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The dataset reports 1) Bibliographic information of each original publication of exotic species impact; 2) number of replicates for controls and experimental treatments; 3) mean ± SD of control and experimental treatments; 4) Hedges’ g effect size and variation of impact; 5) Descriptive information of the recipient sites including latitude, longitude, depth; 6) Descriptive information of the study, including whether it was mensurative of manipulative in the field or laboratory, the level of organization of recorded impacts and the response variable; 7) Descriptive information of the exotic species, including species name, taxonomic group and trophic level; 8) Descriptive information on the recipient species, including taxonomic group and trophic level; 9) Thermal characterization of the recipient site; 10) Latitudinal and thermal characterization of the exotic species range of origin (RO); 11) Characterization of warming projections in the recipient site under RCP4.5 and RCP8.5 projections. Here we provide data on the ecological impacts of exotic marine species on recipient native ecosystems and characterise the thermal niche of both the recipient sites and each exotic species range of origin (RO). In addition, we provide the summertime warming trajectories of the recipient sites under RCP4.5 and RCP8.5 emission scenarios. Together, this dataset characterises the ecological impacts of exotic marine species and the climatic context under which these impacts occur. Overall this database represents 108 studies that have measured impacts of exotic species on recipient marine ecosystems where they were introduced, encompassing 748 observations from 80 sites and 50 species, ranging from primary producers (e.g. seagrass, macroalgae) to predators (e.g. fish, crustaceans, annelids). S.B. received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 659246. S.B., J.S-G and N.M. received funding from the Spanish Ministry of Economy, Industry and Competitiveness (MedShift, CGL2015-71809-P) and Fundación BBVA (project Interbioclima). J.M.P. received funding from the Australian Research Council Centre of Excellence for Coral Reef Studies (CE140100020). D.K.-J. received funding from the Independent Research Fund Denmark (CARMA; 8021-00222B). Author contributions: S.B, J.S-G, N.M and C.M.D. conceived and designed the study. A.A., N.R.G., C.E.L., E.T.A., J.C., D.K-J., N.M., P.M., J.M.P., and J.S-G. constructed the exotic species impacts data set. S.B. and J.S-G., compiled the exotic species range-of-origin dataset and G.J. compiled and analyzed the observed and projected ocean temperature data. S.B. and J.S-G performed the data analyses with contributions from all coauthors. Peer reviewed

Supplementary data tables with the results from Zemp et al. (2019) entitled "Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016", Nature: Data Tables 1a-t | Temporal variabilities for glaciological clusters based on variance decomposition model. Annual results are made available as csv-files for all 20 cluster: Zemp_etal_results_clusters.zip Data Tables 2a-t | Regional and global mass balance and mass change results from 1961-2016. Annual results are made available as csv-files for all 19 regions (cf. RGI 6.0) as well as for the global sum: Zemp_etal_results_regions_global.zip Data Table 3 | Glacier mass changes for Central Europe from 1961-2016. Annual results as in Data Table 2 but with examples for multi-year error calculations are made available as Excel-file: Zemp_etal_results_region-CEU_errorcalcs.xlsx Note: The corresponding full sample of glaciological and geodetic observations for individual glaciers are publicly available from the World Glacier Monitoring Service: http://doi.org/10.5904/wgms-fog-2018-11 Version 1.1 This version provides the results from the glaciological clusters (Data Tables 1a-t) as used by Zemp et al. (2019). In addition, it contains corrected results for region Iceland (Data Table 2, region_6_ISL) and correspondingly for the global sum (Data Table 2, global); the results for the other regions remain unchanged. For more details on the correction of the regional results for Iceland, see Zemp et al. (2019, Nature), Author Correction. Version 1.0 Data tables containing the results for the 20 glaciological clusters (Data Tables 1a-t) as well as for the 19 regions and global sums (Data Tables 2a-t) related to the publication by Zemp et al. (2019, Nature). We note that this version erroneously contains pre-release versions of results for most of the glaciological clusters that were not used in Zemp et al. (2019, Nature). {"references": ["Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., Huber, J., Barandun, M., Machguth, H., Nussbaumer, S.U., G\u00e4rtner-Roer, I., Thomson, L., Paul, F., Maussion, F., Kutuzov, S., and Cogley, J.G. (2019): Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature, http://dx.doi.org/10.1038/s41586-019-1071-0"]}

1. Global climate change has sparked a vast research effort into the demographic and evolutionary consequences of mismatches between consumer and resource phenology. Many studies have used the difference in peak dates to quantify phenological synchrony (match in dates, MD), but this approach has been suggested to be inconclusive, since it does not incorporate the temporal overlap between the phenological distributions (match in overlap, MO). 2. We used 24 years of detailed data on the phenology of a predator–prey system, the great tit (Parus major) and the main food for its nestlings, caterpillars, to estimate MD and MO at the population and brood level. We compared the performance of both metrics on two key demographic parameters: offspring recruitment probability and selection on the timing of reproduction. 3. Although MD and MO correlated quadratically as expected, MD was a better predictor for both offspring recruitment and selection on timing than MO. We argue—and verify through simulations—that this is because quantifying MO has to be based on nontrivial, difficult-to-verify assumptions that likely render MO too inaccurate as a proxy for food availability in practice. 4. Our results have important implications for the allocation of research efforts in long-term population studies in highly seasonal environments. Please contact M.E. Visser for questions regarding (the use of) these datasets (m.visser@nioo.knaw.nl).

The global renewable power support policy dataset was compiled by Sarah Hafner (Anglia Ruskin University, United Kingdom) and Johan Lilliestam (Institute for Advanced Sustainability Studies (IASS), Germany) in February-July 2017 and completed during 2017. The work was led by Johan Lilliestam but each author gathered half of the data. The data was formatted and checked for internal consistency by Tim Tröndle, IASS. All non-commercial users are allowed to use and manipulate our data, but are required to give appropriate attribution. Hence, please cite this data as: Hafner, S. & Lilliestam, J. (2019): The global renewable power support dataset. Institute for Advanced Sustainability Studies (IASS) & Anglia Ruskin University, Potsdam & Cambridge. Doi: https://doi.org/ 10.5281/zenodo.3371375. If you are interested in contributing to and further developing the dataset: please contact Johan Lilliestam (IASS Potsdam). The search was done in publically available sources, including but not limited to the IEA renewables policy database, res-legal.eu, Worldbank data, as well as data from the responsible national ministries. Our data holds information on 10 specific policy instruments explicitly dedicated to the support for expansion of renewable electricity generation 1990-2016; some instruments, including taxation of non-renewables or emission trading, affect other sectors than renewable power, but are mentioned in their original policy description to also be dedicated to increasing renewable power. Our data concerns national policy measures, but ignores policies enacted on higher (e.g. EU-level in Europe) or lower (e.g. state-level policies in Canada, USA) political levels. For example, the “no support” entry for the United Arab Emirates indicates that there were no national-level policies: all policies were, in this case, emirate-specific. The data exists in two versions: one version readable for humans (RE_policies_fullglobal.xlsx) and for each instrument type as .csv. The information in the two versions is identical and differs only in the way it is displayed. Please refer to the metadata file for a detailed description of the dataset and the data categories.

Results for the different scenarios described in Table 4.

In marine fish, stock complexity confers resilience to environmental variability and exploitation. Accurate stock identification is, therefore, a prerequisite to decipher the factors responsible for recruitment variation. We concatenated energetic reproductive investment with otolith microchemistry in order to delineate between stock spawning components in bonga shad Ethmalosa fimbriata in Senegal.Satellite-derived (Moderate-resolution Imaging Spectroradiometer – MODIS Aqua, level 2, 0.1 degrees) sea surface temperatures were assessed at Joal (15 km radius) and Djifer once per sampling week. As no remote sensing data for inland waters are available, the Saloum River's surface water temperatures were recorded in situ once per sampling week with a digital thermometer (ama-digit ad 15 th; precision 0.4%; accuracy 0.4%). At Djifer and Foundiougne, salinity was determined once per sampling week according to the Practical Salinity Scale (PSS-78) with a handheld refractometer (Aqua Medic; precision 0.7%, accuracy 0.2%) using in situ water samples. For Joal, monthly means in MODIS satellite-derived (Aquarius, level 3, 0.5 degrees) sea surface salinities were used.Monthly sampling was conducted at the Senegalese southern coast (SSC) and inside the Saloum River from February to October 2014, during Ethmalosa fimbriata's extended spawning season. Three environmentally contrasted study sites were chosen: Joal (SSC, 14°9.1' N; 16°51.7' W), Djifer (Saloum River's mouth, 13°57.8' N; 16°44.8' W), and Foundiougne (Saloum River's middle reaches, 14°8.1' N; 16°28.1' W). Fish were caught with gill nets (32 - 36 mm mesh size) by local fishermen and immediately stored on crushed ice after landing. Approx. 1000 fish per sampling site and month were examined randomly in order to find stage V females, i.e. mature individuals with ovaries containing fully hydrated oocytes. Sagittal otoliths were extracted, rinsed with ethanol (70%), and stored dry in Eppendorf caps. Females that spawned recently or lost part of their egg batch during handling were rejected. Total wet mass (WM±0.01 g) and total length (LT, nearest mm) were obtained from individual stage V females. Ovaries were dissected, and oocytes were extracted out of one ovary lobe, rinsed with deionized water, and counted under a stereomicroscope. Around 80 hydrated oocytes per fish (ca. 70 for lipid analysis, ca. 10 for protein analysis) were transferred to a pre-weighed tin cap, stored in cryovials, and deep-frozen in liquid nitrogen. Dissected ovaries were transferred to a 4% borax buffered formaldehyde and freshwater liquid for fecundity analysis. Absolute batch fecundity (ABF) was estimated gravimetrically using the hydrated oocyte method for indeterminate spawners. The female's relative batch fecundity (RBF) was calculated by dividing ABF with the ovary free body weight (OFBM). The gonado-somatic index (GSI) of female spawners was calculated by dividing the ovary weight (OM, ±0.0001 g) by the ovary-free body weight (OFBM, ±0.1 g): GSI=OM ×[OFBM]^(-1) × 100. In order to access the nutritional status of female fish, a condition index (CI) was calculated for each individual using WM, LT, and b of the length-weight relationship: CI = WM x LT ^ -3.62 x 1000.Oocytes in tin caps were freeze-dried (24 h) and weighed again to ascertain their dry mass (ODM±0.1 µg). The following equation was employed to calculate oocyte volumes (OV, mm3): OV =4/3 π(d_2/2)^2. Oocyte lipid content was determined by gas chromatography flame ionization detection (GC-FID). Three random samples were processed via GC mass spectrometry to ensure that all lipid classes were detected by the GC-FID. The total lipid content was assessed via summation of all individual lipid classes. For protein analyses, counted oocytes in tin caps were dried at 40°C for >24 h and weighted again for dry weight determination. Total organic carbon (C) and nitrogen (N) content was measured using a EuroVector EuroEA3000 Elemental Analyzer. From the total amount of N in the sample, the protein content was calculated according to Kjeldahl (Bradstreet, 1954), using a nitrogen-protein conversion factor of 6.25. The oocyte gross energy content (J) was calculated on the basis of measured protein and lipid content, which were multiplied by corresponding energy values from literature: The amount of proteins per given oocyte (P, mg) was multiplied by a factor of 23.66 J mg-1 and subsequently added to the total amount of lipids per oocyte (L, mg) multiplied by 39.57 J mg-1 (Henken et al. 1986). Dividing the oocyte's energy content by the oocyte's dry weight allowed for calculation of the oocyte's calorific value (J mg-1). Further, the oocyte energy content of each individual E. fimbriata female was multiplied by its respective relative batch fecundity (RBF) to obtain a standardized estimate of the total amount of energy invested into a single spawning batch per unit body mass (SBEC, J g-1 OFBM): SBEC = [(P × 23.66 J [mg]^(-1) )+(L × 39.57 J [mg]^(-1) )]× RBF.Dried sagittal otoliths were embedded in epoxy resin (Araldite 2020; Huntsman, USA) on glass slides. They were ground from the proximal side down to the nucleus using an MPS2 surface-grinding machine (GN, Nürnberg, Germany) and polished with a diamond-grinding wheel (grain size 15 µm). Concentrations of eight elements (Magnesium, Manganese, Copper, Zinc (Zn), Strontium (Sr), Yttrium, Barium (Ba), and Lead) were determined along transects of up to 2300 μm length along the rostrum's anterior edge on the otolith's proximal side. Analyses were carried out by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) using a NewWave UP193 solid-state laser coupled to a Thermo-Finnigan Element2 ICP-MS. The employed analytical procedure used a pulse rate of 10 Hz, an irradiance of ca. 1 GW cm−2, a spot size of 75 μm, and a traverse speed of 3 μm s−1. For external calibration the glass reference material NIST610 was analysed after every second ablation path, using reference values of Jochum et al. (2011). Data quality was validated by analysing a pressed pellet of NIES22 otolith with a calcium concentration of 38.8 wt. %, as well as through regular analyses of BCR-2G and BHVO-2G standard glasses. Precision was < 2% and the accuracy was < 13% for Zn. Recovery percentages were 100%, 101%, and 114% for Ba, Sr, and Zn, respectively. To account for the substitution of Calcium (Ca) by the divalent elements Ba, Sr, and Zn all element concentrations are given as element:Ca ratios. Mean otolith Ba:Ca, Sr:Ca, and Zn:Ca values throughout the entire female's lifetime are given in this dataset. Supplement to: Döring, Julian; Wagner, Carola; Tiedemann, Maik; Brehmer, Patrice; Ekau, Werner (2019): Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata. Journal of Fish Biology, 1-10

Arctic and boreal peatlands play a major role in the global carbon (C) cycle. They are particularly efficient at sequestering carbon due to their high-water content which makes primary productivity exceed decomposition rates. Though, their future in a climate-change context is quite uncertain in terms of carbon emissions and carbon sequestration.Nuuk-fen site is a well-instrumented greenlandic site of particular interest for testing and validating land-surface models with monitoring of soil physical variables and greenhouse gas fluxes (CH4 and CO2). But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models, as soil carbon is one of the primary drivers of CH4 and CO2 soil emissions. To tackle this issue, we measured for the first time soil carbon and nitrogen density, profiles and stocks in the Nuuk peatland, at the exact location of fluxes monitoring. Measurements were made along two transects. Measurements horizontal resolution is 5 meter, vertical resolution ranges from 5 to 10 cm. Mean soil carbon density is 50.2 kgC/m³. These new data are in the range of those encountered in other arctic peatlands. Supplement to: Morel, Xavier; Hansen, Birger Ulf; Delire, Christine; Ambus, Per Lennart; Mastepanov, Mikhail; Decharme, Bertrand (2020): A new dataset of soil carbon and nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models. Earth System Science Data, 12(4), 2365-2380

ERA5 reanalysis data for the publication "Airborne Wind Energy Resource Analysis" submitted for peer review in Renewable Energy. Generated using Copernicus Climate Change Service Information 2018.