How to model it: Ecological models, in particular simulation
models, often seem to be formulated ad hoc and only poorly analysed. I am
therefore interested in strategies and methods for making ecological modelling
more coherent and efficient. The ultimate aim is to develop preditive models
that provide mechanstic understanding of ecological systems and that are
transparent and structurally realistic enough to support environmental
Pattern-oriented modelling: This is a general strategy of using
multiple patterns observed in real systems as multiple criteria for chosing
model structure, selecting among alternative submodels, and inversely
determining entire sets of unknown model parameters.
Individual-based and agent-based modelling: For many, if not most,
ecological questions individual-level aspects can be decisive for explaining
system-level behavior. IBM/ABMs allow to represent individual heterogeneity,
local interactions, and/or adaptive behaviour
Ecological theory and concepts: I am particularly interested in
exploring stability properties like resilience and persistence.
Modelling for ecological applications: Pattern-oriented modelling
allows to develop structurally realistic models, which can be used to support
decision making and the management of biodiversity and natural resources.
Currently, I am involved in the EU project CREAM, where a suite of population
models is developed for pesticide risk assessment.
Standards for model communication and formulation: In 2006, we published
a general protocol for describing individual- and agent-based models, called the
ODD protocol (Overview, Design concepts, details). ODD turned out to be more useful
(and needed) than we expected. An update of the protocol and its
description appeared 2010(for further details, see
VIBee: Vitality indicators for honeybees: In this project an electronic counter of the bees' flight activity is used to provide new data that could be used
to obtain a more comprehensive picture of the vitality of a honeybee colony. Stress tests are performed, the response of flight
activity registered and then interpreted by mimicing the empirial settings in the simulation model BEEHAVE.
For further information, see:
OCELI: Artificial Intelligence for honeybees. The aim of OCELI is to research and develop a novel technology that will make a decisive contribution to realizing
sustainable agriculture with intact pollinator population. Video footages are analyzed using machine learning to not
only identify and count bees leaving and entering a hive, but also to distinguish between nectar and pollen foragers.
Stress tests in the field and in tunnels are interpreted with the honeybee simulation model BEEHAVE.
CAUSES: Exploring causation in sustainability science. The purpose of the CauSES project is to bring together causal thinking in the social and natural sciences to 1) clarify
different notions, their relationships and compatibility, 2) assess disciplinary approaches in terms of their ability to
capture the complex and social-ecological nature of the problems, and 3) develop integrated approaches to causal analysis for
sustainability science. CauSES is an interdisciplinary research environment that brings together philosophers, ecologists,
institutional economists, social-ecological systems researchers, agent-based modellers and mathematicians.
BioDT: Biodiversity Digital Twin. The Biodiversity Digital Twin prototype provides advanced models
for simulation and prediction capabilities, through practical use cases addressing critical issues related to
global biodiversity dynamics. BioDT exploits the LUMI Supercomputer and employs FAIR data combined with digital
infrastructure, predictive modelling and AI solutions, facilitating evidence-based solutions for biodiversity
protection and restoration. The project responds to key EU and international policy initiatives, including the
EU Biodiversity Strategy 2030, EU Green Deal, UN Sustainable Development Goals, Destination Earth.