When making predictions about ecosystems, we often have available a number of different ecosystem models that attempt to represent their dynamics in a detailed mechanistic way. Each of these can be used as a simulator of large‐scale experiments and make projections about the fate of ecosystems under different scenarios to support the development of appropriate management strategies. However, structural differences, systematic discrepancies and uncertainties lead to different models giving different predictions. This is further complicated by the fact that the models may not be run with the same functional groups, spatial structure or time scale. Rather than simply trying to select a “best” model, or taking some weighted average, it is important to exploit the strengths of each of the models, while learning from the differences between them. To achieve this, we construct a flexible statistical model of the relationships between a collection of mechanistic models and their biases, allowing for structural and parameter uncertainty and for different ways of representing reality. Using this statistical meta‐model, we can combine prior beliefs, model estimates and direct observations using Bayesian methods and make coherent predictions of future outcomes under different scenarios with robust measures of uncertainty. In this study, we take a diverse ensemble of existing North Sea ecosystem models and demonstrate the utility of our framework by applying it to answer the question what would have happened to demersal fish if fishing was to stop.

Marine legislation is becoming more complex and marine ecosystem-based management is specified in national and regional legislative frameworks. Shelf-seas community and ecosystem models (hereafter termed ecosystem models) are central to the delivery of ecosystem-based management, but there is limited uptake and use of model products by decision makers in Europe and the UK in comparison with other countries. In this study, the challenges to the uptake and use of ecosystem models in support of marine environmental management are assessed using the UK capability as an example. The UK has a broad capability in marine ecosystem modelling, with at least 14 different models that support management, but few examples exist of ecosystem modelling that underpin policy or management decisions. To improve understanding of policy and management issues that can be addressed using ecosystem models, a workshop was convened that brought together advisors, assessors, biologists, social scientists, economists, modellers, statisticians, policy makers, and funders. Some policy requirements were identified that can be addressed without further model development including: attribution of environmental change to underlying drivers, integration of models and observations to develop more efficient monitoring programmes, assessment of indicator performance for different management goals, and the costs and benefit of legislation. Multi-model ensembles are being developed in cases where many models exist, but model structures are very diverse making a standardised approach of combining outputs a significant challenge, and there is a need for new methodologies for describing, analysing, and visualising uncertainties. A stronger link to social and economic systems is needed to increase the range of policy-related questions that can be addressed. It is also important to improve communication between policy and modelling communities so that there is a shared understanding of the strengths and limitations of ecosystem models

Reproductive traits in plants tend to evolve rapidly due to various causes that include plant-pollinator coevolution and pollen competition, but the genomic basis of reproductive trait evolution is still largely unknown. To characterize evolutionary patterns of genome wide gene expression in reproductive tissues in the gametophyte and to compare them to developmental stages of the sporophyte, we analyzed evolutionary conservation and genetic diversity of protein-coding genes using microarray-based transcriptome data from three plant species, Arabidopsis thaliana, rice (Oryza sativa), and soybean (Glycine max). In all three species a significant shift in gene expression occurs during gametogenesis in which genes of younger evolutionary age and higher genetic diversity contribute significantly more to the transcriptome than in other stages. We refer to this phenomenon as “evolutionary bulge” during plant reproductive development because it differentiates the gametophyte from the sporophyte. We show that multiple, not mutually exclusive, causes may explain the bulge pattern, most prominently reduced tissue complexity of the gametophyte, a varying extent of selection on reproductive traits during gametogenesis as well as differences between male and female tissues. This highlights the importance of plant reproduction for understanding evolutionary forces determining the relationship of genomic and phenotypic variation in plants.

Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to "calibrate" the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967-2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population-and community-level indicators under alternative management strategies.Résumé : Les modèles de spectres de tailles dynamiques sont reconnus comme offrant une approche efficace pour décrire comment les interactions basées sur la taille peuvent donner lieu à une structure de taille dans les communautés aquatiques. Ce sont des modèles écologiques de complexité moyenne représentant des solutions à des équations différentielles partielles déterminées par les processus dépendant de la taille que sont la prédation, la croissance, la mortalité et la reproduction dans une communauté d'espèces et de tailles interagissant entre elles. Pour être utiles dans la gestion quantitative des pêches, ces modèles doivent être approfondis dans un cadre statistique formel. Des travaux antérieurs ont fait appel à des valeurs moyennes de séries temporelles pour « étalonner » le modèle en utilisant des méthodes d'optimisation, ce qui a le désavantage d'occulter de l'information détaillée contenue dans les séries chronologiques. En utilisant un modèle publié de spectre de tailles multi-espèce paramétré pour la mer du Nord et comptant 12 espèces de poissons interagissant entre elles et les ressources de référence, nous avons calé le modèle sur des données de séries chronologiques en utilisant un cadre bayésien pour la première fois. Nous capturons la période de 1967 à 2010 en employant des estimations annuelles des taux de mortalité par pêche comme entrées au modèle et des séries chronologiques de données de débarquement de pêche pour caler le modèle sur les données de sortie. Nous estimons 38 paramètres clés représentant la capacité de charge de chaque espèce et...