Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks

Abstract

Rising atmospheric levels of carbon dioxide and ozone will impact productivity and carbon sequestration in forest ecosystems. The scale of this process and the potential economic consequences provide an incentive for the development of models to predict the types and rates of ecosystem responses and feedbacks that result from and influence of climate change. In this paper, we use phenotypic and molecular data derived from the Aspen Free Air CO2 Enrichment site (Aspen-FACE) to evaluate modeling approaches for ecosystem responses to changing conditions. At FACE, it was observed that different aspen clones exhibit clone-specific responses to elevated atmospheric levels of carbon dioxide and ozone. To identify the molecular basis for these observations, we used artificial neural networks (ANN) to examine above and below-ground community phenotype responses to elevated carbon dioxide, elevated ozone and gene expression profiles. The aspen community models generated using this approach identified specific genes and subnetworks of genes associated with variable sensitivities for aspen clones. The ANN model also predicts specific co-regulated gene clusters associated with differential sensitivity to elevated carbon dioxide and ozone in aspen species. The results suggest ANN is an effective approach to predict relevant gene expression changes resulting from environmental perturbation and provides useful information for the rational design of future biological experiments.