Migratory animals make globe-spanning movements, link disparate communities and ecosystems and influence their structure and function. Global changes involve processes as diverse as climate change, erection of barriers, deterioration of habitats – all of which may differ in magnitude and direction between places and times. Consequently, many migratory populations have steeply declined and constitute a greater share of threatened species.

Understanding and predicting the threats that migratory populations face and their divergent responses to global changes are challenging endeavours because migrants use geographically distinct places throughout their annual or life cycles, and the conditions on single sites will have implications for the use of other sites – a dependency that needs to be considered in the setup of management and conservation measures.

Furthermore, as migratory animals provide a multitude of services and disservices that are relevant to agriculture, economy and human health, global-change driven changes in migrations will have also implications for other processes with societal relevance, e.g., dispersal of (zoonotic) parasites/pathogens.

For this symposium, we invite contributions using the full range of theoretical approaches that attempt to explain empirical migration patterns and address questions in the realm of understanding the movements and fates of migratory populations, the threats migrants are facing under global changes, the setup of conservation measures, as well as the implications of (altered) animal migrations on processes such as transport of seeds or parasites/pathogens, human-wildlife conflicts, pest control, agricultural damage or pollination.

2. Modelling species migration and range shifts under global change

Heike Lischke, Dynamic Macroecology, Swiss Federal Institute of Forest, Snow and Landscape Research WSL

One of the possible strategies of species in response to global change is to track their suitable habitats by migration (i.e. species range shift). Range shifts have been observed in the past and are expected to increase in the future due to the unprecedented rapidity of anthropogenic environmental change. Beside environmental constraints, species interactions, movement and evolutionary processes can influence species range shifts. Ecological models dealing with range shifts include these processes in different ways. In this symposium, we are interested in applications of different modelling approaches for the simulation of species range shifts, and at different temporal scales (from intra-annual to millions of years) and spatial scales (from landscape to the globe). In particular, we are interested in the following questions: 1) Under which conditions is the modelling of environmental constraints, species interactions and movement required and when do simpler approaches work as well for range shift modelling? 2) How can such range shift models be parametrized and tested? 3) How do the models deal with the computational load?

3. Movement modelling: underlying principles and processes

Jacob Nabe-Nielsen^a, Pernille Thorbek^c, Thomas G. Preuss^b, Joachim Kleinmann^c

^aDepartment of Ecoscience, Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark

^b Bayer AG, Alfred Noble Str 50, 40789 Monheim am Rhein, Germany

^c BASF SE, Speyerer Str. 2, 67117 Limburgerhof, Germany

Movement is a vital part of animal behaviour and has a strong influence on individual fitness; therefore ecological models increasingly take animal movement into account. Since movement is a trajectory through the landscape (or waterbody) that serves a number of purposes, it depends on the corresponding environment. Often animals change their movement behaviour in response to landscape features, their purpose of moving (e.g. foraging versus migration), environmental factors that may limit the movement speed (e.g. vegetation or temperature), their physiological state (e.g. starvation) and perceived risks (e.g. traffic or predation). Human activities lead to changes to environments that reach almost all areas of the globe whether terrestrial or aquatic. In order to understand how such changes affect animal movements, and how this in turn affects their fitness, it is important to be able to predict movement across habitats and landscapes. This knowledge can then be used to assess how populations are influenced by habitat changes and disturbances. However, there is no consensus on the principles of movement modelling and which factors to include for a given research question. Thus, movement models range from very simple diffusion models, over correlated random walk representations to sophisticated models that include perception of environment, energy budgets, trade-offs and adaption of behaviour. In this session we invite abstracts that cover principles and theories about animal movement modelling as well as case studies covering concrete examples or comparison of different approaches.

Modelling methodology

5. Reusable building blocks (RBB) for agent-, and individual-based models

Uta Berger, TU Dresden, Volker Grimm, Helmholtz Centre for Environmental Resarch-UFZ

Agent- and individual-based models (ABM) represent the behavior of organisms and their interactions with their environment. Despite their similarities to models used in engineering, or the video game industry, which are built on established and (semi-)commercial software components, ABMs are usually developed from scratch. This seems logical, since these models are used to produce new knowledge. However, the creativity gained in this way is only apparent and comes at a price: model development is inefficient, takes time, and leads to incoherent model designs with multiple code variations of the same phenomena and processes. In practice, case- or problem-dependent computational codes are rarely reused. This hinders transparency and reusability of models and best practices in science, which are needed to develop robust and effective ABMs that are well aligned with ecological theories at the actor level, as well as general theories at the systems level. It is therefore important to adopt the strategy of other modeling communities and build a repository of Reusable Building Blocks (RBBs). This would allow agent-based modelers to focus their energy on missing pieces, and scientists to efficiently assemble powerful ABMs and focus on their specific research questions. RBBs are submodels of specific processes that are likely to be important in many ABMs in a particular discipline or application, such as plant ecology, or conservation ecology. Established RBBs would have known properties, would be well-tested, and could be re-used in different contexts. They would need to be uploaded to repositories containing: source code, model description, specification of required context, executable demonstration, test reports, and example applications. For this session, contributions are welcome on specific RBB candidates and methodological considerations for modular development of ABMs, including standardization of uploads, version control, incentives for uploads, and pros and cons of using RBBs for theory and further development of ABM.

6. Next-generation advances in individual-based modelling

Aisling Daly, Jan Baetens, Department of Data Analysis and Mathematical Modelling, Ghent University

Individual-based modelling, also known as agent-based modelling, has been widely used in ecology since the 1990s. Since then, much progress has been achieved using this modelling paradigm. The surge in IBMs has been driven by the increasing recognition of significant variability within populations, in terms of age, size, stage, behaviour, and even more features. IBMs are ideally suited for modelling such individual variability between organisms.

Ecologists have used IBMs to tackle problems where population models are not well suited, particularly when it comes to understanding the complexity of ecosystems and how this emerges from the variability of the individuals within them. Hence IBMs have been used for many applied studies, where they can be of substantial aid to management strategies for specific populations in defined locations. IBMs have also been used in many studies for developing and testing theoretical advances. Overall, IBMs are now an established tool in population ecology. However, IBMs are still often considered unsuitable for modelling at the ecosystem level.

To bridge this gap, further progress is needed to address several aspects of the current IBM paradigm, including the lack of a standardized methodology for developing, analyzing and evaluating IBMs, and the fact that independent prediction is usually impossible at the ecosystem level. To meet these challenges, the IBM paradigm in the life sciences is evolving significantly. This conference session would present several examples of how ecologists are achieving progress with ‘next-gen’ IBMs and advancing this modelling paradigm.

We envision talks along the following lines, to cover some of the key challenges facing IBMs in ecological modelling, and how these challenges are being tackled:

· integrating IBMs and bioenergetics theory to improve life-history modelling

· developing cross-scale IBMs that capture multiple levels of biological organization

· integrating IBMs and Bayesian techniques for analysis and inference problems

· improving the standardization of IBM development and analysis

7. New methods and applications in trait-based modelling in ecology

Mateus Dantas de Paula, Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany

Thomas Hickler, Senckenberg Biodiversity and Climate Research Centre (SBiK-F),

Liam Langan, Senckenberg Biodiversity and Climate Research Centre (SBiK-F),

Katrin M. Meyer, Ecosystem Modelling, University of Göttingen, Göttingen, Germany

Simon Scheiter, Senckenberg Biodiversity and Climate Research Centre (SBiK-F)

Britta Tietjen, Theoretical Ecology, Institute of Biology, Freie Universität Berlin, Berlin, Germany

Trait-based approaches are widely used in empirical ecology and are becoming increasingly adopted in ecological modelling. Such models aim to use traits as powerful connectors linking organisms to community dynamics and ecosystem functions, and they enable predictions of community composition and responses to abiotic and biotic change.

Increasingly comprehensive trait data has allowed the identification of major axes of form and function which influence fitness and has facilitated the inclusion of diversity within models. However, there are still challenges that need to be addressed in trait-based modelling, such as incorporating intraspecific trait variability, addressing dynamic shifts in traits due to evolutionary processes, defining how trait values and trade-offs influence birth, growth, reproduction, and death across biotic and abiotic gradients. Despite these challenges, advancement in this field will lead to more flexible and realistic models better suited to the analysis of community responses to environmental change.

This session aims to highlight attempts, both successful and unsuccessful, to address these challenges and their underlying reasons, to give an overview of current trait-based modelling approaches, and to discuss potential routes to further improve trait-based modelling in ecology.

8. Integrative approaches to capture natural disturbance impacts in models

Olalla Díaz-Yáñez, Harald Bugmann, ETH Zurich, Department of Environmental Systems Science, Institute of Terrestrial Ecosystems, Forest Ecology.

Natural disturbances have the capacity to radically change ecosystems such as forests. With climate change, an increase of the frequency and impacts of natural disturbances is expected, and therefore it is likely that the cascading effects across disturbance types will change. Thus, it is crucial that models used to make projections under climate change and to provide management recommendations consider natural disturbances and their interactions in an integrative manner.

Models used to project and better understand the impact of disturbances have taken multiple forms to date, such as empirical or process-based, or from global to geographically limited scales. Advances in high-resolution data such as Earth observation systems as well as more comprehensive and long-term datasets from targeted inventories allow to improve the representation of the spatial and temporal complexity of disturbances and their interactions. However, several aspects related to natural disturbance modelling remain under-explored, such as the inclusion of disturbance interactions or the assessment of the uncertainty of model predictions.

In this session, we will discuss examples of integrative approaches towards better modelling natural disturbances and their interactions. This can be achieved via (1) more accurate and spatially continuous occurrence data to improve or validate models, (2) assessing the uncertainty in projections considering natural disturbance impacts and their interactions, (3) improving the models’ representation of natural disturbances analyzing their structural uncertainty, and (4) considering multi-disturbance impacts on landscape-level patterns such as vegetation shifts or provision of ecosystem services. Overall, in this session we highlight the research frontier on natural disturbance modelling towards robust model formulations and reduced uncertainty.

Model-data integration

9. Digital twins – a new modelling paradigm for ecology?

Koen de Koning, Environmental Systems Analysis Group, Wageningen University,

Digital twins (DTs) are rapidly gaining popularity across scientific domains, such as healthcare, agricultural sciences and urban planning. The concept originates from NASA and is now widely used in engineering and manufacturing. DTs are defined as digital copies of real-world systems or processes that mimic the behaviour of their real-world counterpart through dynamic modelling and continuous synchronisation with real-time (sensor) data.

DTs have proven to be a useful monitoring tool in industry and engineering because they allow accurate, precise, and real-time monitoring of processes that are hard to observe in the real world. Moreover, they can be used for early warning signal detection, experiments, and predictive and prescriptive analytics. DTs are becoming part of the operational work, driven by advances in monitoring and automated retrievals from databases, as well as advances in making models realistic for their purposes. It has become way easier and cheaper to collect data, create models and algorithms using tools, platforms and libraries, run models on large scale infrastructure, compose models and workflows for larger scale and more complex interactions, and create interactive applications that empower end-users to better understand complex systems. This transition is happening across domains, including environmental sciences.

Therefore, there is now a great potential to adopt DTs in the field of ecology and biodiversity conservation, which is worthwhile to explore further. This potential is further enhanced by an increased digitisation of ecology and a call for more evidence-based decision making in biodiversity conservation.

I therefore would like to propose a session on digital twins for ECEM 2023, where I would like to welcome any contributions related to digital twin research (even beyond ecology), ranging from presentations of DTs that are currently being developed, to methodological contributions or opinion pieces that help in moving the discussion forward towards embedding DTs in the ecological modelling toolkit.

10. Combining process-based simulation models and remote sensing data – benefits and limits

Rico Fischer, Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ

Nikolai Knapp, Forest Condition Monitoring, Thünen Institute of Forest Ecosystems

Amanda H. Armstrong, NASA Goddard Space Flight Center

Andreas Huth, Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ

Jürgen Groeneveld, Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ

Process-based simulation models have often been used to better understand ecological and social-ecological systems by projecting potential futures based on scenarios. Many of these applications have been calibrated, validated and analyzed on spatial limited extent. This was often due to limitations in the availability of the appropriate environmental data on relevant spatial extent. With the rapidly increasing availability and accessibility of high-resolution products derived from air- and spaceborne remote sensing data, this limitation often no longer exists. Thus, the potential to expand, transfer, and extrapolate existing process-based simulation models is enormous. However, transferring models that have been designed for a particular ecological and social-ecological context may be challenging. So far, there are first successful attempts with forest models that have integrated various satellite products into forest models for upscaling purposes. Additionally, this is not a one-way street: Remote sensing experts can benefit from process-based simulation tools in order to better understand the impact and nature of the accuracy of the data and interpretation of the signals. For instance, process-based simulation models can facilitate the ground-truthing process of spaceborne estimates. In this session, we will bring together ecological and social-ecological modelers to discuss the benefits, limits and trade-offs in the utilization of remote sensing datasets to inform process-based modeling research. We also invite speakers to explore recent innovative linkages between remote sensing and process-based simulation models.

11. Mathematical and data-driven models in ecohydrology

Ricardo Martinez-Garcia¹, Justin M. Calabrese^1,2,3

¹Center for Advanced Systems Understanding (CASUS) – Helmholtz Zentrum Dresden Rossendorf (HZDR), Görlitz, Germany. ²Department of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany. ³Department of Biology, University of Maryland, College Park, MD, USA.

Water plays a fundamental role in shaping many ecological processes. In particular, water may interact with the biota, the geometry of the habitat, or both to affect community-level properties such as spatial distribution and species composition. Examples include plant-water feedbacks in arid and semi-arid ecosystems that shape plant spatial distributions and directional flow through dendritic river networks that governs riverine biodiversity. In both of these cases, physical water flows play a fundamental role in how the ecosystem responds to varying environmental conditions and stresses, making it essential to understand and quantify how observed patterns originate from their underlying processes.

This session will bring together researchers interested in eco-hydrological models across systems and scales. A key focus of the session will be integrating data and models to investigate how water-organism and water-habitat interactions scale up to explain population, community, and ecosystem-level patterns and processes. The line-up of contributors will include researchers working on different systems and using different modeling techniques, from traditional dynamical systems to more recent data-driven approaches.

12. Advances in forest modelling by using new data sources and methods

Friedrich J. Bohn¹, Andreas Huth^1,2,3, Rico Fischer¹, Amanda Armstrong⁴; Uta Berger⁵

1 Helmholtz Centre for Environmental Research – UFZ

2 University of Osnabrück

3 German Centre for Integrative Biodiversity Research (iDiv), Leipzig

4 Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA.

5 Institute of Forest Growth and Computer Sciences, Technische Universität Dresden

Understanding the processes that shape forest systems functioning, structure, and diversity remains challenging, even though data on such systems are being collected at a rapid pace and at multiple scales. Forest models have a long history in addressing knowledge gaps in the scientific understanding of ecological processes in forested ecosystems. They simulate forest dynamics from local (e.g. succession, disturbance driven mortality, anthropogenic affects) to global spatial scales (e.g. terrestrial feedbacks in the climate system, carbon flux) in short to long term temporal dimensions and measure the consequences of forest management, disturbances or climate change.

New data sources have emerged over the last years rapidly (e.g., automated forest inventories, eddy-flux data, satellite products) and are accompanied by novel and powerful methods of artificial intelligence providing data generation and analysis at a spatio-temporal resolution that were not available so far. Incorporating these approaches at every step of a modeling workflow, allow us to continuously incorporate new data to reduce uncertainty in models, use them for new regions, making them a powerful science-based tool for scenario assessment.

We seek studies that address a broad range of fundamental and applied ecological questions and combine forest modelling with new data sources and methods to promote a deeper understanding of system dynamics over space and time in the context of global change.

13. Model-based data integration as a basis for FAIR ecological modelling, monitoring, and synthesis

Steffen Ehrmann, Carsten Meyer, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig

An essential pillar of understanding how human needs shape climate, land use and biodiversity change lies in building and testing theory and trying to predict recent patterns based on historical data as well as future patterns from current data. To transform society into a more resilient and sustainable modus operandi, we need to understand how biodiversity and resources are allocated to date, where they are needed and how we need to reshape the management status quo. To foster societal transformation, ecological models need to be integrated with other ecological models and models from different domains at the intersection of human-nature interactions, such as landscape planning, civil engineering or sociology and economics (cf. integrated assessment models, etc). Recently, a glut of data is becoming increasingly available, and advances in the sense-making of those data are at the forefront of progress.

Data become much more informative when combined with or used in models, i.e., when integrated with the body of knowledge to supplement it. In this session, we are interested in contributions focusing on methods, techniques, and outputs (modelled data products) that improve or result from model-based data integration and integrate data and models from one or more of the domains mentioned above. Model-data fusion needs to be based on FAIR principles so that we as a community can progress faster towards a comprehensive understanding of the earth system to predict suitable and efficient solutions. Hence, we prefer contributions that promote or are based on FAIR principles.

Response to change

14. Leveraging animal ecophysiology to enhance model predictability in a changing world

Cara A. Gallagher, University of Potsdam, Marianna Chimienti, PhD, Centre d’études biologiques de Chizé, CNRS

As the impacts of environmental change on animal species and biodiversity become increasingly pressing, accurate and reliable predictive methods are urgently needed. To maintain predictability under novel conditions, it is essential that these methods rely on fundamental processes driven by first principles, such as animal energetics and physiology. These processes provide crucial connections between fitness and the environment, and can be measured through a variety of empirical studies, including field and laboratory experiments, as well as through the use of bio-logging and physio-logging devices. The resulting processes and regulatory mechanisms can then be incorporated into flexible ecological models to make predictions in both space and time, or at higher ecological levels. However, measuring and quantifying these processes from heterogeneous and complex datasets remains a significant challenge, and requires the development of novel methodological advancements. Additionally, there is often a disconnect between empirical- and simulation-based animal ecophysiology, and wider ecological applications.

In this session, we invite speakers who have relevant findings and wish to see their results included in simulation models, who have quantified novel ecophysiological mechanisms, or who have incorporated these processes into simulation models. The goal of this session is to highlight the promise, potential, and challenges of developing ecophysiological models that incorporate various aspects of animal ecophysiology, including thermal physiology, toxicology, epidemiology, locomotor performance, bioenergetics, evolutionary physiology, endocrinology, reproductive physiology, and more, to predict outcomes on fitness and demographics under environmental change. This session will provide a valuable opportunity for modellers and empiricists to come together, share their research, and discuss how we can better represent ecophysiological mechanisms in models to enhance our predictive capacity. We anticipate that this session will serve as a catalyst for further discussions, and potentially lead to the formation of a network to identify key mechanisms and outline promising directions for future research.

15. Modelling variation and evolution of traits within individuals, populations and communities

Martin Zwanzig, Chair of Forest Biometrics and Systems Analysis, Technische Universität Dresden, Germany

Viktoriia Radchuk, Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany

Tobias Kürschner, Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research

Thomas Banitz, Department of Ecological Modelling, Helmholtz Centre for Environmental Research– UFZ

Aimara Planillo, Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research

Adaptive expression, mutation and selection of traits are processes inherent to many different systems, resulting in individual plasticity and evolutionary modifications. Besides biotic interactions, changes in environmental conditions represent a strong driver, which can subsequently also impact community dynamics. For all kind of organisms, climate change, increasing anthropogenic pressure and ensuing environmental changes challenge the ability of individuals and populations to adapt to the new conditions, and underline the present importance of understanding such responses. In this regard, ecological models are an indispensable tool to study ecological systems in dynamic environments and, particularly, adaptive responses to changing conditions. This session is intended to present state-of-the-art modelling approaches used to describe and study the variation and evolution of traits within individuals, populations and communities over individual life times or generations. While understanding and predicting responses to future conditions is a clear requirement, much can be learned from dynamic individual responses to past conditions and evolutionary processes, which influenced trait distributions, population persistence and ecosystem dynamics.

For this session, we welcome contributions on specific solutions for modelling variation and evolution of traits at any hierarchical level of ecological systems ranging from genes, to individuals, populations or communities. We encourage submissions based on process- based models, for example following an individual-based approach. We also welcome statistical modelling examples and other suitable contributions independent of the particular methodology or taxon under study.

16. Modelling climate-biosphere feedbacks

Ana Bastos, Max Planck Institute for Biogeochemistry, Jena

Jian Peng, Helmholtz Centre for Environmental Research – UFZ

Nadja Rüger, Leipzig University

Sönke Zaehle, Max Planck Institute for Biogeochemistry

Connections between biodiversity and climate change are important to understand the impacts of ongoing climate change and extremes on ecosystem services. However, they may be equally important to understand how changes in land-use patterns and associated changes/declines in biodiversity may in turn affect local climate, thereby modulating regional scale feedbacks between the biosphere and climate.

In this session, we aim to collect studies and approaches to ecological modelling ranging from data-driven studies, theoretical ecological modelling, model-integration and synthesis approaches and ecological scaling approaches (DGVMs) to understand the imprint of biodiversity on the impacts of climate variability, extremes and climate change on ecosystem stability and ecosystem service provision (such as carbon storage, evaporative cooling and ground water recharge). We are specifically interested in studies investigating feedbacks mechanisms between the biosphere and climate.

17. Sustaining insect pollination in a changing world using mechanistic simulation

Reinhard Prestele, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany

Jürgen Groeneveld, Helmholtz Centre for Environmental Research – UFZ

Insect pollinators are essential for human well being and ecosystem stability. However, insect pollinators face multiple interlinked stresses related to human activities such as land use and land-use change, climatic changes and diseases. Strategies to mitigate further negative effects on insect pollinators, e.g. by adapting beekeeping management and/or the design of agro-ecological measures to support wild pollinators, are urgently needed but often lack scientific underpinning. Mechanistic simulation models can help both in identifying mitigation measures as well as in projecting the effect of mitigation measures on the viability of insect pollinators and the resulting pollination service. Mechanistic models may also support the optimization of land use and beekeeping in complex landscapes. In this session we invite contributions of modellers working with mechanistic simulation models for insect pollinators. We invite studies presenting how mechanistic modelling can improve our understanding of the interactions between insect pollinators and their human influenced environment (e.g., land use, climate, agricultural management) across spatial scales. In particular, we look for contributions that show how mitigation measures can support insect pollinator viability. We invite presentation of case studies, simulation frameworks or innovative model analysis that will move us forward in sustaining pollination services.

Policy support

20. Environmental modelling for policy support – how to build bridges between two worlds

Klaus Hennenberg, Mirjam Pfeiffer, Hannes Böttcher, Oeko-Institut

More than ever ecological modelling has the potential to contribute to evidence-based policy making for achieving necessary transformations of society at regional, national and EU level. This is due to the increasing level of complexity of environmental problems that can only be assessed with models and the need for projections to anticipate the development of environmental indicators. This influence comes with increasing responsibility for ensuring not only accuracy, but also transparency, compatibility and consistency for model developers and users.

Transparency is needed for building trust with stakeholders in tools and methods.
Compatibility with policy processes is needed to ensure model output provides relevant indicators for policy.
Consistency of methodologies and assumptions across different sectors ensures that model results integrate over several environmental topics and help policy to avoid trade-offs and generate synergies.

Prominent examples for the need for environmental modelling in policy making are scenario simulations for assessing impacts of policies for mitigation and adaptation to climate change in the land use sector or bioeconomy policies.

The proposed session will increase awareness of requirements for using modelling for policy making. The session conveners have a long record of policy-oriented research in the field of land use. We call for both policy-oriented model users and model developers to provide their insights of policy support in past or ongoing projects in the land use sector (agriculture, forestry, biomass supply). The session will advance ecological modelling by providing successful strategies for making modelling results relevant and usable for policy:

Approaches to develop co-designed scenarios with stakeholders;
Strategies for communicating model uncertainties;
Tools for presenting results and offering options for exploring assumptions and alternative scenarios;
Multi-model comparisons and ensemble modelling including communication of their results;
Strategies for indicator development for increasing policy relevance of model output.

21. Ecological risk assessment and management – benefits and challenges of ecological modelling

Oliver Jakoby, RIFCON GmbH, Hirschberg, Germany

Andreas Focks, Osnabrück University, Germany

Ensuring the sustainability and longevity of biological diversity and ecosystem functions and services is a major challenge. Risk assessment and risk management of human activities is essential for a sustainable management of ecosystems, to allow for a long-term anthropogenic use of natural resources. Risk assessment evaluates how likely it is that the environment might be impacted as a result of exposure to environmental stressors, such as chemicals, land-use change, diseases, or invasive species, and how severe the impact affects ecosystem properties, while ecological risk management aims at protecting the natural resources and the ecological services they provide.

Ecological models are essential tools for disentangling complex interactions and can be used to assess the risk of different stressors to ecological entities, to evaluate possible risk management options, and to project future developments for individuals, populations, and communities of interest. Particularly, well-established models can support decision making processes since plenty of scenarios and management options can quickly be simulated and uncertainties can be explored. However, there are also several challenges in developing, communicating, and applying ecological models in this context. Models need to be trusted and accepted by the different involved stakeholder groups, if real-world decisions should be built on the model results. Reliable models must be well tested and documented. Further, ecological models for risk assessment need to be applied in a transparent way, appropriate scenarios need to be developed to allow a proper evaluation of the ecological risk, and model results and their implications need to be well communicated.

In this session we invite contributions that address ecological modelling for risk assessment and management, starting from conceptual considerations, including model building, documentation and validation, sensitivity and uncertainty analysis, scenario development and exploration, and stakeholder communication, and also examples where ecological models are applied for risk assessment or management in case-studies on individual, population, ecosystem or landscape-scales.

31. From dynamic forest models to decision support for forest transformation – How can synergies be achieved?

Ulrike Hiltner¹, Gina Marano¹, Marco Mina², Manfred J. Lexer³

¹ ETH Zurich, Dept. Environmental System Sciences, Inst. Terrestrial Ecosystems, Zurich, Switzerland

² Eurac Research, Institute for Alpine Environment, Bolzano/Bozen, Italy

³ University of Natural Resources and Life Sciences, Vienna, Institute of Silviculture, Vienna, Austria

Around the world, forests fulfill important ecosystem functions that are critical to human well-being, such as carbon storage, provision of resources, and protection against natural hazards. Due to global climate change, a sustainable provision of forest functions and ecosystem services is threatened. To determine the impacts of such changes, scientists have been applying dynamic forest models to assess adaptation strategies and management alternatives to the current silvicultural practices to support forest practitioners and policy makers in diverse management decisions.

A variety of dynamic forest models have been successfully used to analyze the impacts of changing environmental conditions, natural disturbances, and forest management on the dynamics of forests and the provision of ecosystem functions. While each model has strengths and shortcomings, the requirements of decision support impose additional challenges for the development of dynamic forest models. In this regard, state-of-the-art research addresses, among other issues, the selection of meaningful indicators of ecosystem services, the accuracy and reliability of short- to long-term projections, and the realistic representation of management regimes. Further, new methods consider, for instance, fundamental population-dynamic processes as well as changes in forest functions under climate change more accurately, they better address societal needs, or make model applications user-friendly.

In this session we would like to explore innovative approaches focusing on the demands of decision support for forest practitioners and policy makers. In this context, we also aim to discuss perspectives for future research in dynamic forest modeling so that forests worldwide can be managed in a climate-smart way in the future. We encourage scientists from a variety of disciplines to contribute new methods and applications of modeling that meet the needs of decision support. Contributions addressing modern communication methods for preparing model results for forest practitioners and policy makers are also welcome.

Theory development

22. Functional responses: from theory to practice

Gian Marco Palamara¹, José A. Capitán², David Alonso³

¹Department of Fish Ecology and Evolution, Eawag, Swiss Federal Institute of aquatic Science and Technology

²Complex Systems Group, Department of Applied Mathematics, Technical University of Madrid

³Theory and Computation in Ecology and Evolution, Center for Advanced Studies of Blanes (CSIC)

Functional response (FR) models are central to describe consumer-resource interactions, as they quantify how per capita average feeding rates respond to density of both consumers and resources. Different mathematical expressions have been proposed to take into account for a variety of foraging mechanisms, and many experiments have been performed to test such expressions against data. Nevertheless, despite the variety of theoretical and empirical studies, a comprehensive understanding of functional responses in all their applications remains elusive. Recently, also due to increasing data availability, there has been rejuvenated interest in revisiting classical FR models, both from a purely theoretical and statistical/empirical perspective. Different studies recently came out, improving theoretical understanding of consumer resource interactions, including new ways of measuring FR, leading to a better integration of theory, data and experiments.

Here we propose a session dedicated to Functional Responses, with the aim of putting together recent modelling developments and their applications. We expect to attract experts in the field to discuss new conceptual, theoretical and empirical frameworks related to functional responses. This session will stimulate the discussions about the assumptions behind classical FR models, and how individual consumer behaviour emerges through eco-evolutionary processes, giving rise to average feeding rates at the population level. Overall, these research efforts have the potential to improve classical expressions, assessing their implications for both simple predator-prey and complex food web dynamics. We are also interested in the potential development of new experimental designs to test theories of the consumer resource interaction. We plan to include in the session: (i) a theoretical part, where we present new avenues of theoretical research, (ii) an inference part where we discuss the empirical challenges of measuring functional responses based on these theoretical developments, and (iii) a synthesis part, where empiricists and theoreticians could converge in the design of future studies.

23. Like a boomerang: How feedback loops affect ecosystem stability and species coexistence

Korinna Theresa Allhoff, University of Hohenheim, Institute of Biology

Ecosystems are inherently complex, meaning that they consist of many interconnected components and that these components affect each other in highly non-trivial ways. The interactions between system components form so-called feedback loops, that is closed chains of cause-and-effect, which are the central element of Systems Dynamics and Dynamical Systems Modelling. For example, consider a two-player system in which species A affects species B and vice versa, creating a 2-link loop that can be either positive or negative. Positive feedback is potentially destabilising. It arises for instance from mutualistic interactions, where both species benefit from each other, which amplifies any small change in abundance in a self-reinforcing way. Negative feedback, on the other hand, counteracts any small change in abundances in a self-dampening way. It can have a stabilising effect on the whole system, as it is the case for antagonistic interactions. More interesting examples of feedback loops are known from larger systems that contain more species and/or a multitude of interaction types, as well as from systems in which evolutionary changes in trait distributions reciprocally interact with ecological dynamics, resulting in eco-evolutionary feedback.

For this session, we invite contributions that focus on various feedback loops from a broad range of different systems. We are interested in specific examples that demonstrate how such feedback can be quantified and under which conditions it plays out as either self-reinforcing or self-dampening, as well as in the question how multiple feedback loops act in concert to determine large-scale system properties, such as conditions for coexistence or the emergence of tipping points. We intend to conclude the session with a synthesizing discussion to gain cross-system insights on how feedback loops shape ecosystem dynamics. Such insights are key for understanding, predicting and managing ecosystem responses to global change.

24. Integrating analytical modeling and individual-based modeling for complexity reduction, upscaling, and to gain causal understanding

Thorsten Wiegand, Andreas Huth, Jürgen Groeneveld

Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ

Facing challenges such as climate change and biodiversity loss requires a deep understanding of the underlying biological systems and how they respond to human intervention. Mechanistic population and community models are key tools in ecology to gain a causal understanding of complex system dynamics and emerging patterns, and to develop concepts that can guide management. Historically, theoretical ecology has been dominated by analytical approaches that have typically state variables, such as population densities, that operate at the macroscale, but use parameters that make assumptions on mean properties of the individuals (such as birth or death rates). Individual-based simulation models (IBMs) have been developed in the 80ties as alternative to allow macroscale dynamics emerging directly from the possibly complex behavior and (local) interaction of individuals at the microscale.

In ecology the development of analytical models and IBMs has largely be proceeded in separate lines, with few attempts to combine the best of these two worlds. However, both approaches have complementary strength and weaknesses, and their integration holds strong promise to foster development of a new generation of hybrid models that provide solutions for longstanding issues such as complexity reduction and upscaling.

This session aims to stimulate integration of analytical modeling and individual-based modeling for complexity reduction, upscaling, and to gain causal understanding of complex phenomena. We welcome submissions that explore concepts of integrating the strength of analytical models and IBMs, and case studies that illustrate the advantages and challenges of such an integration.

Potential topics for this session include, but are not limited to studies:

on the successful integration of analytical models and IBMs
presenting concepts of how to integrate analytical models and IBMs
using analytical approximations of IBMs for upscaling
using IBMs to verify the assumptions of analytical models
presenting hybrid analytical-IBM models

AI and machine learning

25. Integrating mechanistic modeling and machine learning for environmental problem-solving and supporting transformation

Markus Reichstein^1,2, Karin Frank³, Rico Fischer³, Nuno Carvalhais^1,2

¹Max-Planck-Institute for Biogeochemistry, ²ELLIS Unit Jena, ³Helmholtz Centre for Environmental Research – UFZ, Leipzig

Environmental problems, such as climate change, pollution, and biodiversity loss, require the development of strategic solutions that are grounded in a deep understanding of the underlying systems. Traditional approaches to modeling these systems often rely on mechanistic models, such as ordinary differential equations (ODEs), partial differential equations (PDEs), and rule-based systems including Agent-Based Models (ABMs). However, these models can be limited in their ability to capture the complexity and variability of real-world systems, given existing data.

In recent years, there has been a growing interest in combining mechanistic modeling with machine learning to improve the predictive power and understanding of environmental systems. Machine learning techniques, such as neural networks and decision trees, can be used to identify patterns and trends in data that may be difficult to discern using traditional modeling approaches. In addition, agent-based modeling, which simulates the behavior of individual agents within a system, can provide valuable insights into the interactions and emergent behaviors that drive environmental phenomena.

This session will explore the use of mechanistic modeling and machine learning techniques for identifying socio-ecological systems and strategic options to environmental problems. We welcome submissions that demonstrate the integration of these approaches, as well as case studies that showcase the successes and challenges of combining mechanistic and machine learning approaches.

Potential topics for this session include, but are not limited to:

Using machine learning to optimize or augment traditional mechanistic models
Integrating data from multiple sources to improve model accuracy and predictive power
Applying agent-based modeling in conjunction with observations to understand the behavior of agents within environmental systems
Case studies of the successful application of combined mechanistic and machine learning approaches to environmental problems
Challenges and best practices for combining mechanistic modeling and machine learning in environmental problem-solving.

26. Beyond prediction: application of machine learning to gain new ecological insights

Masahiro Ryo, Leibniz Centre for Agricultural Landscape Research/Brandenburg University of Technology

Hanna Meyer, University of Münster

Machine learning is nowadays a popular data-driven modeling approach in ecology. The applications of machine learning have demonstrated the ability of these algorithms to capture nonlinear and nonadditive relationships in complex ecological systems, from local to global scales, from biogeochemistry to biogeography. However, most of the machine learning applications in ecology are limited to model fitting as a black box and subsequent predictions without a rationale behind them. Recent advancements in artificial intelligence, like explainable artificial intelligence (AI), process-aware AI, AI-mechanistic hybrid modeling, and human-AI interaction, indicate that machine learning can be used not only to predict outcomes but also to facilitate mechanistic understanding by learning what the models have learned from data. While this trend is developing rapidly in other fields like physics and climate science, it has been rarely considered in the context of ecology yet. In this session, we welcome contributions from studies that make use of machine learning, deep learning, and AI approaches that go beyond prediction, aiming to facilitate the understanding of ecological mechanisms underlying patterns from data. We hope to discuss which techniques are promising in the ecological domain and how we can better use advanced AI techniques to improve our understanding of ecological systems.

28. The use of artificial intelligence in ecological modelling

Werner Rammer, Rupert Seidl, Ecosystem Dynamics & Forest Management group, School of Life Sciences, Technical university of Munich.

Artificial intelligence (AI) has the potential to revolutionize ecological research by providing new ways to analyze and understand complex ecological systems. In the context of ecological modelling, AI methods such as deep neural networks can be used in multiple ways. For instance, neural networks can be used as meta models, i.e. as a simplified representation of complex ecological models, or as a replacement for individual sub systems within ecological models. In this session, we will explore the use of AI in a variety of ecological contexts, including modeling ecosystem dynamics and understanding the impacts of environmental changes. Welcome are contributions that show the application of AI in ecological modelling that advance our understanding of the natural world, but also that inform the development of conservation and management strategies. Of interest are also contributions that discuss the challenges and limitations of using these approaches, as well as potential future directions for their use in ecological research. Overall, this session will provide a comprehensive overview of the current state of the field and highlight the exciting potential of AI and meta modelling to transform ecological research in the coming years.

Social-ecological systems

29. Ecological Models for the Management of social-ecological Transformations

Hauke Reuter, Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany

Broder Breckling, University of Vechta, Vechta, Germany

A growing consensus has been established to evaluate natural interventions with regard to sustainability criteria. Questions about means, methods and concepts to achieve sustainability goals are getting into the focus of discussions. This is the subject of transformation research. Transformations refer to the transition and change phenomena occurring in this context, which equally encompass the natural space, the social sphere, and the development and application of technological potentials in their common reference. Ecological models make critical contributions to the development, evaluation, and consideration of solution strategies by providing explanations and quantitative estimates of transition phenomena, including those that exist in response to crises in climate change, biodiversity decline, and food security issues. Social-ecological models can contribute knowledge that goes beyond descriptive and analytical methods. Contributors to the session will discuss the following issues and explore them in greater depth using example models.

Environment and landscape design: how do models contribute to estimating potential for achieving greater sustainability
Anthropogenically influenced ecosystem dynamics: What contributions do ecological models make to understanding phase transitions in ecological and environmental relationships
What is the role of ecological models in basic research on transformations and in practical applications
What new opportunities for model development arise from the participatory approaches (living lab, citizen science) that are increasingly being applied in transformation research and what do we gain through new technological possibilities in this interdisciplinary context.

The initiators will present the field of transformation research using current examples and invite a discourse on the importance of ecological models in this context.

30. Modelling change in social-ecological systems – from conceptual reflections to promising case-based examples

Birgit Müller, Helmholtz Centre for Environmental Research – UFZ Leipzig, Germany

Maja Schlüter, Stockholm Resilience Centre, Stockholm University, Sweden

Calum Brown, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

Transforming our use of natural resources and ecosystems services towards more sustainable practices requires understanding the interplay of ecological and social processes and how they shape outcomes. Social-ecological modelling has great potential to enhance understanding of and support action for transformation. However, building and analysing models that incorporate interactions between people’s individual and collective behaviours, societies and their natural environments, still pose major challenges. These challenges can be conceptual (e.g. identification of key social-ecological feedbacks, representing human behaviour and institutions, working across the social and natural sciences), can relate to the complex nature of social-ecological systems (e.g. understanding of causal relationships), or can involve data or technical limitations.

This session aims to discuss and reflect on learnings and modelling strategies to address these challenges across different contexts, such as conservation, sustainable use of natural resources (land, water, marine resources, including for food and energy security), and more broadly the provision of ecosystem services in rural and urban areas. We welcome experiences from the use of different modelling approaches with different conceptual bases (e.g. agent-based models, network models, mathematical models, including participatory approaches).

We suggest that all contributions consider the following three guiding questions to provide the basis for a final overarching discussion:

How did you conceptualise and model social-ecological interactions or feedbacks? What were the challenges? How did you address them?
What are strategies to overcome lack of data or knowledge of complex social-ecological systems, e.g. innovative data collection methods, use of theories?
How can your model (or modelling in general) contribute to knowledge and/or direct support for sustainability transformations?

Sessions