DOI: 10.5061/dryad.c866t1g8k
title: Rebuilding green infrastructure in boreal production forest given future global wood demand

description: Global policy for future biodiversity conservation is ultimately
 implemented at landscape and local scales. In parallel, green
 infrastructure (GI) planning needs to account for socio-economic dynamics
 at national and global scales. Progress towards policy goals must, in
 turn, be evaluated at the landscape scale. Evaluation tools are often
 environmental quality objectives (EQO) indicators. We present three
 management scenarios for a 100,000 hectare boreal forest landscape in
 Sweden in the coming 100 years. The scenarios optimize financial returns
 and account for downscaled projected global demand of wood given a
 middle-of-the road Shared Socioeconomic Pathway (SSP2). We contrast a
 reference scenario meeting the wood demand against an economy scenario
 with no upper harvest limit, and a green infrastructure (GI) scenario
 optimizing the levels of four EQO indicators (the area of old forest, the
 area of mature broadleaf-rich forest, the amount of deadwood and the
 density of large trees). EQO indicators generally reached the highest
 levels in the GI scenario and the lowest levels in the economy scenario.
 Most indicators increased further in set-asides. The financial profit was
 14% lower in the GI and 2% higher in the economy than in the reference
 scenario. These scenarios were used in the associated publication to
 evaluate the future response of eleven model species from three different
 species groups with widely differing habitat requirements. The studied
 species were four bird species, six wood-decaying fungi and one lichen,
 all either of conservation concern or considered indicator species for
 forest of high conservation value. Models and data for the birds and fungi
 have been published previously. The model for the lichen Lobaria
 pulmonaria was created for this study; the underlying data is therefore
 presented here as well. Our study has shown that effects of global SSPs
 can be downscaled and accounted for in planning landscape-scale forest and
 conservation management. Accounting for EQO indicators in the management
 optimization was found to be an effective approach to reveal scenarios for
 reaching targets on both revenue and conservation. Rebuilding green
 infrastructure in the production forest is possible at a relatively minor
 economic cost and to the benefit of species of conservation concern.
description: <strong>Scenario
 data</strong> The three scenarios of forest
 management and conservation were simulated for 100 years, starting from
 2010, using the PlanWise application of the Heureka software suite
 (Wikström et al., 2011). The scenario data given is compiled Heureka
 output data from simulations that optimize the allocation of management
 types to satisfy different targets and constraints. The three scenarios
 are the <em>green infrastructure</em>
 (<em>GI</em>), <em>reference</em>, and the
 <em>economy</em> scenario. All three scenarios have one
 target and one constraint in common: they all target the maximization of
 economic returns in the form of net present value (NPV), and they all are
 constrained to not harvest less than the landscape scale demand. The
 landscape scale demand was derived via downscaling (using SweFor, Eriksson
 et al. 2020) the national demand for Sweden, predicted by GLOBIOM assuming
 the socioeconomic pathway SSP2 and the representative concentration
 pathway RCP2.6. Scenarios differed in additional
 constraints. The <em>GI</em> scenario was additionally
 constrained to simultaneously maximize the mean values of four key
 environmental quality objective (EQO) indicators throughout the simulation
 (the four indicators are: the area of old forest, the area of mature
 broadleaf-rich forest, the amount of deadwood and the density of large
 trees). The <em>reference</em> scenario was additionally
 constrained to not harvest more than the projected landscape scale demand
 (allowed deviation +/- 1%, minimum sawn wood proportion = 40%). The
 <em>economy</em> scenario had no further
 constraints. <strong>Lichen
 data</strong> The occurrences of
 <em>Lobaria pulmonaria</em> were recorded on its host trees in
 Finland, aspen (<em>Populus tremula</em>) and goat willow
 (<em>Salix caprea</em>) during a two-time-point survey in
 Kuhmo, Finland (63°88′ N, 29°18′ E). <em>L. pulmonaria</em>
 occurrences were recorded on aspens (³ 15 cm diameter at breast height;
 DBH; n = 2621) and goat willows (³ 10 cm in DBH; n = 1008). These data are
 accompanied by distances to <em>L. pulmonaria</em> source
 trees (host trees occupied by <em>L. pulmonaria</em> at the
 time of the first survey) in the surrounding landscape, described
 separately for managed stands and reserves of the two-time-point survey.
 The two-time-point survey was conducted in 1997‒1999 by Gu et al. (2001)
 and in 2007‒2010 by Belinchón et al. (2017) and Ronnås et al. (2017) in a
 4500 ha managed landscape and 1100 ha old-growth landscape in Kuhmo,
 Finland. <strong>References</strong> Belinchón, R., Harrison, P. J. , Mair, L., Várkonyi, G., & Snäll T. (2017). Local epiphyte establishment and future metapopulation dynamics in landscapes with different spatiotemporal properties. <em>Ecology</em> 98:741–750. Eriksson, L. O., Forsell, N., Eggers, J., & Snäll, T. (2020). Downscaling of long-term global scenarios to regions with a forest sector model. <em>Forests</em>, 11, 500. Gu, W., Kuusinen, M., Konttinen, T., & Hanski, I. (2001). Spatial pattern in the occurrence of the lichen Lobaria pulmonaria in managed and virgin boreal forests. <em>Ecography</em> 24:139–150. Ronnås, C., Ovaskainen, O., Werth, S., Várkonyi, G., Scheidegger, C. & Snäll, T. (2017). Discovery of long-distance gamete dispersal in a lichen-forming ascomycete. <em>New Phytologist</em> 216:216–226. Wikström, P., Edenius, L., Elfving, B., Eriksson, L. O., Lämås, T., Sonesson, J., Öhmann, K., Wallerman, J., Waller, C., & Klintebäck, F. (2011). The Heureka forestry decision support system: An overview. <em>Mathematical and Computational Forestry & Natural-Resource Sciences</em>, 3(2), 87–94.