The effect of climate change on tropical rainforest vegetation pattern

The effect of climatic change on tropical vegetation is of global and regional concern because of the high biodiversity and the potential feedback to the carbon, water, and nutrient cycles. One of the most critical aspects for assessing broad-scale consequences of climate change is our understanding of how vegetation may change. Models relating vegetation and environmental conditions can be developed for large regions. For a simple application of static models of vegetation–environment relationships, one would have to assume that the probability of species (or vegetation) occurrence conditional on environmental conditions is constant in time (abbreviated as the POCEC assumption). This assumption is critical and difficult. In this paper, we evaluate how the spatial arrangement of forest pattern may constrain vegetation change as predicted by a spatially static artificial neural network (ANN) model. We have relaxed the POCEC assumption by subjoining a spatially dynamic component based on the cellular automata approach. The ANN model quantifies a most suitable forest type based on the conditional probability of vegetation in the environmental space, whereas the cellular automata model imposes spatial constraints on the transition to the best-suited type. We adapt the cellular automata algorithm to successively increase spatial constraints, hence relaxing the POCEC assumption. Our study area is located in Northern Queensland and encompasses 20 000 km2. We evaluate the effect of the +1 °C mean annual temperature and the −10% mean annual precipitation change. A comparison of predictions of vegetation change with the different models indicates that the spatial arrangement of vegetation in the ‘Wet Tropics’ region may impose relatively few constraints for the region's potential change. Depending on the strength of spatial effects included in the models, the predicted future vegetation patterns differ from 1 to 10% of the study area. However, if in addition to spatial constraints ecological constraints also are considered (e.g. prohibiting several transitions that would appear very unlikely to experienced forest researchers), the predictions may differ by as much as 27%, showing a relatively strong dependence of predictions on assumptions about patch-level processes. Furthermore, using different models allows us to assess the uncertainty associated with predictions. The results demonstrate a relative certainty of a predicted decrease of notophyll rainforest types and an increase of medium open forests and woodlands, respectively, whereas the predictions of mesophyll vine forest and wet sclerophyll vegetation differ strongly among different models