Assessing forest yield and site suitability for Eucalyptus grandis x E. urophylla in coastal Zululand, South Africa, under climate change scenarios
Thesis (MFor)--Stellenbosch University, 2022.
ENGLISH ABSTRACT: This study aimed to project future mean annual temperature (MAT), mean annual precipitation (MAP), species site suitability, forest yield and the risk of the Leptocybe invasa pest for Eucalyptus grandis x urophylla (E. g x u) in coastal Zululand of South Africa, under two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and 8.5), each for the intermediate term (2041 – 2060) and long term (2081 – 2100). The study utilized projected future climate variables from Global Circulation Models (GCMs) used in phase five of the coupled Model Intercomparison Project (CMIP5) for use in the R version of 3PG (Physiological Processes Predicting Growth) to simulate forest stand volume. The climate data was also combined with recorded presence of the Leptocybe invasa pest to develop an ecological niche model using the Maximum Entropy (Maxent) model and project the possible risk of the pests’ infestation in the study site. Generally, projected future climates revealed increasing MATs amid reducing MAP over most of the study points in both RCP 4.5 and 8.5 as well as shifts in species site suitability for E. g x u. After validating and testing the r3PG model for use in coastal Zululand using field data, the r3PG runs across the future scenarios projected a pattern of reducing volume yield for E. g x u. A second species that was tested, Pinus elliottii, exhibited a relatively more severe trend of reducing yields from the current scenario through the future scenarios. These projected changes were observed amidst a reducing risk of L. invasa over the study grid points in both pathways by the end of the century. Even though the data had some inaccuracies, acquired from third party sources, and based on assumptions from GCMs, this study shows how integrating projected climate information, processed-based growth models and pest risk models can improve the information available to South Africa’s Forest industry. The integration of these data and models could contribute to the preparedness of the forest industry and inform policymaking towards mitigating uncertain climate futures.
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