Vegetation Modelling - Looking to the Future

Having looked back at how species responded to the changing climates of Northern Hemisphere glaciation and subsequent warming it is now important to look forward to how this may help us model how species will react to future climate change. Historical records are important as they can provide the background for predictions for the future. As shown in a previous post, ‘The Bioclimate Envelope Model - outdated and simple?’, the bioclimate envelope model is still one of the most commonly used models, although it lacks the complexity it simulates. Looking forward, Dynamic Global Vegetation Models (DGVMs) will provide greater capacity for accurate predictions of global shifts of species. 

Some challenges that these models face include (Neilson et al. 2005): 

• ‘Accurately estimating the importance of long-distance dispersal in the establishment and proliferation of species within new vegetation communities.’
• ‘Aggregating information from individual species into the category of plant functional types (PFTs).’
• ‘Incorporating temporal and spatial heterogeneity within large spatial grid cells into the modeling of migration.’

One factor that has to be taken into account is how the warming will affect the ecosystem alteration. Ecosystems tend to change species composition by in situ conversion, where the subdominant species replace the dominant ones (due to different tolerances) or via migration of species from the local region. Adaptability of ecosystems to rapid changes appears to be related to the diversity of species traits that are available to assume old and new functions (Loreau et al. 2002). The more adaptive an ecosystem is the less likely there will be sudden changes in ecosystem function. An example of this can be seen in the southwestern United States with the replacement of the regionally dominant pinyon pine (Pinus remota) with another species (Pinus edulis) during the glacial to interglacial transition (Lanner & Wan Devender 1998). The shift of dominance may have been large but the ecosystem retained its function during the climatic change.

DGVMs are conceived to merge vegetation distribution and ecosystem process models (Cramer et al. 2001). Recent advances in the capabilities of DGVMs have been impressive and include dynamic simulations of vegetation distributions for national and global assessments of climate change (Cox et al. 2000). There have been difficulties with spatial and temporal scales, seeing as current DGVMs often operate with a grid cell size of approximately 50 km on a side, much larger than local dispersal distances. Also, many of the most important processes (e.g., photosynthesis, flowering, and seed set) are also faster than the model time step, which is typically daily to monthly, and must therefore be scaled up. However there will always be a threshold beyond which certain parameters can be simulated. 

DGVMs should be the primary tool for decision makers to evaluate the question of safe rates of climate change, and impacts on carbon and other biosphere feedbacks to the atmosphere. However, migration must also be incorporated into the models. The entire process of migration is lacking in observational data for use in the vegetation models. Data required includes: the processes from flowering and seed set to short and long distance dispersal, establishment and growth, and then to completion of the cycle. Data needs include: quantification of initial establishment and early growth in the face of competition and other biotic interactions. 


Incorporating an expanded concept of PFTs in DGVMs including migration and in situ conversion will be important to model future global ecosystem and species shifts. I have looked at the negative aspects of DVGMs in this post, even through they do represent the most accurate modelling technique for future use. In the next post I will show an example of how these models can be used.