How has plant functional diversity changed in the last century and how is it expected to change in the future?

Mateus is an ecological modeler specializing in dynamic vegetation models and working as a part of the biodiversity and climate center of the Senckenberg society for nature research. His background is environmental sciences, having completed a bachelor’s and Master’s in plant ecology at the Federal University of Pernambuco, with studies which focused on the use of remote sensing and GIS in the analysis of fragmented tropical forest landscapes and their functions. This was followed by a period of work as a consultant in an own company, in which he used remote sensing and GIS to provide forest mapping services for the agriculture and NGO sectors. His PhD studies were carried out in the Helmholtz center for environmental research in Leipzig, Germany, where he analyzed forest fragmentation and biotic interactions in space and in time using a combination of remote sensing and the FORMING forest model. This was followed by a postdoc position in Senckenberg within the DFG funded RESPECT project, where he included plant trait variation in a dynamic global vegetation model (LPJ-GUESS) to study how nutrient limitation shapes plant diversity in a tropical mountain forest elevation gradient.

Research objectives

Understanding global patterns of biodiversity and how climate change may affect them in the future is one of the main open questions in earth system science. In addition, shifts in plant functional diversity are recognized as a main driver in affecting ecosystem processes such as the carbon cycle. Several key plant traits are intrinsically linked to vegetation function, such as photosynthesis, carbon storage and water/nutrient uptake. Therefore, global maps of these traits are invaluable for understanding interactions and threats to the environment and the biosphere, but observations are sparse and not representative for many regions. Current global maps of vegetation traits are generated by filling in the gaps from observations but are primarily based on empirical or statistical relationships between trait observations, climate and soil data, and remote sensing data. These approaches have a low explanatory power, cannot embrace many traits, and are limited since their extrapolation relationships may not be ecologically consistent.