Journal
ECOLOGICAL MODELLING
Volume 419, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ecolmodel.2020.108947
Keywords
Importance value; Sensitivity analysis; Climate change; Bootstrapping simulations
Categories
Funding
- Department of Forestry and Natural Resources, Purdue University
- USDA National Institute of Food and Agriculture, McIntire Stennis project [1017711]
- National Science Foundation [DMS-1555072, DMS-1736364, DMS-1821233]
- U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability Advanced Grid Modeling Program
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Estimation of forest population dynamics is critical for forest management decisions making. In this study, we developed an innovative climate-sensitive matrix model using random forests (RF) algorithm to estimate tree diameter growth, tree mortality, and stand recruitment and consequently predict population dynamics of the central hardwood forests under four climate scenarios (i.e. Representative Concentration Pathway [RCP]2.6, 4.5, 6.0, and 8.5). Based on post-sample validation, this RF matrix (RFMatrix) model was more accurate than the traditional climate-sensitive matrix model and Landis pro 7.0. According to the importance values of all predicted variables, the variability in tree diameter growth, tree mortality, and stand recruitment was mainly explained by local tree and stand-level factors, followed by climatic and anthropogenic factors, and soil factors were the least important for all the species. Additionally, our model predictedthat climate change could substantially reduce total stand basal area. The RFMatrix model and its prediction results could assist future forest population dynamics studies on the central hardwood region under changing climate.
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