The EU aims at reaching carbon neutrality by 2050 and Finland by 2035. We integrated results of three spatially distributed model systems (FRES, PREBAS, Zonation) to evaluate the potential to reach this goal at both national and regional scale in Finland, by simultaneously considering protection targets of the EU biodiversity (BD) strategy. Modelling of both anthropogenic emissions and forestry measures were carried out, and forested areas important for BD protection were identified based on spatial prioritization. We used scenarios until 2050 based on mitigation measures of the national climate and energy strategy, forestry policies and predicted climate change, and evaluated how implementation of these scenarios would affect greenhouse gas fluxes, carbon storages, and the possibility to reach the carbon neutrality target. Potential new forested areas for BD protection according to the EU 10% protection target provided a significant carbon storage (426–452 TgC) and sequestration potential (− 12 to − 17.5 TgCO₂eq a⁻¹) by 2050, indicating complementarity of emission mitigation and conservation measures. The results of the study can be utilized for integrating climate and BD policies, accounting of ecosystem services for climate regulation, and delimitation of areas for conservation.

Biodiversity offsetting is a tool to balance ecological damage caused by human activity with new benefits created elsewhere. Offsetting is implemented by protecting, restoring or managing sufficiently large areas of habitat. While there are concerns about the true feasibility of offsetting, they are becoming a common policy tool world‐wide. Operationally uncomplicated, quantitative approaches to spatial analysis of offsets are rare and their use is often restricted by the availability of suitable spatial data. We describe a practical method for offsets that builds upon two layers of relatively easily sourced spatial data, a balanced spatial priority ranking and a weighted range size rarity map. Together with (a) spatial information about impact and offset areas, and (b) extra parameters for the effectiveness of avoided loss and the amount of leakage expected, we can evaluate whether the proposed offset exchange represents a credible no net loss or net positive impact with an upward trade. The priority ranking and range size rarity maps can be produced in various ways, most notably using existing conservation planning tools. Here we used the standard outputs of the Zonation spatial prioritization software. We illustrate the method and associated visualization in the context of offsetting of boreal forests in Finland, where forests experience high and increasing pressures from forestry and bioenergy sectors. The example is timely as there is political demand for the uptake of biodiversity offset policies in Finland, and boreal forests are the most common biotope. The methods described here are applicable to biomes around the world. The described tools are made available as r scripts that utilize standard Zonation outputs, thus providing direct linkage to any past or future Zonation applications. As a limitation, the present methods only apply to avoided loss offsets.

Here we present a framework for identifying areas with high dead wood potential (DWP) for conservation planning needs. The amount and quality of dead wood and dying trees are some of the most important factors for biodiversity in forests. As they are easy to recognize on site, it is widely used as a surrogate marker for ecological quality of forests. However, wall-to-wall information on dead wood is rarely available on a large scale as field data collection is expensive and local dead wood conditions change rapidly. Our method is based on the forest growth models in the Motti forest simulator, taking into account 168 combinations of tree species, site types, and vegetation zones as well as recommendations on forest management. Simulated estimates of stand-level dead wood volume and mean diameter at breast height were converted into DWP functions. The accuracy of the method was validated on two sites in southern and northeastern Finland, both consisting of managed and conserved boreal forests. Altogether, 203 field plots were measured for living and dead trees. Data on living trees were inserted into corresponding DWP functions and the resulting DWPs were compared to the measured dead wood volumes. Our results show that DWP modeling is an operable tool, yet the accuracy differs between areas. The DWP performs best in near-pristine southern forests known for their exceptionally good quality areas. In northeastern areas with a history of softer management, the differences between near-pristine and managed forests is not as clear. While accurate wall-to-wall dead wood inventory is not available, we recommend using DWP method together with other spatial datasets when assessing biodiversity values of forests.

Forest conservation plays a central role in meeting national and international biodiversity and climate targets. Biodiversity and carbon values within forests are often estimated with models, introducing uncertainty to decision making on which forest stands to protect. Here, we explore how uncertainties in forest variable estimates affect modelled biodiversity and carbon patterns, and how this in turn introduces variability in the selection of new protected areas. We find that both biodiversity and carbon patterns were sensitive to alterations in forest attributes. Uncertainty in features that were rare and/or had dissimilar distributions with other features introduced most variation to conservation plans. The most critical data uncertainty also depended on what fraction of the landscape was being protected. Forests of highest conservation value were more robust to data uncertainties than forests of lesser conservation value. Identifying critical sources of model uncertainty helps to effectively reduce errors in conservation decisions.