Presentation Title
Modeling the effects of global change and associated adaptive silvicultural systems in Northern New Hampshire
Abstract
Global climate change is predicted to have significant but unknown impacts on future forest structure, function, and composition in New England. Combined with a mixed-ownership landscape and dynamic land use history in this region, it may be challenging to meet diverse landowner management objectives using historic silvicultural techniques under future changing conditions. The Adaptive Silviculture for Climate Change (ASCC) project, which aims to provide a toolbox of approaches to forest management for many outcomes under global change, can be employed to break down barriers to communication and implementation between scientists and landowners, resulting in the creation of silvicultural strategies that are both attainable and promote forest resiliency in the face of future changes. This research integrates field measurements from an operational field study at the Second College Grant ASCC project site in Coos County, New Hampshire, with LANDIS-II landscape simulation models to model future forest conditions based on projected changes in climate, disturbance, and associated management regimes. It involves four main management approaches: resistance, resilience, transition, and no management treatments. These stochastic models allow us to evaluate the effectiveness of various adaptive silvicultural strategies at sustaining forest structure, composition, ecosystem services, and landowner management objectives in the context of a changing and uncertain climate future. Resilience and transition treatments are projected to fare the best in terms of maintained ecosystem services under simulated climate models by employing uneven-aged management strategies that emphasize species diversity, structural heterogeneity, and the influx of climate-adapted species. This work underscores the utility of landscape simulation models for evaluating the outcomes of adaptive silviculture and its impacts on future forest structure, function, and composition so as to aid decision making under an uncertain climate future.
Primary Faculty Mentor Name
Anthony D'Amato
Faculty/Staff Collaborators
Anthony D'Amato, Jane Foster, Kevin Evans, Christopher Woodall
Status
Graduate
Student College
Rubenstein School of Environmental and Natural Resources
Program/Major
Natural Resources
Primary Research Category
Food & Environment Studies
Modeling the effects of global change and associated adaptive silvicultural systems in Northern New Hampshire
Global climate change is predicted to have significant but unknown impacts on future forest structure, function, and composition in New England. Combined with a mixed-ownership landscape and dynamic land use history in this region, it may be challenging to meet diverse landowner management objectives using historic silvicultural techniques under future changing conditions. The Adaptive Silviculture for Climate Change (ASCC) project, which aims to provide a toolbox of approaches to forest management for many outcomes under global change, can be employed to break down barriers to communication and implementation between scientists and landowners, resulting in the creation of silvicultural strategies that are both attainable and promote forest resiliency in the face of future changes. This research integrates field measurements from an operational field study at the Second College Grant ASCC project site in Coos County, New Hampshire, with LANDIS-II landscape simulation models to model future forest conditions based on projected changes in climate, disturbance, and associated management regimes. It involves four main management approaches: resistance, resilience, transition, and no management treatments. These stochastic models allow us to evaluate the effectiveness of various adaptive silvicultural strategies at sustaining forest structure, composition, ecosystem services, and landowner management objectives in the context of a changing and uncertain climate future. Resilience and transition treatments are projected to fare the best in terms of maintained ecosystem services under simulated climate models by employing uneven-aged management strategies that emphasize species diversity, structural heterogeneity, and the influx of climate-adapted species. This work underscores the utility of landscape simulation models for evaluating the outcomes of adaptive silviculture and its impacts on future forest structure, function, and composition so as to aid decision making under an uncertain climate future.