Predicting the impact of climate change on forests at management-relevant spatial and temporal scales is a key challenge for forestry science. Making such predictions accurately is currently challenging because the ecological responses of forests to climate change are often altered by the presence of other anthropogenic pressures, such as land use and land cover (LULC) change. While predictive quantitative models of forest responses to climate change are constantly improving, they are time- and data-intensive, making them often impractical to implement as a decision-guiding tool.
However, forest decision makers need actionable information now to choose forest management strategies and prioritise areas for intervention, meaning there is a need to synthesise the best available knowledge and data in a transparent, effective way. The authors suggest that open-source data can be combined with a conceptual model of climate change-LULC interactions to derive maps of LULC-modified climate change risks to different forest types. They illustrate their approach using ancient woodlands in the UK as a case study, mapping risks from changes in average climatic conditions and extreme climatic events, and where these may interact with edge effects from surrounding non-woodland LULC. Surprisingly, the location of UK-wide hotspots of climate change risk to ancient woodlands are overall robust to changing assumptions about climate change-LULC interaction processes; the few areas that appear sensitive to these assumptions should be prioritised for monitoring to increase domain-and forest-type specific knowledge of climate change-LULC interactions.
Their framework can easily be extended when new data or knowledge becomes available, and provides actionable information for forest decision makers on where climate change and land use are likely to create hotspots of ecological change for different forest types.
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