ABSTRACT
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.
Subject(s)
Carbon , Taiga , Uncertainty , Conservation of Natural Resources , Forests , BiodiversityABSTRACT
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.
ABSTRACT
Detailed spatial data are an essential part of land use planning and decision-making. Their spatial resolution sets limitations to their use, as coarse datasets are not suitable for detecting small-scale phenomena. In this study, we explored the effects of spatial resolution on the ecological outcome of a conservation prioritization process in Zonation software. Our study area was in Evo, southern Finland, covering a mosaic of managed and conserved forests. We produced the feature layers describing the forest characteristics using high-resolution remote sensing datasets, object-based mapping methods, and forest site type data. We found that increasing the resolution above the 16 m baseline resolution resulted in substantial errors. The conservation errors were the highest for rare features related to European Aspen, whereas the common features related to dominant tree species could benefit from the growth of cell size. We conclude that adequate spatial resolution is a prerequisite for efficient conservation prioritization, and that the size and spatial distribution of the features affect the resolution requirements.
