless

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are commonly described on the basis of vegetation distribution within the intertidal zone.

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Distribution of mangrove and saltmarsh varies in response to frequency of tidal inundation, resulting

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in environmental gradients in edaphic factors that influence vegetation structure, and subsequently

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affect sedimentary carbon additions by vegetation and carbon losses by decomposition. Current

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sampling approaches and reporting do not adequately account for variability of carbon storage within

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a wetland, and assessments need to capture spatial variation associated with carbon storage to

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improve estimates of potential carbon mitigation services by natural ecosystems. This study

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quantifies the variation in near-surface carbon storage (i.e. upper 30 cm) across an intertidal gradient

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using a stratified sampling approach that recognises vegetation structure. Vegetation distribution and

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structure, as well as sedimentary controls on carbon content, explained variation in carbon storage.

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Saltmarsh near-surface carbon storage varied considerably between structural form. This was less

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evident for mangrove structural forms (i.e. tall, shrub, dwarf), which may be due to mangrove roots

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extending to depths beyond 30 cm. Sedimentary characteristics correlated with carbon content,

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demonstrating considerable influence on near-surface carbon storage within a wetland. The principal

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finding of this study was that variation within a wetland corresponds to the variation between sites.

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Stable carbon isotopes offer a means to identify previous vegetation contributions to sediment,

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associated with an earlier stage of wetland development, likely reflecting previous environmental

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conditions. A stratified sampling approach that recognises vegetation structure provides the capacity

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to account for variability of carbon within a wetland that is inadequately described by current

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sampling protocols.

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1. Introduction

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Saline coastal wetlands, comprising mangrove and saltmarsh, are globally recognised as valuable

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sinks of organic carbon (Chmura et al., 2003; Barbier et al., 2011; Alongi, 2014; Lee et al., 2014;

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Lovelock and Duarte, 2019). These environments may be used to offset anthropogenic carbon

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emissions by sequestering atmospheric carbon in plant biomass and subsequently into the wetland

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substrate (Duarte et al., 2005; Lovelock et al., 2017; Twilley et al., 2017). Mangrove and saltmarsh

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are reported to sustain the highest rates of carbon sequestration per unit area compared to other

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natural ecosystems, primarily due to environmental conditions conducive to preservation of organic

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carbon (Donato et al., 2011; McLeod et al., 2011; Fourqurean et al., 2012; Duarte et al., 2013).

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Saline waterlogged conditions, induced primarily by tidal forcing, inhibit aerobic microbial

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breakdown limiting the release of greenhouse gases to the atmosphere (Kristensen et al., 2008; Dise,

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2009; Fourqurean et al., 2012). Additionally, mangrove and saltmarsh vegetation can increase

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substrate elevations by vertical accretion, primarily through below-ground organic matter additions

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from roots (autochthonous) that augment mineral contributions transported by tidal processes

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(allochthonous). Together these processes facilitate continual burial of organic matter (Hansen and

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Nestlerode, 2013; Lovelock et al., 2013; Saintilan et al., 2013; Kelleway et al., 2016, 2017a).

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Vegetation distribution reflects position in the tidal frame. Distribution of mangrove and saltmarsh

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vary with effects of tidal inundation that influence edaphic factors such as soil salinity, anoxia, and

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nutrient availability (Chen and Twilley, 1999; Feller et al., 2010; Adame and Lovelock, 2011;

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Rogers et al., 2017). Environmental gradients in edaphic factors influence mangrove and saltmarsh

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structure (Lugo and Snedaker, 1974; Clarke, 1993; Twilley et al., 1999; Adam, 2002; Castañeda-

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Moya et al., 2013), often resulting in defined zones broadly associated with tidal inundation

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(Mitchell and Adam, 1989; Woodroffe, 2003; Rogers et al., 2017). In southeast Australia, where

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mangrove and saltmarsh co-exist, the interaction of tides and fluvial contributions with underlying

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geomorphology results in complex distribution of vegetation communities (Owers et al., 2016a).

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Mangroves occur lower within the tidal frame, colonising extensive areas from approximately mean

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sea level to elevations that are frequently inundated in the upper intertidal. Saltmarsh occupy higher

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intertidal areas, occurring up to the limits of highest astronomical tides, where they are replaced by

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upland terrestrial environments such as Melaleuca or Casuarina forests (Adam, 2002; Saintilan,

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2009).

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As inundation is an important control on vegetation distribution, carbon additions to substrates

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correspond to inundation patterns. In addition, as inundation influences substrate physico-chemical

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properties, such as salinity and oxygen availability (i.e. remineralisation) (Krauss et al., 2006; White

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and Reddy, 2009; Adame et al., 2013; Twilley et al., 2017), tides also affect carbon losses by

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decomposition (Chmura et al., 2003; Kayranli et al., 2009; Mitsch et al., 2013; Saintilan et al., 2013).

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Where inundation is relatively frequent, decomposition is reduced, however where inundation is less

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frequent and the surface is only inundated for short periods of time, decomposition is enhanced

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(Bridgham and Lamberti, 2009; Dise, 2009; Verhoeven, 2009). Likewise, carbon additions are

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generally greater in areas frequently inundated compared to areas with irregular or less frequent

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inundation (Lovelock et al., 2013; MacKenzie et al., 2016; Morris et al., 2016). As mangrove exhibit

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a preference for higher inundation frequencies than saltmarsh, they exhibit greater productivity

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(Mangrove, 11.1 Mg C-1 y-1; Saltmarsh, 8.34 Mg C-1 y-1) (Alongi, 2014), biomass (Clarke, 1994;

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Clarke and Jacoby, 1994, Owers et al., 2018) and higher carbon additions to surface sediments

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(Bernal and Mitsch, 2008; Adame et al., 2013). Additionally, as allochthonous carbon additions are

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tidally-borne, areas frequently inundated will have a greater opportunity for sedimentation of mineral

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and organic material (Chmura et al., 2001; Chmura and Hung, 2004; Krauss et al., 2008; Rogers and

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Saintilan, 2008; Rogers et al., 2014; MacKenzie et al., 2016). Structural variation in mangrove and

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saltmarsh vegetation, such as lateral root structures and shoot density of different species, will

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influence retention of tidally-borne allochthonous material (Lovelock et al., 2010, 2013; Saintilan et

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al., 2013; Kelleway et al., 2017a).

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Sediment characteristics, such as grain size and bulk density, have been reported to correlate with

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carbon content and probably mediate some of the variability in carbon storage between sites (i.e.

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vegetation distribution and inundation) (Kelleway et al., 2016; Morris et al., 2016; Xiong et al.,

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2018). Finer sediment grain size and lower bulk density have been shown to correlate with increased

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carbon additions, limiting decomposition by mechanisms that restrict microbial access to organic

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matter (Six et al., 2002; Dungait et al., 2012; Xiong et al., 2018). Accordingly, sediment

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characteristics can enhance preservation or decomposition of carbon associated with position in the

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intertidal. This is evident across geomorphic units within an estuary. Fluvial areas, that are

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characterised by fine-grained sediments, have higher carbon storage than marine-dominated areas,

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where sediments tend to be dominated by sands, despite variation in vegetation composition

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(Saintilan et al., 2013; Kelleway et al., 2016; Hayes et al., 2017; Macreadie et al., 2017). Spatial

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variation in sediment character and vegetation structure reflect the influence of tides, fluvial

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contributions and geomorphology, and may influence carbon additions and losses. Classifying units

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that describe spatial variation may provide an opportunity to differentiate carbon at a higher spatial

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resolution than is achieved by broad vegetation units (i.e. mangrove and saltmarsh). The merit of this

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approach was recently demonstrated at a global scale where carbon storage within mangrove was

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related to ecogeomorphic settings (Rovai et al., 2018).

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Carbon off-setting initiatives such as Reducing Emissions from Deforestation and Forest

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Degradation (REDD+) and Australia’s Emission Reduction Fund (ERF) require carbon storage

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assessments that optimise efficiency and accuracy (Gibbs et al., 2007). To improve the reliability of

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estimates of carbon mitigation by natural ecosystems, accounting for spatial variation will improve

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estimates (Kelleway et al., 2017b). The IPCC outlines tiers of assessment regarding anthropogenic

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greenhouse gas emissions and removals associated with carbon storage (IPCC, 2014). Whilst IPCC

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indicate that estimates will improve when accounting for spatial variation, advice is yet to be

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provided that allows for spatial variation to be adequately accounted for when estimating carbon

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storage (i.e. Kauffman and Donato, 2012; Howard et al., 2014). Accurate quantification of carbon

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additions and bulk carbon storage is important to ensure variability is being accounted for to ensure

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comparisons between sites are comprehensive, as recommended in the Verified Carbon Standard

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(VCS, 2013, 2014, 2015).

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The aim of this study was to explore the variation in near-surface carbon storage (i.e. upper 30 cm)

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across an intertidal gradient using a stratified sampling approach that accounts for vegetation

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structure. Current vegetation is the primary source of carbon additions to near-surface carbon

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storage, yet vegetation distribution and structure broadly reflect the influence of edaphic conditions.

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In addition, decomposition is influenced by edaphic factors that are a response to the interaction of

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tides and fluvial contributions with underlying geomorphology. By presuming that the influence of

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inundation on carbon additions and decomposition is integrated within measures of sediment carbon

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storage, and as carbon storage and vegetation structure are related to inundation, we hypothesise that

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carbon storage correlates with vegetation structure. It is anticipated that vegetation structure can be

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used to characterise the spatial variation in near-surface carbon storage at a higher spatial resolution

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than the broad vegetation units of mangrove and saltmarsh. The specific objectives of this study were

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to:

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1. Establish that variation in near-surface carbon storage (i.e. upper 30 cm) can be described by vegetation structure 2. Characterise near-surface carbon storage and determine relationships between vegetation distribution and structure, and near-surface carbon storage

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3. Quantify near-surface carbon storage sources and analyse relationships with above ground vegetation distribution 4. Develop models that describe the landscape position of vegetation with respect to factors influencing inundation regimes and intertidal gradients

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Existing literature demonstrates that there is considerable variation in carbon storage within broad

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vegetation units of mangrove and saltmarsh (e.g. Adame et al., 2013; Lovelock et al., 2013; Saintilan

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et al., 2013; Rahman et al., 2015; Kelleway et al., 2016; Hayes et al., 2017; Ellison and Beasy, 2018),

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however these are not adequately addressed in current sampling protocols (IPCC, 2014). The intent

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of this study was to apply a new stratified sampling approach that improves carbon storage estimates,

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providing the confidence necessary for carbon accounting required as part of carbon off-setting

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initiatives and national carbon accounts.

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2. Methods

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2.1 Study area

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Along the wave-dominated coastline of southeast Australia, coastal wetlands are restricted to

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estuaries where hydrodynamic conditions are suitable for establishment and growth within the

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intertidal zone (Roy et al., 2001; Sloss et al., 2007). The temperate climate of southeast Australia

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supports co-occurring mangrove and saltmarsh communities (Adam, 1990). Mangroves occur lower

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within the tidal frame, while saltmarsh occupies higher intertidal areas. Avicennia marina is the

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dominant mangrove species in southeast Australia, and Aegiceras corniculatum is also present.

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These species of mangrove exhibit three dominant structural forms within the region, tall mangrove

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(typically 3 to 8 m in height, DBH greater than 15 cm), shrub mangrove (typically 1.3 to 3 m in

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height, DBH less than 15 cm), and dwarf mangrove (typically less than 1.3 m in height, DBH less 7

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than 15 cm) (Owers et al., 2016a). Several dominant structural forms of saltmarsh are also present

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including reed (Phragmites australis), rush (Juncus kraussii), and herbs, grasses and sedges (HGS),

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which includes Sporobolus virginicus, Samolus repens and Sarcocornia quinqueflora (Owers et al.,

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2016a). An ecotone is often present between mangrove and saltmarsh in southeast Australia,

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comprising of mixed and sparsely vegetated areas (Saintilan and Williams, 1999; Kelleway et al.,

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2015). Casuarina glauca woodlands are also extensive beyond the tidal limit (Clarke and Allaway,

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1996).

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Two barrier estuaries were selected in this study region, Minnamurra River (150°50’ E 34°38’ S) and

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Currambene Creek (150°40’ E 35°01’ S), displaying characteristics typical of coastal wetlands in

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southeast Australia, having similar vegetation complexity and edaphic conditions (Saintilan et al.,

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2013) (Figure 1).

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2.2 Near-surface carbon storage

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Sediment cores were collected at each site in each vegetation structural form to quantify sediment

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characteristics and carbon storage. Replicate cores within each vegetation structural form were

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collected where possible at each site. A total of eleven (11) cores were collected at Minnamurra

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River (Figure 1a) and twelve (12) cores at Currambene Creek (Figure 1b). Sediment cores were

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collected using an aluminium pipe with a 75 mm diameter and a sharpened edge to cut through root

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material. Compaction of sediments was determined for each core by measuring the length of sample

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recovery and length of the core penetration (Howard et al., 2014). Sediment cores were recovered for

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the active root zone, upper 30 cm of the sediment profile. Recovered cores were taken back to the

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laboratory and kept at 4°C in a temperature-controlled facility until analysis. Elevation above mean

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sea level (MSL) was recorded for each core location using RTK GPS (error, horizontal 0.008 m

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vertical 0.015 m). Vegetation structural form at each core location was recorded and vegetation

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biomass samples (i.e. living tissue samples) were collected for mangrove (trunk, branch, leaves,

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pneumatophore), saltmarsh (shoot) and Casuarina glauca (branch, needle-like shoots).

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Figure 1. Location of selected study sites in southeast Australia and recovered sediment cores

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characterised by vegetation structural form for a) Minnamurra River and b) Currambene Creek.

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HGS; herbs, grasses and sedges. Data source: Imagery © Land and Property Information [2009].

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Sediment cores were split longitudinally in half and sub-samples collected from one half at 0-2 cm,

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2-4 cm, 5-6 cm, 10-11 cm, 15-16 cm, 20-21 cm, 25-26 cm, and 30-31 cm. Grain size (µm) was

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analysed for each sample using a Malvern Mastersizer 2000 laser diffractometer. To determine

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sediment dry bulk density (g cm-3), samples were oven dried to constant mass at 60°C. Dry sample

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weight was determined and bulk density (BD) was estimated as the ratio between the dry sample

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weight (g) and the wet sample volume (cm3). Each sample was then homogenised by grinding to a

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fine powder using a Retsch three-dimensional Vibrator Mill (Type-MM-2:Haan, Germany). A small

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amount of each sample (1-2 g) was acidified to remove carbonate material by adding 0.1 M

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hydrochloric acid. Samples were then centrifuged and rinsed with Milli-Q water to remove

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remaining acid in sediments, dried to constant mass at 60°C, and ground to a fine powder with

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mortar and pestle. Samples were pelletised and analysed for carbon content (% C).

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Sediment carbon density (C g cm-3) was estimated by multiplying bulk density (g cm-3) and carbon

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content (% C). Carbon storage (Mg C ha-1) for each sediment core was estimated by fitting a linear

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model between each sediment sub-sample and aggregating each centimetre interval. Depth intervals

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were corrected for compaction using the correction factor calculated for each core. Replicate cores

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for each vegetation structural form were used to estimate average carbon storage (Mg C ha-1 ± SE).

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Total near-surface carbon storage (Mg C ha-1 ± SE) was estimated for each site using spatial extent

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of vegetation structural form delineated in Owers et al. 2016a).

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To establish variation in near-surface carbon storage, a nested design was used to test for carbon

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storage variation between vegetation units and their structural forms, within different wetlands (i.e.

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Minnamurra River and Currambene Creek). Initially, one-way analysis of variance (ANOVA) was

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used test for differences in carbon storage between sites. Two-way ANOVA was then used to test for

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differences between vegetation units (i.e. mangrove and saltmarsh) within sites. On the basis of this

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analysis, two-way ANOVA was used to test differences in carbon storage within vegetation

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structural forms within sites. All statistical tests completed in this study were undertaken in JMP

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Version 11 (SAS Institute Inc., Cary, NC, USA) and carried out using a 0.05 level of significance.

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Two-way ANOVA was used to establish relationships between grain size, bulk density and carbon

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content. One-way ANOVA was then used to test for differences between sediment characteristics 10

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(i.e. grain size, BD, % C) and vegetation distribution. Post-hoc Tukeys HSD was performed to test

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for differences between mangrove and saltmarsh and their structural forms. On the basis of this

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analysis, differences between carbon storage (Mg C ha-1) and vegetation distribution were identified

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using one-way ANOVA.

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2.3 Near-surface carbon source

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Stable carbon isotope analysis (δ13C) was undertaken on sediment samples to identify sources of

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carbon additions. Samples were dried to a constant mass at 60°C and ground to a fine powder.

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Sediment samples were acidified by adding 0.1 M HCl to eliminate inorganic carbonates that would

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otherwise affect δ13C values. Treated samples were then pelletised into tin capsules and analysed for

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stable isotopes of carbon using a continuous flow isotope ratio mass spectrometer (CF-IRMS)

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following Mazumder et al. (2010). To aid interpretation of carbon isotope signatures in sediment

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carbon, stable carbon isotope analysis was also undertaken on vegetation biomass. Vegetation δ13C

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was determined for Avicennia marina and Aegiceras corniculatum (trunk, branch, leaves,

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pneumatophores), Casuarina glauca (branch, needle-like shoots) and shoot material of Sporobolus

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virginicus, Juncus kraussii, Sarcocornia quinqueflora, Samolus repens and Phargmites australis.

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One-way ANOVA was used to test for differences between carbon stable isotopes of vegetation

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biomass and sediment carbon.

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2.4 Landscape position of vegetation

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Models were developed to interpret vegetation position in the landscape. These models were derived

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using airborne Lidar data: a digital elevation model (DEM), and hydrological distance to water

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model (HDM), a spatial model representing flow length to open water (i.e. distance to water

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accounting for topographical variation using the DEM) across each study site. Lidar data were

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collected using an ALS50-II airborne sensor between December 2010 and April 2011, with a

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footprint size of 0.62 m2, and accuracy of 0.8 m horizontal and 0.3 m vertical. The developed DEM

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and subsequent HDM were processed to 1 m spatial resolution.

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Initially a DEM was derived from the Lidar data using only ground returns. Although Lidar is

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regarded as highly accurate for modelling topographic surfaces, studies have indicated the inability

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of Lidar to penetrate dense vegetation canopies, resulting in topographic surfaces that overestimate

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elevation (Lefsky et al., 2002; Kuenzer et al., 2011; Schmid et al., 2011). The need to correct the

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DEM was assessed based on comparison of DEM data with elevation data generated from an

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extensive ground-truth campaign between December 2014 and January 2015 at both sites using RTK

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GPS in all vegetation structural forms. Ground-truthed elevations were recorded at both Minnamurra

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River (n = 134) and Currambene Creek (n = 148). To correct the DEM each vegetation structural

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form was delineated (see Owers et al., 2016a) and surface elevation for each structural form reduced

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by the calculated mean value (Table S1) (e.g. for shrub mangrove at Currambene Creek, 0.14 m was

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subtracted from all shrub mangrove DEM values).

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A HDM was produced for both sites using the corrected DEM. The hydrology toolset in ArcGIS 10.2

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(ESRI, 2018) was used to determine flow length to the nearest river or creek. All permanently

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inundated areas were delineated and areas where known obstructions to tidal flow occurred (i.e.

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levees) were corrected to ensure the most representative model could be produced.

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To interpret vegetation position, two-way ANOVA was used to test for differences between

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vegetation distribution, elevation (E) (AHD m) from the DEM, and hydrological distance to water

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(DH) (m) from the HDM. Analysis was stratified on vegetation structural forms within sites on the

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basis of carbon storage analysis between sites (section 3.1). Post hoc Tukeys HSD was performed to

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identify differences between vegetation distribution, E and DH (i.e. mangrove and saltmarsh and their

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structural forms).

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3. Results

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3.1 Near-surface carbon storage

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Mean near-surface carbon storage (Mg ha-1 ± SE) at Minnamurra River (63.16 ± 5.42 Mg C ha-1) was

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greater than Currambene Creek (49.27 ± 6.01 Mg C ha-1) (p < 0.05). Vegetation units (i.e. mangrove

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and saltmarsh) within different wetlands were different (p < 0. f05), and mean mangrove carbon

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storage at Minnamurra River (67.36 ± 6.45 Mg C ha-1) was greater than mangrove at Currambene

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Creek (58.90 ± 4.19 Mg C ha-1). However, carbon storage of vegetation structural forms at

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Minnamurra River and Currambene Creek were not different (p > 0.05). Total near-surface carbon

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storage (Mg ha ± SE) for Minnamurra River (3945.11 ± 121.19) was greater than Currambene Creek

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(3179.56 ± 115.77). Accounting for variation within sites and vegetation units (i.e. mangrove and

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saltmarsh), by a stratified sampling approach that recognises vegetation structure, indicates that

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variation lies within the broader vegetation structural forms of mangrove and saltmarsh.

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Bulk density was positively correlated with grain size however the relationship was weak (r2 = 0.21,

275

p < 0.0001) (Figure 2a). Carbon content and bulk density were highly correlated, where decreasing

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bulk density corresponded to exponential increase in carbon content (r2 = 0.82, p < 0.0001) (Figure

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2c). Increase in carbon content also corresponded to decreasing grain size, however this relationship

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was weak (r2 = 0.22, p < 0.0001) (Figure 2b). Two-way ANOVA verified that carbon content was

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related to bulk density and grain size (r2 = 0.83, p < 0.0001) (Figure 2d). The interaction of these

280

effects (i.e. bulk density and grain size) was not significant at an alpha level of 0.05, however carbon

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content was highly likely to vary on the basis of both factors (p = 0.0664).

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Figure 2. Relationships between surface sediment characteristics a) grain size and bulk density (BD),

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b) log carbon content (% C) and grain size, and c) log carbon content and bulk density. d) Model

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output for observed carbon content and estimated carbon content based on two-way ANOVA where

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bulk density and grain size describe carbon content.

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Bulk density and carbon content varied on the basis of vegetation distribution (p < 0.0001) (Figure

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3a). Mangrove bulk density was lower than both saltmarsh and ecotone vegetation (p < 0.0001),

290

however was not different from Casuarina (p = 0.2922). Conversely, carbon content of saltmarsh

291

and ecotone vegetation was lower than mangrove (p < 0.01) and Casuarina (p < 0.0001) (Figure 3c).

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Although bulk density and carbon content of mangrove structural forms (i.e. tall, shrub, dwarf) were

293

similar, saltmarsh structural forms were different (Figure 3b, d). Reed saltmarsh had greater carbon

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content and lower bulk density than HGS saltmarsh (p < 0.05), however reed saltmarsh was not

295

different to rush saltmarsh (p > 0.05).

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Grain size was similar between mangrove and saltmarsh vegetation (Figure 3e). Differences in mean

297

grain size between mangrove structural forms (i.e. tall, shrub, dwarf) and ecotone vegetation (i.e.

298

mixed and sparse vegetation) were minor (p > 0.05), however differences were detected for

299

saltmarsh structural forms (Figure 3f). HGS saltmarsh had greater mean grain size than rush (p =

300

0.0023) and reed (p = 0.0009) saltmarsh.

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Near-surface carbon storage varied on the basis of vegetation distribution (p = 0.0444). Carbon

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storage was highest for Casuarina (91.09 Mg C ha-1) followed by mangrove (63.13 ± 4.04 Mg C ha-

303

1

), saltmarsh (43.50 ± 7.90 Mg C ha-1) and ecotone (44.07 ± 10.95 Mg C ha-1) (Figure 3i). Saltmarsh

304

carbon storage was lower than mangrove (p = 0.0473) and Casuarina (p = 0.0279), however

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saltmarsh and ecotone communities were similar (p = 0.9624). Carbon storage of mangrove

306

structural forms (i.e. tall, shrub, dwarf) were similar, however differences were detected between

307

saltmarsh structural forms (Figure 3j). Reed saltmarsh carbon storage was greater than HGS

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saltmarsh (p = 0.0285), however reed saltmarsh was not different to rush saltmarsh (p = 0.0834).

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3.2 Near-surface carbon sources

310

Stable carbon isotopes (δ13C) in sediment varied on the basis of vegetation distribution. Mangrove

311

were considerably different from saltmarsh and ecotone vegetation (p < 0.0001) (Figure 3g). These

312

differences were related to vegetation structural form where HGS saltmarsh and sparse vegetation

313

structures had similar isotopic signatures (p = 0.6706) yet were different from all other structural

314

forms (p < 0.05) (Figure 3h).

315

All vegetation biomass in the study area, with the exception of Sporobolus virginicus, exhibited a

316

depleted stable carbon isotope signature associated with C3 photosynthetic pathway (-35‰ to -20‰;

317

Smith and Epstein, 1971; Fry, 2006; Kuzyakov, 2006) (Table 1). Differences were not identified

318

between stable carbon isotopes of C3 vegetation biomass, including Avicennia marina, Aegiceras

319

corniculatum, Samolus repens, Juncus kraussii, Sarcocornia quinqueflora, Phragmites australis, 15

320

Casuarina glauca (p = 0.3625). Sporobolus virginicus biomass samples were relatively enriched (-

321

15.2 ± 0.25‰), exhibiting a stable carbon isotope signature associated with the C4 photosynthetic

322

pathway (-19‰ to -6‰; Smith and Epstein, 1971; Fry, 2006; Kuzyakov, 2006), different from C3

323

vegetation biomass (p < 0.0001).

324

16

325 BD (g cm-3)

Carbon content (% C) Mangrove Ecotone Saltmarsh Casuarina

Mangrove Ecotone Saltmarsh Casuarina

Mangrove Ecotone Saltmarsh Casuarina

80

60

40

20

0

100

Mean grain size (µm)

b)

d)

f)

h)

j) δ13C (‰)

a)

Mean grain size (µm)

Mangrove Ecotone Saltmarsh Casuarina

1 2 3 4 Mangrove Ecotone Saltmarsh Casuarina 1 2

Sh rub Dw arf Mi xed Sp ars e HG S Ru sh Re ed Ca sua rin a

c)

80

60

40

20

0

100

δ13C (‰)

Carbon storage (Mg C ha-1)

Tal l

e)

g)

i) Carbon storage (Mg C ha-1)

BD (g cm-3)

Carbon content (% C)

3 4 5 6 7 8 9

Tal l Sh rub Dw arf Mi xed Sp ars e HG S

Tal l Sh rub Dw arf Mi xed Sp ars e HG S

Tal l Sh rub Dw arf Mi xed Sp ars e HG S

Tal l Sh rub Dw arf Mi xed Sp ars e HG S

Ru sh Re ed Ca sua rin a

Ru sh Re Ca ed sua ri n a

Ru sh Re ed Ca sua ri n a

Ru sh Re ed Ca sua rin a

17

326

Figure 3. Box plots showing sediment characteristics of a) bulk density (BD) for each vegetation

327

community and b) vegetation structural form; c) carbon content (% C) for each vegetation

328

community and d) vegetation structure; e) mean grain size for each vegetation community and f)

329

vegetation structure; g) stable carbon isotopes (δ13C) for each vegetation community and h)

330

vegetation structure; and i) average carbon storage (Mg C ha-1) for each vegetation community and j)

331

vegetation structure. For a-h) horizontal line inside box indicates median, cross indicates mean, the

332

box indicates 25th and 75th percentiles and whiskers indicate maxima and minima. For i-j) no error

333

bar present indicates only one sediment core was collected. HGS; herbs, grasses and sedges.

334

335

Stable carbon isotope signatures for mangrove sediments (-25.4 ± 0.08‰) were similar to mangrove

336

biomass isotopic signatures (-27.0 ± 0.28‰). For saltmarsh sediments, stable carbon isotope

337

signatures were similar to the isotopic signatures of Samolus repens, Juncus kraussii, Sarcocornia

338

quinqueflora, Phragmites australis biomass. Sporobolus virginicus biomass was more enriched than

339

saltmarsh sediment stable carbon isotope signatures (p = 0.0138), however was similar to HGS (p =

340

0.2044) and sparse vegetation (p = 0.1777) structural forms.

341

Table 1. Stable carbon isotopes (δ13C) for mangrove and saltmarsh biomass at study sites. SE;

342

standard error. No SE indicates only one sample for vegetation.

Mangrove

Compartment

δ13C ± SE (‰)

Saltmarsh (shoot)

δ13C ± SE (‰)

Avicennia marina

Trunk

-24.8 ± 0.59

Sporobolus virginicus

-15.2 ± 0.25

Branch

-27.0 ± 0.48

Samolus repens

-29.0 ± 0.04

Leaves

-27.5 ± 0.46

Juncus kraussii

-26.5 ± 0.51

Pneumatophore

-27.6 ± 0.74

Sarcocornia quinqueflora

-26.4 ± 0.13

Trunk

-26.9 ± 0.31

Phragmites australis

-26.7

Branch

-27.6 ± 0.27

Leaves

-27.9 ± 0.09

Casuarina glauca (branch)

-27.5

Pneumatophore

not measured

Casuarina glauca (needle-like shoots)*

-28.1

Aegiceras corniculatum

18

343

344

3.3 Landscape position of vegetation

345

A corrected DEM and HDM were developed for both Minnamurra River (Figure 4a, b) and

346

Currambene Creek (Figure 4c, d). DH increased with increasing E for both sites (p < 0.0001),

347

however these relationships were weak (Minnamurra River r2 = 0.01, Currambene Creek, r2 = 0.16),

348

as demonstrated by the spatial complexity of the HDM at both sites.

19

349 350

Figure 4. Corrected digital elevation model (DEM) and hydrological distance to water model (HDM) for Minnamurra River (a, b) and Currambene Cree

351

(c, d). Data source: Imagery © Land and Property Information [2009]. 20

352

Landscape position of vegetation was associated with E and DH. Vegetation distribution varied on

353

the basis of E (p < 0.0001), DH (p < 0.0001) and the interaction of these effects (E x DH p < 0.0001).

354

Saltmarsh mean E and DH (E = 0.69 m, DH = 236 m) was greater than mangrove (E = 0.36 m, DH =

355

170 m) (Figure 5a, c). Ecotone communities had the smallest elevation range (E 25th percentile =

356

0.53 m, E 75th percentile = 0.64 m), however had the greatest DH range (DH 25th percentile = 86 m,

357

DH 75th percentile = 412 m). Casuarina had a DH range similar to saltmarsh, however was distributed

358

at higher elevations. Mangrove structural forms were readily differentiated (tall E = 0.31 m, DH =

359

102 m; shrub E = 0.36 m, DH = 214 m; dwarf E = 0.45 m, DH = 231 m) (p < 0.0001), however both

360

mixed and sparse vegetation DH were similar (E p < 0.0001, DH p = 0.8610), as was the case with

361

saltmarsh structural forms HGS and rush (E p < 0.0001, DH p = 0.9932) (Figure 5b, d).

362

363

Figure 5. Box plots showing a) elevation characteristics for each vegetation community and b)

364

vegetation structural form, and c) hydrological distance to water for each vegetation community and

365

d) vegetation structural form. Horizontal line inside box indicates median, cross indicates mean, the

21

366

box indicates 25th and 75th percentiles and whiskers indicate maxima and minima. DH; hydrological

367

distance to water. HGS; herbs, grasses and sedges.

368

369

4. Discussion

370

This study found that carbon storage variation within a wetland relates to vegetation distribution and

371

structure, and sediment characteristics. Variation within a wetland and broad vegetation units of

372

mangrove and saltmarsh is accounted for when recognising vegetation structural form. Carbon

373

content varied with bulk density and grain size, where contributions of organic matter to grainsize

374

create conditions for greater carbon content and lower bulk density (Baldock et al., 2004; Kelleway

375

et al., 2016; Xiong et al., 2018). In addition, sedimentary characteristics on carbon content are

376

correlated with vegetation distribution. For mangrove, variability of carbon content associated with

377

vegetation structural form was negligible for near-surface sediment (i.e. upper 30 cm), however a

378

significant difference in carbon content was determined for structural forms of saltmarsh (Figure 3d).

379

Based on the correlation between vegetation distribution and structure, sediment characteristics and

380

near-surface carbon storage, previous environmental conditions must be considered when

381

quantifying carbon storage in deeper sediment as the surface vegetation associated with these

382

sediments at the time of deposition may be different to the vegetation on the current surface.

383

Accordingly, previous environmental conditions may be identified using stable carbon isotopes as

384

indicators of prior vegetation distribution. Combined with suitable mapping to identify within site

385

variability of modern carbon additions, this will improve carbon storage estimates and provide the

386

confidence necessary for national carbon accounts.

387

4.1 Influence of vegetation distribution on near-surface carbon storage and sources

22

388

Carbon content varied in the landscape on the basis of vegetation distribution. Greater carbon content

389

and lower bulk density was found for areas more frequently inundated (i.e. mangrove) than areas in

390

the intertidal that receive irregular or limited flooding (i.e. saltmarsh, ecotone), consistent with other

391

studies (Baldock et al., 2004; Saintilan et al., 2013; Kelleway et al., 2016; Hayes et al., 2017; van

392

Ardenne et al., 2018; Xiong et al., 2018). Casuarina exhibited fine grained substrate with the highest

393

carbon content of all vegetation, likely due to carbon additions at the surface from processes

394

operating above the tidal limits (Clarke and Allaway, 1996; Goel and Behl, 2005). Similar to the

395

ability of mangrove to maintain their position within the tidal frame by adding organic matter to

396

substrates (Rogers and Saintilan, 2008; Krauss et al., 2013; Alongi, 2014), these additions may be a

397

function of vegetation adaptations to remain at elevations beyond the limit of inundation.

398

Furthermore, higher carbon content of mangrove and Casuarina vegetation may be due to higher

399

organic matter input compared to saltmarsh vegetation (Saintilan et al., 2013), facilitated by greater

400

root concentration associated with large woody vegetation (Alongi, 2012; Xiong et al., 2018).

401

This study demonstrates that variability within saltmarsh carbon storage to 30 cm depth at a site can

402

be substantial, and greater than the variation reported between sites for saltmarsh more broadly. HGS

403

saltmarsh, consisting of Sporobolus virginicus, Samolus repens and Sarcocornia quinqueflora, had

404

lower carbon content and higher bulk density than rush (Juncus kraussii) and reed (Phragmites

405

australis) saltmarsh. This may be due to variation in the depth of the rooting zone, where HGS roots

406

do not extend as far as rush or reed saltmarsh, or greater capacity for rush and reed saltmarsh to

407

incorporate above-ground biomass litter into sediment than HGS saltmarsh (Saintilan et al., 2013;

408

Kelleway et al., 2016, 2017a). Irrespectively, distribution of saltmarsh structural forms across the

409

intertidal zone suggests that edaphic factors, together with sediment characteristics, are correlated to

410

carbon content (presumed to integrate carbon additions and decomposition), demonstrating

411

significant variability within a wetland and between sites. Accordingly, comparisons between carbon

23

412

content of saltmarsh in this study and previous studies in southeast Australia that do not account for

413

the same degree of variability (i.e. structural form) will be fraught with errors.

414

Spatial variation in carbon content and bulk density can be substantial, as demonstrated in this study,

415

however current sampling and reporting do not recognise this variability (i.e. Kauffman and Donato,

416

2012; Howard et al., 2014; IPCC, 2014). Existing literature demonstrates substantial variation in

417

sedimentary characteristics within mangrove and saltmarsh (e.g. Chmura et al., 2003; Saintilan et al.,

418

2013; Sanders et al., 2016; Macreadie et al., 2017), yet do not adequately consider sampling location

419

with respect to vegetation distribution in the landscape. For southeast Australia, vegetation structure

420

can be used to indicate position in the landscape and infer tidal position, allowing for comparisons

421

between studies that account for the same variability. For example, rush saltmarsh (Juncus kraussii)

422

in southeast Australia occupies areas with low accommodation space typically towards tidal limits.

423

Saintilan et al. (2013) report bulk density and carbon content for Juncus kraussii (BD = 0.36 g cm-3,

424

% C = 12.6) which correspond to rush saltmarsh in this study (BD = 0.64 ± 0.06 g cm-3, % C = 3.24

425

± 0.72). Furthermore, variation in carbon content and bulk density for saltmarsh in this study (BD

426

min = 0.21 cm-3, max = 1.22 g cm-3; % C min = 0.4, max = 13.8) was comparable to variability for

427

saltmarsh in New South Wales (BD min = 0.23 cm-3, max = 1.46 cm-3; % C min = 0.64, max =

428

20.43; Macreadie et al., 2017), demonstrating equivalent variability within a wetland as between

429

sites. Comparison of carbon content and bulk density for coastal wetland vegetation between

430

different studies must account for variability within a wetland to ensure correct comparisons.

431

4.2 Influence of landscape position on vegetation distribution

432

The distribution of wetland vegetation in the landscape was described in this study by the interaction

433

between elevation and hydrological distance to water. Elevation has been commonly used to indicate

434

inundation dynamics and vegetation distribution of mangrove and saltmarsh (Rogers et al., 2006;

435

Hickey and Bruce, 2010; Crase et al., 2013; Murray-Hudson et al., 2014; Spier et al., 2016; Leong et 24

436

al., 2018), however its capacity as a proxy for inundation patterns is limited. Flooding across

437

intertidal areas is spatially variable, influenced by microtopography, groundwater, surface friction

438

and porosity of sediment, as well as tidal forcing (Cahoon and Reed, 1995; Chmura et al., 2001;

439

Costa et al., 2003; Krauss et al., 2008, 2013). Results in this study demonstrate that describing

440

inundation dynamics using only elevation can lead to weak relationships between elevation and

441

vegetation distribution. For example, 25% of mangrove vegetation occupied elevations above 0.52 m

442

AHD, with some occurring in areas almost at tidal limits, typical of elevations associated with

443

saltmarsh distribution. However, mangrove occupying elevations above 0.52 m generally coincided

444

with considerably less hydrological distance to water, suggesting that they may be regularly

445

inundated or able to access groundwater sources that are nearer to surface substrates at channel

446

margins. Describing inundation patterns and vegetation distribution using only elevation may be

447

suitable in some macrotidal systems, particularly where extensive intertidal habitat has simplified

448

hydrology and zonation of vegetation is obvious (Allen, 2000; Krauss et al., 2013), however these

449

patterns are not easily resolved for microtidal systems (Hanslow et al., 2018) as smaller tidal ranges

450

mean that changes in inundation patterns occur over narrower ranges of elevation (Roy et al., 2001;

451

Woodroffe, 2003).

452

4.3 Implications for carbon storage

453

Near-surface carbon storage of mangrove and saltmarsh in this study ranged from 20.51 Mg C ha-1 to

454

91.09 Mg C ha-1, and these results were broadly consistent with surface carbon storage values

455

reported for other coastal wetlands in temperate settings (Howe et al., 2009; Livesley and Andrusiak,

456

2012; Saintilan et al., 2013; Ewers Lewis et al., 2017; Ellison and Beasy, 2018). There is increasing

457

recognition that carbon storage is more spatially complex than broadscale estimates comparing

458

mangrove and saltmarsh would indicate. Results in this study demonstrate that vegetation

459

distribution and sediment characteristics correlate with carbon content within a wetland and it

25

460

follows that these factors will influence carbon storage. Moreover, variation within an estuary and

461

between sites is adequately described by accounting for vegetation structural form. Near-surface

462

carbon storage of mangrove (63.13 ± 4.04 Mg C ha-1) in this study was broadly similar to previously

463

reported values for southeast Australia (94.20 Mg C ha-1, Howe et al., 2009; 47.4 Mg C ha-1,

464

Livesley and Andrusiak, 2012; 65.6 ± 4.17 Mg C ha-1, Ewers Lewis et al., 2017), where within site

465

variability can be accounted for by sedimentary factors. However substantial variation was observed

466

for saltmarsh (43.50 ± 7.90 Mg C ha-1) in this study compared to previous studies (129.72 Mg C ha-1,

467

Howe et al., 2009; 76.6 Mg C ha-1, Livesley and Andrusiak, 2012; 87.1 ± 4.90 Mg C ha-1, Ewers

468

Lewis et al., 2017). As previously demonstrated with sediment characteristics, this is due to within

469

site variation associated with distribution of saltmarsh structural forms.

470

This study demonstrates an improvement upon approaches that only distinguish spatial variation in

471

near-surface carbon storage on the basis of mangrove and saltmarsh alone. It is proposed that

472

assessments of carbon storage should account for vegetation structure when sampling and reporting

473

carbon storage estimates. This can be achieved most simply by delineating vegetation structural form

474

using remote sensing (Owers et al., 2016a, b). Other high-resolution approaches may further

475

discriminate vegetation structure, by providing a continuous surface (e.g. Lidar) or enhancing

476

discrete units of vegetation such as species level identification (e.g. hyperspectral imagery), enabling

477

further improvements to capture spatial variability in carbon storage (Adam et al, 2009; Heumann,

478

2011; Kuenzer et al., 2011; Klemas, 2013). This will provide greater confidence in carbon storage

479

estimates while optimising efficiency and accuracy using state-of-the-art techniques.

480

Deeper carbon storage, beyond the rooting zone of vegetation, likely does not reflect relationships

481

established with contemporary vegetation. This is because carbon content of deeper sediments is

482

likely to have been contributed by previous vegetation at an earlier stage of wetland evolution (Choi

483

et al., 2001; Bianchi et al., 2013; Rogers et al., 2018, 2019). Results in this study demonstrate that

26

484

stable carbon isotope signatures in surface sediment reflect isotope signatures of vegetation biomass,

485

where some variation in sediment isotope signatures may be explained by detritus and in-situ algal

486

production (France, 1995; Mazumder et al., 2011). Changes in isotopic signature in deeper sediments

487

would likely recognise change in vegetation characteristics, reflecting previous environmental

488

conditions (Choi et al., 2001; Wang et al., 2008; Wang et al., 2011; Bianchi et al., 2013). When

489

quantifying carbon storage in deeper sediment, previous environmental conditions must be

490

considered to avoid incorrect attribution of carbon additions to contemporary vegetation.

491

Relationships established between carbon storage and vegetation distribution in previous research

492

(i.e. carbon storage of mangrove and saltmarsh is similar) may be incorrect where previous

493

environmental conditions associated with Holocene evolution have not been considered.

494

495

Conclusions

496

This study applied a new stratified sampling approach to improve carbon storage assessments by

497

establishing influences on carbon within a wetland. Near-surface carbon storage (i.e. upper 30 cm)

498

was described by vegetation distribution and structure across the intertidal gradient, and sedimentary

499

characteristics that correlate with carbon content. In particular, saltmarsh near-surface carbon storage

500

varied between structural form. Near-surface carbon storage of mangrove structural forms (i.e. tall,

501

shrub, dwarf) was not significantly different, likely due to restricted depth of analysis where tall and

502

shrub mangrove have extended root depth beyond the upper 30 cm. Nevertheless, sedimentary

503

characteristics correlated with carbon content demonstrated considerable influence on near-surface

504

carbon storage within a wetland, where variation within a wetland is of comparable magnitude as

505

variation between sites. Current sampling protocols do not adequately account for variability within a

506

wetland, focusing on broad vegetation units, despite recognising considerable variation within these

507

units. Assessments of carbon storage should recognise vegetation structure when sampling and 27

508

reporting carbon storage estimates. Furthermore, quantifying deeper carbon storage requires

509

consideration of previous environmental conditions, as carbon in deeper sediment may not

510

correspond to processes operating at the wetland surface. Stable carbon isotopes offer a mechanism

511

to recognise change in vegetation characteristics in sediment, associated with an earlier stage of

512

wetland development, likely reflecting previous environmental conditions.

513

514

Acknowledgements

515

The authors would like to thank the many fieldwork assistants that helped ensure high quality data

516

collection; Daniel Owers, Steve Brooks, Kirti Lal, Brent Peterson, Neil Saintilan, Tsun-You Pan,

517

Kate Owers, Daniela Mueller, Catherine Bowie, Laura Mogensen and Junjie Deng. Laboratory

518

assistance was provided by John Morrison and José Abrantes (UOW), and carbon isotope analysis

519

performed by Barbora Neklapilova, Scott Allchin, Jennifer Van-holst (ANSTO) and University of

520

California (Davis) Stable Isotope Facility. NSW National Parks and Wildlife Service and NSW

521

Department of Primary Industries (Fisheries) supported access to field sites. Fieldwork was

522

conducted in accordance with the Office of Environment and Heritage scientific license SL101523

523

and SL101724. Aerial imagery and Lidar data were provided by Land and Property Information,

524

NSW. This research has been conducted with the support of the University of Wollongong Global

525

Challenges Program, the Australian Government Research Training Program Scholarship award to

526

CJO, and Australian Research Council Future Fellowship awarded to KR (FT130100532). The

527

authors have no competing interests to declare.

528

28

529

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1. _Christopher Owers__

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2. _Kerrylee Rogers____

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3. _Debashish Mazumder_

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4. _Colin Woodroffe____

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