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Bioclimatic Modeling of Southern African Bioregions and Biomes Using Bayesian Networks

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Abstract

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.

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Acknowledgements

We are grateful to the anonymous reviewers and editor for their useful comments, which contributed to the final version of the manuscript. We thank Norsys Software Corporation, in particular Brent Boerlage and Jennie Yendall, for the complementary license of their Netica software.

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  1. Swaziland National Trust Commission, P.O. Box 100, Lobamba, Swaziland

    Wisdom M. Dlamini

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  1. Wisdom M. Dlamini
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Correspondence to Wisdom M. Dlamini.

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Dlamini, W.M. Bioclimatic Modeling of Southern African Bioregions and Biomes Using Bayesian Networks. Ecosystems 14, 366–381 (2011). https://doi.org/10.1007/s10021-011-9416-z

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