Can wood supplements increase carbon sequestration in a North Wales

calcareous fen?

A preliminary field study into enzymic latch-mediated geo-engineering

Wayne Watkins

MSc Thesis

School of Biological Sciences

Bangor University

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Summary

Despite covering only 3% of the earth’s surface, peatlands store the equivalent of one third of

the CO2 in the atmosphere as peat and are the most efficient carbon stores of any terrestrial

ecosystem. Despite this, peatlands have been largely overlooked in their potential for geo-

engineering strategies, unlike other ecosystem such as oceans and woodlands. This study set

out to ascertain whether the introduction of exogenous supplementary phenolic compounds

to a peatland would induce, via “enzymic latch-mediated” processes, a suppression in

microbial decomposition with a view to curbing harmful GHG emissions. A phenolic treatment,

control and a procedural control plot of trampled vegetation was set up to test for differences

in extra-cellular hydrolase and phenol oxidase enzyme activities, biogenic trace gases

including CO2, CH4 and N2O and pore water pH, DOC and nutrient concentrations. Results

show phenolic suppression of arylsulphatase by almost half but confounding factors such as

drought and organic carbon “priming” by damaged plants potentially restrict the suppression

of hydrolases at this initial stage. Trace gas emission were not significantly reduced and further

monitoring is required as phenolic leaching from the treatment has a high suppression

potential. It should be noted that monitoring of vegetation and microbial responses to available

nutrients and effects on water quality should continue, as a means of full assessment of the

consequences of phenolic treatment.

Keywords

peatland, geoengineering, extracellular enzymes, fen, GHG gas flux, fen restoration

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Acknowledgements

I would like extend my gratitude to my two academic supervisors Dr Christian Dunn and

Professor Chris Freeman for their encouragement and advice and constant support

throughout the year. I would also like to thank Chris Wynne at the North Wales Wildlife Trust

for supporting the project and providing access to a study site. I would like to thank Dr Tim

Jones for continuous support with methods and general advice throughout the project, Dr

Nathalie Fenner for useful discussions regarding project background and Dr Rachel Gough

for general project advice.

From NRW, I would like to thank Dr Peter Jones for lend of field equipment and general advice

on fens, Katherine Birth for information on fen vegetation and Dylan Lloyd for rainfall data. I

would like to thank Ceri Morris also of NRW for supporting my application to this course of

study. I would like to thanks Dr Luke Ridley of Bangor University for advice on carbon budgets.

I would like to thank Alan Jones in the School of Biological Sciences for help with constructing

GHG chambers and the finance staff for their help with ordering equipment. I would like to

thank Derek Holland for statistical support and Gabrielle Semrau of the dyslexia unit for

support throughout the project.

I would also like to thank fellow students of the Wolfson Carbon Capture Laboratory for their

company during the year and during field visits, especially Imogen German and Beth Lloyd for

help with vegetation surveys and Caroline Scholz for help with field equipment. I would also

like to thank Hannah Prangnell for helpful advice throughout the year. I would also like to thank

Hannah Roberts and all my friends for tolerating me over the last year.

I would also like to thank the ATM project for financial support throughout the year. Lastly, I

would like to thanks my family for their support and apologise for being virtually anonymous

over the last year.

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Table of Contents

1.0 Literature review ............................................................................................................................ 8

1.1 List of Abbreviations .................................................................................................................... 36

1.2 Introduction ................................................................................................................................... 37

1.3 Hypothesis .................................................................................................................................... 40

2.0 Study site ...................................................................................................................................... 41

3.0 Methods ........................................................................................................................................ 43

3.1 Experimental design and treatments ........................................................................................ 43

3.2 Sampling ....................................................................................................................................... 44

3.2.1 GHG sampling .......................................................................................................................... 45

3.2.2 Pore water sampling ................................................................................................................ 45

3.2.3 Soil sampling ............................................................................................................................. 46

3.2.4 Vegetation survey ..................................................................................................................... 46

3.3 Laboratory analysis ..................................................................................................................... 47

3.3.1 GHG analysis ............................................................................................................................ 47

3.3.2 Soil pore water chemistry analysis ........................................................................................ 47

3.3.3 Soil enzyme assay ................................................................................................................... 48

4.0 Quantifying peatland presence and carbon budget estimation ............................................ 50

5.0 Developing a method to estimate the phenolic leaching capacity of wooden spatulas .... 51

6.0 Statistical analysis ....................................................................................................................... 52

7.0 Results .......................................................................................................................................... 53

7.1 The effect on supplementary lignin addition on phenolic concentrations ........................... 53

7.2 Response of extracellular enzymes to phenolic treatment ................................................... 54

7.3 Soil characteristics ....................................................................................................................... 58

7.4 Response pore water chemistry to phenolic supplement ...................................................... 60

7.5 Response of GHG gases to phenolic supplement ................................................................. 62

7.6 Response of pore water nutrients to phenolic supplement ................................................... 66

7.7 Response of vegetation to phenolic supplement .................................................................... 69

7.8 Leachable phenolic potential of wooden spatulas .................................................................. 72

8.0 Discussion..................................................................................................................................... 73

9.0 Suggested further study ............................................................................................................. 79

10.0 Conclusion .................................................................................................................................. 80

11.0 Appendix ..................................................................................................................................... 81

12.0 References ................................................................................................................................. 99

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1.0 Literature review

The potential for financing peatland restoration

using the world’s carbon markets

Peatland characteristics

Peatlands represent a range of wetlands that are characterised by an organic soil but differ in

hydrology, chemistry and consequently vegetation (Laiho 2006). The successional

development in a peatlands, regulated by both allogenic and autogenic factors follows the

order of relatively flat fen forming in a basin, and receiving water from its mineral surroundings,

often termed “minerotrophic” to raised bog termed “ombrotrophic” which is raised above the

surrounding landscape and receives its water, bases and nutrients solely from precipitation

(Gorham 1991). The Ramsar Convention, an intergovernmental treaty that provides a legal

framework for the protection of wetlands including peatlands define a peatlands as;

“Landscapes with a peat deposit that may currently support a vegetation, may not, or may lack

vegetation entirely. The presence of peat or vegetation capable of forming peat is the key

characteristic of peatlands.” (Ramsar Convention, 2002)

Peatlands exist at varying depths depending on age and management pressures with average

depth quoted as 0.5 – 3.5 meters in the former Soviet Union, depending on class of peatland

(Botch et al. 1995), studies however tend to bias towards deeper peats (Turunen et al. 2002).

To class as peatland, minimum peat depths varying from 20 to 70 cm have been cited,

however 30cm has been used by Joosten & Clarke (2002) in order to standardise their global

inventory. The food and agricultural organisation (FAO) (1998) stipulate that a “histosols”

synonymous with organic soil “peat”, if water saturated for long periods must have an organic

C content of at least 12% by weight with no clay present or 18% by weight if at least 60% is

clay or a proportional amount if clay is between 0 and 60 %. If water saturation is never more

than a few day then organic C must be 20% or greater to class as organic. To differentiate

between bogs and fens many authors have attempted to provide a clear distinction, Wheeler

and Proctor (2000) argue that a pH of 5.5 and calcium concentrations provide this, however

Hajek et al. (2006) identify 5 different fens , developing the work of Du Rietz (1954) who split

fens into mesotrophic and mesotrophic, both base their distinction on species indicative of

nutrient status. Gorham (1991) calculates northern latitude peatlands to contain approximately

455 Pg (1 Pg = 1015 g) of carbon (C), however recent calculations of 600 Pg of C of by Yu

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et al. (2012) suggest that this figure could be underestimated. One third of the world’s total

soil C pool, despite only covering 3% of the earth’s surface (346 x 106 hectares) as per

Gorham’s calculation is held in these peatlands. The author measures the C accretion rate as

23 g C m-2 yr-1 for boreal and subarctic peatlands, however Billet et al. (2010) suggest a larger

figure of 35 to 209 g C m-2 yr-1 for UK where the variable maritime climate has allowed 92% of

its peatlands to form as blanket bogs.

A report by the IUCN (2009) suggests that UK peatlands could be sequestering 3 million

tonnes of CO2 per year, however 10 million tonnes are being released to the atmosphere per

year from degraded peatland. The IUCN’s inquiry on peatlands (Bain et al. 2011) state that

80% of the UK’s peatlands had been damaged but peatland restoration offers opportunities

to reduce carbon losses to the atmosphere as CO2. Given the importance of peatlands as

pools of the world’s carbon, a timely review of the mechanisms that allow for vast stores of

peat accumulation and the worlds carbon markets have the potential to fund restoration of

these ecosystems

Decomposition processes in peatlands

Reader & Stewart (1972) estimated leaf litter production of 489 g C m-2 yr-1 and 1750 g C m-2

yr-1 in various Canadian peatlands, whilst Wieder (2001) found 34 - 52 g C m-2 yr-1 entered

long-term C stores in a boreal ombrotrophic bog in Canada. This equates to between 2% and

11% of C captured entering long-term stores as peat. Freeman et al., (2012) states that the

imbalance is due to primary productivity producing senescent vegetation dying faster than it

can be decomposed, decomposition being “the mass loss of organic matter as gas or in

solution, caused by either leaching or consumption by saprophytic organisms” (Laiho 2006).

Decomposition is ultimately controlled by the activity and efficiency of extracellular enzymes

to break down complex high molecular weight hydrocarbons to low molecular weight

hydrocarbons (Freeman et al. 2004). Extracellular enzymes are released by a variety of

bacteria, fungi and plant cells and is the widely accepted mechanism in facilitating breakdown

of detritus for nutrient uptake (Tranvik 1988). Dunn et al., (2014) provides an account of the

roles of various extracellular enzymes and their importance in carbon and nutrient cycling

which is summarised in Table 1. Wetzel (1992) and Appel (1993) suggest that the microbial

metabolism within the reduced peat environment leads to the release and accumulation of

soluble humic (phenolics) and non-phenolic organic acids in the soil organic matter (SOM)

and subsequent inhibition of hydrolase enzyme (hydrolases) activity due to polyphenolic

compounds in the pore water. Wetzel (1992) provides evidence that phenolic compounds

complex with hydrolase enzymes to cause their inactivity by competitive and non-competitive

inhibition.

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Table 1. A summary of the main hydrolase enzymes involved in carbon and nutrient cycling in peatlands.

Hydrolase enzyme Role in peatland nutrient cycling

β-D-Glucosidase Glucosidases degrade plant derived cellulose (C6H10O5), by catalysing the hydrolosis of the glycosidic

linkage between carbohydrates and other molecules, releasing glucose (C6H12O6).

Arylsulphatase Catalyses the removal of sulphate ions from a number of plant derived arylsulphatase esters.

β-D-xylosidase

A hemicellulase involved in the breakdown hemicellulose, which is structurally weaker than cellulose

consisting of sugar monomers including Xylan. More specifically it catalyses the hydrolysis of Xylobiose

(C10H18O9) to Xylose (C5H10O9).

N-acetyl-β-D-glucosaminidase Involved in the breakdown of chitin, a polymer of N-acetyl-β-D-glucosaminidase (NAG) units. In wetland

soils, most nitrogen is bound within Chitin. Therefore the enzyme is important in nitrogen cycling.

Phosphatase Removed a phosphate group from other organic molecules by hydrolosis of phosphoric acid monomers into

phosphate ions and a monomer with a free hydroxyl group.

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Harwood and Parales (1996) suggests that the resonance energy that stabilises the carbon-

carbon bonds in aromatic rings give rise to the recalcitrant nature of phenolic compounds.

There are however a suite of enzymes that can mineralise polyphenolic compounds in the

presence of molecular oxygen (O2). McLatchey and Reddy (1998) provide evidence that

phenol oxidase contains a binding site for aromatic compounds and a distinctly different one

containing copper for molecular O2. Claus (2004) also reveals that phenol oxidases can

mineralise otherwise highly recalcitrant phenolic containing substances such as lignin and

tannins by catalysing the release of reactive oxygen radicals. Zavarzina et al., (2004)

highlight that phenol oxidases are metalloprotein oxidoreductases with varying functional

types. Laccases and tyrosinases polymerise soluble phenolics into insoluble humics and

depolymerise soil humics. Laccases may also oxidise smaller phenolic compounds. Phenol

oxidases are released by micro-organisms such as fungi (Burke & Cairney 2002), bacteria

and actinomycetes (Crawford 1978) and to a lesser extent, certain plants (Gramss et al.

1999).

Pind et al., (1994) suggests that water saturation restricts O2 diffusion within the peat matrix

causing the inhibition of phenol oxidase and subsequent accumulation of phenolic

compounds. Freeman et al., (2001a) highlights that O2 supply is only limiting to phenol

oxidases thus restraining the degradation and oxidation of the inhibitory phenolics. The

importance of this inhibition is highlighted by the fact that any dramatic increase in O2 (resulting

from a lowered water table) could rapidly degrade phenolic compounds causing the inhibitory

effects on the hydrolases to rapidly cease which could result in the release of vast amounts of

carbon dioxide (CO2) to the atmosphere via a dramatic increase in microbial metabolism of

the SOM and plant litter. The authors note, phenolic compounds within the in the peat matrix

represent an “enzymic latch mechanism”, a potent control on decomposition.

Despite this, Toberman et al., (2010) found phenol oxidase activity to decrease with drainage

of a Finnish forested peatland but attributed this to a reduction to co-incident peat acidification

as phenol oxidases have a pH optima of around 7 to 8 (Ruggiero & Radogna 1984). Williams

et al., (2000) reported that in a pH 3.8 Sphagnum dominated peatland phenol oxidase activity

did not change with water table lowering emphasising that the enzyme is sensitive to low pH.

Limpens et al., (2008) suggest that peatlands have a redox potential (i.e. the measure of the

tendency of a compound to acquire electrons) gradient regulated by the depth to water table

which usually fluctuates in the upper peat layer between 5 and 40 cm below the surface,

leaving both an oxic where bacteria respire aerobically and an anoxic layer where anaerobic

respiration occurs. Table 2 shows the order in which final electron acceptors are utilised by

selective micro-organics under the aerobic (positive redox) to anaerobic (negative redox)

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gradient. The authors suggest that primary production provides reduced substances that are

buried as litter or released below ground by vascular roots, creating this gradient to the

atmosphere. The resultant concentration of O2 utilised as the final electron acceptor decreases

with depth through the peat profile (Blodau et al. 2004) as does the relative importance of

aerobic respiration versus heterotrophic respiration, (Moore et al. 2002), rate of litter

decomposition (Chanton et al. 1995) and transport of gases and solutes (Waddington & Roulet

1997).

Wang et al., (1993) suggests that soil redox is the main factor determining the final stage of

anaerobic decomposition, methanogenesis. Under extremely positive redox conditions,

organic matter or CO2 are the only electron acceptors remaining which are reduced to

methane (CH4) by-product by specialised anaerobic archaea called methanogens. In acidic

environments, such as ombrotrophic bogs where Hydrogen ions are high, this happens

through the hydrogenotrophic pathway (CO2 +H2 → 2H20 + CH4), alternatively the acetotrophic

pathway (CH3COOH → CO2 + CH4) or the oxidation of humic derived acetate through

fermentation is utilised.

CH4 by products have the potential to be atmosphere as gas bubbles via ebullition (Kellner et

al. 2004) or diffuse upwards through the anoxic layer where it can be oxidised by facultative

CH4 oxidising bacteria (methanotrophs) under aerobic conditions. According to Op den Camp

et al., (2009) Gammaproteobacteria, Alphaproteobacteria and Verrucomicrobia can utilise

methane CH4 as an energy source via the reaction; CH4 + 2O2 → CO2 + 2H2O (Whalen 2005).

CH4 can also enter the atmosphere from the soil through the aerenchytamous tissues in plants

that pump oxygen into the soil to fuel the electron acceptor pool for aerobic respiraiton as an

adaption to anaerobiosis, CH4 can bypass this aerobic zone travelling up the aerenchyma

tissue entering the atmosphere via diffusion or pressurised internal gas flow via the older plant

leaves (Brix et al. 1992).

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Element Terminal electron acceptor Reduced form Redox potential (mV)

(measure of electron acceptors that

remain in the peatland soil)

Oxygen O2 (oxygen) H2O (water) +250

Nitrogen NO3- (nitrate) NO2

- (nitrite)

↓

N2O or N2

(nitrous oxide,

nitrogen)

250

Manganes

e

Mn4+ (manganic) Mn2+ (manganous) 225

Iron Fe3+ (ferric) Fe2+ (ferrous) +100 to -100

Sulphur SO4 (sulphate) S (sulfide) -100 to -200

Carbon CO2 (carbon dioxide) CH4 (methane) Below 250

Table 2. Redox potential in peatland soils

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Peatland carbon storage and climate regulation

As of 2011 atmospheric concentrations of CO2 were 391 parts per million (ppm) and 1809 ppm

for CH4, exceeding 1750 levels by 40% and 150% respectively due to increased anthropogenic

emissions. Over the period 1880 to 2012 an average global temperature rise of 0.85°C was

recorded. It is predicted that the mean air temperature could increase by 4.8°C in 2100

compared to 2005 levels with CO2 emissions reaching 421 to 936 ppm within the same

timeframe (IPCC 2013). Oechel et al., (1993) suggest of the 750 Pg of carbon currently held

as atmospheric CO2, only 40% more C than is stored in peatlands.

Gorham (1991) highlights that peatlands are considered “unusual” in greenhouse gas (GHG)

scenarios in that they sequester the most important GHG contributor in CO2 during

photosynthesis, while on the other hand they emit can emit vast quantities both CO2 and CH4

which is the second most important GHG. The author realised that the balance between CO2

fixation and release and the balance between CH4 production and consumption should be a

major research topic in light of global warming. Gorham also pointed out that feedbacks such

as increased rates of photosynthesis, decomposition and CH4 emissions are expected under

climatic warming, however, longer growing seasons may be overshadowed by increased

frequencies of summer drought when evapotranspiration exceeds precipitation leading to

lower water-tables. This is significant as aerobic conditions can prevail in a greater proportion

of the peat, leading to rapid oxidation of organic matter via the more efficient aerobic

respiration as opposed to the relatively inefficient anaerobic respiration within the deeper

humified peat.

Loisel et al., (2012) suggest that increased photosythetically active radiation increases

Sphagnum growth rates inducing a negative feedback effect to global warming. However an

increase in CO2 emission found after water table draw-down by various researchers (Freeman

et al. 1993b; Fenner et al. 2005) simulating the potential effects of increased drought

frequency, potentially overshadows the negative feedback to climate change through

increased growth rates. Fenner et al., (2007) suggest that elevated CO2 concentrations can

change vegetation composition of a peatland to favour aerenchymatous Juncus effusus over

Sphagnum species, which is shown to increase CH4 emissions providing a path of least

resistance to the atmosphere (Thomas et al. 1996), although recent studies suggest that

functional group may be more indicative of CH4 emissions (Gray et al. 2013).

Freeman et al., (2004) suggest that under anaerobic conditions the fate of SOM is not that of

rapid oxidation to CO2 via aerobic microbial respiration but an accumulation of dissolved

organic carbon (DOC; organic matter at <0.45 µm) in peat pore water as a result of slow

decomposition due to high phenolic concentrations leading enzymic inhibition. Freeman et al.,

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(2001b) found increasing soil temperature, to simulate warming, stimulated a subsequent

increase phenol oxidase activity, DOC and phenolic release. Fenner et al., (2005) observed

lower DOC and phenolics during drought and a greater diversity and abundance of phenolic

catabolising bacteria but propose that upon re-hydration after severe drought DOC and

phenolics are mobilised and exported from the peat catchment to the receiving waters. Cole

& Caraco (2001) suggest that respiration in a riverine system in North America greatly exceed

the autogenic primary production suggesting large organic inputs from the catchment with the

potential for a positive feedback to elevated CO2 and warming.

Peatland C is highly sensitive, not only too disturbance through climatic warming but any

events that lower the water table. Drainage for forestry has been the most extensive practice

applied to northern latitude peatlands (Toberman et al. 2010), however peat extraction for

energy and horticulture, wind farm development (Waldron et al. 2009) pre-scribed burning,

fertilization, liming and overgrazing are also considered significantly deleterious to a peatlands

carbon storage capacity (Moore 2002). Degraded peatlands are responsible for 25% of global

CO2 emissions from the land-use, land use change and forestry sector (LULUCF) and 75% of

GHG emissions from agriculture in the EU (Joosten et al. 2012)

Freeman et al., (2012) suggests that C sequestration could provide an answer to rising CO2

and associated global warming as not only does it address these symptoms but curbs the

rising CO2 emissions that cause it the problem. The authors states that afforestation has long

been recognised as a CO2 abatement tool but any primary producing ecosystem has the

capacity to store carbon. Dean & Gorham (1998) identify peatlands as the most efficient stores

of C per unit area when compared to various terrestrial and oceanic systems making them an

ideal candidate for C mitigation.

Carbon Stewardship

Carbon sequestration can be defined as the persistent increase in C storage in soil, plant

material or in the sea (Hutchinson et al, 2007) and carbon stewardship is the management of

land to maximise carbon sequestration (Dunn & Freeman 2011).

There is significant evidence that restoration interventions can reverse the CO2 source

function that peatland degradation can trigger, acting as a brake on global warming (Bain et

al. 2011), however Baird et al., (2009) found that restoration does not necessarily return a

peatland to a carbon sink due to initial CH4 increases counteracting the carbon retention. Table

1 highlights the variability of gaseous C flux and the effect of CH4 and aqueous DOC release

when presenting a peatland C balance. Waddington & Roulet (2000) found that although both

years produced net CO2 uptake, when considering net ecosystem C balance (NECB), there

was a net loss in 1993. However when considering climatic, units are converted to CO2e (CO2

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equivalent) which compares the radiative forcing of a tonne of a GHG over a given time period

(e.g. 100 years used by the IPCC) to a tonne of CO2, whereby CH4 is 25 times more potent

(Forster et al. 2007). The site therefore contributes to global warming given the level high level

of CH4 emissions. (Dinsmore et al. 2010) reported a NECB of a peatland used or low intensity

sheep grazing in Scotland as sequestration in both years and due to the low levels of CH4,

GWP resulted in cooling during both years. The differences arise due to both spatial and

temporal variability between and within sites (Bubier et al. 2005; Dinsmore et al. 2009). Bain

et al., (2011) highlight that over the long term that blanket bogs can revert to sink in terms of

C and GWP, 10 to 20 years after restoration with saving of 2.5 tonnes of CO2e per hectare

per year (Figure 1).

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Table 3. Comparisons of the NECB and CO2e of two peatland sites in two subsequent years.

Site Year C m-2 yr-1 Reference In CO2e (accounting for GWP of GHG’s

CO2 CH4 DOC NECB CO2 CH4 Net GHG

Stor Amyran 1992 -10.3 4.1 4.2 -2 Waddington & Roulet, (2000) -37.7 136.9 99.2

Stor Amyran 1993 -3 3.9 6.7 7.6 Waddington & Roulet, (2000) -11 130.2 119.2

Auchencorth Moss 2006 -136 0.3 18.6 -117.1 Dinsmore et al., (2010) -498.3 9.9 -448.5

Auchencorth Moss 2007 -93.5 0.4 32.2 -60.9 Dinsmore et al., (2010) -342.6 12.3 -330.3

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Figure 1. Reproduced from Bain et al., (2011) showing the GWP and net C based GHG fluxes

of blanket bog along the spectrum of natural, drained and restored states.

Bussel et al., (2010) highlighted that no studies have looked at the cumulative GWP of all

GHG’s including nitrous oxide (N20) emitted by peatlands after rewetting. N20 emissions are

often under-represented in trace gas flux budgets from peatlands, Forster et al., (2007)

calculate a GWP of 298 times that of CO2, and however large quantities are only emitted by

peatlands affected by anthropogenic input of N via fertiliser or atmospheric deposition

(Couwenberg 2009a).

Freeman et al., (2012) highlights two ways the “enzymic latch” could be used to increase the

proportion of photosythetically captured C entering the long-term C stores of peatlands. The

authors firstly point out that increasing the expression of the gene in Sphagnum responsible

for phenolic biosynthesis. The bryophyte genus Sphagnum is responsible for over half of the

world’s peat (Rydin & Jeglum 2006), representing 10 to 15% of the entire terrestrial C stock

(Clymo & Hayward 1982). Kuhry & Vitt (1996) attribute the decay resistance of their tissues to

the high polyphenol content in the phenylpropanoid transphagnum acid (TSA) which under O2

restriction in the saturated zone further avoids decay due to the enzyme inhibition processes

explained by the “enzymic latch mechanism” (Freeman et al. 2001a) and low pH (Criquet et

al. 1999) inhibiting on phenol oxidase.

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The second method is the physicochemical enhancement of “enzymic latch” mediated C

retention. Peatlands have been subject to many restoration attempts this, the most common

being raising the water table or “re-wetting” and reducing water flow through erosion gulley

and drainage ditch blocking. These techniques significantly reduced DOC and particulate

organic carbon (POC) export (Holden et al. 2007; Worrall et al. 2007; Wilson et al. 2011). POC

is often overlooked in terms of NECB, Pawson et al., (2008) described an 80% POC

constitution of fluvial C flux in an eroding peat catchment in the South Pennines.

Waddington et al., (2010) used techniques such as ditch blocking, pool creation and dyking to

retain snowmelt along with Sphagnum and straw mulch spreading to retain moisture and

fertilisation facilitate vascular plant growth to act as a companion species in stabilising bare

peat for Sphagnum growth. Restoration attempts converted the site in Minnesota, USA from

source of 245 g C m-2 during the growing season to a sink of 20 g C m-2 two years post

restoration and predict a NECB sequestration 6 – 10 years post restoration. Peacock et al.,

(2013) discovered that by damming ditches in a blanket bog in Wales, deeper pools favoured

Sphagnum whilst the shallower pools favoured vascular Eriophorum cover, whilst Cooper et

al., (2014) observed increases in CH4 emissions following ditch blocking but stated that if

Sphagnum and other non-aerenchytamous plants can quickly establish emissions would be

minimised.

Freeman et al., (2012) suggests geoengineering intervention methods through manipulation

of the “enzymic latch” such as acidification of peatlands to inhibit phenol oxidase activity. The

authors also suggest addition of supplementary phenolics to supress microbial metabolism,

enzyme synthesis and edaphic enzyme activity or burying wood in peatlands to halt decay

could help to curb emissions although there is a paucity of research in applying these methods.

Ostle et al., (2009) states that decisions on land management and policy in the UK must

consider the resilience of carbon sequestration in their overall assessments when considering

maximising ecosystem services as peatlands play a crucial role in the mitigation of climate

change impacts. The authors also highlight that a peatlands propensity for C storage and

release make them the concern of international policies agenda’s for mitigation and abatement

of GHG’s.

Driving forces and financing peatland restoration

Worrall et al., (2009) discovered that when compared to the price of C, UK peatland restoration

was profitable and highlighted the potential for the creation a carbon credits incorporated into

the LULUCF category of the National Greenhouse Gas Inventory (GHGI) instead of the

emissions market. The authors also highlighted that no single management scheme is best

for all peatlands but a targeted approach is needed and that profitability of peatland in carbon

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offsetting schemes is heavily dependent on the price of carbon. Moran et al., (2011)

questioned the GHG abatement potential of restoring peatlands although, acknowledges its

cost effectiveness. However, Moxey (2011; 2014) recognises peatland restoration as an

important climate abatement tool, when compared to other more industry based abatement

measures.

Reed et al., (2013a) reporting for Defra on the possibility of a UK peatland carbon code state

that several potential options for funding peatland restoration and interest has grown in the

potential to stimulate private investment for peatland restoration through carbon markets.

Carbon markets differ in that they can be;

i) Privately for publicly funded

ii) Pay solely for carbon and climate mitigation benefits or pay for a wider range of ecosystem

services derived from restoration

iii) They are international or regional in scope

Compliance vs voluntary markets

The compliance carbon market (the principal one being the Kyoto protocol), is an example of

an international, part public, part private funded mechanism. Although Benwell (2009) argues

that the distinction between the two types are often blurred and the term voluntary is

misleading as no country was forced into ratifying the Kyoto protocol and have the option to

withdraw, in the same way that companies that volunteer to offset their carbon emissions can

be enforced by law and calls for the hybridisation and the divide between the two abandoned.

Peatlands and the compliance market

The United Nations Framework Convention on Climate Change (UNFCCC) is the international

process that provides a regulatory framework for action to reduce and set mandatory levels

for GHG emissions. The Kyoto protocol is its UNFCCC’s regulatory instrument tasked to

achieve the principal objective of the convention;

“Stabilisation of greenhouse gas (GHG) concentrations in the atmosphere at a level

that would prevent dangerous anthropogenic interface with climate system. Such a level

should be achieved within a time-frame sufficient to allow ecosystems to adapt naturally to

climate change, to ensure that food production is not threatened and to enable economic

development to proceed in a sustainable manner” (Secretary of State for Foreign and

Commonwealth 1993).

Dunn & Freeman (2011) provide a detailed review on the subject of peatlands and the Kyoto

protocol in the first commitment period. In the second commitment period (2013 – 2020)

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signatories agreed to 18% reduction in Annex A GHG’s against 1990 levels. Emission

reduction targets are set based on assigned amount units (AAU) each of which represents the

allowance to emit one metric tonne (CO2e). To help as party meet its emission reduction

targets, the protocol outlines market based mechanisms of which the clean development

mechanism (CDM; Article 12), REDD+ (Reducing emissions from deforestation and forest

degradation) and Joint implementation (JI; Article 6). CDM does not currently allow peatland

restoration, however peatswamps could feature as they are limited to afforestation and

reforestation. Unless future rules allow, JI does cover peatland re-wetting as the certificates

they produce are for enhancing removals rather than reducing emissions which peatland

restoration cannot guarantee in the short-term. REDD+ is another Kyoto mechanism whereby

opportunities are offered for developing countries to take financial incentives for carbon

sequestration projects. The Indonesian Ministry of Forestry are testing this global market with

peatland restoration supported by developed countries and the World Bank. Certified

emissions reduction units are then be tradable on compliance markets like CDM helping to

buffer against the fluctuation in the price of carbon compared to competing land uses such as

oil palm plantation (Butler et al. 2009). However, current Defra GHG reporting guidelines

forbid UK companies from meeting their Kyoto obligations from land activities and there are

currently no buffers against uncertainty of emission reduction so may work against a country

if not managed properly.

Article 1(a) (IV) of the protocol explicitly states modes via which these reductions could be

achieve focusing on renewable energies whilst Article 3, paragraph 3 states that;

“net changes in GHG emissions by sources and removal by sinks resulting from direct human-

induced land-use change and forestry activities, limited to afforestation, reforestation and

deforestation since 1990, measured as verifiable changes in carbon stocks in each

commitment period, shall be used to meet the commitment under this Article of each party

included in Annex 1”.

However, a decision at COP-17 in Cancun 2011, decided that all forest management will be

mandatory. Article 3, paragraph 4, allows for accounting for cropland and grazing land

management on a voluntary basis. Joosten et al., (2012) highlight that grazing, cropland or

forestry management is also applicable to peatlands and the decision to include voluntarily

“wetland drainage and re-wetting” (WDR) under Article 3.4 in the second commitment period

will be largely redundant to peatlands in the EU as 90% of peatlands fall under the other

categories, although the decision gives peatland restoration legitimacy and as a climate

mitigation activity. Under WDR, although not exclusive to peatlands should include emissions

22

reductions from any peatland restoration since 1990, accompanied by emissions from

peatland drainage.

Bain et al., (2011) report that national GHGI methodologies under Kyoto did not properly

address the potential GHG abatement from to peatland restoration, suggesting that due to

climatic and seasonal variations in peatlands, developing a measurable, reportable and

verifiable (MRV) protocol for monitoring provided difficulties. However, Joosten & Couwenberg

(2009) state that affordable methodologies are now available which opens the door for

mandatory peatland GHG accounting. Such policies may include adding such proxies as

water-level and vegetation to GHG flux models such as the chamber or eddy co-variance

method the models are based on. If Annex 1 parties can include the negative flux from existing

peatlands and emission reduction from restoring peatlands under LULUCF to their national

GHGI, it could help reduce the amount of emission cuts and therefore, the investment they

have to make from other sectors such as industry as well as helping to restore peatlands

(Freeman et al. 2012).

Peatlands and the Voluntary market

Non-mandatory carbon trading also occurs through the voluntary carbon market, aimed at

businesses and individuals looking to offset their GHG emissions. Through this mechanism

verified emission reduction units based on CO2e as with the mandatory market are verified by

a third party and cannot be used for Kyoto derived emissions accounting.

Benwell (2009) states that “the difference in the emission reduction targets mandated by

international, regional and national law and the cuts that will eventually need to be made in

order to stabilise the climate can be seen as “negative spaces” for emissions trading which

have started to be filled by the voluntary market and the voluntary components of the

mandatory markets”. Such “negative spaces” have been narrowed by a surge in voluntary

restoration projects designed to offset CO2 emissions. The author states following reasons

why private companies are agreeable to sponsoring projects voluntarily;

i) A genuine wish to reduce GHG emissions,

ii) A marketing opportunities for the carbon neutral or green image,

iii) Shareholder or institutional pressure,

iv) Threat of future climate litigation,

v) Influence over future regulations.

23

Market schemes for carbon sequestration through peatland restoration

Since degraded peatlands are responsible for large GHG emissions, they pose a high risk to

society, albeit one that falls outside of compliance markets. However, some private businesses

are already prepared to pay for ecosystem services or the “benefits that people obtain from

ecosystems” (UK NEA) through informal transactions (Chris Miller, Lancashire Wildlife trust;

pers. comm.) motivated by corporate social responsibility (CSR). Paying for ecosystem

services (PES) forms a new way at approaching ecological restoration with a healthy

biodiversity largely underpinning these services (Natural Environment White paper)

If significant peatland restoration is to be viable for any national GHGI’s, carbon offsetting

schemes or carbon stewardship projects the results be measurable, results based and

verifiable (MRV) so they can be quantified and reported in a consistent and transparent way.

Joosten et al., (2012) highlight that in terms of practicality the voluntary market is the only

market capable of providing finance to peatland restoration projects.

Peatland Carbon Code

In the UK, an accredited voluntary carbon market, the Peatland Carbon Code (PCC) was

established by the Ecosystem Markets Taskforce after the 2011 natural environment white

paper emphasised the creation of new markets to pay for ecosystem services (PES)

The PCC is designed to facilitate business sponsorship of peatland restoration to deliver the

principal ecosystem service benefit of climate mitigation via carbon stewardship, or managing

for carbon sequestration optimisation in an open, credible and verifiable format. This code will

enable peatland restoration economically viable ahead of competing land-uses (Reed et al.

2013a). However as Reed et al., (2013b) suggest peatland restoration can provide a win:win

as it provides multiple ecosystem services such as biodiversity gain and improved water

quality and run-off attenuation on land that is already highly degraded and may not support

alternative land use such grouse management or sheep grazing in the future due to policy or

practicality but if future government policy favours food provision over ecological restoration

there would be negative consequences for carbon sequestration, water quality and

biodiversity.

The pilot phase however only covers blanket bog ecosystem to offer a better opportunity for

restoration benefits as they make up 95% of UK peatlands. To meet the requirements of the

pilot phase peat depth must be at least 50 cm but may lack peat forming vegetation (inactive)

or active where peat is currently forming and accumulating or supports peat forming vegetation

but is not currently laying down peat. Peat must have been lost through human activities such

as peat extraction, human-induced pear slides, wildfire, sever erosion exacerbated by

24

overgrazing, pollution, burning or agricultural wastage of peat but can easily be safeguarded

to active building peat bog status and eligible. Eligible restoration activities include “re-wetting

or re-wetting in combination with other land management change required to re-establish

species that are normally peat forming, for example Sphagnum moss”

The code provides guidance for quantification of carbon sequestration in a way that gives

potential sponsors the confidence that enables them to report green saving to stakeholders,

thus allowing peatland restoration on a significant scale (Bain et al. 2011). This is achieved

through independent quality assurance via third party verification by UK accreditation service

(UKAS) accredited auditors or “certification bodies” to assess claims on climate and other

benefits. Registries are publicly viewable and projects are validation to ensure land is eligible

UKAS accredited auditors or “certification bodies” to assess claims on climate and other

benefits.

Aimed at the corporate social responsibility (CSR) market, the pilot phase does not allow the

generation of “carbon credits” for trade on international carbon markets or carbon offsetting

schemes. Assuming it is possible to trade carbon from peatland restoration in the future,

additional verification to international standards (e.g. Verified Carbon Standard; VCS) which

is currently the largest benchmark standard for voluntary carbon trading from land-use which

can include peatlands. Emission reductions cannot be used for corporate carbon accounting,

unless future Defra GHG reporting guidelines allow (IUCN 2013)

Although the Kyoto protocol created an international market for carbon under UNFCC,

legislative changes at EU and country level for these markets to be used to support peatland

restoration in Europe (PCC) although LULUCF changes to add WDR increases the potential

for carbon stewardship involving peatlands in the future Dunn & Freeman (2011). Until then

the voluntary market is leading the way in developing methods for financing peatland

restoration through carbon credit schemes and Table 3 compares the major market schemes

for generating carbon credits via peatlands restoration.

25

Table 4. Comparison of the main differences in the major market schemes for peatland restoration pertinent to Europe.

Name and

scope of

market

scheme

Peatland Carbon Code (PCC)

MoorFutures Standard

Verified Carbon Standard (VCS)

National Regional International

Brief

description

Located in the UK, the PCC is a scheme

designed to facilitate business sponsorship

of peatland restoration motivated by

corporate social responsibility. It provides

an open, credible and verifiable basis for

good peatland restoration practice and is

currently in its pilot phase

Located in the Mecklenburg-Vorpommern and

Brandenburg regions of Germany, the

MoorFutures Standard is a scheme designed

to facilitate business sponsorship of peatland

restoration motivated by carbon offsetting by

provides by generating Moorfutures credits

(emission reduction certificates) that

companies are invited to purchase

(MoorFutures credit = 1t CO2e. saved)

Provides an international service for

corporate sponsorship and quantification

of emission reduction and removals from

peatland restoration and other land uses

by generating verified carbon units (VSC

PRC credits) under the project category

“Peatland Rewetting and Conservation”

(PRC) which allows for two types of

peatland restoration “Rewetting of

drained peatlands” (RDP) and

Conservation of partially drained and

undrained peatlands” (CUPP) the later

more pertinent to peatswamps.

26

Approach to

additionally of

land and GHG

reductions

Land additionally

Land is additional if…

There are no legal orders for restoration,

land may have conservation objectives

but insufficient funds to achieve them.

Sponsorship covers at least 15% of costs.

Other land uses are more financially

viable and other barriers are overcome.

MoorFutures leverage on credibility of the

State‘s regulatory system and the high

capacity of university and administration to

develop, manage and monitor the holding of

MoorFuture credits.

GHG emissions additionally

VCS methodology “Tool for the

demonstration and assessment of

additionally” in VCS AFOLU project

activities.

The amount of emission reductions

generated is calculated as the difference

between project and baseline emissions

Approach to

MRV

Baseline assessment

IPCC Tier 2 methodology or Tier 3

methodology

Baseline shall be based on a continuation

of the current land use in the absence of

the project and is calculated at the start

using

Either 5 years direct GHG measurements using eddy co-variance or closed chamber methods or by using the GEST (GHG Emission Site Type) proxy vegetation method of (Couwenberg et al. 2011). This approach uses a matrix system that classifies vegetation according to their water-table and the presence of aerenchymatous species, whilst also considering nutrient status, pH, and land-use. Original condition of vegetation cover, soil type (inc peat depth and estimated C content) are described

Baseline assessment

Forward looking baseline (using GEST)

whereby the results of the “with project”

scenario are compared with the reference

scenario that would have occurred without

implementation of the project

Baseline assessment

Baseline shall be based on a

continuation of the current land use in

the absence of the project and reviewed

every 10 years

For a forested peatland use GEST and

C stock changes in tree biomass to

calculate average annual net CO2e

emissions

For a non-forested peatland use GEST

to calculate average annual emissions.

Water-levels can be used on bare peat

Otherwise GEST approach is used.

CH4 can be neglected in baseline as

seen as negligible from drained

peatlands.

27

Based on ecosystem function and GHG

flux pathways (less on water-tables which

are less useful for blanket bogs has led to

development of Standard Emission

Values for different blanket bog states

(Birnie & Smyth, 2013)

Monitoring protocol

1 -5 years after validation and thereafter

at periods of 10 years via tracking GHG

flux using vegetation proxies (GEST).

Monitoring protocol

Water levels alone or GEST

No emission reduction can be claimed

beyond peat depletion time (the time at

which no intervention would have led to

complete oxidation of all peat and thus

no GHG emissions)

Approach to

permanence

30-100 year project duration

Risk assessment and covenants in

property sale. GHG buffer credits to buffer

against uncertainty in fluxes

30 – 50 year project duration

Water-table registered in official land use

plans and binding should land change hands.

The maximum quantity of GHG emission

reductions that may be claimed by a

project is limited to the difference in peat

C stock between the project and the

baseline scenario after 100 years.

28

Approach to

leakage and

double

counting

Double counting

Publicly viewable register of UK peatland

code project in later phases. Registry to

act as a meeting platform for buyers and

sellers.

Leakage

The land manager shall give confirmation

of whether he plans to change or intensify

land use outside the project area as a

result of the project. Create leakage

management zones, with the project area

where other ecosystem goods and

services can be provided.

Double counting

All projects are registered in a ledger publicly

viewable at www.moorfutures.de so emission

savings are not inadvertently sold twice.

Leakage

Follows VCS criteria

Double counting

Requires retirement or cancellation of an

equivalent number of credits from the

compliance market (i.e. Kyoto) in

countries where VCS and Kyoto are both

operating.

Leakage

Market leakage does not occur due to

methodological approaches as pre-

project land use must have been

forestry, peat extraction sites

(abandoned within the last two years

prior to project start date) or agriculture

where crop production has been

abandoned with the last two years prior

to start date. Ecological leakage is

minimized by ensuring hydrological

connectivity between project site and

adjacent area’s is kept to a minimum

through the use of buffer strips or an

impermeable dam (VCS methodology

v3, 2011)

Approach to C

trading and

corporate C

accounting

Credits not tradable and not used for

corporate C accounting under pilot code

Not traded as they are voluntary certificates

designed for long-term investments

Certificates are used in balance carbon

budgets in corporate C accounts.

Criteria for peatland carbon credits is

regionally dependent.

Biodiversity

New for Moorfutures standard version 2

Biodiversity

This scheme deals with carbon only.

Other international schemes such as the

climate, community and biodiversity

29

Approach to

co-benefits

Market research found that investors were

content with a narrative of biodiversity

based published literature to be included

in the environmental statement.

Standard: Biodiversity evaluation site types

(BEST) approach

Premium: Indicator species / groups

approach.

Water quality improvement

Standard: N emission site types (NEST)

approach

Premium: N & P modelling.

Flood retention

If part of planning, modelling retention volume

and reduction of peak run-off.

Ground water storage

If part of planning, modelling available water

volume and levels

Local cooling

Standard: Evapotranspiration Energy

Site Types (EEST) approach

Premium: modelling

standard (CCB) are usually used in

tandem with VCS (Tanneberger &

Witchmann 2011)

30

Future research recommendations

This review concludes that more research to alleviate some problems encountered

with restoring and financing peatland restoration. Research should focus on the

following areas;

Minimising the effects of CH4 after peatland re-wetting.

Developing more regional carbon credit schemes that are inexpensive to run,

MRV-able, and low risk to both donors and receivers with possible public

sector match funding.

Developing methodologies to make peatland restoration cheaply MRV-able

Research in peatland geoengineering to increase the percentage of

photosythetically captured carbon entering long-term C stores

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36

1.1 List of Abbreviations

DOC Dissolved organic carbon

GHG Greenhouse gas

NWWT North Wales Wildlife Trust

PCC Peatland Carbon Code

FAO Food and Agriculture Organisation

LOI Loss on ignition

SOM Soil organic matter

FID Flame ionisation detector

SAC Special Area of Conservation

UK United Kingdom

NEE Net ecosystem exchange

NVC National Vegetation Classification

MUF 4-methylumbelliferone

ʟ-DOPA ʟ-3,4-dihydroxy phenylalanine

ANOVA Analysis of variance

37

1.2 Introduction

Peatlands are freshwater inland wetland ecosystems with organic soils (Mitsch & Gosselink

2011) of at least 30 cm (Limpens et al. 2008). They depend on water as the single most import

factor for their persistence and stability (Freeman et al. 1998b). Despite, covering less than

3% of the earth’s surface (Gorham 1991), they store the carbon equivalent of two thirds of the

entire atmospheric CO2 pool (IPCC 2007) as peat which is defined as “the dead or decaying

plant remains that have formed in situ under waterlogged conditions” (Ramsar 2002). Phenols

are compounds which consist of a benzene ring with a directly attached hydroxyl group,

compounds containing phenols are known as aromatic or phenolic compounds or “phenolics”.

The vast majority of naturally occurring phenolics are derived from lignin, which forms up to

50-60 % of the dry peat biomass (Clymo 1983). Furthermore, soluble phenolic compounds

have been recognised a potent inhibitors of microbial activity and extracellular hydrolase

enzymes (Wetzel 1992; Pind et al. 1994; Freeman et al. 2001a; Fenner & Freeman 2011).

The existence of peat is attributed to low rates of decomposition relative to primary production

(Freeman et al. 2012) and the incomplete decomposition of lignin in the water saturated

peatland environment leads to the accumulation of these phenolic (humic) acids (Fenner et

al. 2005a).

Limpens et al., (2008) suggest that peatlands have a redox potential (i.e. the measure of the

tendency of a compound to acquire electrons) gradient regulated by the depth to water table,

which usually fluctuates between 5 and 40 cm below ground level. This saturation gradient

leaves a deep anoxic and shallow oxic peat layer. In the deep anoxic layer, inefficient

fermentative microbial metabolism by methanotrophic archaea (methanogens) using

hydrogen or acetate as a terminal electron acceptor, results in methane (CH4) production and

the accumulation of aqueous dissolved organic carbon (DOC) instead of continual oxidation

of organic matter to CO2 as experienced in the shallow oxic layer (Gammelgaard et al. 1992).

However, in the oxic layer, obligate aerobic methanotrophic bacteria (methanotrophs) are

capable of oxidising the CH4 produced as it diffuses upwards to the aerobic layer.

Aerenchymatous plants however, can prevent this oxidation by “shunting” CH4 to the

atmosphere through their aerenchyma tissues with oxygen travelling down in the opposite

direction to aerate the rhizosphere, as an adaptation to living in constant anaerobiosis (Brix et

al. 1992). Protective gas bubbles can also prevent the oxidation of CH4, which are then

released at the peat surface as ebullitions (Baird et al. 2004).

A major rate limiting step to the decomposition of peat is the cleavage of phenolic compounds

by phenol oxidase, an edaphic oxidoreductase family of extracellular enzymes (Zavarzina et

al. 2004) which require bimolecular oxygen (O2) (McLatchey & Reddy 1998). Phenol oxidases

38

are released by micro-organisms such as fungi (Burke & Cairney 2002), bacteria and

actinomycetes (Crawford 1978) and to a lesser extent, plants (Gramss et al. 1999) and

catalyse various stages of phenolic decomposition (Toberman et al. 2010). pH changes can

greatly affect phenol oxidase activity which operates less efficiently in acidic environments

(Pind et al. 1994). As depth to the saturated layer (i.e. the water table) increases, rapid gas

diffusion leads to elevated O2 in the unsaturated (oxic) zone, releasing phenol oxidase from

its O2 constraints. The resultant degradation of phenolic compounds leads to a

biogeochemical cascade whereby hydrolase enzymes, released from phenolic inhibition can

efficiently decompose and mineralise non-phenolic compounds such as cellulose and

hemicellulose resulting in a gaseous CO2 end product. Freeman et al., (2001a) termed this

process an “enzymic latch” on decomposition as O2 constraints on phenol oxidase allow the

build-up phenolics preventing the re-release of 500 Gt of carbon (Yu 2012) back to the

atmosphere as CO2 thus stabilising the peatland carbon stock. The DOC produced under

anaerobic conditions has a 50% soluble phenolic (inhibitor) constitution (Wetzel 1992; Fenner

et al. 2001; Toberman et al. 2008; Peacock et al. 2013) thus making it stimulatory to its own

accumulation (Fenner et al. 2009).

By adding additional phenolic compounds to a peatland, both hydrolase and phenol oxidase

activities are potentially curbed by direct inhibition of hydrolase enzymes leading to a

suppression of general microbial community as a reduction in organic matter breakdown leads

to less available mineral nutrients and labile carbon substrates (DOC) for microbial

metabolism. This is important as DOC can become quickly decomposed or photolysed to CO2

with increased O2 and light attenuation in receiving waters outside the peatland environment.

DOC can also has deleterious effects on drinking water quality, aesthetically unappealing,

water companies remove DOC in an expensive process which leaves a risk of carcinogenic

trihalomethane formation with chlorine used in the purification process if the DOC is not fully

removed (Fenner et al. 2009).

The biogeochemical cascade described above leads to a reduction of biogenic trace gas

emissions, the end-products of microbial metabolism. The CO2 end-product of aerobic

decomposition and CH4 and N2O, the end-products of anaerobic decomposition are important

trace gases (Greenhouse gases; GHG). Therefore, the addition of supplementary phenolics

can reduce the strain on global warming as proposed by Freeman et al., (2012). Increased

atmospheric GHG concentrations in the atmosphere, responsible for radiative forcing could

lead to negative feedbacks loops to global warming such as increased evapotranspiration

leading to increased depth to water table (Gorham 1991).

39

With this said, peatlands can act both as a source or sink of GHG depending upon climate,

litter composition and nature of the microbial communities (Limpens et al. 2008; Kayranli et al.

2010). This delicate balance has been influenced by human intervention since the 16th century

(Tanneberger & Witchmann 2011) by practices such as drainage, with the aim of lowing the

water table for agricultural production. Since then, peatlands have been drained for numerous

purposes such as forestry (Toberman et al. 2010), peat extraction and urbanisation (Joosten

et al. 2002). Thus, drained peatlands can thus act as a CO2 source and pristine peatlands

generally act as a CO2 sink with the opposite for CH4. Nitrous oxide (N2O), a potent GHG and

by-product of anaerobic denitrification is often underrepresented in peatland GHG budgets,

although it is less important in sites with a poor nutrient status (Couwenberg 2009a). Although

CH4 is 23 times more potent GHG than CO2 over 100 years (Forster et al. 2007), it is generally

accepted that the CO2 sequestration capacity of pristine peatland more than offsets the

detrimental climate impacts of increased CH4 emissions (Couwenberg 2009a). Restoring

degraded peatlands back to a CO2 sink function by drain blocking can thus act as a “brake” on

global warming with a positive feedback to “global cooling” (Bain et al. 2011). Although initial

CH4 increases (Wilson et al. 2009) can counteract any benefit from CO2 sequestration, the

long-term benefits of re-wetting are that of a return to a net carbon sink or at least a lesser

source ,with research focussing on efforts to lessen the impact of CH4, such as favouring the

re-establishment of Sphagnum over aerenchymatous species on blanket bogs (Cooper et al.

2014; Peacock et al. 2014). Potential reductions in DOC (Wilson et al. 2011) are also a benefit

but research in this area remains scant and conflicting (Fenner et al. 2009; Fenner et al. 2011;

Peacock et al. 2014).

Climate is the most important determinant of the distribution and character of peatlands (Clark

et al. 2010; Loisel et al. 2012). Anthropogenic global warming as a result of increased GHG

emissions, including the contribution of the degraded peatlands, can exert a strong influence

on a peatlands’ unique ecosystem services, due to increased summer drought frequency and

changes in rainfall patterns (Mitchell & Warrilow 1987). Peatlands are therefore important

ecosystems not only for their carbon storage capacity (Laiho 2006) but for both GHG (Kimmel

& Mander 2010) and water quality mitigation as well as numerous other ecosystem services

(Zedler & Kercher 2005).

The appeal of peatlands as carbon and climate mitigation systems has received recent

attention with the upsurge of voluntary carbon credit schemes (IUCN 2013) as well as a

voluntary reporting activity under the Kyoto protocol (Joosten et al. 2012). In the United

Kingdom (UK), Worrall et al., (2009) highlights that with targeted management practices to

maximise carbon storage, the costs involved in peatland restoration per unit of carbon stored

are sufficiently low to be profitably financed by carbon offsetting projects and funded by the

40

carbon market. It is the intention of this project, to investigate whether supplementary

phenolics can increase carbon storage, potentially sparking its appeal as a restoration

measure used either with re-wetting or stand alone, with the ability attract a premium “carbon

credit” on the carbon market for the additional CO2 emission reductions. Therefore, the aim of

this study is to ascertain whether the addition of supplementary phenolics to a fen as a

potential geoengineering (climate manipulation) strategy to both protect peatlands and the

atmosphere from harmful CO2 emissions will lead to reduced carbon loss by curbing trace gas

emissions via manipulation of the “enzymic latch”.

1.3 Hypothesis

To increase the proportion of photosynthetically captured carbon that enters long-term carbon

stores, the hypothesis will be examined that;

The addition supplementary phenolic compounds (lignin rich wooden spatulas) to a fen will

increase pore water phenolic concentrations thus reducing decomposition rates by triggering

an “enzymic latch-mediated” biogeochemical cascade whereby;

a) The activity extracellular hydrolase enzymes will be lower in the phenolic treated plots

due to direct phenolic inhibition.

b) Microbial metabolism will be supressed in the phenolic treated plots due to reduced

enzyme mediated nutrient and carbon cycling therefore;

i) De novo synthesis of extracellular enzymes (phenol oxidase and hydrolases)

will be lower in the phenolic treated plots than controls.

ii) DOC and nutrients will be lower in the phenolic treatment than control plots.

iii) Biogenic trace gases (CO2 CH4 and N2O) will be lower in the phenolic treatment

than control plots.

iv) pH will be lower in the phenolic treatment than control plots.

41

2.0 Study site

Study site description, importance and management

Cors Goch is a glacially eroded valley mire (active peatland) situated on the Isle of Anglesey

in north Wales just above sea level. The mire is based on a former Holocene lake stratigraphy

with the hydrogeological control of a Carboniferous limestone-sandstone contact leading to

the development of a transitional peatland or poor fen via flow-through succession or

“topogenous development” (Gilman & Newson 1982; Mitsch & Gosselink 2000). The fen is

supplied by both diffuse and point-source carbonate rich spring water from an underlying

Carboniferous aquifer. This gives rise to calcareous (figure 1) and alkaline type fen habitat

with a number of protected specialist plant assemblages (Beamish & Farr 2013).

The fen is divided east to west by a promontory rock which gives rise wet east basin and a

drier west basin both dominated by “brown mosses”, Shoenus nigricans (black bog-rush).

Sedges and communities dominated by Cladium mariscus, Phragmites australis and Juncus

subnodulosus. The eastern Anglesey calcareous and alkaline fens are collectively known as

the “Anglesey Fens” and designated as a Special Area of Conservation. The site is also listed

under the Ramsar Convention as part of the Anglesey and Llyn Fens suite of base-rich fens.

Figure 1. Cors Goch calcareous fen and location of the study plot (UK National grid reference SH502816)

42

Rationale for selection of study site

The site was selected as it is within the ownership and management of the North Wales

Wildlife Trust (NWWT) who have sponsored this project. Aided by the data collected within in

this study, NWWT have the potential to benefit from a “voluntary carbon credit scheme” such

as the UK Peatland Carbon Code (PCC) to fund future restoration work at the site.

Furthermore, the code requires quantification of the presence of organic soil (peat) to consider

a project (table 3). Data collected within this study confirmed peatland presence under the

Food and Agriculture Organisation (FAO) definitions using percentage carbon content and

depth of organic layer as defining parameters. The definitions are summarised as follows;

a) With reference to organic soil qualification the FAO (1998) state “soils that are

saturated with water for long periods must contain at least 18% organic carbon if the

clay content is 60% or more or 12% organic carbon if the mineral fraction contains no

clay to qualify as organic. If the clay content falls between 0% and 60% then a

proportional carbon content is reached. However, soils never saturated with water for

more than a few days must contain at least 20% organic carbon to class as organic”.

b) With reference to horizon depth the FAO (2014) state “starting ≤ 40 cm and ending ≤

100cm of soil surface, an organic soil must contain a combined thickness of either ≥

60 cm of organic material if ≥ 75% of the material (by volume) consists of moss fibres

or ≥ 40 cm of organic material if consisting of other materials to class as a Histosol,

which is virtually synonymous with peat (Couwenberg 2009a).

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3.0 Methods

3.1 Experimental design and treatments

A “phenolic manipulation” experimental plot of 8 x 11 meters was established on calcareous

fen at Cors Goch (figure 2). More specifically, the plot is situated on a relatively ”open” canopy

Cladio-molinietum first described by Wheeler (1980) and further sub-classified as ‘Cladio-

Molinietum a’ owing to the dominance of Cladium mariscus and the absence of Eupatorium

cannabinum and Lythrum salicaria (Bosanquet et al. 2009). Mean water table depth during

the study period was estimated at ≤ 10 - 20 cm below peat surface for the majority of the study,

apart from the 28/08/14 where a higher water level was noted, particularly in replicate L where

a few centimetres of standing water was observed. The plot was not fenced off from grazers

and 5 horses grazed the site over the post-treatment period, introduced around the 7th august

2014. Any dunging of the plot by wild animals or livestock during the experiment was left in

situ.

Phenolic concentrations were manipulated by addition of lignin rich compounds in the form of

disposable non-sterile universal wooden tongue depressors (spatulas). The spatulas, (Oncall

medical supplies, ref UN975, 100 box adult) designed for oral examination in the medical

practice consisted of a flat wooden blade approximately 15 cm in length by 1.6 cm in width by

0.1 cm thickness, smoothed and rounded at both ends. They are useful for scientific study as

they and have a uniform size and weight and are clean but not sterilised. The lignin constitution

of wood cell walls has also been studies for decades (Bamford & Campbell 1936; Stone et al.

1971) and are abundant in phenolic compounds (Xu et al. 2014). The rationale for this type of

phenolic treatment was based upon Dunn et al., (2013). The author used spatulas of identical

dimensions in a similar phenolic manipulation experiment using peatland mesocosms. The

most successful spatula density in the authors study was scaled for this field study (appendix

A3).

The spatulas were inserted into the peat vertically so that the entire length is buried (appendix

A2). Trampling was decided as a procedural control (Dytham 2009) as unavoidable heavy

trampling of the vegetation was observed during phenolic treatment. Thus, twelve 2 x 2 meter

plots were randomly assigned to control, phenolic treatment (treatment) and trampled in a

block design, giving four replicates per factor with a 1 meter buffer strip (see appendix A1 for

design and alpha-numeric code). Phenolic addition took between two and four days per

replicate (in the order ALJG) and trampled plots experienced roughly 15 minutes of

equally distributed trampling (i.e. walking up and down the plot).

44

Figure 2. Cors Goch fen set with the context of the wider agricultural catchment. The

experimental plot (UK National grid reference SH502816) is represented by a red rectangle.

Map reproduced with Bing maps. North is directly up.

3.2 Sampling

The study was carried out in summer/autumn 2014 with plot establishment on the 11th July

2014 using plastic (15 mm overflow pipes; Screwfix) markers (importantly, not to wooden) to

mark quadrat corners. Treatment occurred between the 7th and 20th August 2014 following a

period of pre-treatment measurements. GHG fluxes, pore water chemistry and soil enzyme

activity were then monitored regularly for treatment effect (table 1).

Table 1. Full sampling regime of the study. Tick indicates samples were collected, tick and

shaded indicates that data was statistically analysed. Cross indicates that no data was

collected.

Variable sampled

Date of

sampling visit Trace gases Water quality Soil enzymes

Vegetation

survey

Pre-treatment sampling visits

14/07/14 x

22/07/14 x x

31/07/14 x x

07/08/14 x x

Post-treatment sampling visits

28/08/14 x

05/09/14 x x x

11/09/14 x x

17/09/14 x x

24/09/14 x x

30/09/14

45

3.2.1 GHG sampling

CO2, CH4 and N2O fluxes were measured using the closed static chamber technique. 12

chambers were constructed from semi-transparent 4 Litre NalgeneTM wide-mouth HDPE

(High-density polyethylene) bottles with opaque screw lids (Thermo Fisher Scientific Inc.)

modified by removing the base (Freeman et al. 1994). The semi-transparent nature of the

chamber accounts for the diurnal dark/light cycle thus giving a diurnally adjusted net

ecosystem exchange (NEE). A Suba-seal® septa was inserted into the lid through a pre-drilled

hole measured to the exact width of the septa to ensure an airtight seal, which was tested with

water. Chambers were inserted into the peat to 2.5 cm without lid in each replicate at midday,

ensuring a tight seal. Gas collection was initiated by sealing the lid and sampled immediately

to generate a background gas concentration and after one hour of headspace accumulation

to generate a flux. A 20 ml Plastipak TM syringe fitted with a TERUMO® needle (0.5x25mm)

was used to withdraw gas samples through the septa. One initial purge of the syringe into the

chamber was followed by gas extraction and subsequent injection into a pre-evacuated 12 ml

Labco Exetainer®. Samples were stored in the dark at room temperature until analysis. A time

period of one hour was pre-determined by test for linearity which indicted that gas

accumulation was linear over this time period (appendix A1). Chamber position was

randomised each week as it was agreed that no equipment (i.e. collars) were left on site. Dark

chambers were not used as recent studies by Gunther et al., (2014) suggest that using

transparent and not dark chambers to measure CH4 flux where convective plants such as

Phragmites australis dominate, almost doubles the flux estimate due to sensitivities of the

internal pressurised gas flow in convective plants, to changes in irradiation.

3.2.2 Pore water sampling

Two rhizons with a 10 cm long glass fibre wire porous part (mean pore size 0.15 µm) and a

12 cm PVC tube with female luer lock (rhizosphere.com; part no. 19.21.21F) were used to

collect approximately 40 ml of pore water per replicate. They were installed at the start of the

experiment and remained in situ for the duration. The porous part was inserted into a 45 º pre-

augured hole to prevent rainwater infiltration contaminating the sample. On each visit suction

was applied to each rhizon with a 20 ml PlastipakTM syringe into which the filtrate was

collected. Black tape covered the sunlight exposed half of the syringe to prevent potential

photolysis of DOC (Wetzel 1992). Syringes were collected after approximately 2 hours and

transported back to the laboratory for re-filtration (i.e. collected after gas sampling had ended

as not to disturb the ground during gas sampling).

46

3.2.3 Soil sampling

One peat sample (ca. 60 grams) per replicate was collected by trowel and hand from within 5

to 10 cm of the peat surface, where biological and enzyme activity is known to be greatest

(Gammelgaard et al. 1992; Freeman et al. 1995). Labelled airtight polythene bags containing

the samples were then transported to the laboratory and stored in the dark at 4 °C until enzyme

analysis within one week of sampling (Kang & Freeman 2009). Soil temperature was

measured adjacent to the sample point using a digital thermometer inserted to 10 cm depth.

3.2.4 Vegetation survey

Species composition and percentage cover of each species, leaf litter and bare ground was

assessed using the National Vegetation Classification method (NVC) (Rodwell 1991),

whereby the whole 2 x 2 meter plot was representative. The survey was conducted by the

author and one other surveyor with the estimate of percentage cover recorded as the mean

estimate of the two surveyors to reduce personal bias.

47

3.3 Laboratory analysis

3.3.1 GHG analysis

CH4 and CO2 concentrations were analysed using a gas chromatograph fitted with a flame

ionisation detector (FID) for CO2 and CH4 detection and electron capture device (ECD) for

N2O detection. Standard analytical grade reference gases (Scientific and Technical Gases

Ltd) were used for calibration and to check for instrument drift. Low, medium and high

concentration standards were placed at the start and end of the vial rack with a low

concentration standard placed after every 10th sample.

Gas fluxes were then estimated using the following equation;

F = dC/dt x rho x V/A F = flux (umol m-2 s-1)

dC = change in mixing ratio (umol/mol)

dt = change in time (s)

rho = density of air (mol m-3)

= pressureMb*100/(8.314*(T+273.15))

V = volume (m3)

A = area (m2)

3.3.2 Soil pore water chemistry analysis

Conductivity, pH and filtration

Pore water from both collection syringes were combined in one clean 50 ml amber glass bottle

and conductivity and pH readings were taken. Pore water pH was taken with a Mettler-Toledo

seveneasy pH meter from a 5ml sub-sample, which was discarded after measurement to avoid

contamination with ions. The collection syringes were rinsed twice with distilled water and

used to re-filter the sample with a BIOFILTM syringe filter (pore size 0.45 µm) into 2 acid

cleaned 20 ml NalgeneTM vials to remove any particulates that had entered the sample during

collection. Samples are stored in the dark at 4 ºC until analysis.

Dissolved organic carbon (DOC) absorbance at 254 nm proxy

DOC has been estimated using different absorbance wavelengths in the literature. Peacock

et al., (2013) discovered that 254 nm performed with the most reliability as a single wavelength

48

proxy. Therefore, to determine DOC concentrations the authors’ proxy was used with the

following equation;

y = 22.7x - 2.04

y = required DOC value

x = absorbance value at 254 nm.

Pore water phenolic concentrations

Phenolic compound concentrations were assayed using the ratios recommended by Box

(1983). To 1 ml of pre-filtered sample, 50 µl Folin-Ciocalteau phenol reagent (Sigma) and 150

µl of 200 mg/l-1 Na2CO3 was added. The mixture was incubated at room temperature for 1 hour

15 minutes in 1.5 ml Eppendorf vials. A standard curve was prepared by adding the same

quantity of chemicals to 0 – 10 mg/l-1 in a dilution series consisting of 0, 1, 2, 4, 6, 8, 10 mg/l-

1 phenol solution from which the phenolic concentrations were determined. The reaction was

terminated by centrifuging samples and standards at 10,000 r.p.m for 5 minutes to reduce any

precipitate and reduce potential for quench (reduction in fluorescence intensity). The colour

change of reactants was measured at 750 nm to give a reading of the phenolic concentrations

in the sample.

Pore water nutrient concentrations

Pore water anion and cation nutrient concentrations were analysed on pre-filtered sample

using ion chromatography on a Metrohm auto-analyser.

3.3.3 Soil enzyme assay

Soil preparation and assay conditions

Each soil sample was prepared by hand homogenising for 5 minutes followed by the removal

of adhering debris such as large roots, stones and macroinvertebrates to give the sample a

more consistent texture. Assays were performed at field temperature to estimate quantity of

actual enzyme activity as thermal optima for microbial processes shifts with seasonal

temperature change (Fenner et al. 2005b).

Hydrolase assay

A laboratory assay of the enzymic hydrolysis of the bond between fluorogenic 4-

methylumbelliferone (MUF) and 5 hydrolase model substrates on exposure to aqueous soil

extracts was used as a proxy to measure the activity of 5 soil extracellular hydrolase enzymes;

β-D-glucosidase (B), arylsulphatase (S), β-D-xylosidase (X), N-acetyl-β-D-glucosaminidase

(N) phosphatase (P) in situ using the method of Freeman et al., (1995) adapted by Dunn et

49

al., (2014). The term “hydrolase” has been used in this study to describe the enzyme activity

that in the presence of water hydrolyse non-phenolic high molecular weight plant material to

form low molecular weight compounds. Whilst bonded to the model substrate, MUF does not

fluoresce, however upon reaction with the free hydrolase enzymes in the aqueous soil, the

bond is broken by hydrolysis, allowing the MUF to fluoresce. This is measured at 450nm,

excitation 330nm and related to target enzyme activity. The calibration curve is made correct

for “quench” by subtracting the standard concentrations from zero and plotting a graph from

which the flourescense is calculated.The flourescense values are then converted to enzymic

activity with the following equation;

Enzyme activity = F/Mdry/t assay/8 F flourescense value Mdry dry weight of 1 gram of wet soil t assay time of assay (60 minutes or 45 for phosphatase) 8 correct for original soil to substrate mixture in Stomacher bags

Six 1 gram (±0.02) peat sub-samples were weighed (Ohaus Precision standard digital

balance) per replicate and placed into individual stomacher bags (Seward) labelled B, S, X,

N, P and Std (standard) for all treatment. 7 ml of relevant model substrate solution and 7 ml

of ultra-pure water, all maintained at field temperature, was added to the relevant stomacher

bag. All bags were homogenised for 30 seconds using a paddle blender (Seward Stomacher

80) set to normal. Bags B, S, X & N were then incubated for 1 hour, and P for 45 minutes at

field temperature then transferred to 1.5 ml Eppendorf vials and centrifuged at 10,000x r.p.m

for 5 minutes. Std bags were added to 2 x 2 ml Eppendorf vials and centrifuged

simultaneously. 50 µl of ultra-pure water and 250 µl of each enzyme supernatant were then

pipetted into a black 96 well black microplate (Sterilin, Cambridge, UK) and analysed on a

Spectramax® M2e plate reader at 450nm, excitation at 330 nm. A standard curve was

prepared by adding 50 µl of 0 – 40 µl range of MUF free acid solution to 250 µl of Std

supernatant and analysed as above (Dunn et al. 2014).

Phenol oxidase assay

A laboratory assay of the enzymic oxidation of ʟ-dihydroxy phenylalanine (ʟ-DOPA; a phenolic

amino acid model substrate) on exposure to aqueous soil extracts was used as a proxy to

estimate the combined activity of soil extracellular phenol oxidase enzymes in situ using the

method of Pind et al., (1994) adapted by Dunn et al., (2014). In the presence of O2, the main

classes of phenol oxidase enzymes (laccases, tyrosinases and catechol oxidase) all rapidly

oxidise phenols into the red pigmented dopachrome (2-carboxy-2, 3-dihydroindole-5,6-

quinone)

50

Two peat sub-samples of 1 gram (±0.02) were weighed (Ohaus Precision standard digital

balance) and placed into individual stomacher bags (Seward) labelled POx-B (blank) and POx-

L (model substrate). 9 ml of ultrapure water maintained at 4°C was added to each pair of bags

and homogenised for 30 seconds using a laboratory paddle blender (Seward Stomacher 80)

set to normal. Ten ml of ʟ-DOPA was then added to the POx-L bags and 10ml of ultrapure

water was added to the Pox-B bags. The POx-B bags containing no model substrate is

subtracted from the ʟ-DOPA bags to give the phenol oxidase activity. The pair are blended for

a further 30 seconds on normal setting then incubated at 4°C for 10 minutes. After the

incubation period 3 x 1.5 ml Eppendorf vials were filled from each pair and centrifuged at

10,000 x r.p.m for 5 minutes. 300 µl of resultant supernatant from each vial was then pipetted

into a clear 96 well microplate (Sterilin, Cambridge, UK) and analysed on a Spectramax® M2e

plate reader at 475 nm.

Enzyme activity was calculated using a formula based on Beer-Lambert Law;

C = A/bԐb

and activities are expressed in µmol dicq g-1 min-1 as detailed in Dunn et al., (2014)

Dry weight, soil moisture and soil organic matter

A quantity of soil sample was added to a porcelain crucible (of known weight) weighed, dried

in an oven at 105 °C and reweighed to ascertain dry weight and percentage soil moisture

gravimetrically. Crucibles were then ignited at 550 °C after which the inorganic ash was re-

weighed to ascertain percentage soil organic matter (SOM) and thus percentage organic

carbon, 50% of SOM (Joosten et al. 2002) gravimetrically via the loss on ignition (LOI) method.

4.0 Quantifying peatland presence and carbon budget

estimation

To quantify presence of a peatland total depth of peat was measured at four corners of the

plot using connectable drain rods and organic carbon content was assessed by collecting peat

with a Russian corer down to one meter (half cylinder Russian auger) and assessing organic

carbon content at 10 cm intervals. The core was split into 10 x 10 cm sections and 3 cm of

intact core was collected in a sealable polythene bag for gravimetric analysis as explained

above. The less intact core was used to assess peat against the Von post system, a quick

field method for assessing humification (state of decomposition).

Bulk density at 10 cm intervals was determined using the following equation.

Pb = Volume / dry weight

51

Volume = Surface area of core (i.e. the Russian corer) x length of core (3 cm collected)

Surface area of core r2 / 2 (as corer is half cylinder)

5.0 Developing a method to estimate the phenolic leaching

capacity of wooden spatulas

To determine the phenolic leaching capacity of wooden spatulas a standardised method was

needed. I hereby propose a density dependant method based on the spatula density used for

phenolic supplement. The method (appendix A3) for the scaling of phenolic treatment is

dependent on GHG chamber surface area which can be transferred to the surface area of the

base of a 500 ml Schott Duran® laboratory clear glass bottle. These glass bottles were

selected as they are roughly the same height as the wooden spatulas and commonly used.

1. 580 ml of ultra-pure water is added to 2 clean, dry 500 ml Schott Duran® laboratory

glass bottles per replicate (kitchen foil was used to cover the glass bottles to minimise

light penetration)

(*580 ml ensures that the spatulas remain submerged and exposed to limited surface

air space, replicating peatland conditions)

2. To one bottle add the required number of spatulas based on treatment density and

cap, the other bottle is capped with no treatment to ascertain background

concentrations of phenolics in pore water.

3. Place bottles on orbital shaker at room temperature for 48 hours at 100 r.p.m.

4. Terminate the incubation after 48 hours and pipette 20 ml of sample into a sterile 20ml

PlastipakTM syringe fitted BIOFIL® syringe filter and drive filtrate into an acid clean

NalgeneTM vial for storage and analysis. Phenolics assay is then performed using the

method of Box (1983) as detailed above.

52

6.0 Statistical analysis

All data from experimental plots were compared using a one-way ANOVA taking Welch’s

adjusted F-ratio, which has apparent versatility when equal variances are not assumed (Field

2009) and a Games-Howell post hoc comparison. Where data was not normally distributed

(identified by the Shapiro-Wilks test) and log10 or square root transformation did not result in

normal distribution, a Kruskal-Wallis test was carried out with a Mann-Whitney post hoc test

with exact p values and Bonferroni correction applied (Field 2009). Percentage data was

transformed using an arcsine transformation and treated the same way as continuous data.

Correlations between variables were sought using a Spearman’s rank correlation. All statistic

were ran on IBM SPSS statistics version 22. One pre-treatment and two post-treatment

sampling visits were selected form the time-series for statistical analysis. For pre-treatment,

the 7th August 2014 was chosen (pore water chemistry and GHG flux) as this was the last

sampling day immediately prior to treatment. The 14 August 2014 was chosen for soil being

the only pre-treatment sampling week. For post-treatment, the first visit after treatment and

the last sampling visit were chosen, representing key milestones of the experiment.

53

7.0 Results

7.1 The effect on supplementary lignin addition on phenolic

concentrations

Before comparing the post-treatment differences, pre-treatment differences were compared

according to their assigned future treatments to ascertain whether any pre-existing differences

in pore water phenolic concentrations existed. ANOVA revealed that there were no significant

differences in phenolic concentrations between any of the plots during the week immediately

prior to treatment (table 3). Pre-treatment mean phenolic concentrations (all plots) fluctuated

between 1.3 and 3.8 mg L-1, showing no persistent unidirectional change between the 14th

July 2014 and the 7th August 2014 (figure 3). Upon treatment, mean phenolic concentrations

in the treatment plot (3.8 ±0.5 mg L-1) increased to a greater extent than in the control (2.5

±0.2 mg L-1) and trampled plot (2.7 ±0.2 mg L-1) however there were no significant differences.

Phenolic concentrations in the treatment remained elevated above control and trampled plots

in the third week after treatment (11/09/2014) and above but only marginally by the fifth week

after treatment. By the final sampling week, ANOVA revealed that phenolics were significantly

higher in the treatment when compared to the trampled plot (p = 0.041, F = 5.892, n = 4)

however the control plot displayed higher phenolic concentrations than the treatment plot. This

was the only time that phenolics were higher in the control plot during post-treatment. The

highest mean phenolic concentrations during post-treatment were observed in the treatment

plot (3.8 ±0.5 mg L-1) on the 28/08/2014, however this was equal to the highest mean phenolic

concentration observed pre-treatment (3.8 ±0.2 mg L-1) on the 14/07/14. Phenolics correlate

positively with soil moisture in the treatment (n = 12, r = 0.874 p < 0.001) and trampled plots

(n = 12, r = 0.748, p < 0.05) but negatively in the control plot (insignificant).Phenolics also

correlate positively with soil temperature in all plots (n = 12; control r = 0.601, p < 0.05;

treatment r = 0.622, p = < 0.05; trampled r = 0.868, p < 0.001).

54

Figure 3. Time series plot of means plus standard error for phenolic concentrations. Vertical

dashed line indicates time of treatment. Solid line = control, dashed line = phenolic treatment

and dotted line = vegetation trampling. Data shown from the start (14 July 2014) of the pre-

treatment water sampling period to the end of post-treatment sampling period (30 September

2014).

7.2 Response of extracellular enzymes to phenolic treatment

Hydrolases enzymes

ANOVA revealed there was a significant difference for β-D-xylosidase activity which was

higher in the trampled plot (0.91 ±0.06 nmoles g-1 min-1 MUF released) than the control plot

(0.52 ±0.08 nmoles g-1 min-1 MUF released) in the week immediately following treatment

(figure 4a), a similar response was observed with phosphatase but this was not significant.

There were no significant differences between any of the plots during pre-treatment or post-

treatment for the activity of β-D-glucosidase, arylsulphatase, N-acetyl-β-D-glucosaminidase

and phosphatase (table 2). It is noteworthy that activities of β-D-glucosidase, N-acetyl-β-D-

glucosaminidase, xylosidase and phosphatase became elevated at the end of September to

their highest levels in all three plots. Arylsulphatase also showed a similar temporal

response however activities in the treatment did not rise with the control and trampled plots

in late September. All hydrolases negatively correlate with phenolics, soil moisture and soil

temperature at 10 cm with the exception of arylsulphatase in the treatment but these

exceptions are not significant.

Phenol oxidase

There were no significant differences in phenol oxidase activity either before or after treatment

(table 2). It is interesting to note that the heightened phenol oxidase activities following

treatment was never surpassed in late September as observed with the hydrolase enzymes

(figure 4f). Phenol oxidase correlates positively with phenolics in the trampled and treatment

plots but negatively in the control (insignificant but unexpected correlations).

55

a)

b)

c)

56

d)

e)

f)

Figure 4. Time series plot of means plus standard error for five extra-cellular nutrient cycling hydrolase enzymes and phenol oxidase. Vertical dashed line indicates time of treatment. Solid line = control, dashed line = phenolic treatment and dotted line = vegetation trampling. a) β-D-glucosidase b) arylsulphatase c) β-D-xylosidase d) N-acetyl-β-D-glucosaminidase e) phosphatase f) phenol oxidase.

57

Table 2. Post-hoc comparisons for extra-cellular hydrolases and phenol oxidase enzymes between C = control, T = (phenolic) treatment, VT = vegetation trampling, B = β-D-glucosidase, S = arylsulphatase, X = β-D-xylosidase, N = N-acetyl-β-D-glucosaminidase, P = phosphatase. Shaded grey cells contain a significant result with the significant p in bold.

Sampling

date B S X N P

Phenol

oxidase

14/07/2014

n = 4

F = 0.070

C v T

p = 0.924

C v VT

p = 0.991

T v VT

p = 0.935

n = 4

F = 0.569

C v T

p = 0.997

C v VT

p = 0.545

T v VT

p = 0.930

n = 4

F = 1.200

C v T

p = 0.856

C v VT

P = 0.374

T v VT

P = 0.535

n = 4

F = 1.472

C v T

p = 0.736

C v VT

p = 0.342

T v VT

p = 0.794

n = 4

F = 3.031

C v T

p = 0.101

C v VT

p = 0.187

T v VT

p = 0.767

n = 4

F = 0.677

C v T

p = 0.883

C v VT

p = 0.483

T v VT

p = 0.892

28/08/2014

n = 4

F = 0.060

C v T

p = 0.988

C v T

P = 0.988

T v VT

p = 0.930

n = 4

F = 0.503

C v T

p = 0.889

C v VT

p = 0.593

T v VT

p = 0.795

n = 4

F = 6.012

C v T

p = 0.525

C v VT

P < 0.05

T v VT

p = 0.871

n = 4

F = 1.087

C v T

p = 0.620

C v T

p = 0.390

T v VT

p = 1.000

n = 4

F = 2.996

C v T

p = 0.988

C v T

p = 0.988

T v VT

p = 0.930

n = 4

F = 0.962

C v T

p = 0.993

C v VT

p = 0.841

T v VT

p = 0.841

30/09/2014

n = 4

F = 2.193

C v T

p = 1.000

C v T

p = 0.163

T v VT

P = 0.481

n = 4

F = 0.936

C v T

p = 0.423

C v VT

p = 0.997

T v VT

p = 0.443

n = 4

Chi square

= 1.414

Exact p =

0.544

n = 4

F = 0.665

C v T

p = 0.548

C v VT

p = 0.990

T v VT

p = 0.511

n = 4

Chi square

= 0.471

Exact p =

0.872

n = 4

Chi square

= 2.346

Exact p =

0.348

58

7.3 Soil characteristics

Table 3. Soil parameters from an exploratory core using a Russian auger down to one meter depth.

Depth in peat

profile (cm)

Von Post scale

rating

Organic carbon

content (%)

Bulk density

(g/cm3)

0 – 10 H3/H4 44 0.08

10 – 20 H3/H4 43 0.12

20 – 30 H7/H8 44 0.16

30 – 40 H7/H8 44 0.13

40 – 50 H7/H8 44 0.10

50 – 60 H9/H10 46 0.13

60 – 70 H9/H10 44 0.14

70 – 80 H9/H10 45 0.16

80 – 90 H9/H10 45 0.14

90 – 100 H9/H10 44 0.11

Soil organic matter Mean soil organic matter in the trampled plot sharply increased to 93.5 % in the week

immediately following treatment, however not significantly different to the control (90.6%) and

treatment plots (90.7%). There were no significant differences in soil organic matter before or

after treatment (figure 5a).

Soil temperature Soil temperature at 10cm depth decreased consistently during the 4 sampling visits equating

to a total drop of 3.1 °C (figure 5b). Pre-treatment control mean was 16.4 ºC (±0.2) in July

which fell to 13.3 ºC (±0) by the end of September.

Soil moisture

ANOVA revealed a pre-existing significant difference in soil moisture percentage in the week

before treatment between the plots that become assigned treatment (89.09% ±0.23) and

trampled (87.91% ±0.07) (F = 10.003, n = 4, p = 0.024). During post-treatment however, no

significant differences were detected between any plots (table 3). Soil moisture decreased in

all plots between July and September, the control plot decreased by 1.7% whereas the

treated plot fell by 3.1% overall.

59

a)

b)

c)

Figure 5. Time series plot of means plus standard error for a) percentage soil organic matter, b) percentage soil moisture and c) soil temperature at 10 cm. Vertical dashed line indicates time of treatment. Solid line = control, dashed line = phenolic treatment and dotted line = vegetation trampling.

60

7.4 Response pore water chemistry to phenolic supplement

Response of pH and electrical conductivity

Mean pH values in the control site fluctuated between 6.6 and 6.9 over the duration of the

study. Mean pH in the treated and trampled plots never deviated more than 0.1 pH units from

the control during the study (figure 6a). As such there no were significant differences in pore

water pH. See appendix 4 for explanation of conductivity data loss.

Response of dissolved organic carbon

There were no pre-existing significant differences detected in pore water DOC concentrations.

During the pre-treatment phase, DOC concentrations fluctuated between 19.75 (±1.20) and

27.9 (±3.26). During the week immediately following treatment mean treatment DOC

concentrations peaked at 30.85 (±2.96), the highest post-treatment concentration, but the

differences were not significant. In the weeks that followed, mean DOC concentrations in the

treatment plot dropped and by the fifth week after treatment mean DOC concentrations were

lower in the treatment than the control plot. DOC concentrations in the trampled plot were

consistently lower than both treatment and control plots throughout post-treatment. In the sixth

week post-treatment an ANOVA test revealed that DOC was significantly lower in the trampled

plot when compared to the control plot (F = 6.312 n = 4 p < 0.05). Excluding the peak

experienced on the 28th August both treatment and trampled plot DOC concentrations fell in

September and started to level off towards the end of the month. DOC concentrations in the

control plot increased over the duration of the study, whereas treatment DOC was lower in the

third pre-treatment week than during post-treatment. DOC correlates positively with phenol

oxidase in the trampled (significant) and treatment plots but negatively in the control plot.

a)

61

b)

Figure 6. Time series plot of means plus standard error for a) pH b) dissolved organic carbon. Vertical dashed line indicates time of treatment. Solid line = control, dashed line = phenolic treatment and dotted line = vegetation trampling. See appendix 4 for samples loss details.

Table 4. Post-hoc comparisons for pH, DOC, phenolics, % soil organic matter, SOM and % soil moisture between C = control, T = (phenolic) treatment, VT = vegetation trampling. Shaded grey cells contain a significant result with the significant p in bold.

Sampling

date

pH DOC (abs254

proxy)

Phenolics % SOM % Soil

Moisture

14/07/2014

n = 4

F = 0.010

C v T

p = 0.993

C v VT

p = 1.000

T v VT

p = 0.992

n = 4

F = 0.298

C v T

p = 0.718

C v VT

p = 0.748

T v VT

p = 0.999

n = 4

F = 0.256

C v T

p = 0.793

C v VT

p = 0.765

T v VT

p = 0.991

n = 4

F = 1.218

C v T

p = 0.375

C v VT

p = 0.281

T v VT

p = 0.293

n = 4

F = 0.023

C v T

p = 0.250

C v VT

p = 0.953

T v VT

p < 0.05

28/08/2014

n = 4

F = 1.194

C v T

p = 0.601

C v VT

p = 0.304

T v VT

p = 0.639

n = 4

F = 3.446

C v T

p = 0.627

C v VT

p = 0.370

T v VT

p = 0.174

n = 4

F = 2.182

C v T

p = 0.186

C v VT

p = 0.862

T v VT

p = 0.252

n = 4

F = 1.534

C v T

p = 0.998

C v VT

p = 0.281

T v VT

p = 0.293

n = 4

F = 3.359

C v T

p = 0.871

C v VT

p = 0.442

T v VT

p = 0.079

30/09/2014

n = 4

F = 0.112

C v T

p = 0.910

C v VT

p = 0.992

T v VT

p = 0.950

n = 4

F = 6.312

C v T

p = 0.295

C v T

p < 0.05

T v VT

p = 0.139

n = 4

F = 5.892

C v T

p = 0.769

C v VT

P = 0.200

T v VT

P < 0.05

n = 4

F = 0.140

C v T

p = 0.998

C v VT

p = 0.921

T v VT

p = 0.865

n = 4

F = 2.947

C v T

p = 0.871

C v T

p = 0.442

T v VT

p =0.079

62

7.5 Response of GHG gases to phenolic supplement

CH4 and N2O response to phenolic treatment

Pre-treatment comparisons

Before comparing the post-treatment fluxes, pre-treatments were compared according to their

assigned future treatments to ascertain any pre-existing flux differences. A Kruskal-Wallis test

revealed that there were no significant differences in CH4 fluxes during the week immediately

prior to treatment (table 6). A large range of means (0.87 – 11.82 mg CH4 m-2 h-1) was

observed during the first sampling visit (all treatments). A flux stabilisation was observed by

the second sampling week owing to the narrower range of means (0.52 – 1.45 mg CH4 m-2 h-

1) (Figure 8b). There were no significant differences in N2O flux throughout the study (figure

8c).

Post-treatment comparisons

Analysis of post-treatment data by ANOVA revealed a significantly higher CH4 flux observed

in the treatment when compared to the control during the week immediately following

treatment (F = 9.896 n = 4 p = 0.038). However, no significant differences were observed

between trampling and both treatment and control. Emissions from treatment and trampled

plots increased when compared to the week immediately prior to treatment but decreased in

the control plot. It should be noted that the treatment plot experienced a peak in mean CH4

emissions during the second sampling week (8.14 ± 3.17 mg CH4 m-2 h-1). This peak was

accompanied by a concurrent but smaller increase from the control but a decrease in

emissions from the trampled plot (table 5). Although this spike accounted for the highest mean

post-treatment, it was not as high as the pre-treatment mean observed for the plot that would

become trampled (11.82 ± 10.22 mg CH4 m-2 h-1). By the final three weeks of sampling CH4

flux fluctuations from all three plots had stabilised (Figure 7b) and ANOVA revealed that no

statistical differences existed between any of the plots during the last sampling week (table 6).

63

Table 5. CH4 flux (mg CH4 m-2 hr-1) immediately before and two & three weeks after treatment

with the difference between consecutive sampling visits. Pre week 1 = 7th August. Treatment

started immediately after pre week 3 and ended on the 20th August. The first sampling visit

(Post 1) was the 28th August. Post 2 = 5th September.

Factor

Mean CH4 flux

08/07/2014

(pre week 3)

Mean CH4 flux

28/07/2014

(post week 1)

Difference

(pre week 3

& post

week 1)

Mean CH4 flux

05/09/2014

(post week 2)

Difference

(post week 1

& 2)

Control 1.29 (±1.14) 0.01 (± 0.02) -1.28 3.07 (±2.55) + 3.06

Phenolic

treatment 0.22 (±0.12) 0.60 (± 0.13) -0.38 8.51 (±3.17) +8.45

Trampled 0.00 (±0.08) 2.82 (± 1.57) 2.82 2.32 (±1.13) -0.5

Regulators of CH4 flux and other interactions

There is a significant negative correlation between soil temperature at 10 cm and CH4 flux in

the control plot (n = 12, r = -0.725, p = 0.008) with (non-significant) positive correlations

between them in the trampled and treatment plots. There was a positive correlation between

phenolics and CH4 in the treatment (n = 28 12, r = 0.466, p = 0.012) and trampled plots

(insignificant) but a negative correlation between them in the control plot (insignificant)

CO2 response to phenolic treatment

Before comparing the post-treatment fluxes, pre-treatments were compared according to their

assigned future treatments to ascertain any pre-existing flux differences. ANOVA revealed

that there were no significant differences in CO2 flux between any of the plots during the week

immediately prior to treatment (table 6). CO2 flux continued to decrease for a week after

treatment in all plots then experienced a sharp increase in the second week after treatment

for both treatment and trampled plots, unlike CH4 flux which experienced the sharp increase

in the treatment plot only (see Figure 8b). In the third week after treatment, the CO2 flux for

the trampled plot decreased sharper than in the treatment plot and by the final three weeks of

sampling visit all three plot fluxes had stabilised.

There is a steep temporal decline in mean CO2 flux values from the control plot between the

22nd July 2014 when the flux was 563.30 mg CO2 m-2 h-1 (±159.43) and the 28th August 2014

where it had fallen to 117.05 mg CO2 m-2 h-1 (±48.51). During mid-September CO2 flux in the

control plot was rising, however, flux from the treatment was falling. During post-treatment,

64

two of the six sampling visits produced lower CO2 fluxes in the treatment than in the control

compared to one in three during pre-treatment. This is proportional, however it is interesting

to note that the two instances where CO2 flux was lower in the control were the last two

sampling visits (figure 7).

Figure 7. Carbon dioxide flux for control and treatment (n = 4). Vertical dashed line indicates time of treatment. Light grey solid bars = control and black patterned bars = phenolic treatment. Data are mean plus standard error.

Regulators of CO2 flux and other interactions

CO2 is positively correlated with soil temperature in the control and trampled plots but

negatively correlated with soil temperature in the treatment (unexpected negative correlation

but insignificant). CO2 has a positive correlation with soil moisture in the trampled (n = 12, r =

7.18, p < 0.05) and treatment plots (insignificant) but negative correlation with soil moisture in

the control plot.

65

a)

b)

c)

Figure 8. Time series plot of means plus standard error for a) carbon dioxide b) methane c)

nitrous oxide. Vertical dashed line indicates time of treatment. Solid line = control, dashed line

= phenolic treatment and dotted line = vegetation trampling. Data shown from the start (22

July 2014) of the pre-treatment GHG sampling period to the end of post-treatment sampling

period (30 September 2014).

66

Table 6. Post-hoc comparisons for carbon dioxide, methane and nitrous oxide. C = control, T

= treatment, VT = vegetation trampling. Shaded grey cells contain a significant result with the

significant p in bold.

Sampling

date CO2 CH4 N2O

07/08/14

n = 4

F = 0.668

C v T

p = 0.564

C v VT

P = 0.766

T v VT

P = 0.863

n = 4

Chi square = 4.178

Exact p = 0.125

n = 4

F = 0.166

C v T

p = 0.829

C v T

P = 0.982

T v VT

P = 0.954

28/08/14

n = 4

F = 0.313

C v VT

p = 0.951

C v T

p = 0.703

T v VT

p = 0.759

n = 4

F = 0.896

C v T

p < 0.05

C v VT

p = 0.313

T v VT

p = 0.439

n = 4

F = 0.734

C v T

p = 0.541

C v VT

p = 0.481

T v VT

p = 0.969

30/09/14

n = 4

F = 0.256

C v VT

p = 0.996

C v VT

P = 0.808

T v VT

p = 0.762

n = 4

F = 0.067

C v VT

p = 0.923

C v VT

P = 0.985

T v VT

p = 0.965

n = 4

Chi square = 1.414

Exact p = 0.544

7.6 Response of pore water nutrients to phenolic supplement

Bromide levels in the treatment plot become elevated after treatment (figure 9d). ANOVA

revealed that treatment plots had significantly higher bromide concentrations than the control

(F = 8.927, n = 4, p < 0.05) and trampled plots (F = 8.927, n = 4, p = 0.021) during the final

sampling visit on the 30th September 2014. Furthermore, treatment had the opposite effect for

sulphate concentrations with treatment having significantly lower concentrations than both

control (U = 0.000, n = 4, p = 0.021, r = -1.1545) and trampled plots (U = 0.000, n = 4, p <

0.05, r = -1.1545) identified by the Mann-Whitney test.

There were no significant differences, pre or post-treatment in any of the following nutrient

anions following treatment; nitrate, nitrite, phosphate, chloride and fluoride. There were no

significant changes for any nutrient cations, as such plots are displayed in appendix A9. It is

also noteworthy that mean nitrate levels became elevated post-treatment, with concentrations

in the control of 0.5 mg L-1 (±0.4) and trampled 0.8 mg L-1 (±0.6) during the fifth week post-

67

treatment, however, an observed rise was not observed in the treatment (0.06 mg L-1 ±0.03)

during the same week (figure 9d).

a)

b)

c)

68

d)

e)

f)

g)

Figure 9. Time series plot of means plus standard error for a) fluoride b) chloride c) nitrite d) bromide e) nitrate f) phosphate g) sulphate. Vertical dashed line indicates time of treatment. Solid line = control, dashed line = phenolic treatment and dotted line = vegetation trampling. See appendix 4 for samples loss details.

69

Table 7. Post-hoc comparisons for selected anion nutrients. C = control, T = treatment, VT = vegetation trampling. Shaded grey cells contain a significant result with the significant p in bold.

Sampling

date Bromide Nitrate Phosphate Sulphate

07/08/2014

n = 4 F = 1.712

C v T p = 0.436 C v VT

P = 0.438 T v VT

p = 0.887

n = 4 F = 1.513

C v T p = 0.997 C v VT

p = 0.283 T v VT

P = 0.487

n = 4 Chi square =

4.415 Exact p = 0.139

n = 4 F = 0.75

C v T p = 0.866

C v T p = 0.970

T v VT p = 0.842

28/08/2014

n = 4 F = 3.800

C v T p = 0.082 C v VT

p = 0.994 T v VT

p = 0.173

n = 4 F = 0.438

C v VT p = 0.733 C v VT

p = 0.612 T v VT

p = 0.883

n = 4 Chi square =

3.363 Exact p = 0.242

n = 4 F = 9.577

C v T p = 0.68 C v VT

p = 0.365 T v VT

p = 0.056

30/09/2014

n = 4 F = 0.018

C v T p = 0.842 C v VT

p < 0.05 T v VT

p < 0.05

n = 4 F = 0.875

C v T p = 0.649 C v VT

p = 0.972 T v VT

p = 0.503

n = 4 Chi square =

1.128 Exact p = 0.610

n = 4 chi square = 8.346

p < 0.005 Mann-Whitney tests

*C v T p = 0.021

r = -1.1545 U = 0.000 *T v VT

p = 0.021 r = -1.1545 U = 0.000

*Bonferroni correction

applied, adjusted critical p value is 0.025)

7.7 Response of vegetation to phenolic supplement

There were no pre-existing significant differences in percentage cover of leaf litter or the three

dominant wetland plant species present, namely Phragmites australis, Cladium mariscus and

Myrica gale. Mean percentage cover of leaf litter increased in the treatment plots by nearly

twice as much as the control and plots (figure 10a). During the final week after treatment a

Kruskal-Wallis test detected a significant difference in leaf litter (p < 0.05), however the Mann-

Whitney test could not provide conclusive evidence as to which plots were significantly

different (control v treatment; p = 0.029 see table 8 for critical p value). Myrica gale decreased

in the treatment and trampled plots by 18 and 14 times respectively compared to the control

plot (figure 10d) as identified by the Kruskal-Wallis test but the Mann-Whitney failed to

70

distinguish which plots were significantly different. There were no significant differences in

Phragmites australis or Cladium mariscus percentage cover post-treatment (table 8). Full

photo-monitoring and vegetation survey results are displayed in appendix A10.

Table 8. Post-hoc comparisons for dominant vegetation and leaf litter. C = control, T = treatment, VT = vegetation trampling.

Sampling

date Leaf litter Myrica gale

Cladium

mariscus

Phragmites

australis

14/07/2014

n = 4

Chi square = 2.426

Exact p = 0.321

n = 4

F = 3.830

C v T

p = 0.084

C v VT

P = 0.142

T v VT

p = 0.954

n = 4

F = 0.469

C v VT

p = 0.588

C v VT

p = 0.833

T v VT

p = 0.762

n = 4

F = 0.762

C v T

p = 0.451

C v VT

P = 0.795

T v VT

p = 0.642

30/09/2014

n = 4

chi square = 8.355

p < 0.05

Mann-Whitney

test

*C v T

p = 0.029

r = -1.183

U = 0.000

*T v VT

p = 0.86

r = -1.024

U = 1.000

*Bonferroni

correction applied,

adjusted critical p

value = 0.025)

n = 4

chi square = 6.575

p < 0.05

Mann Whitney U

test

C v T

p = 0.029

r = -1.183

U = 0.000

C v VT

p = 0.286

r = 0.8335

U = 3.000

*Bonferroni

correction applied,

adjusted critical p

value = 0.025)

n = 4

F = 1.036

C v VT

p = 0.530

C v VT

P = 0.511

T v VT

P = 0.870

n = 4

Chi square = 2.503

Exact p = 0.291

71

Figure 10. Plot of means plus standard error for the dominant vegetation cover in the plot. a) Leaf litter b) Phragmites australis c) Cladium

mariscus d) Myrica gale . Data shown from pre-treatment vegetation survey on the 14 July 2014 (solid light grey bar) and post-treatment

vegetation survey on the 30 September 2014 (patterned dark bar).

72

7.8 Leachable phenolic potential of wooden spatulas

Mean phenolic concentrations in the sample containing the wooden spatulas was 11.7 mg L-

1, whereas mean phenolic concentrations in the samples containing ultrapure water was 0.4

mg L-1 which is almost a 30 fold difference (figure 11). The small amount of phenolics in the

ultrapure water can be explained by the calibration graph not intersecting zero (appendix A6).

Figure 11. Difference in phenolic concentrations of ultrapure water with and without wooden spatulas present. Spatulas were rotated at 100 r.p.m for 48 hours to assess the leachable phenolic potential. Data are means with standard error.

73

8.0 Discussion

Phenolics and Dissolved organic carbon

Supplementing the fen with lignin rich wood significantly increased the concentration of

phenolics in the treatment plot by the sixth week post-treatment when compared to the

trampled plot with a likelihood that phenolic increases were due to leaching from the spatulas.

Phenolics in the trampled plot remained lower than the undisturbed control for a majority of

the study offering further evidence to support this assumption. A laboratory study also

reinforced this assumption by demonstrating the high phenolic leaching capacity of the

spatulas.

Alternatively, the initial rise in phenolic concentrations in the treatment plot may also stem from

a simultaneous rise in DOC possibly originating from fresh labile root exudate carbon of

trample damaged plants known as the “priming effect” (Derrien et al. 2014) as indicated by

the spike in xylosidase activity in the trampled and treatment plots. Increased labile carbon

sources made available through disturbance of leaf litter and damage to vegetation stimulate

microbial activity which in turn may result in the partial degradation of insoluble recalcitrant

phenolics (Toberman et al. 2008). This is supported by Hamilton et al., (2008) who found that

defoliated (damaged) grassland species exudate labile carbon through their roots

hypothesising that this occurrence is a protection mechanism whereby readily utilisable

energy sources are exuded by damaged plants, stimulating rhizosphere microbial activity thus

enhancing nitrate mineralisation for re-assimilation and tissue repair. Thus, a feedback loop

occurs in which the plant, ultimately serving to protect itself sustains primary production and

future higher trophic herbivory. This argument is also supported by the increase in N-acetyl-

β-D-glucosaminidase activity overriding any potential suppression provided by the elevated

phenolic concentrations in the treatment plot.

The increase in phenolics in the treatment also coincides with the heavy August rains which

may have further stimulated DOC and phenolic increases (Fenner et al. 2005a). This was

followed by a decrease in phenolic concentrations and coincided with a month long drought in

September. Rain gauge data indicates 19.85 ml rain fell at a neighbouring fen (Cors

Erddreiniog) situated within 3km of the study site (see appendix A7 & A8 for rain gauge

location and data). This equates to a six-fold reduction in rainfall when compared to mean

September rainfall at Cors Erddreiniog over the previous 7 years. Also, three and five-fold less

rainfall fell in September 2014 than in July and August 2014 respectively and a 21 day period

in mid-September experienced only 0.2 ml of rainfall at Cors Erddreiniog. The UK Met Office

74

(2014) report that 2014 was the driest September since records began in 1910 but occurred

within a wet year.

Despite the drought, phenolics in the treatment plot were significantly higher than the trampled

plot by the sixth week post-treatment. The drop off in phenolics (and DOC) in the treatment

plot by the fifth week post-treatment is likely to be explained by a combination of points a, b,

c and d.

a) Tipping and Woof (1990) observed that DOC and particularly phenolic rich compounds

tend to fall after periods of drought due to the physical adsorption onto precipitated

particles drawing it out of solution.

b) A reduction in moisture constraints on phenol oxidase leading to an increased

depletion of phenolics is also supported by various laboratory and field studies (Pind

et al. 1994; Freeman et al. 2004b; Fenner et al. 2005a) and explained by processes

involved in the “enzymic latch” (Freeman et al. 2001a; Fenner & Freeman 2011).

c) Phenol oxidase catabolising bacterial diversity can increase following drought (Fenner

et al. 2005a) and after soil “priming” by labile carbon sustaining phenolic decomposition

after the initial “priming effect” (Fontaine et al. 2003).

d) The positive correlations between phenol oxidase and pH may also partly explain the

trend, as phenol oxidase operates more efficiently nearer the lower limit (pH 8) of its

optimal range (Pind et al. 1994), thus directly or indirectly (through partial phenolic

degradation) increasing the solubility of DOC and therefore phenolics (Toberman et al.

2008).

The drought seemed to cause a steady decline in soil moisture throughout the post-treatment

phase irrespective of treatment, however phenolics correlated positively with soil moisture in

the trampled (significant) and treatment but negatively with soil moisture in the control plot.

The observed correlation could be explained by plant senescence occurring later in the year

with undisturbed control conditions (Fenner et al. 2001; Fenner et al. 2005b) which was not

observed in the treatment or trampled plots as trampling may have forced leaf litter deposition

earlier than usually expected in the season. This is also backed up by the larger increase in

leaf litter deposition in the treatment (almost double) and trampled plots relative to the control

after six weeks. This argument is strengthened by the spike in phosphatase, xylosidase

(treatment and trampled) and SOM (trampled) relative to the control plot following treatment.

75

The “priming effect” of fresh root exudates stimulating enzyme activity in the treatment and

trampled plot also supports this argument and suggests that decompositions may be limited

by phosphate as well as labile carbon at Cors Goch (Lynch & Whipps 1990; Baggs et al. 2003;

Freeman et al. 2004a; Kang et al. 2005b). This timing of natural senescence coupled with late

September rainfall potentially washing out DOC that had accumulated under “undisturbed

drought” conditions may also explain why phenolics (and DOC) increased in the control plot

after the sixth week (Freeman et al. 1998b; Fenner et al. 2001; Fenner et al. 2005a).

The positive correlation between phenol oxidase and phenolics (and DOC) in the treatment

and trampled plots could be explained by the relative availability of phenolics in these plots

following trampling disturbance relative to the control. Toberman et al., (2010) attributed this

trend to the formation of fully soluble phenolics outweighing the initial stages of phenolic

breakdown from recalcitrant insoluble phenolics. In this study the “priming effect” as described

above may have led to this scenario through microbial activity stimulating partial degrading of

insoluble phenolics. The treatment period also occurred “pre-drought” and coincided with more

favourable soil temperatures for enzyme activity in August (Kang et al. 2001; Freeman et al.

2001b; Fenner et al. 2005b; Kang et al. 2005a) exacerbating this “priming effect” . The “late”

drought and the potential recalcitrance of the available phenolic material in the control plot

relative to the treatment and trampled plots may also explain the unexpected significant

positive correlation between phenolics and soil temperature in all three plots.

Greenhouse gases

CH4 fluxes have a complexity of regulatory variables including soil temperate, sulphate

concentration, water table, (Freeman et al. 1993b, Kang et al. 1998) and drought frequency

(Hughes et al. 1999). Gray et al., (2013) propose that plant functional type is the master

controller of CH4 flux. Turetsky et al., (2014) also suggest fen CH4 fluxes are more sensitive

to vegetation and less sensitive to temperature than bogs or swamps. CH4 fluxes in this study

are comparable with Kang et al., (1998) who found ranges between 0.32 & 240 mg m-2 day-1

but observed a temporal decline in CH4 flux from July to September. This suggests that

decreasing soil temperatures, along with high sulphate concentrations and low soil moisture

observed in this study during September should also favour a temporal decline in CH4

emissions. However, a significant negative correlation between soil temperature and CH4 was

observed in the control plot but not the trampled or treatment plot which may be explained

points a and/or b.

a) It is acknowledged that the GHG chambers used in the experiment lead to self-

selection of the shorter vegetation, leaf litter and bare ground due to height constraints.

As a result of the variations in type of vegetation cover between sampling points, not

76

only will an underestimated CH4 flux be observed by not accounting for taller vegetation

which formed a majority of the cover, but can potentially lead to discrepancies as plants

minimise CH4 oxidation by proving shortcuts to the atmosphere through

aerenchymatous tissue (Whiting & Chanton 1992) and stimulate methanogenesis via

root exudation (Whiting et al. 1991). Roura-Carol & Freeman (1999) also found that

excluding plants significantly reduced CH4 emissions from Welsh riparian peat cores

supporting this argument.

b) A result of reduced xylosidase activity in the control plot relative to the trampled

(significantly) and treatment plots in the week immediately following treatment and/or

significantly less leaf litter found in the control plot at the end of the study. Restrictions

on labile carbon, a potential substrate for methanogenesis (Whiting et al. 1991;

Freeman et al. 2004a) during late August would be limiting to CH4 production.

Moreover, conditions favourable to CH4 production, assuming high rainfall and higher

soil moisture approximated to high water table, were prevalent in late August.

The slight although significant difference in CH4 flux observed between treatment and control

plots immediately following treatment is consistent with increased disturbance as speculated

by previous studies (Cooper et al. 2014). This may also explain the positive correlations

between CH4 and phenolics in the treatment (significant) and trampled plotx but negative

correlation in the control plot.

The spike in CH4 emissions in the treatment plot may be delayed response to the “priming

effect” as the inhibitory effects of the phenolics and soil moisture on decomposition became

less pronounced during onset of drought. This may have been exacerbated by CH4 “ebullition

events” resulting from pressure changes following water table drawdown consistent with

Moore et al., (1990) on a northern fen. Indeed, the lack of spike in SOM following treatment in

the treatment plot suggests an inhibitory effect as a large peak was observed in the trampled

plot. However, the first post-treatment sampling occurred 1-19 and 8 days prior to sampling in

the treatment and trampled plots respectively, and the strongest impact of the “priming effect”

in the treatment plot on SOM was potentially missed due to its transient nature (Hamilton et

al. 2008; Bird et al. 2011).

The “levelling off” of CH4 emissions in all plots following the post-treatment spikes could be

explained by either drought conditions maintaining an elevated ratio of aerobic to anaerobic

conditions, thus favouring methanotrophy over methanogenesis (Gorham 1991; Freeman et

al. 2004a) and/or elevated levels of sulphate supressing methanogenesis due to competition

with sulphate reducing bacteria for substrate (Freeman et al. 1994) or a slight reduction in

77

productivity experienced during drought stress (Dowrick et al. 2006) which is backed up by

the a slight but unexpected increase in CO2 flux in the control plot during September.

Importantly, there was no in increase in N2O flux following the September drought, as re-

wetting following drought can increase emissions in as observed in previous laboratory and

field studies (Freeman et al. 1993b; Freeman et al. 1997b). The range of N2O flux means in

this study is comparable to Kang et al., (1998) despite the stimulation of N-acetyl-β-D-

glucosaminidase providing sources of nitrate and labile carbon which has the potential to

stimulate denitrification (Rogers et al. 1998).

The observed temporal decline in CO2 flux can be attributed to less favourable optimal

temperatures for enzyme activity and therefore metabolism during autumn/winter. CO2

increases in the control between the 11th and 24th of September, during the period of drought

are consistent with the findings of previous laboratory drought studies in peatlands (Freeman

et al. 1993a). The negative correlation between soil temperature and CO2 flux in the treatment

plot (insignificant) can be explained by the “late” timing period of drought possibly coupled with

a low sample number. The timing of drought can also explain the positive correlations between

CO2 and soil moisture in the trampled plot (n = 12, r = 0.718, p <0.05) and treatment plot

(insignificant). It is also noted that a larger sample size may have returned the expected

positive correlation. Self-selection of litter / bare ground / shorter vegetation may have also

effected results, as well as providing a flux overestimation as a large proportion of the

photosynthesis will occur in the taller plants due to their overall dominance. Although

experimentally, it is too early to draw any firm conclusions regarding CO2 emission reductions,

the treatment has returned lower CO2 fluxes from the last two sampling weeks when compared

to the “undisturbed control”.

Nutrients and enzyme-substrate interactions

Sulphate rose in all plots to varying degrees of sharpness in late September. This increase in

sulphate may be explained by one or a combination of points a, b or c.

a) A reduction in assimilation by plants and dissimilation by sulphate reducing bacteria in

the autumn upon cessation of the peak growing season (Urban et al. 1989).

b) A product of September drought mediated re-oxidation of sulphide, the major form of

reduced insoluble sulphur in wetlands, being released upon heavy rainfall in late

September (Freeman et al. 1993a; Eimers et al. 2007; Whitfield et al. 2010) which can

enhance natural autumn/winter pulses of sulphate in peatlands (Hughes et al. 1997).

78

c) A product of September drought conditions leading to heightened arylsulphatase

activity mediated by lower levels of phenolic suppression in the control and trampled

plots, consistent with Freeman et al., (1997a) who found increased sulphatase

activities following drought. However, it is interesting that with supplementary

phenolics, sulphate levels remained significantly lower than the control plot and could

be explained by a suppression of process in point b. Freeman et al., (2004b) observed

a significant 47% increase in sulphate concentrations by switching phenolic rich water

with ultrapure water, in effect the reverse treatment to the present study but conforming

to the same inhibition properties.

Bromide concentrations have been shown to increase dramatically following a rewetting event.

Hughes et al., (1996) attributed this to physical leaching and increased solubility rather than

an effect of increased mineralisation during drought. Neal et al., (1997) attribute strong

seasonal cycles of bromide to an autumnal pulse during natural plant senescence and decay

which are then leached out upon disturbance. Therefore, the significantly higher bromide

concentrations in treatment compared to both trampled and control plots could be attributed

to a one or a combination of points a, b and c.

a) A disturbance effect to such magnitude in the treatment which was only partially

replicated in the trampled plot. Figure 10a highlights this well, showing a greater

difference in leaf litter deposition in the treatment relative to the trampled plot.

b) Phenolic inhibition of mineralisation in the treatment sustaining naturally high levels of

bromide which would usually dissipate naturally with time (Hughes et al. 1998) as seen

to a greater extent in the trampled and control plots.

c) An intrinsic property of the spatulas used as a source of treatment, which would

warrant further investigation.

The increase in nitrate concentrations in the control and trampled plots and lack of increase

in the treatment plot during post-treatment cannot be explained by a greater suppression on

N-acetyl-β-D-glucosaminidase in the treatment plot. It is likely that increased oxidation of

ammonium by aerobic nitrifying bacteria (Freeman et al. 1993a) during drought was observed

in the control and trampled which was not observed in the treatment as a consequence of pre-

existing higher soil moisture levels in the treatment relative to the trampled (significantly) and

control plot. The activities of nitrifying bacteria may have been lower in the treatment relative

to the other plots under pre-existing “moister” conditions and the onset of drought only served

to increase nitrifyer activity in the control and trampled plots’ relative to the treatment. This is

79

supported by studies on microbial gene diversity suggesting shifts in microbial activity

following short-term droughts (Fenner et al. 2005a; Jaatinen et al. 2007; Kim et al. 2008).

General temporal comparisons

The late summer/early autumn timing of the experiment represents the peak and early decline

in soil temperatures for calcareous fens in North Wales with water table being at its lowest

during this time of year (Kang et al. 1998). September is also the peak time for hydrolase

enzyme activity (Freeman et al. 1998b; Kang & Freeman 1999) in Welsh peatlands which was

alos observed in this study. The high concentrations of calcium and magnesium, SOM around

89% and pH around 6-7 are comparable to Kang & Freemans’ (1999) study at Cors Goch in

Juncus and Festuca vegetation, although soil temperature was roughly 2 °C lower when

compared to July – September in the authors’ study. Sulphate concentrations in September

were 5 fold lower in the same study, however the authors’ attributed this to high sulphate

deposition (possibly marine origin) rather than mineralisation. Nitrate concentrations were five-

fold lower in a nearby fen (Kang et al. 1998) during the same time period.

9.0 Suggested further study

In future studies, a continuous monitoring regime of water table would be beneficial especially

as the fen is an open system in terms of hydrology. It would also be beneficial to replicate this

study using bogs as more than half of the world’s peatlands originate from Sphagnum (Fenner

et al. 2004), which offers a different hydrochemistry to fen peat (Mitsch & Gosselink 2011) and

to research alternative sources of phenolic treatments.

It is also recommended that heavy vegetation trampling is kept to a minimum in order to avoid

detrimental impacts on the vegetation community, the foundation for active peatland growth.

One way of achieving this is to conduct any treatment post-growing season. Indeed, Freeman

et al., (2012) highlight that although geoengineering can be controversial on “pristine” peatland

ecosystems it can prove useful if used along with other traditional conservation measures such

as re-wetting by ditch blocking or re-seeding degraded systems, however further research is

needed to investigate the full application of this novel conservation measure.

80

10.0 Conclusion

It is clear that additional phenolic concentrations are supressing the activity of hydrolase

enzymes in the treatment plot as observed with arylsulphatease, but it remains unclear as to

whether the source of the “supplementary” phenolic compounds are a product of leaching

from the wood or disturbance of the plants producing a “priming effect”, despite the procedural

controls put in place. As the only enzyme that displayed a clear, although insignificant

reduction in activity was arylsulphatase, it seems likely that this enzyme is less sensitive to

the buffers of the “priming effect” as the fresh inputs of labile organic matter through root

exudates seemed to override the effects of the phenolic compounds’ potential additional

suppression in the other hydrolase enzymes. Moreover, the project post-treatment phase

experienced a month long drought in September which can strongly influence some of the

biogeochemical processes in wetlands. As water is the chief driver of biogeochemical

processes in wetland, it is not surprising to find these results during “natural” drought periods.

It is however notable, that suppression of sulphate mineralisation continued even with drought

conditions which is a promising result. The timing of the study coinciding with early plant

senescence and a record breaking dry September month also provided an interesting but

complex dynamic and the results should be put into context of this. Although there were no

significant decreases in CO2, it is promising that four weeks after the initial disturbance, CO2

was lower in the treatment plot than the “undisturbed” control. However, it would be unwise to

estimate any CO2 emission reductions at this early stage due to confounding effects of

disturbance on pore water chemistry and soil organic matter. It is also interesting to find that

apparent autogenic increases in bromide were not ameliorated as efficiently in the presence

of phenolic inhibitors (treatment plot) and could have consequences for water quality and plant

nutrient interactions.

It should also be taken into account that these are early stages of this project and as such

these short-term findings should be regarded with a little caution. Further monitoring is

necessary to assess the impacts of long-term phenolic supplementation of fen peat with wood

to rigorously test the hypothesis first proposed by Freeman et al., (2012) and the focus of this

study. Furthermore it is still too early to assess the full effects of trampling and phenolic

enrichment on the interactions and potential competition for nutrients between the microbial

and plant communities (Freeman et al. 1998a).

81

11.0 Appendix

A1. Randomised block design

Appendix A1. Randomised block showing how plots were assigned to undisturbed control (0, white squares), trampled (1, light orange squares) and phenolic treatment (2, dark orange squares) in the study. Light blue = 1 meter buffer strip

A2. Phenolic treatment addition: developing a field methodology

Appendix A2. Method of spatula submersion into the peatland. Boxes of 100 spatulas were

emptied over a plot replicate (4 x 100 at a time) with one box approximately covering each

quarter of the 2 x 2 m replicate. Sticks were then inserted half way into the peat by hand and

then either inserted the remainder of the way be hand until top of the spatula was below the

peat surface or if this was impeded by a woody rhizosphere making submersion by hand

difficult the Duck tape was used to strike the spatula in to ground with force until it submersion.

Logistically speaking, phenolic addition was best accomplished whilst lay on the ground for

increased comfort. Long heavy duty rubber gloves and no bare skin are recommended for

82

health and safety purposes whilst commencing work in Cladium mariscus which has a serrated

edge capable of damaging skin.

A3. Phenolic treatment density calculations

Part 1

The density of wooden spatula treatment was based on the density at which significant results

were achieved by Dunn (2013). The GHG chamber radius used to measure gases was

identical to the mesocosm radius therefore GHG measurements could be up scaled based on

area of GHG chamber as both studies used roughly cylindrical chambers to determine GHG

fluxes.

a) Area of GHG chamber used in Dunn (2013) = 78.45 cm2

b) Area of GHG chamber used in this study = 176.71 cm2

c) Number of spatulas that provided the best results in Dunn (2013) = 6

b/a = 2.25

Therefore, area b is 2.25 times larger than area a, thus requires 2.25 times more spatulas to

upscale appropriately in this study based on chamber area.

c x 2.25 = 13.5

d) Therefore, spatula density = 13.5 per 176.71 cm2

Part 2

In order to sample at the same spatula density for each field visit, the area of a plot replicate

needed to be considered to extrapolate how many spatulas were needed to achieve this

density

e) Area of 2m x 2m replicate = 400,000 cm2

e/b = 226.35

Therefore, the amount of spatulas required per plot replicate to achieve an even density was

calculated as;

226.35 x 13.50 = 3055

The total number of spatulas required for four replicates was therefore 12,223

*As the average weight of 16 spatulas was 2.4 grams this equates to 7.3 kg of wood per

replicate, which is roughly 1/3 to 1/2 the weight of a standardised wooden shipping pallet

depending upon country.

A4. Sample loss

Anion, cation and pH data was removed from plots B and E (both trampled) for the sampling

visit on the 11/09/2014. B was removed due to potential contamination with hydrochloric acid

used to acid clean the 20 ml NalgeneTM vials in which samples are stored and E was removed

due to potential contamination with the pH probe, therefore n = 2 for this population. Electrical

conductivity data was removed from the experiment due to instrument calibration error.

A5. Linearity tests for GHG’s

a)

83

b)

c)

Appendix A5. Test for linearity after 30, 60 & 120 minutes to determine the time of trace gas a) carbon dioxide b) methane c) nitrous oxide saturation concentrations in the chamber headspace.

A6. Phenolics calibration graph

84

Appendix A6. Phenolic concentration calibration graph used to determine the

leachable phenolic concentrations of the wooden spatulas.

A7. Distance to rain gauge

Appendix A7. Distance between automated weather station at Cord Erddreiniog to

the west and the study site to the east. White line represents 2.6 km. North is directly

up.

A8. Rainfall data at Cors Erddreiniog weather station

85

Appendix A8. Total rainfall in mm over the project period from 14th July 2014 to the 30 September 2014 at Cors Erddreiniog

A9. Time series of means for cations

a)

b)

c)

86

d)

e)

Appendix A9. Time series plot of means plus standard error for a) ammonium b) potassium c) calcium d) magnesium e) sodium. Vertical dashed line indicates time of treatment. Solid line = control, dashed line = phenolic treatment and dotted line = vegetation trampling. See appendix 4 for samples loss details.

87

A10. Photo-monitoring results and vegetation survey

Appendix A10. Photo-monitoring results and vegetation survey tables with % cover and % cover change for all replicates before

and after manipulation. Replicates (A-L) correspond with appendix A1. Bold = % cover increase.

Control Plot C % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Bryophytes Bare ground

Leaf litter Hypericum perforatum

20 20 50 50 5 20 20 0

25 5 5

70 3

20 35 1

25% -75% -90% 40% -40%

maintained 75% new

88

Control Plot F % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Bryophytes Bare ground

Leaf litter Galium uliginosum

Hydrocotyle vulgaris Cirsium palustre

Hypericum perforatum Potentilla palustris unknown sedge

50 5 40 45 5 5 40 5 5 1 0 0 0

30 3 5

60 2 3

50 1 2 1 1 1 1

-40% -40% -88% 33% -60% -40% 25% -80% -60%

maintained new new new

89

Control Plot H % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Phragmites australis Cladium mariscus

Bryophytes Bare ground

Leaf litter Hypericum perforatum

15 20 30 1 5

45 0

15 0 75 1 5 50 1

0% -100% 150% 0% 0%

11% new

90

Control Plot I % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Hypericum perforatum

Galium Bryophytes Bare ground

Leaf litter Prunella vulgaris

Ranunculus flammula Potentilla palustris 2 x unknown forbs

30 12 35 15 1 2 5 5 25 1 1 0 0

30 1 5

60 1 0 2 5

50 0 0 1 1

maintained -92% -86%

maintained maintained

-100% -60%

maintained 100% -100% -100% new new

91

Treatment Plot A % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Bryophytes Bare ground

Leaf litter Potentilla palustris

40 5 60 20 5 10 40 1

15 2 1

60 2 3

65 1

-63 -60 -98 200 -60 -70 63 0

92

Treatment Plot G % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Phragmites australis

Cladium mariscus Hypericum perforatum

Bryophytes Bare ground

Leaf litter Cirsium palustre Viola palustris

20 50 50 1 2 5 35 1 0

5 1

80 0 1 2

60 0 1

-75 -98 60

-100 -50 -60 71

-100 new

93

Treatment Plot J % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Bryophytes Bare ground

Leaf litter Equisetum palustre

30 5 70 60 2 5 35 1

5 0 2

30 0 2

80 0

-83% -100% -97% -50% -100% -60% 129% -100%

94

Treatment Plot L % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Phragmites australis

Cladium mariscus Bare ground

Leaf litter

30 25 40 20 30

5 0

40 3

75

-83% -100%

maintained -85% 150%

95

Trampled Plot B % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Bryophytes Bare ground

Leaf litter Luzula campestris

50 15 45 60 10 10 40 0

20 1 0

60 2 3

50 1

-60% -93% -100%

maintained -80% -70% 25% new

96

Trampled Plot D % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Bryophytes Bare ground

Leaf litter Potentilla palustris unknown sedge

Hydrocotyle vulgaris

20 1 30 40 2 5 50 0 0 0

15 0 1

70 2 5

60 1 1 1

-25% -100% -97% 75%

maintained maintained

20% new new new

97

Trampled Plot E % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Juncus subnodulosus Phragmites australis

Cladium mariscus Hypericum perforatum

Bryophytes Bare ground

Leaf litter Potentilla palustris

20 10 50 50 5 10 5 30 1

5 3 1

45 0 1 1

60 1

-75% -70% -98% -10% -100% -90% -80% 100%

maintained

98

Trampled Plot K % cover pre-treatment (14

July 2014)

% cover post-treatment

(30 September 2014)

% cover change

Species

Myrica gale Phragmites australis Cladium mariscus

Bryophytes Bare ground

Leaf litter

30 40 30 5

40 30

5 2

60 1

20 50

-83% -95% 100% -80% -50% 67%

99

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