KING’S COLLEGE LONDON
University of London
Linking Soil Organic Carbon (SOC) Storage
to Ecological Successional Stages in the
Urban Plant Communities of the London
Borough of Southwark, UK
Sophie Page
(0726511)
31st August 2012
This dissertation is submitted as part of an MSc degree in ‘Carbon:Science, Society &
Change’ at King’s College London
King’s College London
Sophie Page
KING’S COLLEGE LONDON
UNIVERSITY OF LONDON
DEPARTMENT OF GEOGRAPHY
MA/MSc DISSERTATION
I, Sophie Page
hereby declare (a) that this Dissertation is my own original
work and that all source material used is acknowledged therein;
(b) that it has been specially prepared for a degree of the
University of London; and (c) that it does not contain any
material that has been or will be submitted to the Examiners of
this or any other university, or any material that has been or
will be submitted for any other examination.
This Dissertation is: 11,763 words.
Signed: …………………………………………...…………….
Date: 30 August 2012
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Abstract
Soil samples were taken from a range of sites and depths across the London borough of
Southwark and grouped by successional stage inferred through the UK Phase 1 Habitat
Classification Survey. Loss-on-ignition analysis showed a significant variation in soil
organic carbon between habitats, with the strongest variation between grassland and
wildflower meadow, and mature woodland types (p=.000). Variation was strongest in
the upper sampling depth (0-5cm) (p=<.001). Findings suggest late successional stages
provide the most suitable amount and type of plant detritus suitable for SOC
accumulation in urban soils.
Keywords:
Soil Organic Carbon (SOC), Succession, Woodland, Loss-on-Ignition, Urban
Acknowledgements
I would like to thank Rob Francis for his support and advice throughout my studies,
both undergraduate and postgraduate, especially in providing much needed guidance
for everything statistics-related. I would also like to express my gratitude towards
Trevor Blackall for his time and patience during my never-ending lab work. Finally, I
would like to thank my family for encouraging and allowing me to continue my studies,
and for putting up with my constant worrying!
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List of Contents
LIST OF TABLES............................................................................................................................................. 5
LIST OF ABBREVIATIONS, ACRONYMS AND SYMBOLS ............................................................................. 6
1.0 INTRODUCTION ...................................................................................................................................... 7
2.0 LITERATURE REVIEW ............................................................................................................................ 9
2.1 CONTEXT OF RESEARCH ................................................................................................................................... 9
2.2 THE SOIL CARBON POOL ................................................................................................................................ 10
2.3 GLOBAL & VERTICAL DISTRIBUTION OF SOC ................................................................................................... 12
2.4 CONTROLS ON SOIL ORGANIC CARBON ............................................................................................................ 13
2.5 INFLUENCE OF SUCCESSION ON SOC ................................................................................................................ 16
2.6 MEASURING OR ESTIMATING SOC................................................................................................................... 20
3.0 SOUTHWARK: BACKGROUND & URBAN ECOLOGY ........................................................................... 23
3.1 PHYSICAL CHARACTERISTICS OF SOUTHWARK .................................................................................................. 24
3.2 ECOLOGY OF SOUTHWARK .............................................................................................................................. 25
3.3 CLASSIFYING SUCCESSION IN SOUTHWARK ....................................................................................................... 25
4.0 RESEARCH QUESTIONS & HYPOTHESES ............................................................................................ 26
4.1 RESEARCH QUESTIONS................................................................................................................................... 26
4.2 NULL & ALTERNATIVE HYPOTHESES ............................................................................................................... 27
5.0 METHODOLOGY .................................................................................................................................... 27
5.1 SAMPLE COLLECTION..................................................................................................................................... 27
5.2 SAMPLE HANDLING & PREPARATION .............................................................................................................. 28
5.3 ANALYTICAL METHODS.................................................................................................................................. 29
5.4 DATA TREATMENT & STATISTICAL ANALYSES ................................................................................................. 30
6.0 RESULTS ................................................................................................................................................ 31
6.1 OVERVIEW OF RESULTS ................................................................................................................................. 32
6.2 SOIL ORGANIC CARBON.................................................................................................................................. 34
6.3 SOIL MOISTURE CONTENT.............................................................................................................................. 37
6.4 LASER PARTICLE SIZE ANALYSIS ..................................................................................................................... 38
6.5 STATISTICAL ANALYSES ................................................................................................................................. 41
7.0 DISCUSSION ........................................................................................................................................... 44
7.1 LIMITATIONS ................................................................................................................................................ 44
7.2 KEY FINDINGS ............................................................................................................................................... 45
8.0 CONCLUSION ......................................................................................................................................... 49
9.0 APPENDIX I: ETHICS & RISKS FORMS ................................................................................................. 51
10.0 APPENDIX II: LIST OF SITE CODES .................................................................................................... 52
11.0 REFERENCES CITED ............................................................................................................................ 53
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List of Tables
TABLE 1: NULL AND ALTERNATIVE HYPOTHESES ........................................................................................................ 27
TABLE 2: SUMMARY OF ALL VARIABLES ANALYSED ..................................................................................................... 33
TABLE 3: GRAPHIC REPRESENTATION OF DECLINING SOC WITH SAMPLING DEPTH ............................................. 33
TABLE 4: SUMMARY OF DATA EXPLORATION OF SOC ................................................................................................. 35
TABLE 5: SIGNIFICANCE VALUES OF THE MANN-WHITNEY U-TEST FOR SOC GROUPED BY HABITAT
CLASSIFICATION. ....................................................................................................................................................... 43
List of Figures
FIGURE 1: PROCESSES AFFECTING THE MOVEMENT OF SOIL ORGANIC CARBON......................................................... 8
FIGURE 2: A GRAPHIC REPRESENTATION OF THE SOIL ORGANIC CARBON POOL ..................................................... 10
FIGURE 3: VERTICAL DISTRIBUTION OF CARBON PROPORTIONAL TO 1M. ............................................................... 12
FIGURE 4: THE MOST RECENT GLOBAL MAP OF SOIL ORGANIC CARBON TO A DEPTH OF 1.5M. ............................ 13
FIGURE 5: THE RELATIONSHIP BETWEEN SOIL RESPIRATION RATE AND TEMPERATURE ..................................... 15
FIGURE 6: THE RELATIONSHIP BETWEEN RAINFALL (MM) AND SOIL ORGANIC CARBON ...................................... 16
FIGURE 7: COMPREHENSIVE ANALYSIS OF HOW SUCCESSIONAL ADVANCES ALTER SOIL CARBON ....................... 17
FIGURE 8: ALLOCATION OF ORGANIC CARBON IN DIFFERENT ASPECTS OF VEGETATION....................................... 18
FIGURE 9: THE RELATIONSHIP OF EXPOSURE TIME AND WEIGHT UPON COMBUSTION .......................................... 22
FIGURE 10: SOUTHWARK PARK, LONDON ..................................................................................................................... 23
FIGURE 11: BEDROCK GEOLOGY OF SOUTHWARK ........................................................................................................ 24
FIGURE 13: THE PERCENTAGE OF DIFFERENT HABITAT TYPES IN SOUTHWARK .................................................... 25
FIGURE 14: MEAN SOC (%) AT EACH SAMPLING DEPTH, AVERAGED ACROSS 7 HABITAT CLASSIFICATIONS ... 34
FIGURE 15: BOXPLOTS OF AVERAGED SOC SCORES ACROSS ALL HABITATS. ........................................................... 35
FIGURE 16: BOXPLOTS OF SOC SCORES AVERAGED ACROSS THREE SAMPLING DEPTHS ....................................... 36
FIGURE 17: FREQUENCY DISTRIBUTION OF ALL SOC SCORES .................................................................................... 37
FIGURE 18: BOXPLOTS OF SOIL MOISTURE, AS A PERCENTAGE OF FIELD-FRESH SAMPLE WEIGHT ..................... 37
FIGURE 19: SCATTER PLOT OF SOIL MOISTURE (%) AND SOC (%) ......................................................................... 38
FIGURE 20: FREQUENCY DISTRIBUTION OF AVERAGE PARTICLE SIZE, USING PHI (ɸ) ............................................ 39
FIGURE 21: PERCENTAGE SAND, SILT & CLAY ACROSS ALL SAMPLING LOCATIONS ................................................ 40
FIGURE 22: SCATTER PLOT OF AVERAGE SOIL ORGANIC MATTER (% OF DRY WEIGHT) AND PARTICLE SIZE .... 41
FIGURE 23: RELATIONSHIP BETWEEN SOIL ORGANIC CARBON AND DRY ROOT BIOMASS...................................... 47
FIGURE 24: SUB-DISTRICTS WITHIN THE LONDON BOROUGH OF SOUTHWARK. .................................................... 52
List of Equations
EQUATION 1: LOSS-ON-IGNITION........................................................................................................................ 30
EQUATION 2: SOIL ORGANIC MATTER............................................................................................................... 30
EQUATION 3: SOIL ORGANIC CARBON................................................................................................................ 31
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List of Abbreviations, Acronyms and Symbols
BC
Before Christ
C
Carbon
CO2
Carbon Dioxide
DEFRA
Department for Environment, Food and Rural Affairs
g
Grams
GtC
Gigatonnes of Carbon (109g)
JNCC
Joint Nature Conservation Committee
Kg
Kurtosis
m
Metres
mm
Millimetres
n
Number of Samples
NPP
Net Primary Productivity
p
Critical Value of Significance
PgC
Petagrams of Carbon (1015g)
Sh
Skewness
Sp
Spearmans Rho Correlation Coefficient
SOC
Soil Organic Carbon
SOM
Soil Organic Matter
TOC
Total Organic Carbon
TIC
Total Inorganic Carbon
µm
Microns
X2
Chi-Squared
Φ
Phi
σg
Sorting
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1.0 INTRODUCTION
“The thin layer of soil that forms a patchy covering over the continents controls our
own existence and that of every other animal of the land. Without soil, land plants as we
know them could not grow, and without plants no animals could survive.” (Carson,
1963: 53). Though the image of soil portrayed in Rachel Carson’s widely credited Silent
Spring is somewhat romanticised, it does emphasize the vital role that soils provide in
nurturing the natural environment. Composed of mineral particles and organic remains
(Thomas & Goudie, 2000) soil is the principal store of nutrients and water for the
terrestrial biosphere (Trudgill, 2001). Soil also provides several other important
ecosystem services including carbon storage, water and nutrient cycling & regulation,
support for biodiversity (Daily et al. 1997) and even flood management (DEFRA, 2011).
Notions of the value of soil have been expressed for centuries, reaching as far back as
300 BC in the botanical works of Greek philosopher Theophrastus (Trudgill, 2001).
Though the notion of soil as vital to life has always been active, throughout history a
contrast has existed between social constructs of soil resources and mankind’s (mis)use
of them. Human activities have historically been known to cause soil degradation
through unsustainable cultivation practices and the advent of intensive and mechanised
agriculture in the late 1800s (Schlesinger et al. 2000). John Steinbeck’s ‘The Grapes of
Wrath’ (1951) fictionalises the plight of farmers during the Dust Bowl, a period of
severe soil erosion caused by extensive cultivation of American and Canadian prairie
lands during the Great Depression. In more recent years pollution from industrial
activity has played a role in contaminating soil resources (De Kimpe & Morel, 2000).
Despite advances in science and technology there is still a strong inconsistency between
the high value we place upon soil ecosystem services (UK National Ecosystem
Assessment, 2011; Dominati et al. 2010) and our treatment of them. Most recently these
discrepancies have arisen within the climate change domain (Bouwman, 1990).
Soils are an important component of the global carbon cycle, holding one of the largest
terrestrial pools of carbon (C) estimated at approximately 1500 Petagrams of carbon
(PgC), twice as large as the atmospheric pool (Lal, 2004a; Schimel et al. 2000).
Consisting of both organic (SOC) and inorganic carbon (SIC) the global soil C pool is a
system of dynamic equilibrium (Lal, 2004a) though soil stocks of carbon are currently
in decline at an estimated rate of loss of ~0.8 GtC year-1 (Schlesinger et al. 2000). This
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represents a significant component of the flux of CO2 into the atmosphere (Figure 1) and
a decline in the potential of soil carbon in mitigating the threat of future global warming
(Lal, 2004b).
Figure 1: Processes affecting the movement of soil organic carbon (SOC) between the carbon
pool and atmosphere (from Lal, 2004a).
Soil carbon affects and is affected by plant productivity in the overlying terrestrial
biosphere (Jobbagy & Jackson, 2000) and historically human activities, namely
cultivation, have played a large role in the depletion of C stocks (Schlesinger et al. 2000;
Bouwman, 1990). Pre-industrial cultivation of virgin lands is believed to be responsible
for a loss of 20-40% of native SOC (Mann, 1986; Davidson & Ackerman, 1993) and the
intensive style of agriculture practised since the late 19th century has contributed
further (Bouwman, 1990). Identifying land management practices that are the most
sympathetic to soil carbon stocks now form a key part of climate change mitigation
literature and policy (IPCC, 2007), with the potential to turn agricultural land from a
carbon source to a sink (Lal, 2004b; Post & Kwon, 2000).
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2.0 LITERATURE REVIEW
2.1 Context of Research
While climate change remains a highly contentious issue there is now a consensus
among scientists that human activities are causing unequivocal warming of the Earth
through the release of greenhouse gases into the atmosphere (IPCC, 2007a). Emissions
of carbon dioxide (CO2) by fossil fuel combustion have been rising dramatically since
1750 (Keeling et al. 2005) and consequently strategies for climate change mitigation are
comprised mostly of efforts to reduce ‘CO2 sources’ and improve ‘CO2 sinks’ (IPCC,
2007b). The scale and dynamicity of the terrestrial soil carbon pool means that small
changes to carbon storage could have significant effects on levels of atmospheric CO2
(Lal, 2004a). The organic carbon pool is currently being depleted at a rate of
approximately 0.8 Pg C year-1 (Schlesinger et al. 2000) however this is a reversible
process and a growing body of research exists on the potential of the soil carbon pool to
become to become a sink rather than a source of carbon (Jones et al. 2005; Guo &
Gifford, 2002; Post & Kwon, 2000; White et al. 2000; Post et al. 1982). Mitigation
strategies of this type cite changes in land-use and management as showing the greatest
potential to enhance soil carbon sequestration (Post & Kwon, 2000). Terrestrial
biomass supplies soil with most of the organic matter that comprises the global soil
carbon pool thus strategies to enhance carbon stocks commonly involve controlling
vegetation so as to gain the greatest input of decomposing plant material into the
ground (FAO, 2001).
Much of the focus has been upon improving the fertility and productivity of agricultural
soils (Freibauer et al. 2004 (Europe); Smith et al. 2000 (UK); Robertson et al. 2000
(USA)) cited as having a sink capacity of an additional 0.9
0.3 Pg C year-1 (Lal, 2004; a
Lal 2000b; Lal & Bruce, 1999). This potential is based upon the substantial loss of SOC
from agricultural soils as a result of the cultivation of virgin land and the intensive
practices used upon them (Paustian et al. 2000). The conversion of land-use from nonurban to urban also has the potential to drastically transform the SOC pool and its fluxes
to and from the atmosphere (Lal, 2004b). SOC in urban landscapes has been observed at
higher densities than in suburban and rural areas (Pouyat et al. 2002) once again
suggesting the role that human activities play in controlling the amount of carbon
stored in the world’s soils. Urban areas are growing in size and population and their
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burden on the climate system is evident; cities alone cover 2% of global land cover yet
are commonly cited as accounting for 70% of global greenhouse gas emissions (UNHABITAT, 2011). Little is known about the effects that a sprawling urban land cover has
on the biogeochemical cycles of soil (Byrne, 2007) though urban soils do have the
capacity to store much greater quantities of SOC (Lorenz & Lal, 2009). With improved
knowledge urban soils could be adapted enhance their carbon storage and thus to
mitigate climate change (Lorenz & Lal, 2009).
2.2 The Soil Carbon Pool
Soils hold the largest store of carbon in the terrestrial environment (Schlesinger, 1990
& 1995; Jobbagy & Jackson, 2000; Wigley & Schimel, 2000). Several attempts have been
made to quantify the pool of organic carbon in soils (Bouwman, 1990) though the most
comprehensive studies cite 1500 Pg as the most accurate estimate (Schlesinger, 1990,
1995; Eswaran et al. 1993; Post et al., 1982; Schlesinger, 1977). Carbon is transferred to
the soil store by the CO2-fixing biosphere whose dead plant residues, including leaves,
roots and tree debris, contribute organic matter to the soil surface (Schlesinger, 1997).
This ‘litter layer’ of plant detritus on the surface of the soil approximates 55 Pg C year-1
(Wigley & Schimel, 2000) and as the global value of net primary production (NPP) is of a
similar value (50 Pg C yr-1; Potter et al., 1993) the residence time of surficial litter is
roughly 1 year. The journey of organic matter through mineral soil is relatively simple
(Figure 2) with only small amounts accumulating as permanent additions in the lower
depths of the soil profile.
Figure 2: A graphic representation of the soil organic carbon pool in dynamic equilibrium (from
Schlesinger, 2000).
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Organic matter in the soil is compartmentalised into three fractions; fresh and
decomposing plant residues, decomposed humic materials, and a small proportion of
living microbial organisms (Bouwman, 1990). It is these organisms which are
responsible for transporting carbon to the lower depths of the soil profile through the
decomposition of plant residues. A large proportion of organic matter will be
decomposed quickly in the upper layers of the soil and carbon returned to the
atmosphere as CO2 (Sumner, 1999). This efflux contributes to the collective term ‘soil
respiration’ which describes the process by which the carbon is returned to the
atmosphere by soil fauna, roots, and microbial organisms (Schlesinger, 2000). This is an
important factor in the cycling of soil carbon and its global value is estimated at a flux of
68 Pg C year-1 (Raich & Schlesinger, 1992).
The remaining organic matter which is not readily decomposed undergoes the slow
process of “humification”, an advanced form of decomposition that ultimately produces
a set of humic substances (fulvic acid, humic acid and humus) which are chemically
stable and resistant to further breakdown (Ghabbour & Davies, 2001). The organic
compounds precursory to humus are numerous, though lignin, a compound commonly
derived from woody biomass, is thought to be one of the main sources (Foth, 1984).
Though only a small amount of the initial carbon input to the soil ends up as humus, this
part of the soil profile has a turnover time of thousands of years (Jacobson et al., 2000)
and so is considered a permanent C accumulation, holding almost 75% of total SOC
(Holmén, 2000). Processed soil organic matter such as this belongs to what is known as
the “recalcitrant carbon” pool; a long-term store of stable organic carbon with a mean
residence time (MRT) of hundreds to thousands of years. (Cheng et al., 2007). The
“labile carbon” pool is associated with organic material with a much shorter MRT
(several years to decades, though this varies with depth- see Section 2.3), and includes
plant residues and rapidly decomposing detritus.
The rate of long-term carbon accretion is estimated at 0.4 PgC year-1 (equating to 0.7%
of global NPP) and the rate of humification that may contribute to the relcalcitrant
carbon pool annually is between 0.2 and 12 g C per m2 (Schlesinger, 1990). The SOC
pool is currently being depleted at a rate of approximately 0.8 PgC year-1 primarily as a
result of the conversion of natural vegetation for agricultural purposes (Bouwman,
1990). Prior to the advent of widespread cultivation the soil carbon pool was thought to
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be in a near steady-state, remaining unchanged in its magnitude (Schlesinger, 2000).
Cultivation led to a lower input of organic matter, an increase in organic compounds
which rapidly decompose and a lack of physical protection against decomposition (Post
& Kwon, 2000).
2.3 Global & Vertical Distribution of SOC
The dynamics of the soil carbon pool and its origins in above- and belowground plant
biomass result in a vertical distribution of organic carbon that decreases with depth
(Jobbagy & Jackson, 2000 & 2001). Organic matter decays exponentially with depth
(Bernoux et al., 1998) though the allocation of carbon within the soil profile varies
greatly with location, the main controls on which are thought to be climate, soil type
and vegetation (Jobbagy & Jackson, 2000; see Sections 2.4 & 2.5). These controls are
thought to have the strongest impact upon the top 0.2 metres (m) of soil in most biomes
(Figure 3), though soil texture shows strongest influence within deeper layers (Jobbagy
& Jackson, 2000). Studies of the vertical distribution of SOC are typically limited to 1m,
within which the global estimate is approximately 1500 PgC (Schlesinger, 1990, 1995;
Eswaran et al. 1993; Post et al., 1982; Schlesinger, 1977). Interestingly when a second
meter is included, this estimate rises by 60% (Batjes, 1996).
Figure 3: Vertical distribution of carbon proportional to 1m in grasslands, shrublands and
forests from a study of <2500 soil profiles (from Jobbagy & Jackson, 2000).
Attempts to map the global distribution of SOC are similarly inconclusive and must rely
on regression models that incorporate numerous environmental variables in order to
predict SOC across the world (Meentemeyer et al., 1985; Zinke et al., 1984). Given the
difficulty and accuracy issues in creating them, mapped global distributions of SOC are
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rare. Lawrence & Slater (2008) have produced the most recent global SOC map (Figure
4) using data from the ORNL Distributed Active Archive Centre used to create a gridded
representation (1° by 1°) of soil down to a depth of 1.5m. This shows that per kg m -2
SOC is highest in Siberia, Canada and Scandinavia, and lowest in arid and semi-arid
regions such as northern Africa. Climate has long been thought of as the main control
on the global distribution of SOC (Meentemeyer et al., 1985) but as its foundation lies in
NPP, increasingly vegetation is seen as a controlling factor (Post et al., 1982; Jobbagy &
Jackson, 2000; Kucharik et al., 2000; Cramer et al., 2001).
Figure 4: The most recent global map of soil organic carbon to a depth of 1.5m (from Lawrence
& Slater, 2008; using ORNL DAAC data).
2.4 Controls on Soil Organic Carbon
i. Mineralogy
The underlying geological material from which soils are formed can influence the
amount of organic carbon that is stored in overlying layers. Thorn et al. (1997)
discovered a positive relationship between SOC and the amount of non-crystalline
minerals in parent materials, influencing carbon storage on the same magnitude as
climate or vegetation. Further to this, acidic rocks are considered to be less fertile than
more basic rock types, a factor which ensures more inputs of SOM from the terrestrial
biosphere (Bouwman, 1990).
ii. Pedology
SOC may be influenced by a number of edaphic factors: soil texture, moisture, structure,
fertility and preservation capacity (Bouwman, 1990). Heavy-textured soils with larger
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particle sizes (sand) show slower rates of decomposition and thus slower rates of SOC
storage (Christensen, 2001; Olk and Gregorich, 2006). Coarser soils may also slow
carbon storage by limiting the movement of microorganisms that decompose organic
matter (Bouwman, 1990). Finer, clay soils tend to provide the best environment for SOC
storage owing to their negative surface charge which encourages adsorption of nutrient
cations under a strong bond (Trudgill, 2001). This phenomenon is well documented
(Jobbagy & Jackson, 2000; Krull et al., 2001 ;) and a linear relationship between clay
content and SOC has been recognised (Schimel et al., 2004).
Water saturation favours reduction over oxidation processes (Smithson et al. 2004),
which are detrimental to the soil organisms responsible for the decomposition of plant
debris (Schlesinger et al. 2000) thus lowering the possibility of SOC storage (Rutherford
et al., 1992). Soil fertility has the opposite reaction: soils richer in minerals will favour
quicker decomposition (Bouwman, 1990). High mineral content occurs when fertilizer
is applied and when plant root uptake is minimal, and results in the stimulation of
microorganisms (Bouwman, 1990).
iii. Climate
Soil organic carbon is determined by the balance of inputs derived from NPP, to outputs
from decomposition (Schlesinger 1977) and climatic factors influence both sides of this
equation.
Temperature
has
a
varied
influence
on
vegetation
productivity,
simultaneously enhancing and limiting plant growth across the globe (Nemani et al.,
2003). Slightly elevated temperatures can stimulate NPP through the process of CO2
fertilisation (Friedlingstein et al., 1995) while extreme temperatures can limit plant
growth over much of the Earth’s surface (Nemani et al., 2003). Furthermore increasing
temperatures cause a nitrogen cycle feedback which results in accelerated nitrogen
release and stimulated plant growth. Schimel et al. (1994) attribute much of the
correlation between temperature and SOC storage to this feedback.
There is great interest surrounding the influence of temperature on soil decomposition
as a result of projected temperature increases under global warming scenarios
(Davidson & Jannsens, 2006). While the phenomenon varies across the globe, it is
widely accepted that soil decomposition increases under warming conditions;
shortening the amount of time that soil carbon is stored for (Kirschbaum, 2000).
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Warming conditions could thus result in an accelerated efflux of carbon back into the
atmosphere, something that could be extremely detrimental to the balance of the global
carbon cycle (Davidson & Jannsens, 2006). The positive relationship between ambient
or soil-surface temperature and decomposition rates (Figure 6) is explained by an
Arrhenius type equation (Lloyd & Taylor, 1994). As temperature increases the
activation energy, or the amount of energy required to begin decomposition, decreases
(Lloyd & Taylor, 1994). This phenomenon however, varies across the globe with the
strongest temperature sensitivity in temperate biomes resulting (Lloyd & Taylor, 1997).
Figure 5: The relationship between soil respiration rate and temperature surface soil or
ambient air for a variety of data sets from Lloyd & Taylor (1994).
The relationship between temperature and soil organic carbon is also greatly affected
by precipitation (and thus soil moisture), and vice versa. For example Franzmeier et al.
(1995) found that if soil moisture was constant, then a temperature increase of 10° C
could result in SOM being reduced by up to a half. Soil moisture itself provides a varying
influence on soil organic carbon, with precipitation cited as having both positive and
negative correlations with SOC depending on climate. In arid and semi-arid areas it is
generally accepted that rainfall has a positive correlation with organic matter
decomposition (Bouwman, 1990) (Figure 7). Birch (1958) observed that upon the
rewetting of dry soils the decomposition rate of humic substances increased
dramatically, though Derner & Schuman (2007) found the relationship between SOC
and precipitation to be strongest in the 0 to 10cm soil depths. The study by Derner &
Schuman (2007) on the Great Plains of the U.S. also discovered that the threshold figure
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of annual precipitation at which soil carbon stops being enhanced by rainfall was
approximately 440mm (at 0-10cm) and 600mm at the 0-30cm depth. Conversely in
moist, tropical biomes it is a decrease in precipitation which increases carbon efflux
from the soil to the atmosphere. Cleveland et al. (2010) conducted an experimental
drought and observed that reduced rainfall resulted in increased soil respiration rates,
and thus an outflow of carbon in the form of CO2 into the atmosphere. This phenomenon
had been attributed to amelioration in the anoxic soil conditions that constant water
saturation had created.
Figure 6: The relationship between rainfall (mm) and soil organic carbon values in the Negev
Desert, Israel (from Shem-Tov et al., 1997).
2.5 Influence of Succession on SOC
The carbon carrying capacity of soil is dependent on a number of factors like climate
and soil type but first and foremost it is dependent on the nature of the vegetation
inhabiting it (Guo & Gifford, 2002). The classification of vegetation and ecological
succession theory are strongly linked (Morin, 2011) and as a result much of the
literature surrounding the influence of plants on soil organic carbon, groups vegetation
on its successional status (Gale & Grigal, 1987; Brown & Luo, 1990; Guo & Gifford,
2002). The influence of succession upon the storage of organic carbon in soil (SOC) is
primarily reflected in the net primary production (NPP) of habitats (Foote & Grogan,
2010) though also concerns the impact upon soil parameters (Van der Kamp et al.,
2009). Theoretically NPP increases between early and mid-successional stages, peaks
when leaf area index has climaxed (Chapin et al., 2002) and then stabilises at a lower
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level, reaching equilibrium with the habitat above in mature, late stages (Schlesinger,
2000). Global NPP and SOC levels correspond with one another (Potter et al.¸1993;
Schlesinger, 2000) and so vegetative changes strongly linked to the soil carbon pool,
albeit with a time lag (Foote & Grogan, 2010). The influence of succession on soil
parameters has long been recognised (Connell & Slatyer, 1977) and this manipulation
extends to SOC (Van der Kamp et al., 2009). The effects of succession on soil organic
carbon are mostly documented as change over time, a comprehensive example of which
is by Guo & Gifford (2002) illustrated in Figure 8.
Figure 7: Comprehensive analysis of how successional advances alter soil carbon by Guo &
Gifford (2002). Error bars represent confidence limits at 95% level and numbers in brackets
show the number of observations.
i.
Type of Plant Detritus
As previously mentioned, it is lignin, an organic compound found mainly in woody
biomass, which is responsible for the largest inputs of organic matter into the soil
carbon pool (Foth, 1984). With reference to individual plant parts it is the leaves, roots
and well-decayed wood that contain the largest quantities of lignin compounds and
fresh wood and twigs that contain the least (Pastor & Post, 1986). As lignin is
decomposed very slowly it represents a long-term carbon store. Conceptually, habitats
with high inputs of ligneous detritus like mature forest stands should show the largest
amount of recalcitrant carbon storage (Pastor & Post, 1986). Guo & Gifford (2002)
however, found that in ecosystems of eastern North America when secondary
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succession allowed pasture land to progress into forest, a decline in soil carbon stocks
ensued. This effect was thought to occur as a result of differential above/below-ground
biomass allocation (see Section 2.5.ii.) though was only found to occur in coniferous
stands and not deciduous. This finding highlights the influence of species composition
on SOC (Kirby & Potvin, 2007), whereby deciduous species such as Elm, Oak, Ash and
Birch are seen to produce litter that decomposes more quickly than coniferous trees
such as Pine, the leaves of which are rich in lignin (Foth, 1984).
ii.
Aboveground/Belowground Biomass Allocation
Woodland, representing a mature, late successional stage, often has a large quantity of
slow-decomposing biomass above ground (Kirby & Potvin, 2007; Figure X) and a deep
rooting system (Jackson et al., 1996). The rich NPP that characterises these habitats
often means that when forest succession occurs there is an associated accumulation of
SOC (Foote & Grogan, 2010; Devine et al., 2011) and when forests are cleared there is a
decline (Allen, 1985; Guo & Gifford, 2002).
Figure 8: Allocation of organic carbon in different aspects of vegetation above and below
ground, grouped by habitat type (from Kirby & Potvin, 2007).
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This is not always the case however, as grasslands are often quoted as having a SOC
storage potential equal to, or even greater than, mature forests (Breemen & Feijtel,
1990). This is because grasslands often allocate more biomass to their root system
(Kirby & Potvin, 2007), resulting in large quantities of organic matter belowground
where even distribution within the soil profile, among other factors, favours long-term
carbon storage (Rasse et al., 2005). Rasse et al. (2005) even identified that the mean
residence time of root-derived soil carbon was longer than shoot-derived C and Post &
Kwon (2000) cite some grasslands as having the ability to contribute up to 90% of soil
organic matter. Favouring belowground root-allocation is a phenomenon common in
early successional species primarily as a result of their inherent genetic potential for a
thorough environmental exploration subsequent to pioneering a habitat (Gale & Grigal,
1987). Species colonising late successional stages are generally more shallow-rooted,
tapping resources near the soil surface (Pastor & Post, 1986).
Referring once more to the close relationship between NPP and SOC, Pastor & Post
(1986) propose that soil organic carbon is at a maximum within mature, early
successional stages (Van der Kamp, 2009) when NPP is also at a peak. In Michigan, U.S.
where Aspen (Populus grandidentata) is succeeded by Pine and Oak forest Cooper
(1981) found that NPP was highest immediately before P. grandidentata dies off. After
NPP peaks and vegetation cover eventually stabilises, after a time-lag soil organic
matter will decline and fluctuate around a steady-state level, where the pool of SOC is at
the same magnitude as the carbon held in living biomass (Breemen & Feijtel, 1990).
This equilibrium between the terrestrial biosphere and soil organic carbon is thus only
achieved in climax communities.
iii.
. Species Complexity
The concept of species complexity, succession and SOC features little in soil carbon
literature though is based upon the traditional ecological theory that species diversity is
assumed to peak in pre-climax stages (Connell, 1978). In the U.S. high species diversity
has been seen to favour high soil carbon stocks (Gebhart et al., 1994) and a shift in the
opposite direction has culminated in low SOC storage in (Wedin & Tilman, 1996).
Beyond pre-climax stages vegetative complexity is highest, but biomass declines and
reaches a steady-state in conjunction with soil carbon stocks (Fujisaka et al., 1998).
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2.6 Measuring or Estimating SOC
A wide range of literature exists on methods for the determination of SOC as it is often
this which is used to assess soil quality for a range of services (Schumacher, 2002). At
present destructive techniques are favoured in the measurement and estimation of SOC
and are based upon quantitative measurements of either the organic matter (used as a
rough estimate of SOC) or carbon dioxide lost during the process (Carter & Gregorich,
2008). Non-destructive techniques are relatively new to the field (Wielopolski et al.,
2005) and those that are already in use generate mainly qualitative data, characterising
the structural characteristics of organic compounds within the soil (Schumacher, 2002).
There is no single, universal method for estimating SOC as many of the widely-adopted
destructive techniques have a similar balance of pros and cons (Nelson & Sommers,
1996).
i.
Wet Oxidation
The Walkley-Black method (1934) for determining organic matter provides the
foundation for many chemical oxidation techniques (Blaisdell et al., 2003). It works
upon the principle that a known amount of chromic acid is added to a set weight of soil
and the organic matter present is oxidised (Tinsley, 1950). The organic matter content
is thus determined by the weight loss subsequent to oxidation. There are several issues
with this technique however; firstly the chromate waste produced is a hazardous
chemically and as such must be properly disposed of (Sahrawat, 1982). Secondly this
technique is has been seen to produce results only accurate within soils of 0.4-0.8%
total organic carbon (TOC) (Vos et al., 2007). Lastly, oxidation by chromic acid favours
only easily-oxidisable organic compounds (Schulte, 1995).
ii.
Hydrogen Peroxide Digestion
This technique is once again based on the weight loss incurred after chemical
destruction of organic matter within a soil sample, though it is hydrogen peroxide
(H2O2) which is used to oxidise SOM (Schumacher, 2002). Similarly there are issues
surrounding this method; the oxidation of SOM is often incomplete (Schumacher, 2002)
and varies between soil types (Robinson, 1927).
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iii.
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Dry Combustion
Dry combustion methods often require pre-treatment of soil samples with acid to
remove inorganic carbonates such as calcite and dolomite so as not to interfere with
organic carbon values (Schumacher, 2002). If carbonates are present then hydrochloric
acid (HCl) is added to remove all traces of inorganic carbon. The dry combustion
technique may then proceed, with samples being combusted in a stream of oxygen at
high temperatures that often exceed 1000°C (Nelson & Sommers, 1996). Organic carbon
is then inferred from the amount of CO2 released during combustion and detected by
infrared or thermoconductivity analysers (Schumann, 2002). While the extreme
temperatures used during this technique ensure that all organic carbon is released it is
difficult to ensure that contamination of atmospheric CO2 does not influence final
readings (Nelson & Sommers, 1996). Furthermore the technique is expensive and
therefore often employed only for commercial uses (Schumann, 2002)
iv.
Loss-on-Ignition (LOI)
LOI is an inexpensive destructive technique for acquiring the OM content of soil samples
requiring only a muffle furnace, drying cabinet and scales, equipment that is often
readily available in most laboratories, and relatively safe and simple to use. LOI also
works upon the principle of gravimetric determination of organic matter after its
combustion, though this time at lower temperatures in a muffle furnace (Dean, 1974).
Preparation of samples is important; soil must be dried prior to combustion so that final
weights are reflective of the organic matter lost and not the soil moisture (Schumacher,
2002). The ignition temperature is also an important factor of LOI: combustion of OM
begins at 200°C and is almost completely destroyed at 550 °C (Allen, 1989) and
inorganic carbonates begin to breakdown at 700°C (Santisteban et al., 2004). Most
recent uses of LOI use ignition temperatures of between 350-440°C (Soon & Abboud,
1991) though to ensure complete destruction some studies use temperatures closer to
the upper limit of OM combustion (Allen, 1989; Heiri et al., 2001; Santisteban et al.,
2004). The exposure time obviously is dependent on the amount of organic matter
within the sample though a comprehensive analysis by Heiri et al. (2001) of the times
required for complete OM combustion shows that after 5 hours exposure at 530°C
median weight loss was 98.3%. Figure 5 shows that weight loss increases with exposure
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time however after 4 hours this loss is thought to be associated with loss of volatile
salts, structural water (from clay) and inorganic carbon (Dean, 1974; Heiri et al., 2001).
Figure 9: The relationship of exposure time and weight upon combustion (from Heiri et al.,
2001).
Values of SOM approximated through LOI can be converted to organic carbon based
upon a ratio of organic matter to carbon. A standard value for this ratio is 1.724 based
on the assumption that organic matter is 58% carbon (Howard, 1966; Allen, 1989;
Nelson & Sommers, 1996). There is no universal conversion factor as the SOM: SOC ratio
differs as a result of numerous factors (between 1.72 & 2.5; Schumann, 2002) but when
testing soils of a similar nature it is acceptable to use the same ratio (Allen, 1989).
v.
Non-Destructive Techniques
Non-destructive techniques do not rely on quantification of organic matter as a proxy
for SOC but can provide direct estimations without the extraction of OM. Nuclear
Magnetic Resonance spectroscopy had, until the development of in-situ techniques,
been the most popular non-destructive method (Preston et al., 1994). NMR
spectroscopy distinguishes the chemical structures that are characteristic of newly
formed OM in samples to give an idea of the amount of organic soil carbon (Schumann,
2002). Advancements in the field of SOC measurement have led to the development of
Inelastic Neutron Scattering (INS) which can provide non-invasive SOC measurement in
situ (Johnston et al.¸2000). INS works on the principle of gamma-ray spectroscopy
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which can process information on multiple elements at once (Wielopolski et al., 2005).
This technique is still under development, though marks important progress for the
field as it will allow quantification and monitoring of soil carbon pools (Wielopolski et
al., 2005).
3.0 SOUTHWARK: BACKGROUND & URBAN ECOLOGY
The following section will firstly present a comprehensive overview of the physical
characteristics (Section 3.1) of the chosen sampling location in the Borough of
Southwark, south-east London (Figure 10). So that the relationship between succession
in urban plant communities and soil organic carbon can be quantified, a summary of the
ecological features and green space management practises in the borough has been
provided (Section 3.2). The section will finish with a summary of how successional
stages were classified for the purposes of the study, according to habitat types labelled
using the British Habitat Classification published by the Joint Nature Conservation
Committee (2010).
Figure 10: Southwark Park, London (Google Images, 2012).
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3.1 Physical Characteristics of Southwark
The London Borough of Southwark is a large borough in south east London, England
(Figures 11 & 12) with an area of 2,886 hectares and a resident population of
approximately 286,000 (in 2011; Southwark BAP, 2012).
Figure 11: Bedrock geology of Southwark (outlined section) (from BGS, 2012). Bedrock is
primarily Lambeth Group sand, silt, clay & gravel (orange) with sections of Thames Group
(mauve); Figure 12: Map of the London Borough of Southwark: central, outlined section (from
Southwark Council, 2012a).
The bedrock geology of Southwark is primarily ‘Lambeth Group’ sand, silt, clay and
gravel; a sedimentary bedrock formed approximately 55 to 58 million years ago (BGS,
2012). Surface soils data for the London area is yet to be released by the British
Geological Survey who is progressively surveying urban areas in the UK under the GBASE (2012) program. Elevation determined by a hand-held GPS device varied greatly
across all sampling sites in the borough, with the highest elevation (270 metres above
sea level) found in the far south at One Tree Hill wood.
The climate of Southwark during June, 2012 was determined by average 1971-2000
values for a nearby weather station in Greenwich. Maximum temperatures in June
reach, on average, 20.2° and minimum temperatures reach 11.1°C, and rainfall for the
month is 53mm which falls over approximately 8 days (Met Office, 2012b). During the
month of sampling (June, 2012) the temperature in Southwark was 0.3°C below average
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and precipitation was exceptionally heavy compared to previous years, with the month
recorded as the wettest June since 1910 (Met Office, 2012b).
3.2 Ecology of Southwark
The borough contains over 130 parks and open green spaces (Figure), 59 of which have
been classified of Sites of Importance for Nature Conservation and 5 of which are Local
Nature Reserves (Southwark BAP, 2012). Devised in 2004 the Southwark Biodiversity
Action plan supports the borough’s strong heritage in urban ecological conservation,
and is home to Britain’s first urban ecological park (Stave Hill Ecological Park)
(Southwark BAP, 2012). Despite being a highly-populated, inner-city borough
Southwark contains a diverse network of habitat patches, some of which include
valuable Ancient woodland, home to populations of internationally-threatened species
such as the stag beetle (Southwark BAP, 2010). As one of the greenest boroughs in
London, management of green space in Southwark is largely distributed by the
Biodiversity Action Plan which serves to protect valuable habitats and create new
habitats of rich biodiversity (Southwark Council, 2012b). The maintenance and creation
of wildflower meadows is an important part of the borough’s BAP, to encourage rare
wildlife and a pleasing aesthetic (BAP, 2012).
Figure 13: The percentage of different habitat types in Southwark (from Southwark BAP,
2012).
3.3 Classifying Succession in Southwark
The patchy distribution of urban plant communities (Rebele, 1994) means that in
Southwark there are few examples of “natural” seral stages and instead plant
communities are often halted at early stages of secondary succession by varying
degrees of human disturbance (Niemelä, 1999). While their ecological complexity may
make urban ecosystems appear a difficult area to study, this ecological mosaic
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exemplifies a wide range of communities at different stages of ecological succession. In
order to sample organic carbon in soils supported by vegetation at range of successional
stages, green spaces were grouped into habitat types classified using the JNCC Phase 1
Habitat Classification system (2010). This field survey technique has the highest degree
of repeatability for modified environments (Stevens et al., 2004) so was deemed
suitable for use in urban ecosystems. Sites were initially chosen to be representative of
all sub-districts within Southwark (see Appendix 2) and then surveyed using the Phase
1 Habitat Classification prior to soil sampling. Habitats were chosen to encompass a
range of successional stages, though a high level of human disturbance meant that
stages often represented urban “plagioclimax” seres (Tansley, 1949) as construction,
recreation and maintenance disturbances did not allow succession to progress naturally
(Niemelä, 1999). Seven habitat classifications were used: 1. Improved Grassland; 2.
Semi-Improved Grassland; 3. Wildflower Meadow; 4. Scrub; 5. Plantation Woodland; 6.
Semi-Natural Woodland & 7. Ancient Woodland (JNCC, 2010). Though wildflower
meadow was not classified within the Phase 1 Habitat Survey, because of the high
importance that Southwark BAP places upon these habitats, they were included in soil
surveying.
4.0 RESEARCH QUESTIONS & HYPOTHESES
Following a comprehensive review of the literature surrounding soil organic carbon and
its relationship with ecological succession, this study will seek to answer the following
questions.
4.1 Research Questions

Does the storage of soil organic carbon vary between stages of succession in
urban plant communities?

At what depth is soil organic carbon most variable?

At which successional stages are conditions the most favourable to soil organic
carbon storage?

What are other external influences are there on the organic carbon content of
soils and does succession affect these also?
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4.2 Null & Alternative Hypotheses
Table 1: Null and alternative hypotheses
Hypothesis
HH0
HH1
There is no significant variation in SOC between habitats in different stages of
succession
There is a significant variation in SOC between habitats in different stages of
succession
HD0
There is no significant variation in SOC with depth in the soil profile
HD1
There is a significant variation in SOC with depth in the soil profile
HD2
SOC declines with depth in the soil profile
5.0 METHODOLOGY
Identifying the possible relationship between ecological succession and soil organic
carbon required three stages of methodology; sample collection and handling, lab
analyses and statistical analyses. The organic carbon content of soil was approximated
using values of organic matter acquired through thermogravimetric methods,
specifically the prevalent loss-on-ignition technique (Soon & Abboud, 1991; Nelson &
Summers, 1996; Heiri et al., 2001). 180 samples were collected and analysed in total,
representing urban habitats at 7 different stages of succession (see Section 3.3). Soil
collection was standardised using the British Geological Survey Field Procedures
(Johnson, 2005) as a guide. Lab analysis was carried out at the John B. Thornes Earth
Surface Materials laboratory at King’s College London over two weeks in June, 2012.
5.1 Sample Collection
Careful consideration of extrinsic variables was required so as to observe the
relationship between soil carbon and succession only. Surface soil type, parent material
and land management practices had been considered during the selection of greenspace
sites within Southwark (see Section 3.1) but at a smaller scale the influence of slope had
to be considered when choosing plots. Water saturation favours reduction over
oxidation processes (Smithson et al. 2004) which are detrimental to the soil organisms
responsible for the decomposition of plant debris (Schlesinger et al. 2000). For this
reason if a sloping landform was present within the habitat, plots were placed at the
highest point on the slope, determined by use of a handheld GPS device. In accordance
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with BGS field procedures at each site a square plot (of 100m2) was determined and 5
sample points located; one in each corner and one in the centre of the plot. To gain
insight into vertical variation in soil organic carbon soil was taken at three depths from
each sampling point; 0-5cm, 5-15cm and 15-30cm. Samples from each of the 5 points
within a plot were mixed and 1 composite sample created for each depth, resulting in 3
depth samples at each plot. Composite sampling was important in removing site bias
and gaining a representative average of the plot (Boone et al. 1999). Vegetation was
cleared and the layer of plant residue removed at each sampling point until the mineral
soil was exposed. In accordance with British Geological Survey sampling methods
(Johnson, 2005) a hand-held auger was then inserted into the ground and samples
taken at each depth. To avoid cross-contamination care was taken to clean the auger
between depths and to avoid soil from upper layers falling deeper within the hole.
Samples were collected in the growing season and under the same climatic and weather
conditions (weather remained consistently dry for the duration of sampling, though
rainfall had been persistent prior to sampling: see Section 3.1) to standardise results
(Boone et al. 1999).
5.2 Sample Handling & Preparation
All samples were taken during June 2012 and were analysed in the lab no more than 4
days after collection.
Prior to collection samples were promptly stored in sealed
polythene bags to retard moisture loss. As the correct procedure for storing soil
samples for carbon analysis is still ambiguous (Boone et al. 1999) the samples were
stored, sealed, at room temperature and promptly submitted for lab analysis. Organic
compounds may naturally be lost during the handling process owing to a range of
factors from microbial degradation to oxidation and volatilization but this loss is so
small (<1% of TOC) it is considered as insignificant (Schumacher, 2002). Preparation for
lab analysis firstly required the removal of discrete organic detritus which could lead to
disproportionately higher values of organic matter (Blaisdell et al. 2003). While care
was taken to remove larger inorganic stones and gravel, these are not removed during
heating and so do affect the accuracy of results (Schumacher, 2002). Small ceramic
crucibles were washed, dried, labelled and weighed empty, then filled ¾ full with
prepared sample (and weighed once more) ready for analysis. Weights were
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determined using an OHAUS Pioneer precision balance with a readability of .0000
grams.
5.3 Analytical Methods
i.
Moisture Loss
Moisture content of the soil samples was determined by the weight loss between fieldfresh and dried samples. Samples were dried in the lab drying oven overnight (min. 12
hours) at a low temperature (70:C) so that organic matter was not affected
(Schumacher, 2002) until weight remained at a constant level. Samples were then
placed in a dessicator for 2 hours to eliminate residual moisture and then weighed.
ii.
Organic Matter
Organic matter was used as a rough estimate of total organic carbon content and was
approximated using the loss-on-ignition technique; a gravimetric method that estimates
TOC by removal of organic matter through controlled heating and the weight-loss this
incurs. The few samples that had a high clay content had clumped and hardened during
drying so were ground using a pestle and mortar to a consistency that matched the
majority of samples. This allowed organic compounds to be burnt more readily (Boone
et al., 1999). Samples were then placed in a Carbolite AAF1100 muffle furnace at 550:C
for a minimum of 6 hours (Allen, 1989) until samples had achieved a relatively constant
weight. Samples were left to cool slightly in the furnace then placed in the dessicator
once more to absorb moisture, left to cool to ambient temperature and then weighed a
final time. The difference between the dried weight and burnt weight of each sample
was then recorded as loss-on-ignition values.
iii.
Particle Size Analysis
In order to assess the different fractions of sand, silt and clay the laser particle sizing
technique was used on a composite sample (all three depths) for each site. All samples
were analysed using the Malvern Mastersizer 2000 Laser Particle Sizer which works
upon the principle that light is scattered by grains at varying degrees and this
information is then converted to site-specific particle size distributions (Campbell,
2003). Although the laser sizing is sometimes cited as underreporting the clay fraction
of soil owing to its assumption that grains are uniformly spherical (Campbell, 2003), the
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technique is often cited as producing accurate, consistent, and repeatable results
(Loizeau et al., 1994; Buurman et al., 1997; Beuselinck et al., 1998). Other techniques
such as sieving and pipette analysis produce similar issues in underreporting clay
fractions (Konert & Vandenberghe, 1997) though are deemed to produce results that
are less reproducible in a lab environment (Eshel, 2004). Prior to analysis in the particle
sizer particles were passed through a 2mm sieve to removal large gravel particles.
During the analysis samples were all subjected to 60 seconds of ultrasonication order to
break up aggregate material and sample the individual sand, silt and clay fractions
(Sperazza et al., 2004). Methods of analysis including absorption and obscuration levels
were used according to Sperazza et al. (2004) who present a comprehensive analysis of
particle size analyses from a wide range of experiments.
5.4 Data Treatment & Statistical Analyses
i.
Loss-on-Ignition
As the loss-on-ignition method determines organic matter there are a series of
conversions that must take place before values of organic carbon content can be
achieved (Schumacher, 2002). LOI values are calculated by subtraction of weight after
combustion from the dried weight of samples:
(1)
Where LOI= loss-on-ignition; Wd= dried sample weight; Wc= weight after combustion.
Subsequent to this values are converted to organic matter content (SOM) as a
percentage of the dried sample weight:
(2)
Lastly, in order to determine organic carbon content (SOC) from organic matter it is
necessary to apply a conversion factor (Schumacher, 2002). Though this value varies
depending on soil features, the traditional 1.724 ‘Van Bemmelen’ conversion factor, that
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assumes SOM contains 58% organic carbon, was used as it is a standardised
approximation used in several studies (Burt, 2004):
(3)
ii.
Particle Size Distribution
Widely-used computer program GRADISTAT was used to rapidly analyse particle-size
statistics and produce mean, mode, sorting and skewness values over all composite
samples that constituted all three sampling depths at each site. Values were
geometrically scaled and displayed in microns, and were also logarithmically
transformed into phi (Φ) values so that graphical representations of data were
appropriate (Blott & Pye, 2001). The GRADISTAT program uses Folk and Ward (1957)
graphical methods and nominal grain-size classifications as these are the most
appropriate for cross-comparison with other studies (Blott & Pye, 2001).
iii.
Statistical Analysis
After a detailed exploration of data statistical computer analysis program PASW
Statistics 18.0 was used to assess statistical significance of variation of all variables
between both habitat types, and sampling depths. Normality tests were carried out to
assess whether or not to use non-parametric tests. As SOC data was not-normally
distributed, a non-parametric Kruskal-Wallis ANOVA was used followed by paired
Mann-Whitney tests to assess where variation was the greatest. Post-hoc MannWhitney tests were corrected for inflated Type 1 error rate using the Bonferonni
Correction (Field, 2000) which lowered the 0.05 critical value (p) of significance to
0.024.
6.0 RESULTS
The following section examines the results of lab analyses on soil samples collected in
June, 2012, with the primary focus on soil organic carbon (SOC) values obtained
through the loss-on-ignition technique. Secondary to this, analyses were also conducted
to obtain values for soil moisture content and the particle size distribution of all 180
samples. The discussion begins with a synopsis of the analyses and then expands upon
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individual variables, using detailed data exploration to identify any possible influences
upon them. The section ends by examining the results of statistical analyses on all
variables.
6.1 Overview of Results
Table 2 presents a summary of the 180 samples analysed for soil organic carbon (SOC),
moisture content and particle size distribution, with values averaged at each of the
three sampling depths (0-5; 5-15;15-30cm) across seven habitat classifications. Mean
SOC (%) is uniformly highest in the upper most sampling depth, at a maximum in
‘ancient woodland’ (30.03%) and a minimum in ‘wildflower meadow’ (4.1%) habitats.
Variation from the mean does not appear to show any logical trend between either
habitat or sampling depth. Ancient woodland sites (habitat 7.) show a high mean SOC
and a large amount of variance (standard deviation= 11.14) though a low median score,
indicating the presence of possible outliers. Moisture content, as a percentage of fieldfresh soil samples, shows a pattern for higher values in the upper layers though no
logical variation between habitats, as is expected given the breadth of locations within
each classification. Percentages of sand, silt and clay have been averaged for each
habitat type though for the same reason should show no logical variation between
classifications. The three particle sizes have been averaged within habitats to illustrate
how SOC may be influenced by clay content, as it is the finest particles which are known
to encourage organic matter accumulation (Tisdall & Oades, 1982; Tiessen & Stewart,
1983; Oades, 1984; Parton et al., 2001). Table 3 shows a better example of how high
clay content tends to correlate with high SOC, and also a trend for high SOC and
moisture content.
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Table 2: Summary of all variables analysed on 180 samples at John B. Thornes Lab, King’s College London
in June, 2012.
Sample
No. of
Std.
SOCa
Medianb
Moisturec
Sandd
Siltd
Clayd
Depth
Samples
Deviation
(cm)
(n)
(%)
(SOC)
(SOC)
(%)
(%)
(%)
(%)
0-5
6.99
3.34
27.41
1. Improved
grassland
19.45
5-15
27
4.58
1.40
5.32
22.42
0.34
79.98
15-30
4.64
1.85
18.59
0-5
8.49
3.49
34.64
2. Semiimproved
15.04
5-15
30
7.63
5.12
6.03
33.39
0.28
84.45
grassland
15-30
5.74
3.00
26.00
0-5
6.64
2.53
26.03
3. Wildflower
meadow
17.56
5-15
24
4.10
0.57
4.27
19.89
81.54
0.57
15-30
4.51
0.96
17.20
0-5
12.64
6.01
44.46
4. Scrub
24.99
5-15
24
7.67
2.73
8.41
32.04
74.04
0.60
15-30
8.39
3.01
32.32
0-5
9.66
4.25
39.31
5. Plantation
woodland
17.92
5-15
24
9.21
5.59
7.74
36.22
81.59
0.37
15-30
8.37
6.58
35.20
9.01
3.53
46.53
6. Semi-natural 0-5
woodland
84.40
15.02
5-15
27
6.39
1.85
5.52
47.03
0.31
15-30
5.66
1.35
43.07
0-5
7. Ancient
30.03
11.14
56.91
woodland
64.54
34.02
5-15
24
8.11
2.24
9.77
27.70
0.78
15-30
8.53
3.00
26.49
a
SOC as percentage of dry-weight averaged at each depth across 7 habitats comprised of multiple sites; bMedian score across
SOC at all depths; cPercentage moisture content of field-fresh samples; d Percentage sand, silt and clay averaged for each of 7
habitats obtained through laser particle size analysis
Habitat
Table 3: Graphic representation of declining SOC with sampling depth and the relationships between SOC
and moisture, and clay content and SOC.
Habitat
1.
2.
3.
4.
5.
6.
7.
Sample Depth
(cm)
Improved grassland
0-5
5-15
15-30
Semi-improved grassland 0-5
5-15
15-30
Wildflower meadow
0-5
5-15
15-30
Scrub
0-5
5-15
15-30
Plantation woodland
0-5
5-15
15-30
Semi-natural woodland 0-5
5-15
15-30
Ancient woodland
0-5
5-15
15-30
33
SOC
(%)
6.99
4.58
4.64
8.49
7.63
5.74
6.64
4.10
4.51
12.64
7.67
8.39
9.66
9.21
8.37
9.01
6.39
5.66
30.03
8.11
8.53
Moisture
(%)
27.41
22.42
18.59
34.64
33.39
26.00
26.03
19.89
17.20
44.46
32.04
32.32
39.31
36.22
35.20
46.53
47.03
43.07
56.91
27.70
26.49
Clay
(%)
0.34%
0.28%
0.57%
0.60%
0.37%
0.31%
0.78%
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6.2 Soil Organic Carbon
Figure 14 once again shows that there was a strong variation in SOC between all seven
habitat classifications, with ancient woodland displaying the strongest concentration in
the upper sampling depth. High SOC values in scrub and ancient woodland habitats also
display the greatest variation, with error bars indicating +1 standard deviation being
SOC (%)
longest within these classifications.
45
40
35
30
25
20
15
10
5
0
0-5cm
5-15cm
15-30cm
Figure 14: Mean SOC (%) at each sampling depth, averaged across 7 habitat classifications.
Error bars represent +1 standard deviation.
Table 4 supports this, showing high variance values and wide ranges for both habitats.
Maximum SOC values reached 40% which occurred in ancient woodland at the
Hitherwood sites possibly as a result of high clay content as confirmed by laser particle
size analysis (see Section 6.4) and visual descriptions of soil samples in the field.
Averaged SOC across the three sampling depths confirms organic carbon is most
abundant in the first 0-5cm of the soil profile (Table 4) which is in agreement with
literature supporting the exponential decrease of carbon with depth (Torn et al., 1997;
Jobbagy & Jackson, 2000; Lorenz & Lal, 2005; Pan et al., 2008). Figure 15 shows that
median scores across all seven habitat types do not differ dramatically, but that there is
great variation within the interquartile and upper quartile range in ancient woodland
habitats that is responsible for skewness in values.
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Table 4: Summary of data exploration of SOC averaged for habitat classifications and sampling
depths separately.
Habitat
Mean
(%)
Median
(%)
Minimum
(%)
Maximum
(%)
Range
(%)
Std.
Deviation
(%)
Variance
(%)
1.
Improved Grassland
5.41
5.32
1.56
14.50
12.93
2.53
6.41
2.
Semi-improved
Grassland
7.29
6.03
2.97
19.97
16.99
3.91
15.29
3.
Wildflower
Meadow
5.08
4.27
2.41
10.48
8.07
1.91
3.62
4.
Scrub
9.56
7.74
4.61
24.18
19.58
5.34
28.46
5.
Plantation
Woodland
9.08
8.41
3.03
22.01
18.98
4.59
21.03
6.
Semi-natural
Woodland
7.02
5.68
3.85
12.55
8.71
2.76
7.61
7.
Ancient Woodland
15.48
9.77
5.19
40.13
34.95
12.51
156.54
0-5
11.66
8.38
2.41
40.13d
37.72
9.19
84.39
5-15
6.81
5.50
1.90
21.69e
19.79
3.57
12.78
15-30
6.44
5.52
1.56
24.18f
22.62
3.49
12.17
Sampling Depth (cm)
Figure 15: Boxplots of averaged SOC scores across all habitats.
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Figure 16 once again illustrates that there is a great range in SOC values in the 0-5cm
sampling depth across the whole dataset. As this is the section of mineral soil closest to
the litter layer variance could be greatest here as a result of varying types of plant
material entering the soil (Kirby & Potvin, 2007; Shrestha & Lal, 2007). Median scores of
the two lower sampling layers appear to be quite similar and show an even spread of
scores between the upper and lower quartiles.
Figure 16: Boxplots of SOC scores averaged across three sampling depths: 0-5, 5-15 and 1530cm.
As a whole, the entire SOC dataset showed a non-normal distribution in KolmogorovSmirnov normality testing, displaying a positive skewness (Sk= 3.03) with values
grouped in lower scores and a leptokurtic distribution (K= 10.44). Overall scores of
organic carbon were grouped close to the mean (8.31%) with almost 89% of scores
lying within ±1 standard deviation (Figure 17). The site that contained the highest
average SOC content (21.55%) was at Hitherwood in the Dulwich district of Southwark.
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Frequency Distribution f(y)
50
88.89% of data within
1 stdv
40
7.23% data above +1 stdv
30
20
10
0
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
SOC (%)
Figure 17: Frequency distribution of all SOC scores showing the majority of scores were
grouped close to the mean.
6.3 Soil Moisture Content
Figure 18 represents averages at three sampling depths of soil moisture content, as a
percentage of field-fresh sample weights. Median scores vary across depths and scores
in the 0-5cm category are skewed in the upper quartile, supporting the findings of Table
4 which shows a variance figure of 84.39 at this depth.
Figure 18: Boxplots of soil moisture, as a percentage of field-fresh sample weight, show varying
median scores across all three sampling depths.
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The scatter plot below (Figure 19) shows a weak positive correlation between SOC and
soil moisture content (R2= 0.35) that may result from the stimulation of microbial
decomposition by rainwater (Derner & Schuman, 2007). This is, however, unlikely as
stimulation of decomposition processes requires extended periods of precipitation to
alter microbial processes (Bouwman, 1990) and as samples were taken under similar
weather conditions and within several miles of one another, soil moisture differences
are probably negligible.
45
40
y = 0.2636x - 0.4087
R² = 0.3454
35
SOC (%)
30
25
20
15
10
5
0
0
10
20
30
40
50
Soil Moisture (%)
60
70
80
Figure 19: Scatter plot of soil moisture (%) and SOC (%) illustrating a weak positive correlation
(R2= 0.35) between the two variables.
6.4 Laser Particle Size Analysis
Results from laser particle size analysis were averaged for all sites in order to gain an
overall distribution of the grain sizes sampled across the London borough of Southwark,
and statistical programme GRADISTAT (Blott & Pye, 2001) used to create the
distribution below (Figure20). The frequency distribution below a normally-distributed
dataset (Kg= 1.02) with very little skewness (Sk= 0.16). Average particle size across the
dataset was 2.557 phi which is within the medium-fine sand grain classification (Blott &
Pye, 2001). The particles were poorly sorted (σg= 1.63) showing large variation in grain
size that is expected of a dataset comprising of 50+ sampling locations.
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16
Class weight (%)
14
12
10
8
6
4
2
0
-7
-5
-3
-1
1
3
5
Particle diameter (ɸ)
7
9
11
Figure 20: Frequency distribution of average particle size, using phi (ɸ) values across entire
dataset. Sand particles range between 0-4, silt: 4.5-8.5 & clay 9-16 (Wentworth, 1922).
The composite samples (all 3 sampling depths) for each site were analysed for particle
size distribution and have been displayed below (Figure 21; for site codes see Appendix
II) to illustrate whether or not % clay content was particularly high at any locations, as
it is this grain size which impacts upon organic carbon accumulation (Tisdall & Oades,
1982; Tiessen & Stewart, 1983; Oades, 1984; Parton et al., 2001). There is little
variation in clay content between locations, though the Hitherwood site (HA) in Dulwich
shows a slightly higher percentage than others. Grain size is primarily sand, though
again Hitherwood (HA) and Dulwich Upper Wood (DUI) sites show variation with
slightly higher quantities of silt.
39
100%
Particle Size Content
90%
80%
70%
60%
Clay
50%
Silt
40%
Sand
30%
20%
10%
BGS
BGW
BL
BPL
BRI
BRL
BRW
BW
CG
CNCA
CNCB
DKW
DPB
DUI
DWBR
GG
HA
HBB
HB
LGL
LGW
LTB
LTW
NC
OTB
PRCP
PRC
PRCW
PRPA
PRPB
PRPBR
PRPP
PRPW
RWB
RWP
RWPI
SFA
SFB
SHA
SHB
SHR
SHWB
SL
SWA
WL
WO
0%
Site
Figure 21: Percentage sand, silt & clay across all sampling locations obtained using laser particle size analysis and the GRADISTAT statistical
analysis programme (Blott & Pye, 2001). Site codes are listed in Appendix II.
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Values of mean particle size (ɸ) were plot against soil organic matter (%) to see if a
relationship existed between the two whereby fine grain size would attract
accumulations of organic matter. Particle size transformed by phi was used and a weak
positive correlation (R2=0.33) was identified. Larger phi values indicate finer grain size,
with ɸ values between 9 and 16 representing clay (Blott & Pye, 2001). Averaging
particle size for each site meant that clay fractions were not represented on the
correlation, however Figure AA still illustrates that finer grain sizes correlated with
higher percentages of organic matter.
25
y = 4.9643x - 4.2969
R² = 0.333
SOM (%)
20
15
10
5
0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Particle Size (ɸ)
Figure 22: Scatter plot of average soil organic matter (% of dry weight) and particle size
represented as phi (ɸ). A weak positive correlation can be identified (R2= 0.33).
6.5 Statistical Analyses
Statistical analysis was carried out on soil organic carbon data using PASW Statistics
(Version 18.0) in order to accept or reject the null hypotheses that there is not a
statistically significant difference in SOC scores between either habitat classifications
(HH0) or between sampling depths (HD0). As the SOC dataset was, as a whole and
between habitats and depths, non-normally distributed (see Section 6.2) the nonparametric Kruskal-Wallis ANOVA, Mann-Whitney U-Test and Spearman Rho
Correlation analyses were used. Levene’s test does not assume a normal distribution so
could be used on SOC scores (Field, 2000).
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i.
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Homogeneity of Variance: Levene’s Test
The Levene’s test was used to test the assumption that variance is equal between
habitat classifications and sampling depth. The assumption of homogeneity of variance
between habitat groups was rejected as variances were significantly different (test
statistic based on mean= 15.29; p= <.001). Assumed equality of variance between
sampling depths was also rejected (test statistic= 15.29; p=<.001).
ii.
Kruskal-Wallis One-Way Analysis
Following the Levene’s test the Kristal-Wallis test was conducted to ascertain whether
there was a significant difference (p= <.05 or.001) between SOC scores in the seven
habitat classifications or three sampling depths. As SOC data breached the normality
assumption of a one-way ANOVA the Kruskal-Wallis test was used though noting the
loss of accuracy in using this technique (Field, 2000). The test results allow for the
rejection of both HH0 and HD0 and the acceptance of alternative hypotheses HH1 and
HD1 as there was a statistically significant difference between habitat types (chi-squared
x2= 47.74; p=.000 & significant at both .05 and .001 levels) and sampling depth
(x2=28.19; p= .000). As organic carbon is thought to decline exponentially with depth in
the soil (Torn et al., 1997; Jobbagy & Jackson, 2000; Lorenz & Lal, 2005; Pan et al., 2008)
the post-hoc Jonckheere-Terpstra test was conducted in order to test the alternative
ordered hypothesis (HD2) that SOC declines with sampling depth. The test confirmed
that there was a significant trend for descending mean SOC from 0-5cm (highest) to 1530cm (J-T statistic= 3564; z-score= -4.82).
iii.
Mann-Whitney U-Test
To further investigate results of the Kruskal-Wallis test and in order to ascertain where
the greatest variations lay between habitat types and sampling depth a non-parametric
t-test, the Mann-Whitney U-Test, was conducted on grouped SOC scores. In order to
avoid inflation of the Type 1 error with repeated tests, a Bonferonni correction was
applied, lowering the 0.05 critical value of significance to 0.024. Table 5 shows several
statistically significant (p=<.024) variations between habitat types. The strongest
differences were between improved grassland and both semi-natural (6>1) and ancient
woodland types (7>1), and between wildflower meadow and scrub (4>3), plantation
(5>3) and ancient woodland (7>3) types. There was no significant difference in SOC
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within lower habitat types (1-3) though was within the most mature habitats (seminatural vs. ancient woodland p=<.001; 7>6).
Table 5: Significance values of the Mann-Whitney U-Test for SOC grouped by habitat
classification (1. Improved grassland; 2. Semi-improved grassland; 3. Wildflower meadow; 4.
Scrub; 5. Plantation woodland; 6. Semi-natural woodland; 7. Ancient woodland).
Habitat Type
1.
2.
3.
4.
5.
6.
7.
1.
-
0.66
.428
.002
(4 >1)
.000
(5>1)
.076
.000
(7 >1)
2.
0.66
-
.003
(2 >3)
.164
.013
(5>2)
.811
.001
(7 >2)
3.
.428
.003
-
.000
(4 >3)
.000
(5 >3)
.002
(6 >3)
.000
(7 >3)
4.
.002
.164
.000
-
.343
.227
.037
5.
.000
.013
.000
.343
-
.027
.248
6.
.076
.811
.002
.227
.027
-
.001
(7 >6)
7.
.000
.001
.000
.037
.248
.001
-
Mann-Whitney U-testing on SOC grouped by sampling depth showed no significant
differences between SOC at the two lower depths, though when paired with the 0-5cm
sampling depth there were strong differences (p=.000 for both 5-15 and 15-30cm).
iv.
Spearman Rho Correlation
The Spearman Rho Correlation test conducted on particle size and soil moisture
content, both averaged for each site, revealed that the weak positive correlation
between SOC and mean particle size (ɸ) identified in scatter plots (see Section .4) was
not significant (p=.108). There was however, a significant correlation between soil
moisture and SOC (Sp= .269; p= <.001).
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7.0 DISCUSSION
The following section will discuss how the results obtained through laboratory and
statistical analyses relate to initial hypotheses, in the context of the surrounding
literature (Section 7.2). To put the results into perspective the discussion will begin
with recognition of any limitations of the study (Section 7.1).
7.1 Limitations
i. Site Selection
As the root of this study lies in how soil organic carbon is affected by the habitat type
residing the ground above, most of the limitations involve external factors which could
also be altering carbon content. Given the complexity of urban soils one of the main
limitations of selecting unbiased sites was ensuring that samples were taken from soil
that was representative of the habitat. The Southwark Biodiversity Action Plan (2010)
allowed for the avoidance of newly-created habitats for sampling selection (2010),
though this could not remove the influence that differences between ‘natural’ soils (e.g.
in Ancient woodland) and ‘urban’ soils (e.g. lawns and parkland) could have had on SOC.
Soils such as those within grassland and wildflower meadow may be of composite
origin, including added organic material (Beyer et al., 1995).
ii. Sampling Methods
Use of the British Geological Survey’s G-BASE field sampling methods (Johnson, 2005)
allowed for the standardised collection of samples with minimal limitations. Though
care was taken to ensure that samples at each depth did not contaminate lower layers,
in the few soils that were particularly dry this was unavoidable. Use of a soil auger to
sample SOC is thus a limitation in the study, something which could be avoided using
either a model-based (Allen et al., 2010) approach or in-situ measurement of SOC
(Schumacher, 2002).
The total depth to which SOC was sampled in the soil profile may provide another
limitation to the study. A soil profile pit would have allowed for the distinguishing of
horizons, including the labile vs. recalcitrant carbon pools, however given the breadth of
sites this was unfeasible. Harrison et al. (2011) report that sampling at shallow depths
(within 0-20cm) provides an inaccurate estimation of SOC, however declare that the
majority of soil samples are often of a shallow-depth due to cost and the difficulties of
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sampling at lower profiles. Inaccuracy of shallow-sampling is contested by Batjes
(1996) whose comprehensive study of global soil carbon pools proposed that 50% of
SOC was contained, to a depth of 1m, within the upper most 30cm of the soil profile.
iii. Laboratory Analyses
Limitations of the loss-on-ignition technique for estimating soil organic carbon are
outlined in section 2.6.iv. but following completion of lab analysis a further query over
the accuracy of LOI-derived SOC arose. There was evidence in one furnace load that
during combustion air pockets had expanded and caused displacement of material
around the inner-edge of crucibles. While undetectable, this could have either lead to
contamination of surrounding samples or the loss of surplus weight from a sample,
creating anomalous values of organic matter.
Laser particle-sizing is generally considered an efficient and more advanced laboratory
method than sieve analysis though because the analysis is based upon the assumption
that grains are spherical the technique may not be completely accurate,
underestimating grains in the finer, clay sizes (Konert & Vandenberghe, 2008).
7.2 Key Findings
i. Soil Organic Carbon & Succession
The patchy distribution of urban plant communities (Rebele, 1994) meant that in
Southwark borough there are few examples of “natural” seral stages and instead
communities (classified by the Phase 1 Habitat Survey) are often halted at early stages
of secondary succession by varying degrees of human disturbance (Niemelä, 1999).
While their ecological complexity may make urban ecosystems appear a difficult area to
study, this ecological mosaic exemplifies a wide range of communities at different stages
of ecological succession. As a result, habitats were chosen according to the successional
stage they best represented in a natural ecosystem.
The findings of the statistical analysis of SOC values provide support both for and
against literature surrounding how succession may influence the storage of organic
carbon in soils. The successional stage at which a community is at is seen to influence
soil carbon storage principally through control of organic matter input (Foote & Grogan,
2010; Wardle, 2011). A significant variation was found in the median SOC scores of
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seven different habitats in Southwark, London (p=<.001), representing ecosystems at
various stages of succession. Conceptually, mature, preclimax communities will be the
most favourable to SOC storage, reflecting the peak in net primary production that
occurs at this stage (Tate et al., 2000; Guo & Gifford, 2002; Foote & Grogan, 2010).
Following succession into a climax community, habitats see a decline in NPP and, as this
is strongly linked with SOC (Potter et al.¸1993; Schlesinger, 2000), a stabilisation of soil
carbon levels at a lower rate. In Southwark while ancient woodland sites, representing
mature, late-successional stages, demonstrated the highest values of SOC (mean:
15.40%), scrub habitats, representing mature, mid-successional stages, produced
similarly high values (mean: 9.56%). A high mean SOC score in ancient woodland habit
was in part due to large variation in values in the upper quartile, whereas scrub habitats
showed significantly less variation. Further to this, a Mann-Whitney U-Test revealed
that the two habitats did not show a statistically significant difference. Scrub habitats
across Southwark were principally dense communities dominated by Rubus fruticosus,
Urtica diocia, and young Acer pseudoplatanus, representing the transitional stage from
open herbaceous vegetation to woodland. This finding supports ecological theory and
evidence that mature, pre-climax stages contribute high levels of organic matter to the
soil.
The dominance of SOC values of ancient woodland sites does not concur with studies
that cite woodland as being less favourable to SOC storage (Guo & Gifford, 2002; Post &
Kwon, 2000; Pastor & Post, 1986). This may be the result of a time-lag between peak
NPP occurring and becoming reflected in soil organic carbon values (Breeman & Feijtel,
1990). Several locations within the ‘ancient woodland’ classification are residual
patches of what was once the ‘Great North Wood’, a natural oak forest that has extended
across south London for centuries. Patches of scrub vegetation in Southwark may
however, have only reached, or still be climbing towards a peak in NPP as a result of
human disturbance, and so corresponding SOC values are yet to be reflected. Human
disturbance could also account for the strongly significant variation between seminatural woodland, a climax community of secondary succession, and ancient woodland
(p=<.001). Mature late-successional stages like ancient woodland have had a long
period of time to stabilise their SOC levels, but also much of Southwark’s ancient
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woodland is protected under Local Nature Reserve status (Southwark BAP Report,
2010) so is protected against further human disturbance.
ii. Variation across Sampling Depths
Variation in SOC across habitats was strongest in the upper most sampling depth (05cm) (Figure 16) reflecting the relationship between succession and belowground
biomass as a control over SOC (Kirby & Potvin, 2007; Shrestha & Lal, 2007). In a
comparison of land-uses Shrestha & Lal (2007) found that variation in SOC was greatest
in surface soil (0-5cm) as a result of a direct correlation with dry root biomass (Figure
23).
Figure 23: Relationship between soil organic carbon and dry root biomass; ** indicates
significance at .002 level (from Shrestha & Lal, 2007).
While strong variation between habitats in topsoil does indicate an influence of
succession in Southwark, in the long-term this does not reflect in their varying ability to
store carbon. Soluble fresh residues in surface soil do have a rapid sensitivity to
vegetative changes (Parton et al., 1987) though a small turnover period of less than 1
year (Spain et al., 1983). Particulate organic carbon stored in the lower layers is more
recalcitrant and a turnover period of 10 years (Spain et al., 1983; Cheng et al., 2007).
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iii. Urban Plagioclimax Seres
Urban habitats are often typified by plant communities at early successional stages
owing to high levels of human disturbance such as construction, gardening or recreation
(Niemelä, 1999). Successional development is halted by disturbance (Hale, 1987) and as
a result, urban habitats do not progress beyond what Tansley (1949) describes as a
“plagioclimax”.
Plagioclimax communities in Southwark represented low habitat
complexity, with grasslands dominated by few species. Habitats 1-3 all represented
urban plagioclimax seres as they all received some form of human disturbance through
mowing and/or removal of biomass (Southwark BAP Report, 2010). This could explain
the lack of difference between SOC scores in lower habitat classifications. Removal of
biomass in agricultural land is often cited as producing lower than expected soil organic
carbon levels (Post & Kwon, 2002; Freibauer et al., 2004; Lal, 2004). Further to human
disturbance, wildflower meadows in Southwark are comprised mainly of annual species
which do not provide the same degree of permanent vegetative cover as grasslands in
the borough do (Brown & Lugo, 1990). This lack of permanent aboveground biomass
could explain why average SOC in these habitats displayed the lowest mean and median
carbon values (Table 4).
iv. Particle Size Distribution & SOC
Field observations of soil appearance and laser particle size analysis confirmed that
maximum values of clay content in soil samples corresponded to higher values of soil
organic carbon. Finer, clay particles have a negative surface charge, to which organic
matter is attracted to in a strong bond through the process of adsorption (Trudgill,
2001). Jones et al. (2004) have identified a linear relationship between soil clay content
and SOC. As the maximum clay content occurred in the same sites in which soil organic
carbon was highest (ancient woodland sites Dulwich & Hitherwood) it would appear
that particle size could explain the large variation of SOC scores in the upper quartile of
Ancient Woodland sites, lessening the aforementioned influence of succession. Mean
particle size (Φ) at each site was correlated with corresponding soil organic matter
values to see if a lower particle sizes (represented by high phi values) corresponded to
high organic matter content (Figure 22) however the correlation was deemed not
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significant (p=>.05). However as sites were averaged, clay particles (>8.5Φ) were not
represented in the correlation.
v. Soil Moisture & Organic Carbon
Though a scatter plot and statistical analysis confirmed a positive correlation between
soil organic carbon and soil moisture content, this result is negligible owing to the
homogeneity of factors influencing soil moisture across sampling locations. Sampling
sites were chosen according to similar topography to eliminate water saturation that
may occur at the base of slopes (Smithson et al, 2004) and as all sampling occurred
within a radius of several miles and over a time period of unchanging weather
conditions, climatic influence could be eliminated also. A more probable explanation for
the correlation between SOC and soil moisture involves the control of soil texture on
soil moisture retention (Gee & Bauder, 1986). The capacity for soil to be held in
macropores is strongly related to particle size with fine, clay particles experiencing high
electrostatic attraction of water molecules and large sand grains allowing rapid
drainage of water (Rawls et al., 1982; Vereecken et al., 1989). Soil organic matter itself
is also cited as increasing the available water capacity of soils by improving infiltration,
drainage and aeration (Hudson, 1994).
Further to this, soil moisture in temperate biomes may act as a positive influence on
SOC storage up until a threshold value after which soil oxidation processes decline
(Derner & Schuman, 2007). Overall, soil decomposition rates may have been low during
the study period owing to extremely high rainfall in June 2012 (200% above 1971-2000
average; Met Office, 2012a) though this would do little to long-term storage rates.
8.0 CONCLUSION
The findings of this study highlight the influence that succession has upon the storage of
organic carbon within the soils of urban plant communities, central to which is the
relationship between net primary productivity and the supply of organic matter. Habitat
types classified by the Phase 1 Habitat Classification Survey were used to represent
seven varying stages of ecological succession in an urban ecosystem. Early stages of
succession were seen to favour lower values of soil organic carbon, thought to be a
result of the creation of plagioclimax communities through frequent human
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disturbance. In later-stage plant communities human disturbance had less of an
influence over net primary productivity and this corresponded with higher SOC values.
Literature suggested that mature, mid-successional stages (as represented by ‘Scrub’
habitat in the study) should favour the highest soil organic carbon, as it is at this stage at
which net primary productivity is highest. Contrastingly, ancient woodland habitat
showed the highest amounts of soil carbon storage thought to result from a time-lag in
the soil carbon pool reaching equilibrium with NPP. The influence of high clay content
at mature late-successional stages was also thought to play a part, though more
investigation was needed following no significant correlation between the two.
There is great difficulty in classifying successional stages in complex urban ecosystems
as a result of the high level of human disturbance (Niemelä, 1999; Rebele, 1994) and
findings from this study should be considered alongside this. However the findings of
this study do suggest the strong role that mature, mid- and late-successional plant
communities have in the long-term storage of organic carbon. Currently, soil carbon
sequestration does not feature in the Mayor of London’s Climate Change Mitigation and
Energy Strategy (2011) and while the important role of urban greenspace is recognised,
it is merely as a part of adaption plan to buffer rising temperatures (The London Plan,
2011). The direct role that urban plant communities have in sequestering carbon is
clearly under-recognised and merits further consideration so as to preserve London’s
natural sinks.
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9.0 APPENDIX I: Ethics & Risks Forms
(see following page)
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10.0 APPENDIX II: List of Site Codes
NB: Some sites featured one or more habitat classifications and so were sampled several times.
Code in bold refers to district within borough (B= Bermondsey; BB= Borough/Bankside; C=
Camberwell; D= Dulwich; NPR= Nunhead/Peckham Rye; R= Rotherhithe; W= Walworth)
BGS- Brenchley Gardens (NPR)
PRPBR- Bird in Bush Park (NPR)
BGW- Brunswick Gardens (C)
PRPP- Honor Oak (NPR)
BL- Bellenden Road Nature Garden (C)
PRPW- Brimmington Gardens (NPR)
BP- Belair Park North (D)
RWB- Russia Dock Woodland (R)
BPL- Belair Park South (D)
RWP- King’s Stair Gardens (R)
BR- Bermondsey Spa (B)
SFA- Aspinden Road Nature Garden (BB)
BRL- Belair Nature Garden (D)
SFB- Sunray Gardens (D)
BRW- Brunswick Park (C)
SHA- Sydenham Hill North (D)
BW- Dicken’s Square (BB)
SHB- Sydenham Hill South (D)
CG- Camberwell Green (C)
SHR- Stave Hill Ecological Park North (R)
CNCA- Camberwell New Cemetery (NPR)
SHWB- Stave Hill Ecological Park South (R)
CNCB- Camberwell Old Cemetery (NPR)
SWA- Southwark Park (B)
DKW- Dog Kennel Hill Wood (C)
WL- Nursery Row (W)
DPB- Dulwich Park (D)
WO- Warwick Gardens (C)
DUI- Dulwich Wood East (D)
DWBR- Dulwich Wood West (D)
GG- Goose Green (NPR)
HA- Hitherwood North (D)
HB- Hitherwood South (D)
HBB- Herne Hill Stadium Meadow D)
LGL- Lucas Gardens (C)
LGW- Lettsom Gardens North (C)
LTB- Lettsom Gardens South (C)
NC- Nunhead Cemetery (NPR)
Figure 24: Sub-districts within the London
borough of Southwark (from SouthEastCentral
Forum, 2011).
PRCP- Peckham Rye Park (NPR)
PRCW- Peckham Rye Common (NPR)
PRPA- Peckham Rye Park Wood (NPR)
PRPB- Peckham Rye Common Wood (NPR)
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11.0 REFERENCES CITED
Allen, J.C. (1985) Soil Response to Forest Clearing in the United States and the Tropics: Geological and
Biological Factors. Biotropica 17, 15-27
Allen, S.E. (1989) Nutrient Elements. In: Chemical Analysis of Ecological Materials (2nd Ed) Allen, S.E.
(ed) pp 81-159
Allen, S.E., Pringle, M.J., Page, K.L. & Dalal, R.C. (2012) A Review of Sampling Designs for the
Measurement of Soil Organic Carbon in Australian Grazing Lands. The Rangeland Journal 32,
227-246
Archer, D. (2010) The Global Carbon Cycle. Woodstock, Oxfordshire: Princeton University Press
Batjes, N.H. (1996) Total Carbon and Nitrogen in the Soils of the World. European Journal of Soil Science
47(2) 151-163
Bernoux, M., Arrouayas, D., Cerri, C.C. & Bourennane, H. (1998) Modeling Vertical Distribution of Carbon
in Oxisols of the Western Brazilian Amazon (Rondonia) Soil Science 163(12) 941-951
Beuselinck, L., Govers, G., Poesen, J., Degraeur, G. & Froyen, L. (1998) Grain-size analysis by laser
diffractometry: comparison with the sieve-pipette method. Catena 32, 193-208
Beyer, L., Blume, H.P., Elsner, D.C. & Willnow, A. (1995) Soil Organic Matter Composition and Microbial
Activity in Urban Soils. Science of the total Environment 168(3) 267-278
Birch, H.F. (1958) The effect of soil drying on humus decomposition and nitrogen availability. Plant and
Soil 10(1) 9-31
Blaisdell, R., Conant, R., Dick, W., Dobermann, A., Izzauralde, C., Ransom, M., Rice, C., Robertson, P., Stuth,
J. & Thompson, M. (2003) Recommended Procedures for Collecting, Processing and Analysing
Soil Samples in CASMGS Research Plots. Consortium for Agricultural Soils Mitigation of
Greenhouse Gases 4.1 Working Group Report (Online)
http://www.casmgs.colostate.edu/vignette/448_8___recommended%20procedures.pdf
[Accessed 2 June 2012]
Blott, S.J. & Pye, K. (2001) GRADISTAT: A grain size distribution and statistics package for the analysis
of unconsolidated sediments. Earth Surface Processes and Landforms 26, 1237-1248
Boone, R.D., Grigal, D.F., Sollins, P., Ahrens, R.J. & Armstrong, D.E. (1999) Chapter 1: Soil Sampling,
Preparation, Archiving and Quality Control In: Standard Soil Methods for Long-Term Ecological
53
King’s College London
Sophie Page
Research. Robertson, G.P., Coleman, D.C., Bledsoe, C.S. & Sollins, P. Oxford University Press: New
York. pp 3-28
Bouwman, A.F. (1990) Soils and the Greenhouse Effect. Chichester: John Wiley and Sons
Breemen, V.N. & Feijtel, T.C.J. (1990) Soil processes and properties involved in the porudction of
greenhouse gases, with special relevance to soil taxonomic systems. In: Bouwman, A.F. (ed) Soils
and the Greenhouse Effect. John Wiley and Sons: Chichester, pp 195-223
British Geological Survey (2012) Geology of Britain Viewer (Online)
http://mapapps2.bgs.ac.uk/geoindex/home.html [Accessed 21 June 2012]
Brown, S. & Lugo, A.E. (1990) Effects of Forest Clearing and Succession on the Carbon and Nitrogen
Content of Soils in Puerto Rico and US Virgin Islands. Plant and Soil 124, 53-64
Byrne, L.B. (2007) Habitat Structure: A Fundamental Concept and Framework for Urban Soil Ecology.
Urban Ecosystems 10, 255-274
Buurman, P., Pape, T. & Muggler, C.C. (1997) Laser Grain-Size Determination in Soil Genetic Studies 1.
Practical Problems. Soil Science 162, 211-218.
Burt, R. (2004) Soil Survey Laboratory Methods Manual. Natural Resources Conservation Service
(Online)
http://www.scribd.com/doc/15751720/Soil-Survey-Lab-Methods-Manual-2004-
USDA [Accessed 21 May 2012]
Campbell, J.R. (2003)Limitations in the Laser Particle Sizing of Soils. In: Advances in Regolith (Roach, I.C.
ed) pp 38-42
Carson, R. (1963) Silent Spring. London: Hamish Hamilton
Carter, M.R. & Gregorich, E.G. (2008) Soil Sampling and Methods of Analysis (2nd ed) Taylor & Francis:
Boca Raton, FL
Chapin, F.S., Matson, P.A. & Mooney, H.A. (2002) Principles of terrestrial ecosystem ecology. Springer:
New York
Cheng, L., Leavitt, S.W., Kimball, B.A., Pinter Jr., P.J., Ottman, M.J., Matthias, A., Wall, G.W., Brooks, T.,
Williams, D.G. & Thompson, T.L. (2007) Dynamics of Labile and Recalcitrant Soil Carbon Pools in
a Sorghum Free-Air CO2 Enrichment (FACE) Agroecosystem. Soil Biology & Biochemistry 39,
2250-2263
54
King’s College London
Sophie Page
Christensen, B.T. (2001) Physical Fractionation of Soil and Structural and Functional Complexity in
Organic Matter Turnover. European Journal of Soil Science 52(3) 345-353
Cleveland, C.C., Wieder, W.R., Reed, S.C. & Townsend, A.R. (2010) Experimental Drought in a Tropical
Rain Forest Increases Soil Carbon Dioxide Losses to the Atmosphere. Ecoloy 91(8) 2312-2323
Coleman, M.D., Dickson, R.E. & Isebrands, J.G. (2000) Contrasting Fine-Root Production, Survival and
Soil CO2 Efflux in Pine and Poplar Plantations. Plant and Soil 225, 129-139
Connell, J.H. & Slatyer, R.O. (1977) Mechanisms of Succession in Natural Communities and Their Role in
Community Stability and Organization. The American Naturalist 111(982) 1119-1144
Connell, J.H. (1978) Species Diversity in Tropical Rainforests and Coral Reefs. Science 199(4335) 13021310
Cooper, A.W. (1981) Above-ground Biomass Accumulation and Net Primary Production During the First
70 Yers of Succession in Populus grandidentata Stands on Poor Sites in Northern Lower
Michigan. In: Forest Succession: Concepts and Application (West, D.C., Shugart, H.H. & Botkin, D.B.
eds). Springer-Verlag: New York
Cramer, W.M., Bondeau, A., Woodward, I.F., Prentice, I.C., Bett, R.A., Brovkin, V., Cox, P.M., Fisher, V.,
Foley, J.A., Friend, A.D., Kucharik, C., Lomas, M.R., Ramankutty, N., Sitch, S., Smith, B., White, A. &
Young-Molling, C. (2001) Global response of terrestrial ecosystem structure and function to CO2
and climate change: results from six dynamic global vegetation models. Global Change Biology
7(4) 357-373
Daily, G.C., Matson, P.A. & Vitousek, P.M. (1997) Ecosystem Services Supplied by Soil. In: Daily, G.C., ed.
Nature’s Services: Societal Dependence on Natural Ecosystems. Washington: Island Press, 1997,
113-132
Davidson, E.A. & Ackerman, I.L. (1993) Changes in Soil Carbon Inventories Following Cultivation of
Previously Untilled Soils. Biogeochemistry 20(3) 161-193
Davidson, E.A. & Janssens, I.A. (2006) Temperature Sensitivity of Soil Carbon Decomposition and
Feedbacks to Climate Change. Nature 440, 165-173
Dean, W.E. (1974) Determination of Carbonate and Organic Matter in Calcareous Sediments and
Sedimentary Rocks by Loss on Ignition; Comparison with Other Methods. Journal of Sedimentary
Research 44(1) 242-248
55
King’s College London
Sophie Page
DEFRA (2011) Natural Environment White Paper ‘The Natural Choice: securing the value of nature’
(Online) http://www.official-documents.gov.uk/document/cm80/8082/8082.asp [Accessed 13
June 2012]
De Kimpe, C.R. & Morel, J.-L. (2000) Urban Soil Management: A Growing Concern. Soil Science 165(1)
31-40
Derner, J.D. & Schuman, G.E. (2007) Carbon Sequestration and Rangelands: A Synthesis of Land
Management and Precipitation Effects. Journal of Soil and Water Conservation 62(2) 77-85
Devine, S., Markewitz, D., Hendrix, P. & Coleman, D. (2011) Soil Carbon Change Through 2m during
Forest Succession Alongside a 30-Year Agroecosystem Experiment. Forest Science 57(1) 36-50
Dominati, E., Patterson, M. & Mackay, A. (2010) A Framework for Classifying and Quantifying the
Natural Capital and Ecosystem Services of Soils. Ecological Economics 69(9) 1858-1868
Eshel, G., Levy, G.J., Mingelgrin, U. & Singer, M.J. (2004) Critical Evaluation of the Use of Laser Diffraction
for Particle-Size Distribution Analysis. Soil Science Society of America Journal 68(3)736-743
Eswaran, H., Berg, E.V.D. & Reich, P. (1993) Soil Science Society of America Journal 57, 192-194
FAO (2011) Soil Carbon Sequestration For Improved Land Management. Food and Agricultural
Organisation of the United Nations World Soil Resources Report (96) (Ed: Robert, M.)
Field, A. (2000) Discovering Statistics using SPSS (2nd ed.) Sage: London Foote, R.L. & Grogan, P. (2010)
Soil Carbon Accumulation During Temperature Forest Succession on Abandoned Low
Productivity Agricultural Lands. Ecosystems 13, 795-812
Folk, R.L. & Ward, W.C. (1957) Brazos River bar: a study in the significance of grain size parameters.
Journal of Sedimentary Petrology 27, 3-26
Foth, H.D. (1984) Fundamentals of Soil Science. John Wiley & Sons: Chichester
Freibauer, A., Rounsevell, M.D.A, Smith, P. & Verhagen, J. (2004) Carbon Sequestration in the
Agricultural Soils of Europe. Geoderma 122(1) 1-23
Friedlingstein, P., Prentice, K.C., Fung, I.Y., John, J.G., & Brasseur, G.P. (1995) Carbon-Biosphere-Climate
Interactions in the Last Glacial Maximum Climate. Journal of Geophysical Research 100, 72037221
56
King’s College London
Sophie Page
Fujisaka, S., Castilla, C., Escobar, G., Rodrigues, V., Veneklass, E.J., Thomas, R. & Fisher, M. (1998) The
Effects of Forest Conversion on Annual Crops and Pastures: Estimates of Carbon Emissions and
Plant Species Loss in a Brazilian Amazon Colony. Agriculture, Ecosystems and Environment 69(1)
17-26
Gale, M.R. & Grigal, D.F. (1987) Vertical Root Distributions of Northern Tree Species in Relation to
Successional Status. Canadian Journal of Forest Research 17(8) 829-834
G-BASE (2012) British Geological Survey Geochemical Baseline Survey of the Environment (G-BASE)
GeoIndex Onshore (Online) http://mapapps2.bgs.ac.uk/geoindex/home.html [Accessed 21 June,
2012]
Gebhart, D.L., Johnson, H.B., Mayeux, H.S. & Polley, H.W. (1994) The CRP Increases Soil Organic Carbon.
Journal of Soil and Water Conservation 49(5) 488-492
Gee, G.W. and Bauder, J.W. (1986) Particle-size analysis. p. 383-411. In A Klute (ed.) Methods of
Soil Analysis, Part 1. Physical and Mineralogical Methods. Agronomy Monograph No. 9
(2ed). American Society of Agronomy/Soil Science Society of America: Madison, WI.
Ghabbour, E.A. & Davies, G. (2001) Humic Substances. Molecular Details and Applications in Land and
Water Conservation. Taylor & Francis: New York
Google Earth (2012) Google Earth (Version: 6.0.3.2197)
Guo, L.B. & Gifford, R.M. (2002) Soil Carbon Stocks and Land Use Change: A Meta Analysis. Global
Change Biology 8, 345-360
Hale, M. (1987) Urban Ecology: A Problem of Definition? Journal of Biological Education 21(1) 14- 16
Harrison, R.B., Footen, P.W. & Strahm, B.D. (2011) Deep Soil Horizons: Contribution and Importance to
Soil Carbon Pools and in Assessing Whole-Ecosystem Response to Management and Global
Change. Forest Science 57(1) 67-76
Heiri, O., Lotter, A.F. & Lemcke, G. (2001) Loss On Ignition as a Method for Estimating Organic and
Carbonate Content in Sediments: Reproducibility & Comparability of Results. Journal of
Paleolimnology 25, 101-110
Holmén, K. (2000) The Global Carbon Cycle. In: Earth Systems Science: From Biogeochemical Cycles to
Global Change. Jacobson, J.M, Charlson, R.J., Rodhe, H. & Orians, G. (eds) Academic Press: New
York, pp 282-321
57
King’s College London
Sophie Page
Hudson, B.D. (1994) Soil organic matter and available water capacity. Journal of Soil and Water
Conservation 49, 180–194
IPCC (2007a) Climate Change 2007: The Physical Science Basis. Contribution of Working Group 1 to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., D. Qin,
M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and
H.L. Miller (eds.) Cambridge: Cambridge University Press
IPCC (2007b) Climate Change 2007: Mitigation of Climate Change. Contribution of Working Group III to
the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. B. Metz, O.R.
Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds). Cambridge: Cambridge University Press
Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E. & Schulze, E.D. (1996) A Global
Analysis of Root Distribution. Oecologia 108, 389-411
Jobbagy, E.G. & Jackson, R.B. (2000) The Vertical Distribution of Soil Organic Carbon and Its Relation to
Climate and Vegetation. Ecological Applications 10 (2) 423-436
Jobbagy, E.G. & Jackson, R.B. (2001) The Distribution of Soil Nutrients with Depth: Global Patterns and
the Imprint of Plants. Biogeochemistry 53, 51-77
Johnson, C. (2005) 2005 G-BASE Field Procedures Manual. British Geological Survey Internal Report
IR/05/097. 130pp (Online) http://nora.nerc.ac.uk/5190/1/2005proceduresmanual.pdf
[Accessed 28 May 2012]
Johnson, C.T., Bish, D.L., Echert, J. & Brown, L.A. (2000) Infrared and Inelastic Neutron Scattering Study
of the 1.03 and 0.95nm Kaolinite-Hydrazine Intercalation Complexes. Journal of Phyiscal
Chemistry B 104(33) 8080-8088
Joint Nature Conservation Classification (2010) Handbook for Phase 1 Habitat Survey: A technique for
environmental audit. (Online)
http://jncc.defra.gov.uk/PDF/pub10_handbookforphase1habitatsurvey.pdf [Accessed 28
March, 2012]
Jones, C., McConnel, C., Coleman, K., Cox, P., Falloon, P., Jenkinson, D. & Powlson, D. (2005) Global
Climate Change and Soil Carbon Stocks; Predictions from Two Contrasting Models for the
Turnover of Organic Carbon in Soil. Global Change Biology 11(1) 154-166
Keeling, C.D., S.C. Piper, R.B. Bacastow, M. Wahlen, T.P. Whorf, M. Heimann, and H.A. Meijer. (2005)
Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and oceans from 1978 to
58
King’s College London
Sophie Page
2000: observations and carbon cycle implications. In J.R. Ehleringer, T.E. Cerling, and M.D.
Dearing (eds.), A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems.
83-113, Springer Verlag, New York.
Kirby, K.R. & Potvin, C. (2007) Variation in carbon storage among tree species: Implications for the
management of a small-scale carbon sink project. Forest Ecology & Management 246, 208-221
Kirschbaum, J. (2000) Will Changes in Soil Organic Matter Act as a Positive or Negative Feedback on
Global Warming? Biogeochemistry 48, 21-51
Konert, M. & Vandenberghe, J. (1997) Comparison of Laser Grain Size Analysis with Pipette and Sieve
Analysis: A Solution for the Underestimation of the Clay Fraction. Sedimentology 44(3) 523-535
Krull, E.S., Baldock, J.A. & Skjemstad, J.O. (2001) Importance of Mechanisms and Processes of the
Stabilisation of Soil Organic Matter for Modelling Carbon Turnover. Functional Plant Biology
30(2) 207-222
Kucharik, C.J., Foley, J.A., Delire, C., Fisher, V.A., Coe, M.T., Lenters, J.D., Young-Molling, C., Ramankutty, N.,
Norman, J.M. & Gower, S.T. (2008) Testing the performance of a dynamic global ecosystem
model: Water balance, carbon balance, and vegetation structure. Global Biogeochemical Cycles
14(3) 795-825
Lal, R., Kimble, J.M., Levine, E. & Stewart, B.A. (1995) World Soils and Greenhouse Effect: An Overview.
In: Soils and Global Change. Lal, R., Kimble, J.M., Levine, E. & Stewart, B.A. (Eds.) CRC Lewis
Publishers: Boca Raton pp1-7
Lal, R. & Bruce, J.P. (1999) The potential of world cropland soils to sequester C and mitigate the
greenhouse effect. Environmental Science and Policy 2, 177–185.
Lal, R. (2000) World cropland soils as a source or sink for atmospheric carbon. Advances in Agronomy
71, 145–191
Lal, R. (2004a) Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science
304, 1623-1627
Lal, R. (2004b) Soil Carbon Sequestration to Mitigate Climate Change. Geoderma 123(1-2) 1-22
Lawrence, D.M. and A.G. Slater (2008) Incorporating organic soil into a global climate model. Climate
Dynamics 30(2-3) 14-160
59
King’s College London
Sophie Page
Lloyd, J. & Taylor, J.A. (1994) On the temperature dependence of soil respiration. Functional Ecology
8(3) 315-323
Loizeau, J.L., Arbouille, D. & Santiago, S. (1994)Evaluation of a wide range laser diffraction grain size
analyser for use with sediments. Sedimentology 41, 353-361
Lorenz, K. & Lal, R. (2005) The Depth Distribution of Soil Organic Carbon in Relation to Land Use and
Management and the Potential of Carbon Sequestration in Subsoil Horizons. Advances in
Agronomy 88(13), 35-66
Lorenz, K. and Lal, R. (2009). Biogeochemical C and N cycles in urban soils. Environment International.
35:1-8
Mann, L.K. (1983) Changes in Soil Carbon Storage after Cultivation. Soil Science 142(5) 279-288
Mann, L.K. (1986) Changes in Soil Carbon Storage after Cultivation. Soil Science 142(5) 279-288
Meentemeyer, V., Gardner, J. & Box, E.O. (1985) World Patterns and Amounts of Detrital Soil Carbon.
Earth Surface Processes and Landforms 10(6) 557-567
Met Office (2012a) Met Office Confirms Wettest June in a Century (Online)
http://www.metoffice.gov.uk/news/releases/archive/2012/wettest-June [Accessed 22 August,
2012]
Met Office (2012b) June 2012 UK Overview (Online)
http://www.metoffice.gov.uk/climate/uk/2012/june.html [Accessed 26 August 2012]
Morin, P. (2011) Community Ecology (2nd ed). John Wiley & Sons: Chichester
Nelson, D.W. & Sommers, L.E. (1996) Total Carbon, Organic Carbon, and Organic Matter In: Methods of
Soil Analysis Part 3: Chemical Method, Sparks, D.L. Soil Science Society of America Inc: Madison,
USA. pp 961-1010
Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J., Myneni, R.B. & Running,
S.W. (2003) Climate-Driven Increases in Global Terrestrial Net Primary Production from 19821999. Science 300(5625) 1560-1563
Niemelä, J. (1999) Is there a need for a theory of urban ecology? Urban Ecosystems 3, 57-65
Oades, J.M. (1984) Soil Organic Matter and Structural Stability: Mechanisms and Implications for
Management. Plant and Soil 76 (1-3) 319-337
60
King’s College London
Sophie Page
Olk, D.C., & E.G. Gregorich (2006) Meaningful pools in determining soil carbon and nitrogen dynamics.
Soil Science Society of America Journal 70, 967-974
Pan, G., Wu, L., Li, L., Zhang, X., Gong, W. & Wood, Y. (2008) Organic Carbon Stratification and Size
Distribution of Three Typical Paddy Soils from Taihu Lake Region, China. Journal of
Environmental Sciences 20, 456-463
Parton, W.J., D.S. Schimel, C.V. Cole, and Ojima, D.S. (1987). Analysis of factors controlling organic matter
levels in Great Plains Grasslands. Soil Science Society of America Journal 51, 1173–1179
Parton, W.J., Schimel, D.S., Cole, C.V. & Ojima, D.S. (2001) Analysis of Factors Controlling Soil Organic
Matter Levels in Great Plains Grasslands. Soil Society of America Journal 51(%) 1173-1179
Pastor, J. & Post, J.M. (1986) Influence of Climate, Soil Moisture, and Succession on Forest Carbon and
Nitrogen Cycles. Biogeochemistry 2(1) 3-27
Paustian K., Andren, O., Jansen, H.H., Lal, R., Smith, P., Tian, G., Tiessen, H., Van Noordwijk, M. & Woomer,
P.R. (1997) Agricultural Soils as a Sink To Mitigation CO2 Emissions. Soil Use and Management
13, 230-244
Paustian, K., Six, J. & Elliot, E.T., Hunt, H.W. (2000) Management Options for Reducing CO2 Emissions
from Agricultural Soils. Biogeochemistry 48, 147-163
Post, W.M., Emanuel, W.R., Zinke, P.J. & Stangenberger, A.G. (1982) Soil Carbon Pools and World Life
Zones. Nature 298, 156-159
Post, W.M. & Kwon, K.C. (2000) Soil Carbon Sequestration and Land-Use Change: Processes and
Potential. Global Change Biology 6 (3) 317-328
Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A. & Klooster, S.A. (1993)
Terrestrial ecosystem production: A process model based on global satellite and surface data.
Global Biogeochemical Cycles 7, 811–841
Pouyat, R., Groffman, P., Yesilonis, I. & Hernandez, L. (2002) Soil Carbon Pools and Fluxes in Urban
Ecosystems. Environmental Pollution 116, 107-118
Preston, C.M., Newman, R.H. & Rother, P. (1994) Using 13C Cpmas NMR to Assess Effects of Cultivation
on the Organic Matter of Particle Size Fractions in a Grassland Soil. Soil Science 157(1) 26-35
Raich, J.W. & Schlesinger, W.H. (1992) The Global Carbon Dioxide Flux in Soil Respiration and Its
Relationship to Vegetation and Climate. Tellus B 44(2) 81-99
61
King’s College London
Sophie Page
Rasse, D.P., Rumpel, C. & Dignac, M-F. (2005) Is Soil Carbon Mostly Root Carbon? Mechanisms for a
Specific Stabilisation. Plant & Soil 269, 341-356
Rawlins, B.G., Vane, C.H., Kim, A.W., Tye, A.M., Kemp, S.J. & Bellamy, P.H. (2008) Methods for estimating
types of soil organic carbon and their application to surveys of UK urban areas. Soil Use and
Management 24 (1) 47-59
Rawls, J.W., Brakensiek, D.L. & Saxton, K.E. (1982) Estimation of Soil Water Properties. Transactions of
the American Society of Agricultural Engingeers 25(5) 1316-13328
Rebele, F. (1994) Urban Ecology and Special Features of Urban Ecosystems. Global Ecology and
Biogeography Letters 4(6) 173-187
Robertson, G.P., Paul, E.A. & Harwood, R.R. (2000) Greenhouse Gases in Intensive Agriculture:
Contributions of Individual Gases to the Radiative Forcing of the Atmosphere. Science 289,
1922-1925
Robinson, W.O. (1927) The determination for organic matter in soils by means of hydrogenperoxide.
Journal of Agricultural Research 34, 339-356.
Roth, C.H.B., Meyer, B. & Frede, H.G. (1985) A portable rainfall simulator to study factors affecting
runoff, infiltration and soil loss. Catena 12, 79-85
Rutherford, D.W., Chiou, C.T. & Eberl, D.D. (1997) Effects of Exchanged Cation on the Microporosity of
Montmorillonite. Clas and Clay Minerals 45(4) 534-543
Santisteban, J.I., Mediavilla, R., Lopez-Pamo, E., Dabrio, C.J., Blanca Ruiz Zapata, M., Garcia, J.G., Castano,S.
& Martinez-Alfaro, P.E. (2004) Loss on Ignition: a qualitative or quantitative method for organic
matter and carbonate mineral content in sediments? Journal of Paleolimnology 32(3) 287-299
Schimel, D.S., Braswell, B.H., Holland, E.A., McKeown, R., Ojima, D.S., Painter, T.H., Parton, W.J. &
Townsend, A.R. (1994) Climatic, Edaphic and Biotic Controls over Storage and Turnover of
Carbon in Soils. Global Biogeochemical Cycles 8(3) 279-293
Schimel, D.S. (1995) Terrestrial Ecosystems and the Carbon Cycle. Global Change Biology 1(1) 77-91
Schimel, D., Enting, I.G., Heimann, M., Tigley, T.M.L., Raynaud, D., Alves, D. & Siegenthaler, U. (2000) CO2
and the Carbon Cycle. In: Wigley, T.M.L. & Schimel, D.S. (2000) The Carbon Cycle. Cambridge:
Cambridge University Press
Schlesinger, W.H. (1977) Carbon Balance in Terrestrial Detritus. Annual Review of Ecological Systems. 8,
51-81
62
King’s College London
Sophie Page
Schlesinger, W.H. (1990) Evidence from Chronosequence Studies or a Low Carbon-Storage Potential of
Soils. Nature 348, 232-234
Schlesinger, W.H. (1997) Biogeochemistry. An Analysis of Global Change (2nd Ed.) San Diego, California:
Academic Press
Schlesinger, W.H., Palmer Winkler, J. & Megonigal, J.P. (2000) Soils and the Global Carbon Cycle. In:
Wigley, T.M.L. & Schimel, D.S. The Carbon Cycle, 2000. Cambridge: Cambridge University Press
Schulte, E.E. (1995) Recommended Soil Organic Matter Tests. In: Recommended Chemical Soil Test
Procedures for the North Central Region. Danke, W.C. (Ed) Cooperative Extension Service, North
Dakota State University: Fargo, ND
Schumacher, B.A. (2002) Methods for the Determination of Total Organic Carbon (TOC) in Soils and
Sediments. U.S. Environmental Protection Agency: Washington D.C. (Online)
http://www.epa.gov/esd/cmb/research/papers/bs116.pdf [Accessed 2 June 2012]
Shem-Tov,S., Zaady, E., Groffman, P.M. & Gutterman, Y. (1999) Soil Carbon Content Along a Rainfall
Gradient and Inhibition of Germination: A Potential Mechanism for Regulation Distribution of
Plantago Coronopus. Soil Biology and Biochemistry 31(9) 1209-1217
Shrestha, R.K. & Lal, R. (2007) Soil Carbon and Nitrogen in 28-Year-Old Land Uses in Reclaimed Coal
Mine Soils of Ohio
Smith, P., Powlson, D.S., Smith, J.U., Falloon, P. & Coleman, K. (2000) Meeting the UK’s Climate Change
Commitments: Options for Carbon Mitigation on Agricultural Land. Soil Use and Management 16,
1-11
Smithson, P., Addison, K. & Atkinson, K. (2004) Fundamentals of the Physical Environment (3rd Ed.)
London: Routledge
Soon, Y.K. & Abboud, S. (1991) A Comparison of Some Methods for Soil Organic Carbon Determination.
Communications in Soil Science and Plant Analysis 22 (9-10) 943-954
SouthEastCentral Forum (2011) Map of Sub-Districts of Southwark (Online)
http://www.southeastcentral.co.uk/threads/where-is-nunhead-historically-part-of-peckhamor-not.1424/ [Accessed 28 August 2012]
Southwark BAP Report (2010) Southwark Biodiversity Action Plan Report 2006-2010. Southwark
Council: London (Online )
http://www.southwark.gov.uk/downloads/download/287/working_for_southwarks_wildlife
[Accessed 18 March 2012]
63
King’s College London
Sophie Page
Southwark BAP (2012) Southwark Biodiversity Action Plan 2012-2018L Making Space for Natural
Neighbours. Southwark Council: London (Online)
http://www.southwark.gov.uk/downloads/download/1782/southwark_biodiversity_action_pl
an_2006-2010 [Accessed 18 March 2012]
Southwark Council (2012a) Overview Map (Online)
http://maps.southwark.gov.uk/connect/index.jsp?tooltip=yes [Accessed 26 August, 2012]
Southwark Council (2012b) Southwark Ecology (Online)
http://maps.southwark.gov.uk/connect/index.jsp?tooltip=yes [Accessed 26 August, 2012]
Spain, A. V., R. F Isbell, and Probert, M.E. (1983) Soil Organic Matter. In: Soils: An Australian Viewpoint
(CSIRO, Melbourn: Ed) Academic Press: London. Oo 551-563
Sperazza, M., Moore, J.N., Hendrix, M.S. (2004) High-Resolution Particle Size Analysis of Naturall
Occuring Very Fine-Grained Sediment through Laser Diffractometry. Journal of Sedimentary
Research 74(5) 736-743
Steinbeck, J. (1951) The Grapes of Wrath. Melbourne: Heinemann
Stevens, J.P., Blackstock, T.H., Howe, E.A. & Stevens, D.P. (2004) Repeatability of Phase 1 Habitat Survey.
Journal of Environmental Management 73(1) 53-59
Sumner, M.E. (1999) Handbook of Soil Science. Taylor & Francis: Boca Raton, FL
Tansley, A. G. (1949) The British Isles and their vegetation. Cambridge University Press: Cambridge
Tate, K.R., Scott, N.A., Ross, D.J., Parshotam, A. & Claydon, J.J. (2000) Plant Effects on Soil Carbon Storage
and Turnover in a Montane Beech (Nothofagus) Forest and Adjacent Tussock Grassland in New
Zealand. Australian Journal of Soil Research 38, 831-835
Thomas, D.S.G. & Goudie, A. (2000) The Dictionary of Physical Geography. Oxford: Blackwell Publishing
Ltd.
Thorn, K.S., Christensen, H.E.M, Shigeta, R., Huddler, D., Shalaby, L., Lindberg, N-HC., Schutt, C.E. (1997)
The Crystal Structure of a Major Allergen from Plants. Cell Press 5(1) 19-32
Tiessen, H. & Stewart (1983) Particle-size Fractions and their Use in Studies of Soil Organic Matter: II.
Cultivation Effects on Organic Matter Composition in Size Fractions. Soil Society of America
Journal 47(3) 509-514
Tinsley, J. (1950) Determination of organic carbon in soils by dichromate mixtures. P. 161-169. In
Trans. 4th Int. Congr. Soil Sci., Vol. 1 Hoitsemo Brothers, Groningen, the Netherlands.
64
King’s College London
Sophie Page
Tisdall, J.M. & Oades, J.M. (1982) Organic Matter and Water-Stable Aggregates in Soils. Journal of Soil
Science 33(2) 141-163
Torn, M.S., Trumbore, S.E., Chadwick, O.A., Vitousek, P.M. & Hendricks, D.M. (1997) Mineral Control of
Soil Organic Carbon Storage and Turnover. Nature 389, 170-173
Trudgill, S. (2001) The Terrestrial Biosphere. Environmental Change, Ecosystem Science, Attitudes and
Values. Essex: Pearson Education Ltd.
Trumbore, S.E., Chadwick, O.A. & Amundson, R. (1996) Rapid Exchange Between Soil Carbon and
Atmospheric Carbon Dioxide Driven by Temperature Change. Science 272(5260) 393-396
UN-HABITAT (2011) Global Report on Human Settlements 2011- Cities and Climate Change. Press
Release: Hot Cities- Battle-Ground for Climate Change (Online)
http://www.unhabitat.org/downloads/docs/GRHS2011/P1HotCities.pdf [Accessed 24 July
2012]
UK National Ecosystem Assessment (UK NEA) (2011) The UK National Ecosystem Assessment: Synthesis
of the Key Findings. Cambridge: UNEP-WCMC
Van der Kamp, J., Yassir, I. & Buurman, P. (2009) Soil Carbon Changes Upon Secondary Succession in
Imperata Grasslands (East Kalimantan, Indonesia). Geoderma 149(1-2) 76-83
Vereeckhen, H., Maes, J., Feyen, J. & Darius, P. (1989) Estimating the Soil Moisture Retention
Characteristic From Texture, Bulk Density, and Carbon Content. Soil Science 148(6) 389-403
Vos, B.D., Lettens, S., Muys, B. & Deckers, J.A. (2007) Walkley-Black Analysis of Forest Soil Organic
Carbon: Recovery, Limitations and Uncertainty. Soil Use and Management 23(3) 221-229
Walkley, A. & Black, A. (1934) An examination of the degtjareff method for determining soil organic
matter, and a proposed modification of the chromic acid titration method. Soil Science 37(1) 2938
Wardle, D.A., Jonsson, M., Bansal, S., Bardgett, R.D., Gundale, M.J. & Metcalfe, D.B. (2011) Linking
Vegetation Change, Carbon Sequestration and Biodiversity: Insights from Island Ecosystems in a
Long-Term Natural Experiment. Journal of Ecology 100(1) 16-30
Wedin, D.A. & Tilman, D. (1996) Influence of Nitrogen Loading and Species Composition on the Carbon
Balance of Grasslands. Science 274, 1720-1723
Wentworth, C.K. (1922) A Scale of Grade and Class Terms for Clastic Sediments. Journal of Geology 30,
377-392
65
King’s College London
Sophie Page
White, A., Cannell, M.G.R. & Friend, A.D. (2000) CO2 Stabilization, Climate Change and the Terrestrial
Carbon Sink. Global Change Biology 6(7) 817-833
Wielopolski, L., Z. Song, I. Orion, A.L. Hanson, and G. Hendrey. (2005) Basic considerations for Monte
Carlo calculations in soil. Appl. Radiat. Isot. 62:97–107.
Wigley, T.M.L. & Schimel, D.S. (2000) The Carbon Cycle. Cambridge: Cambridge University Press
Zinke, P., Stangenberger, A.G., Post, W.M., Emanuel, W.R. & Olsen, J.S. (1984) Worldwide Organic Soil
Carbon and Nitrogen Data. Environmental Sciences Division Publication 2212
66