Daily growth rates for Thermal Performance Curve (TPC) of Chaetoceros simplex after about 100 generations of evolution at seven temperatures, 12-34 degrees C. 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/778749 Copyright: https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0

Current algal biomass research still focuses mainly on identifying and growing monocultures that produce high amounts of lipids or other target compounds. However, monocultures might have a lower efficiency of utilizing resources, due to their limited physiological resource use possibilities, compared to algal polycultures. Recent studies showed that species diversity enhances the lipid production of microalgae. To identify the general patterns of enhanced lipid production in diverse microalgal communities it is essential to investigate links between resource use complementarity and the corresponding lipid production.

Predicting the effects of multiple global change stressors on microbial communities remains a challenge because of the complex interactions among those factors. Here, we explore the combined effects of major global change stressors on nutrient acquisition traits in marine phytoplankton. Nutrient limitation constrains phytoplankton production in large parts of the present-day oceans, and is expected to increase owing to climate change, potentially favouring small phytoplankton that are better adapted to oligotrophic conditions. However, other stressors, such as elevated pCO(2), rising temperatures and higher light levels, may reduce general metabolic and photosynthetic costs, allowing the reallocation of energy to the acquisition of increasingly limiting nutrients. We propose that this energy reallocation in response to major global change stressors may be more effective in large-celled phytoplankton species and, thus, could indirectly benefit large-more than small-celled phytoplankton, offsetting, at least partially, competitive disadvantages of large cells in a future ocean. Thus, considering the size-dependent responses to multiple stressors may provide a more nuanced understanding of how different microbial groups would fare in the future climate and what effects that would have on ecosystem functioning. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.

Environments change, for both natural and anthropogenic reasons, which can threaten species persistence. Evolutionary adaptation is a potentially powerful mechanism to allow species to persist in these changing environments. To determine the conditions under which adaptation will prevent extinction (evolutionary rescue), classic quantitative genetics models have assumed a constantly changing environment. They predict that species traits will track a moving environmental optimum with a lag that approaches a constant. If fitness is negative at this lag, the species will go extinct. There have been many elaborations of these models incorporating increased genetic realism. Here, we review and explore the consequences of four ecological complications: non-quadratic fitness functions, interacting density- and trait-dependence, species interactions and fundamental limits to adaptation. We show that non-quadratic fitness functions can result in evolutionary tipping points and existential crises, as can the interaction between density- and trait-dependent mortality. We then review the literature on how interspecific interactions affect adaptation and persistence. Finally, we suggest an alternative theoretical framework that considers bounded environmental change and fundamental limits to adaptation. A research programme that combines theory and experiments and integrates across organizational scales will be needed to predict whether adaptation will prevent species extinction in changing environments. Copyright: CC BY 4.0

A synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. Trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients.

Environmental factors that interact with increasing temperature under the ongoing global warming are an urgent issue determining marine phytoplankton's performance. Previous studies showed that nutrient limitation alters phytoplankton responses to temperature and may lower their temperature optima (T-opt), making them more susceptible to high temperatures. The generality of this relationship is unknown, as very few species were tested. Here we investigated how growth rate depended on temperature at two contrasting nitrogen concentrations in six marine diatoms isolated from different thermal environments, including the tropics. Low nitrate had a significant effect on thermal performance in five of the six species. The effect size was larger around the optimum temperature for growth, resulting in flattened thermal performance curves but no shift in T-opt. While that trend is independent of the thermal regime from which each species was isolated, the implications for the phytoplankton response to global warming may be region dependent.

Environments change, for both natural and anthropogenic reasons, which can threaten species persistence. Evolutionary adaptation is a potentially powerful mechanism to allow species to persist in these changing environments. To determine the conditions under which adaptation will prevent extinction (evolutionary rescue), classic quantitative genetics models have assumed a constantly changing environment. They predict that species traits will track a moving environmental optimum with a lag that approaches a constant. If fitness is negative at this lag, the species will go extinct. There have been many elaborations of these models incorporating increased genetic realism. Here, we review and explore the consequences of four ecological complications: non-quadratic fitness functions, interacting density- and trait-dependence, species interactions and fundamental limits to adaptation. We show that non-quadratic fitness functions can result in evolutionary tipping points and existential crises, as can the interaction between density- and trait-dependent mortality. We then review the literature on how interspecific interactions affect adaptation and persistence. Finally, we suggest an alternative theoretical framework that considers bounded environmental change and fundamental limits to adaptation. A research programme that combines theory and experiments and integrates across organizational scales will be needed to predict whether adaptation will prevent species extinction in changing environments. This article is part of the theme issue 'Integrative research perspectives on marine conservation'.

We compiled the most comprehensive data setof phytoplankton and other marine protists in terms of sizes, shapes, genus, and species names. Samples were obtained from seven globally distributed marine areas: Baltic Sea, North Atlantic (Scotland), Mediterranean Sea (Greece and Turkey), Indo-Pacific (the Maldives), South-western Pacific (Australia), Southern Atlantic (Brazil). See details inRyabov et al Ecology Letters 'Shape matters: the relationship between cell geometry and diversity in phytoplankton' Files original data: 6Regions_Original.xlsx andBalticSea_Original.xlsxinclude genus and species names, linear dimenstions and identified shapes of phytoplankton cells Combined dataset: Baltic+6Regions_genera_sizes.xlsx calculated surface, volume and other geometric characteristis for both original datasets the data is aggregated by Genus, Site and Shape. See details in: Ryabov et al Ecology Letters 'Shape matters: the relationship between cell geometry and diversity in phytoplankton' Data sources The data sources include two datasets. The first dataset represents the results of monitoring in several stations in Baltic Sea over the past 25 years (with interval 1-2 months from May to November) and contains information on phytoplankton species and heterotrophic dinoflagellates covering a total of 308 genera. The second dataset includes a biogeographical snapshot survey of phytoplankton assemblages obtained by Ecology Unit of Salento University performed during summer in 2011 and 2012 in six coastal areas with different biogeographical conditions (ecoregions) around the globe (Roselli et al. 2017). This survey included 3 concurrent data replicas from each of 116 local sites. This data covers a total of 193 genera sampled from 23 ecosystems of different typology (coastal lagoons, estuaries, coral reefs, mangroves and inlets or silled basins). The data used in this study are available online (ICES CEIM; LifeWatch ERIC), see also Data availability for the data included in manuscript submission. Sampling methods and dataset description The measurements for the Baltic dataset were done by the HELCOM Phytoplankton Expert Group (PEG), and described in more detail by Olenina et al. (2006). The phytoplankton samples were taken in accordance with the guidelines of HELCOM (1988) as integrated samples from surface 0-10, or 0-20m water layers, using either a rosette sampler (pooling equal water volumes from discrete depths: 1; 2,5; 5; 7,5 and 10 m) or a sampling hose. The samples were preserved with acid Lugols solution (Willen 1962). The inverted microscope technique (Utermohl 1958) was used for identification of the phytoplankton species. After concentration in a sedimentation 10-, 25-, or 50-ml chamber, phytoplankton cells were measured for the further determination of species-specific shape and linear dimensions. All measurements were performed under high microscope magnification (400-945 times) using an ocular scale. The second dataset includes the results of sampling of three to nine ecosystems per ecoregion and three locations for each system, yielding a total of 116 local sites replicated three times. Phytoplankton were collected with a 6 mum mesh plankton net equipped with a flow meter for determining filtered volume. Water samples for phytoplankton quantitative analysis were preserved with Lugol (15 mL/L of sample). Phytoplankton were examined following Utermohl (1958). Phytoplankton were analysed by inverted microscope (Nikon T300E, Nikon Eclipse Ti) connected to a video-interactive image analysis system (L.U.C.I.A Version 4.8, Laboratory Imaging). Taxonomic identification and linear dimension measurements were performed at individual level on 400 phytoplankton cells for each sample. Overall, the data on 142 800 cells are included. The data on the dimensions of the same species were averaged for each replicate. If you use this data please cite some of the following papers Ryabov et al Ecology Letters 'Shape matters: the relationship between cell geometry and diversity in phytoplankton' Olenina, I., Hajdu, S., Edler, L., Andersson, A., Wasmund, N., Busch, S., et al. (2006). Biovolumes and size-classes of phytoplankton in the Baltic Sea. HELCOM Balt.Sea Environ. Proc., 106. Roselli, L., Litchman, E., Elena, S., Cozzoli, F. & Basset, A. (2017). Individual trait variation in phytoplankton communities across multiple spatial scales. J. Plankton Res., 39, 577-588. If you need to calculate surface area and volume, you can useMATLAB and Phytonscripts from: https://github.com/AlexRyabov/Cell-shape Ask alexey.ryabov@uol.de, if you need a Julia or R script for that. Copyright: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

Fatty 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/780178 Copyright: https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0

Fatty acid profiles by biovolume 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/780155 Copyright: https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0