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Rebuilding green infrastructure in boreal production forest given future global wood demand

Cite this dataset

Moor, Helen; Eggers, Jeannette; Fabritius, Henna; Snäll, Tord (2022). Rebuilding green infrastructure in boreal production forest given future global wood demand [Dataset]. Dryad.


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.


Scenario data

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 green infrastructure (GI), reference, and the economy 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 GI 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 reference scenario was additionally constrained to not harvest more than the projected landscape scale demand (allowed deviation +/- 1%, minimum sawn wood proportion = 40%). The economy scenario had no further constraints.

Lichen data

The occurrences of Lobaria pulmonaria were recorded on its host trees in Finland, aspen (Populus tremula) and goat willow (Salix caprea) during a two-time-point survey in Kuhmo, Finland (63°88′ N, 29°18′ E). L. pulmonaria 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 L. pulmonaria source trees (host trees occupied by L. pulmonaria 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.


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. Ecology 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. Forests, 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. Ecography 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. New Phytologist 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. Mathematical and Computational Forestry & Natural-Resource Sciences, 3(2), 87–94.


Swedish Research Council for Environment Agricultural Sciences and Spatial Planning, Award: 2016-01949