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Climate change and long-term fire management impacts on Australian savannas Simon Scheiter1, Steven I. Higgins2, Jason Beringer3,4 and Lindsay B. Hutley5 1

Biodiversity and Climate Research Centre (LOEWE BiK-F), Senckenberg Gesellschaft f€ur Naturforschung, Senckenberganlage 25, 60325 Frankfurt am Main, Germany; 2Department of

Botany, University of Otago, Dunedin 9054, New Zealand; 3School of Geography and Environmental Science, Monash University, Melbourne, Vic., Australia; 4School of Earth and Environment, University of Western Australia, Crawley, WA, Australia; 5Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT, Australia

Summary Author for correspondence: Simon Scheiter Tel: +4969 970751617 Email: [email protected] Received: 24 April 2014 Accepted: 18 September 2014

New Phytologist (2015) 205: 1211–1226 doi: 10.1111/nph.13130

Key words: adaptive dynamic global vegetation model (aDGVM), Australia, carbon sequestration, climate change, CO2 fertilization, fire, management, savanna.


Tropical savannas cover a large proportion of the Earth’s land surface and many people are dependent on the ecosystem services that savannas supply. Their sustainable management is crucial. Owing to the complexity of savanna vegetation dynamics, climate change and land use impacts on savannas are highly uncertain.
We used a dynamic vegetation model, the adaptive dynamic global vegetation model (aDGVM), to project how climate change and fire management might influence future vegetation in northern Australian savannas.
Under future climate conditions, vegetation can store more carbon than under ambient conditions. Changes in rainfall seasonality influence future carbon storage but do not turn vegetation into a carbon source, suggesting that CO2 fertilization is the main driver of vegetation change. The application of prescribed fires with varying return intervals and burning season influences vegetation and fire impacts. Carbon sequestration is maximized with early dry season fires and long fire return intervals, while grass productivity is maximized with late dry season fires and intermediate fire return intervals.
The study has implications for management policy across Australian savannas because it identifies how fire management strategies may influence grazing yield, carbon sequestration and greenhouse gas emissions. This knowledge is crucial to maintaining important ecosystem services of Australian savannas.

Introduction Tropical savannas are a mixture of trees and C4 grasses and they dominate the seasonal tropics and subtropics of the world (Hutley & Setterfield, 2008). People living in these regions are dependent on the ecosystem services that savannas supply, such as food production, biodiversity and climate regulation (De Groot et al., 2002),, and savannas are home to many browsing and grazing animals. The balance between tree and grass abundance is a critical structural and functional variable that exhibits a wide spatial variation across the savanna biome (Lehmann et al., 2014). Shifts in this ratio may impart significant impacts on ecosystem functioning and ecosystem services. Vegetation shifts favouring woody vegetation over grasses can result in a loss of grazing productivity and altered fire activity, which may further accelerate shifts towards woody vegetation (Higgins et al., 2000; Higgins & Scheiter, 2012). By contrast, a reduction of tree abundance may lead to a reduction of carbon sequestration (Scheiter & Higgins, 2009), changes in water cycling and a loss of habitat and biodiversity (Cumming et al., 1997; Setterfield et al., 2010). Many tropical and subtropical regions support both an open grassland or savanna state and a closed forest state. Vegetation Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

shifts between these states can be abrupt when critical thresholds in environmental conditions or management intensities are exceeded (Hirota et al., 2011; Staver et al., 2011; Higgins & Scheiter, 2012; Moncrieff et al., 2014). Because of the complexity of grass–tree interactions, the response of savannas to climate change and management is highly uncertain and an integrated assessment of the relative importance of climatic and anthropogenic impacts on savanna vegetation is required (Beringer et al., 2014; IPCC, 2014b). Land administrators will require information on the response of these ecosystems over large temporal and spatial scales in order to manage them for the maintenance of important ecosystem services. Australian savannas are one of the most intact savanna regions of the world and they provide a ‘living laboratory’ (Hutley et al., 2011) to investigate the interactions among vegetation, soils, climate change and fire management. Australian savannas occupy almost 2000 000 km2 across the tropics of northern Australia and they are characterized by relatively intact vegetation, limited topographic relief and low population pressure and disturbance (Woinarski et al., 2007). In these savanna regions, only restricted areas of land are cleared for intensive agricultural development. Australian savannas experience one of the most seasonal savanna New Phytologist (2015) 205: 1211–1226 1211 www.newphytologist.com

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climates in the world. Despite this seasonality, woody vegetation shows limited seasonal adjustment of leaf-scale photosynthesis (Eamus, 1999; Cernusak et al., 2011) or tree water use (O’Grady et al., 1999; Hutley et al., 2000) and it is dominated by evergreen Eucalyptus and Corymbia tree species (Hutley et al., 2011). By contrast, African savannas are dominated by deciduous woody vegetation and carbon gain is typically restricted to the wet season (Higgins et al., 2011). Over the last 10 yr, a landscape-scale, fire-based greenhouse gas emissions abatement scheme has been implemented in Australian savanna (Russell-Smith et al., 2009a,b). By prescribing fire frequency and the season of burning, land managers have influenced fire intensities, fire impacts on vegetation and greenhouse gas emissions. In the Northern Territory of Australia, 56% of the mesic savannas (> 1200 mm annual rainfall) burn at least every other year, and shifts in fire management from high-severity, late dry season fires to low-intensity, early dry season fires can reduce greenhouse gas emissions by c. 50%, relative to premanagement baseline emissions (Russell-Smith et al., 2009a,b). Fire combusts biomass, and a large proportion of the carbon fixed by vegetation is released into the atmosphere. Van der Werf et al. (2003) estimated that, at the global scale, 25% of the net primary productivity (NPP) in savannas can be consumed by fires. In the Northern Territory of Australia, c. 50% of the greenhouse gas emissions can be attributed to fire (Woinarski et al., 2007). The bistable nature of savannas implies that many savannas could sequester substantially more carbon with a shift in fire regime (Grace et al., 2006; Beringer et al., 2007). Changing fire regimes also implies a risk of rapid biome shifts (Higgins & Scheiter, 2012) and changes in the flux of ecosystem services. The development of long-term management strategies requires the consideration of climate change. Over the last 50 yr, rainfall in northwest Australia has been increasing by c. 1 mm yr1 via increased wet season rainfall, an effect possibly resulting from changes in the dynamics of the Australian monsoon or impacts from enhanced Asian aerosol production and rainfall generation (Rotstayn et al., 2007; Kanniah et al., 2010). The Intergovernmental Panel on Climate Change (IPCC, 2013) projects future climate change to include increasingly variable precipitation regimes as well as warming. A higher frequency of extreme rainfall events from intense convective storms, a lower frequency of rainfall days, and longer intervening dry seasons are likely (Shi et al., 2008). For African savanna systems, Scheiter & Higgins (2009) and Higgins & Scheiter (2012) projected regime shifts from C4-dominated savanna systems towards woodlands and forests under future conditions because elevated CO2 promotes C3 vegetation (woody plants), in contrast to C4 grasses. This effect leads to a competitive advantage of trees over grasses and influences the grass–tree ratio (Scheiter & Higgins, 2012). The response of vegetation to elevated CO2 is likely to be influenced by the projected change of mean and seasonality of precipitation. For instance, Fensham et al. (2009) found that drought can cause tree mortality and thereby offset CO2 fertilization effects in xeric savannas in Australia. Increasing temperature and changes in the length of the growing season could induce changes in fire regimes. New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

Savanna ecosystems are vulnerable to climate change (Bowman et al., 2001) and it is not clear whether Australian savanna systems will undergo similar regime shifts as African savannas. Australian savannas are dominated by Eucalyptus and Corymbia woody species that have a significant resprouting ability that may confer greater resistance to fire mortality than African species (Lawes et al., 2011). Systematic studies of the region are needed to sustainably manage these ecosystems. Given the uncertainties in the response of Australian savannas to future environmental change and fire management, we investigate the sensitivity of Australian savannas to these disturbances. Such assessments require dynamic vegetation modelling approaches (Prentice et al., 2007), providing a detailed representation of vegetation dynamics and the capacity to simulate ecophysiological processes and vegetation patterns for extended temporal and spatial scales. Models are a powerful tool to investigate multiple combinations of climate change and management. Here, we use the adaptive dynamic global vegetation model (the aDGVM, Scheiter & Higgins, 2009), which has been developed to simulate grass–tree dynamics in the tropics and subtropics. Because the aDGVM has been developed for African vegetation, we reparametrize and benchmark the aDGVM for northern Australian savanna systems. We then use the modified model to investigate the sensitivity of Australian savannas to changes in CO2, temperature, mean and seasonality of rainfall until 2100, and different long-term fire management strategies. We assess how carbon storage in savannas is influenced by these factors and whether climate change implies similar shifts towards tree-dominated states as projected for Africa (Scheiter & Higgins, 2009; Higgins & Scheiter, 2012). We further test how prescribed burning interacts with climate change to define the amount of carbon stored by vegetation in 2100.

Materials and Methods Model description We use the aDGVM (Scheiter & Higgins, 2009), a ‘state of the art’ dynamic vegetation model for tropical grass–tree systems. A full model description is provided in Scheiter & Higgins (2009); the salient features are summarized in the following. The aDGVM integrates routines commonly used in DGVMs to simulate plant physiological processes (Prentice et al., 2007) with novel processes that allow plants to dynamically adjust carbon allocation and leaf phenology to the environmental conditions. For instance, the aDGVM simulates changes of growing season length in response to climate change and increasing carbon allocation to roots in more water-limited environments (Scheiter & Higgins, 2009). The aDGVM is an individual-based model and it simulates state variables such as biomass, height and photosynthetic rates of single plants. This individual-based approach allows more realistic simulations of the impacts of herbivores (Scheiter & Higgins, 2012) and fire (Scheiter & Higgins, 2009) on vegetation as these impacts depend on characteristics of individual plants. Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist The aDGVM simulates fire regimes based on climate conditions and the vegetation state. In the model, potential fire intensity (I, kJ m1 s1) is a function of fuel biomass, fuel moisture and wind speed (Higgins et al., 2008). Fuel biomass consists mainly of grass biomass. A fire starts if the I exceeds a constant threshold of 300 kJ m1 s1 (Van Wilgen & Scholes, 1997) and if an ignition event (e.g. lightning strike) occurs. Days with fire ignition events are randomly generated. The number of ignitions yr–1 decreases with increasing tree cover (described by model parameter i2; see the Model fitting and benchmarking section) because unshaded grasses desiccate faster than shaded grasses and their probability of ignition is higher. Fire spreads into the simulated vegetation stand if a random number exceeds a predefined value (denoted as fire ignition probability Pfire; Scheiter & Higgins, 2009). The parameter Pfire is a model constant (but see the Model fitting and benchmarking section). This condition ensures that fire does not spread as soon as a fire ignition event occurs. Fire removes above-ground grass biomass and, depending on the intensity and tree height, fire will also remove fractions of above-ground tree biomass (‘topkill effect’; Higgins et al., 2000). Both grasses and trees have a high potential to resprout and regrow from root reserves after fire (Bond & Midgley, 2001). The aDGVM only requires generally available environmental input data (daily values for precipitation, daily or monthly values for temperature, atmospheric CO2 concentration, humidity and wind speed, site data for soil carbon, soil nitrogen, wilting point and field capacity) and it typically simulates vegetation at 1 ha stands. Model parametrization and benchmarking The original version of the aDGVM has been developed and parametrized for African savannas. Scheiter & Higgins (2009) showed that the aDGVM can simulate the broad vegetation patterns in Africa better than alternative vegetation models and that it can simulate biomass observed in a long-term fire exclusion experiment in the Kruger National Park, South Africa (Experimental Burn Plots; Higgins et al., 2007). Trial simulation runs, however, showed that the model parameterization developed for Africa yielded poor model–data agreement when applied to Australian savannas. The following paragraphs describe how we adjusted the aDGVM to Australian savannas. Model parameterization The aDGVM simulates ‘typical’ grass and tree types of the savanna ecosystem. In the model, these typical vegetation types are characterized by a set of plant traits defining leaf characteristics, carbon allocation, plant allometry, demography and fire response. For this study, we modified values of selected traits by using field data from Australian savannas (see Table 1). We further used the topkill functions for the dominant savanna tree genus Eucalyptus as provided by Cook et al. (2005) instead of topkill functions used in Scheiter & Higgins (2009). Cook et al. (2005) use different functions for small trees (Psmall) and adult trees (Ptall) and they define a tree’s topkill probability, Ptopkill, as the maximum of these probabilities: Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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Psmall ðI ; DÞ ¼ 1  ½1  0:098 I þ 0:037I logðDÞ

Eqn 1

Ptall ðI ; DÞ ¼ 1  ½1 þ 0:029I  0:010I logðDÞ

Eqn 2

Ptopkill ¼ max ðPsmall ; Ptall Þ

Eqn 3

Here, I is the fire intensity (in kJ m1 s1) calculated by the aDGVM fire model and D is the stem diameter at breast height (DBH, in m) of an individual tree. Model fitting and benchmarking To improve simulation results for Australian savannas, we fitted the model simultaneously to flux data and structural information from two savanna sites at Howard Springs (12°290 39.12″S, 131°090 09.00″E, 1695 mm mean annual precipitation (MAP)) and Daly River (14°090 33.12″S, 131°230 17.16″E, 1190 mm MAP) by using a genetic optimization algorithm (DEoptim, Mullen et al., 2011). Flux data used for model fitting were daily values of gross primary productivity (GPP), ecosystem respiration (Re) and evapotranspiration (Et). We used data for the period from 2001 to 2006 at Howard Springs (Beringer et al., 2007, 2011a,b) and for 2008–2010 at Daly River (Beringer et al., 2011a,b; Cresswell et al., 2011). We forced the model by using daily precipitation, net radiation and temperature measured at the flux tower sites. Vegetation and site characteristics are given in Hutley et al. (2011). The optimization algorithm adjusted 11 model parameters that define stomatal conductance (two parameters, mtree and mgrass; notation of parameters as described by Scheiter & Higgins, 2009), Vcmax of grasses (one parameter), light competition (four parameters, lc, lg, lt and ld), fire (two parameters, Pfire and i2) and tree demography (two parameters, mortality Pcarb = Pcomp = Pfrost, seed germination φgerm). We used these parameters because their values are uncertain and difficult to obtain from field experiments. We restricted the range of parameter values for the optimization to ensure that the fitted values remained plausible. The optimization algorithm minimized a sum-of-squares

Table 1 Parameter values used for Australian savannas in this study Name SLA H1 H2 d1 d2 Dmrd a0L a0S a0R lL Ptopkill

Description

Tree 2

1

Specific leaf area (m kg ) Allometry parameter, plant height Allometry parameter, plant height Allometry parameter, stem diameter Allometry parameter, stem diameter Maximum rooting depth (m) Carbon allocation to leaves Carbon allocation to stem Carbon allocation to roots Leaf longevity (d) Topkill probability

5.9 3.6 0.27 0.61 100 2 0.2 0.3 0.5 270 xtopkill

Grass 8 3.5 0.5 – – 0.4 0.6 – 0.4 160 –

Values are provided for the ‘typical’ grass and tree types simulated by the adaptive dynamic global vegetation model (aDGVM). Names of parameters are similar to the model description provided in Scheiter & Higgins (2009). New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

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function defined by above-ground biomass, daily GPP, daily Re, daily Et and tree size distribution of mature stands. Original and fitted model parameters as well as parameter ranges for the optimization are provided in Table 2. Following Morales et al. (2005), we used the root mean square error and the correlation coefficient for a data–model comparison. The reparameterized model version was then benchmarked against GPP data and basal area data at the Northern Australian tropical transect (NATT) to ensure that it was valid at larger spatial scales. We therefore simulated vegetation at 20 NATT sites (see Kanniah et al., 2011 for site details) using historical climate data for the period from 1900 to 2010 provided by the Australian Water Availability Project (AWAP, Raupach et al., 2009, 2011). We used minimum and maximum monthly temperatures, mean monthly precipitation and net radiation from this data set. The GPP data were derived by remote sensing (MODIS) and calibrated using flux tower measurements (Kanniah et al., 2011). For basal area we used data compiled by Hutley et al. (2011) and a regression line that relates basal area to mean annual precipitation (Williams et al., 1996). We benchmarked simulated fire frequency in the northern Australian savanna regions against fire frequency obtained from the MODIS burned area product (Roy et al., 2002, 2005, 2008). We therefore simulated vegetation in the savanna regions between 1900 and 2010 at a 0.5° grid resolution using historical AWAP climate data (Raupach et al., 2009, 2011). Fire frequency at the 0.5° resolution was obtained from the high-resolution MODIS burned area product by counting the number of fire events in the period between April 2000 and April 2013 in each of the 0.5° cells. For the comparison, we used simulated fire frequency between 2000 and 2010. Climate forcing Climate data In this study, we simulated vegetation for the period between 1900 and 2100 (see the Simulation experiments section) and we used two different climate forcing data sets. For the period 1900–2010, we forced the model with daily historical

climate data generated by AWAP (Raupach et al., 2009, 2011). For the period 2011–2100 we forced the model with CO2 concentrations, temperature and precipitation trends estimated from monthly time series of the ECHAM5 simulations for IPCC (2007) SRES A1B, A2 and B1 (Roeckner, 2005a,b,c). In scenario A1B, CO2 increases to c. 700 ppm in 2100, and precipitation and temperature increase on average by 11% and 4°C, respectively, in the study region. In scenario A2, CO2 increases to c. 830 ppm in 2100, precipitation decreases by 2.4% and temperature increases on average by 5.2°C. In scenario B1, CO2 increases to c. 540 ppm, precipitation decreases by 19% and temperature increases by 3.1°C. Fig. S1 (Supporting Information) shows the temperature and precipitation trends used for the simulations and trends projected for the fifth IPCC assessment report (RCP scenarios; IPCC, 2013, 2014a,b). Seasonality of rainfall In addition to trends in MAP, we explored how changes in the seasonality of precipitation between 2012 and 2100 could influence future vegetation. Although the IPCC (2007) scenarios project changes in seasonality, we forced the model with more extreme seasonality changes. This allows us to investigate in more detail how different climate variables interact to modify vegetation and to obtain a wide range of possible vegetation changes. To manipulate seasonality, we resampled daily rainfall sequences generated by the aDGVM. Resampling ensures that the MAP of the original and the modified rainfall sequences are equal, whereas the size of rainfall events and their distribution within a year change. To reduce seasonality of precipitation we randomly selected two days, i and j, of the year. We then added 75% of the precipitation of day i to the precipitation of day j, irrespective of the precipitation of day j, and reduced the precipitation of day i by the same amount. This procedure was repeated R times. The parameter R is predefined and defines the degree to which seasonality should be reduced; higher R indicates less seasonality. This algorithm increases the number of rainy days yr–1 and reduces the average amount of rainfall per event. To increase seasonality of precipitation we used a similar procedure; however, we only

Table 2 Original parameter values for African savannas (Scheiter & Higgins, 2009) and fitted parameter values for Australian savannas as used in this study Name

Description

Original

Fitted

Min

Max

Pfire i2 lc lg lt ld Vcmax mtree mgrass φgerm Pcarb Pcomp Pfrost

Fire ignition probability Parameter for ignition sequence generator Light competition: trees shade trees Light competition: grasses shade trees Light competition: trees shade grasses Light competition: grasses shade grasses Maximum carboxylation rate of grasses Ball–Berry parameter of trees Ball–Berry parameter of grasses Seed germination probability of trees Tree mortality rate: carbon deficiency Tree mortality rate: competition Tree mortality rate: frost

0.01 0.1 0.35 0.25 0.4 0.5 Variable 9 4 0.25 0.001 Pcarb Pcarb

0.021 0.1 0.99 0.2 0.25 0.16 9.75 5.48 10 0.0039 0.000 67 Pcarb Pcarb

0.005 0.05 0 0 0 0 2 2 2 0.000 01 0.0001

0.05 0.15 1 1 1 1 20 20 20 0.3 0.1

Columns ‘Min’ and ‘Max’ provide the parameter ranges used for the model fitting. Variable names and original values were taken from the model description provided by Scheiter & Higgins (2009). New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist added precipitation of day i to precipitation of day j if both precipitation values were greater than zero and if precipitation of day i was less than precipitation of day j. This procedure ensures that the number of extreme rainfall events increases, whereas the total number of rainy days is constant or decreases. The procedure is repeated R times, where R defines the degree to which rainfall seasonality should be increased; higher R indicates more rainfall seasonality. In simulation experiments, seasonality changes progressively. We therefore set the parameter R to zero in a selected initial year and then increased R by a constant value ΔR yr–1(R = 0 in initial year, R = ΔR in first year, R = 2ΔR in second year, etc.) Hence, resampling is done ΔR times in the first year, 2ΔR times in the second year, and so on. We used different rainfall seasonality indexes to quantify seasonality changes generated by this routine and to compare seasonality in IPCC projections and simulation runs (see also the Climate change impacts in savanna regions section, Methods S1, Table S1, Figs S2, S3). Simulation experiments Climate change impacts in savanna regions In a first simulation experiment, we created an ensemble of future projections for the Australian savanna region. We therefore conducted transient forward simulations to the year 2100 at a 0.5° resolution using the Australian parameterization of the model. We follow Fox et al.’s (2001) definition of Australian savannas, which defines savannas as regions in Northern Australia receiving rainfall of > 600 mm yr–1. Other variables such as temperature or species composition were not considered in this definition. For each grid point, we ran a 100 yr model spin-up to ensure that the model was in equilibrium with the environment. To account for the variability between different IPCC scenarios, simulations were conducted for IPCC (2007) SRES A1B, A2 and B1 (see the Climate forcing section). For each scenario, we conducted simulations where mean and seasonality of precipitation were manipulated. More specifically, we conducted simulations with and without the projected changes in MAP and simulations where seasonality of rainfall was not manipulated, increased or decreased. We set R to zero in 2012 and then increased R by a constant value ΔR = 15 yr–1 for both increasing and decreasing amounts of seasonality. The ensemble includes changes of seasonality similar to changes in IPCC scenarios but also more extreme increases and decreases (Table S1). Simulations were conducted with fire as simulated by the aDGVM fire submodel. Overall, an ensemble of 18 simulation runs was conducted (three IPCC scenarios, two scenarios for MAP, three scenarios for seasonality). For the model analysis we used snapshots of the simulated vegetation state in 2012 and 2100 as well as time series of simulated vegetation. We repeated these simulation runs for the Howard Spring and Daly River study sites to compare simulated carbon (GPP, Re) and water fluxes (Et) for current (2008–2012) and future (2096– 2100) conditions. Quantification of CO2, rainfall and temperature effects In a second simulation experiment, we investigated in more detail Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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how changes in environmental conditions between now and 2100 interact to influence future vegetation. We therefore conducted a full factorial experiment with the four factors, CO2, temperature, mean and seasonality of precipitation. Simulations were conducted for SRES A1B. For CO2, temperature and MAP, we used the two levels, ‘variable stays at ambient level’ and ‘variable changes according to SRES A1B’, and for seasonality of rainfall we used the levels ‘seasonality not manipulated’ and ‘seasonality increases by ΔR = 15’ (16 simulation scenarios in total). We conducted these simulations for the Northern Australian savanna area at a 0.5° resolution. We used an ANOVA and a g2 analysis to quantify the effects. Fire management In the third experiment, we investigated how long-term fire management interacts with climate change to influence vegetation dynamics and carbon emissions in the northern Australian savanna regions. Vegetation was simulated with fire regimes being defined by the aDGVM fire module for the period 1900–2011. After 2011, we introduced different prescribed fire regimes with fixed fire return intervals between 1 and 10 yr (in 1 yr steps) and different fire seasonality. We simulate early dry season fires, where fires occur in May or June, and late dry season fires, where fires occur between middle of August and middle of October. The day of a fire occurrence within these periods is randomly selected. The fire intensity is calculated using the aDGVM fire model (see the Model description section). The selection of fire regimes follows fire management strategies applied in Australian savannas (Russell-Smith et al., 2009a,b); these authors define early dry season fires occurring before July and late dry season fires occurring in July–December. We simulated vegetation for IPCC SRES A1B and in the presence or absence of changes in rainfall seasonality until 2100 (ΔR = 15). We plotted different model output variables in 2100 to explore long-term impacts of the different fire regimes on the vegetation state.

Results Fluxes and stand structure at the site scale Gross primary productivity, respiration and evapotranspiration simulated by the aDGVM agreed well with data measured at Howard Springs and Daly River in Northern Territory (Fig. 1; Table 3). The model better represented fluxes at Howard Springs than fluxes at Daly River. The aDGVM simulated evergreen phenology for trees at these sites. GPP and Et of trees are maintained throughout the whole year. The model projects that grasses are deciduous and only active in the wet season (not shown). These phenological properties are, in the aDGVM, not prescribed but emerge from the leaf phenology submodel. The model also simulated tree height distribution in good agreement with observed values (Fig. 1; Table 3). Note that additional disturbance agents, such as cyclones, influence the observed tree height distribution (Hutley & Beringer, 2010; Hutley et al., 2013). These agents are not considered in our simulations. Model fitting resulted in modified parameters for Australia as opposed to the model version for Africa (Table 2). A comparison New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

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−6

GPP and Re (g C m–2 d–1)

4 2 0 −2 −4 −6 −8

GPP and Re (g C m–2 d–1)

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2001

2002

2003

2004

2005

2006

2007

2008

2009

2003

2004

2005

2006

2011

7 6 5 4 3 2

2007

2008

2009

Year

30 20 10 0

0

20

40

60

No. of trees ha–1

80

40

100

Year

No. of trees ha–1

2010

1

Evapotranspiration (mm d–1) 2002

2011

0

8 6 4 2

2001

2010

Year

0

Evapotranspiration (mm d–1)

Year

0.5 2 3.5 5 6.5 8 9.5

12

14

16

18

20

22

24

26

28

30

Height (m, in 0.5 m classes)

0.5 2 3.5 5 6.5 8 9.5

12

14

16

18

20

22

24

26

28

30

Height (m, in 0.5 m classes)

Fig. 1 Data–model comparison of fluxes and tree height distribution at Howard Springs and Daly River. Daily time series of both data and model results were smoothed by using 30 d sliding averages. The upper panels show gross primary productivity (GPP) and respiration (Re), and the middle panels show evapotranspiration (Et). Tree height distribution is depicted in 0.5 m classes. Grey lines/bars represent data; black lines/bars represent aDGVM simulation results. See the Model parameterization and benchmarking section for a description of the field data used for the comparison.

suggests differences in light competition and tree demography between African savanna trees and Australian Eucalyptus species. Tree–tree competition (lc) was stronger in the parametrization for Australia compared with the original parametrization. Therefore, grass–tree competition (lg) and grass–grass (ld) competition were weaker. The probabilities for seed germination (φgerm) and mortality of trees (Pcarb, Pcomp, Pfrost) were lower in the Australian parametrization. The model performance at the plot scale gave us the confidence to infer vegetation patterns at larger spatial scales. Model simulations for study sites along the Northern Australian tropical transect (Hutley et al., 2011) broadly captured the range of annual GPP data (Kanniah et al., 2011; Fig. 2a) and basal area (Williams et al., 1996; Hutley et al., 2011; Fig. 2b). The aDGVM captured the broad patterns of fire frequency derived New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

from the MODIS burned area product (Roy et al., 2002, 2005, 2008; Fig. 3). Agreement was higher in the northern and western parts of the study area than in the southeastern parts. Climate change projections at the regional scale The aDGVM projected increased biomass storage in savanna vegetation under future conditions, despite substantial variation within the simulated ensemble (Fig. 4). In the following paragraphs, we focus on SRES A1B only. When no changes in seasonality were assumed, above-ground biomass in vegetation increased, on average, from 14.2 to 32.8 t ha1 in 2100 (an increase of 18.6 t ha1), such that the carbon storage in the whole savanna region increased from 2.6 Pg C to 5.4 Pg C (Fig. 5a,b). Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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The modelled future vegetation state is influenced by changes in the seasonality of precipitation. When the seasonality of precipitation was decreased and precipitation was distributed more uniformly over the year, above-ground biomass increased by 31.5 to 45.7 t ha1 and carbon storage for the savanna region increased from 2.6 Pg C to 7 Pg C (Fig. 5a,d). When seasonality of precipitation increased until 2100, above-ground biomass still increased, but only by 12.9 to 27.1 t ha1. Under such conditions, the vegetation in the savanna regions could only store 3.5 Pg C (Fig. 5a,c). Relative changes in biomass and GPP were sensitive to relative changes in mean and seasonality of rainfall. High increases in GPP were simulated for high amounts of precipitation change (Fig. 6a) and a low decrease in rainfall seasonality (Figs 6b, S4). Regions that experienced higher increases in rainfall seasonality had a lower potential to increase GPP. Yet both biomass and GPP increased in most regions irrespective of changes in rainfall patterns, indicating that CO2 fertilization effects drove the biomass increase and offset changes in rainfall regimes. This is also supported by the full factorial experiment (second simulation experiment), where changes of CO2, temperature, mean and variability of rainfall were present or absent (Table S2). The ANOVA shows that only CO2 had a significant effect on future tree

Table 3 Comparison between adaptive dynamic global vegetation model (aDGVM) simulation results and observed data Cor

Data

aDGVM

39.8 34.3 70.1 37.9

0.65 0.52 0.67 0.89

3.81 2.82 1.35 3.13 9.7 661 62.1

3.47 2.42 1.05 3.61 7.6 590 66.5

51.7 46.1 98.4 51.5

0.71 0.61 0.67 0.85

3.65 2.75 1.24 2.33 8.3 230 63.5

2.38 1.76 0.63 2.74 5.6 450 43.3

(a)

8 6 0

500

1000

Basal area (m2 ha–1)

10

(b)

0

Fig. 2 Comparison of simulated and observed gross primary productivity (GPP) and basal area along the Northern Australian Tropical Transect (NATT). (a) GPP data are annual means obtained from remote sensing (Kanniah et al., 2011); bars show minimum and maximum values. (b) Basal area is plotted against mean annual precipitation (MAP). Data points for basal area were taken from Hutley et al. (2011) (black diamonds), and the regression line has been fitted by Williams et al. (1996) (black line) using basal area data from the NATT. Red squares are aDGVM simulation results.

Observed GPP (g C m–2 yr–1)

1500

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biomass, and the g2 analysis revealed large CO2 effects (g2 = 0.97) and small effects of temperature, mean and seasonality of precipitation (g2 < 0.01).

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In the aDGVM, fire regimes are influenced by the amount of fuel biomass, which mainly consists of grass biomass. The ensemble for the study region revealed that climate change influenced grass growth and thereby both fire intensities and fire return intervals (Fig. 7). The highest fire activity was simulated when seasonality of precipitation decreased (Fig. 7e,f). Despite substantial increases of woody biomass, tree cover was not sufficient to suppress grass growth and fire. When the seasonality of precipitation increased, simulated fire activity was reduced, particularly at the southern, more arid end of the study region (Fig. 7c,d), because grass biomass and fuel biomass decreased. Climate change influenced modelled carbon and water fluxes at the site scale. When rainfall seasonality was not manipulated, both GPP and Re were higher in 2100 than in 2012, whereas Et values were similar (Fig. 8; Table 4). When the seasonality of precipitation decreased, GPP, respiration and evapotranspiration increased (Fig. 8; Table 4). When the seasonality of precipitation increased, GPP and respiration were slightly higher under future conditions than under ambient conditions, whereas Et decreased (Fig. 8; Table 4). Despite the differential responses of fluxes to different rainfall regimes, ecosystem water-use efficiency (calculated as the quotient between net ecosystem production (NEP = GPP – Re) and Et) increased in all scenarios by a similar amount (between 0.18 and 0.24; Table 4). Impact of fire management Fire management can influence the vegetation state, fire characteristics and carbon emissions. The aDGVM simulates increases

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Fig. 5 Simulated above-ground biomass (in t ha1) in the northern Australian savanna regions for 2012 and projected changes between 2012 and 2100 under IPCC SRES A1B. (a) Biomass at ambient conditions; (b) simulated biomass change when seasonality of precipitation was not manipulated between 2012 and 2100; (c) seasonality increases; and (d) seasonality decreases between 2012 and 2100. For these simulations we used ΔR = 15 (see the Seasonality of rainfall section). Simulations were conducted at 0.5° grid resolution. We used snapshots for 2012 and 2100 from transient simulation runs for these figures. The legend in panel (b) also applies to panels (c) and (d). Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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manipulated between 2012 and 2100, above-ground tree biomass differed by c. 5 t ha1 between early and late dry season fires. Moisture content of fuel biomass was higher in early fires such that fire intensities were lower than in late dry season fires. Biomass consumption was lower in early fires than in late fires. When averaged over longer periods, the average biomass consumption per year decreased as fire return intervals increased (Fig. 9). That is, although biomass consumption and carbon emissions per fire increase as the fire return interval increased, biomass consumption was highest for short return intervals when averaged over a longer period. Rainfall seasonality did not influence the general patterns of these responses, but it shifted the absolute values of the simulated model parameters to higher or lower values (Fig. 9).

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Relative change in seasonality of MAP until 2100 Fig. 6 Sensitivity of gross primary productivity (GPP) to changes in mean annual precipitation (MAP, a) and seasonality of precipitation (b) between 2012 and 2100. Sensitivity was calculated by dividing the difference between GPP in 2100 and in 2012 by the difference between MAP (or seasonality of precipitation) in 2100 and 2012. Values were then normalized between 1 and 1. Each point represents one study site in the savanna study region. Simulations were conducted for increasing seasonality of precipitation (ΔR = 15). Seasonality was measured by summing the normalized absolute values of the differences between mean monthly rainfall and overall monthly mean rainfall (Walsh & Lawler, 1981). See also Methods S1 for the calculation of seasonality indexes and Fig. S4 for relationship between gross primary productivity and different seasonality indexes.

in biomass and GPP of trees when the fire return interval increases from 1 to 10 yr (Fig. 9). Grass GPP decreased at higher fire return intervals, whereas grass biomass was relatively stable for different return intervals. This can be explained by grass–tree competition; frequent fire reduces tree cover and allows grasses to grow in open landscapes, whereas long fire return intervals promote tree growth and suppress grasses. Longer fire return intervals reduce the number of trees, indicating more tall trees. Changes in grass and tree biomass imply changes in fuel biomass, fire intensities, biomass combustion by fire (Fig. 9) and thereby the emission of carbon and other greenhouse gasses (not shown). The aDGVM projects that the season of burning (early or late dry season fires) influenced GPP and biomass (Fig. 9). For instance, when the seasonality of precipitation was not Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

The aDGVM simulation results show that fire management and climate change can strongly influence the amount of carbon stored in north Australian savannas. Thus, climate change (IPCC SRES A1B) may increase carbon storage in vegetation by c. 2.8 to 5.4 Pg C in 2100. This effect can potentially be explained by CO2 fertilization effects on C3 vegetation (Ehleringer et al., 1997) and by increasing water-use efficiency at elevated CO2 concentrations. In the aDGVM, these factors translate into a competitive advantage of trees over grasses, suppression of grass growth, reduced fire activity and hence vegetation shifts towards tree dominance. Simulated vegetation change up to 2100 was, however, strongly influenced by changes in rainfall. Decreased rainfall seasonality and longer growing seasons increased the potential of vegetation to sequester carbon. By contrast, increased seasonality with shorter growing seasons and severe droughts decreased the carbon storage potential, suggesting that increases in rainfall seasonality have the potential to dampen CO2 fertilization effects. Both GPP and biomass were generally higher in 2100 than under ambient conditions, irrespective of the selected simulation scenario and even though the manipulation of rainfall seasonality was more extreme than it is in the IPCC scenarios. This result suggests, together with results from a factorial experiment, that the increase in CO2 had a larger impact on future savanna vegetation than changes in rainfall patterns. Simulated vegetation responses to rainfall variability are consistent with previous studies. Liedloff & Cook (2007) used the FLAMES model to show that the basal area in Australian savannas decreased as the standard deviation of monthly rainfall increased. Kanniah et al. (2013) used satellite data to show that carbon uptake and carbon fluxes in northern Australian savanna systems were sensitive and correlated to precipitation patterns. Fensham et al. (2009) found that drought may induce tree mortality in xeric Australian savannas and offset CO2 fertilization. Whether this mechanism is crucial in the aDGVM needs to be tested. Model projections for future conditions are influenced by many sources of uncertainty, and whether CO2 fertilization will have a significant impact on future vegetation at the ecosystem level is highly debated (see Settele et al., 2014). Empirical studies New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

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of recent vegetation trends can help to assess simulation results. Aerial photography surveys over the last 50 yr have revealed contrasting patterns of shifts in woody cover and savanna–forest New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

boundaries in Australia. Bowman et al. (2001) reported vegetation thickening in Litchfield National Park over a 50 yr period (1941–1994) via an expansion of closed forest into savanna and Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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woody encroachment into grasslands potentially driven by changes in rainfall, fire regimes and CO2. Savanna woody cover remained relatively stable over this period (Bowman et al., 2001). Bowman et al. (2008) observed an increase in woody cover in both savanna and floodplain ecosystems within Kakadu National Park, Northern Territory. They examined herbivore density increases and declines over this period and concluded that observed increases in woody cover were weakly related to herbivore density, and were more likely to be driven by elevated CO2, increasing rainfall and changing fire regimes experienced during the study period (1964–2004). However, this conclusion is disputed by Petty & Werner (2010), who found a stronger influence of buffalo numbers on woody cover in a similar study. Banfai & Bowman (2006) attributed observed forest expansion in the Kakadu National Park primarily to changes in CO2 and precipitation Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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but also to fire. Murphy et al. (2014) analysed data from different northern Australian national parks and found that woody biomass was relatively stable with weak trends of wood thickening in the period 1994–2004. Empirical studies in South African savannas reported increases in woody vegetation and shrub encroachment and these vegetation shifts were attributed to changes in CO2 rather than to changes in land use (Kgope et al., 2010; Wigley et al., 2010; Buitenwerf et al., 2012). Bodart et al. (2013) found that woody vegetation cover in Africa is reduced mainly by human pressure and that the impacts of climate change are minor compared with the extraction of wood from forested land or the conversion of wooded land. Donohue et al. (2013) used satellite data to show that in warm and arid environments, a 14% increase in atmospheric CO2 caused an increase of green foliage cover by 5–10% New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

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2.88 7.65 4.77 2.15 5.91 3.76 0.73 1.74 1.01 3.30 4.09 0.79 0.22 0.43 0.21

2.88 5.36 2.48 2.15 4.08 1.93 0.73 1.28 0.55 3.30 3.20 0.10 0.22 0.40 0.18

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Simulations were conducted for different rainfall scenarios (less and more rainfall seasonality in 2100; no manipulation between ambient and 2100). Time series of the residuals are provided in Fig. 8. Differences with the results in Table 3 can be attributed to different climate forcing data used for the simulations (measurements from eddy flux experiments at the sites in Table 3, Australian Water Availability Project (AWAP) data in Table 4). GPP, gross primary productivity; Re, respiration; NEP, net ecosystem productivity; Et, evapotranspiration; WUE, water-use efficiency.

and they stated that CO2 fertilization effects are occurring. Although CO2 fertilization effects have been found in greenhouse experiments (Kgope et al., 2010), empirical evidence for CO2 fertilization effects at the ecosystem level is weak because free air carbon dioxide enrichment (FACE) experiments in savannas are rare. An exception is OzFACE (Stokes et al., 2005); this experiment found evidence for enhanced growth of Acacia and Eucalyptus species, higher soil respiration and higher water-use efficiency under elevated CO2 (Williams et al., 2008). In this study, we only considered carbon stored in vegetation, although substantial amounts of carbon are stored in soils. Chen et al. (2003) estimated that 74% of the carbon in a northern Australian savanna system is stored in soil organic matter and only 24% is stored in live biomass. How soil carbon stocks respond to fire or climate change is still uncertain. Richards et al. (2011) showed in a modelling study that soil carbon storage increased when fire return intervals increased from 1 to 5 yr but slightly decreased for less frequent fires. Together with our results, this implies that carbon storage in savanna ecosystems is maximized when fire return intervals are long. By contrast, field experiments show only weak impacts of fire on soil carbon (Beyer et al., 2011). More sophisticated soil models that simulate how microbial communities decompose dead material and how these communities respond to environmental change and fire are essential to assess the full carbon cycle under current and future conditions (Ostle et al., 2009). Consideration of black carbon and charcoal may further improve carbon accounting in savannas. New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

In this study, we fitted the aDGVM to flux data and tree size data measured in Australian savannas and achieved good agreement between data and model results. Agreement was comparable to values reported by previous DGVM studies; for instance, Morales et al. (2005) reported coefficients of correlation of between 0.61 and 0.95 in a model comparison experiment for European forests, whereas coefficients of correlation were between 0.52 and 0.89 in this study. Although the fit between modelled and observed time series is not perfect, we achieved good agreement at larger spatial scales (along the NATT), such that model fitting increased our confidence for model projections. This demonstrates that the indirect parametrization of DGVMs is a useful method to reduce model uncertainty (Hartig et al., 2012). The model fitting also suggests that DGVM parametrization at more regional scales could help to improve model projections, compared with DGVMs parametrized at the global scale. Model fitting may generate hypotheses related to ecosystem functioning; we found, for example, that Australian and African savannas differ in the competitive interactions between grasses and trees and in tree demography. Fensham & Bowman (1992) observed intense competition in Eucalyptus, and Lawes MJ et al. (unpublished) found different tree mortality rates in Australian and African savannas. A further investigation of these differences is required. Long-term fire management that manipulates fire return intervals and burning season influences the projected vegetation state, fire characteristics and fire impacts of vegetation in 2100. The aDGVM simulated that long fire return intervals maximized carbon stored in vegetation and minimized biomass consumption when averaged over a longer period. Early dry season, low-intensity fires showed higher carbon storage and less biomass consumption than late dry season fires. Low-intensity fires also avoided severe damage of tree canopies and thereby promote trees. Our findings support the application of prescribed early dry season fires because these fires minimize vegetation damage and carbon emissions (Russell-Smith et al., 2009a,b, 2013). How these differences in fire management influence the emissions of other greenhouse gasses is a key area of future model development and model application. Our findings have implications for the grazing industry across northern Australia. Currently, low-impact, low stocking grazing is common on native pastures across Western Australia and the Northern Territory, whereas land use is more intense in Queensland. From the aDGVM simulations, we can conclude that fire return intervals of c. 4 yr and late dry season fires are optimal for grazing because such return intervals maximize grass GPP and grass biomass. Dyer & Stafford-Smith (2003) modelled interactions among fire behaviour, tree response, pasture growth, grazing utilization and animal productivity in a grazed semiarid savanna ecosystem in the Northern Territory. They also found that prescribed late dry season fire could maintain long-term productivity of the system and maximize the economic benefit. Under this regime, fire damage of woody vegetation is high enough to suppress recruitment and maintain grass productivity to support grazing. Obviously, this management strategy is contrary to management strategies that aim to maximize carbon sequestration Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

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and minimize greenhouse gas emissions (Meyer et al., 2012). Our modelling framework serves as a tool to investigate how fire management can be optimized to achieve long-term management goals in Australian savanna systems, considering both grazing industry and the reduction of greenhouse gas emissions. Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

Acknowledgements Financial support to S.S. and S.I.H. was provided by Hesse’s Landes-Offensive zur Entwicklung wissenschaftlich-€okonomischer Exzellenz (LOEWE) and by the Deutsche Forschungsgemeinschaft New Phytologist (2015) 205: 1211–1226 www.newphytologist.com

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(DFG). Travel support was provided for L.B.H. by the LOEWE BiK-F. Support was also provided by the Australian Research Council projects DP0344744 and DP130101566 plus Australia’s Terrestrial Ecosystem Research Network (TERN) for financial support of the flux tower infrastructure. Aspects of this work were funded by the Australian Research Council (DP0344744, DP0772981, DP130101566, LP0774812, LP100100073). J.B. is funded under an ARC FT (FT1110602).

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Fig. S1 Anomalies of temperature and mean annual precipitation used for simulations and anomalies of IPCC (2013) RCP scenarios. Fig. S2 Rainfall seasonality in the study region at ambient climate conditions. Fig. S3 Comparison of rainfall seasonality indexes for ambient climate conditions. Fig. S4 Sensitivity of GPP to changes in rainfall seasonality. Table S1 Seasonality changes used in simulations and seasonality changes in IPCC scenarios (Roeckner, 2005a,b,c) Table S2 Effects and results of g2 analysis in the full factorial experiment Method S1 Definitions of four rainfall seasonality indexes. Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Supporting Information Additional supporting information may be found in the online version of this article.

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