The factors regulating phytoplankton community composition play a crucial role in structuring aquatic food webs. However, consensus is still lacking about the mechanisms underlying the observed biogeographical differences in cell size composition of phytoplankton communities. Here we use a trait-based model to disentangle these mechanisms in two contrasting regions of the Atlantic Ocean. In our model, the phytoplankton community can self-assemble based on a trade-off emerging from relationships between cell size and (1) nutrient uptake, (2) zooplankton grazing, and (3) phytoplankton sinking. Grazing ‘pushes’ the community towards larger cell sizes, whereas nutrient uptake and sinking ‘pull’ the community towards smaller cell sizes. We find that the stable environmental conditions of the tropics strongly balance these forces leading to persistently small cell sizes and reduced size diversity. In contrast, the seasonality of the temperate region causes the community to regularly reorganize via shifts in species composition and to exhibit, on average, bigger cell sizes and higher size diversity than in the tropics. Our results raise the importance of environmental variability as a key structuring mechanism of plankton communities in the ocean and call for a reassessment of the current understanding of phytoplankton diversity patterns across latitudinal gradients.

It is well-established that when equilibrium is attained for two species competing for the same limiting resource in a stable, uniform environment, one species will eliminate the other due to competitive exclusion. While competitive exclusion is observed in laboratory experiments and ecological models, the phenomenon seems less common in nature, where static equilibrium is prevented by the fluctuating physical environment and by other factors that constantly change species abundances and the nature of competitive interactions. Trait-based models of phytoplankton communities appear to be useful tools for describing the evolution of large assemblages of species with aggregate group properties such as total biomass, mean trait, and trait variance, the latter representing the functional diversity of the community. Such an approach, however, is limited by the tendency of the trait variance to unrealistically decline to zero over time. This tendency to lose diversity, and therefore adaptive capacity, is typically "solved" by fixing the variance or by considering exogenous processes such as immigration. Exogenous processes, however, cannot explain the maintenance of adaptive capacity often observed in the closed environment of chemostat experiments. Here we present a new method to sustain diversity in adaptive trait-based models of phytoplankton communities based on a mechanism of trait diffusion through subsequent generations. Our modeling approach can therefore account for endogenous processes such as rapid evolution or transgenerational trait plasticity.

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Aim Develop a biogeographical classification of phytoplankton size distributions for the Atlantic Ocean and predict the global phytoplankton size composition based on prevailing environmental conditions. Location Atlantic Ocean and Global Ocean Methods Using phytoplankton size composition data, nutrient concentrations (nitrite+nitrate, phosphate, and silicate), irradiance, temperature and zooplankton abundances of the Atlantic Meridional Transect programme, we derived and tested an environmental classification method of phytoplankton size distribution with a k‐means clustering technique. We then used principal component and Dirichlet multivariate regression analyses to disentangle the relative influence of different environmental conditions on the phytoplankton size composition. Subsequently, we evaluated different probabilisitic models and selected the most parsimonious one to estimate the global phytoplankton size distributions in the world oceans based on global climatology data of the World Ocean Atlas 2009. Results Based only on prevailing environmental conditions and without a priori knowledge concerning, for example, the position of oceanic fronts, the primary productivity, the distribution of organisms or any geographical information, our classification method captures the size structures of phytoplankton communities across the Atlantic. We find a strong influence of temperature and nitrite+nitrate concentration on the prevalence of the different size classes, and we present evidence that both factors may act independently on structuring phytoplankton communities. While at low nitrite+nitrate concentrations temperature has a major structuring impact, at high nitrite+nitrate concentrations its influence is reduced. Finally, we show that the global distribution of phytoplankton community size structure can be predicted by a probabilistic model based only on temperature and nitrite+nitrate. Main conclusion The global distribution of phytoplankton community size structure can be predicted with good approximation using a parsimonious probabilistic model forced by only temperature and nitrite+nitrate data.

The history of Easter Island and its supposed social-ecological collapse is often taken as a grim warning for the modern world. However, while the loss of a once lush palm forest is largely uncontested, causes and timing of the collapse remain controversial, because many paleoecological and archaeological data are afflicted with considerable uncertainties. According to a scenario named ecocide, the overharvesting of palm trees triggered a dramatic population decline, whereas a contrasting view termed genocide deems diseases and enslavement introduced by Europeans as the main reasons for the collapse. We propose here a third possibility, a slow demise, in which aspects of both ecocide and genocide concur to produce a long and slow decline of the society. We use a dynamic model to illustrate the consequences of the three alternatives with respect to the fate of the paleoecological system of the island. While none of the three model scenarios can be safely ruled out given the uncertainties of the available data, the slow demise appears to be the most plausible model scenario, in particular when considering the temporal pattern of deforestation as inferred from radiocarbon dates of charcoal remains.

Abstract. Biodiversity is one of the key mechanisms that facilitate the adaptive response of planktonic communities to a fluctuating environment. How to allow for such a flexible response in marine ecosystem models is, however, not entirely clear. One particular way is to resolve the natural complexity of phytoplankton communities by explicitly incorporating a large number of species or plankton functional types. Alternatively, models of aggregate community properties focus on macroecological quantities such as total biomass, mean trait, and trait variance (or functional trait diversity), thus reducing the observed natural complexity to a few mathematical expressions. We developed the PhytoSFDM modelling tool, which can resolve species discretely and can capture aggregate community properties. The tool also provides a set of methods for treating diversity under realistic oceanographic settings. This model is coded in Python and is distributed as open-source software. PhytoSFDM is implemented in a zerodimensional physical scheme and can be applied to any location of the global ocean. We show that aggregate community models reduce computational complexity while preserving relevant macroecological features of phytoplankton communities. Compared to species-explicit models, aggregate models are more manageable in terms of number of equations and have faster computational times. Further developments of this tool should address the caveats associated with the assumptions of aggregate community models and about implementations into spatially resolved physical settings (onedimensional and three-dimensional). With PhytoSFDM we embrace the idea of promoting open-source software and encourage scientists to build on this modelling tool to further improve our understanding of the role that biodiversity plays in shaping marine ecosystems.

It is expected that climate change will have significant impacts on ecosystems. Most model projections agree that the ocean will experience stronger stratification and less nutrient supply from deep waters. These changes will likely affect marine phytoplankton communities and will thus impact on the higher trophic levels of the oceanic food web. The potential consequences of future climate change on marine microbial communities can be investigated and predicted only with the help of mathematical models. Here we present the application of a model that describes aggregate properties of marine phytoplankton communities and captures the effects of a changing environment on their composition and adaptive capacity. Specifically, the model describes the phytoplankton community in terms of total biomass, mean cell size, and functional diversity. The model is applied to two contrasting regions of the Atlantic Ocean (tropical and temperate) and is tested under two emission scenarios: SRES A2 or "business as usual" and SRES B1 or "local utopia." We find that all three macroecological properties will decline during the next century in both regions, although this effect will be more pronounced in the temperate region. Being consistent with previous model predictions, our results show that a simple trait-based modeling framework represents a valuable tool for investigating how phytoplankton communities may reorganize under a changing climate.

Abstract. Understanding Earth system dynamics in the light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing inter-disciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple time-scales; and (3) data-model integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. Latest developments in machine learning, causal inference, and model data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.