Conference themes
AMEMR welcomes all submissions that use modelling approaches to better understand and predict marine ecosystems and support marine sustainability. We invite submissions on all aspects of model development and application across spatial, temporal and complexity scales, utilising well-established and cutting-edge ideas and approaches, focusing on modelling theory and practice. Sessions will cover all parts of the marine ecosystem, from the atmosphere to the seabed, microbes to whales, estuaries to the open ocean. AMEMR is a forum to share successes and challenges, discuss methodological ‘nitty-gritty’ of marine ecosystem modelling and debate about the future of our field.
This year the conference is organised across four themes. During abstract submission you will be asked to select all themes that apply to your work.
This year the conference is organised across four themes. During abstract submission you will be asked to select all themes that apply to your work.
1: New recipes in the cookbook: including new processes, parameterisations and data in models
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We use different strategies to develop better models for improved understanding and prediction of the marine environment. These may include updating existing models with new processes, improved parameterisations and data assimilation, or starting from scratch with completely new model structures. Additional challenges include balancing need for high spatial and temporal resolutions with biological realism and an appropriate level of model complexity. Sometimes the learning from our models may be that certain processes are not important and do not need to be included, at least in some circumstances. This theme could be considered the ‘bread and butter’, or foundation, of AMEMR.
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2: Tasting notes: advancing model validation and uncertainty assessment
Despite significant advances in marine ecosystem modeling, knowing whether we can be confident in the results our models produce remains a major challenge. Uncertainties in models can arise from structural choices in the representation of physical, biogeochemical, and ecological processes, model parameterisations, uncertainties in external forcing datasets, and limitations in observational data used for model calibration, evaluation and initialization. In data-driven models, uncertainties may also arise from the quality and spatio-temporal coverage of training datasets, as well as model architectures. Critical knowledge gaps in our understanding of the marine ecosystem can also be identified through uncertainties in models. This theme focuses on the quantification and reduction of uncertainties in marine ecosystem models, validation of models using improved and novel observational datasets and highlights where we need better understanding of the marine system. We also welcome presentations on ensemble approaches and model intercomparison projects as tools for identifying, contextualising and reducing uncertainty.
3: New gadgets in the kitchen: machine learning and AI
Machine learning and artificial intelligence (ML/AI) are advancing rapidly, opening new opportunities for marine ecosystem modelling. These include hybrid approaches that combine ML/AI with traditional models to improve simulations through learned parametrisations and bias correction, as well as for downscaling, deriving new indicators, and estimating uncertainties. ML/AI can also replace parts or entire models with emulators, potentially reducing computational costs by orders of magnitude. Explainable AI offers further opportunities to understand emergent model behaviour and inform model development. This theme explores how AI can interact with traditional modelling, including the provocative question of whether it will eventually take over. Of course, ML/AI aren’t without their limitations, and we welcome submissions where these are explored.
4: Serving up answers to the big questions: applying marine ecosystem models to answer the current 'grand challenges'
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Marine ecosystem models are critical tools to better understand, predict and offer solutions to current grand challenges, such as climate change, biodiversity loss and pollution. Beyond the physical, biogeochemical and ecological components of these challenges, models can also help us understand the interaction with humans and society via human impacts on ecosystems and changes to ecosystem services (e.g. food security). In turn, we need to effectively communicate model results and their uncertainties beyond the scientific community, to both support informed decision-making and educate the wider public and society. We invite submissions that address any dimension of these grand challenges, including pitfalls encountered, lessons learned, and best practices.
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