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Rates of aerobic metabolism vary considerably across evolutionary lineages, but little is known about the proximate and ultimate factors that generate and maintain this variability. Using data for 131 teleost fish species, we performed a large-scale phylogenetic comparative analysis of how interspecific variation in resting and maximum metabolic rates (RMR and MMR, respectively) is related to several ecological and morphological variables. Mass- and temperature-adjusted RMR and MMR are highly correlated along a continuum spanning a 30- to 40-fold range. Phylogenetic generalized least squares models suggest RMR and MMR are higher in pelagic species and that species with higher trophic levels exhibit elevated MMR. This variation is mirrored at various levels of structural organization: gill surface area, muscle protein content, and caudal fin aspect ratio (a proxy for activity) are positively related with aerobic capacity. Muscle protein content and caudal fin aspect ratio are also positively correlated with RMR. Hypoxia-tolerant lineages fall at the lower end of the metabolic continuum. Different ecological lifestyles are associated with contrasting levels of aerobic capacity, possibly reflecting the interplay between selection for increased locomotor performance on one hand and tolerance to low resource availability, particularly oxygen, on the other. These results support the aerobic capacity model of the evolution of endothermy, suggesting elevated body temperatures evolved as correlated responses to selection for high activity levels. Killen et al Am Nat Table S1Data used for the analysis by Killen et al. 2016, American Naturalist.Fish_PhylogenyPhylogeny used for the analysis by Killen et al. 2016, American Naturalist.

CzIPMR code to estimate the recovery time for Cystoseira zosteroides populations after a major disturbance at different temperature scenarios treatments. In addition, stochastic population growth rate (��s) and quasi-extinction probability at increasing frequency of two major disturbances at increasing temperature scenarios. These analyses correspond to the figures 4 and 5 of Capdevila et al. 2018 JEcol.MixedEffectsparamsParameter values needed for the Integral Projection Models used to model the life cycle and population dynamics of Cystoseira zosteroides. This includes seven demographic processes: 1.survival (��), 2.growth (��), 3.fertility (��), 4.recruits per capita (��(N)), 5.probability of settlement of recruits (��), 6.early survival of recruits (��s) and 7.recruits size probability distribution.IPMFunctionsFunctions required to run the CzIPM.R script. This script contains the description of the growth, survival and fecundity functions used to build the IPMs.1. The best-fitted model for survival (��) was a logistic mixed effect model including size as fixed factors and population nested in years as a random factor. 2. For growth (��), the best-fitted model was a linear mixed effect model, with size as fixed factor and population nested in year as random factor. 3. Fertility (��(z)), was estimated as the relation between reproductive status (reproductive vs. non-reproductive) and size with a binomial regression. 4. Recruitment per capita (��(N)) is density-dependent in C. zosteroides (Capdevila et al., 2015), so a generalized linear model with Poisson error distribution and a log-link function was fitted, correlating the recruit:adult ratio as a function of the adult density. 5. To model the effect of temperature on the probability of settlement (��) we used a generalized linear mixed models (GLMM), with a Poisson error distribution and a logit link function, the independent variable was the number of zygotes, temperature was treated as a fixed variable and we used the ID of each quadrat of the Petri dishes as a random variable. 6. To model the effect of temperature and time (fixed factors) on germling survival (��s), we used a GLMM with a binomial error distribution and a logit link function, with the ID of each quadrat of each Petri dish as a random variable to deal with the lack of independence between observations repeated at different times and a binomial error distribution was assumed to deal with the binary response variable (survive vs. die). 7. The size distribution of recruits was estimated as a normal probability function. In addition, the function required to project the density-dependent and stochastic IPMs is provided.modsumDensity-dependent function, relating the number of Cystoseira zosteroides recruits with the number of adults. It is a generalized linear model (GLM) with Poisson error distribution and a log-link function, correlating the recruit:adult ratio with the adult density. This file is needed to run the code CzIPM.R.settData on the impacts of temperature (16��C, 20��C and 24��C) on the settlement of Cystoseira zosteroides early stages. This file is needed to perform the projections in CzIPM.R code.survrecData on the impacts of the temperature treatments (16��C, 20��C and 24��C) to early survival of Cystoseira zosteroides. This file is required to run the code CzIPM.R. 1. Understanding the combined effects of global and local stressors is crucial for conservation and management, yet challenging due to the different scales at which these stressors operate. Here we examine the effects of one of the most pervasive threats to marine biodiversity, ocean warming, on the early life stages of the habitat-forming macroalga Cystoseira zosteroides, its long-term consequences for population resilience and its combined effect with physical stressors. 2. First, we performed a controlled laboratory experiment exploring the impacts of warming on early life stages. Settlement and survival of germlings were measured at 16��C (control), 20��C and 24��C and both processes were affected by increased temperatures. Then, we integrated this information into stochastic, density-dependent integral projection models (IPM). 3. Recovery time after a minor disturbance significantly increased in warmer scenarios. The stochastic population growth rate (��s) was not strongly affected by warming alone, as high adult survival compensated for thermal-induced recruitment failure. Nevertheless, warming coupled with recurrent physical disturbances had a strong impact on ��s and population viability. 4. Synthesis: The impact of warming effects on early stages may significantly decrease the natural ability of habitat-forming algae to rebound after major disturbances. These findings highlight that, in a global warming context, populations of deep-water macroalgae will become more vulnerable to further disturbances, and stress the need to incorporate abiotic interactions into demographic models.

This submission consists of 40 eddy covariance datasets collected from six shallow sites in the Baltic Sea over an 18 month period. Hourly fluxes were extracted from the high-density data streams and were used to compute daily rates of benthic metabolism (gross primary production (GPP), respiration (R), and net ecosystem metabolism (NEM); in mmol O2 m-2 d-1). These were converted to C assuming an O2 : C of 1.0 for GPP and R. A description of the flux data processing protocol is given in the manuscript. These datasets were used to compute annual rates of GPP, R, and NEM at each habitat site. The annual rates were then used to investigate (i) phototrophic biomass turnover rates, by comparing the GPP rates with standing phototrophic biomass measurements, and (ii) the regional importance of benthic metabolism, by upscaling the annual rates to habitat distribution maps. This dataset includes all data on standing biomass and habitat extent. Attard et al. LO LettersDaily benthic metabolism rates, annual integrated rates, biomass turnover rates, and spatial upscaling estimates presented in Attard et al. LO LettersMetadata template_Attard et alMetadata template for Attard et al. LO Letters dataset

Outputs from simulations done with the ecosystem model Ecopath with Ecosim. Data contain future estimates of catch and biomass calculated for a given climate scenario (i.e. IPCC projection), for each species included in the model.

North Atlantic high sea survey in Norway

Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/). In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein. {"references": ["Benedetti, F., Vogt, M., Hofmann-Elizondo, U., Righetti, D., Zimmermann, N.E., Gruber, N. Major restructuring of marine plankton assemblages under global warming. Nature Communications. 2021."]} # To read the raster layers in R use: library("raster"); library("rgdal") raster <- raster::stack("Nat.Comms.2021.Benedetti_et_al._Fig1+Fig2_species_richness+composition.grd")

Key enzymes for autotrophic pathways, and enzymes of reference set used for comparison of read mapping vs SwissProt database.

Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal

Most of the thermal tolerance studies on fish have been performed on juveniles and adults, whereas limited information is available for larvae, a stage which may have a particularly narrow range in tolerable temperatures. Moreover, previous studies on thermal limits for marine and freshwater fish larvae (53 studies reviewed here) applied a wide range of methodologies (e.g. the static or dynamic method, different exposure times), making it challenging to compare across taxa. We measured the Critical Thermal Maximum (CTmax) of Atlantic herring (Clupea harengus) and European seabass (Dicentrarchus labrax) larvae using the dynamic method (ramping assay) and assessed the effect of warming rate (0.5 to 9°C h-1) and acclimation temperature. The larvae of herring had a lower CTmax (lowest and highest values among 222 individual larvae, 13.1 – 27.0 °C) than seabass (lowest and highest values among 90 individual larvae, 24.2 – 34.3 °C). At faster rates of warming, larval CTmax significantly increased in herring, whereas no effect was observed in seabass. Higher acclimation temperatures led to higher CTmax in herring larvae (2.7 ± 0.9°C increase) with increases more pronounced at lower warming rates. Pre-trials testing the effects of warming rate are recommended. Our results for these two temperate marine fishes suggest using a warming rate of 3 - 6 °C h-1: CTmax is highest in trials of relatively short duration, as has been suggested for larger fish. Additionally, time-dependent thermal tolerance was observed in herring larvae, where a difference of up to 8°C was observed in the upper thermal limit between a 0.5- or 24-h exposure to temperatures >18°C. The present study constitutes a first step towards a standard protocol for measuring thermal tolerance in larval fish. Thermal limits of early life stages of fishThis dataset includes the raw data on the effect of warming rate, acclimation temperature and ontogeny on the critical thermal maximum of Atlantic herring and European seabass larvae on sheet "CTmax_herring+seabass_Fig2". On sheet "Upper_limits_herring_Fig3", results on the time dependence of upper thermal limits of Atlantic herring are displayed. Finally, review data from upper and lower thermal limits of larval fish are summarized in sheet "Thermal_limits_review_Fig4".Moyano_et_al_rawData.xlsx

Support assets for "VISIR-I.b: waves and ocean currents for energy efficient navigation", by G. Mannarini and L. Carelli, gmd-2018-292 (Geosci. Model Dev. Discussions): -graphs_gmd-2018-292.tar.gz Graphs for running VISIR computations - figTab_DATA_v2.tar.gz: Source data for Figures and Tables Please refer to https://doi.org/10.5281/zenodo.2563074 for the full release of VISIR-1.b source code.