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Climate change mitigation is a global response that requires actions at the local level. Quantifying local sources and sinks of greenhouse gases (GHG) facilitate evaluating mitigation options. We present an approach to collate spatially explicit estimated fluxes of GHGs (carbon dioxide, methane and nitrous oxide) for main land use sectors in the landscape, to aggregate, and to calculate the net emissions of an entire region. Our procedure was developed and tested in a large river basin in Finland, providing information from intensively studied eLTER research sites. To evaluate the full GHG balance, fluxes from natural ecosystems (lakes, rivers, and undrained mires) were included together with fluxes from anthropogenic activities, agriculture and forestry. We quantified the fluxes based on calculations with an anthropogenic emissions model (FRES) and a forest growth and carbon balance model (PREBAS), as well as on emission coefficients from the literature regarding emissions from lakes, rivers, undrained mires, peat extraction sites and cropland. Spatial data sources included CORINE land use data, soil map, lake and river shorelines, national forest inventory data, and statistical data on anthropogenic activities. Emission uncertainties were evaluated with Monte Carlo simulations. Artificial surfaces were the most emission intensive land-cover class. Lakes and rivers were about as emission intensive as arable land. Forests were the dominant land cover in the region (66%), and the C sink of the forests decreased the total emissions of the region by 72%. The region's net emissions amounted to 4.37 ± 1.43 Tg CO 2 -eq yr -1 , corresponding to a net emission intensity 0.16 Gg CO 2 -eq km -2 yr -1 , and estimated per capita net emissions of 5.6 Mg CO 2 -eq yr -1 . Our landscape approach opens opportunities to examine the sensitivities of important GHG fluxes to changes in land use and climate, management actions, and mitigation of anthropogenic emissions., Competing Interests: Declaration of competing interest We have no known competing financial interests or personal relationships that have influenced the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Animals use and select habitat at multiple hierarchical levels and at different spatial scales within each level. Still, there is little knowledge on the scale effects at different spatial levels of species occupancy patterns. The objective of this study was to examine nonlinear effects and optimal-scale landscape characteristics that affect occupancy of the Siberian flying squirrel, Pteromys volans , in South- and Mid-Finland. We used presence-absence data ( n  = 10,032 plots of 9 ha) and novel approach to separate the effects on site-, landscape-, and regional-level occupancy patterns. Our main results were: landscape variables predicted the placement of population patches at least twice as well as they predicted the occupancy of particular sites; the clear optimal value of preferred habitat cover for species landscape-level abundance is a surprisingly low value (10% within a 4 km buffer); landscape metrics exert different effects on species occupancy and abundance in high versus low population density regions of our study area. We conclude that knowledge of regional variation in landscape utilization will be essential for successful conservation of the species. The results also support the view that large-scale landscape variables have high predictive power in explaining species abundance. Our study demonstrates the complex response of species occurrence at different levels of population configuration on landscape structure. The study also highlights the need for data in large spatial scale to increase the precision of biodiversity mapping and prediction of future trends.

The boreal region is facing intensifying resource extraction pressure, but the lack of comprehensive biodiversity data makes operative forest conservation planning difficult. Many countries have implemented forest inventory schemes and are making extensive and up-to-date forest databases increasingly available. Some of the more detailed inventory databases, however, remain proprietary and unavailable for conservation planning. Here, we investigate how well different open and proprietary forest inventory data sets suit the purpose of conservation prioritization in Finland. We also explore how much priorities are affected by using the less accurate but open data. First, we construct a set of indices for forest conservation value based on quantitative information commonly found in forest inventories. These include the maturity of the trees, tree species composition, and site fertility. Secondly, using these data and accounting for connectivity between forest types, we investigate the patterns in conservation priority. For prioritization, we use Zonation, a method and software for spatial conservation prioritization. We then validate the prioritizations by comparing them to known areas of high conservation value. We show that the overall priority patterns are relatively consistent across different data sources and analysis options. However, the coarse data cannot be used to accurately identify the high-priority areas as it misses much of the fine-scale variation in forest structures. We conclude that, while inventory data collected for forestry purposes may be useful for forest conservation purposes, it needs to be detailed enough to be able to account for more fine-scaled features of high conservation value. These results underline the importance of making detailed inventory data publicly available. Finally, we discuss how the prioritization methodology we used could be integrated into operative forest management, especially in countries in the boreal zone.