
Turbulent waters of Lake Müggelsee: a symbol of the challenges facing our waters in the wake of global change. The consequences and possible solutions are the focus of a new IGB programme area. | © David Ausserhofer/IGB
The new programme area aims to improve our understanding of the complex responses of freshwater ecosystems and their diverse communities to the challenges of global change, in order to enable more accurate predictions. “By combining interdisciplinary approaches and innovative scientific methods, we aim to set priorities for protecting biodiversity and securing the vital ecosystem services that freshwater systems provide to humanity,” explained IGB Director Professor Luc De Meester.
The programme area focuses on the integration of modelling and empirical research, considering different levels of ecological organisation – from individuals to whole ecosystems – and spatial scales. This approach aims to provide deep insights into the ecological dynamics of freshwater systems, while developing important groundwork for evidence-based policy advice and sustainable freshwater management.
The Joint Science Conference of the Federal Government and the Länder decided at the end of 2024 to permanently increase the core budget of IGB, which is necessary for the implementation of this new programme area. After a three-year start-up phase, the special funding will be transferred to the core budget in the final phase of its development and will then be available for an unlimited period as additional annual funding of around 2.2 million euros. In addition, IGB will contribute more than 500,000 euros per year from its own resources.
As a result, additional expertise will be built up in at least eight new research groups. Three of these groups are already up and running. We asked them what topics they are focusing on and what challenges they face in the process:
Sami Domisch, you are one of the speakers for the new programme area and your group is conducting research on global freshwater biodiversity. In order to be able to predict changes, species and habitats must first be documented. This is particularly challenging underwater. What approach do you take?
Sami Domisch: Our group takes the spatial approach, because the spatial distribution of species is usually the most basic and best-available information: where do species occur, and what factors contribute to their distribution? There are two caveats to answering these questions: first, freshwater biodiversity monitoring is unevenly distributed around the world, which means that such species occurrence data first needs to be mobilised or even digitised for further analysis. Secondly, and perhaps even more fundamentally, we need standardised information on the distribution of freshwater bodies worldwide, together with environmental information describing their characteristics.
We focus on such data mobilisation and data generation on a global scale. By combining these two approaches, we perform biogeographic analyses of spatial freshwater biodiversity in different parts of the world. These analyses can then be used, for example, to detect changes in species distributions, to identify possible environmental factors contributing to such changes, to assess differences in taxonomic or functional biodiversity, or to use spatial prioritisation to identify areas that are important for supporting freshwater biodiversity.
Anne McLeod, you recently established the new Computational Ecology research group at IGB. What challenges and opportunities do you see for the use of such methods in ecological research, particularly in terms of better predictions?
Anne McLeod: We are in the midst of a data revolution, where it is becoming easier and easier to obtain and analyse data, whether from remote sensing, permanent in-situ sensors or R packages. More data is not necessarily better data, just as the use of remote techniques is not a replacement for fieldwork, but rather a complement. Luckily, we are seeing simultaneous improvements in computing power and computer-based methods that are accessible to ecologists. This means you don’t need a degree in computer science to work with a multitude of data sources, from satellite imagery to routine water quality surveys.
Instead, we can focus on being ecologists, because the reading and research still needs to be done – there are no large datasets or sophisticated analyses to overcome poorly thought-out questions and ill-defined hypotheses. However, the combination of increasing computing power, open-source science, and high-resolution data is very exciting for predictive ecology. It means we can be more ambitious with our predictions, balancing our longer-term expectations and models of equilibrium dynamics with short-term iterative forecasts, similar to those used in meteorology, where models are continually tested, updated, and improved as new data becomes available and further insights are gained.
Daniel Stouffer, you study complex systems ecology, such as the emergent phenomena that arise from interactions between species. How do theoretical and data-driven approaches help unravel these dynamics?
Daniel Stouffer: In ecology, essentially everything is connected. Just as what happens in one place can ripple through to another, something that happens to one population can percolate through a community both directly (e.g. to the predator of that species) and indirectly (e.g. to a “superpredator” of that predator). For more than a century, ecologists have used mathematical models to try to better understand the consequences of these direct and indirect interactions, in particular to explore what makes a community stable or resilient to disturbance. Despite this rich history, there are still many unknowns regarding the extent to which such models match what happens between real species in the laboratory or in the field.
To conserve biodiversity in the face of global change, practitioners will ultimately need specific insights tailored to specific systems of interest, and we expect these will shine brightest when they are guided by theory. In our group, we therefore combine data with theory to develop more realistic mathematical models, while striving to make these models tractable enough to be applied to diverse real-world communities. Indeed, theory can also help us to make the best use of all the hard-won data at our disposal, and can provide crucial predictions for phenomena that we have never really been able to test experimentally or quantify observationally.
About the research group "Complex Systems Ecology" >
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