Numerical models help us to make sense of complicated data or to test our hypotheses. For this reason, they are increasingly used in studies and analyses supporting fisheries and ecosystem management decisions. Sound participatory management requires that different actors – from fishermen to scientists – can understand and trust these models, which implies that assumptions behind the models are presented in a clear, honest and transparent way. It is also essential that uncertainties in our knowledge and observations are well represented in the models.
When we study complex ecological systems, like the Barents Sea food-web, the numerical models can become themselves rather complicated, and the numerical modelling principles and terminology used by scientists are sometimes barely accessible to non-scientists, therefore hindering a fruitful dialogue. In a newly published article, Nansen Legacy scientists, Benjamin Planque, proposes the use of a new kind of simple models as a practical step to enable scientists and non-scientists alike to better share their understanding of how model assumptions, data and model outputs are linked.
The proposed model approach is based on the principles of chance and necessity (CaN), and explicitly acknowledges our limited capacity to observe and model ecological processes. A central element of this model approach is that the model outputs cover a range of possible ecosystem states and dynamics, rather than striving to deliver one best estimate. The existence of these multiple possibilities represents the starting point for discussions among modellers, managers, and stakeholders.
Learn about the controls and limits to observational capabilities
Using a simplified representation of the Barents Sea food-web and annual field-based biomass estimates, Planque and his coworker Christian Mullon constructed an explorative CaN model for the Barents Sea. The objectives for this model were to learn about the controls of the system while recognizing the limits to our understanding and to our observational capabilities. With help of the model, Planque and Mullon investigated how species and trophic interactions may have varied in the past and to which degree these variations can explain the changes in the Barents Sea ecosystem that have been observed.
Fig. above: Simplified trophic structure of the Barents Sea ecosystem. Arrows indicate flow of biomass from prey to predators (plain, green), within the same trophic functional group (dotted, red) and towards fisheries (dashed, blue).
Fig. above: A representative set of sampled trajectories of biomasses. The thick black lines correspond to input trajectories, which are best estimates of primary production and biomasses, as well as reported landings. Thin upper and lower red lines indicate upper and lower biomass constrains. Grey ribbons encompass the 5-95% quantiles of all sampled trajectories. A subsample of ten trajectories is shown in thin coloured lines.
Are historical data compatible?
A question Planque and Mullon address with this ‘food-web assessment’ model is whether the historical data from several trophic groups, from plankton to whales, are compatible. They show that it is possible to explain observed changes in biomass across the Barents Sea food-web in a way that is consistent with our assumptions about trophic flows (who eats whom) and ecological (metabolic and life-history) theory.
The reconstruction of the trophic flows shows that most species groups have a high turnover and that the increase in demersal fish biomass (mostly Atlantic cod) in recent decades has impacted trophic exchanges throughout of the food-web. It suggests that the most species groups have been controlled by top-down pressures (the predator controlling the abundance of the prey) rather than the reverse, bottom-up. Given incomplete and uncertain observations, the outputs of the model provide many possible ‘histories’ of the Barents Sea food web dynamics. These can serve as the basis for the discussion between modellers, managers and stakeholders.