Shifting from even-aged management to less intensive forestry in varying proportions of forest land in Finland: impacts on carbon storage, harvest removals, and harvesting costs

Vol.:(0123456789) 1 3 European Journal of Forest Research (2019) 138:219–238 https://doi.org/10.1007/s10342-019-01163-9 ORIGINAL PAPER Shifting fromeven‑aged management tolessintensive forestry invarying proportions offorest land inFinland: impacts oncarbon storage, harvest removals, andharvesting costs JariVauhkonen1 · TuulaPackalen1 Received: 4 June 2018 / Revised: 24 December 2018 / Accepted: 8 January 2019 / Published online: 21 January 2019 © The Author(s) 2019 Abstract Many studies have reported increased multi-functionality and financial profits due to a shift from evento uneven-aged forest management. However, little is known (from long-term experiences or predictions) how alternative management systems could affect national-scale wood production and carbon storage, if adopted over very large areas. We analysed these effects using an area-based framework, in which multiple Markov chain models were used to simulate the development of forests according to different management systems. Classification of forests to wood availability categories was used to determine the system to be applied. We enhanced the framework to allow shifts between management systems that correspond to enforced or voluntary changes in forest use. Simulations of extensive shifts from conventional even-aged management to alternative silvicultural systems revealed interesting developmental patterns that cannot be directly deduced from studies that upscale from smaller areas. Our results show that the amount of carbon stored by Finnish forests can be increased by applying less intensive management systems, although this has trade-offs in terms of harvests and associated financial costs. The level of trade-offs differed depending on the type of forest that shifted between management systems and whether areas were also assumed to be completely set aside from forestry. These differences were further pronounced if the desired harvest levels and their allocation changed along with the management system. If the studied attributes were considered at the same relative scale and with equal weighting, the extensive shifts to alternative management systems exhibited the strongest impact on harvesting costs. Keywords European Forestry Dynamics Model (EFDM)· Forest resource projection· Integrated forest management· Matrix model· National Forest Inventory (NFI) data· Scenario analysis Introduction Currently, several international and national strategies strive for a transition from a fossilto a bio-based economy, which calls for an increase in the use of (forest) biomass for products, such as biofuels and energy, chemicals, polymers, and wood-based structures. However, concerns in regard to fragmentation, degradation, and loss of forest habitats have been increasingly expressed and international and national agreements have been signed to reverse this trend. In particular, the restoration of at least 15% of degraded ecosystems by 2020 (CBD 2010), also known as Aichi Target 15, is widely accepted as a national and global conservation target. However, maintaining both high economic forest yields and the viability of forest species involves trade-offs. In Finland, for example, Kotiaho etal. (2016) have estimated that meeting Aichi Target 15 would cost between 12 and 23 billion euro, or 368–658 million euro per annum, when only forests and peatlands are considered and if the conservation actions were prolonged until 2050. For comparative purposes, annual stumpage earnings amount to approximately 1.5 billion euro (MAF 2015). Communicated by Miren del Rio. Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1034 2-019-01163 -9) contains supplementary material, which is available to authorized users. * Jari Vauhkonen jari.vauhk[email protected] 1 Bioeconomy andEnvironment Unit, Natural Resources Institute Finland (Luke), Yliopistokatu 6, 80100Joensuu, Finland 220 European Journal of Forest Research (2019) 138:219–238 1 3 Rather than segregating the forest and conservation sectors, a good compromise could be achieved by combining cost-effective conservation actions with the sustainable use of forests and peatlands (see also Kotiaho etal. 2016). For example, the management of a proportion of forest land as “multi-use conservation landscapes” (MUCLs; Hanski 2011) is a practical and cost-effective means to aid forest conservation strategies, in addition to the strict protection of forest networks (see also Stevens and Montgomery 2002). More precisely, Hanski (2011) has proposed that management of a third of the land as MUCLs, and the strict protection of a third of this area, would be sufficient for the conservation needs of specialist species, particularly if the MUCLs were composed of aggregated habitat patch clusters and could be connected to existing protected area networks (see also Rybicki and Hanski 2013). However, the work cited above does not specify feasible silvicultural practices or management intensities for the proportion of MUCLs (twothirds) that are not strictly protected. Yet, we can assume that such areas could be managed according to the principles of multiple-use (Fürstenau etal. 2007) or integrated forest management (Diaci etal. 2011), which support the production of ecosystem services other than just species conservation based on a more diverse set of silvicultural practices, compared to conventional rotation forestry or even-aged management systems. Forestry practices in Finland have been based on evenaged management since World War II (Kuuluvainen etal. 2012), but options for forest management practices will clearly increase in the future. By options, we refer to the various forms of uneven-aged management, such as continuous cover forestry as defined by Pukkala (2016a). Continuous cover forestry essentially differs from even-aged management in that it avoids clear felling and planting by utilizing thinnings from above and by promoting natural regeneration. These choices may convert stands towards uneven-aged forest structures, although converging to a steady-state structure of any kind is not required (Pukkala 2016a). Continuous cover forestry is expected to become more common, because of its potential to supply multiple ecosystem services (Pukkala 2016b; Peura etal. 2018) and reduce the financial costs related to regeneration and other silvicultural operations (Pukkala 2016a) compared to even-aged management (see also Knoke 2012; Kuuluvainen etal. 2012; Puettmann etal. 2015; Nieminen etal. 2018). Comparisons of alternative forest management systems and subsequent trade-off analyses are typically based on long-term observations (Sutherland etal. 2016; Strengbom etal. 2018), metamodelling (Lafond etal. 2017) or simulations. The latter have been carried out at the forest stand or small forest holding level (Pukkala etal. 2011; Pukkala 2016a, b; Carpentier etal. 2017), the landscape level (> 100km2; Triviño etal. 2015; Diaz-Balteiro etal. 2017; Peura etal. 2018), and the regional level (> 1000km2; Schröter etal. 2014; Pang etal. 2017). However, long-term experiences or predictions as to how alternative practices may affect national-scale wood production if adopted over very large areas are not known. With the exception of recently formulated growth (Bollandsås etal. 2008; Pukkala etal. 2013) and thinning models (Pukkala etal. 2015; Vauhkonen and Pukkala 2016), most conventional forest simulators and projection tools have been developed for even-aged forestry systems. Conventional models for forest development would, therefore, extrapolate outside the original population if applied in uneven-aged forests. Moreover, detailed forest-specific thinning instructions might not reconcile with large-area projections based on aggregated spatial scales (cf., Verkerk etal. 2014; Creutzburg etal. 2017; Mouchet etal. 2017). As such, there is a need for flexible tools that can combine detailed instructions with projection capabilities for large areas. From the perspective of regional or national-level wood production, the areas subject to conservation or integrated management reduce the amount of Forests Available for Wood Supply (FAWS; Alberdi etal. 2016). From the point of view of provisioning of non-wood forest products or other ecosystem services, it is useful to also simulate the development of area and growing stock in the remaining areas—i.e. in Forests Not Available for Wood Supply (FNAWS) and Forests with Restrictions on Availability for Wood Supply (FRAWS; see also Vauhkonen and Packalen 2017). Extensive forest inventories, such as the National Forest Inventory (NFI), also provide data for the simulation of transitions due to growth or management for forests prioritized for uses other than solely wood production. Several approaches have been presented for the projection of the future development of forest resources based on transition probability matrices of stand-specific diameter classes (e.g. Bollandsås etal. 2008; Schou and Meilby 2013; Roessiger etal. 2016). However, corresponding Markov chain models based on transition matrices of forest size and structure classes (e.g. Vauhkonen and Packalen 2017) could be more suitable for area-based projections of forest dynamics based on the NFI data. Vauhkonen and Packalen (2017) simulated the development of forest size and structure classes derived from NFI data by combining multiple Markov chain models for different silvicultural systems. In their study, classification of forests to wood availability categories determined which system was applied. Wood availability depended on administrative forest use restrictions as recorded in the NFI data. Areas where forestry operations were forbidden (FNAWS) or restricted to selective harvests for the enhancement of ecosystem services other than wood supply (FRAWS) were distinguished from FAWS. The future development of FAWS was simulated with even-aged management; FRAWS with 221European Journal of Forest Research (2019) 138:219–238 1 3 continuous cover forestry, in which the final felling was replaced by thinning from above; and only the natural processes were simulated for FNAWS. The assumption that restrictions on forest use determine the silvicultural system is a simplification that might be unrealistic, especially, if large proportions of FAWS diverge from even-aged management in the future, as reasoned above. However, the FAWS in our inventory data are largely composed of even-aged stands; forest management instructionswere based on even-aged silviculture until the final measurements of the 11th Finnish NFI in 2013. Thus, increasing the proportion of alternative management systems in FAWS fundamentally means that more forests will be managed in a similar fashion to FRAWS or FNAWS are obligatory managed because of administrative forest use restrictions. We propose that simulation of shifts between these categories should allow further examination of the national-scale effects of moving from evenaged, rotation forest management to alternative silvicultural regimes, which may result from enforced political decisions to promote less intensive forestry or increased nature conservation or voluntary changes in the use of forests. The objectives of this study are (1) to enhance the Markov chain modelling framework by the simulation of shifts between forest management systems and (2) to use the developed framework to assess the trade-offs between carbon storage, harvest removal, and harvesting costs due to these shifts. We first derived benchmark projections of the future development of forests according to silvicultural systems associated with current wood availability categories as described in the previous paragraph. We then repeated the simulations with varying proportions of even-aged forests managed according to continuous cover forestry or set aside to assess the effects of diverging from business-asusual management in an increasing proportion of forest land. Methods Simulation framework anddata We simulated the future development of forests in Finland using an area-based Markov chain model (cf. Vauhkonen and Packalen 2017). Our analyses considered almost the entire forest land of Finland, excluding northernmost areas and forests located in the southern archipelago, which are considered to have a low importance in terms of wood supply of Finland. The total area of forests on productive and poorly productive forest land was 21.28 million ha, with an initial growing stock of 2234 million m3 (the 11th Finnish NFI–NFI11; measurements in 2009–2013), which equates to approximately 95% of the entire growing stock in Finland. As in Vauhkonen and Packalen (2017), the forests were assigned to wood availability categories according to forest use restrictions in the NFI data: initially, 10.1% and 10.6% of the total forest area were classified as FNAWS and FRAWS, respectively, with the remainder classified as FAWS. In order to study the national-scale effects of diverging from the current management system, varying proportions of forest were removed from FAWS and assigned to either FRAWS or FNAWS. These were subsequently simulated for future development along with forests that are currently assigned to these categories due to administrative forest use restrictions. Specifically, forests that received even-aged management in the benchmark simulations were either assigned to continuous cover forestry (in the proportion transited to the FRAWS category) or set aside (FNAWS) at the beginning of the simulations. The selection of land transited from FAWS to the other categories was simulated according to four different strategies that mimic the different drivers and land availability in the transitions. The aim was to mimic the establishment of either (a) MUCLs as a variant of the concept proposed by Hanski (2011), which consist of both continuous cover forestry and set-aside areas; or (b) multi-use landscapes (MULs) managed by continuous cover forestry without setaside areas. The four strategies were obtained by emphasizing the selection of forests with high (strategies MUCLhigh, MULhigh) or low (MUCLlow, MULlow) proxy conservation values, determined as a function of the maturity of the trees, tree species composition, and site fertility (Appendix 2; Lehtomäki etal. 2015). Because of its formulation, the proxy might act as a surrogate not only for the biodiversity features of conservation interest, but also the more general multiple-use potential of a forest. As further discussed in Sect.4.1, the four strategies generally cover the range of production possibilities, by reducing the management intensity in extensive proportions of the different forest types. Each land transition strategy was composed of eight proportions p, where p was either 5, 10, …, or 40% of the FAWS land area, which was then added to either FRAWS or FNAWS. The proportions were selected by first computing the conservation value (consval) of each NFI plot, as described in Appendix 2, and the cumulative distribution of consval separately for each of the 15 forestry regions (or Forest Centres as distinguished by the Finnish NFI). The number of plots required to represent p% of the FAWS land area was selected at equal intervals from the cumulative distribution of each forestry region as follows: • MUCLhigh p% of FAWS was first selected according to consval. A third of this area was further selected according to the cumulative distribution of consval computed for plots in the selected p% and assumed to transit to FNAWS (no management). The remaining proportion of p% was assumed to transit to FRAWS and to be managed as continuous cover forestry. The emphasis in the new 222 European Journal of Forest Research (2019) 138:219–238 1 3 MUCLs established according to this strategy was on FAWS with a high conservation value. One-third of this selection (with an emphasis on forests with the highest conservation values) was set aside. • MUCLlow As with MUCLhigh, but the land area to transit was selected according to the inverse of consval, excluding plots with consval = 0 (i.e. bare land). The emphasis in the new MUCLs was on FAWS with a low conservation value. One-third of the forests with the highest conservation values in the first selection were completely set aside. • MULhigh p% of FAWS was selected according to consval and assumed to transit to FRAWS to be managed as continuous cover forestry. The emphasis in the new MULs was on FAWS with high conservation value. No additional forest areas were set aside. • MULlow As with MULhigh, but the land area to transit was selected according to the inverse of consval, excluding plots with consval = 0 (i.e. bare land). The emphasis in the new MULs was on FAWS with low conservation value. No additional forest areas were set aside. Simulations offorest development As the Markov chain model, we used v. 2.0. of the European Forestry Dynamics Model (EFDM), which is implemented in the R statistical modelling environment (R Core Team 2016) and can be downloaded from https ://githu b.com/ecjrc/efdm as open source under the European Union Public License (EUPL). The EFDM approach is based on arranging the forest area into matrix cells according to ecological and socioeconomic factors and simulating the development of the resulting matrices. In the simulations, the area represented by each matrix cell is managed according to a set of pre-defined activities, which may transit the area to other cell(s) depending on the transition probabilities associated with the activities. In practice, the forest area distribution after one simulation step (i + 1) is obtained as a multiplication of the area in state i by the probability that the area receives one of j activities and the activity-conditional transition probabilities (for details, see Sirkiä 2012 or Packalen etal. 2014). The simulations were carried out in five-year time-steps, which correspond to the measurement interval in the data used to derive the transition probabilities. In total, ten steps were simulated (i.e. the last year in the simulation period is approximately 2060, depending on the initial measurement year). The initial forest state, (business-as-usual) transition and activity probabilities, and output coefficients for the model were derived from the NFI11 data according to the workflow presented by Vauhkonen and Packalen (2017) based on using permanent NFI plots as pairwise data for natural processes. Appendix 2 also illustrates the process of deriving the initial state and the simulation of the future development of one example forest. In the following sections, we briefly describe the parameterization, but note that parameters and their effects are explained in more detail in Vauhkonen and Packalen (2017). Initial state The EFDM is parameterized by matrices with dynamic (e.g. age, volume) and static (e.g. geographical region, site fertility) dimensions (henceforth “factors”). Similar to Vauhkonen and Packalen (2017), we defined the dynamic factors separately for forests to be managed using different silvicultural attributes, such as age and volume for evenaged management systems or stem number and volume for continuous cover forestry. The natural processes of FNAWS were simulated using age and volume matrices; no differences would have been observed if stem number and volume matrices were applied (Vauhkonen and Packalen 2017). The continuous measurements from the NFI were classified using age classes of 0, 5, 10, …, 120, 120+ years, whereas the class limits for both the volume and stem number were determined as the values of the 10th, 20th, …, 90th and 95th quantiles of the pairwise observations made from the permanent NFI plots. The class limits (presented in Appendix 1) were defined by Vauhkonen and Packalen (2017) with a motivation to obtain an approximately equal amount of pairwise observations per class and to reduce the value range of the last class by halving the number of observations included in it. These dynamic factor matrices were derived separately by applying the following static factors: (1) known land-use restrictions: FAWS, FRAWS, FNAWS; (2) forest ownership: private, public + other; (3) site fertility: a total of five categories that correspond to the four taxation classes that productive forests are assigned in the Finnish NFI + a fifth class that includes all poorly productive forest land; (4) dominant species: pine, spruce, deciduous trees. Management activities andtheir probabilities Possible management activities were “no management” (i.e. simulation of natural processes only), “thinning”, and “regeneration harvest”. A “thinning” always referred to a management thinning and was implemented as a thinning from below for both FAWS and FRAWS. A “regeneration harvest” was implemented either as a final felling in the even-aged management system (simulated for FAWS) or a thinning from above in continuous cover forestry (for FRAWS), as described in Sect.2.2.3. The probabilities of the activities were determined in two steps. First, the NFI data were used to compute the mutual proportions of the activities, resulting in two alternative allocations of the activities: (1) a business-as-usual allocation 223European Journal of Forest Research (2019) 138:219–238 1 3 (ABAU) based on the proportions of permanent NFI plots not managed, thinned, or regenerated during the most recent 5-year period; or (2) a schoolbook-allocation (ASB) based on the proportions of the NFI plots that should be managed within the next 5years strictly according to forest managementinstructions. The proportions were based on the NFI records that were made according to the instructions that prevailed at the time of the measurements [for more details, see Yrjölä (2002)]. The two alternative allocations are class specific (cf. Figure1 of Vauhkonen and Packalen 2018); following the initial proportions of ASB would, in general, involve proposing much more management activities than Fig. 1 Effects of wood availability on carbon storage at the end of the simulation period (top panel), total harvest removals during the simulation period (middle), and average harvesting costs over the simulation period (bottom) under businessas-usual activity and transition probabilities. The x-axes of the sub-figures indicate the area diverging from even-aged management. The black-filled dots represent simulations with the current proportions of Forests Available for Wood Supply (FAWS), Forests Not Available for Wood Supply (FNAWS) and Forests with Restrictions on Availability for Wood Supply (FRAWS). The lines depict scenarios, where the proportion of the FAWS area corresponding to different levels of p% was shifted from FAWS to the other two categories according to the four strategies described in Sect.2.1. The open circles denote a hypothetical situation, where all forests are considered as FAWS and simulated according to the even-aged management system 224 European Journal of Forest Research (2019) 138:219–238 1 3 ABAU. Second, the activity probabilities were iterated to the level that yielded a specified total roundwood harvest level. As in Vauhkonen and Packalen (2017), the iteration was carried out by repeatedly multiplying the activity probabilities ≠ 0 by 1.01 or 0.99, depending on the sign of the difference between the goal and the harvest level given by the current activity probabilities, until the harvesting goal was met or the activity probabilities could not be changed. The specified total harvest goal was calculated from two harvest levels provided in the National Forest Strategy of Finland (MAF 2015): the business-as-usual level (65 million m3/a) and the desired future level (80 million m3/a). Because our analyses considered about 95% of the total growing stock in Finland, both goals were multiplied by this proportion to yield the final harvest targets of approximately 62 million m3/a or 76 million m3/a, respectively. Activity probabilities were iterated to meet these harvesting goals only at the beginning of the simulations. Thereafter, the same proportion of the land area was managed in every simulation step, i.e. the volume harvested after the first simulation step depended on how the forest class distribution evolved during the simulations. Transition probabilities The transition probabilities of the natural processes (growth) were derived using pairwise observations from the permanent NFI plots (total: 11,987 or about 23% of the plots), which were measured at approximately five-year intervals between NFI11 and the earlier inventory (NFI10). Positive differences in total volumes on plots with no treatments, based on data that could be matched with certainty between the two subsequent inventories, were recorded as the pairwise data. The estimated transitions, therefore, included growth and mortality, but not potential reductions due to calamities or natural disturbances, for example. The transitions due to management activities were based on simulations of their expected development. The forests affected by final fellings were forced to transit to the beginning of the even-aged rotation. Their early development was simulated conservatively, such that 25% of the final-felled area moved to volume class #2 (Appendix 1) in the first simulation step after final felling, and the remainder thereafter. A thinning simulator was implemented to derive pairwise observations due to the treatments. The simulator determined the trees to be removed following two types of instructions: (1) a thinning from below corresponding to conventional instructions for forest management and (2) a thinning from above with an intensity corresponding to an interest rate of 3%, as predicted by Eq.2 in Pukkala etal. (2015). As detailed in Vauhkonen and Packalen (2017), these simulations were applied to plots with an initial basal area > 10m2/ha and a mean height > 10m, which corresponds to commercial thinnings. In addition, pre-commercial thinnings were simulated for the less mature plots with a thinning need recorded by the NFI. These thinnings were implemented as thinning from below, but instead of applying a thinning curve for the harvest removal, the aim was always to retain a residual stand with approximately 1000 trees/ha. In mimicking an operational implementation, the trees to be cut were distributed to different parts of the diameter distribution, as described in detail by Vauhkonen and Packalen (2017). The thinnings took place at the beginning of each simulation step. The growth of the forests thinned from below was simulated by applying the transition probabilities of forests not managed in the simulation step where the thinning took place. The transition probabilities were simulated using the same pairwise data for both age-volume and stem numbervolume classes and can be expected to develop similar to established forests (cf. Vauhkonen and Packalen 2017). However, differences may occur between the management systems due to assumptions on the early development after the regeneration harvest. To assess the sensitivity of these assumptions on the management systems, all figures of Sect.3 were alternatively reproduced using the same methodology, but with different assumptions for the early development of even-aged and continuous cover forestry. Specifically, the parameters above were modified such that 75% of the final-felled area moved to the next volume class that was already in the first simulation step after final felling; and the forests thinned from above were simulated for growth in the same simulation step that the harvest occurred, similar to forests thinned from below, i.e. they were assumed to recover rapidly despite heavy thinning. A comparison of the full results indicated increasing differences, i.e. uncertainties towards the end of the simulation period. However, the management systems were not essentially different from each other, based on either of the results, so our analyses focused on the results from the conservative early development simulations, while the results of the more rapid development are provided as Electronic Supplementary Material for the reader who wishes to evaluate the degree of sensitivity in the simulations. Output coefficients As the simulations only considered the development of the forest area distribution, separate transformation coefficients were determined to derive further information for the variables of interest (carbon storage, harvest removal, and harvesting costs). The coefficients were determined as the mean values of the NFI plots within the dynamic classes (Appendix 1) as follows: 225European Journal of Forest Research (2019) 138:219–238 1 3 • Carbon storage The total aboveand below-ground biomass of each NFI plot was first computed by estimating stem, branch, foliage, stump, and root biomass with the models described in Repola (2008, 2009). The biomass components were multiplied by species-specific expansion factors (approximately 0.5; see Table1 in Pukkala 2014) and summed to obtain the carbon content in each plot. The transformation coefficients were computed as mean values for the volume classes (Appendix 1) using all NFI plots. • Harvest removal The proportions of logand pulp wood of the total volume obtained from the different harvests were first computed from the estimates of assortment volumes obtained by theoretically bucking the trees in the NFI plots, following the cut-to-length harvesting method. The total fellings were computed by summing these proportions. Coefficients for the thinnings were determined as mean volumes of logs for sawn wood and pulp wood from the NFI plots for which the thinnings were simulated. The coefficients for the final fellings were based on similar mean values in all NFI plots and determined using both age and volume classes. • Harvesting costs The time expenditure of cut-to-length logging and roadside-transportation of the trees in the NFI plots was estimated using the models described in Rummukainen etal. (1995). The models were applied with the assumption that one hectare of forest represented by an NFI plot was cut to the timber assortments as described above. The models assumed higher time expenditure for thinning-types of harvests compared to final fellings, but typical terrain conditions and with no entry time for the logging equipment, i.e. the time expenditure values only depended on the amount of timber assortments obtained as a result of the different types of harvests. As in Vauhkonen and Pukkala (2016), the harvesting costs were computed with the assumption that operating a harvester and a forwarder cost 80 €/h and 57 €/h, respectively. The transformation coefficients for harvesting costs (expressed as €/m3) were computed as mean values for the volume classes based either on all NFI plots (final fellings) or plots for which the thinnings were simulated (thinnings). Results Total effects underbusiness‑as‑usual (BAU) management Assuming BAU management and the current wood availability categories, the total carbon storage in the aboveand below-ground living biomass was 883.2 million tonnes carbon at the end of the simulation period. In total, 4057.1 million m3 of roundwood was harvested during the simulation (average: 73.8 million m3/a, with a variation from 61.9 to 79.5 million m3/a between the simulation steps). The unit costs of harvesting varied from 16.9 to 21.5 €/m3 (average: 18.85 €/m3) between the simulation steps. Figures1 and 2 show how these values developed according to the varying proportions of wood availability. All land transition alternatives resulted in increased carbon storage in the aboveand below-ground living biomass at the end of the simulation period, compared to current wood availability (Fig.1, top panel). The MUCLlow alternative resulted in the highest levels of carbon storage (903.8–1090.1 million tonnes carbon, depending on p%). The higher the p% value assigned to MUCLlow, the greater the difference to the other land transition alternatives. The latter behaved similarly when compared to each other in terms of the increase in carbon storage and the magnitude of this increment as a function of p% (Fig.1, top panel). The reduction in potential harvest removal was greatest in the MUCLhigh alternative, followed by the MUCLlow alternative (Fig.1, middle panel). Total harvest removals (computed for 55years by temporally allocating the harvests to the beginning of the simulation steps in EFDM) varied from 3984.5 to 3371.2 million m3 in MUCLhigh, and from 3980.0 to 3508.6 million m3 in MUCLlow. Removal depended on the p%: the higher the value, the greater the difference to the harvest removals of BAU management and wood availability. Harvest removals in the other two landuse transition alternatives varied from 4051 to 3887 million m3 according to p%, i.e. the difference to BAU management was much less compared to the MUCLhigh and MUCLlow alternatives. The difference in harvest removals produced by the other two strategies remained constant despite the increase in p%. The MULhigh alternative resulted in the least reduction in harvest removals among the considered land transition alternatives. Harvesting costs increased in conjunction with the increasing proportion of land transited from FAWS, compared to BAU management and wood availability. This result applied to all land transition alternatives. However, both the MUCLlow and MULlow alternatives increased costs slightly, with unit costs varying between 19.0 and 21.15 €/ m3, depending on the p% value. In addition, these costs were not affected to any extent by the increase in p%. On the contrary, there was a strong positive correlation between the p% value and costs in the MULhigh and MUCLhigh strategies. The latter strategy also resulted in the highest unit costs (19.9–30.2 €/m3, depending on the p% value). Temporal effects underBAU management Carbon storage in the aboveand below-ground living biomass, harvest removals, and harvesting costs developed 226 European Journal of Forest Research (2019) 138:219–238 1 3 over time until the end of the simulation period (Fig.2). As mentioned in Sect.2.2.2 and further discussed in Sect.4, the harvests of the first simulation step were iterated to the same level in all land transition alternatives, so the differences begin to show immediately after the first simulation step. Carbon storage started to increase in approximately two steps (10years) after the land transitions, whereas the increase in the harvesting costs took place immediately. With respect to carbon storage, the land transition alternatives differed in magnitude, but the trends were similar over time. The land-use transition alternatives differed more with respect to harvest removals and costs. While Fig.1 suggests that the increase in p% almost equally affected the total harvest levels based on the MULhigh and MULlow land transitions, these alternatives clearly differed in terms of temporal development pattern over the entire simulation (Fig.2). In particular, the harvests of MULlow were closer to BAU over the first few simulation steps, whereas the harvests of MULhigh approached those of BAU towards the end of the simulation, eventually exceeding that level. MULhigh also differed from the other strategies in that the highest p% value allowed the greatest increases in harvest towards the end of the simulation. The temporal development of harvesting costs also differed depending on land transition strategy. In alternatives with emphases on high consval, the costs increased considerably due to the land transition, but their temporal development resembled BAU (i.e. the costs decreased slightly over time). In alternatives with Fig. 2 Temporal development of carbon stock (top row), harvest removals (middle), and harvesting costs (bottom), assuming different degrees of wood availability. The grey bars of each sub-figure depict the development under business-as-usual activity and transition probabilities and current wood availability. The eight lines of each subfigure denote shifts of p% (p = 5, 10, …, or 40; see Sect.2.1) of the land area of Forests Available for Wood Supply (FAWS) to Forests with Restrictions on Availability for Wood Supply (FRAWS) or Forests Not Available for Wood Supply (FNAWS) according to the four land transition strategies 227European Journal of Forest Research (2019) 138:219–238 1 3 emphases on low consval, the costs did not initially differ from BAU, but instead increased over time. Joint effects due toshifts betweenwood availability categories andchanges intotal harvesting levels andallocation Altering harvest levels and their allocations affected the levels of carbon storage in the aboveand below-ground living biomass, harvest removals, and harvesting costs under current wood availability, as illustrated in Fig.3 in a scale normalized to the level of the initial values of these attributes assuming BAU management and current wood availability (grey bars in the leftmost column of Fig.3). Relative to that situation, more harvest removals were obtained at lower costs when the harvest allocation was changed from ABAU to ASB. This change also increased carbon storage towards the end of the simulation. Carbon storage decreased and harvesting costs increased when the ABAU harvest allocation was maintained, but the proportion of harvested areas was increased at the beginning of the simulation to correspond with the harvest goals outlined in the National Forest Strategy. However, the allocation of increased harvest levels (according to ASB) somewhat compensated for these effects Fig. 3 The temporal development of carbon stock (top row), harvest removals (middle), and harvesting costs (bottom), assuming different degrees of wood availability and harvesting. The y-axes are presented in a scale normalized to the level of the initial values of these attributes assuming business-as-usual activity and transition probabilities, the development of which is illustrated in sub-figure “ABAU; 62 mill” according to the applied allocation of harvests and the total amount of harvests in m3/a for the first simulation step. The grey bars depict the development assuming current wood availability, while the coloured lines indicate p = 25% of the land area of Forests Available for Wood Supply (FAWS) shifted to Forests with Restrictions on Availability for Wood Supply (FRAWS) or Forests Not Available for Wood Supply (FNAWS) according to the different strategies (refer to Fig.1 caption for the interpretation of the symbols). The black lines indicate the hypothetical situation where all forests are considered as FAWS and simulated according to the even-aged management system 234 European Journal of Forest Research (2019) 138:219–238 1 3 Appendix2 The parameterization of the EFDM requires that NFI data are classified into factors that represent the initial forest state. Transition probabilities, management activities, and output coefficients are specified for each factor combination (Vauhkonen and Packalen 2017). The present study differs in that more than one initial state was defined for the simulations by transferring different proportions of FAWS to the other wood availability categories according to a conservation value proxy (cf. Sect.2.1). This appendix aims to (numerically) exemplify the computations related to the conservation value (1), classification of the NFI data to obtain the initial state (2) and running the EFDM simulations (3) on an example forest. Consider that an NFI plot without forest use restrictions was located on private land in southern Finland. In total, 19 trees of spruce, birch, and aspen species that were growing on the most fertile soil were measured, with the breast height diameters (d; measurements in mm) distributed as follows: dspruce = {323, 265, 219, 249, 238, 427, 291, 345, 313, 407, 445, 505, 407, 402}; dbirch = {231, 199, 177, 147}; daspen = {414}. Conservation value proxy The proportion of land transferred from FAWS to the other categories was selected according to a conservation value proxy (adapted fromLehtomäki etal. 2015): where diameter and volume are the species-specific mean diameter and growing stock volume of a plot, f() is a speciesspecific transformation function used to convert the diameter to a conservation value index based on expert knowledge, w is a weighting that corresponds to site fertility, and sp is a species index. To compute consval, the trees measured from each NFI plot were assigned to groups of pine, spruce, birch, and other deciduous species. The median and maximum values of the mean diameters of these species were (1) consval = ∑ sp [wsp ×f(diametersp)×volumesp] , computed for each of the 15 forestry regions in Finland. Species-specific asymptote and scaling parameters (specifically, mod_asym, and parameters with suffices lavdia and ravdia from the file parameters-esmk.csv; Lehtomäki 2015) were related to the median and maximum values (as carried out in file gis.calculate.index.R; Lehtomäki 2015). The parameters produced sigmoidal transformation functions (Fig.6) that were used to transform the species-specific mean diameters to conservation indices between 0 and 1. Figure6 shows the species-specific mean diameters of the example plot, the resulting conservation index values and sigmoidal transformation functions for every forestry region and species. Smaller diameters yield greater transformed values in deciduous species than in coniferous species. For birch, this transformation was strongly forestry region specific, in that occurrences of large birch trees yielded a higher conservation value in regions where the median diameter of birch is low. The same species-specific weightings for site fertility, as used by Lehtomäki etal. (2015) in their “Coarse with classes” workflow for conservation prioritization based on multi-source NFI data, were extracted. The example plot above was assigned site weightings of 3.0, 4.0, and 7.0 for spruce, birch and aspen, respectively, as it was located on a Units in m3/ha for volume, 1/ha for stem number, and years for age Table 1 (continued) Age classesa 21. (95, 100] 22. (100, 105] 23. (105, 110] 24. (110, 115] 25. (115, 120] 26. (120, ∞) Fig. 6 Sigmoidal transformations from species-specific mean diameter to conservation value. Black, red, blue, and green lines show the transformation functions for pine, spruce, birch, and other deciduous trees, respectively, and the coloured dots show how the trees measured from the example plot related to the transformation functions. Separate lines are drawn to depict the different forestry regions. Note that for some species, the transformation is determined piecewise, with different asymptote and scaling parameters applied for diameters below and above the region-specific median value. (Color figure online) 235European Journal of Forest Research (2019) 138:219–238 1 3 highly fertile soil. To obtain the final consval for the plot, the transformed diameters were multiplied by species-specific total stem volume and weighting for site fertility and summed over the species in a plot. The total consval value (907.12) for the example plot was thus obtained as a sum of Eq.1 applied per species as follows: According to the example above, the conservation value would be lower for plots with the same volume but with less species. Site weightings affect the result considerably; the weightings are reduced to 1.0–1.5 for conifer species on less fertile soils. The weightings are also reduced for deciduous species, although less severely, and the occurrence of deciduous species in the least fertile soils are also weighted slightly higher. Overall, the consval index was found to reflect well the growing stock, species and site variation, relative to variation within forestry regions (Fig.6). (2) consvalspruce =3.0 ×0.78 ×310.38 m 3 ∕ha ≈ 725.8 (3) consvalbirch =4.0 ×0.02 ×60.734 m 3 ∕ha ≈ 4.9 (4) consvalaspen =7.0 ×1.0 ×25.212 m 3 ∕ha ≈ 176.5 EFDM input data The example plot represents an area of 350ha when computed according to NFI methodology for forest area estimation (cf. Vauhkonen and Packalen 2017). It is classified in the initial forest area distribution matrix as a spruce-dominated, private FAWS in the highest taxation class according to the static factors (Sect.2.2.1). It is further classified to volume class #12, stem number class #6, and age class #17 according to its total volume (396m3/ha), stem number (730 stems/ha), age (78years), and class limits (Appendix 1). A similar classification is applied for each plot in both the full and pairwise data, and these data sources are used to derive the initial state, activity/transition probabilities, and output coefficients as described in Sect.2.2 and in more detail by Vauhkonen and Packalen (2017). EFDM simulations The forest area distribution after one simulation step is obtained by two matrix multiplications (see Sect.3 in Packalen etal. 2014): (1) the current area distribution is multiplied by the activity probabilities, which yields separate matrices that represent the area of each cell affected by each activity; and (2) the aforementioned intermediate matrices are multiplied by the transition probabilities, which yields the area distribution in the next step. Figure7 depicts the Fig. 7 The proportion of different management activities applied to the example plot on the first step of the simulations. The activity probabilities vary depending on the choice between businessas-usual allocation (ABAU) or schoolbook-allocation (ASB) and whether the forest is considered as Forests Available for Wood Supply (FAWS) or Forests with Restrictions on Availability for Wood Supply (FRAWS) and, therefore, simulated using agevolume or stem number-volume matrices, respectively 236 European Journal of Forest Research (2019) 138:219–238 1 3 activity probabilities that are applied to the example plot, depending on whether the plot is considered either as FAWS or FRAWS and managed according to either ABAU or ASB. Finally, the proportions are adjusted by two alternative total harvesting amounts, but these effects are omitted here for clarity purposes. The area managed by thinnings or final fellings produces harvested removals. According to the output coefficients for the plot in the first simulation step, final felling of the plot described above would yield in total 200.5m3/ha and 162.8m3/ha of logs for sawn wood and pulp wood, respectively, at an estimated (total) cost of 8.76 €/m3. The corresponding values are 57.8m3/ha and 89.1m3/ha for thinning from below (total cost of 15.6 €/m3) and 129.9m3/ha and 95.8m3/ha (total cost 12.3 €/m3) for thinning from above. The transition probabilities shift the initial area of the plot to multiple matrix cells. Using age-volume classes, the forest that is not managed gains age, but remains in the (highest) volume class. Thinnings from below transit the area to volume classes from #7 upwards, the most common class being #10. Using stem number, about half of the volume remains in the same stem number and volume class. Harvests shift the area from stem number/volume classes #3/#6 upwards, the most common target class being #4/#7. Notably, natural processes with stem number may shift the area both up and down in terms of classes, and shift the area downwards from both thinning from below and above, although based on different thinning rules. In the next simulation step, the forest area of the receiving classes is further updated using the activity and transition probabilities of the specific classes. Figure8 shows how the initial area of 350ha in one class evolved during the 10 simulation steps based on either agevolume and stem number-volume classes. Because of the multiple transitions that take place due to activity and transition probabilities, it might not be feasible to track the development of forests in a single class, but the overall development of the class structure is deemed realistic with respect to the properties of the forest. References Alberdi I, Michalak R, Fischer C, Gasparini P, Brändli UB, Tomter SM, Kuliesis A, Snorrason A, Redmond J, Hernández L, Lanz A, Vidondo B, Stoyanov N, Stoyanova M, Vestman M, Barreiro S, Marin G, Cañellas I, Vidal C (2016) Towards harmonized assessment of European forest availability for wood supply in Europe. For Pol Econ 70:20–29 Alrahahleh L, Ikonen VP, Kilpeläinen A, Torssonen P, Strandman H, Asikainen A, Kaurola J, Venäläinen A, Peltola H (2016) Effects of forest conservation and management on volume growth, Fig. 8 Distribution of age-volume (above) and stem numbervolume (below) classes after simulating 10 time-steps and applying class-specific activity and transition probabilities to the initial 350ha of forest. Refer to Appendix 1 for the interpretation of class numbers on the x-axes. The grey tones within the bars depict volume classes, the darkest grey referring to the lowest number of volume class and vice versa 237European Journal of Forest Research (2019) 138:219–238 1 3 harvested amount of timber, carbon stock, and amount of deadwood in Finnish boreal forests under changing climate. Can J For Res 47:215–225 Arponen A, Lehtomäki J, Leppänen J, Tomppo E, Moilanen A (2012) Effects of connectivity and spatial resolution of analyses on conservation prioritization across large extents. Conserv Biol 26:294–304 Biber P etal (2015) How sensitive are ecosystem services in European forest landscapes to silvicultural treatment? Forests 6:1666–1695 Bollandsås OM, Buongiorno J, Gobakken T (2008) Predicting the growth of stands of trees of mixed species and size: a matrix model for Norway. Scand J For Res 23:167–178 Bose AK, Weiskittel A, Kuehne C, Wagner RG, Turnblom E, Burkhart HE (2018) Does commercial thinning improve stand-level growth of the three most commercially important softwood forest types in North America? For Ecol Manag 409:683–693 Carpentier S, Filotas E, Handa IT, Messier C (2017) Trade-offs between timber production, carbon stocking and habitat quality when managing woodlots for multiple ecosystem services. Environ Conserv 44:14–23 CBD (2010) Convention on biological diversity: COP 10 decision X/2. Strategic plan for biodiversity 2011–2020. https ://www.cbd.int/ decis ion/cop/?id=12268 . Accessed 27 Dec 2017 Creutzburg MK, Scheller RM, Lucash MS, LeDuc SD, Johnson MG (2017) Forest management scenario in a changing climate: tradeoffs between carbon, timber, and old forest. Ecol Appl 27:503–518 Diaci J, Kerr G, O’Hara K (2011) Twenty-first century forestry: integrating ecologically based, uneven-aged silviculture with increased demands on forests. Forestry 84:463–465 Diaz-Balteiro L, Alonso R, Martínez-Jaúregui M, Pardos M (2017) Selecting the best forest management alternative by aggregating ecosystem services indicators over time: a case study in central Spain. Ecol Indic 72:322–329 Fürstenau C, Badek FW, Lasch P, Lexer MJ, Linder M, Mohr P, Suckow F (2007) Multiple-use forest management in consideration of climate change and the interests of stakeholder groups. Eur J For Res 126:225–239 Grassi G, den Elzen MG, Hof AF, Pilli R, Federici S (2012) The role of the land use, land use change and forestry sector in achieving Annex I reduction pledges. Clim Change 115:873–881 Hanski I (2011) Habitat loss, the dynamics of biodiversity, and a perspective on conservation. Ambio 40:248–255 Heinonen T, Pukkala T, Mehtätalo L, Asikainen A, Kangas J, Peltola H (2017) Scenario analyses for the effects of harvesting intensity on development of forest resources, timber supply, carbon balance and biodiversity of Finnish forestry. For Pol Econ 80:80–98 Heinonen T, Pukkala T, Asikainen A, Peltola H (2018) Scenario analyses on the effects of fertilization, improved regeneration material, and ditch network maintenance on timber production of Finnish forests. Eur J For Res 137:93–107 Hynynen J, Salminen H, Ahtikoski A, Huuskonen S, Ojansuu R, Siipilehto J, Lehtonen M, Eerikäinen K (2015) Long-term impacts of forest management on biomass supply and forest resource development: a scenario analysis for Finland. Eur J For Res 134:415–431 Kärkkäinen L, Haakana H, Hirvelä H, Packalen T (2018) Using a decision support system to study impacts of land use policies on wood procurement possibilities of the sawmill industry—a case study at regional and municipal levels. Pol Econ, For. https ://doi. org/10.1016/j.forpo l.2017.10.002 Knoke T (2012) The economics of continuous cover forestry. In: Pukkala T, von Gadow K (eds) Continuous cover forestry, managing forest ecosystems 23. Springer, Dordrecht, pp 167–193 Kotiaho JS, Kuusela S, Nieminen E, Päivinen J, Moilanen A (2016) Framework for assessing and reversing ecosystem degradation— report of the Finnish restoration prioritization working group on the options and costs of meeting the Aichi biodiversity target of restoring at least 15 percent of degraded ecosystems in Finland. Reports of the Ministry of Environment 15en, Ympäristöministeriö, Helsinki. http://julka isut.valti oneuv osto.fi/handl e/10024 /74862 . Accessed 27 Dec 2017 Kuuluvainen T, Tahvonen O, Aakala T (2012) Even-aged and unevenaged forest management in boreal Fennoscandia: a review. Ambio 41:720–737 Lafond V, Cordonnier T, Mao Z, Courbaud B (2017) Trade-offs between ecosystem services in uneven-aged mountain forests: evidences using Pareto fronts. Eur J For Res 136:997–1012 Lehtomäki J (2015) zsetup-esmk: PONE publication (Version 1.1). Zenodo file repository. https ://doi.org/10.5281/zenod o.27240 . Accessed 22 Oct 2018 Lehtomäki J, Tomppo E, Kuokkanen P, Hanski I, Moilanen A (2009) Applying spatial conservation prioritization software and highresolution GIS data to a national-scale study in forest conservation. For Ecol Manag 258:2439–2449 Lehtomäki J, Tuominen S, Toivonen T, Leinonen A (2015) What data to use for forest conservation planning? A comparison of coarse open and detailed proprietary forest inventory data in Finland. PLoS ONE 10(8):e0135926. https ://doi.org/10.1371/journ al.pone.01359 26 Lundström J, Öhman K, Rönnqvist M, Gustafsson L (2016) Considering future potential regarding structural diversity in selection of forest reserves. PLoS ONE 11(2):e0148960. https ://doi. org/10.1371/journ al.pone.01489 60 MAF (2015) Ministry of Agriculture and Forestry: National Forest Strategy 2025—Government Resolution of 12 February 2015, Ministry of Agriculture and Forestry, Helsinki. http://mmm.fi/ en/nfs. Accessed 27 Dec 2017 Mehtätalo L, Peltola H, Kilpeläinen A, Ikonen VP (2014) The response of basal area growth of Scots pine to thinning: a longitudinal analysis of tree-specific series using a nonlinear mixed-effects model. For Sci 60:636–644 Miina J, Pukkala T, Kurttila M (2016) Optimal multi-product management of stands producing timber and wild berries. Eur J For Res 135:781–794 Moilanen A, Anderson BJ, Eigenbrod F, Heinemeyer A, Roy DB, Gillings S, Armsworth PR, Gaston KJ, Thomas CD (2011) Balancing alternative land uses in conservation prioritization. Ecol Appl 21:1419–1426 Montoro Girona M, Rossi S, Lussier JM, Walsh D, Morin H (2017) Understanding tree growth responses after partial cuttings: a new approach. PLoS ONE 12(2):e0172653. https ://doi.org/10.1371/ journ al.pone.01726 53 Mouchet MA, Rega C, Lasseur R, Georges D, Paracchini ML, Renaud J, Stürck J, Schulp CJE, Verburg PH, Verkerk PJ, Lavorel S (2017) Ecosystem service supply by European landscapes under alternative land-use and environmental policies. Int J Biodiv Sci Ecosyst Serv Manag 13:342–354 Neumann M, Moreno A, Mues V, Härkönen S, Mura M, Bouriaud O, Lang M, Achten WMJ, Thivolle-Cazat A, Bronisz K, Merganič J, Decuyper M, Alberdi I, Astrup R, Mohren F, Hasenauer H (2016) Comparison of carbon estimation methods for European forests. For Ecol Manag 361:397–420 Nevalainen S (2017) Comparison of damage risks in even-and unevenaged forestry in Finland. Silva Fenn. https ://doi.org/10.14214 / sf.1741 Nieminen M, Hökkä H, Laiho R, Juutinen A, Ahtikoski A, Pearson M, Kojola S, Sarkkola S, Launiainen S, Valkonen S, Penttilä T, Lohila A, Saarinen M, Haahti K, Mäkipää R, Miettinen J, Ollikainen M (2018) Could continuous cover forestry be an economically and environmentally feasible management option on drained boreal peatlands? For Ecol Manag 424:78–84 238 European Journal of Forest Research (2019) 138:219–238 1 3 Packalen T, Sallnäs O, Sirkiä S, Korhonen K, Salminen O, Vidal C, Robert N, Colin A, Belouard T, Schadauer K, Berger A, Rego A, Louro G, Camia A, Räty M, San-Miguel J (2014) The European forestry dynamics model: concept, design and results of first case studies. JRC Science and Policy Reports Volume 93450, EUR 27004. Publications Office of the European Union, Luxembourg. https ://doi.org/10.2788/15399 0 Pang X, Nordström EM, Böttcher H, Trubins R, Mörtberg U (2017) Trade-offs and synergies among ecosystem services under different forest management scenarios—the LEcA tool. Ecosyst Serv 28:67–79 Peura M, Burgas D, Eyvindson K, Repo A, Mönkkönen M (2018) Continuous cover forestry is a cost-efficient tool to increase multifunctionality of boreal production forests in Fennoscandia. Biol Conserv 217:104–112 Puettmann KJ, Wilson SM, Baker SC, Donoso PJ, Drössler L, Amente G, Harvey BD, Knoke T, Lu Y, Nocentini S, Putz FE, Yoshida T, Bauhus J (2015) Silvicultural alternatives to conventional evenaged forest management—What limits global adoption? For Ecosyst 2:8. https ://doi.org/10.1186/s4066 3-015-0031-x Pukkala T (2014) Does biofuel harvesting and continuous cover management increase carbon sequestration? For Pol Econ 43:41–50 Pukkala T (2016a) Plenterwald, Dauerwald, or clearcut? For Pol Econ 62:125–134 Pukkala T (2016b) Which type of forest management provides most ecosystem services? For Ecosyst 3:9. https ://doi.org/10.1186/ s4066 3-016-0068-5 Pukkala T, Kellomäki S, Mustonen E (1988) Prediction of the amenity of a tree stand. Scand J For Res 3:533–544 Pukkala T, Lähde E, Laiho O, Salo K, Hotanen JP (2011) A multifunctional comparison of even-aged and uneven-aged forest management in a boreal region. Can J For Res 41:851–862 Pukkala T, Lähde E, Laiho O (2013) Species interactions in the dynamics of even-and uneven-aged boreal forests. J Sustain For 32:371–403 Pukkala T, Lähde E, Laiho O (2015) Which trees should be removed in thinning treatments? For Ecosyst 2:32. https ://doi.org/10.1186/ s4066 3-015-0056-1 R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https ://www.R-proje ct.org/. Accessed 6 Oct 2017 Repola J (2008) Biomass equations for birch in Finland. Silva Fenn 42:605–624 Repola J (2009) Biomass equations for Scots pine and Norway spruce in Finland. Silva Fenn 43:625–647 Roessiger J, Ficko A, Clasen C, Griess VC, Knoke T (2016) Variability in growth of trees in uneven-aged stands displays the need for optimizing diversified harvest diameters. Eur J For 135:283–295 Rummukainen A, Alanne H, Mikkonen E (1995) Wood procurement in the pressure of change: resource evaluation model till year 2010. Acta For Fenn 248:1–98 Rybicki J, Hanski I (2013) Species–area relationships and extinctions caused by habitat loss and fragmentation. Ecol Lett 16(S1):27–38 Schou E, Meilby H (2013) Transformation of even-aged European beech (Fagus sylvatica L.) to uneven-aged management under changing growth conditions caused by climate change. Eur J For Res 132:777–789 Schröter M, Rusch GM, Barton DN, Blumentrath S, Nordén B (2014) Ecosystem services and opportunity costs shift spatial priorities for conserving forest biodiversity. PLoS ONE 9(11):e112557. https ://doi.org/10.1371/journ al.pone.01125 57 Sirkiä S (2012) Methodology and system design—Appendix1 in Anon. Developing and testing a prototype for European forestry dynamics model (EFDM), Framework contract for the provision of forest data and services in support to the European Forest Data Centre. Specific Contract 10 Report, Reference: 2007/S 194-235358 of 09/10/2007. https ://githu b.com/ec-jrc/efdm/blob/ maste r/docum ents/EFDMi nstru ction s/Seija _Mathe matic s_behin d_EFDM.pdf Accessed 14 Dec 2018 Sirkiä S, Lehtomäki J, Lindén H, Tomppo E, Moilanen A (2012) Defining spatial priorities for capercaillie Tetrao urogallus lekking landscape conservation in south-central Finland. Wildl Biol 18:337–353 Solberg B, Bergseng E, Lindstad BH (2017) Assessing national impacts of international environmental regimes for biodiversity protection and climate mitigatio in boreal forestry—experiences from using a quantitative approach. For Pol Econ 85:147–160 Stevens J, Montgomery C (2002) Understanding the compatibility of multiple uses on forest land—a survey of multiresource research with application to the Pacific Northwest. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. https ://www.fs.usda.gov/trees earch /pubs/4393. Accessed 25 Jan 2018 Strandström M (2017) Timber harvesting and long-distance transportation of roundwood 2016. In: Metsätehon tuloskalvosarja 1b/2017, http://www.metsa teho.fi/timbe r-har ve sting -and-long-dista ncetrans porta tion-of-round wood-2016/. Accessed 25 Jan 2018 Strengbom J, Axelsson EP, Lundmark T, Nordin A (2018) Trade-offs in the multi-use potential of managed boreal forests. J Appl Ecol 55:958–966 Sutherland IJ, Bennett EM, Gergel SE (2016) Recovery trends for multiple ecosystem services reveal non-linear responses and longterm tradeoffs from temperate forest harvesting. For Ecol Manag 374:61–70 Triviño M, Juutinen A, Mazziotta A, Miettinen K, Podkopaev D, Reunanen P, Mönkkönen M (2015) Managing a boreal forest landscape for providing timber, storing and sequestering carbon. Ecosyst Serv 14:179–189 van der Plas F etal (2018) Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol Lett 21:31–42 Vauhkonen J, Packalen T (2017) A Markov chain model for simulating wood supply from any-aged forest management based on National Forest Inventory (NFI) data. Forests 8(9):307. https :// doi.org/10.3390/f8090 307 Vauhkonen J, Packalen T (2018) Uncertainties related to climate change and forest management with implications on climate regulation in Finland. Ecosyst Serv 33(B):213–224 Vauhkonen J, Pukkala T (2016) Selecting the trees to be harvested based on the relative value growth of the remaining trees. Eur J For Res 135:581–592 Vauhkonen J, Ruotsalainen R (2017) Assessing the provisioning potential of ecosystem services in a Scandinavian boreal forest: suitability and tradeoff analyses on grid-based wall-to-wall forest inventory data. For Ecol Manag 389:272–284 Verkerk PJ, Mavsar R, Giergiczny M, Lindner M, Edwards D, Schelhaas MJ (2014) Assessing impacts of intensified biomass production and biodiversity protection on ecosystem services provided by European forests. Ecosyst Serv 9:155–165 Yrjölä T (2002) Forest management guidelines and practices in Finland, Sweden and Norway. Internal Report 11, European Forest Institute, Joensuu. http://www.efi.int/files /attac hment s/publi catio ns/ir_11.pdf. Accessed 27 Dec 2017