Making spatial-temporal marine ecosystem modelling better – A perspective

Abstract

Marine Ecosystem Models (MEMs) provide a deeper understanding of marine ecosystem dynamics. The United Nations Decade of Ocean Science for Sustainable Development has highlighted the need to deploy these complex mechanistic spatial-temporal models to engage policy makers and society into dialogues towards sustainably managed oceans. From our shared perspective, MEMs remain underutilized because they still lack formal validation, calibration, and uncertainty quantifications that undermines their credibility and uptake in policy arenas. We explore why these shortcomings exist and how to enable the global modelling community to increase MEMs’ usefulness. We identify a clear gap between proposed solutions to assess model skills, uncertainty, and confidence and their actual systematic deployment. We attribute this gap to an underlying factor that the ecosystem modelling literature largely ignores: technical issues. We conclude by proposing a conceptual solution that is cost-effective, scalable and simple, because complex spatial-temporal marine ecosystem modelling is already complicated enough.

Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions

“…Second, the models can in principle be run 100,000s of times -even Ecospace -while resampling inputs and hence generate "big data" about a hypothetical ecosystem. Such research is technically challenging (Steenbeek et al, 2021a), yet becoming feasible because of the unprecedented computing power available. It is facilitated by the EcoSampler plugin (Steenbeek et al, 2018), which allows resampling of biological model parameters such that the required mass balance is retained.…”

“…Such data sets could also be used in global sensitivity analyses, allowing quantification of the direct and indirect contributions of model inputs describing stressors to changes of outputs describing ecosystem condition (Bracis et al, 2020;Saltelli et al, 2004). The generated data could simultaneously contribute rigorous uncertainty analyses, which are crucial for high-stakes applications in environmental policy and practice (Saltelli and Funtowicz, 2014;Steenbeek et al, 2021a). Of course, such data and analyses would describe an artificial "world in the model" (Morgan, 2012) as opposed to a real-world ecosystem -even if all relevant pathways of effects could be represented via realistic response functions.…”

Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions

“…Such data sets could also be used in global sensitivity analyses, allowing quantification of the direct and indirect contributions of model inputs to changes in the outputs (Bracis et al, 2020;Saltelli et al, 2004). The generated data could simultaneously contribute rigorous uncertainty analyses, which are crucial for high-stakes applications in environmental policy and practice (Saltelli and Funtowicz, 2014;Steenbeek et al, 2021a). Of course, such data and analyses would describe an artificial "world in the model" (Morgan, 2012) as opposed to a real-world ecosystem -even if all relevant pathways of effects could be represented via realistic response functions.…”

Modelling the Mediterranean Sea ecosystem at high spatial resolution to inform the ecosystem-based management in the region

“…To fully utilize these tools in an evidence-based policy context, marine ecosystem models are required to be spatially and temporally explicit to facilitate the communication between scientists and policy makers in identifying species/group of species and areas of major concern and planning targeted actions such as MPA site selections 15 , catch level reductions 16 and nutrients control measures 14 . Spatial marine ecosystem modelling is, however, a challenging component of higher trophic level (HTL) ecosystem approaches (e.g., models that consider food web dynamics beyond phytoplankton and zooplankton and usually include main food web components) 14 , 17 and, for this reason, its use in the policy making process has been limited. Main obstacles reside, for example, with the lack of spatial data for many species and ecosystems 18 , and with the difficulties of accounting for environmental effects, trophic interactions, and human activities (e.g., fishing) when predicting the distribution of marine species in space and time 19 .…”