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UvA-DARE (Digital Academic Repository) 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. Link to publication 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 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl) 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