Predicting how aquatic ecosystems will respond to increasing pressures — from changing climate to increasing demands for ecosystem services — is extremely challenging, due to complexities of aquatic ecosystems themselves, as well as their interactions with adjacent ecosystems and exogenous forcing. Process-based aquatic ecosystem models are powerful tools to simulate long-term ecosystem processes. A recent paradigm in modeling is to combine mechanistic knowledge of ecosystem processes with data from long-term monitoring and high-frequency measurements in a data-driven deep learning model.
We develop and apply aquatic ecosystem models to research:
Physical Limnology and Metabolism
Understanding mixing in lakes is critical for accurately simulating the distribution of heat, nutrients, and gases, which directly influence ecological processes such as primary production and biogeochemical cycling. So, how does mixing affect aquatic metabolism, which is a key limnological concept to infer information about carbon, dissolved oxygen, nutrient and energy cycling in aquatic ecosystems? As metabolism distills water quality information and carbon cycling into a complex metric with a high order of information, it conceptually allows the inference of how freshwater ecosystems respond to environmental change. Our research aims to quantify how temporal and spatial feedbacks between biotic (phytoplankton succession, zooplankton grazing) and abiotic processes (stratification, ice formation, nutrient runoffs) will be affected by climate change and land use changes. Our workhorse for these kind of studies are vertical one-dimensional lake models, which are powerful tools for understanding ecosystem dynamics.
Take a look at: Bucak et al. (2026), Ladwig et al. (2022), Moore, Mesman, Ladwig, Feldbauer et al. (2021), Ladwig et al. (2021).
Ecological Knowledge-Guided Machine Learning
Merging process-based modeling with deep learning shows promising results on how to improve lake modeling by training deep learning models on synthetic and observed data. Still such hybrid models have not yet widely been developed and tested for ecological research question. Developing novel hybrid models could revolutionize our applications of aquatic ecosystem models by providing improved projections that are still ecologically and physically valid.
Take a look at: Ladwig et al. (2023) and Eco-KGML.
Ecosystem Functioning
Beyond physics and water quality, ecological patterns and dynamics across species shape an aquatic ecosystem. But how will climate change and anthropogenic factors affect biodiversity and phenological succession patterns? We are conducting empirical, laboratory and theoretical studies to research how ecosystem functioning in freshwater ecosystems develops and evolves. Theoretically, we are interested in developing conceptual models on how phenological shifts as well as mismatches can change future ecosystem structures.
Take a look at: Rohwer, Ladwig et al. (2023).
"The purpose of computing is insight, not numbers."
- Richard W. Hamming