Biological ensemble modeling to evaluate potential futures of living marine resources

Abstract

Natural resource management requires approaches to understand and handle sources of uncertainty in future responses of complex systems to human activities. Here we present one such approach, the "biological ensemble modeling approach," using the Eastern Baltic cod (Gadus morhua callarias) as an example. The core of the approach is to expose an ensemble of models with different ecological assumptions to climate forcing, using multiple realizations of each climate scenario. We simulated the long-term response of cod to future fishing and climate change in seven ecological models ranging from single-species to food web models. These models were analyzed using the "biological ensemble modeling approach" by which we (1) identified a key ecological mechanism explaining the differences in simulated cod responses between models, (2) disentangled the uncertainty caused by differences in ecological model assumptions from the statistical uncertainty of future climate, and (3) identified results common for the whole model ensemble. Species interactions greatly influenced the simulated response of cod to fishing and climate, as well as the degree to which the statistical uncertainty of climate trajectories carried through to uncertainty of cod responses. Models ignoring the feedback from prey on cod showed large interannual fluctuations in cod dynamics and were more sensitive to the underlying uncertainty of climate forcing than models accounting for such stabilizing predator-prey feedbacks. Yet in all models, intense fishing prevented recovery, and climate change further decreased the cod population. Our study demonstrates how the biological ensemble modeling approach makes it possible to evaluate the relative importance of different sources of uncertainty in future species responses, as well as to seek scientific conclusions and sustainable management solutions robust to uncertainty of food web processes in the face of climate change.

Regional trade-offs from multi-species maximum sustainable yield (MMSY) management options

“…Our model framework has room for improvements, in particular regarding environmental influences on recruitment (Köster et al 2009), density-dependent growth (Casini et al 2011, Gårdmark et al 2013 and processes accounting for changes in the spatiotemporal overlap of cod and sprat (Eero et al 2012). Nevertheless, we are confident in the range of simulated outcomes.…”

Observing and modelling phytoplankton community structure in the North Sea

“…Hence, simple assessment of the skill of models in predicting outcomes (validation -Mackinson, 2014), model comparisons (e.g. Kwiatkowski et al, 2014), and the clear treatment of the uncertainty associated with predictions (Thorpe et al, 2015;Gårdmark et al, 2013;Stewart and Martell, 2015;Tebaldi and Knutti, 2007) are needed to increase the confidence in and uptake of models (Hyder et al, 2015).…”

Extinctions in Ecological Communities : direct and indirect effects of perturbation on biodiversity

“…It seems that the initial inertia, the abrupt shift and/or hysteresis can be caused by positive feedbacks between the system state and the species it benefits, including effects on species interactions (Scheffer et al 2001, Norström et al 2009, Huss et al 2013. Overall, this goes to show, that when studying responses to perturbation, there is indeed good reason to do so in a community setting of species interactions (see also Gårdmark et al 2012). …”