Frontiers in Forests and Global Change (Feb 2024)
Quantitative methods for integrating climate adaptation strategies into spatial decision support models
Abstract
With the onset of rapid climate change and the legacy of past forest management and fire suppression policies, the capacity for forested landscapes to maintain core functionality and processes is being challenged. As such, managers are tasked with increasing the pace and scale of management to mitigate negative impacts of future large disturbances and improve resilience and climate adaptation of large landscapes. Such efforts require consensus building, with partners and stakeholders to determine where to allocate scarce resources. We present a methodology to identify strategic (where to go) and tactical (what to do) priorities across large landscapes to assist in project level planning. The model integrates a spatial assessment of current ecosystem resource conditions and spatial outputs from a landscape succession and disturbance simulation model (LANDIS-II) to assess the potential to achieve desired conditions under climate change with ongoing disturbances. Based on the expected trajectory of landscape conditions over time, the model applies fuzzy logic modeling to provide quantitative support for four management strategies (Monitor, Protect, Adapt, and Transform) across the landscape. We provide an example application of these methods targeting sustainable carbon loads across a 970,000 ha landscape in the central Sierras in California. By including future landscape conditions in the model, decisions made at the stand-level are inherently tied to and influenced by larger landscape-level processes that are likely to have the greatest impact on future landscape dynamics. The methods outlined here are able to incorporate multiple metrics to capture the many resources targeted by management. Model outputs could also be used as inputs into spatial optimization models to assess tradeoffs and synergies among treatment options and to aid in long-term planning.
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