Land (Jan 2021)
Combining Tree Species Composition and Understory Coverage Indicators with Optimization Techniques to Address Concerns with Landscape-Level Biodiversity
Abstract
Sustainable forest management needs to address biodiversity conservation concerns. For that purpose, forest managers need models and indicators that may help evaluate the impact of management options on biodiversity under the uncertainty of climate change scenarios. In this research we explore the potential for designing mosaics of stand-level forest management models to address biodiversity conservation objectives on a broader landscape-level. Our approach integrates (i) an effective stand-level biodiversity indicator that reflect tree species composition, stand age, and understory coverage under divergent climate conditions; and (ii) linear programming optimization techniques to guide forest actors in seeing optimal forest practices to safeguard future biodiversity. Emphasis is on the efficiency and effectiveness of an approach to help assess the impact of forest management planning on biodiversity under scenarios of climate change. Results from a resource capability model are discussed for an application to a large-scale problem encompassing 14,765 ha, extending over a 90-years planning horizon and considering two local-climate scenarios. They highlight the potential of the approach to help assess the impact of both stand and landscape-level forest management models on biodiversity conservation goals. They demonstrate further that the approach provides insights about how climate change, timber demand and wildfire resistance may impact plans that target the optimization of biodiversity values. The set of optimized long-term solutions emphasizes a multifunctional forest that guarantees a desirable local level of biodiversity and resilience to wildfires, while providing a balanced production of wood over time at the landscape scale.
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