caa a22 4500 445383836 CHVBK 20180317143025.0 cr unu---uuuuu 170323e20110401xx s 000 0 eng 10.1007/s10021-011-9416-z doi (NATIONALLICENCE)springer-10.1007/s10021-011-9416-z Dlamini Wisdom Swaziland National Trust Commission, P.O. Box 100, Lobamba, Swaziland aut Bioclimatic Modeling of Southern African Bioregions and Biomes Using Bayesian Networks [Elektronische Daten] [Wisdom Dlamini] This study uses Bayesian networks (BNs) to simulate the spatial distribution of southern African biomes and bioregions using bioclimatic variables. Two Tree-Augmented Naïve (TAN) BN models were parameterized from 23 bioclimatic variables using the expectation-maximization (EM) algorithm. Using sensitivity analyses, the relative influence of each variable was determined using the mutual information from which six bioclimatic variables were selected for the final models. Precipitation of the warmest quarter and extra-terrestrial solar radiation was found to be the most influential variables on both bioregion and biome distributions. Isothermality was the least influential bioclimatic variable at both bioregion and biome levels. Overall correspondence was very high at 93.8 and 87.1% for biomes and bioregions, respectively, whereas classification errors were obtained in transition areas indicating the uncertainties associated with vegetation mapping around margins. The findings indicate that southern African bioregions and biomes can be classified and mapped according to key bioclimatic variables. Spatio-temporal, in particular, monthly and quarterly variations in both precipitation and temperature are found to be ecologically significant in determining the spatial distribution of biomes and bioregions. The findings also reflect the hierarchical relationship of biomes and bioregions as a function of local bioclimatic gradients and interactions. The results indicate the ecological significance of bioclimatic conditions in ecosystem science and offer the opportunity to utilize the models for predicting future responses and sensitivities to climatic changes. Springer Science+Business Media, LLC, 2011 Bayesian network nationallicence bioclimate nationallicence biome nationallicence bioregion nationallicence Southern Africa nationallicence tree-augmented naïve nationallicence Ecosystems Springer-Verlag 14/3(2011-04-01), 366-381 1432-9840 14:3<366 2011 14 10021 https://doi.org/10.1007/s10021-011-9416-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10021-011-9416-z text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Dlamini Wisdom Swaziland National Trust Commission, P.O. Box 100, Lobamba, Swaziland aut NATIONALLICENCE 773 0- Ecosystems Springer-Verlag 14/3(2011-04-01), 366-381 1432-9840 14:3<366 2011 14 10021 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer