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Heathland ecosystems, human impacts and time: A long term heathland trial
investigating ecosystem changes that occur after exposure to climate change,
elevated N deposition and traditional vegetation management practices
Kopittke, G.R.
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Citation for published version (APA):
Kopittke, G. R. (2013). Heathland ecosystems, human impacts and time: A long term heathland trial
investigating ecosystem changes that occur after exposure to climate change, elevated N deposition and
traditional vegetation management practices
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Download date: 18 Jun 2017
8|
Chapter 1
Introduction
Photo (left) Snow on Calluna vulgaris heathland. Photographer Gillian Kopittke 2012.
1
Introduction
Humans influence the environment around them and, while natural environmental
variability exists, scientific evidence indicates that the rapid climate changes of the past
century are dominantly human-induced (IPCC 2007a). Anthropogenic emissions of
greenhouse gases, such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (NOX),
and of aerosols (microscopic airborne particles or droplets) have resulted in increased
atmospheric concentrations of these compounds (Le Treut et al. 2007). This has altered
global temperatures due to absorption of outgoing radiation by the greenhouse gases and
increased reflection / absorption of incoming solar radiation by aerosols (Forster et al.
2007). Global hydrological cycles have also been linked to these temperature changes,
with the recent warming trends explaining much of the global drying trend (1.7% per
decade) observed between 1950 and 2008 (Dai 2011a).
Subsequent deposition of the anthropogenic compounds back to the earth’s surface
directly affects local ecosystem processes. Deposition of NOx, NH3 and SOx can change
carbon (C) cycling patterns, affect the species composition of ecosystems and result in soil
acidification (Bobbink et al. 2010, van Breemen and van Dijk 1988). This may gradually
alter the ecosystem type and indirectly change the land surface characteristics (Friedrich
et al. 2011).
Changes to the land surface also occur directly from human activities, such as through
deforestation, vegetation management, crop irrigation or construction of cities. These
changes to land use can also influence the rate of energy and water exchange with the
atmosphere, which results in changes to C fluxes, hydrological cycles and temperature
feedback patterns (Forster et al. 2007, Houghton 2003, Pongratz et al. 2006). These
feedback patterns can amplify or dampen atmospheric concentrations of greenhouse
gases.
The complexity of climate systems and the many interactions that occur place limits on
accurately predicting either the future global climate or the ecosystem responses
(Solomon et al. 2007). Ecosystem processes may respond differently to regional changes,
or interact asymmetrically with other processes within the same ecosystem, leading to
unpredicted outcomes (Walther et al. 2002). To form better predictions, it is necessary to
investigate ecosystem biogeochemical responses over longer time frames, so that the
changes in ecosystem-climate feedbacks (e.g. C cycle) can be determined as they respond
to anthropogenic activities.
The opportunity to study these anthropogenic influences existed at a shrubland
ecosystem located in Oldebroek, the Netherlands. Shrublands are widespread across
Europe and have been identified by the IPCC (2007) as an ecosystem vulnerable to climate
changes. Oldebroek is one of six long term shrubland study sites established across Europe
to investigate variations in key ecosystem functions in response to climate change (e.g.
10 | Chapter 1
Emmett et al. 2004, Gorissen et al. 2004, Peñuelas et al. 2007). The shrubland at the
Oldebroek site is dominated by the dwarf shrub Calluna vulgaris (L.) Hull and is termed a
heathland. The vegetation has traditionally been managed by humans, experienced
elevated N deposition rates for more than half a century and had a climate manipulation
trial operating for 14 years.
2
Vegetation Management
Heathlands are managed landscapes that developed about 4000 years ago as a result of
forest clearing followed by low intensity agro-pastoral uses such as grazing, burning, turf
cutting and vegetation harvesting for fodder or fuel (Gimingham 1985, Sparrius 2011). In
the Netherlands, the traditional management of heathlands involved grazing by sheep and
horses in all seasons, with small heath areas periodically cut on a three to five year cycle
to provide winter forage for stabled cattle (Diemont and Heil 1984, Webb 1998). During
autumn, farmers dug into the organic horizon (peat), which allowed it to weather and dry
during the winter. This was then spread on the stable floor to absorb animal excrement
until it was removed and applied as fertilizer to nearby arable plots (Webb 1998). This led
to a net export of organic matter and nutrients from the heathlands and developed
ecosystems with low levels of plant available nutrients (Diemont and Heil 1984, Wessel et
al. 2004).
This traditional maintenance preserved the open landscape of heathlands until the mid to
th
late 19 century when chemical fertilizers were introduced and afforestation of former
‘waste lands’ occurred, which led to traditional forms of land-use dying out (Webb 1998).
In recent times, landscape management has been reinstated to protect and conserve
heathland flora and fauna. This is generally undertaken by cutting, burning or sod-cutting
on a cyclical basis, producing relatively large patches of even-aged vegetation (Webb
1998).
In between these management related disturbances, Calluna plants grow and biomass
accumulates both in the soil and the vegetation (Diemont and Heil 1984, Gimingham
1985). Following disturbances, the Calluna plants become established and there is a net
gain of aboveground biomass within the ecosystem. After ~6 years, the plants develop a
hemispherical shape of up to 50 cm in height with 100% groundcover. The net biomass
gain continues until ~15 years and after this age, net loss of aboveground biomass begins
to occur. After ~25 years, the central branches die and grasses establish in the vegetation
gaps (Gimingham 1985). If heathlands are not managed, grass encroachment or
succession to a forest occurs as influenced by N deposition, frost periods and heather
beetle attacks (Aerts 1989, Diemont and Heil 1984, Marrs 1986).
Management practices preserve these heathland ecosystems and the associated flora and
fauna. However, does the frequency of the management cycle impact the C feedback
cycles or affect carbon sequestration? Carbon storage on three heathland ages is
Introduction | 11
investigated in Chapter 2 and the C loss associated with soil respiration for three
heathland ages is the subject of Chapter 3.
3
Elevated N Deposition
Acid atmospheric emissions within Europe reached a peak between the mid-1960s and
-1
-1
-1
1980s with deposition rates in the order of 40 to 80 kg N ha year and 30 to 80 kg S ha
-1
year recorded in the Netherlands (van Breemen and van Dijk 1988). Then, between 1985
and 1995, there were strong reductions in emissions observed (particularly for SO2) as a
result of the introduction of air pollution abatements (Fowler et al. 2007, Jaarsveld and
Bleeker 2004). Emissions have continued to decline in Europe, with European Union (EU)
-1
-1
countries reducing N (NOx + NH3) output by 42% (22,162 Gg year to 12,753 Gg year ) and
-1
-1
SOx output by 82% (24,857 Gg year to 4,575 Gg year ) between 1990 and 2010 (EEA
2012). In some countries, such as the Netherlands, even greater reductions have been
observed, with a 57% reduction of N emissions between 1990 and 2010 (EEA 2012). With
the significant reductions in anthropogenic acid emissions and subsequent deposition,
questions have been posed on whether there is still an ecological threat (Alewell et al.
2000).
Although there have been significant reductions, the rates of wet N deposition in the
Netherlands continue to be recorded above critical loads (Bobbink et al. 2010, Boxman et
al. 2008, Schmidt et al. 2004). Elevated N deposition above critical loads can impact on
ecosystem dynamics by decreasing soil pH, affecting species composition and changing C
allocation patterns. In low-nutrient ecosystems, the increased deposition reduces nutrient
limitations and alters plant growth through increased C allocation and increased litter
production in woody shrub species (Bobbink et al. 2010, Marcos et al. 2003). Additionally,
changes in the leaf chemistry and litter quality can also occur, which in many cases
accelerates the litter decomposition rate (Kozovits et al. 2007, Nierop and Verstraten
2003). However, it has been shown that this is not the case for Calluna, as Calluna leaf
decomposition rates do not increase with increased N, up to a maximum application rate
-1
of 80 kg N ha (van Vuuren and van der Eerden 1992). Therefore, the accumulation of C in
Calluna plants and heathland soil is expected to be relatively pronounced. This is
addressed as part of Chapter 2, in which C stocks were quantified on different ages of
heathland.
Apart from reducing nutrient limitations to plants, elevated N deposition also contributes
additional protons to the soil system which participate in proton transfer processes and
can eventually lead to soil acidification (Reuss et al. 1987, van Breemen et al. 1983). In
soils that are already acidic, such as at the Oldebroek heathland (pH 3.7–4.9), the proton
participates in one of two important transfer processes: (1) cation exchange reactions
with the soil complex, and (2) reactions of protons with the soluble amorphous
components of the soil, such as the release of inorganic aluminium species by dissolution
of soil constituents (Reuss et al. 1987). The leaching of these metal ions (e.g. the base
12 | Chapter 1
cations) is irreversible and once they are leached they are unavailable for any future
neutralizing reactions. This leads to a permanent change in soil pH (van Breemen et al.
1983). The effect of the interaction of ambient N deposition rates on the soil acidification
of a heathland ecosystem are investigated in Chapter 5.
4
Climate Change
Changes to the global climate have affected temperature and precipitation patterns in the
th
20 century, with increases in average annual surface temperature of 0.8°C observed in
most of Europe (Alcamo et al. 2007). Precipitation trends were more spatially variable,
with increases of 10 to 40% observed in northern Europe and decreases of up to 20%
observed in some parts of southern Europe (Alcamo et al. 2007). There have also been
changes in the seasonal distribution of precipitation, with mean winter precipitation
increasing in most of Atlantic and northern Europe (Klein Tank et al. 2002).
Over the next 100 years, it is predicted that a drying trend will occur in Europe which will
affect the southern regions, while in western Europe the frequency of wet and dry periods
is predicted to increase (Dai 2011a, Dai 2011b). Specifically for the Netherlands,
wintertime precipitation is predicted to increase between 3.5 and 7% per degree of global
warming, while summertime precipitation shows a decrease (van den Hurk et al. 2007).
Overall, this will result in a slight to moderate drying trend from 2000–2099 for the
1
Netherlands, as indicated by an increase in the Palmer Drought Severity Index (PDSI ) of
between -1 and -2 (Dai 2011b). These particularly dry summer scenarios and the increased
intensity of extreme daily precipitation will require additional management attention in
the near future (van den Hurk et al. 2007).
Drought periods can impact ecosystem functioning in a number of ways. Plants can be
affected in a physiological and structural manner, such as reducing their enzymatic activity
or closing stomata to minimize water loss, with a resulting decrease in photosynthesis and
respiration rates (Chaves et al. 2002, van der Molen et al. 2011). Reductions in leaf area
can also occur due to early senescence and leaf shedding, or arrest of leaf expansion
(Peñuelas et al. 2007, van der Molen et al. 2011). The response of plant roots may also
vary, with increases in root to shoot ratio, root length or root area occurring, which
enables increased water and nutrient uptake, although these responses vary between
plant species (Liu and Stützel 2004, Rodrigues et al. 1995). Microbial processes in the soil
are also affected, with reductions of soil water films inhibiting the diffusion of substrates,
reducing the soil C mineralization rate and decreasing subsequent CO 2 release (Davidson
and Janssens 2006, Jensen et al. 2003). These changes can affect key ecosystem functions,
such as C storage, nutrient cycling and species composition (IPCC 2007b, Wessel et al.
2004). Therefore, increasing drought frequency is likely to impact these processes,
1
The PDSI is an index of meteorological drought used for monitoring and ranges from −10 (dry) to
+10 (wet) with values below −3 representing severe to extreme drought.
Introduction | 13
particularly the C cycle and C feedback loops, more strongly in the future (van der Molen
et al. 2011).
Identifying these potential feedbacks is an important aspect of climate research. Further,
it is also important to assess if feedbacks alter in response to human activities and if they
could result in significant or undesirable climate responses (Denman et al. 2007).
5
Carbon Cycles
The C cycle (Figure 1) is of particular interest when discussing climate change. Terrestrial
ecosystems store large quantities of C in their vegetation and soil, can release or absorb
globally relevant quantities of the greenhouse gases CO2, CH4 and NOX, emit aerosols and
control exchanges of energy and water between atmosphere and the land surface
(Denman et al. 2007, Heimann and Reichstein 2008). Ecosystems are influenced by the
local climate. In turn, the climate is influenced by the feedbacks from the ecosystem. This
occurs through the nonlinear interactions of the climate and biogeochemical cycles, which
can result in multiple feedbacks to the climate that might amplify or dampen atmospheric
concentrations of greenhouse gases (Denman et al. 2007).
Quantifying and modeling these feedback mechanisms is difficult due to the limited
understanding of C cycling processes within different ecosystem types, and particularly
within the soil components of these ecosystems (Heimann and Reichstein 2008). The
dominant C fluxes at an ecosystem level are the:

C gain from:
o photosynthesis (PG); and
o deposition of dissolved organic C (DOC).

C loss from:
o plant respiration (RP);
o soil respiration (RS), which includes:
▪ root respiration (RA: autotrophic respiration);
▪ microbial respiration (RH: heterotrophic respiration);
o cutting (grazing) and export of vegetation; and
o leaching of DOC.
Investigating these C fluxes and feedback loops, such as the belowground loops shown in
Figure 2, and quantifying C storage in ecosystems will allow us to better model and predict
the influence of anthropogenic activities on the climate (Denman et al. 2007). The C
storage and C loss from the soil of three ages of heathlands are investigated in Chapter 2
and Chapter 3, respectively. The C loss from the soil of a heathland after 14 years of
climate manipulation is investigated in Chapter 4.
14 | Chapter 1
-1
Figure 1 The global carbon cycle for the 1990s showing the annual fluxes in Gt C year : preindustrial ‘natural’ fluxes in black and ‘anthropogenic’ fluxes in red (Denman et al. 2007). ‘GPP’ is
annual gross (terrestrial) primary production.
Figure 2 Three potential belowground C feedback loops that could be induced by climate change,
showing a) potential interactions between microbial metabolism and the physics of permafrost
thawing and carbon release, b) the ‘microbial priming effect’ and c) interactions between the carbon
and nitrogen cycles, adapted from Heimann and Reichstein (2008). Pink arrows denote effects of
terrestrial ecosystems on climate, orange arrows denote effects of climate change on terrestrial
ecosystems, and black arrows denote interactions within ecosystems.
Introduction | 15
6
Scope of this Thesis
The main objective of this research was to investigate the changes that occurred in a
heathland soil that was exposed to three anthropogenic disturbance regimes: the
traditional heathland management practices (cyclical vegetation removal), elevated N
deposition and a predicted climate change scenario (repeated annual drought). To
accomplish this objective, the research questions posed were:






What are the carbon pools of the studied heathland and does the increasing age
of the vegetation lead to increasing carbon accumulation? (Chapter 2)
If the frequency of the heathland cutting regime decreases and communities
become older, is there a change in the annual soil CO2 emissions? (Chapter 3)
How can model selection procedures be used to select appropriate models for
autotrophic soil respiration and heterotrophic soil respiration? (Chapter 3)
Has a long-term, repeated annual drought resulted in lower soil CO2 emissions
and a lower overall annual C loss? (Chapter 4)
If the N deposition rate at the site has followed the recent decreasing trend
observed across Europe, do the heathland soils show recovery from soil
acidification? (Chapter 5)
What is the effect of a field-scale repeated annual drought on the soil
acidification trend, when considered in conjunction with an elevated N
deposition rate? (Chapter 5)
Contrasting with earlier works on European heathlands, this thesis examines multiple
anthropogenic conditions that influence the ecosystem. These conditions are examined
separately within each chapter but attention is also given to the interactions that occur
with the other anthropogenic conditions.
7
7.1
Experimental Approach
Site Description
Investigations were undertaken at a dry heathland located at Oldebroek in the
Netherlands (Figure 3). The trial site is situated approximately 90 km to the north-east of
Amsterdam and is 25 meters above sea level (Table 1). The experimental area is relatively
flat in the west and rises in the east and north-east onto a gentle slope with a south-1
western aspect. The climate is humid temperate and average rainfall is 1018 mm year ,
with the mean monthly rainfall greatest in July (Figure 4). The ecosystem is N saturated,
-1
-1
with high bulk N deposition rates (10.7–37.4 kg N ha year ) and leaching rates (3.9–65.7
-1
-1
kg N ha year ) between 1998 and 2011. The system was also subjected to high rates of
-1
-1
SO4 deposition (4.2–6.2 kg S ha year ) with subsequently high leaching rates (2.7–11.7 kg
-1
-1
S ha year ) between 2008 and 2011. Further site details are provided in Table 1.
16 | Chapter 1
Figure 3 The Oldebroek site layout showing the vegetation ages (12, 19 and 28 years), the long term
trial plots (green rectangles), the trenching trial plots (small black squares), the carbon stock
assessment points (red circles) and the location of Oldebroek within the Netherlands (inset). The long
term trial plots are denoted by ‘C’ for Control treatment, ‘D’ for Drought treatment and ‘W’ for
Temperature Warming treatment. The trenching trial plots are denoted by ‘U’ for Untrenched, ‘T’ for
Trenched and
for Trenched Validation plots.
Introduction | 17
Table 1 Description of the Oldebroek Trial Location
Category
Description
Location
Co-ordinates
Elevation
Slope
Climate
ASK Oldebroek, Oldebroekse heide, Province of Gelderland, Netherlands
52°24’N 5°55’E
25 m ASL
2%
Temperate, humid.
Avg Rainfall
Avg Air Temperature
1018 mm year
January: 2.0 °C
-1
July: 17.8 °C
Annual: 10.1 °C
-1
Figure 4 Rainfall (mm month ) at the Oldebroek site, showing Control rainfall (averaged over three
gauges) as a solid black line and Drought rainfall (averaged over 3 gauges) as a dashed orange line.
Plot (a) shows the monthly rainfall between December 1998 and August 2012. Plot (b) shows the
average yearly rainfall pattern where the bars denote standard errors of the mean arising from interyear variability and the dotted horizontal lines represent the average monthly rainfall across all years
for Control and Drought treatments.
7.1.1
Site Vegetation
The dominant vascular species at the site is Calluna vulgaris (L.) Hull. This grows to a
maximum height of 75 cm and provides approximately 95% of the groundcover. Also
18 | Chapter 1
present are Deschamspia flexuosa, Molinia caerulea and Pinus sylvestris. The dominant
non-vascular species is Hypnum cupressiforme Hedw. with two ecological phenotypes, one
growing under Calluna protection and the other adapted to areas of more light between
Calluna plants (Sass-Gyarmati et al. 2012). A list of species identified in the Control and
Drought treatments of the long term trial is provided in Table 2. Indicative chemical values
for Calluna new shoots, flowers and bulk litter are provided in Table 3.
Table 2 Plant species composition and mean percentage cover in the Climate Trial Control and
Drought treatments in October 2011 (Sass-Gyarmati et al. 2012). Mean cover percentages were
obtained from 20 cm x 20cm quadrats (n=15 per treatment).
Species
Mean % Cover in Treatment
Form
Control
Drought
96.00%
84.00%
Molinia caerulea (L.) Moench
Nardus stricta L.
Prunus serotina Ehrh.
Rumex acetosella L.
Perennial shrub
Sedge
Evergreen shrub /
tree
Grass
Grass
Deciduous tree
Perennial herb
Non-vascular Plants
Cladonia sp.
Campylopus introflexus (Hedw.) Brid.
Ceratodon purpureus (Hedw.) Brid.
Dicranum scoparium Hedw.
Dicranella heteromalla (Hedw.) Schimp.
Hypnum cupressiforme Hedw.
Pohlia nutans (Hedw.) Lindb.
Polytrichum juniperinum Hedw.
Polytrichum longisetum Sw. ex Brid.
Lophozia ventricosa (Dicks.) Dum.
Cephaloziella hampeana (Nees) Schiffn.
Aphanothece sp.
Lichen
Moss
Moss
Moss
Moss
Moss
Moss
Moss
Moss
Liverwort
Liverwort
Cyanobacteria
11.00%
0.66%
Vascular Plants
Calluna vulgaris (L.) Hull
Carex pilulifera
Juniperus communis
0.50%
0.50%
28.00%
2.00%
1.85%
16.00%
0.50%
80.00%
11.00%
14.00%
24.00%
90.00%
3.00%
2.00%
0.20%
0.26%
0.80%
-1
Table 3 Chemical composition (mg g ) of Calluna vulgaris new shoots, flowers and bulk litter in 1998
(van Meeteren 2005).
Plant Part
Shoots
Litter
Flowers
C
N
522
543
540
13.8
11.8
11.2
Element Concentration (mg g-1)
P
S
K
Ca
0.65
0.61
0.71
1.54
-
4.33
-
3.94
3.53
-
Mg
Mn
1.24
-
0.27
0.20
-
‘-‘ indicates concentration not analyzed.
Introduction | 19
The heathland has historically been managed by cutting and removal of the aboveground
vegetation on a 7 to 8 year cycle, however, this cutting cycle had not occurred in the study
area for a period of time. The site was divided into three areas in which cutting last
occurred in different years as a result of fire break creations. The ages of the three
communities were established using both site knowledge and by counting the growth
rings in the Calluna stems. The oldest heathland area (the Old community) was
determined to be approximately 28 years of age at the time of the investigation. The long
term climate trial was situated in this area. The vegetation on the south-eastern third of
the research site was approximately 19 years of age (the Middle community) and the
southern portion of the site was last cut in the year 2000, being 12 years old in 2012 (the
Young community).
7.1.2
Site Soil
The parent material at Oldebroek is a fluvioglacial deposit consisting of gravelly, white,
quartz-rich sands originally deposited by eastern rivers and pushed by glaciers in the
Saalien (Koster 1995). Overlying these deposits is a cover sand, which was dispersed by
winds during the Weichselien, and a thin layer of drift sand (30 cm) which dates from the
Middle Ages (van Meeteren 2005).
The soil at Oldebroek is a nutrient-poor, well drained, acid sandy haplic podzol with a
mormoder humus form. The surface organic horizon ranged between 1.4 and 8 cm thick,
and included OL and OF horizons with a discernible OH horizon present in most locations. A
clear boundary existed from the organic horizon to the bleached eluvial A horizon, where
frequent roots were observed. A clear and irregular boundary existed to an illuvial B
horizon, which was characterized by layers of accumulated humic or sesqueoxide
compounds. This graded to an illuvial BC horizon with few roots and some mottling.
Buried organic, eluvial or illuvial horizons were present in a number of profiles. The
boundary between organic and mineral soil (A horizon) was designated as 0 cm. The
chemical characteristics of the soil horizons are summarised in Table 4.
Table 4 Soil Properties at the Oldebroek Trial Site
Soil Chemistrya
Depth (cm)
pH
EC (S cm-1)
NO3 (mol kg-1)
PO4 (mol kg-1)
C/N ratio
Soil Moistureb %
a
b
Organic Horizons
L+F
H
Ae
Bs
Mineral Horizons
1BC
2BC
C
+8.0 to +1.4
3.7
197.9
646.6
+1.4 to 0
3.9
92.0
216.2
0 to 5.5
3.9
88.7
20.2
5.5 to 13
4.0
73.2
62.4
13 to 21
4.5
32.3
22.1
21 to 27
4.4
46.3
47.6
>27
4.9
30.8
13.1
1589
40.4
104.8
126
17.7
47.1
4.6
27.7
15.7
1.4
18.0
14.9
0.1
16.7
6.3
0.1
18.5
6.3
0.1
11.7
6.3
Water extraction of 1:5 for organic horizons and 1:1 for mineral horizons
Soil moisture at field capacity obtained following a rainfall event, reported as a percentage (g g-1 dry weight soil)
20 | Chapter 1
7.2
Experimental Design
7.2.1
Experimental Layout
The experimental locations were established within an 80 m x 100 m area, where three
heathland communities of different ages converged (Figure 4). At this convergence area, a
series of chronosequence studies were established using a ‘space for time’ approach to
investigate the differences between the three community ages for C stock (Chapter 2) and
C loss through soil respiration (Chapter 3). This was considered an appropriate approach
for a study undertaken on a species-poor community that followed a convergent
successional trajectory (Walker et al. 2010). The microbial soil respiration and root
respiration were separated using a trenching trial with eight experimental plots (60 cm x
60 cm) within each heathland age. Four of these plots had the aboveground biomass
removed with a narrow trench excavated to 50 cm around the plot borders and a nylon
mesh placed in the trench to prevent the new root incursion into the plots. These
‘Trenched’ plots were used to measure microbial respiration on each community age. The
other four ‘Untrenched’ plots were used to measure total soil respiration on each
community age plots.
The long term trial was established on the western portion of the study area, within the
Old heathland community. This commenced in 1998 and offers an ongoing, annual
assessment of the Calluna heathland ecosystem (Beier et al. 2004). Nine experimental
plots of 5 m x 4 m (with a 0.5 m buffer strip around the perimeter) were established in a
homogeneous area of the heathland in 1998. Three treatments were allocated randomly:
a Control, a prolonged drought during the growing season (Drought) and a passive nighttime warming treatment (Warming). The Warming treatment is not investigated within
this thesis and is therefore, not discussed further.
Light scaffold structures were built over all the plots to ensure that any impact from the
scaffolding (e.g. shading and sheltering) occurred in all plots (Beier et al. 2004).
Retractable curtains were supported on the frames over the Drought plots. During the
drought period, rain sensors activated the motor to extend a transparent cover over the
Drought plots when rain was detected and retracted the cover when the rain stopped.
Outside the drought period, no manipulation was applied and all plots received the same
precipitation amount. The drought periods and rainfall patterns are shown in Figure 5a.
The Drought and the Control treatments were used to investigate C loss from soil
respiration (Chapter 4) and soil acidification trends (Chapter 5).
While the plots were established in 1998, the experimental treatments began in May
1999. Further details on the design of the treatment manipulations can be found in Beier
et al. (2004). In September 2009, an area of 1 m x 2 m was harvested in each of the plots,
to simulate the effects of heathland cutting management.
Introduction | 21
7.2.2
Experimental Measurements
Soil sampling on the long term climate manipulation trial was undertaken in 1998 to
characterize the soil chemistry of the plots prior to treatment application. Soil sampling of
the three heathland ages was undertaken between 2011 and 2012 to determine (a)
carbon stocks to an approximate depth of 30 cm, (b) root biomass within the organic and
0–5 cm mineral soil layers and (c) microbial biomass of the organic horizon. Soil solution
was sampled at the base of the rooting zone on the long term climate manipulation trial
between December 1998 and April 2012.
Soil respiration was measured on the experimental plots of both the long term climate
manipulation trial, between July 2010 and August 2012, and the three ages of heathland,
between May 2011 and August 2012. Net ecosystem exchange (NEE) and ecosystem
respiration (ER) was measured on the long term climate trial between November 2010
and August 2012 and on the three ages of heathland between August 2011 and August
2012. These NEE and ER measurements were used to calculate gross photosynthesis and
to provide a measure of plant activity for inclusion as an explanatory variable in the soil
respiration models.
Biomass measurements were conducted annually on the long term climate manipulation
plots in August (1998–2003 / 2005 / 2008–2011) using a non-destructive pinpointing
method (Peñuelas et al. 2004). Absolute biomass measurements were obtained using
destructive methods from the long term climate manipulation trial in September 2009 and
from the three ages of heathland in May 2011.
Climate data, such as air and soil temperature, photosynthetically active radiation (PAR)
and soil moisture was recorded at the site during the experimentation period.
7.2.3
Model Selection Process
Modeling the effect of human activities on the feedback between ecosystem and climate
requires not only an understanding of biogeochemical processes, but also the selection of
an appropriate model to make predictions. These models can be designed for a wide
range of purposes, such as the Atmosphere-Ocean General Circulation Models (AOGCM),
which provide quantitative estimates of future climate change at continental and larger
scales (Randall et al. 2007). At an ecosystem scale, models that incorporate multiple
ecosystem processes can be used to predict C cycles over a long time period, such as the
FöBAAR model for forests (Keenan et al. 2012). At a smaller scale, models are also used to
predict a single flux within the C cycle, such as the C loss from soil respiration (Webster et
al. 2009). This thesis aimed to quantify soil respiration C loss from the three heathland
ages (Chapter 3) and from the long term climate manipulation treatments (Chapter 4);
thus a soil respiration model was required.
Choosing an appropriate model is not necessarily straight forward, with models using
different structures, process representations, input scenarios, species, locations and scales
of application (Medlyn et al. 2011). When selecting an appropriate model for data
22 | Chapter 1
analysis, consideration should be given to the purpose of the model, deciding which
measures are important to the outcome and what variable interactions need to be taken
into account (Kadane and Lazar 2004). Given the wide range of possible models, a sensible
approach is to start with a number of plausible models that are suitable for the model aim.
The models that are poor are deselected, leaving a subset of models for further
consideration (Kadane and Lazar 2004).
To identify the plausible models, a conceptual diagram is often used to show the essential
elements of the problem, such as the variables, forcing functions and the interrelations
which will be expressed by mathematical equations (Jørgensen and Bendoricchio 2001).
The conceptual diagram prepared for the soil respiration modeling in Chapter 3 is shown
in Figure 5. The plausible models that are developed from this conceptual diagram may
have different variable complexity and different structures and these are all then
compared through a pre-defined model selection process.
Figure 5 A conceptual model of the ecosystem processes affecting autotrophic soil respiration and
heterotrophic soil respiration. State variables are shown in pink filled boxes, forcing variables in blue
outlined boxes and the interactions are shown with black arrows.
The plausible models are calibrated and, if relevant, a sensitivity analysis undertaken to
select the model variables. Provided that the models under consideration match the
model aim, calibration of the models aims to obtain an appropriate or optimal parameter
set which gives the best agreement between the model output and the measured values,
according to a pre-defined objective function (Jørgensen and Bendoricchio 2001). The
sensitivity analysis is undertaken by changing combinations of variables and parameters to
distinguish between those that significantly impact on the system behavior and those that
have minimal impact on the system. In this process, it should be noted that all models are
simplifications and therefore the most important processes will be included, but not all
the details will be accounted for in the model (Jørgensen and Bendoricchio 2001). A
subset of models is then selected using the following criteria:
Introduction | 23



Are the parameters statistically significant? Reject models in which the
parameters are not significant.
Are the parameters feasible according to literature values and experience? Reject
models in which the parameters are unreasonable.
Are the model predictions within the range of the observation data and do they
follow expected patterns? Compare the goodness-of-fit measures (such as the
root mean square error (RMSE)) for the remaining models and select the models
with the lowest RMSE values.
Calibration should always be followed by some form of validation. This can be done by
testing a model against an independent set of data, preferably in a different experiment of
environment, or alternatively via cross-validation where the available single-source data is
repeatedly split into calibration and validation sets (Jørgensen and Bendoricchio 2001).
Performance in validation should be measured in the same way as during calibration. The
validation performance shows how well the model generalizes, that is, performs in a new
situation for which the model is supposed to be applicable. Based on this process, the
model with the best validation performance is selected for use in the data interpretation.
24 | Chapter 1