Our research employs cutting-edge Data Science tools, such as machine learning, artificial intelligence, and big-data computational frameworks, to unravel the underlying mechanisms driving biodiversity. We work to understand and forecast global distribution patterns, ranging from genes to entire ecosystems, and from past climate fluctuations to future climate change scenarios. Our analyses consider anthropogenic impacts, such as climate change, habitat degradation, overexploitation, and the introduction of invasive species. We share our data under the FAIR principle (easy to find, access and reuse) to citizens, managers, and policymakers so that they can make informed decisions on the best strategies for protecting, conserving, and managing biodiversity.

Climate change, habitat degradation, overexploitation, and biological invasions are among the major threats to global biodiversity and ecosystem functioning. These threats are leading to the loss of species and altered energy flows, which can have severe impacts on human health, wellbeing, and economic activities. Current efforts to halt the biodiversity crisis are falling short, highlighting the urgency for policy-relevant research that quantifies the effect of anthropogenic impacts under contrasting scenarios to inform citizens, managers, and policymakers on the best strategies for protecting, conserving, and managing biodiversity. Our team is devoted to developing the necessary tools and providing the relevant information for evidence-based, long-term proactive actions to protect our planet's biodiversity and ecosystems.

Our central question is “what are the consequences of anthropogenic impacts to global biodiversity and ecosystem functioning?”. In particular we aim to:
understand the underlying mechanisms driving the global distribution of biodiversity;
project the consequences of anthropogenic impacts to the distribution of biodiversity, in particular those associated with global climate change under contrasting scenarios (e.g., Paris Agreement expectations vs. reduced mitigation strategies);
identify priority conservation areas to safeguard biodiversity for future generations (e.g., climatic refugia) to inform global conservation and management strategies.