We Must Continue Monitoring Our Planet…

Environmental monitoring is probably one of the most relevant activities in continuous development in environmental sciences. Collected data is widely used to understand natural phenomena and their complex relationship with anthropogenic activities. Furthermore, as we saw in the previous post, data acquisition is playing a crucial role in models developing not only for validation processes, but also as input data for parameters definitions.

One appropriate question of using models to estimate the effects of climate change on biodiversity is; how accurate is the estimation or prediction of these effects? Normally, more accurate input data should entail less uncertainty of models. Even though there are many factors affecting the accuracy of models outcomes, such as user expertise, it is particularly significant the effect of observational datasets in models performance. Good examples of this can be found in models applied on biodiversity.In addition to technical challenges of monitoring activities, a huge challenge of collecting biosphere data is the inherent difficulty to measure significant variables. In effect, as Medvigy and Moorcroft (2011) argue, some relevant parameters to biosphere models are difficulty to obtain directly, like carbon distribution. As a solution to this, monitoring data can be used to estimate indirectly variables which are difficult to obtain through direct measurements. This is a significant contribution of monitoring data to environmental modelling and it represents a useful method in order to minimize models uncertainties (Medvigy and Moorcroft, 2011).

Estimated shift of vegetation gradient over a 30 years period, Santa Rosa Mountains, Southern California, USA (Breshears et Al, 2008)

It has also been recognized the needs of improving models introducing more detailed observational data, in this case, related to plant´s distribution patterns. As Thuiller et al. (2007) suggest, the introduction of vegetation dynamic data in models conceptualization represents an opportunity of improvement for Dynamic Global Vegetation Models (DGVMs). Indeed, the same Author assert that plants migration and their distribution patterns are consider relevant sources of uncertainties in prediction models in the climate change context. Similarly, Breshears et al.,2008 argue that some variables such as migration rates and variability in ecosystem properties are highly sensitive to climate change. Therefore, these variables should be carefully considered in models input and parameters definition.It seems to be clear that including monitoring data, such as patterns of plants distribution according to a desired scale of analysis, should lead to a reduction of uncertainties in the prediction of biodiversity response due to global environmental change. However, this requires a previous systematisation of complex information whose availability in a proper space and time scales, it is not ensured for every area of interest. We have to consider this before.