Use of ecosystem models in marine governance
Sakari Kuikka,
Professor,
FEM –group, Environmental Change and Global Sustainability -research program, University of Helsinki Finland
Barcelona held, during Novemeber 2010, the MARIFISH-ICES Joint Workshop on Integrated Ecosystem Modelling with the aim of building capacity to understand and manage marine ecosystems in a changing world. A good representation of ecosystem models aimed at simulating an end-to-end representation of the ecosystem was shown at the workshop. Slide by slide, these presentations summarized years of intelligent thoughts by some of the brightest minds in the field of marine ecosystem research.
In a workshop structure that was unusual, the organizers also gave the floor to the clients of this work, those who pay the research for its development. The European Commission is a big actor for this role in Europe through funds provided by DG Research, including the E4 Unit in charge of fishing and aquaculture. An officer from this unit offered a more pessimistic view. In fact, the analysis by Philippe Moguedet was rather devastating. His sharp criticism was not on the internal structure or the logic of the models but on the fact that he could not identify who was implementing these tools for the managing porpoises that justified their creation.
To cut the head of the messenger is an option since Tigranes war against Rome and the words of the officer, despite being valid in the scientific arena by its internal consistency based on facts, was followed by a vivid reaction of the room. The discussion evidenced that part of the failure was coming from the final-user side and their (lack of) willingness to incorporate innovative concepts in their decision-taking process. It is also unbalanced to expect from marine-ecosystem modeling more capabilities than it is presently requested to other areas of research not dealing with the complexities of living being. Meteorology was mentioned as an instance where huge model-implementation and data-gathering programs only allow a prediction time of the order of days. Marine-ecosystem models cannot count on these massive investments to better constraint their outputs. However, it was also obvious that many of the models developed ad hoc to manage resources under the ecosystem approach had problems to perform that function.
An interesting set of debates cascaded in the following days of the workshop from this, rather chocking, initial picture. Without denying its limitations, part of the workshop members advocated for the understanding that can be gained by knitting the details of ecosystem functioning into mathematical complex structures. Without denying its heuristic value, a smaller portion of the participants was more sensitive to the perils this design involves for managing purposes. Dr. Moguedet’s words were seen as evidence of their inherent potential to be used for transmitting a too optimistic view of science and its capacity to predict precisely the outcomes of management actions on ecosystems. Although both positions were by no mean exclusivist the emphasis in either view was present along the intensive exchanges held during these days.
A very first and basic question to consider is what was wrong? Why an officer from the European Commission with an unquestionable experience in managing programs of fishery research has reached to the conclusion that tools developed so far to implement the ecosystem approach might be nice to the scientist eye but useless to the rest of the society.
Some context may help to analyze this unreasonable divorce. Stock collapses along the 20th century evidenced that statistics alone are not a reliable tool to provide scientific support for the management of living resources. The first collapse of a fish stock cannot be predicted by modeling its statistical behavior in the past.
At the present state of this heuristic path, the structure of these models can accommodate the variables needed to apply the ecosystem approach for the management of marine resources. The models use spotless mathematics to construct the equations that simulate these variables and the interaction among them. These equations demand a large number of variables but these are incorporated in a fashion coherent with the existing knowledge of ecosystem functioning, a knowledge that still needs development but certainly is not small.
Therefore, it seems that we have models able to focus the problem and to focus it with a firm basis constructed over decades of scientific thought. Being this the case, why these tools that look solid are not widespread used for knowledge-based decision-making? A non-negligible part of this lack of transfer comes from the honest position of the model developers and the limitations they perceive in their own tools. Projecting a wrong diagnose or prognose in the taking of decisions may have societal consequences beyond those of hypothesis fencing in scientific journals. This is a challenging arena when the dynamics of the conceptual representation under operation by models is perceived only as an approximation to the real ecosystem functioning. In words of the famous modeler George E. P. Box: “…all models are approximations. Essentially, all models are wrong,… ; the practical question is how wrong do they have to be to not be useful”
Alternatives exist to avoid projecting the ecosystems over a mechanical-universe determinism where they hardly fit. The modern implementation of the Bayesian approach is able to cartography over mathematical structures not only our knowledge but also our uncertainties on ecological processes. In doing so, they do not produce point but probabilistic estimates to compute uncertainty and risk that are crucial to decision making. They offer us a consistent frame to describe the uncertainties of our diagnoses and prognoses given the available data and hypotheses, a strategy considered as superior both to model nature and to represent the uncertainties associated to this modeling. These techniques can also consistently compute not only parametric but structural uncertainty by analyzing together several alternative theories (causal structures) to describe the natural phenomena. This is a major advantage to model mid and high trophic levels where data can be used to learn about the several hypotheses that scientific literature proposes to describe their dynamics.
Email: sakari.kuikka@helsinki.fi
Expert article 3096
> Back to Baltic Rim Economies 5/2021
To receive the Baltic Rim Economies review free of charge, you may register to the mailing list.
The review is published 4-6 times a year.