Natural climate solutions, such as reforestation, are increasingly called for to lower atmospheric CO2 concentrations and prevent further warming of the climate. Predictive modeling of forest stand dynamics provides a quantitative framework that can be used to select the tree species with the highest carbon (C) uptake potential for restoration efforts based upon site-specific and species-specific data. To simulate stand dynamics and compare C uptake and storage potential of three pine species across Florida, I developed individual-based models that combined environmentally-sensitive tree growth models with background mortality taken from the literature. Growth model parameters were estimated using space for time (SFT) substitution and mortality model parameters were estimated from published literature possibly introducing biases into model development. Therefore, to explore these possible biases, parameters of both growth and mortality models were calibrated through a Bayesian inversion technique using forest growth and mortality data. Simulated results of total above-ground biomass (AGB), for both calibrated and SFT models, were compared with Forest Inventory and Analysis (FIA) re-measurement data. Model results demonstrate that SFT substitution adequately predicted growth rate of P. taeda, P. palustris, and P. elliottii, while Bayesian inversion helped to calibrate parameters in mortality functions reported in published literature. The results highlight the possible benefit of using SFT substitution in tree growth models, helping to save time and resources, as this modeling framework can be easily replicated for forests in other states using open-sourced data from FIA and globally gridded raster data of climate and edaphic properties.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1679 |
| Date | 01 January 2021 |
| Creators | Formanack, Alicia |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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