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The boreal biome exchanges large amounts of carbon (C) and greenhouse gases (GHGs) with the atmosphere and thus significantly affects the global climate. A managed boreal landscape consists of various sinks and sources of carbon dioxide (CO2), methane (CH4), and dissolved organic and inorganic carbon (DOC and DIC) across forests, mires, lakes, and streams. Due to the spatial heterogeneity, large uncertainties exist regarding the net landscape carbon balance (NLCB). In this study, we compiled terrestrial and aquatic fluxes of CO2, CH4, DOC, DIC, and harvested C obtained from tall‐tower eddy covariance measurements, stream monitoring, and remote sensing of biomass stocks for an entire boreal catchment (~68 km2) in Sweden to estimate the NLCB across the land–water–atmosphere continuum. Our results showed that this managed boreal forest landscape was a net C sink (NLCB = 39 g C m−2 year−1) with the landscape–atmosphere CO2 exchange being the dominant component, followed by the C export via harvest and streams. Accounting for the global warming potential of CH4, the landscape was a GHG sink of 237 g CO2‐eq m−2 year−1, thus providing a climate‐cooling effect. The CH4 flux contribution to the annual GHG budget increased from 0.6% during spring to 3.2% during winter. The aquatic C loss was most significant during spring contributing 8% to the annual NLCB. We further found that abiotic controls (e.g., air temperature and incoming radiation) regulated the temporal variability of the NLCB whereas land cover types (e.g., mire vs. forest) and management practices (e.g., clear‐cutting) determined their spatial variability. Our study advocates the need for integrating terrestrial and aquatic fluxes at the landscape scale based on tall‐tower eddy covariance measurements combined with biomass stock and stream monitoring to develop a holistic understanding of the NLCB of managed boreal forest landscapes and to better evaluate their potential for mitigating climate change. [ABSTRACT FROM AUTHOR]

Interspecific interactions are crucial in determining species occurrence and community assembly. Understanding these interactions is thus essential for correctly predicting species' responses to climate change. We focussed on an avian forest guild of four hole‐nesting species with differing sensitivities to climate that show a range of well‐understood reciprocal interactions, including facilitation, competition and predation. We modelled the potential distributions of black woodpecker and boreal, tawny and Ural owl, and tested whether the spatial patterns of the more widespread species (excluding Ural owl) were shaped by interspecific interactions. We then modelled the potential future distributions of all four species, evaluating how the predicted changes will alter the overlap between the species' ranges, and hence the spatial outcomes of interactions. Forest cover/type and climate were important determinants of habitat suitability for all species. Field data analysed with N‐mixture models revealed effects of interspecific interactions on current species abundance, especially in boreal owl (positive effects of black woodpecker, negative effects of tawny owl). Climate change will impact the assemblage both at species and guild levels, as the potential area of range overlap, relevant for species interactions, will change in both proportion and extent in the future. Boreal owl, the most climate‐sensitive species in the guild, will retreat, and the range overlap with its main predator, tawny owl, will increase in the remaining suitable area: climate change will thus impact on boreal owl both directly and indirectly. Climate change will cause the geographical alteration or disruption of species interaction networks, with different consequences for the species belonging to the guild and a likely spatial increase of competition and/or intraguild predation. Our work shows significant interactions and important potential changes in the overlap of areas suitable for the interacting species, which reinforce the importance of including relevant biotic interactions in predictive climate change models for increasing forecast accuracy. [ABSTRACT FROM AUTHOR]