Research

Pflanzen in Glaskolben, die in einer Kühlung stehen

By taking a quantitative view of ecological systems, we conduct our research in the following areas:

Biometrics: Statistical methods in the environmental sciences, in particular problem cases of distribution analysis (spatial autocorrelation, collinear predictors)
Statistical ecology: application of statistical methods for the analysis and theorization of ecological issues
Forest measurement and surveying: Further development and testing of measurement methods and equipment for terrestrial surveys and inventories. Development of methods for volume determination, species prognosis and increment determination
Analysis and modeling of ecological networks (focus on pollinator networks)
Bayesian statistics, in particular application in hierarchical statistical models and for quantifying error propagation
Evidence-based ecosystem services: Scientific quality of the ecosystem services concept by classifying the study situation
Nature conservation topics with statistical relevance (e.g. by evaluating databases, complex statistical analyses or more complicated population models)
Statistical parameterization of forest models; this is less about the development of forest models and more about the estimation of model parameters from measured environmental data
Causes of biodiversity, from the local to the global scale
Behavioral ecology of mammals, especially habitat preferences and movement patterns

Research projects

Collaborative Research Centre

Small Data

In many areas of science, particularly in environmental science, process models have been developed to represent our knowledge of the mechanisms underlying fluxes and states. We hypothesize that such process knowledge can significantly improve purely data-driven neural networks on small datasets. Therefore, we aim to extend the approaches for process models in neural networks by enriching neural networks with existing biophysical process knowledge and using explainable AI to improve process models.

Collaborative Research Center

ECOSENSE

The interdisciplinary research project ECOSENSE will investigate all relevant scales in a next generation ecosystem research assessment. Our vision is to detect and predict critical changes in ecosystem functioning based on the understanding of hierarchical process interactions. To this end, ECOSENSE will develop, implement and test a new, versatile, distributed, cost-effective, autonomous and intelligent sensor network based on novel microsensors tailored to the specific needs in remote and harsh forest areas.

Research Training Group

ConFoBi

ConFoBi (Conservation of Forest Biodiversity in Multiple-Use Landscapes of Central Europe) is a Research Training Group (RTG) at the University of Freiburg funded by the German Research Foundation (DFG). ConFoBi pursues two overarching goals:
(1) the qualification of early career researchers through a structured doctoral program for leadership positions within and outside academia
(2) the implementation and execution of an excellent research program.

Research Unit

REASSEMBLY

REASSEMBLY aims to understand the dynamics of networks to uncover the rules of network dissolution and reconstruction in a highly diverse tropical lowland rainforest ecosystem. We investigate the dynamics of natural forest recovery from agriculture along a chronosequence and the contribution of rebuilt networks to the resilience of ecosystem processes to disturbance. We compare the evolution of predator-prey, plant-pollinator and plant-seed dispersal networks as well as decomposition networks between mammals, dung beetles and seeds and between deadwood, ants, termites and beetles.
Our research group is funded by the German Research Foundation (DFG).

About us

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Teaching

Our courses and information about theses

Equipment

Overview of research equipment and facilities

Our team

Introduction of our team members and list of contact details

Peters, A., Smith, A. F., Henrich, M., Dormann, C. F., & Heurich, M. (2025). Temporal displacement of the mammal community in a protected area due to hunting and recreational activities. Ecological Applications, 35(7), e70118. https://doi.org/10.1002/eap.70118
Plein, M., Feigel, G., Zeeman, M., Dormann, C. F., & Christen, A. (2025). Using Gradient Boosting for gap-filling to analyze temperature and humidity patterns in an urban weather station network in Freiburg, Germany. Urban Climate, 62, 102496. https://doi.org/10.1016/j.uclim.2025.102496
Wesselkamp, M., Chantry, M., Pinnington, E., Choulga, M., Boussetta, S., Kalweit, M., … & Balsamo, G. (2025). Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand. Geoscientific Model Development, 18(4), 921-937. https://doi.org/10.5194/gmd-18-921-2025
Feigel, G., Plein, M., Zeeman, M., Metzger, S., Matzarakis, A., Schindler, D., & Christen, A. (2025). High spatio-temporal and continuous monitoring of outdoor thermal comfort in urban areas: a generic and modular sensor network and outreach platform. Sustainable Cities and Society, 105991. https://doi.org/10.1016/j.scs.2024.105991
Heger, T., Elliot‐Graves, A., Kaiser, M. I., Morrow, K. H., Bausman, W., Dietl, G. P., … & Jeschke, J. M. (2025). Looking beyond Popper: how philosophy can be relevant to ecology. Oikos, 2025(2), e10994. https://doi.org/10.1111/oik.10994
Oehler, F., Hagen, R., Hackländer, K., Walton, Z., Ashish, K., & Arnold, J. (2025). How do red foxes (Vulpes vulpes) explore their environment? Characteristics of movement patterns in time and space. Movement Ecology, 13(1), 4. https://doi.org/10.1186/s40462-024-00526-1

All Publications of the department