Projecting forest outcomes for Prince Edward Island National Park under climate change using a process-based forest landscape model
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Date
2022-12
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University of New Brunswick
Abstract
Climate change is projected to have profound impacts on the Acadian Forest ecosystem. Large uncertainties (climate and future disturbances effects on stand composition, structure) make it difficult to determine the best course of action (management). Novel forest simulation models allow us to grow the forest under a changing climate and disturbance regimes, assess vulnerabilities, and test different management strategies.
In this project, iLand (v1.1.1), a landscape-scale process-based forest model that offers a novel approach for assessing the feedback between individual trees and their environment (ecosystem processes, climate, and disturbance), was calibrated, and used for the first time in the Acadian Forest Region (AFR). We applied the model to the forest of Prince Edward Island National Park (PEINP), a highly degraded forest with increased vulnerability to climate change. PEINP is an ideal landscape for the initial regional application of this model because of high intensity inventory data available for set up and calibration. The Park is also representative of the heavily disturbed forest found throughout the AFR, providing a framework for future studies to be conducted in the region using iLand.
Forest outcomes were quantified through various stand measures and discussed in relation to the management goals of the Park and implications of climate change for the AFR. As the accessibility and capacity of process-based forests models increases, this project provides a case study for forest managers looking to expand their toolbox to deal with climate change and increasing disturbance activity.