Helsinki group

Office: 5619 (Biocenter 3)
E-mail:

Emilie is a Postdoctoral researcher working on numerous projects within REC and the Spatial Food Web Ecology Group. She is an applied pollinator ecologist by training, with a special interest in the ecology of non-bee pollinators such as moths. Her current research addresses: 

• The relationship between biodiversity, land-use changes and ecosystem functioning.
• The species- and community- level temperature niche changes in response to climate change.
• The ecology of pollinators in agricultural and forest systems.

Office: room 4602 (Biocenter 3)
Email:

Tanel Vahter is a Marie Skłodowska-Curie Postdoctoral Fellow in the Spatial Food Web Ecology group at the University of Helsinki. 

His research explores how past land-use changes continue to shape soil biodiversity today, focusing on delayed species loss and “extinction debts” in soil communities. Using molecular data and historical landscape records, he studies how land-use history influences soil fungi, bacteria, and mesofauna. By combining ecological network and statistical modelling approaches, his work aims to reveal which species traits promote persistence or extinction and how this knowledge can guide biodiversity conservation in managed ecosystems.

Office: room 4602 (Biocenter 3)
Email:

I am a community ecologist interested in how abiotic and biotic factors shape species interactions, particularly those between plants and microbes. In my postdoctoral work, I aim to expand this research by exploring how insects and other trophic groups influence these interactions.

• species interactions
• community ecology
• plant-microbial interactions
• microbiome

E-mail:

My main research interests are how we can describe the ways species fit into ecological networks and what the network perspective can add to our understanding of ecology. I am currently investigating how phenological change in plants and insects affects pollination networks, including crops that we humans depend on. I am also interested in how we can draw better conclusions from messy and missing data.