Shape matters: the relationship between cell geometry and diversity in phytoplankton

Size and shape profoundly influence an organism’s ecophysiological performance and evolutionary fitness, suggesting a link between morphology and diversity. However, not much is known about how body shape is related to taxonomic richness, especially in microbes. Here we analyse global datasets of unicellular marine phytoplankton, a major group of primary producers with an exceptional diversity of cell sizes and shapes and, additionally, heterotrophic protists. Using two measures of cell shape elongation, we quantify taxonomic diversity as a function of cell size and shape. We find that cells of intermediate volume have the greatest shape variation, from oblate to extremely elongated forms, while small and large cells are mostly compact (e.g. spherical or cubic). Taxonomic diversity is strongly related to cell elongation and cell volume, together explaining up to 92% of total variance. Taxonomic diversity decays exponentially with cell elongation and displays a log-normal dependence on cell volume, peaking for intermediate-volume cells with compact shapes. These previously unreported broad patterns in phytoplankton diversity reveal selective pressures and ecophysiological constraints on the geometry of phytoplankton cells which may improve our understanding of marine ecology and the evolutionary rules of life

Toward a stoichiometric framework for evolutionary biology. Oikos

2005. Toward a stoichiometric framework for evolutionary biology. Á/ Oikos 109: 6 Á/17. Ecological stoichiometry, the study of the balance of energy and materials in living systems, may serve as a useful synthetic framework for evolutionary biology. Here, we review recent work that illustrates the power of a stoichiometric approach to evolution across multiple scales, and then point to important open questions that may chart the way forward in this new field. At the molecular level, stoichiometry links hereditary changes in the molecular composition of organisms to key phenotypic functions. At the level of evolutionary ecology, a simultaneous focus on the energetic and material underpinnings of evolutionary tradeoffs and transactions highlights the relationship between the cost of resource acquisition and the functional consequences of biochemical composition. At the macroevolutionary level, a stoichiometric perspective can better operationalize models of adaptive radiation and escalation, and elucidate links between evolutionary innovation and the development of global biogeochemical cycles. Because ecological stoichiometry focuses on the interaction of energetic and multiple material currencies, it should provide new opportunities for coupling evolutionary dynamics across scales from genomes to the biosphere

Marine Phytoplankton Temperature versus Growth Responses from Polar to Tropical Waters – Outcome of a Scientific Community-Wide Study

"It takes a village to finish (marine) science these days" Paraphrased from Curtis Huttenhower (the Human Microbiome project) The rapidity and complexity of climate change and its potential effects on ocean biota are challenging how ocean scientists conduct research. One way in which we can begin to better tackle these challenges is to conduct community-wide scientific studies. This study provides physiological datasets fundamental to understanding functional responses of phytoplankton growth rates to temperature. While physiological experiments are not new, our experiments were conducted in many laboratories using agreed upon protocols and 25 strains of eukaryotic and prokaryotic phytoplankton isolated across a wide range of marine environments from polar to tropical, and from nearshore waters to the open ocean. This community-wide approach provides both comprehensive and internally consistent datasets produced over considerably shorter time scales than conventional individual and often uncoordinated lab efforts. Such datasets can be used to parameterise global ocean model projections of environmental change and to provide initial insights into the magnitude of regional biogeographic change in ocean biota in the coming decades. Here, we compare our datasets with a compilation of literature data on phytoplankton growth responses to temperature. A comparison with prior published data suggests that the optimal temperatures of individual species and, to a lesser degree, thermal niches were similar across studies. However, a comparison of the maximum growth rate across studies revealed significant departures between this and previously collected datasets, which may be due to differences in the cultured isolates, temporal changes in the clonal isolates in cultures, and/or differences in culture conditions. Such methodological differences mean that using particular trait measurements from the prior literature might introduce unknown errors and bias into modelling projections. Using our community-wide approach we can reduce such protocol-driven variability in culture studies, and can begin to address more complex issues such as the effect of multiple environmental drivers on ocean biota.EL and MKT were in part supported by the National Science Foundation (NSF) grants DEB-0845932 and OCE-0928819. TAR and KAW were supported by NSF grant OCE-0727227. UP was supported by NSF grants OCE-0926711 and OCE-1041038. PWB and RS were supported by the New Zealand Royal Society Marsden Fund and the Ministry of Science and Innovation. RMK and KH were in part supported by National Oceanic and Atmospheric Administration (NOAA) Monitoring and Event Response for Harmful Algal Blooms (MERHAB) grant NA04NOS4780239 and NSF grant OCE-0238347. DAH and FX-F were supported by NSF grants OCE-0942379, OCE-0962309, and OCE-117030687. MRM was partially supported by NSF grant OCE-0722395 and a NOAA The Ecology and Oceanography of Harmful Algal Blooms (ECOHAB) grant NA06NO54780246. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes

The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers

The ecological causes of functional distinctiveness in communities

Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages

Trait-based approaches to zooplankton communities

Zooplankton are major primary consumers and predators in most aquatic ecosystems. They exhibit tremendous diversity of traits, ecological strategies and, consequently, impacts on other trophic levels and the cycling of materials and energy. An adequate representation of this diversity in community and ecosystem models is necessary to generate realistic predictions on the functioning of aquatic ecosystems but remains extremely challenging. We propose that the use of trait-based approaches is a promising way to reduce complexity while retaining realism in developing novel descriptions of zooplankton in ecosystem models. Characterizing zooplankton traits and trade-offs will also be helpful in understanding the selection pressures and diversity patterns that emerge in different ecosystems along major environmental gradients. Zooplankton traits can be characterized according to their function and type. Some traits, such as body size and motility, transcend several functions and are major determinants of zooplankton ecological strategies. Future developments of trait-based approaches to zooplankton should assemble a comprehensive matrix of key traits for diverse groups and explore it for general patterns; develop novel predictive models that explicitly incorporate traits and associated trade-offs; and utilize these traits to explain and predict zooplankton community structure and dynamics under different environmental conditions, including global change scenarios. © 2013 The Author

The allometry of the smallest: superlinear scaling of microbial metabolic rates in the Atlantic Ocean

Prokaryotic planktonic organisms are small in size but largely relevant in marine biogeochemical cycles. Due to their reduced size range (0.2 to 1 mu m in diameter), the effects of cell size on their metabolism have been hardly considered and are usually not examined in field studies. Here, we show the results of size-fractionated experiments of marine microbial respiration rate along a latitudinal transect in the Atlantic Ocean. The scaling exponents obtained from the power relationship between respiration rate and size were significantly higher than one. This superlinearity was ubiquitous across the latitudinal transect but its value was not universal revealing a strong albeit heterogeneous effect of cell size on microbial metabolism. Our results suggest that the latitudinal differences observed are the combined result of changes in cell size and composition between functional groups within prokaryotes. Communities where the largest size fraction was dominated by prokaryotic cyanobacteria, especially Prochlorococcus, have lower allometric exponents. We hypothesize that these larger, more complex prokaryotes fall close to the evolutionary transition between prokaryotes and protists, in a range where surface area starts to constrain metabolism and, hence, are expected to follow a scaling closer to linearity.Versión del editor8,951

Understanding and predicting harmful algal blooms in a changing climate: A trait‐based framework

Abstract The worldwide proliferation of harmful algal blooms (HABs) both in freshwater and marine ecosystems make understanding and predicting their occurrence urgent. Trait‐based approaches, where the focus is on functional traits, have been successful in explaining community structure and dynamics in diverse ecosystems but have not been applied extensively to HABs. The existing trait compilations suggest that HAB taxa differ from non HAB taxa in key traits that determine their responses to major environmental drivers. Multi‐trait comparisons between HAB‐forming and other phytoplankton taxa, as well as within the HAB groups to characterize interspecific and intraspecific differences will help better define ecological niches of different HAB taxa, develop trait‐based mechanistic models, and better identify environmental conditions that would likely lead to HABs. Building databases of HAB traits and using them in diverse statistical and mechanistic models will increase our ability to predict the HAB occurrence, composition, and severity under changing conditions, including the anthropogenic global change

Fatty acid profiles per cell of replicate populations of Thalassiosira pseudonana, selected at 16 and 31C for ~500 generations and assayed at 4 temperatures

Dataset: T. pseudonana fatty acids per cellFatty acid profiles per cell of replicate populations of Thalassiosira pseudonana, selected at 16 and 31C for ~500 generations and assayed at 4 temperatures For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/780178NSF Division of Ocean Sciences (NSF OCE) OCE-1638958, NSF Division of Ocean Sciences (NSF OCE) OCE-1638804, NSF Division of Ocean Sciences (NSF OCE) OCE-163883