Fire and Climate: the Implications of Global Change in the Cape Floristic Region of South Africa

• Main Author: Wilson, Adam Michael
• Format: Text
• Language: English
• Published: OpenCommons@UConn 2012
• Subjects: Biology; Ecology|Climate Change|Remote Sensing; Antarctic; Antarctic Ocean; The Antarctic; Antarc*
• Online Access: https://opencommons.uconn.edu/dissertations/AAI3510544; http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3510544

Climate change threatens to affect ecological dynamics at regional to global scales. This dissertation quantifies the role of climate in driving ecological processes in the Cape Floristic Region (CFR) of South Africa, a region of extraordinary biodiversity. In the second chapter an extensive database of observed wildfires and high-resolution meteorological data were used to build a novel spatially and temporally varying survival model. This model was used to analyze fire regimes in the Cape Floristic Region (CFR) of South Africa during the period 1980–2000. The analysis revealed an important influence of seasonally anomalous weather on fire probability and identified that the Antarctic Ocean Oscillation (AOO) is also associated with fire risk. In the third chapter, a Hierarchical Bayesian model was used to assess the relationship between biomass measurements collected at finee scales (2x3 in) with the normalized difference vegetation index (NDVI), a satellite derived metric. The analysis revealed a strong correlation between NDVI and biomass and supports the use of NDVI in spatiotemporal analysis of vegetation dynamics in Mediterranean shrubland ecosystems. In the fourth chapter post-fire ecosystem recovery was modeled using NDVI observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite as a function of age, topography, and climate. The analysis identified the important role of climate in driving the recovery process and suggests that this critical ecosystem property will be sensitive to climate change. In the fourth chapter a climate-aided Bayesian kriging approach is used to interpolate 20 years of daily meteorological observations (maximm and minimum temperature and precipitation) to a 1 arc-minute grid for the CFR. Independent validation data revealed overall predictive performance of the interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum temperature, minimum temperature, and precipitation, respectively.^