Hilbert, David W., and Ostendorf, Bertram (2001) The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecological Modelling, 146 (1-3). pp. 311-327.

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Abstract

A feedforward artificial neural network, coupled with a regional GIS (geographic information system), is described that is being used to assess the potential impacts of climate change on a complex landscape of tropical forests. The model quantifies the relative suitability of environments for 15 forests classes using the best information that is available: a structural-environmental classification of forest types, vegetation maps and spatial estimates of environmental variables. Inputs to the model include climate variables, soil parent material classes and terrain variables. The model is highly successful at distinguishing the relative suitability of environments for the forest classes with 75% of the forest mosaic accurately predicted by the model at a one hectare resolution over more than two million hectares. The model was used to estimate potential forest distributions in several climates occurring since the end of the last glacial period. These distributions shift dramatically in response to scenarios representing past climates. Certain locations are occupied by a forest class in only some climates while others are always occupied by the same class despite large changes in regional mean annual temperature and precipitation. Using the model to assess the possible impacts of future climate change and estimating the pre-settlement distribution of forest types in the region is also discussed. The coupling of neural networks with a cellular automata model is also described as a means to assess the importance of spatial constraints on the potential redistribution of forest types in the future. The usefulness of artificial neural networks when applied to vegetation change studies in our region suggests that this approach could be applied in many tropical regions, where floristic diversity is high and mechanistic understanding is comparatively low.

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