Current research themes
The ecology of species distributions
Species distribution modelling (SDM) seeks to use distribution data to understand the controls on species distribution and to forecast potential future distributions of species. In our research we have been trying to add more biological detail into such models (see this overview Higgins et al. 2012, Journal of Biogeography). In particular we have adapted the Thornley Transport Resistance model (a model that simulates growth and allocation in plants) for species distribution modelling (see Higgins et a. 2012, Journal of Biogeography). This model not only describes species distributions well, but has proven to be surprisingly useful. For example, we were able to use the model to understand why some species become invasive, whereas others fail to invade (see Higgins and Richardson 2014, PNAS).
The ecology of biomes
Biomes are major vegetation formations, defined by structural and functional attributes rather than by floristic species composition. Recent research has made it clear that that different floristic instances of the same biome may respond very differently to climate change. We have shown that this is because functional and structural attributes of some biomes are more strongly influenced by evolutionary history than by selection (see Moncrieff et al. 2014, Global Ecology and Biogeography). And indeed when we examine the climatic profiles of the world’s biomes it is clear that biomes are not well constrained by climate (Moncrieff, Hickler and Higgins 2015, Global Ecology and Biogeography). We have proposed a new functional definition of biomes that circumvents some of these problems (Higgins et al. 2016, Global Change Biology). This paper builds on analyses of global patterns in leaf phenology we have been working on (e.g. Buitenwerf, Rose and Higgins, 2015 Nature Climate Change)
Dynamic Global Vegetation Models (DGVMs)
Dynamic global vegetation models aim to use biophysical principles to predict how vegetation is influenced by climate, soils and disturbances. We developed a DGVM in 2009 (which we called aDGVM, Scheiter and Higgins 2009, Global Change Biology) that allowed a better representation of how fire and plants interact. We have used this model to explore potential future vegetation states in Africa and Australia (e.g. Scheiter et al. 2015, New Phytologist). A few years ago we switched to developing a trait-based DGVM (which we called aDGVM2). aDGVM2 allows individual plant traits to evolve within simulation runs (see Scheiter, Langan and Higgins 2013, New Phytologist for an overview). Essentially this forces the model designer to focus more on trade-offs between traits than the trait values themselves. Although this dramatically simplifies the model parameterisation process, it does challenge our knowledge of fundamental trade-offs in ecology: this in turn set priorities for empirical work. Working with DGVMs is a sobering experience since it confronts one with the fact that our capacity to anticipate the plant communities that will assemble as climates change is rudimentary (see Higgins 2017, Ecosystems). Our work with the aDGVM2 is an attempt to model the assembly process by using a trait based approach that primarily focuses on trade-offs between traits and competitive interactions between species.