Advancing Global Ecological Modeling Capabilities to Simulate Future Trajectories of Change in Marine Ecosystems

Coll, Marta; Steenbeek, Jeroen; Pennino, María Grazia; Buszowski, Joe; Kaschner, Kristin; Lotze, Heike K.; Rousseau, Yannick; Tittensor, Derek P.; Walters, Carl J.; Watson, Reg; Christensen, Villy

doi:10.3389/fmars.2020.567877

Cited by 54 publications

(70 citation statements)

References 139 publications

(225 reference statements)

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“…Future developments can certainly include essential EDSS features such as comprehensive usability and uncertainty assessments (Walling and Vaneeckhaute, 2020). As all ecosystem management questions have temporal and spatial components, we can also extend OceanViz to connect to spatial-temporal ecosystem models such as Ecospace, the spatial-temporal module of EwE (Serpetti et al, 2017;Coll et al, 2020), and to explicitly represent the impacts of climate change according to the available forecasting scenarios (Tittensor et al, 2018). We can extend OceanViz to incorporate the effects of hazardous substances and litter if the connected ecological model provides said features, and OceanViz can be made to incorporate socio-economic impact analysis and include non-aquatic species in its considerations if underlying models provide these abilities -providing stakeholder sessions have such needs.…”

Section: Discussionmentioning

confidence: 99%

Using Gaming Technology to Explore and Visualize Management Impacts on Marine Ecosystems

Steenbeek

Felinto²,

Pan

et al. 2021

Front. Mar. Sci.

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We have developed an approach that connects a complex and widely used scientific ecosystem modeling approach with a game engine for real-time communication and visualization of scientific results. The approach, OceanViz, focuses on communicating scientific data to non-scientific audiences to foster dialogue, offering experimental, immersive approaches to visualizing complex ecosystems whilst avoiding information overload. Within the context of ecosystem-based fisheries management, OceanViz can engage decision makers into the implicit operation of scientific software as an aid during the decision process, and it can be of direct use for public communication through appealing and informative visualizations. Beside a server-client architecture to centralize decision making around an ecosystem model, OceanViz includes an extensive visualization toolkit capable of accurately reflecting marine ecosystem changes through a simulated three-dimensional (3D) underwater environment. Here we outline the ideas and concepts that went into OceanViz, its implementation and its related challenges. We reflect on challenges to scientific visualization and communication as food-for-thought for the marine ecosystem modeling community and beyond.

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Section: Discussionmentioning

confidence: 99%

Using Gaming Technology to Explore and Visualize Management Impacts on Marine Ecosystems

Steenbeek

Felinto²,

Pan

et al. 2021

Front. Mar. Sci.

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“…MEMs are inherently subject to structural uncertainty due to their simplification of complex ecosystem dynamics ( Collie et al, 2016 ) and parameterizations (e.g., Anderson et al, 2010 ). Insights into the impact of structural uncertainty can be garnered through ensemble modelling approaches ( Lewis et al, 2021 ; Lotze et al, 2019 ), flexibility in the coupling between biophysical forcing and ecosystem models ( Tittensor et al, 2018 ), and flexibility in the inclusion of ecological mechanisms within complex models ( Audzijonyte et al, 2019 ; Coll et al, 2020 ). Hybrid models that adaptively switch the mathematical representations of sub-models to best suit the state of an ecosystem ( Gray and Wotherspoon, 2015 ), are a promising yet underexplored approach for addressing structural uncertainty.…”

Section: Reviewmentioning

confidence: 99%

“… Initialization and internal variability (IIV) uncertainty relates to the uncertainty in adequately representing the initial conditions, temporal variabilities, and numerical sensitivities within complex models that may lead to “deterministic chaos” ( Anderson et al, 2010 ). Promising approaches include adopting a modular design to model construction that allows for bypassing internal computations with the advice from dedicated expert models ( Christensen et al, 2014 ; Coll et al, 2020 ; Steenbeek et al, 2016 ). Climate models quantify IIV uncertainty by starting models at different times with different realizations (e.g., Nadiga et al, 2019 ), which is hard to achieve for marine ecosystem models that have much more complex starting states to represent the living components in the system (e.g., Skogen et al, 2021 ).…”

Section: Reviewmentioning

confidence: 99%

“…For more complex models, adaptive parameter sensitivity screening ( Pantus, 2007 ) may serve to greatly reduce the number of needed parametric uncertainty iterations. Uncertainties in observations ( Skogen et al, 2021 ) and driver data ( Coll et al, 2020 ) can be considered as parametric uncertainty too, and should be treated as such in uncertainty assessments. Scenario uncertainty relates to the inability to accurately define future contexts within which the dynamic conditions captured within a MEM play out.…”

Section: Reviewmentioning

confidence: 99%

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Making spatial-temporal marine ecosystem modelling better – A perspective

Steenbeek

Buszowski

Chagaris

et al. 2021

Environmental Modelling & Software

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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.

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“…Hence their measurement in both process studies and operationally are critical in the development and calibration of these models and therefore our understanding and operational awareness of ocean health. Further the outputs from such lower trophic levels can be used to force models predicting higher trophic levels from "primary producers to top predators" (Coll et al, 2020) under a range of climate and hence ocean chemistry change scenarios. Therefore, the measurement of ocean chemistry is critical to understanding the health and productivity (including fisheries and bioresources) of our oceans in a changing climate and in mitigating negative effects.…”

Section: Introductionmentioning

confidence: 99%

Industry Partnership: Lab on Chip Chemical Sensor Technology for Ocean Observing

et al. 2021

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We introduce for the first time a new product line able to make high accuracy measurements of a number of water chemistry parameters in situ: i.e., submerged in the environment including in the deep sea (to 6,000 m). This product is based on the developments of in situ lab on chip technology at the National Oceanography Centre (NOC), and the University of Southampton and is produced under license by Clearwater Sensors Ltd., a start-up and industrial partner in bringing this technology to global availability and further developing its potential. The technology has already been deployed by the NOC, and with their partners worldwide over 200 times including to depths of ∼4,800 m, in turbid estuaries and rivers, and for up to a year in seasonally ice-covered regions of the arctic. The technology is capable of making accurate determinations of chemical and biological parameters that require reagents and which produce an electrical, absorbance, fluorescence, or luminescence signal. As such it is suitable for a wide range of environmental measurements. Whilst further parameters are in development across this partnership, Nitrate, Nitrite, Phosphate, Silicate, Iron, and pH sensors are currently available commercially. Theses sensors use microfluidics and optics combined in an optofluidic chip with electromechanical valves and pumps mounted upon it to mix water samples with reagents and measure the optical response. An overview of the sensors and the underlying components and technologies is given together with examples of deployments and integrations with observing platforms such as gliders, autonomous underwater vehicles and moorings.

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Advancing Global Ecological Modeling Capabilities to Simulate Future Trajectories of Change in Marine Ecosystems

Cited by 54 publications

References 139 publications

Using Gaming Technology to Explore and Visualize Management Impacts on Marine Ecosystems

Using Gaming Technology to Explore and Visualize Management Impacts on Marine Ecosystems

Making spatial-temporal marine ecosystem modelling better – A perspective

Industry Partnership: Lab on Chip Chemical Sensor Technology for Ocean Observing

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