Earth system simulations show that the occurrence of bistability in tropical savanna-forest ecosystems is continentdependent

Transcription

1 Chapter 4 Earth system simulations show that the occurrence of bistability in tropical savanna-forest ecosystems is continentdependent Verheijen, L.M. 1, Aerts, R. 1, Van Bodegom, P.M. 1,2 1 VU University Amsterdam, Systems Ecology, Department of Ecological Science, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands 2 Leiden University, Institute of Environmental Sciences, Einsteinweg 2, 2333 CC, Leiden, the Netherlands

2 Chapter Abstract Tropical systems are important to global climate and carbon budgets, as evaluated by earth system models and applied in global change assessments. An important characteristic of tropical systems is the bistability of savannas and forest, observed at intermediate precipitation levels. Under these conditions, savannas and forests are alternative stable states, with fire determining the occurrence of either savannas or forests. However, a crosscontinental analysis of the extent to which earth system models are capable of capturing such bistability has not yet been investigated. In this study, we analyzed climate change scenarios with the Max Planck Institute earth system model (MPI-ESM) with atmospheric CO 2 concentrations rising from pre-industrial conditions to about 925 ppm at the end of the 21 st century. We analyzed vegetation responses in tropical Asia, Africa and South-America and show that a simple generic fire model within an ESM can capture observed bistable patterns. Indicators of bistability, i.e. bimodality of woody cover and different relationships between woody cover vs. fire for savannas and forest, are strongly continent-dependent despite similar underlying mechanisms of vegetation dynamics. Bistability is initially only present in Asia when vegetation is in equilibrium and has disappeared by the end of the 21 st century. When functional variation in the traits within plant functional types is included in the MPI-ESM, more continents show signs of bistability, also around This shows that inclusion of trait variation, thought to allow vegetation to adapt to environmental changes, also affects bistability patterns. The results have important implications for modeling of bistability by ESMs (and other regional to global models), because the potential of bistability loss introduces additional uncertainties in predicting vegetation responses to global change. 4.2 Introduction Understanding the response of the tropical vegetation to climate change in the 21 st century is essential, as they are hotspots of biodiversity, store a large amount of carbon (Pan et al., 2011; Saatchi et al., 2011), have the highest global carbon productivity (Beer et al., 2010) and affect global climate (Snyder et al., 2004). Therefore, both earth system models (ESMs) as well as (regional) dynamic global vegetation models (DGVMs) aim to capture current and predict future responses of tropical vegetation, either in the context of assessing the impact of climate change on the world (Ciais et al., 2013) or by focusing on specific tropical continents (Cox et al., 2004; Scheiter & Higgins, 2009; Galbraith et al., 2010). Evidence suggests (Hirota et al., 2011; Staver et al., 2011b) that tropical savannas and forests are in a bistable situation, where two distinct alternative stable states, savannas or forests, can be present within a similar environment (Fig. 1). Each of these states has its 88

3 Bistability in the tropics own basin of attraction that is separated by an unstable state (Scheffer et al., 2001). These basins of attraction make the system resilient to environmental change, because switches between the two states will only occur when a change in an environmental driver pushes a state across a certain bifurcation point or threshold (T in Fig. 1), making the system move to the alternative state (Scheffer et al., 2001; Schröder et al., 2005). Characteristics of bistable systems are abrupt shifts in time (instead of gradual changes), sharp (spatial) boundaries between states and existence of strong stabilizing biological feedbacks (Scheffer et al., 2001; Schröder et al., 2005; Murphy & Bowman, 2012). Figure 1. A bistable system with alternative stable states occurring at intermediate precipitation levels. These states are savannas and forests with either low or high woody cover, respectively. Passing a certain fire threshold (T), this will shift one state into another (dotted lines). In the case of forest and savanna dynamics, this will result from a shift from forest to savannas when fire is sufficiently high. Stable areas are solid lines, unstable areas dashed line). Bistability in the tropics, resulting in either forests or savannas, is present at intermediate precipitation levels, with tropical forests dominating at high precipitation levels and grasslands at low levels. At these intermediate precipitation levels (depending on the study between mm year -1 (Staver et al., 2011b), mm year -1 (Staver et al., 2011a) or from ~650 mm year -1 onwards (Sankaran et al., 2005)), a bimodal distribution of woody cover can be observed, with higher frequencies of both low woody cover, reflecting savannas, and high woody cover, reflecting forest (again thresholds are study-dependent: savannas <50 % woody cover and forests >75 % according to Staver et al. (2011a) or forests >55-60 % according to Staver et al. (2011b), or savannas <60% woody cover and forests >60% according to Hirota et al. (2011)). This bimodality of woody cover is found across all tropical continents, according to analyses of satellite-derived data (Hirota et al., 2011; Staver et al., 2011b). 89

4 Chapter 4 Within intermediate precipitation ranges, fire is thought to be the dominant driver of this bimodal distribution of woody cover (Fig. 1), because fire prevents transitions of savannas to forests if fire frequency is high enough (Sankaran et al., 2005; Staver et al., 2011a; Staver et al., 2011b). However, also other factors, like biogeography and species composition, edaphic (nutrient availability, soil conditions) and historical controls and herbivory may co-determine the distribution of woody cover (Sankaran et al., 2008; Murphy & Bowman, 2012; Lehmann et al., 2014; Van Nes et al., 2014). So far, bistability and bimodality of vegetation distribution have been analyzed within Africa applying (regional) models (Staver et al., 2011a; Higgins & Scheiter, 2012; Staver & Levin, 2012). Both regional and global vegetation models predict expansion of woody vegetation (when not accounting for human impacts), as well as non-woody expansion into the Sahara ( greening ) by elevated atmospheric CO 2 (Heubes et al., 2011; Higgins & Scheiter, 2012). Increased CO 2 concentrations favor C3-photosynthesis over C4- photosynthesis, and the enhanced growth of C3-species will promote woody sapling establishment after fires (Baudena et al., 2015). Combined with a general increase in water use efficiency of plants upon elevated CO 2, which promotes deeper-rooted (woody) species because of deeper water infiltration, this results in woody expansion into the savannas (Bond & Midgley, 2000). The (eastern) Amazon basin in South-America is thought to exhibit bistability as well, but here, in contrast to Africa, models reveal there is a probability of transitions from forest to savanna systems (Oyama & Nobre, 2003; Malhi et al., 2009). An increased mortality of woody vegetation in the Amazonian basin has been attributed to a projected reduction in precipitation and increase in temperature, causing an increased dry season period, but the main driver of this reduction in forest cover is model-dependent (Malhi et al., 2009; Galbraith et al., 2010). In addition, (seasonal) drought is expected to promote fire, further stimulating woody collapse (Brando et al., 2014), especially when combined with deforestation (Malhi et al., 2009). However, the likelihood of such a collapse of woody vegetation has been debated, due to uncertainties in projected changes in precipitation regimes (Malhi et al., 2009; Rammig et al., 2010; Good et al., 2013), uncertainties in adaptation of vegetation (Poulter et al., 2010) and because intact tropical forests may be more resilient than models predict (Malhi et al., 2008). In northern Australia, the possibility of occurrence of alternative stable states (i.e. savannas vs. forests) within similar environmental conditions and the role of fire (and other environmental drivers) has been investigated as well (Warman & Moles, 2009), but projections concerning the response of Asia and tropical Australia are scarce (see Hughes (2003) for an overview for Australia). Comparing vegetation responses of the different tropical regions is not straightforward, because different model configurations, model setups and scales (regional vs. global models) have been used to investigate the response of tropical natural vegetation 90

5 Bistability in the tropics of the different continents separately. Regional models concerning tropical areas are of great value to understand the behavior of these ecosystems because they tend to more sophisticatedly reflect relevant ecological processes of these ecosystems, and have more advanced fire models that include fire dynamics relevant for savannas-forest dynamics (Staver et al., 2011a; Higgins & Scheiter, 2012; Staver & Levin, 2012). However, they do not allow for a between-region comparison. In contrast, ESMs allow for cross-continental comparisons within a similar climate change scenario, but they use generic mechanisms, not adapted to specific ecosystems, to model vegetation and fire dynamics. As ESMs are used in global change assessments, knowing how these models capture some major (observed) vegetation patterns is critically important. Given that there are intercontinental differences in the relationship between woody cover and precipitation, as apparent from the occurrence of forests in South-America at drier conditions than in Africa or Australia (Murphy & Bowman, 2012; Lehmann et al., 2014), the different tropical continents can also be expected to respond differently to climate change. Cross-continental analyses of the tropical response to climate change have been conducted with ESMs (Gumpenberger et al., 2010; Huntingford et al., 2013), but, to our knowledge, no comprehensive analysis of bistability across the entire tropical region, consisting of South-America, Africa and Asia (including northern Australia) has been conducted within one modeling framework. Therefore, our goal was to determine whether relatively simple generic mechanisms currently implemented in an ESM allow for bistability of tropical systems across the different tropical continents, and investigate how these systems change over time. We therefore analyzed climate change scenarios with increasing atmospheric CO 2 concentrations from pre-industrial conditions to about 925 ppm at the end of the 21 st century. Vegetation adaptation to environmental changes is thought to play a critical role in correctly representing carbon, water and nutrient fluxes and feedbacks in vegetation models (Van Bodegom et al., 2012; Pavlick et al., 2013) as well as in representing bistability (Hoffmann et al., 2012). Implementation of trait variation within the MPI-ESM has shown to modify carbon fluxes as well as global vegetation distribution (Verheijen et al., 2013), Verheijen et al., 2015). We therefore also investigated the effect of vegetation adaptation on bistability patterns across the continents. 91

6 Chapter Materials and Methods Representation of vegetation dynamics and disturbances JSBACH (Raddatz et al., 2007; Brovkin et al., 2009) is the land surface component of the MPI-ESM, developed by the Max Planck Institute for Meteorology in Germany. It simulates fluxes of water, carbon and heat between land and atmosphere and also includes vegetation dynamics. In JSBACH, vegetation types cover different fractions of a grid cell, and its dynamics (i.e. expansion or shrinkage) within a cell is based on establishment, competition and natural or disturbance-induced mortality. Vegetation dynamics and disturbances are described in detail in Brovkin et al. (2009) and Reick et al. (2013). Woody vegetation is defined as shrubs and trees (tropical and extra-tropical evergreen and deciduous trees and raingreen and deciduous shrubs) and non-woody vegetation consists of both C3- and C4-grasses. Expansion of woody and non-woody vegetation is based on different succession rates after disturbances, reflecting light competition and differences in growth rate. Grasses have higher colonization rates than woody plant functional types (PFTs) when large areas of a grid cell are still uncolonized. However, with increasing vegetation cover, woody PFTs are favored, reflecting their superiority to compete for light. As a consequence, woody PFTs have an advantage over grasses in the absence of fire. There is no productivity-based competition between woodies or non-woodies, only within a growth form (e.g. among woody PFTs), and neither is there competition for water (only via water limitations on productivity within woody and non-woody PFTs). In addition, competition between vegetation types is absent when vegetation expansion into formerly unhospitable land is concerned (so-called desert dynamics). In JSBACH, natural mortality is a fixed, PFT-dependent, proportion of cover. Disturbance-induced mortality is caused by either wind breaks or wild fires. Disturbances by wind breaks do not affect grasses, and are dependent on wind force and speed. Fire occurs only when litter is dry enough to ignite (represented by a long-term relative air humidity) and aboveground litter (both woody and green (leaves) litter) is sufficiently dense to fuel fires. Fire damage (area and carbon burned) increases with decreasing humidity, and grasses are more severely affected by fire than woody vegetation. Fire removes all the aboveground litter from burned areas, and a fraction of the vegetation biomass, similar to the fraction of burned area to total area. Carbon emissions by fire therefore represent both litter and vegetation biomass burned. After fire, the remaining areas are open for recolonization. Even though the global fire model SPITFIRE (Thonicke et al., 2010) has recently been coupled to JSBACH (Lasslop et al., 2014), at the moment of simulations, this was not yet available. 92

7 Bistability in the tropics Including trait variation We derived empirical relationships between observational plant trait data and climate data and implemented these relationships in JSBACH. The main source of the trait data was the TRY database (Kattge et al., 2011), see Verheijen et al. (2013) for an overview of trait data references. For three originally static PFT-dependent traits in JSBACH sufficient trait data could be obtained; these traits were specific leaf area (SLA, fresh leaf area per dry mass), maximum carboxylation rate at a reference temperature of 25 C (Vcmax 25 ) and maximum electron transport rate at 25 C (Jmax 25 ). Based on previous-year local (grid cell) climate, these traits were recalculated every year for each PFT within the model. This allowed for PFT-specific spatial and temporal variation in traits, reflecting responses of vegetation to the environment and affecting vegetation functioning. For an elaborate description of the applied method, we refer to Verheijen et al. (2013). For a more detailed description of the inclusion of additional trait responses to CO 2, we refer to Verheijen et al. (2015). Simulation setup JSBACH was run coupled to ECHAM6, the atmospheric model of the MPI-ESM (Stevens et al., 2013), but sea surface temperatures and sea ice were prescribed. This setup allowed the internal simulation of climate and fluxes between vegetation and atmosphere. We performed transient simulations from 1851 to 2100, using sea surface temperature and sea ice and radiative, ozone and aerosols forcing from historical simulations ( ) and projections ( ) from the C5MIP project (Giorgetta et al., 2013). We followed the RCP8.5LR climate change scenario with prescribed rising atmospheric greenhouse gases, with rising CO 2 to ppm CO 2 around 2100 (Van Vuuren et al., 2011). Only natural vegetation was simulated; no crops or pastures were included, and there were no effects of anthropogenic land use change. For a detailed description of the simulation setup, we refer to Verheijen et al. (2015). We analyzed two simulations: a simulation with the default fixed trait settings (DEF simulation) and a simulation where fixed traits were replaced by in space and time varying PFT-dependent trait values (VARTR simulation). For the analysis, we compared 10 year means of woody cover (%), precipitation (mm year -1 ), mean annual temperature (C ) and fire emissions (kg C m -2 year -1 ) at the start and end of the simulation (mean of and , referred to as 1851 and 2100, respectively). For fire, we used fire emissions instead of burned area, as it accounts for litter built up, which is thought to influence interactions between fire and vegetation cover (Baudena et al., 2015). 93

8 Chapter 4 Selected tropical areas The geographical tropical regions of South-America, Africa and Asia were defined by the areas between the Tropics of Cancer and Capricorn (23.4 N and 23.4 S). Upper boundaries in Africa and lower boundaries in Australia (transitions with deserts) were adjusted based on occurrence of vegetation: areas where vegetation was never dominant during the simulated period ( ) were left out. The lower boundary of Africa was extended beyond the Tropics of Capricorn to include South-Africa. Vegetation definitions PFTs were aggregated into woody vegetation and non-woody vegetation, the former consisting of shrubs and trees, and the latter consisting of both C3- and C4-grasses. Shrubs were most abundant in Africa with cover between 7.3% and 4.7% of the area (in 1851 and 2100, respectively) in the default model. In the other regions (except for South America in 1851, 1.3 %) and in the variables traits model, shrubs covered less than 1% of the area. Because there is no consistency in literature about woody coverage in savannadefinitions, we determined boundaries between savannas and forests for each continent separately. These cut-off values of woody cover were derived from bimodality curves (see section Bimodality below) and should fall between the 50-75% cover range (Staver et al., 2011a). Systems with less than 10% woody cover were considered to be grasslands (Scheiter & Higgins, 2009; Lenton, 2013). Testing for bistability Although proving bistability is difficult and only feasible with manipulation experiments such as fire-removal experiments (Schröder et al., 2005; Murphy & Bowman, 2012), we evaluated the possibility of bistability at the different continents by analyzing bistability indicators (Scheffer & Carpenter, 2003; Schröder et al., 2005). These indicators were bimodality of the state variable (woody cover) and dual relationships (i.e. two different relationships have a better explanatory power than a single relationship) between the different states (savannas and forests) and control factors (precipitation and fire). We did not test for abrupt shift in the state variable over time, because changes in woody cover (especially shifts from savannas to forests) might take several decades and are therefore difficult to test for. Instead, as bistable systems are thought to be more resilient than other systems to changes in the environment (Scheffer et al., 2001), we compared the number of shifts between forests and savannas over time and compared differences in responses of woody cover to environmental changes within and outside bistable regions (i.e. within and outside intermediate precipitation levels). 94

9 Bistability in the tropics Bimodality Bimodality of woody cover was investigated at each of the different continents with the R- package flexmix (version ) for finite mixture models regression (Grün & Leisch, 2007; Yin et al., 2014), which assumes that a distribution can be explained by one or a number of normal distributions. It makes use of the expectation-maximization (EM) algorithm to fit models. We tested models assuming 1 to 4 modal distributions and compared different criteria to find the best model: the Akaike information criterion (AIC), Bayesian information criterion (BIC) and integrated completed likelihood criterion (ICL). BIC was graphically the most consistent estimate, while other studies indicated ICL was the best criterion (Yin et al., 2014; Baudena et al., 2015). Therefore, bimodality was thought to be present i. whenever one of these three criteria indicated such a distribution, ii. the two peaks of the bimodal distribution were well developed and separated by an area with low probability densities, and iii. this latter area occurred within the expected fire-mediated bimodality range (fractional cover between , following Staver et al. (2011a)). We evaluated bimodality of woody cover for different precipitation regimes; , and mm year -1. From mm year -1 onwards, most woody cover in grid cells was 40 % or higher, indicating there were mainly forests. When analyzing more aggregated precipitation ranges; e.g mm year -1, bimodality disappeared in many cases for mm year -1 (probably because a too wide precipitation regime was taken), but for mm year -1 results were relatively similar to either and mm year -1. Therefore, we took the mm year -1 range for further analysis. Bimodality is an indicator of bistability, but can also be explained by a bimodal distribution of underlying drivers (Scheffer & Carpenter, 2003). We therefore investigated the distribution of precipitation and fire at the given mm year -1 precipitation range (Fig. S1-S3). Occasionally, distributions of these drivers did show a bimodal distribution, but there was no relationship with the occurrence of bimodal woody cover. Dual relationships For each of the different continents, we tested the existence of different (dual) relationships of woody cover in response to precipitation and fire for savannas and forests in the mm year -1 precipitation range where bimodality was observed. We evaluated whether an ANCOVA (with vegetation type as fixed factor) better explained the observed variation in woody cover than a single regression line. If the models differed significantly and the ANCOVA was the best model (highest R 2 adj), dual relationships were said to be present if forests and savannas had significantly different (p<0.05) slopes (i.e. the effect of vegetation type on the slope was significantly different). 95

10 Chapter 4 Vegetation shifts and change in woody cover For each continent, shifts from savannas to forests and vice versa between 1851 and 2100 were compared between the precipitation range where bimodality occurred and the region outside this precipitation range. Because these regions consisted of different numbers of grid cells, the number of shifts was normalized to the size of each region. We also investigated if there were different relationships between change in woody cover (between 1851 and 2100) and change in underlying drivers (precipitation and fire) between these regions. We again compared an ANCOVA (with precipitation range as a fixed factor) with a single regression to determine if dual relationships existed for the different regions at each of the different continents. 4.4 Results Bimodality at the different continents Bimodal distributions differed per continent and changed over time. Although a distribution with two peaks was observed in the DEF simulation in Asia in 1851 and in Africa and South-America in both 1850 and 2100 (Fig. 2, Table 1), only the distribution in Asia in 1851 reflected a fire-driven bimodal distribution. In all other cases, the peaks were not well separated or the depression in probability density separating the two peaks was outside the expected fire-mediated bimodality range. However, although South-America did not show such bimodal distribution, woody cover in 2100 seems to move towards such a distribution. In the VARTR simulation, distributions with two peaks were observed in Asia in 1851, in Africa in 1851 and 2100 and in South-America in Except for Africa in 2100, this could also represent fire-mediated bimodality. In either simulation, bimodal distributions were never stable over time, and either disappeared (both simulations) or appeared (VARTR only). 96

11 Bistability in the tropics Figure 2. Probability density distribution of fractional woody cover at mm year -1 precipitation. Fitted bimodal distributions were plotted when bimodality was the best fit according to either AIC, BIC or ICL. Solid lines, if this can reflect fire-driven bimodality, dashed lines if this is not the case. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 97

12 Chapter 4 Table 1. AIC, BIC and ICL values for 1-3 modal distributions at mm year -1 precipitation (4-modal distributions were never the best fit and were therefore omitted). Best fit in bold. DEF; default simulation, VARTR; variable trait simulation, AS; Asia, AF; Africa, SAM; South-America. DEF AS AF SAM peaks: AIC BIC ICL No of cells: VARTR AIC BIC ICL No of cells: Dual relationships To evaluate dual relationship between the state variables, different threshold for woody cover to delineate savannas from forests were taken for different continents and simulations. This was done to account for the fact that the depression between the two peaks of the bimodal distributions was continent- and simulation-dependent. In the DEF simulation, no bimodality was observed in Africa and thresholds based on the depressions were very low in this simulation. Thresholds were therefore set at 50% instead of 40% to fall within the suggested range by Staver et al. (2011a) (boundaries between savannas and forests between 50-75% woody cover). For South-America, no bimodality was observed either, but the threshold based on the depressions (70%) still fell within the suggested bistability range. For the DEF simulation, this resulted in thresholds of 60%, 50% and 70% 98

13 Bistability in the tropics (below these threshold savannas were said to be present, above forests) for Asia, Africa and South-America, respectively, while this was 55%, 60% and 65% for the VARTR simulation. For all models, an ANCOVA (with vegetation type as fixed factor) better explained the observed variation in woody cover than a single regression line, but the effect of vegetation type on the slope was not always significant (i.e. forests and savannas did not always have significantly different slopes). However, only the continents where bimodality was observed needed to exhibit dual relationships. Within the mm year -1 precipitation range on those continents where bimodality of woody cover was observed (marked grey in Table 2), savannas and forests showed different dual relationships between woody cover and precipitation in most cases: there were significantly different relationships for savannas and forests in Asia in 1851 in the DEF simulation and Africa in 1851 and South-America in 2100 in the VARTR simulation, but not in Asia in 1851 in the VARTR simulation (Fig. S4, Table S1). In contrast, for the relationship between woody cover and fire, dual relationships were present for savannas and forests on all of the continents where bimodality of woody cover was observed around 1851 and 2100 (Fig. 3, Table 2). Figure 3. Relationship between woody cover and fire for savannas and forest at mm year -1 precipitation. Solid lines reflect fitted lines by ANCOVA, while dashed line reflect single linear regression. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 99

14 Chapter 4 Table 2. P-values of intercepts and slopes of ANCOVAs of woody cover ~fire. Non-significant terms in bold. Continents with bimodality are marked grey. Abbreviations similar to Table 1. AS AF SAM DEF Intercept p< p< p< p< p< p< Fire (slope) p< p< p< p< p< p< Effect of vegetation type on intercept p< p< p< p< p< Effect of vegetation type on slope p< R 2 adj VARTR Intercept p< p< p< p< p< p< Fire (slope) p< p< p< Effect of vegetation type on intercept p< p< p< p< p< Effect of vegetation type on slope p< p< R 2 adj Shifts between savannas and forests in bimodal regions between 1851 and 2100 Because bistable areas are thought to be more resilient to change, we evaluated those changes in woody cover ( ) that resulted in a shift from savannas to forests or vice versa (i.e. across continent-dependent bimodality thresholds). Although simulations differed in the number of shifts, those in the VARTR simulation were not consistently larger or smaller than in the DEF simulation most of the time (Table 3). When bimodality was present around 1851, more shifts and more shifts from forests to savannas occurred within the intermediate precipitation range (the bimodal region) than outside this precipitation range (Asia in both simulations and Africa in VARTR simulation). In South-America, where bimodality appeared (in VARTR, potentially in DEF), there were fewer shifts. In a small percentage of grid cells where shifts from forests to savannas occurred, climate change has resulted in precipitation levels outside the bistable range ( mm year -1 ) around 2100 (values between brackets in Table 3). In the VARTR simulation, more shifts within such grid cells occurred. 100

15 Bistability in the tropics Table 3: Percentage of shifts from savannas to forests and vice versa (relative to the number of grid cells within a given precipitation range). Between brackets, the percentage of shifts having a precipitation level outside the mm year -1 range in 2100 is indicated. Continents with bimodality in either 1851 or 2100 are marked grey. Abbreviations similar to Table 1. Precipitation range in 1851: mm year -1 <500 and >1750 mm year -1 total area DEF VARTR Shifts (%) AS AF SAM AS AF SAM forest->savanna 6.6 (0.7) 11.0 (0) 22.4 (1.9) 5.0 (1.4) 8.8 (2.0) 24.8 (5.6) savanna->forest 0.7 (0) 1.0 (0) 0.0 (0) 1.4( 0) 2.7 (0) 2.3 (0) total shifts forest->savanna savanna->forest total shifts forest->savanna savanna->forest total shifts Changes in woody cover upon climate change Large changes in both precipitation and fire occurred over time, with a strong increase in fire given a certain precipitation range (Fig. 4). We therefore also compared how woody cover changed between 1851 and 2100 in relation to these changes in precipitation and fire in the regions within and outside the mm year -1 precipitation range of Relationships between changes in woody cover and changes in fire within and outside the intermediate precipitation range were most of the time significantly different on the continents where bimodality occurred, except for Africa in the VARTR simulation (Fig. 5, Table 4). For the relationship with changes in precipitation, only 2 out of the 4 continents with bimodality had significant different relationships (Asia in the DEF simulation and Africa in the VARTR simulation, Fig. S5, Table S2). 101

16 Chapter 4 Figure 4. The relation between fire and precipitation in 1851 and The mm year -1 precipitation range is marked grey. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). Table 4. P-values of intercepts and slopes of ANCOVAs of change in woody cover ~ change in fire. Nonsignificant terms in bold. Continents with bimodality in either 1851 or 2100 are marked grey. Abbreviations similar to Table 1. DEF VARTR AS AF SAM AS AF SAM Intercept p< p< Change in fire (slope) p< p< p< p< p< p< Effect of precipitation range on intercept Effect of precipitation range on slope p< p< R 2 adj

17 Bistability in the tropics Figure 5. Relationship between change in woody cover and change in fire (between 1851 and 2100) for woody cover within and outside the mm year -1 precipitation. Solid lines reflect fitted lines by ANCOVA, while dashed line reflect single linear regression. Left 3 panels (a,c,e) default simulation (DEF), right 3 panels (b,d,f) variable trait simulation (VARTR). 103

18 Chapter Discussion Generic fire models can create bistability In this study we showed the existence of bistability in an ESM which has generic mechanisms for vegetation-fire interactions. Within intermediate precipitation ranges ( mm year -1 ) bimodality of woody cover could be observed. In addition, on the different continents where this bimodality was observed, woody cover showed structurally different relationships with fire but not with precipitation, suggesting that fire is the underlying driver of bimodal distributions. These bistability indicators were continent-dependent. In the DEF simulation, there were only clear indicators of bistability (bimodal distributions of woody cover at intermediate precipitation levels and different relationships between woody cover and fire for savannas and forests) in Asia around When more adaptive responses of vegetation to environmental drivers were allowed for by including trait variation, more continents showed signs of bistability; besides Asia, both Africa in 1851 and South-America in 2100 also revealed bimodal distributions of woody cover and dual relationships of woody cover and fire between savannas and forests. These results clearly show that including trait variability affects bistability prevalence. Bistable systems are not stable over time Bistability-indicators disappeared over time. This disappearance might explain the lack of structural differences in relationships between woody cover changes and changes in fire or precipitation within and outside the intermediate precipitation range on bistable continents. It might also explain why in contrast to expectations, there were more shifts between savannas and forests within than outside the intermediate precipitation range, because when bistability disappears, the system might not be more resilient to environmental change. In contrast, fewer shifts occurred in regions within the mm year -1 precipitation range when bimodality appeared (South-America in VARTR, potentially DEF), suggesting bistability of the system does result in less change. Including adaptive responses of vegetation did not modify the number of shifts; there were not structurally fewer shifts between savannas and forests within the VARTR simulation, which would have been be expected if adaptive vegetation is more resilient against environmental change. Larger differences between simulations might be expected if variation in traits related to the main driver of bistability had been included. Variation in fire related traits like bark thickness or resprouting capacity (Ratnam et al., 2011; Hoffmann et al., 2012) might increase the responsiveness of the system, although observational trait data is not yet sufficient to determine global trait-climate relationships 104

19 Bistability in the tropics for such traits, and in JSBACH, these traits are not yet present at all. Although SLA of savanna and forests trees differs (Ratnam et al., 2011), this might not be a trait important in modifying vegetation behavior in response to fire. This disappearance of bistability over time makes it very likely that the suggested bistability of the savanna-forest systems are not resilient to the strong changes in CO 2, temperature and precipitation as occurring between 1851 and Although bistable systems are resilient to small environmental changes (Scheffer et al., 2001), this climate change might result in such large changes in environmental drivers that new conditions are not suitable to sustain bistability of savanna-forest systems anymore, for example when the bistable system is pushed outside its environmental boundary conditions. However, only a small percentage of the vegetation shifts occurred in conditions where precipitation changed that much that it was outside the mm year -1 precipitation range (values between brackets in Table 3). Instead, it is very likely that the strong increase in fire in Asia and Africa has caused the disappearance of these bistable systems. Looking at South- America, fire increases as well, but does not reach levels as high as in Africa in 2100 (Fig. 4). Although in South-America fire does reach similar levels as in Asia, there are still areas with low fire emissions (not present in Asia and less present in Africa). These conditions possibly allow for bimodality being present in South-America in the VARTR simulation around 2100, and explain why in the DEF simulation, woody cover moves towards such a distribution with alternative stable states. Such alternative stable states in the (eastern) Amazon of South-America are also present in other models and have been shown to potentially shift from forests towards desert upon changes in precipitation and dry season water stress (Oyama & Nobre, 2003; Malhi et al., 2009). Although bistability indicators disappeared over time, this does not necessarily mean that new bistable equilibria cannot become apparent anymore. In 1851, the systems were in equilibrium, but strong climate change, especially during the 21 st century moves these systems away from equilibrium. When climate will become more stable (by stabilizing CO 2 concentrations), these systems may find new equilibria again, which might be a bistable system as well. Around 2100 bistability indicators were found in South- America in VARTR suggesting re-establishment of equilibrium under these conditions. Complexity of fire-mediated bistability Baudena et al. (2015) suggested that bimodality is not observed in JSBACH, because in the current model version there is no difference in litter from woody or non-woody vegetation as driving force of fire. As a consequence, because trees contribute more to the total litter stock, fire is more strongly driven by litter of trees than of grasses, resulting in a decrease in fire when woody cover gets sufficiently low. However, although woody cover showed less clear relationships (more variability) with fire below ~40% cover on some continents (Fig. 105

20 Chapter 4 3), large fire emissions were still possible in our simulations. In addition, above this ~40% threshold, an increase in woody cover resulted in a decrease in fire emissions, in line with observations that fires are suppressed above ~40 % of woody cover (Archibald et al., 2009). Moreover, grasses showed a positive relationship with fire emissions (Fig. S6), suggesting there might be a positive feedback between grasses and fire as well, which is thought to be an important mechanism of preventing establishment of woody vegetation and transition from savanna systems to forests (Staver et al., 2011a). Our analysis shows that there are clear signs of bistability in our model. To prove however, that this also means that there is actual fire-mediated bistability, manipulation experiments need to be conducted (Schröder et al., 2005), which in a model-setup would mean, for example, to run simulations without fire as well. Such simulations could indicate which areas are truly bistable, i.e. become forests in the absence of fire and savannas when fire is included (Higgins & Scheiter, 2012). However, this might still not be conclusive in a coupled setup, where the different drivers and vegetation can interact and feedback to each other, resulting in different environmental conditions in simulations (Verheijen et al., 2013), making comparisons between simulations difficult. Implications ESMs are used to investigate the earth system response to climate change within CMIP5 assessments (Ahlstrom et al., 2012; Arora et al., 2013). They inevitably simplify reality (due to computational limitations) and use a generic approach to model vegetation dynamics. In reality, tropical vegetation might have different biophysical properties (e.g. different albedos, photosynthetic parameters), bioclimatic limits or different phenologies compared to temperate vegetation, while in ESMs mechanisms of competition and disturbances are assumed to be similar across different continents. Although tropical forests and savannas are functionally very different ecosystems, they are not treated differently in these models (Ratnam et al., 2011). Knowing the extent to which such global models can produce vegetation patterns and behavior resembling observations is therefore pivotal. When computational feasible, there is still the need of improving these general global models by including ecological mechanisms affecting vegetation dynamics, such as included in more specialized (regional) models. Those models contain fire modules that include fire dynamics relevant in savannas, e.g. life-stage or size-dependent sensitivities to fire, differentiation between fire and shade sensitivity of savanna and forest trees, or direct competition for water between grasses and trees (Staver et al., 2011a; Higgins & Scheiter, 2012; Scheiter et al., 2012; Staver & Levin, 2012; Baudena et al., 2015), although these models still lack sophisticated representations of other important drivers as well, like herbivory (Sankaran et al., 2008; Murphy & Bowman, 2012) and soil conditions (Lehmann et al., 2014). However, an ESM with a simple fire module (as in the MPI-ESM) also allows 106

21 Bistability in the tropics for continent-dependent bistability of these systems, increasing confidence in the reliability of the output of these models upon climate change. The suggested different expression and loss of bistability across continents has major implications for modeling of future ecosystem responses on these continents. It shows that upon climate change, continental differences in ecosystem responses will likely be observed. More importantly, although bistable systems are thought to be more resilient to small perturbations, these systems are not necessarily better at dealing with the large environmental changes caused by the effects of climate change. Not only (relatively) abrupt shifts between savannas and forests across thresholds within bistable conditions can occur, but disappearance of these bistable systems and related ecological mechanisms is not unlikely either. If bistability is lost, these systems will change into more gradual responding systems, with consequently different relationships with environmental drivers. This introduces extra uncertainties into these models and makes it extra difficult to predict vegetation behavior upon climate change. 4.6 Acknowledgment This study has been financed by the Netherlands Organization for Scientific Research (NWO), Theme Sustainable Earth Research (project number TKS09-03) and has been supported by the TRY initiative on plant traits ( db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (idiv) Halle Jena Leipzig. The authors would also like to thank Victor Brovkin from the Max Planck Institute for Meteorology in Hamburg, Germany, for the opportunity to work with JSBACH and Mara Baudena from Utrecht University for her help with the flexmix modeling in R. 107

22 Chapter Supplementary material Figure S1 Probability density distributions of precipitation Figure S1. Probability density distributions of precipitation at mm year -1 precipitation. Fitted bimodal distributions were plotted when bimodality was the best fit according to either AIC, BIC or ICL. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 108

23 Bistability in the tropics Figure S2 Probability density distributions of fire Figure S2. Probability density distributions of fire emissions at mm year -1 precipitation. Fitted bimodal distributions were plotted when bimodality was the best fit according to either AIC, BIC or ICL. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 109

24 Chapter 4 Figure S3 Probability density distributions of temperature Figure S3. Probability density distributions of temperature at mm year -1 precipitation. Fitted bimodal distributions were plotted when bimodality was the best fit according to either AIC, BIC or ICL. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 110

25 Bistability in the tropics Figure S4 Relationship between woody cover and precipitation for savannas and forest Figure S4. Relationship between woody cover and precipitation for savannas and forest at mm year -1 precipitation. Solid lines reflect fitted lines by ANCOVA, while dashed line reflect single linear regression. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 111

26 Chapter 4 Table S1 P-values of intercepts and slopes of ANCOVAs of woody cover ~ precipitation Table S1. P-values of intercepts and slopes of ANCOVAs of woody cover ~precipitation. Non-significant terms in bold. Continents with bimodality are marked grey. Abbreviations similar as Table 1. AS AF SAM DEF Intercept p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 Precipitation (slope) p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 Effect of vegetation type on intercept p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 Effect of vegetation type on slope p<0.001 R 2 adj VARTR Intercept p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 Precipitation (slope) p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 Effect of vegetation type on intercept p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 Effect of vegetation type on slope p<0.001 p< p<0.001 p<0.001 R 2 adj

27 Bistability in the tropics Figure S5 Relationship between change in woody cover and change in precipitation Figure S5. Relationship between change in woody cover and change in precipitation (between 1851 and 2100) for woody cover within and outside the mm year -1 precipitation. Solid lines reflect fitted lines by ANCOVA, while dashed line reflect single linear regression. Left 3 panels (a,c,e) default simulation (DEF), right 3 panels (b,d,f) variable trait simulation (VARTR). 113

28 Chapter 4 Table S2 P-values of intercepts and slopes of ANCOVAs of change in woody cover ~ change in precipitation Table S2. P-values of intercepts and slopes of ANCOVAs of change in woody cover ~ change in precipitation. Non-significant terms in bold. Continents with bimodality in either 1851 or 2100 are marked grey. Abbreviations similar as Table 1. DEF VARTR AS AF SAM AS AF SAM Intercept p< p< p< Change in precipitation (slope) p< p< p< Effect of precipitation range on intercept p< p< p< Effect of precipitation range on slope p< p< p< R 2 adj Figure S6 Relationship between non-woody cover and fire for savannas and forest Figure S6. Relationship between non-woody cover and fire for savannas and forest at mm year -1 precipitation. Left 6 panels (a,b,e,f,i,j) default simulation (DEF), right 6 panels (c,d,g,h,k,l) variable trait simulation (VARTR). 114

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