Marine Ecosystem Models (MEMs) are increasingly forced with Earth System Models (ESMs) to better understand marine ecosystem dynamics, and to analyse the effects of alternative management efforts for marine ecosystems under potential scenarios of global change. However, policy and commercial activities typically occur on seasonal-to-decadal time scales, a time span widely used in the global climate modelling community but where the skill level assessments of MEMs are in their infancy. This is mostly due to technical hurdles that prevent the global MEM community from performing large ensemble simulations with which to undergo systematic skill assessments. Here, we developed a novel distributed execution framework constructed of low-tech and freely available technologies to enable the systematic execution and analysis of linked ESM / MEM prediction ensembles. We apply this framework on the seasonal-to-decadal time scale, and assess how retrospective forecast uncertainty in an ensemble of initialised decadal Earth System Model predictions affects a mechanistic and spatiotemporal explicit global MEM. Our results indicate that ESM internal variability has a relatively low impact on the MEM predictability in comparison to the broad assumptions related to reconstructed fisheries. We also observe that the results are also sensitive to the ESM specificities. Our case study warrants further systematic explorations to disentangle the impacts of climate change, fisheries scenarios, MEM internal ecological hypotheses, and ESM variability. Most importantly, our case study demonstrates that a simple and free distributed execution framework has the potential to empower any modelling group with the fundamental capabilities to operationalize marine ecosystem modelling.

By evaluating genetic variation across the entire genome, one can address existing questions in a novel way while new can be asked. Such questions include how different local environments influence both adaptive and neutral genomic variation within and among populations, providing insights not only into local adaptation of natural populations, but also into their responses to global change and the exploitation-induced evolution. Here, under a seascape genomic approach, ddRAD genomic data were used along with environmental information to uncover the underlying processes (migration, selection) shaping European sardines (Sardina pilchardus) of the Western Mediterranean and adjacent Atlantic waters. This information can be relevant to the (re)definition of fishery stocks, and their short-term adaptive potential. We found that studied sardine samples form two clusters, detected using both neutral and adaptive (outlier) loci suggesting that natural selection and local adaptation play a key role in driving genetic change among the Atlantic and the Mediterranean sardines. Temperature and especially the trend in the number of days with sea surface temperature (SST) above 19oC was crucial at all levels of population structuring with implications on species’ key biological processes, especially reproduction. Our findings provide evidence for a dynamic equilibrium where population structure is maintained by physical and biological factors under the opposing influences of migration and selection. Given its dynamic nature, such a system postulates a continuous monitoring under a seascape genomic approach that can benefit by incorporating a temporal as well as a more detailed spatial dimension.