Next-generation dynamic global vegetation models: learning from community ecology

Abstract

Dynamic global vegetation models (DGVMs) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they define vegetation and by their simplistic representation of competition. We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a site's biotic and abiotic conditions. The aDGVM2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life-history strategies that are suggestive of colonization-competition trade-offs. The aDGVM2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.