Adaptation is necessary to cope with or take advantage of the effects of climate change on socio-ecological systems. This is especially important in the forestry sector, which is sensitive to the ecological and economic impacts of climate change, and where the adaptive decisions of owners play out over long periods of time. Relatively little is known about how successful these decisions are likely to be in meeting demands for ecosystem services in an uncertain future.

This paper explores adaptation to global change in the forestry sector using CRAFTY-Sweden, an agent-based model that represents large-scale land-use dynamics, based on the demand and supply of ecosystem services. Future impacts and adaptation within the Swedish forestry sector were simulated for scenarios of socio-economic change (Shared Socio-economic Pathways) and climatic change (Representative Concentration Pathways, for three climate models), between 2010 and 2100.

Substantial differences were found in the competitiveness and coping ability of land owners implementing different management strategies through time. Generally, multi-objective management was found to provide the best basis for adaptation. Across large regions, however, a combination of management strategies was better at meeting ecosystem service demands.

Results also show that adaptive capacity evolves through time in response to external (global) drivers and interactions between individual actors. This suggests that process-based models are more appropriate for the study of autonomous adaptation and future adaptive and coping capacities than models based on indicators, discrete time snapshots or exogenous proxies. Nevertheless, a combination of planned and autonomous adaptation by institutions and forest owners is likely to be more successful than either group acting alone.


    The competitiveness of management strategies differs between future scenarios.
    Multi-objective forest management was found to provide the best basis for adaptation.
    Adaptation depends on external drivers and the dynamics of interactions among actors.Process-based models are particularly appropriate for studying adaptation processes.
    Process-based models are most appropriate for studying future adaptive capacity.

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