Adaptive management for competing forest goods and services under ...

Ecological Applications, 22(8), 2012, pp. 2065–2077 Ó 2012 by the Ecological Society of America

Adaptive management for competing forest goods and services under climate change CHRISTIAN TEMPERLI,1 HARALD BUGMANN,

AND

CHE´ ELKIN

Swiss Federal Institute of Technology, ETH Zurich, Department of Environmental Systems Science, Forest Ecology, Universita¨tstrasse 22, CH-8092 Zu¨rich, Switzerland

Abstract. Developing adaptive forest management strategies is essential to maintain the provisioning of forest goods and services (FGS) under future climate change. We assessed how climate change and forest management affect forest development and FGS for a diverse casestudy landscape in Central Europe. Using a process-based forest model (LandClim) we simulated forest dynamics and FGS under a range of climate change and management scenarios in the Black Forest, Germany, which is shaped by various management practices. We focused on the interdependencies between timber production and forest diversity, the most valued FGS in this region. We found that the conversion to more drought-adapted forest types is required to prevent climate change-induced forest dieback and that this conversion must be the target of any adaptive management, especially in areas where monocultures of drought-sensitive Norway spruce (Picea abies) were promoted in the past. Forest conversion takes up to 120 years, however, with past and future adaptive management being the key drivers of timber and forest diversity provision. The conversion of drought-sensitive conifer monocultures maintains timber production in the short term and enhances a range of forest diversity indices. Using uneven-aged forest management that targets a drought-adapted, diverse, and resilient species mixture, high species diversity can be combined with timber production in the long term. Yet, the promotion of mature-stand attributes requires management restrictions. Selecting future adaptive management options thus implies the consideration of trade-offs between forest resource use and environmental objectives, but also the exploitation of synergies between FGS that occur during forest conversion. Lastly, the large impact of past management practices on the spatial heterogeneity of forest dynamics underpins the need to assess FGS provisioning at the landscape scale. Key words: Black Forest, Germany; climate change; ecological indices; ecosystem goods and services; landscape model; multi-objective forest planning; Norway spruce monoculture conversion; scenario analysis.

INTRODUCTION Adaptive forest management (Walters 1986, Heinimann 2010) has been proposed to enhance the provision of forest ecosystem goods and services under uncertain future conditions such as climate change. In this context the desired forest conditions that best meet the demands for forest goods and services (FGS) must first be defined, and second, the management pathways that allow the forest to be converted to this target state need to be identified (Pretzsch et al. 2008). This involves evaluating alternative management practices that enhance resilience to environmental changes by promoting diversity in tree species and stand structure (Spiecker et al. 2004), by adapting the tree species mixture to anticipated future climate conditions (Chmura et al. 2011), or by favoring certain species through planting or thinning. However, defining the target state and suitable conversion pathways is a nontrivial task, as it strongly depends on Manuscript received 2 February 2012; revised 31 May 2012; accepted 1 June 2012. Corresponding Editor: V. C. Radeloff. 1E-mail: [email protected]

legacies from past management as well as uncertainty in future driving forces such as climate change. We next review four key challenges to identifying desired target states (including FGS production levels) and appropriate management practices to achieve those goals. First, target states must accurately consider shifts in forest growth, forest structure, and species distributions under future climate taking into account the uncertainties associated with climate change and forest dynamics. Second, forests develop slowly, so during periods of rapid climate change they are likely to be in disequilibrium with the environment, leading to changed competition and possibly mortality (Niinemets 2010). The rate of climate change therefore determines the achievable target states and—equally important—the dynamics of FGS during this transition. Third, past management practices can have a large impact on how forests respond to climate change (Farrell et al. 2000). For example, in commercial forests drought resistance was often sacrificed by promoting species in even-aged monocultures outside their natural distribution range (Klimo 2000). Thus, the degree to

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Ecological Applications Vol. 22, No. 8

CHRISTIAN TEMPERLI ET AL.

TABLE 1. Current climate and regional circulation model realizations for the IPPC AR4 A1b emission scenario at 828 m a.s.l. in the northern Black Forest (Germany) study landscape. Temperature (8C)

Precipitation (mm)

Climate scenario

Annual

Summer (Apr–Sep)

Winter (Oct–Mar)

Annual

Summer (Apr–Sep)

Winter (Oct–Mar)

Current climate (1950–2000) SMHI (2081–2100) MPI (2081–2100)à HCCPR (2081–2100)§

7.1 9.3 10.7 11.7

12.4 14.6 16.0 17.3

1.8 4.0 5.4 6.1

1086 1041 1011 1042

573 491 471 473

513 550 540 569

The RCA30/CCSM3 model (Kjellstro¨m et al. 2011) by the Swedish Meteorological and Hydrological Institute (SMHI). à The CLM/ECHAM5 model (Hollweg et al. 2008) by the Max-Planck-Institute for Meteorology (MPI). § The HadRM3Q0/HadCM3Q0 model (Collins et al. 2006) by the Hadley Center for Climate Prediction and Research (HCCPR).

which past management actions have moved forests away from the target state needs to be assessed. Fourth, changes in forest management can have immediate but also delayed effects on the provision of several FGS (Knoke et al. 2008, McCarney et al. 2008). Thus, the trade-offs between FGS and shifts in tradeoffs over time need to be considered if robust management strategies are to be devised (Lexer and Brooks 2005). Most studies on adaptive forest management have addressed these challenges in isolation and have not included their aggregated impacts. For example, gap models have often been used to evaluate how climate and site factors will influence forest properties (Bugmann 2001). With the incorporation of forest management, they provide the necessary functionality to assess the interacting effects of management and climate on FGS provision (Lasch et al. 2002, 2005, Fu¨rstenau et al. 2006, Seidl et al. 2007, Pabst et al. 2008, Kint et al. 2009, Rasche et al. 2011). However, gap models capture neither landscape-scale disturbances nor the spatial distribution of site factors (Bugmann et al. 2000, Perry and Enright 2006). The latter needs to be considered because it drives the spatial variation in forest dynamics, which in turn interacts with disturbances (Turner et al. 1998) and the timing and spatial allocation of management (Gustafson and Rasmussen 2002). Here we consider how interactions between past forest management and the rate of climate change influence the ability of alternative management plans to maintain the competing FGS timber production and forest diversity. To achieve these goals we employed a dynamic forest model that combines local-scale succession with landscape-scale disturbances and forest management. We evaluated how climate change will influence forest development and FGS through time for a case-study region in Central Europe. We assessed the impact of a range of adaptive management strategies on forest development and the provision of FGS, emphasizing the trade-offs between FGS with respect to management strategies. We contrasted the short- and long-term effects of management decisions to illustrate the importance of developing robust adaptive management plans.

MATERIALS

AND

METHODS

Study landscape The 2 3 10 km study landscape at the western edge of the Northern Black Forest (48840 0 N, 8813 0 E) ranges from 250 to 1050 m above sea level (a.s.l.); the climate is oceanic (Table 1). This landscape features three distinct areas with respect to environmental conditions and forest management (Fig. 1). In the low-elevation area (,500 m a.s.l.), soil water-holding capacity (cf. Henne et al. 2011) is higher (.15 cm) than in the middle and uppermost areas (500–800 and 800–1050 m a.s.l., respectively; 6–15 cm; data provided by Forstliche Versuchsanstalt Baden-Wu¨rttemberg, Freiburg, Germany). Under the latter conditions, a mixed European beech (Fagus silvatica L.) forest would develop naturally, with oaks (Quercus spp.) increasing in proportion toward lower elevations and silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) Karst) toward higher elevations (Mu¨ller et al. 1992, Ludemann 2010). However, as in large parts of Central Europe, Norway spruce was promoted throughout this landscape for economic reasons. At low elevations this led to unevenaged mixed Norway spruce–silver fir forests with intermixed European beech and Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco var. menziesii ), mostly on dry sites. The mid- and high-elevation forests are a mosaic of even-aged, almost pure Norway spruce stands of different age (map of forest stands provided by Forstliche Versuchsanstalt Baden-Wu¨rttemberg). LandClim model The forest landscape model LandClim was designed to study forest dynamics determined by climate, soil properties, and forest management as well as fire and wind disturbance (Schumacher et al. 2004, Schumacher and Bugmann 2006). Landscapes are simulated on a 25 3 25 m grid, thus capturing the forest’s response to large-scale disturbance events while simulating highly resolved site conditions. Within each grid cell, tree regeneration, growth, mortality, and competition are modeled using a simplified gap model with an annual time step (Bugmann and Solomon 2000). The latter includes 32 tree species parameterized for Central

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CLIMATE-ADAPTIVE ECOSYSTEM MANAGEMENT

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FIG. 1. The location of the study landscape (northern Black Forest) in Germany and its subdivision into altitudinal areas and stands. Stands can be distinguished by different shades of gray: dark gray in the low-elevation area, intermediate gray in the midelevation area, and light gray in the high-elevation area. Contour lines indicate elevation (m a.s.l.).

Europe (Henne et al. 2011). The landscape-scale processes of fire, wind, forest management, and seed dispersal are simulated in 10-year time steps. LandClim has been used to simulate current (Schumacher et al. 2006, Schumacher and Bugmann 2006) and historic (Colombaroli et al. 2010, Henne et al. 2011) forest dynamics in the Swiss Alps and the Colorado Front Range (USA). The simulation of potential natural vegetation (PNV) in the study landscape corresponds well to local PNV maps and paleoecological records (Mu¨ller et al. 1992, Ludemann 2010). Climate data and scenarios Monthly temperature and precipitation data were available from meteorological stations (1950–2000) and from three Regional Circulation Model simulations of the IPCC AR4 (Intergovernmental Panel on Climate Change, Fourth Assessment Report [2007]) A1B emission scenario (2001–2100), hereafter referred to as ‘‘climate scenarios’’ (Table 1). These climate scenarios were selected to encompass the uncertainty in climatechange predictions. Spatial climate data interpolated to a 1-ha-grained elevation model (SRTM-3) were provided by the Research Unit Landscape Dynamics of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Simulation of forest management scenarios The landscape was divided into multiple management units for which regimes with specific objectives are

defined. In our case study each of the three elevation areas was considered as a separate management unit and was further divided into stands using boundaries from the local stand map (Fig. 1). We employed five management regimes, two representing past management practices (1–2) and three alternative adaptive management scenarios (3–5). To quantify silvicultural operations (see Appendix A for details), we used descriptions of the management regimes that are currently applied in or recommended for the study area (MLR 1999, Spiecker et al. 2004, Duncker et al. 2007). Common to all adaptive management regimes was the objective of converting even-aged spruce monocultures to uneven-aged forest types. Yet the management regimes varied in the degree to which they promote the adaptation of the species composition to warmer and drier conditions and in the extent to which timber production vs. forest diversity is favored (Fig. 2): 1) In the past several centuries, forests at higher elevations (500–1050 m a.s.l.) were managed as even-aged Norway spruce (EN) stands, whereby highest possible timber production was achieved by clear-cutting stands when dominant trees reached a target diameter of 45 cm dbh. Following clearcutting, the stands were replanted with Norway spruce and thinned to increase growth and to maintain the monoculture.

FIG. 2. Adaptive and past (dashed boxes) management regimes. The selection represents a gradient from timber to forest diversity provision-oriented management regimes (left to right) and whether or not the species composition is actively adapted to climate change (upper vs. lower row). Note: The past management regime at low elevation, i.e., uneven-aged mixed forest, was applied as an adaptive management regime at mid- and high elevation.

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2) Uneven-aged mixed forest (UM) management was applied at lower elevations (,500 m a.s.l.). We used this management regime as an adaptive strategy to convert the even-aged Norway spruce forests at mid- and high elevation. To combine timber production with the promotion of forest diversity, a structurally rich Norway spruce-dominated forest with continuous cover was promoted. Naturally regenerating deciduous trees, Douglas-fir, and silver fir comprise 20–40% basal area of the species mixture. 3) The second adaptive strategy aims at forest diversity promotion by conversion to natural vegetation (NV). To this end, Norway spruce is thinned strongly. Otherwise, forest management is restricted to a minimum of infrastructure maintenance (e.g., hiking trails). 4) The third adaptive strategy converts stands to uneven-aged mixed Douglas-fir/silver fir (UD) using thinning and target-diameter harvesting. Thus, windthrow resistance is improved and the species mixture is adapted to a warming climate while valuable coniferous timber is still produced (Schu¨tz et al. 2006). 5) Last, the current forest is converted to an unevenaged mixed oak (UO) forest by means of stand openings, planting of downy oak (Quercus pubescens Willd.), and subsequent promotion of other droughtadapted species by thinning. Thereby the species mixture is adapted to drier conditions, and forest diversity as well as resilience to disturbance are promoted (Wermelinger et al. 2008, Lindner et al. 2010). Model initialization and simulation setup We simulated the past management regimes under current climate until the model reached a pseudoequilibrium state, which agreed well with recent local inventory data. These data were used to initialize the simulation experiments under the current climate (1950– 2000) and the three climate scenarios. For every climate setting, we simulated the past regimes (EN and UM) as business-as-usual scenarios; at low elevation, we subsequently simulated the three (NV, UD, and UO) and at mid- and high elevation the four adaptive management regimes (UM, NV, UD, and UO). By applying all adaptive management regimes to the whole study landscape we were able to assess the influence of elevation specific past forest management on the future provision of forest goods and services (FGS). We started these simulations with the climate data of the year 2001 and used the transient climate data to simulate to 2100. We then simulated another 100 years assuming, as a conservative zero-order approximation, that climate would stabilize toward the end of the 21st century. For the 22nd century we used a 100-year time series that was constructed by randomly sampling from the climate

data of 2081–2100. We replicated each simulation 15 times using a different initialization data set. We analyzed the output data for each cell, decade, and simulation run, then averaged across the 15 replicates, and finally aggregated the cell-specific data to the three elevation areas. Assessment of forest state and forest goods and services provision LandClim outputs were converted to metrics on forest state and FGS provision. To assess total biomass and species composition, we summed up tree biomass per species and evaluated its development over time. For timber production we used the summarized harvested and thinned species-specific biomass, which includes leaves and branches (see Table A1 in Appendix A). To characterize forest diversity we considered two aspects: tree species diversity and stand structure. Tree species diversity has been used numerous times to assess the ecological value of forests (e.g., Lasch et al. 2002, Seidl et al. 2007); it accounts for the number of species and their relative abundance, both key factors determining resilience (Elmqvist et al. 2003). We used Shannon’s diversity index (H ) as follows: H¼

S X ðpi 3 ln pi Þ

ð1Þ

i¼1

where pi is the proportion of stems of species i in a cell, and S is the number of species. The diversity of many forest-dwelling species depends more on stand structure than on tree species diversity (McElhinny et al. 2005). To indirectly capture this second aspect of forest diversity other studies rated stands according to the occurrence of tree and shrub layers (Lasch et al. 2002), used indices describing stand structural complexity (Kint et al. 2009), or focused on deadwood abundance (Fu¨rstenau et al. 2006, Seidl et al. 2007). We use an index that measures a range of mature stand attributes that reflect successional development. We employ this stand maturity index (SMI) because diversity of many organism groups is higher in old stands due to the high abundance of microhabitats (Spies and Franklin 1991, McElhinny et al. 2005). Old stands comprise (1) old and large trees that feature cavities (Michel et al. 2011); (2) tree size variation, which fosters bird diversity (Rosenvald et al. 2011); and (3) deadwood that provides habitat for a range of specialized organisms (DeWalt et al. 2003, Lassauce et al. 2011). Thus we used as base indices (Bi ) median age, median height, standard deviation of height and deadwood biomass as B1-4, respectively. To account for right-skewed distributions, we applied a log-transformation and normalized the base indices using the following upper limits (Li ): 200 years; height, 30 m (mean), 5 m (SD); and 5 Mg/cell as L1-4, respectively, because above a certain threshold the base indices do not further benefit forest diversity (Eq. 2a). Thresholds were based on the distributions in simulations of

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potential natural vegetation and verified by empirical data (Mu¨ller and Bu¨tler 2010). We combined the transformed and normalized base indices as h1 to h4 equally weighted by multiplication (Eq. 2b), i.e., implying that all attributes must be present to support forest diversity: logðBi þ 1Þ ;1 ð2aÞ hi ¼ min logðLi Þ

SMI ¼

4 Y

hi :

ð2bÞ

i¼1

For all indices, we considered trees .12 cm dbh. To compare the development of the FGS indices across time as well as between scenarios and elevations, we normalized them to the maximum values found in the whole scenario space, time frame, and study landscape. RESULTS Simulation results were similar for the mid- and highelevation area due to similar management histories and soil properties. We therefore focus on high and low elevations here. The full set of simulation results can be found in Appendix B: Figs. B1–B7. Climate effects on forest properties and forest goods and services Forest properties.—Forests under business-as-usual management exhibited severe biomass reductions under climate change due to a rapid drought-induced dieback of Norway spruce in the second half of the 21st century (50–90% Norway spruce biomass decline within 20–30 years; Fig. 3, panels 1a–c and 2e–g); biomass losses were higher at low elevation and varied with the climate change scenario. The onset of drought-induced Norway spruce mortality (2050–2080) was determined by the combination of long-term climate trends and short-term (annual) climate variability. The impact of climate change on species composition depended on its magnitude. This was most apparent under the NV (natural vegetation) regime, where forest management ceased after the removal of Norway spruce. Under current climate, simulated natural species composition was a European beech-dominated forest with intermixed sessile oak (Quercus petraea Liebl.), Douglas-fir, and silver fir (Fig. 3, panels 3d, h). With increasing climate change (SMHI , MPI , HCCPR), Douglas-fir was projected to become more dominant, with silver fir, European beech, sessile oak, and smallleaved lime (Tilia cordata Mill.) increasing as well (Fig. 3, panels 3a–c and 3e–g). The biomass share of each species varied with elevation. These climate-changedriven dynamics in species composition were less prominent under management prescriptions that favor specific species, especially under UO (uneven-aged mixed oak) management, where heavy Douglas-fir

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thinning was applied to promote the less competitive downy oaks. Forest goods and services, FGS.—Climate change drastically impacted on timber production and forest diversity if business-as-usual management was continued (Fig. 4, panels 1a vs. 1d and 2e vs. 2h). In contrast, under adaptive management the climate change impact was small (Fig. 4, panels 2a–5d and 3e–5h; Appendix B: Figs. B2–B4). The climate-change-induced decrease in Norway spruce resulted in decreasing Norway spruce timber production that remained low until the end of the simulation period (Fig. 4, panels 1a and 2e). Under the adaptive management regimes, timber harvest recovered as more drought-adapted species established (Fig. 4, panels 2a, 4a, 5a, and 4e, 5e). The share of these drought-adapted species in total harvested biomass increased with the magnitude of climate change. However, the harvested species composition varied more among management regimes than among climate scenarios, indicating that the management impact was stronger than the climate impact (Fig. 4; Appendix B: Figs. B2–B4). At the beginning of the scenario simulation (2001), Shannon diversity was low under EN (even-aged Norway spruce) management at high elevation compared to the uneven-aged mixed forest at low elevation. Shannon diversity increased with the drought-induced loss of large canopy trees (;50% fewer trees .40 cm dbh in 2100) and, more importantly, the lower survival of planted Norway spruce saplings (;90% in year 2000 vs. 40% in year 2100). Hence, increased light availability and decreased competition from planted saplings allowed for a wide range of species to regenerate and survive. Increased regeneration of a diverse array of species contributed to relatively high Shannon diversity after ca. 2080, even though these species were heavily thinned and made up a small share of total biomass (Fig. 3, panels 1a–c and Fig. 4, panel 1a). Droughtinduced mortality of large trees caused deadwood to accumulate, especially under NV management, where trees were not harvested and thus died naturally. These increasing deadwood amounts caused the stand maturity index (SMI) to rise, particularly at low elevation (Fig. 4; Appendix B: Figs. B5–B7). Effects of adaptive management on forest properties and forest goods and services Forest properties.—Each of the management prescriptions reduced total forest biomass compared to the NV treatment, with the magnitude depending on the management alternative and climate change scenario (Fig. 3). In the long term, the adaptive management alternatives led to a species composition according to their targets (Appendix A: Table A1). Forest goods and services.—In the short term (ca. 2001–2080), timber production was primarily determined by the management prescription used to promote

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FIG. 3. Development of species-specific live biomass aggregated for the high-elevation (top panels) and low-elevation (bottom panels) areas under four climate scenarios (shown on the right side of the rows) and five management regimes (columns). For midelevation areas see Appendix B, for climate scenario information see Table 1, and for management regimes see Appendix A.

the species transition. Management regimes that resulted in intensive removal of Norway spruce (NV, UD, and UO) resulted in high timber production in the short term. Conversely, long-term timber yields (2081–2200) were determined by the target species mixture and the

degree to which management promoted drought-resistant species (Fig. 4). Conversion to Douglas-fir forests (UD) resulted in harvestable volumes that exceeded those achieved under the business-as-usual management regimes, even under

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FIG. 4. Development of normalized forest goods and services (FGS) indices aggregated for the high-elevation (top panels) and low-elevation (bottom panels) areas under current climate and the HCCPR climate scenario (Table 1) in rows and under five management regimes (Appendix A) in columns (see Appendix B for mid-elevation and intermediate climate scenarios). Shown are the species-specific harvested biomass (polygons), Shannon’s diversity index (Shannon H ), and the stand maturity index (SMI). Panel annotations correspond to panel annotations in Fig. 3 regarding climate and management scenarios.

the most severe climate change scenario (Fig. 4, panels 4a and 4e). The past EN management suppressed species other than Norway spruce almost completely and led to low Shannon diversity at mid- and high elevations (Fig. 4, panel 1d; Appendix A: Table A1). In contrast, all adaptive management alternatives, which do not aim to maintain such monocultures, resulted in the gradual establishment of a range of different species and thus increased Shannon diversity. While moderate thinning in the UM (uneven-aged mixed forest) regime resulted in the highest Shannon diversity in the long term (Fig. 4, panels 2a–2h), the heavy Norway spruce thinning prescription under the other three adaptive management regimes increased species diversity strongly in the short term. However, after the removal of Norway spruce (ca. 2081–2200), the increasing dominance of either the natural (NV) or the targeted species mixture (UD and UM) resulted in a reduction of Shannon diversity. This effect was most pronounced at low elevation with the promotion of

natural vegetation in the initially highly diverse unevenaged mixed forests (Fig. 4, panels 3a–5h). The conversion of even-aged Norway spruce forests to uneven-aged forest types at high elevation promoted mature stand attributes (SMI) in two ways (Fig. 4, panels 2a–5d). First, the change to uneven-aged management regimes increased the variability of tree heights. Second, reduced thinning and harvesting intensities, or even the cessation of harvest under the NV regime, led to increased density- and age-induced mortality and thus the accumulation of deadwood. In contrast, potential deadwood was removed from the forest with the early and intense thinning applied under the EN regime (Appendix B: Figs. B5–B7). Trade-offs between forest goods and services In general, with decreasing management intensity (EN . UD . UM . NV), relative FGS provision shifted from timber production to forest diversity, indicating a trade-off between the two FGS (Fig. 5). The key exception to this trend was that management for UO

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FIG. 5. The relative provision of forest goods and services (FGS) under five management regimes and the current climate and the HCCPR climate scenario (solid and open circles) on a gradient between timber-oriented and forest diversity-oriented FGS provision in the short term (2001–2080) and the longer term (2081–2200). The position of the circles shows the difference (D) between harvested biomass (T ) and the averaged forest diversity indices, Shannon’s diversity index (H ) and stand maturity index (SMI) using the normalized indices that were presented in Fig. 4: D ¼ mean (H, SMI) – T; D ¼1 means maximum timber and no forest diversity; D ¼ 1 means no timber and maximum forest diversity; and D ¼ 0 means equal timber and forest diversity provision. The indices were averaged across the time period prior to and after the Norway spruce decline in ca. 2080. The management regimes were ranked by management intensity (1–4), except for uneven-aged mixed oak (X), where management intensity varies between time periods. Note the shift in relative FGS provision under the different management regimes with respect to business-as-usual management under current climate (vertical dashed line).

resulted in lower Shannon diversity and SMI in the long term compared to what may be expected based on the low intensity of this management scenario. At high elevation, under all management regimes relative FGS provision shifted toward increased forest diversity and a loss in timber production, with the magnitude of the shift depending on the climate and the management scenario. In contrast, at low elevation relative FGS provision shifted toward timber production under all management scenarios because forest diversity decreased. During the Norway spruce decline (2001–2080), changes in relative FGS provision at high elevation were driven by climate- and conversion managementinduced reductions of Norway spruce harvest and concomitant increases in both Shannon diversity and SMI (Fig. 4, panels 1a–5a). With the continuation of EN management between 2081 and 2200, the balance between timber and forest diversity was further shifted toward forest diversity, despite the high management intensity. After the Norway spruce decline (2081–2200), at all elevations and irrespective of the past management regime, timber harvest under the high-intensity UD regime came at the price of reduced forest diversity. In contrast, under the NV regime timber production was

sacrificed for the promotion for both Shannon diversity and SMI at high elevation and SMI at low elevation. DISCUSSION Future forest development and the provision of forest goods and services (FGS) are strongly influenced by the interactions between climate change, past management, and the range of adaptive management regimes. While based on stands in the Northern Black Forest, Germany, our findings are generally applicable to regions where climate change threatens the persistence of commercially important tree species (Spiecker et al. 2004). Previous work considered a subset of these drivers (Farrell et al. 2000, Yousefpour and Hanewinkel 2009) and focused either on the landscape scale (Gustafson and Rasmussen 2002, Hauer et al. 2010) or the stand scale (Briceño-Elizondo et al. 2008, Kint et al. 2009). We suggest that the consideration of both the stand and landscape scale are important for identifying desired target states and suitable pathways for adaptive management. Stand dynamics driven by competition and management will determine which conversion pathways are feasible. However, the desired target state must be evaluated at the landscape scale for three reasons: First, FGS are normally demanded at this scale (de Groot et al. 2010). Second, the desired target state must reflect the landscape constraints that are imposed

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by past management practices (Saura et al. 2011). Third, the implementation of forest management is necessarily spatial and interacts with landscape heterogeneity. Identifying desired target states Shift in species composition.—In line with many other studies (e.g., Bugmann 1997, Morin et al. 2008, Fyllas and Troumbis 2009) our results show that the definition of the target state must consider a shift to more droughtadapted species under climate change. Appropriate species mixtures will be dependent upon site conditions, climate projections, and the climate change response of individual species, all of which are subject to uncertainty. In our case, there are three key components making up the latter uncertainty: (1) the drought tolerance of European beech (Meier and Leuschner 2008) vs. Douglas-fir (Anekonda et al. 2002); (2) the competitive relationship between the two species (Reyer et al. 2010); and (3) the species-specific resistance to disturbances and pathogens (Schu¨tz et al. 2006, Watt et al. 2010). Disconnect between species composition and climate.— The past promotion of drought-sensitive Norway spruce resulted in a disequilibrium between species composition and climate (Mu¨ller et al. 1992, Ludemann 2010). As climate changes, this disequilibrium will increase, ultimately resulting in stress-induced mortality (Allen et al. 2010). We found that this disconnect is most pronounced if Norway spruce continued to be promoted and climate change was severe (HCCPR scenario), but it occurred already under moderate climate change (SMHI scenario) and also at high-elevation sites, where precipitation was larger (see Table 1 for scenario information). The Norway spruce dieback started when temperature increased beyond ;28C. This highlights the high drought sensitivity of Norway spruce forests that were planted nearby or even beyond their natural hotdry distribution limit (Ko¨lling 2007, Hanewinkel et al. 2010). Importantly, we found that the dieback of droughtsensitive species reduces forest diversity (cf. Morin et al. 2008). In our case study this was particularly true for the currently species-rich uneven-aged mixed forest at low elevations. With the dieback of Norway spruce the provision of timber could also not be maintained. Therefore, forests need to be converted by promoting fast-growing and more drought-tolerant species such as Douglas-fir or European beech. In addition, promoting a diverse species mixture allows for the flexible adaptation of management to climate change over time and spreads the risk associated with the promotion of tree species with uncertain resistance to drought and disturbances (Fischer et al. 2006). Identifying suitable conversion strategies Past forest management constrains forest conversion.— Irrespective of the management history, our simulation results indicate a drought-induced decline in forest biomass at mid- and high elevation mostly due to the

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dieback of Norway spruce, and at lower elevation also due to the loss of European beech, silver fir, and other species. Clearly, past management practices determined the time that is required for conversion to a droughtadapted species mixture and the sensitivity of the forest during this transition period. In even-aged Norway spruce forests, the share of Norway spruce biomass was reduced to ,30% within 70–120 years of conversion, while this took only 10–40 years in the uneven-aged mixed forest, where Norway spruce was less dominant and the target species were already present. With light availability being critical for regeneration (Beaudet et al. 2011), the establishment of the target species in the evenaged Norway spruce forest was additionally hampered by the dense, light-impermeable Norway spruce overstory during the first decades of conversion. Thus, past management is an important factor that needs to be considered when adaptive strategies are evaluated, as it influences forest dynamics for a long time (here, up to 120 years). However, given sufficient time and resources for accompanying measures to bridge temporal losses in FGS provision, the management goals of adaptive regimes can be achieved irrespective of how forests were managed in the past. Management effects.—Our simulation results support the view that adaptive management can counteract the negative effects of climate change on FGS provision (Shanin et al. 2011). Management practices and the growth performance of the favored tree species under climate change determined the temporal dynamics of timber production. If at the beginning of the conversion period (2001) Douglas-fir trees were favored through thinning, they reached harvestable size by ca. 2100 so that by then timber yields could even exceed preconversion amounts. If, however, stands were opened for the purpose of oak plantings (Appendix A: Table A1), timber yields were high in the short term (2001– 2081), but low subsequently as the slow-growing downy oak only reached harvestable size by the end of the simulation period. Shifts in forest management altered competition, which has strong consequences for forest biodiversity. Not surprisingly, Shannon diversity increased when management promoted a diverse species mixture and reduced light competition during the conversion from even-aged Norway spruce to uneven-aged forests. In contrast, the promotion of drought-adapted Douglas-fir or downy oak in species-diverse and structurally rich uneven-aged mixed forests decreased light availability in the understory as Douglas-firs and downy oaks gained height. This reduced growth (Schumacher et al. 2004) in the small size classes (12–20 cm dbh), and therewith also reduced Shannon diversity. During conversion, thinning intensity determined the rate of species establishment and thus Shannon diversity. The moderate thinnings applied under uneven-aged mixed forest (UM) management increased Shannon diversity gradually until it peaked in 2200, because

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thinnings created light conditions that maintained natural regeneration and a mixture of shade-tolerant and shade-intolerant tree species (Beaudet et al. 2011). However, moving toward more natural and climateadaptive forest attributes does not imply a monotonic increase in tree species diversity. Under natural vegetation (NV) management Shannon diversity peaked in ca. 2120 due to the heavy Norway spruce thinnings. Similar to the situation in forest reserves (Heiri et al. 2009), European beech and Douglas-fir became increasingly dominant in our simulations. They outcompeted lightdemanding species and thus gradually reduced Shannon diversity and tree size variation after 2120. Hence, in uneven-aged mixed forests the conversion to NV fosters forest diversity primarily through the accumulation of deadwood and the abundance of large trees (Nilsson et al. 2002). Balancing adaptive management for different forest goods and services.—Adaptive management will affect the trade-offs among the different FGS. In general we found that with increasing management intensity higher timber yields can be achieved, but forest diversity is reduced. Other studies also found this general trade-off (Fu¨rstenau et al. 2006, Briceño-Elizondo et al. 2008, Kint et al. 2009), but we specifically focused on its temporal development while considering different aspects of forest diversity. Hence, we were able to show that, depending on the management history, during certain parts of the conversion pathway trade-offs occur, while in other parts there are synergies. These trade-offs and synergies can occur between timber production and tree species diversity but also between timber production and the promotion of mature-stand attributes (stand maturity index, SMI; cf. Figs. 4 and 5). Our results also indicate that distinct forest diversity aspects, such as tree species diversity and mature stand attributes, may have to be balanced against each other (cf. Seidl et al. 2007). In general we found a synergetic relationship between timber production and forest diversity in situations where harvest increases light availability in the understory. By contrast trade-offs occur when timber production is enhanced by favoring a low number of productive tree species or when the promotion of forest diversity (e.g., through retaining deadwood and large trees) requires restrictions in timber harvest. Surprisingly, in the short term (2001–2080) we found that implementing adaptive management aiming to convert even-aged pure Norway spruce forests brings about a synergy between timber production and forest diversity in the same short term (2001–2080). This is because all of the adaptive management strategies we tested, including conversion to NV, incorporated a continued harvesting of the climatically maladapted Norway spruce. Therefore, conversion maintained Norway spruce harvest in the short term while increasing tree species diversity. In contrast, if adaptive management is applied to uneven-aged mixed forests, trade-offs have to be considered: (1) the conversion to a more

drought-adapted species mixture (e.g., dominance by Douglas-fir or downy oak) has to be balanced against a reduction in tree species diversity; but (2) the promotion of deadwood and large trees by converting to NV comes at the cost of low timber yield, as management intensity decreases (Michel et al. 2011, Remm and L~ ohmus 2011). Only when a longer-term evaluation of differences in adaptive management regimes is conducted (2001–2200 in our case study) do the impacts of different management actions on the interactions between timber production and forest diversity become fully evident. We found that the promotion of Douglas-fir constitutes an option to achieve high timber yields under drier climate conditions. However, favoring Douglas-fir comes at the cost of lower tree species diversity and detrimental effects on soil fertility (Augusto et al. 2002), ground vegetation (Barbier et al. 2008), and the bird community (Gossner and Utschick 2004). Similar to Norway spruce monocultures, pure Douglas-fir forests are highly susceptible to pathogens, the damage of which is projected to increase with climate change (Stone et al. 2008, Watt et al. 2010). Considering this long-term perspective, a synergy between timber production and forest diversity may be achieved by applying moderate intensity UM (uneven-aged mixed forest) management. It yields similar amounts of timber as Norway spruce forests under current climate while constituting a management intensity that corresponds to an intermediate disturbance regime (Connell 1978), which maintains high tree species diversity. Finally, different aspects of forest diversity have to be balanced against high timber yields. Long-term tree species diversity is best promoted by moderate intensity management, while fostering mature-stand attributes (SMI) requires at least a partial cessation of forest management (Michel et al. 2011, Remm and L~ ohmus 2011). The simulated mixed oak forests provide little timber, and their forest diversity provision is low compared to the other adaptive management regimes. However, mixed oak forests have benefits that are not reflected by the indices we used to assess forest diversity. Both Shannon’s diversity index and the stand maturity index are not sensitive to the importance of individual tree species in providing habitat for other organisms or toward their contribution to forest resilience. For example, oak forests often support a rich fauna (e.g., Sweeney et al. 2010) and are considered as particularly resistant to drought (Zweifel et al. 2007) and pathogens (Wermelinger et al. 2008). Conclusion To prevent climate-change-induced forest dieback at the landscape scale, species composition needs to be the target of an effective adaptive management strategy, especially where the difference between current and target species composition is magnified by past management practices. Due to uncertainty in (1) climate change predictions, (2) the suitability of species under

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future climates, and (3) the future demands for forest goods and services (FGS), the target species mixture should be diverse so that forest management can be continuously adapted as time unfolds. Going beyond previous studies, we integrated three key factors driving future forest dynamics—past management, climate change, and adaptive future management—in a consistent framework and analyzed the interacting effects of these factors on timber production and forest diversity through time. Our results suggest that up to 120 years need to be allowed for reaching targeted forest states. Dynamics during the transition are important, and are highly dependent on past management and the specific implementation of adaptive management. Our assessment clearly demonstrates that adaptive management does not necessarily impose a trade-off between resource use and conservation objectives. In contrast, ‘‘win-win’’ situations accrue along some of the conversion pathways. If past management is a key driver of future forest dynamics then adaptive management should be developed at the landscape scale so that the impact of past and current forest management on forest heterogeneity and forest dynamics are accounted for. ACKNOWLEDGMENTS We thank Ju¨rgen Zell (FVA) for providing forest inventory data, spatial data on management implementation, and soil water holding capacity, and Dirk Schmatz (WSL) for providing downscaled climate scenario data. We also thank Kay Karius for providing case study landscape information and for inspiring discussions, and two anonymous reviewers for improving the manuscript. This research was funded by the MOTIVE project within the European commission’s 7th framework program (grant agreement number 226544). LITERATURE CITED Allen, C. D., et al. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660– 684. Anekonda, T. S., M. C. Lomas, W. T. Adams, K. L. Kavanagh, and S. N. Aitken. 2002. Genetic variation in drought hardiness of coastal Douglas-fir seedlings from British Columbia. Canadian Journal of Forest Research 32:1701– 1716. Augusto, L., J. Ranger, D. Binkley, and A. Rothe. 2002. Impact of several common tree species of European temperate forests on soil fertility. Annals of Forest Science 59:233–253. Barbier, S., F. Gosselin, and P. Balandier. 2008. Influence of tree species on understory vegetation diversity and mechanisms involved—a critical review for temperate and boreal forests. Forest Ecology and Management 254:1–15. Beaudet, M., B. D. Harvey, C. Messier, K. D. Coates, J. Poulin, D. D. Kneeshaw, S. Brais, and Y. Bergeron. 2011. Managing understory light conditions in boreal mixedwoods through variation in the intensity and spatial pattern of harvest: a modelling approach. Forest Ecology and Management 261:84–94. Briceño-Elizondo, E., D. Ja¨ger, M. J. Lexer, J. Garcia-Gonzalo, H. Peltola, and S. Kelloma¨ki. 2008. Multi-criteria evaluation of multi-purpose stand treatment programmes for Finnish boreal forests under changing climate. Ecological Indicators 8:26–45.

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SUPPLEMENTAL MATERIAL Appendix A Detailed description of simulated management regimes (Ecological Archives A022-112-A1). Appendix B Seven figures showing simulation results for the mid-elevation part of the case study landscape and for intermediate climate change scenarios as well as for the development of mature stand attributes (Ecological Archives A022-112-A2).