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Scenario tools are widely used to support policymaking and strategic planning. Loss of biodiversity, climate change, and increase in biomass demand ways to project future forest resources considering e.g. various protection schemes, alterations to forest management, and potential threats like pests, wind, and drought. The European Forestry Dynamics Model (EFDM) is an area-based matrix model that can combine all these aspects in a scenario, simulating large-scale impacts. The inputs to the EFDM are the initial forest state and models for management activities such as thinning, felling or other silvicultural treatments. The results can be converted into user-defined outputs like wood volumes, the extent of old forests, dead wood, carbon, or harvest income. We present here a new implementation of the EFDM as an open-source R package. This new implementation enables the development of more complex scenarios than before, including transitions from even-aged forestry to continuous cover forestry, and changes in land use or tree species. Combined with a faster execution speed, the EFDM can now be used as a building block in optimization systems. The new user interface makes the EFDM more approachable and usable, and it can be combined with other models to study the impact of climate change, for example.

Land-based mitigation measures are needed to achieve climate targets. One option is the mitigation of currently high greenhouse gas (GHG) emissions of nutrient-rich drained peatland forest soils. Continuous cover forestry (CCF) has been proposed as a measure to manage this GHG emission source; however, its emission reduction potential and impact on timber production at regional and national scales have not been quantified. To quantify the potential emission reduction, we simulated four management scenarios for Finnish forests: (i) The replacement of clear-cutting by selection harvesting on nutrient-rich drained peatlands (CCF) and (ii) the current forest management regime (BAU), and both at two harvest levels, namely (i) the mean annual harvesting (2016–2018) and (ii) the maximum sustainable yield. The simulations were conducted at the stand scale with a forest simulator (MELA) coupled with a hydrological model (SpaFHy), soil C model (Yasso07) and empirical GHG exchange models. Simulations showed that the management scenario that avoided clear-cutting on nutrient-rich drained peatlands (i.e. CCF) produced approximately 1 Tg CO 2 eq. higher carbon sinks annually compared with BAU at equal harvest level for Finland. This emission reduction can be attributed to the maintenance of a higher biomass sink and to the mitigation of soil emissions from nutrient-rich drained peatland sites.

Research Highlights: We show the difference in the long-term effects on economic and ecological forest values between four forest management scenarios of a large representative forest landscape. The scenarios were largely formulated by stakeholders representing the main views on how to manage north-European forests. Background and Objectives: Views on how to balance forest management between wood production and biodiversity differ widely between different stakeholder groups. We aim to show the long-term consequences of stakeholder-defined management scenarios, in terms of ecological and economic forest values. Materials and Methods: We simulated management scenarios for a forest landscape in Sweden, based on the management objectives and strategies of key stakeholders. We specifically investigated the difference in economic forest values coupled to wood supply and ecological indicators coupled to structural biodiversity between the scenarios over a 100-year period. The indicators were net present value, harvest, growing stock and increment, along with deadwood volume, the density of large trees, area of old forests and mature broadleaf-rich forests. Results: We show that the scenarios have widely different outcomes in terms of the studied indicators, and that differences in indicator outcome were largely due to different distributions in management regimes, i.e., the proportion of forest left unmanaged or under even-aged management or continuous cover forest, as well as specific retention practices. Retention and continuous cover forestry mitigate the negative effects that clear-cut forestry has upon biodiversity. Conclusions: We found that an increase in the forest area under the continuous cover forestry regime could be a cost-efficient way to increase structural diversity in managed boreal forests. On the other hand, no single management regime performed best with respect to all indicators, which means that a mixture of several management regimes is needed to balance conflicting objectives. We also show that the trade-off between economic and ecological indicators was not directly proportional, meaning that an increase in structural biodiversity may be obtained at a proportionally low cost with appropriate management planning.

Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass(AGB) and stem volume (VOL) was investigated. The main objective of this study was totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9%and 26.4% when field measurements were used in training the ABA. When ITDmeasurements were used in training, the respective accuracies ranged between 28.5%–34.9%and 29.2%–34.0%. Overall, the results show that accurate plot-level AGB estimates can beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping.

Aim: Climate change is expected to have major impacts on terrestrial biodiversity at all ecosystem levels, including reductions in species-level distribution and abundance. We aim to test the extent to which land use management, such as setting-aside forest from production, could reduce climate-induced biodiversity impacts for specialist species over large geographical gradients. Location: Sweden. Methods: We applied ensembles of species distribution models based on citizen science data for six species of red-listed old-forest indicator fungi confined to spruce dead wood. We tested the effect on species habitat suitabilities of alternative climate change scenarios and varying amounts of forest set-aside from production over the coming century. Results: With 3.6% of forest area set-aside from production and assuming no climate change, overall habitat suitabilities for all six species were projected to increase in response to maturing spruce in set-aside forest. However, overall habitat suitabilities for all six species were projected to decline under climate change scenario RCP4.5 (intermediate-low emissions), with even greater declines projected under RCP 8.5 (high emissions). Increasing the amount of forest set-aside to 16% resulted in significant increases in overall habitat suitability, with one species showing an increase. A further increase to 32% forest set-aside resulted in considerably more positive trends, with three of six species increasing. Main conclusions: There is interspecific variation in the importance of future macroclimate and resource availability on species occurrence. However, large-scale conservation measures, such as increasing resource availability through setting aside forest from production, could reduce future negative effects from climate change, and early investment in conservation is likely to reduce the future negative impacts of climate change on specialist species.

Climate change is expected to drive the distribution retraction of northern species. However, particularly in regions with a history of intensive exploitation, changes in habitat management could facilitate distribution expansions counter to expectations under climate change. Here, we test the potential for future forest management to facilitate the southward expansion of an old-forest species from the boreal region into the boreo-nemoral region, contrary to expectations under climate change. We used an ensemble of species distribution models based on citizen science data to project the response of Phellinus ferrugineofuscus, a red-listed old-growth indicator, wood-decaying fungus, to six forest management and climate change scenarios. We projected change in habitat suitability across the boreal and boreo-nemoral regions of Sweden for the period 2020–2100. Scenarios varied in the proportion of forest set aside from production, the level of timber extraction, and the magnitude of climate change. Habitat suitabilities for the study species were projected to show larger relative increases over time in the boreo-nemoral region compared to the boreal region, under all scenarios. By 2100, mean suitabilities in set-aside forest in the boreo-nemoral region were similar to the suitabilities projected for set-aside forest in the boreal region in 2020, suggesting that occurrence in the boreo-nemoral region could be increased. However, across all scenarios, consistently higher projected suitabilities in set-aside forest in the boreal region indicated that the boreal region remained the species stronghold. Furthermore, negative effects of climate change were evident in the boreal region, and projections suggested that climatic changes may eventually counteract the positive effects of forest management in the boreo-nemoral region. Our results suggest that the current rarity of this old-growth indicator species in the boreo-nemoral region may be due to the history of intensive forestry. Forest management therefore has the potential to compensate for the negative effects of climate change. However, increased occurrence at the southern range edge would depend on the dispersal and colonization ability of the species. An increase in the amount of set-aside forest across both the boreal and boreo-nemoral regions is therefore likely to be required to prevent the decline of old-forest species under climate change.

Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making.

This paper introduces a modeling approach for the assessment of policy options within the forest-based bioeconomy. The feedback between the forestry dynamics model and the economic model of the global forest-based sector of the proposed framework is essential, not only for response analysis as to the development of forest resources and for a correct assessment of future harvesting potentials, but also for the assessment of the impact of different management regimes on wood-based product markets. Test runs of the modeling framework on a Swedish case highlight the necessity of considering timber assortments for a comprehensive integration of forest resources and wood-based commodity market dynamics. Hence, the composition of harvest demand in terms of timber assortment affects the allocation of harvesting activities and, consequently, the development of forest resources (and thus future harvest potentials), as well as the production, trade and consumption of wood-based products.

In this paper a new concept for modeling uneven-aged forests (UEAF) is presented. The term UEAF in this article encloses all forests that deviate from the even-aged structure. The matrix model is area-based, in that the forest under study is described by a distribution of areas over fixed state-spaces spanned by stem number and volume per hectare classes. Dynamics is introduced as transitions of areas inside the state-space during the simulation. Harvesting activities and the occurrence of calamities are explicitly handled. The model is designed to be suitable for large-scale analyses. The concept was tested in an application to Austrian National Forest Inventory (NFI) data. Results shown, including a comparison to older inventory data, indicate that it is worth further elaborating on the concept and the model. The work will be continued and in the next step the model concept will be applied in several other countries.