Modelling Biodiversity; Simulating a Complex Environmental Dynamic

It has widely been recognized that climate change produces effects in vegetation distribution (McMahon et al., 2011), therefore, different approaches have arisen in order to understand, measure and predict the effect of environmental change on the biosphere.

Dynamic Global Vegetation Models (DGVMs) have extensively been used to study the biodiversity dynamic (e.g. Sato, 2009, Moncrieff et al., 2015, Kucharik et al., 2006, and Halofsky et al., 2013). This approach integrates diverse knowledge of vegetation dynamics, including physiology and biogeochemistry, for different climate scenarios in order to predict vegetation and ecosystem changes due to climate change. Although these models represent advanced tools to develop biodiversity simulations, it has been argued that they are not able to obtain suitable results at landscapes scales (Hickler et al., 2004, Halofsky et al., 2013). An interesting study carried out by Halofsky et al., 2013 combines DGVMs approach with State and Transition Models (STMs). Although the last ones simulate vegetation changes by disturbances at local or landscape scales, normally these do not consider the effects by climate dynamics. This study offered a novel modelling approach combining the strengths of two different models families, in order to predict vegetation changes by climate change at landscape scale. The model showed satisfactory results which were contrasted with monitoring data available to the study area.

Forest inventory plots and potential vegetation in Oregon, obtained from DGVM-STM model; modal vegetation class estimated for 1971-2000 (recent historical) and forecast for 2100 (Halofsky et al., 2013)

Another interesting study was conducted by Medvigy and Moorcroft (2011) in the Harvard Forest, northeaster USA, and Quebec. The researchers assessed the performance and accuracy of a specific biosphere model called Ecosystem Demography Version 2 (ED2) whose outcomes were contrasted against biodiversity data obtained through a continuous monitoring in the study area for 11 years (see Figure below). The developed approach consisted in the application of two different models parameterizations; one of them using conventional setting (from literature) and a second one using parameters estimated with the available data from monitoring. According to the study, the second model had a better response than the conventional one, and it was proposed as a powerful tool to predict and forecast different biodiversity variables of interest in that specific area. 

Forest inventory: Northeaster USA (green) - Quebec (blue) (Medvigy and Moorcroft, 2011)

This approach represents an effective tool since the monitoring data was used not only in the validation process but also in the prediction parameters definition. However the lack of data could be a relevant restriction for this approach in areas without available and suitable information.

Similarly to DGVMs and STM models, the presented ED2 approach based fundamental processes of its development on biosphere data. This highlights the fundamental role of environmental monitoring in biodiversity modelling. In the next post we will analyse the relevance of the biosphere monitoring and its close relationship with the accuracy of biodiversity models.