My interest in palaeoecology, vegetation dynamics, fire history and human impact on terrestrial ecosystems mainly concerns the interpretation of long-term disturbances (causes/consequences) and the possible connection to the ongoing climate change.

I have previous experience in the analysis of Holocene pollen and charcoal samples from peat bogs, mires and soils. I’m also skilled at sedimentological laboratory analyses, at the interpretation of palaeomagnetic data, at various analytical techniques and at stratigraphic and palaeoenvironmental interpretations. I have some knowledge in statistical analysis and in programming using R.

My present research at the Lund University Centre for Studies of Carbon Cycle and Climate Interaction (LUCCI, http://www.lucci.lu.se/) is mainly focused on the reconstruction of global/regional biomass burning over the Holocene based on sedimentary charcoal records. The most important aim is to better understand past fire history and environmental dynamics in order to use palaeo-data for validating dynamic global vegetation models (DGVMs) designed to project potential future responses to climate, vegetation and anthropogenic land-cover variations. More in detail, I’m trying to compare palaeodata from pollen and charcoal to vegetation and fire-indices simulated with LPJ-GUESS (a DGVM developed by researchers from Lund University (Sweden), from the Potsdam Institute for Climate Impact Research (Germany), the Max-Planck Institute for Biogeochemistry (Germany) and from a number of other institutes).

Due to multiple interactive factors influencing fire patterns (such as atmosphere–climate–fire–vegetation feedbacks at continental/regional scales and fire–vegetation–soil–land use feedbacks at local scales), driving forces of biomass burning are still highly uncertain. I’m firmly convinced that palaeoecological reconstructions can provide an important source of information about how our climate system actually works and that the assessment of the effect of fire regime during the past will give us important information to help predicting what might happen in the future.