Hazard warning: model misuse ahead

Dickey‐Collas, Mark; Payne, Mark; Trenkel, Verena M.; Nash, Richard D.M.

doi:10.1093/icesjms/fst215

Cited by 54 publications

(37 citation statements)

References 48 publications

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“…However, it is also clear that there is no "right" answer to identifying the "best" model, and the choice should ultimately be driven by the question being asked (Dickey-Collas et al, 2014). Nevertheless, the continued application of such techniques in marine science represents a valuable opportunity to improve the treatment of structural uncertainty in this field.…”

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confidence: 99%

Uncertainties in projecting climate-change impacts in marine ecosystems

Payne

Barangé

Cheung

et al. 2015

ICES Journal of Marine Science

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Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.

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confidence: 99%

Uncertainties in projecting climate-change impacts in marine ecosystems

Payne

Barangé

Cheung

et al. 2015

ICES Journal of Marine Science

Self Cite

140

112

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show abstract

“…This is not to say that useful forecasts cannot be built upon empirical knowledge: indeed nearly all of the products highlighted here are based on correlations between physical variables in the ocean and biological responses. However, mechanistic knowledge is generally regarded as providing a strong footing for forecasting both biological (Guisan and Zimmermann, 2000;Dickey-Collas et al, 2014) and economic (e.g., Haynie and Pfeiffer, 2012) aspects of marine systems, particularly in cases where extrapolation beyond the range of conditions seen in the training set (e.g., under climate change) is required. Nevertheless, it is important to note that there is often little choice but to employ correlative approaches: while the difficulties of predicting fishstock recruitment based on empirical relationships with the environment have long been recognized (e.g., Myers, 1998), skilful mechanistic solutions to this problem still appear far off.…”

Section: How To Go Forwardmentioning

confidence: 99%

“…Marine science has been limited for many years by its focus on describing, rather than predicting, systems. Expanding our knowledge beyond the empirical toward the mechanistic can be expected to greatly improve the quality of our understanding and our predictive capability (Dickey-Collas et al, 2014;Urban et al, 2016). Incorporating behavior, allowing for adaptive responses, and modeling organisms in terms of their full life-cycle are all key elements that can be expected to be seen in the next generation of models and deliver gains in predictive skill, challenges in parameterising such models notwithstanding (Urban et al, 2016).…”

Section: Future Needsmentioning

confidence: 99%

Lessons from the First Generation of Marine Ecological Forecast Products

et al. 2017

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Recent years have seen a rapid expansion in the ability of earth system models to describe and predict the physical state of the ocean. Skilful forecasts ranging from seasonal (3 months) to decadal (5-10 years) time scales are now a reality. With the advance of these forecasts of ocean physics, the first generation of marine ecological forecasts has started to emerge. Such forecasts are potentially of great value in the management of living marine resources and for all of those who are dependent on the ocean for both nutrition and their livelihood; however, this is still a field in its infancy. We review the state of the art in this emerging field and identify the lessons that can be learnt and carried forward from these pioneering efforts. The majority of this first wave of products are forecasts of spatial distributions, possibly reflecting the inherent suitability of this response variable to the task of forecasting. Promising developments are also seen in forecasting fish-stock recruitment where, despite well-recognized challenges in understanding and predicting this response, new process knowledge and model approaches that could form a basis for forecasting are becoming available. Forecasts of phenology and coral-bleaching events are also being applied to monitoring and industry decisions. Moving marine ecological forecasting forward will require striking a balance between what is feasible and what is useful. We propose here a set of criteria to quickly identify "low-hanging fruit" that can potentially be predicted; however, ensuring the usefulness of forecast products also requires close collaboration with actively engaged end-users. Realizing the full potential of marine ecological forecasting will require bridging the gaps between marine ecology and climatology on the one-hand, and between science and end-users on the other. Nevertheless, the successes seen thus far and the potential to develop further products suggest that the field of marine ecological forecasting can be expected to flourish in the coming years.

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“…Predicting the outcomes of the management actions with precision becomes progressively more challenging as the number of major forcing factors and pressures increase since they can occur in previously unseen combinations (Dickey-Collas et al, 2014). Uusitalo et al (2016) approached this problem in the Baltic Sea case by using three distinct modeling approaches to evaluate how different combinations of fisheries management and nutrient abatement can be expected to affect the ecosystem status of the Baltic Sea, thus linking the MSFD pressure descriptors D5-eutrophication and D3-commerical fish and shellfish.…”

Section: Modeling To Evaluate Management Scenariosmentioning

confidence: 99%

Uses of Innovative Modeling Tools within the Implementation of the Marine Strategy Framework Directive

et al. 2016

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In Europe and around the world, the approach to management of the marine environment has developed from the management of single issues (e.g., species and/or pressures) toward holistic Ecosystem Based Management (EBM) that includes aims to maintain biological diversity and protect ecosystem functioning. Within the European Union, this approach is implemented through the Marine Strategy Framework Directive (MSFD, 2008/56/EC). Integrated Ecosystem Assessment is required by the Directive in order to assess Good Environmental Status (GES). Ecological modeling has a key role to play within the implementation of the MSFD, as demonstrated here by case studies covering a range of spatial scales and a selection of anthropogenic threats. Modeling studies have a strong role to play in embedding data collected at limited points within a larger spatial and temporal scale, thus enabling assessments of pelagic and seabed habitat. Furthermore, integrative studies using food web and ecosystem models are able to investigate changes in food web functioning and biological diversity in response to changes in the environment and human pressures. Modeling should be used to: support the development and selection of specific indicators; set reference points to assess state and the achievement of GES; inform adaptive monitoring programs and trial management scenarios. The modus operandi proposed shows how ecological modeling could support the decision making process leading to appropriate management measures and inform new policy.

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Hazard warning: model misuse ahead

Cited by 54 publications

References 48 publications

Uncertainties in projecting climate-change impacts in marine ecosystems

Uncertainties in projecting climate-change impacts in marine ecosystems

Lessons from the First Generation of Marine Ecological Forecast Products

Uses of Innovative Modeling Tools within the Implementation of the Marine Strategy Framework Directive

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