Publications

This dataset compiles narrative and quantitative evidences of impacts of warming on organisms living in the Mediterranean Sea published in the literature until year 2014 as well as sea surface temperature values. The search of evidences of impact was conducted using ISI Web of Knowledge and the keywords “ (Mediterranean) AND ((Bio* OR Eco*)) AND ((warming OR temperature)) AND ((Marine OR Ocean*)) NOT ((paleo*))”. Only evidences from articles directly attributing the changes observed in marine biota to warming are included in the dataset. The dataset comprises biological impacts observed after single warming events as well as during longer-term observations. The dataset provides information about the study conducted (i.e. year or period of years of the study, site name and geographical coordinates, spatial scale of the study, species name, taxonomic group, whether the species is sessile or mobile, native or introduced), the type, magnitude and activation energy (Brown et al. 2004) of biological responses. The values on sea surface temperature (SST) are derived from satellite images. SST is calculated as the mean august SST (SSTaugust) and as the 99th percentile of SST (SST99p) in the year and location the impact was recorded. The SST anomaly is calculated as the SST99p with respect to the averaged august temperature computed for the period 1960-1985. The calculations to estimate activation energy and detailed information about the sources of SST data are described in Marba et al. (submitted). Access and reuse: The database is subject to a Creative Commons Attribution-Noncommercial-ShareAlike International licence 4.0. Contact person for enquiries: Nuria Marba (nmarba@imedea.uib-csic.es).

Abstract. We use observations of dissolved inorganic carbon (DIC) and total alkalinity (TA) to assess the impact of ecosystem metabolic processes on coastal waters of the eastern Red Sea. A simple, single-end-member mixing model is used to account for the influence of mixing with offshore waters and evaporation/precipitation, and to model ecosystem-driven perturbations on the carbonate system chemistry of coral reefs, seagrass meadows and mangrove forests. We find that (1) along-shelf changes in TA and DIC exhibit strong linear trends that are consistent with basin-scale net calcium carbonate precipitation; (2) ecosystem-driven changes in TA and DIC are larger than offshore variations in > 85 % of sampled seagrass meadows and mangrove forests, changes which are influenced by a combination of longer water residence times and community metabolic rates; and (3) the sampled mangrove forests show strong and consistent contributions from both organic respiration and other sedimentary processes (carbonate dissolution and secondary redox processes), while seagrass meadows display more variability in the relative contributions of photosynthesis and other sedimentary processes (carbonate precipitation and oxidative processes).

Photolysis is a major removal pathway for the biogenic gas dimethylsulfide (DMS) in the surface ocean. Here we tested the hypothesis that apparent quantum yields (AQY) for DMS photolysis varied according to the quantity and quality of its photosensitizers, chiefly chromophoric dissolved organic matter (CDOM) and nitrate. AQY compiled from the literature and unpublished studies ranged across 3 orders of magnitude at the 330 nm reference wavelength. The smallest AQY(330) were observed in coastal waters receiving major riverine inputs of terrestrial CDOM (0.06-0.5 m3 (mol quanta)-1). In open-ocean waters, AQY(330) generally ranged between 1 and 10 m3 (mol quanta)-1. The largest AQY(330), up to 34 m3 (mol quanta)-1), were seen in the Southern Ocean potentially associated with upwelling. Despite the large AQY variability, daily photolysis rate constants at the sea surface spanned a smaller range (0.04-3.7 d-1), mainly because of the inverse relationship between CDOM absorption and AQY. Comparison of AQY(330) with CDOM spectral signatures suggests there is an interplay between CDOM origin (terrestrial versus marine) and photobleaching that controls variations in AQYs, with a secondary role for nitrate. Our results can be used for regional or large-scale assessment of DMS photolysis rates in future studies.

High solar radiation along with extreme transparency leads to high penetration of solar radiation in the Red Sea, potentially harmful to biota inhabiting the upper water column, including zooplankton. Here we show, based on experimental assessments of solar radiation dose-mortality curves on eight common taxa, the mortality of zooplankton in the oligotrophic waters of the Red Sea to increase steeply with ambient levels of solar radiation in the Red Sea. Responses curves linking solar radiation doses with zooplankton mortality were evaluated by exposing organisms, enclosed in quartz bottles, allowing all the wavelengths of solar radiation to penetrate, to five different levels of ambient solar radiation (100%, 21.6%, 7.2%, 3.2% and 0% of solar radiation). The maximum mortality rates under ambient solar radiation levels averaged (±standard error of the mean, SEM) 18.4±5.8% h−1, five-fold greater than the average mortality in the dark for the eight taxa tested. The UV-B radiation required for mortality rates to reach ½of maximum values averaged (±SEM) 12±5.6 h−1% of incident UVB radiation, equivalent to the UV-B dose at 19.2±2.7 m depth in open coastal Red Sea waters. These results confirm that Red Sea zooplankton are highly vulnerable to ambient solar radiation, as a consequence of the combination of high incident radiation and high water transparency allowing deep penetration of damaging UV-B radiation. These results provide evidence of the significance of ambient solar radiation levels as a stressor of marine zooplankton communities in tropical, oligotrophic waters. Because the oligotrophic ocean extends across 70% of the ocean surface, solar radiation can be a globally-significant stressor for the ocean ecosystem, by constraining zooplankton use of the upper levels of the water column and, therefore, the efficiency of food transfer up the food web in the oligotrophic ocean.

2012 ASLO Aquatic Sciences Meeting: Voyages of Discovery, 8-13 July 2012, Lake Biwa, Shiga, Japan

1. Abstract The pandemic of the COVID-19 disease extended from China across the north-temperate zone, and more recently to the tropics and southern hemisphere. We find no evidence that spread rates decline with temperatures above 20 °C, suggesting that the COVID-19 disease is unlikely to behave as a seasonal respiratory virus.

Macroalgae are the most widely distributed marine vegetated habitats and contribute to marine carbon cycling and storage but with limited empirical documentation of long-term burial. To evaluate long-term burial of macroalgal-derived carbon in Arctic sediments, we analyzed eDNA from six dated sediment cores from off the coast of West Greenland (79°N-60°N). We applied metabarcoding of 18S rRNA genes to selected sediment layers covering the past ∼2600 years, assessed spatio-temporal patterns of macroalgal taxa, and evaluated climatic drivers of macroalgal change using proxies for past sea surface conditions. Macroalgal DNA was present in all cores and 86.5 % of samples. Orders of brown algal (Laminariales, Fucales) were more prevalent than red or green orders and we found no consistent changes in the identity of macroalgal taxa, despite significant changes in sea surface conditions (temperature, sea ice, salinity) over this period. However, locally, we observed a significant decline in richness of macroalgal taxa buried over the last c. 800 years (Qeqertarsuup Tunua, Disko Bay). This decline coincided with the onset of the Little Ice Age and greater fluctuations in sea surface conditions. Overall, we demonstrate that macroalgae can be preserved for millennia in marine sediments along Greenland's west coast, documenting that macroalgae contribute to long-term carbon burial in the Arctic although their quantitative significance is still unknown.

In the Brown et al. study 'Increased food supply mitigates ocean acidification effects on calcification but exacerbates effects on growth' they show disagreement with the tested hypothesis and data analysis methodology used in our 2016 study. We acknowledge careful criticism and a constructive dialogue are necessary to progress science and address these issues in this reply.Replying to: Brown et al. Sci. Rep. 8 (2018); https://doi.org/10.1038/s41598-018-28012-w .

Abstract Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/ .