BUSCADOR RECOLECTA

The dataset provides data on survival, rhizome elongation (cm day-1), recruitment rate (day-1), net population growth rate (day-1), gross primary production (GPP; mmol 02 day-1 gDW-1), respiration (R; mmol 02 day-1 gDW-1) and net production (NP; mmol 02 day-1 gDW-1) of exotic (Greece and Cyprus; Mediterranean) and native (Saudi Arabia; Red Sea) Halophila stipulacea populations exposed to 12 seawater temperature treatments ranging from 8 to 40°C., Peer reviewed

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., 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., 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)., Peer reviewed

Data on biogeochemical characteristics (210Pb geochronologies, density, organic matter, organic carbon concentration, stable carbon isotopes, carbon stocks and carbon burial rates) and on the detection of Halophila stipulacea with eDNA in seagrass sediments cores from H. stipulacea, Cymodocea nodosa and Posidonia oceanica meadows collected in the Eastern Mediterranean (Greece and Cyprus)., This work was funded by the Spanish Ministry of Economy and Competiveness (Project MEDSHIFT, CGL2015-71809-P), the Spanish Ministry of Science, Innovation and Universities (SUMAECO, RTI2018-095441-B-C21) and King Abdullah University for Science and Technology (3834 KAUST-CSIC Research Collaboration and base line funding to CMD)., Peer reviewed

[Geographic location of data collection] Mediterranean basin, seagrass meadows of Posidonia oceanica and Cymodocea nodosa in coastal regions, max. depth 17m., [File List] datacompilation_med_seagrass_metabolic_rates_hendriks.csv, readme.txt., [Relationship between files, if important] readme provides background information for csv datafile., [Additional related data collected that was not included in the current data package] dissolved nutrients for author data (available upon request)., [Description of methods used for collection/generation of data] Data on metabolic rates was extracted from the literature, through a literature search (March 2020) on SCOPUS and the Web of Science using the keywords “Posidonia”, OR “Cymodocea”, OR “Seagrass”, AND “Productivity”, OR “Metabolism” and manually screened for data on metabolism in the Mediterranean basin. This database was extended with unpublished data from the authors and data from dedicated sampling campaigns in 2016 in Mallorca (Western Mediterranean) and 2017 in the Eastern basin (Crete and Cyprus). We compiled data from multiparametric sensors, and data using the benthic chambers methodology with a temporal cover from 1982 to 2019., [Methods for processing the data] For benthic chambers, reported metabolic rates were extracted from the literature. For measurements with multiparametric sensors we used time series of dissolved oxygen (DO, in mg/L), salinity and temperature (C) measured in P. oceanica and/or C. nodosa meadows. With the time series of dissolved oxygen (DO), temperature (°C) and salinity we calculated the metabolic rates of the seagrass habitats using a modification of the model of Coloso et al., (2008) implemented in MATLAB (version 7.5. the Mathworks Inc.) explained in detail in Vaquer-Sunyer et al., (2012). Wind speed was estimated at each station for the same interval as oxygen measurements to predict k660 (air-sea gas transfer velocity for oxygen at 20º C and salinity 35) based on Kihm et al., (2010) and Cole et al., (1998). Schmidt number equations for seawater according to Wanninkhof (1992) were used for the k calculation from k660. As the cubic model equals the model proposed by Wanninkhof et al., (1999) for short-term winds this parameterization by Kihm et al., (2010) is used. Meteorological data (windspeed) for the deployment period was obtained from the Agencia Estatal de Meteorología (AEMET) for the stations in Mallorca, from the Cyprus Department of Meteorology for Cyprus sampling sites and from the Hellenic National Meteorological Service for the locations in Crete.--, [Standards and calibration information] Sensors were calibrated before each deployment; oxygen sensors (Hach LDOTM) were calibrated using the water saturated air method calibration. For validation of salinity, specific conductance calibrations were performed with 50.000uS/cm buffers. For depth measurements, pressure readings were corrected for specific conductance., [Environmental/experimental conditions] Coastal seagrass meadows with max. 17m depth., [Describe any quality-assurance procedures performed on the data] Negative respiration rates (oxygen production) at night for sensor deployments, were discarded as this was interpreted as an indication for the influence of lateral advection and passing of different water masses. Therefore, we trimmed the dataset to contain only measurements where this influence was not detected. Respiration rates were notated as oxygen consumption (positive values, literature reports differ in notation)., [People involved with sample collection, processing, analysis and/or submission, please specify using CREDIT roles https://casrai.org/credit/: Conceptual idea IEH and NM. Data collection in the field MW, SF, RVS, IEH, NM. Literature compilation IEH and AEM. Data curation AEM and IEH., [Data-specific information] 1. Number of variables: 21. 2. Number of cases/rows: 151. 3. Variable List: Reference, Journal, Methodology, Year, Month, Season, Site, Region, Latitude, Longitude, Species, Temperature_C, Salinity, Depth, NCP, NCP_SD, CR, CR_SD, GPP, GPP_SD, Wind_m_s. 4. Missing data codes: Empty cell. 5. Specialized formats or other abbreviations used: C (degree Celcius), SD (Standard Deviation), m_s (Meter per second). Depth in meter. Latitude and Longitude in Decimal Degrees (DD)., The data is a compilation of information on metabolic rates of Mediterranean seagrasses obtained by two different methodologies (benthic incubations and multiparametric sensors) from published literature and data from the authors., The Spanish Ministry of Economy and Competitiveness (Project MEDSHIFT, CGL2015-71809-P). Project RTI2018-095441-B-C21 (SUMAECO) from the Spanish Ministry of Science, Universities and Innovation. SF was supported by a “Margalida Comas” postdoctoral scholarship, funded by the Balearic Islands Government. Also funding was received from “projectes de recerca La Caixa en àrees estratègiques” (2018) through a grant to IEH at the University of the Balearic islands., datacompilation_med_seagrass_metabolic_rates_hendriks.csv, readme.txt, Peer reviewed

[Description of methods used for collection/generation of data] Collection of data from extraction of articles retrieved from the literature (Web of Knowledge and SCOPUS, accessed July 2015 and updated May 2021). Papers reporting estimates of the effect of coastal plants on current and wave attenuation in vegetated coastal habitats identified using search terms: “Seagrass*” [All Fields] OR “Mangrove*” [All Fields] OR “Salt marsh*” [All Fields] OR “Macrophyte*” [All Fields] AND “engine*” [All Fields] OR “wave attenuation” [All Fields] OR “flow modification” [All Fields]. The in total 963 papers retrieved were analyzed for quantitative estimates, supplemented with papers and documents containing data meeting the requirements of the analyses contained within the references of the papers retrieved, resulting in a data set containing a total of 1372 estimates derived from 95 individual articles with a temporal cover from 1982 to 2020., [Methods for processing the data] Results from field and laboratory studies were used, but not numerical models. When information was given for multiple observations with different vegetation parameters and/or hydrodynamic parameters, we included several data points per study, but only included 1 measurement (max. distance) when the same structural parameters had repeated measurements for different distances within the vegetation. Where authors reported values for current reduction these were used directly, always making sure a non-vegetated (bare) reference value was used to calculate reduction in the vegetation. When data was (re)calculated from separate reported values the formulas used for current reduction, dU, were calculated as: dU/U0 = (U0-Uv)/U0 With U as the current speed over a reference unvegetated region U0 and through a vegetated region Uv in m s-1 respectively. Where the information was provided in the selected studies, we calculated the wave energy reduction, dE, defined as (Knutson et al. 1982): dE/E0 = ((E0-Ev))/E0 Where E is the energy without vegetation (E0) and within the vegetation (Ev) respectively. The wave height reduction per meter r (Mazda et al 1997) was calculated as: r = dH/(H0x) = ((H0-Hv))/(H0x) Where x is the length of the vegetation field. When multiple measurements were done with the same vegetation settings (i.e. density, water height) at different distances into the vegetation, we took the maximum distance evaluated. The effect of vegetation on current and wave attenuation was represented by the decay coefficients, KiH, (Kobayashi et al., 1993) and KiU (m-1), representing the relative decrease in significant wave height (KiH), and current velocity (KiU) with distance into the vegetated fringe (x, bed length) calculated as, kiH=1/x ln(1-dH/H0 )=1/x ln(Kt ) and kiU=1/x ln(1-dU/U0 ) Where Kt is the wave transmission coefficient. We used the same literature sources that were used for the data were collection, to compile relevant vegetation structural parameters, specifically, shoot or stem density and emergence ratio (defined as hveg/h). For stiffness we used Young’s bending modulus (E, in N mm-2), when this parameter was not available from the same source, we completed the data with species specific values from literature (e.g. Zhu et al. 2020 for salt marshes, de los Santos et al. 2016; La Nafie et al. 2012; Soissons et al. 2017 for seagrasses and van Hespen et al. 2021 for mangroves). When no value was known, the value for the family was used or an average for the group (i.e., saltmarsh, seagrass, etc.) obtained from the compiled values., [Relationship between files] Readme provides background information for xlsx datafile., [People involved with sample collection, processing, analysis and/or submission] https://casrai.org/credit . Idea and concept C.M.D and I.J.L, design and discussion of content during workshops I.E.H., N.M., B.v.W., T.J.B., I.J.L, C.M.D. Database compilation I.E.H, M.M., A.G.M and N.M. Analysis of data I.E.H.. All authors contributed to the writing and editing of the manuscript., Funding for this data collection supplied by the MedShift project, CGL2015-71809-P (MINECO/FEDER) and baseline funding from King Abdullah University of Science and Technology to C.M.D. I.E.H. was supported by grant RYC-2014-15147, co-funded by the Conselleria d'Innovació, Recerca i Turisme of the Balearic Government (Pla de ciència, tecnologia, innovació i emprenedoria 2013-2017) and the Spanish Ministry of Economy, Industry and Competitiveness., Data_coastal_vegetation_adaptation.xlsx, readme.txt, Peer reviewed

The dataset compiles seagrass upper thermal limits (Tlimit) for survival, growth or biomass loss published in the literature and obtained by conducting a search on Web of Knowledge using the keywords combinations seagrass AND (temperature OR warming) and seagrass AND ("thermal limit" OR "thermal threshold" OR "critical temperature" OR "thermal niche”). The reference lists of the papers obtained with these searches were screened for additional relevant data. The dataset only includes data of seagrass populations growing submersed within their native geographical range. Tlimit are derived from empirical observations of seagrass die-off events attributed to heat waves, in combination with other simultaneous stressors (hypersalinity, Carlson et al 2018; low light availability, Moore and Jarvis 2008, Moore et al., 2014), or mesocosm experiments. Seagrasses in mesocosm experiments were exposed to at least 2 temperature treatments above average in situ summer temperature that extended the experimental thermal range beyond the Tlimit. Seagrasses were exposed to experimental temperatures for 6 to 120 days depending on the study. The Tlimit was defined as: a) the upper temperature at which shoot survival, shoot growth or biomass above optimal temperature started to decline in experimental studies; or b) the seawater temperature during the heat wave that triggered die-off events. For each study, the compiled dataset includes the species name, location and coordinates of the population studied, the Tlimit, the approach (i.e. experimental or empirical), the year the study was conducted and the data source. For experimental studies, the dataset also includes the temperature treatments seagrasses were exposed to. For each population studied, we obtained mean annual seawater temperature values for the 5 years before the thermal tolerance experiment or observation was conducted from the ORAS4 ocean reanalysis (Balmaseda, Mogensen, Weaver, 2013), which provides monthly 3D temperature global fields from 1958 to present with a spatial resolution of 1 degree in the horizontal and ~10 m in the vertical. Those temperatures aim at representing the regional characteristics, rather than the local features which cannot be captured by the coarse spatial resolution, [Relationship between files] The file "variables_Marbà_et_al_ 2022.xlsx" defines the variables used in the dataset. The full references of the sources of data compiled in the dataset are provided in the file "References_Dataset_Marba_et_al_2022.docs"., [Environmental/experimental conditions] The dataset includes target experimental temperatures and average annual seawater temperature natural populations were exposed to, calculated for the 5 years before conducting the experiment or the occurrence of seagrass mass-mortality event., Dataset of seagrass upper thermal limits for survival, growth or biomass loss derived from empirical observations of seagrass die-off events attributed to heat waves or mesocosm experiments., This work was funded by the Spanish Ministry of Economy, Industry and Competivness with the projects MedShift (CGL2015-71809-P), SumaEco (RTI2018-095441-B-C21) and Clifish (CTM2015-66400-C3-2-R), the European Union’s Horizon 2020 SOCLIMPACT project (grant agreement No 776661) and the King Abdullah University of Science and Technology (KAUST subaward number 3834). S.B. was supported by a Juan de la Cierva Formación contract funded by the Spanish Ministry of Economy, Industry and Competitiveness., File List: - variables_Marbà_et_al_ 2022.xlsx - dataset_Marbà_et_al_2022_(seagrass thermal limits).xlsx - References_Dataset_Marba_et_al_2022.docs, Peer reviewed