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 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

Among bacteria and archaea, maximum relative growth rate, cell diameter, and genome size are widely regarded as important influences on ecological strategy. Via the most extensive data compilation so far for these traits across all clades and habitats, we ask whether they are correlated and if so how. Overall, we found little correlation among them, indicating they should be considered as independent dimensions of ecological variation. Nor was correlation evident within particular habitat types. A weak nonlinearity (6% of variance) was found whereby high maximum growth rates (temperature-adjusted) tended to occur in the midrange of cell diameters. Species identified in the literature as oligotrophs or copiotrophs were clearly separated on the dimension of maximum growth rate, but not on the dimensions of genome size or cell diameter.

Predicting how food webs will respond to global environmental change is difficult because of the complex interplay between the abiotic forcing and biotic interactions. Mechanistic models of species interactions in seasonal environments can help understand the effects of global change in different ecosystems. Seasonally ice-covered lakes are warming faster than many other ecosystems and undergoing pronounced food web changes, making the need to forecast those changes especially urgent. Using a seasonally forced food web model with a generalist zooplankton grazer and competing cold-adapted winter and warm-adapted summer phytoplankton, we show that with declining ice cover, the food web moves through different dynamic regimes, from annual to biennial cycles, with decreasing and then disappearing winter phytoplankton blooms and a shift of maximum biomass to summer season. Interestingly, when predator-prey interactions were not included, a declining ice cover did not cause regime shifts, suggesting that both are needed for regime transitions. A cluster analysis of long-term data from Lake Baikal, Siberia, supports the model results, revealing a change from regularly occurring winter blooms of endemic diatoms to less frequent winter bloom years with decreasing ice cover. Together, the results show that even gradual environmental change, such as declining ice cover duration, may cause discontinuous or abrupt transitions between dynamic regimes in food webs.

Predicting how food webs will respond to global environmental change is difficult because of the complex interplay between the abiotic forcing and biotic interactions. Mechanistic models of species interactions in seasonal environments can help understand the effects of global change in different ecosystems. Seasonally ice-covered lakes are warming faster than many other ecosystems and undergoing pronounced food web changes, making the need to forecast those changes especially urgent. Using a seasonally forced food web model with a generalist zooplankton grazer and competing cold-adapted winter and warm-adapted summer phytoplankton, we show that with declining ice cover, the food web moves through different dynamic regimes, from annual to biennial cycles, with decreasing and then disappearing winter phytoplankton blooms and a shift of maximum biomass to summer season. Interestingly, when predator-prey interactions were not included, a declining ice cover did not cause regime shifts, suggesting that both are needed for regime transitions. A cluster analysis of long-term data from Lake Baikal, Siberia, supports the model results, revealing a change from regularly occurring winter blooms of endemic diatoms to less frequent winter bloom years with decreasing ice cover. Together, the results show that even gradual environmental change, such as declining ice cover duration, may cause discontinuous or abrupt transitions between dynamic regimes in food webs.

The spread of an enteric pathogen in the human gut depends on many interacting factors, including pathogen exposure, diet, host gut environment, and host microbiota, but how these factors jointly influence infection outcomes remains poorly characterized. Here we develop a model of host-mediated resource competition between mutualistic and pathogenic taxa in the gut that aims to explain why similar hosts, exposed to the same pathogen, can have such different infection outcomes. Our model successfully reproduces several empirically observed phenomena related to transitions between healthy and infected states, including (1) the nonlinear relationship between pathogen inoculum size and infection persistence, (2) the elevated risk of chronic infection during or after treatment with broad-spectrum antibiotics, (3) the resolution of gut dysbiosis with fecal microbiota transplants, and (4) the potential protection from infection conferred by probiotics. We then use the model to explore how host-mediated interventions-namely, shifts in the supply rates of electron donors (e.g., dietary fiber) and respiratory electron acceptors (e.g., oxygen)-can potentially be used to direct gut community assembly. Our study demonstrates how resource competition and ecological feedbacks between the host and the gut microbiota can be critical determinants of human health outcomes. We identify several testable model predictions ready for experimental validation.