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  • PANGAEA
  • 2015-2019  (847)
  • 1920-1924
  • 2016  (847)
Collection
Keywords
Publisher
Years
  • 2015-2019  (847)
  • 1920-1924
Year
  • 1
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    Surface Ocean CO2 Atlas (SOCAT) V4 (2016)
    PANGAEA
    Details
    Publication Date: 2024-05-02
    Description: The Surface Ocean CO2 Atlas (SOCAT) is a synthesis activity by the international marine carbon research community (〉100 contributors). SOCAT version 4 has 18.5 million quality-controlled, surface ocean fCO2 (fugacity of carbon dioxide) observations with an accuracy of better than 5 µatm from 1957 to 2015 for the global oceans and coastal seas. Automation of data upload and initial data checks speeds up data submission and allows annual releases of SOCAT from version 4 onwards. SOCAT enables quantification of the ocean carbon sink and ocean acidification and evaluation of ocean biogeochemical models. SOCAT represents a milestone in research coordination, data access, biogeochemical and climate research and in informing policy.
    Keywords: SOCAT; Surface Ocean CO2 Atlas Project
    Type: Dataset
    Format: application/zip, 1265 datasets
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    DATA PANGAEA
  • 2
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    Surface Ocean CO2 Atlas (SOCAT) V3 (2016)
    PANGAEA
    In:  Supplement to: Bakker, Dorothee C E; Pfeil, Benjamin; Landa, Camilla S; Metzl, Nicolas; O'Brien, Kevin M; Olsen, Are; Smith, Karl; Cosca, Catherine E; Harasawa, Sumiko; Jones, Steve D; Nakaoka, Shin-Ichiro; Nojiri, Yukihiro; Schuster, Ute; Steinhoff, Tobias; Sweeney, Colm; Takahashi, Taro; Tilbrook, Bronte; Wada, Chisato; Wanninkhof, Rik; Alin, Simone R; Balestrini, Carlos F; Barbero, Leticia; Bates, Nicolas R; Bianchi, Alejandro A; Bonou, Frédéric Kpédonou; Boutin, Jacqueline; Bozec, Yann; Burger, Eugene; Cai, Wei-Jun; Castle, Robert D; Chen, Liqi; Chierici, Melissa; Currie, Kim I; Evans, Wiley; Featherstone, Charles; Feely, Richard A; Fransson, Agneta; Goyet, Catherine; Greenwood, Naomi; Gregor, Luke; Hankin, Steven; Hardman-Mountford, Nicolas J; Harlay, Jérôme; Hauck, Judith; Hoppema, Mario; Humphreys, Matthew P; Hunt, Christopher W; Huss, Betty; Ibánhez, J Severino P; Johannessen, Truls; Keeling, Ralph F; Kitidis, Vassilis; Körtzinger, Arne; Kozyr, Alexander; Krasakopoulou, Evangelia; Kuwata, Akira; Landschützer, Peter; Lauvset, Siv K; Lefèvre, Nathalie; Lo Monaco, Claire; Manke, Ansley; Mathis, Jeremy T; Merlivat, Liliane; Millero, Frank J; Monteiro, Pedro M S; Munro, David R; Murata, Akihiko; Newberger, Timothy; Omar, Abdirahman M; Ono, Tsuneo; Paterson, Kristina; Pearce, David J; Pierrot, Denis; Robbins, Lisa L; Saito, Shu; Salisbury, Joe; Schlitzer, Reiner; Schneider, Bernd; Schweitzer, Roland; Sieger, Rainer; Skjelvan, Ingunn; Sullivan, Kevin; Sutherland, Stewart C; Sutton, Adrienne; Tadokoro, Kazuaki; Telszewski, Maciej; Tuma, Matthias; van Heuven, Steven; Vandemark, Doug; Ward, Brian; Watson, Andrew J; Xu, Suqing (2016): A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 8(2), 383-413, https://doi.org/10.5194/essd-8-383-2016
    Details
    Publication Date: 2024-06-12
    Description: The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.5 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.4 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This living data publication documents changes in the methods and data sets used in this new version of the SOCAT data collection compared with previous publications of this data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014).
    Keywords: SOCAT; Surface Ocean CO2 Atlas Project
    Type: Dataset
    Format: application/zip, 3657 datasets
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    DATA PANGAEA
  • 3
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    Coccosphere geometry measurements from culture experiments on the coccolithophore species Calcidiscus leptoporus, Calcidiscus quadriperforatus and Helicosphaera carteri (2016)
    PANGAEA
    In:  Supplement to: Sheward, Rosie M; Poulton, Alex J; Gibbs, Samantha J; Daniels, Chris J; Bown, Paul R (2017): Physiology regulates the relationship between coccosphere geometry and growth phase in coccolithophores. Biogeosciences, 14(6), 1493-1509, https://doi.org/10.5194/bg-14-1493-2017
    Details
    Publication Date: 2023-02-13
    Description: Coccolithophores are an abundant phytoplankton group that exhibit remarkable diversity in their biology, ecology and calcitic exoskeletons (coccospheres). Their extensive fossil record is a testament to their important biogeochemical role and is a valuable archive of biotic responses to environmental change stretching back over 200 million years. However, to realise the full potential of this archive for (palaeo-)biology and biogeochemistry requires an understanding of the physiological processes that underpin coccosphere architecture. Using culturing experiments on four modern coccolithophore species (Calcidiscus leptoporus, Calcidiscus quadriperforatus, Helicosphaera carteri and Coccolithus braarudii) from three long-lived families, we investigate how coccosphere architecture responds to shifts from exponential (rapid cell division) to stationary (slowed cell division) growth phases as cell physiology reacts to nutrient depletion. These experiments reveal statistical differences in coccosphere size and the number of coccoliths per cell between these two growth phases, specifically that cells in exponential-phase growth are typically smaller with fewer coccoliths, whereas cells experiencing growth-limiting nutrient depletion have larger coccosphere sizes and greater numbers of coccoliths per cell. Although the exact numbers are species-specific, these growth-phase shifts in coccosphere geometry demonstrate that the core physiological responses of cells to nutrient depletion result in increased coccosphere sizes and coccoliths per cell across four different coccolithophore families (Calcidiscaceae, Coccolithaceae, Isochrysidaceae and Helicosphaeraceae), a representative diversity of this phytoplankton group. Building on this, the direct comparison of coccosphere geometries in modern and fossil coccolithophores enables a proxy for growth phase to be developed that can be used to investigate growth responses to environmental change throughout their long evolutionary history. Our data also show that changes in growth rate and coccoliths per cell associated with growth-phase shifts can substantially alter cellular calcite production. Coccosphere geometry is therefore a valuable tool for accessing growth information in the fossil record, providing unprecedented insights into the response of species to environmental change and the potential biogeochemical consequences.
    Type: Dataset
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, 350.8 kBytes
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    DATA PANGAEA
  • 4
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    Compilation of scientific results of the EU project BENGAL (2016)
    PANGAEA
    Details
    Publication Date: 2024-06-13
    Description: This data collection presents the compilation of scientific results of the EU project BENGAL.
    Keywords: 12812-002; 12913-002; 12914-003; 12923-002; 12923-005; 12923-013; 12925-004; 12925-008; 12926-001; 12926-002; 12930-004; 12930-005; 12930-006; 12930-007; 12930-010; 12930-014; 12930-015; 12930-017; 12930-018; 12930-022; 12930-023; 12930-025; 12930-026; 12930-027; 12930-028; 12930-029; 12930-032; 12930-034; 12930-035; 12930-036; 12930-037; 12930-038; 12930-039; 12930-040; 12930-044; 12930-045; 12930-046; 12930-048; 12930-049; 12930-052; 12930-055; 12930-059; 12930-061; 12930-063; 12930-064; 12930-065; 12930-066; 12930-068; 12930-071; 12930-073; 12930-075; 12930-078; 12930-081; 12930-082; 12930-084; 12930-087; 12930-093; 12930-095; 13077-001; 13077-004; 13077-006; 13077-012; 13077-014; 13077-015; 13077-018; 13077-019; 13077-021; 13077-023; 13077-024; 13077-025; 13077-026; 13077-035; 13077-036; 13077-047; 13077-057; 13077-058; 13077-059; 13077-060; 13077-062; 13077-063; 13077-065; 13077-069; 13077-070; 13077-071; 13077-072; 13077-078; 13077-087; 13077-089; 13077-090; 13077-093; 13077-096; 13077-097; 13077-098; 13077-099; 13078-006; 13078-008; 13078-010; 13078-011; 13078-012; 13078-013; 13078-015; 13078-016; 13078-017; 13078-018; 13078-019; 13078-027; 13078-029; 13078-031; 13078-037; 13078-038; 13200-001; 13200-004; 13200-005; 13200-007; 13200-008; 13200-009; 13200-010; 13200-011; 13200-012; 13200-016; 13200-017; 13200-018; 13200-020; 13200-021; 13200-022; 13200-024; 13200-025; 13200-026; 13200-027; 13200-028; 13200-029; 13200-030; 13200-032; 13200-033; 13200-035; 13200-036; 13200-039; 13200-041; 13200-045; 13200-046; 13200-047; 13200-048; 13200-049; 13200-051; 13200-052; 13200-053; 13200-058; 13200-059; 13200-060; 13200-061; 13200-062; 13200-063; 13200-065; 13200-068; 13200-069; 13200-070; 13200-071; 13200-073; 13200-074; 13200-075; 13200-077; 13200-078; 13200-080; 13200-081; 13200-082; 13200-083; 13200-084; 13200-087; 13200-089; 13200-090; 13200-091; 13200-093; 13200-094; 13200-096; 13200-099; 13201-001; 13201-002; 13201-005; 13368-003; 13368-004; 13368-007; 13368-008; 13368-012; 13368-014; 13368-015; 13368-019; 13368-022; 13368-023; 13368-024; 13368-025; 13368-026; 13368-028; 13368-030; 13368-036; 13368-039; 13368-040; 13368-042; 13368-044; 13368-045; 13368-047; 13368-048; 13368-049; 13368-051; 13368-052; 13368-053; 13368-055; 13368-056; 13368-057; 13370-004; 13370-005; 13370-006; 13627-005; 13627-008; 13627-010; 13627-011; 13627-012; 13627-014; 13627-015; 13627-017; 13627-022; 13627-023; 13627-024; 269; 356; 362; 372; 373; 54301-002; 54301-003; 54301-005; 54301-008; 54301-009; 54301-010; 54301-012; 54301-014; 54301-016; 54301-019; 54301-021; 54301-023; 64PE123; ALBEX lander; Autonome colonisation module; Baited free-fall benthic amphipod trap; BC; Bengal; BENGAL; Benthic Biology and Geochemistry of a North-eastern Atlantic Abyssal Locality; BIO; Biology; BN; Bottom net; Bottom water sampler; Box corer; BWS; CH135; Challenger; Chalut à perche (6 m beam trawl); CMA; CP; CTD/Rosette; CTD-RO; Current meter, Aanderaa; D217; D222/1; D222/2; D226; D229; D231; D236; D237; DEMAR; DI236_08-1; DI236_11-1; DI236_16-1; DI236_18-1; DI236_21-1; DI236_23-1; DI236_25-1; DI236_28-1; DI236_29-1; DI236_31-1; DI236_34-1; DI236_42-1; DI236_45-1; DI236_49-1; Discovery (1962); D-MOC-01; D-MOC-02; D-MOC-03; D-MOC-04; D-MOC-07; Dy222_FFR-05; FFR; FFR-01; FFR-02; FFR-04; Free vehicle respirometer; FT-04; FTS; GBGL; GBGL-01; GBGL-02; Göteborg lander; IMBC; IMBC lander; KASTEN; Kasten corer (1 m**2); M36/4; M36/4_MC1; M36/4_MC4; M36/4_MC5; M36/5; M36/5_MC26; M36/5_MC27; M36/5_MC28; M36/6; M36/6_368FFR; M36/6_371BWS; M36/6_372MUC; M36/6_373MUC; M36/6_375MSN; M36/6_380MSN; M36/6_381BWS; M36/6_BWS-19; M36/6_BWS-20; M36/6_MC33; M36/6_MC38; M36/6_MC41; M42/2; M42/2_363-1; M42/2_365; M42/2_366; M42/2_367; M42/2_368-2; M42/2_368-3; M42/2_370; M42/2_373; M42/2_374-2; M42/2_374-3; M42/2_377-1; M42/2_377-5; M42/2_377-6; M42/2_380-2; M42/2_380-3; M42/2_380-4; M42/2_381; M42/2_384-1; M42/2_385; M42/2_386; M42/2_388-1; M42/2_388-2; M42/2_391-2; M42/2_397-1; M42/2_397-3; M42/2_417; M42/2_418; M42/2_419; M42/2_420; M42/2_421-2; M42/2_421-3; M42/2_421-5; M42/2_422; M42/2_424-1; M42/2_424-2; M42/2_424-4; M42/2_425; M42/2_426-2; M42/2_429-1; M42/2_429-2; M42/2_430; M42/2_432-1; M42/2_433; M42/2_434-1; M42/2_434-2; M42/2_436; M42/2_438; M42/2_BWS-01; M42/2_BWS-02; M42/2_BWS-04; M42/2_BWS-05; M42/2_BWS-09; M42/2_BWS-10; M42/2_BWS-12; M42/2_CTD-03; M42/2_CTD-05; M42/2_CTD-06; M42/2_CTD-07; M42/2_CTD-08; M42/2_CTD-09; M42/2_CTD-13; M42/2_CTD-22; M42/2_CTD-24; M42/2_CTD-25; M42/2_CTD-28; M42/2_CTD-29; M42/2_CTD-31; M42/2_MC-04; M42/2_MC-09; M42/2_MC1; M42/2_MC2; M42/2_MC27; M42/2_MC28; M42/2_MC29; M42/2_MC-30; M42/2_MC31; M42/2_MC-32; M42/2_MC34; M42/2_MC-34; M42/2_MC4; M42/2_MC5; M42/2_MC6; M42/2_MC7; M42/2_MC8; MACOL; MCB57; MCB57-74; MCB92; MCS; MEGAC; MegaCorer; Meteor (1986); MOC; MOC1; MOCNESS opening/closing plankton net; MOCNESS opening/closing plankton net 1 sqm; MSN; MUC; MULT; MultiCorer; MultiCorer, small; MultiCorer Barnett pattern (12-57); MultiCorer Barnett pattern (4-57.8-74); MultiCorer Barnett pattern (8-92); Multiple investigations; Multiple opening/closing net; NIOZL; OTSB14; PAP; PAP-XIX; PAP-XV; PAP-XVIII; PAP-XX; PAP-XXIIIa; Pelagia; Photo sledge; PLG123; PLG123/12-1; PLG123/13-2; PLG123/13-3; PLG123/13-5; PLG123/13-6; PLG123/13-7; PLG123/14-1; Porcupine Abyssal Plain; RESP; Respirometer; RK127; RK128; RK130; SAPS; Sediment profile imagery; Semi-balloon trawl; SEP; South Atlantic Ocean; Spade box corer; Stand-alone pumps; Trap, sediment; TRAPS; VEGBOXC; Vertical amphipod trap; VET
    Type: Dataset
    Format: application/zip, 515 datasets
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    DATA PANGAEA
  • 5
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    X-ray fluorescent (XRF) ratios and diatom groups of sediment core GeoB13813-4 (2016)
    PANGAEA
    In:  Supplement to: Perez, Laura; García-Rodríguez, Felipe; Hanebuth, Till J J (2016): Variability in terrigenous sediment supply offshore of the Río de la Plata (Uruguay) recording the continental climatic history over the past 1200 years. Climate of the Past, 12(3), 623-634, https://doi.org/10.5194/cp-12-623-2016
    Details
    Publication Date: 2023-03-03
    Description: The continental shelf adjacent to the Río de la Plata (RdlP) exhibits extremely complex hydrographic and ecological characteristics which are of great socioeconomic importance. Since the long-term environmental variations related to the atmospheric (wind fields), hydrologic (freshwater plume), and oceanographic (currents and fronts) regimes are little known, the aim of this study is to reconstruct the changes in the terrigenous input into the inner continental shelf during the late Holocene period (associated with the RdlP sediment discharge) and to unravel the climatic forcing mechanisms behind them. To achieve this, we retrieved a 10 m long sediment core from the RdlP mud depocenter at 57 m water depth (GeoB 13813-4). The radiocarbon age control indicated an extremely high sedimentation rate of 0.8 cm per year, encompassing the past 1200 years (AD 750-2000). We used element ratios (Ti / Ca, Fe / Ca, Ti / Al, Fe / K) as regional proxies for the fluvial input signal and the variations in relative abundance of salinity-indicative diatom groups (freshwater versus marine-brackish) to assess the variability in terrigenous freshwater and sediment discharges. Ti / Ca, Fe / Ca, Ti / Al, Fe / K and the freshwater diatom group showed the lowest values between AD 850 and 1300, while the highest values occurred between AD 1300 and 1850. The variations in the sedimentary record can be attributed to the Medieval Climatic Anomaly (MCA) and the Little Ice Age (LIA), both of which had a significant impact on rainfall and wind patterns over the region. During the MCA, a weakening of the South American summer monsoon system (SAMS) and the South Atlantic Convergence Zone (SACZ), could explain the lowest element ratios (indicative of a lower terrigenous input) and a marine-dominated diatom record, both indicative of a reduced RdlP freshwater plume. In contrast, during the LIA, a strengthening of SAMS and SACZ may have led to an expansion of the RdlP river plume to the far north, as indicated by higher element ratios and a marked freshwater diatom signal. Furthermore, a possible multidecadal oscillation probably associated with Atlantic Multidecadal Oscillation (AMO) since AD 1300 reflects the variability in both the SAMS and SACZ systems.
    Keywords: Center for Marine Environmental Sciences; MARUM
    Type: Dataset
    Format: application/zip, 4 datasets
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  • 6
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    Seasonal changes in the D/H ratio of fatty acids of pelagic microorganisms (2016)
    PANGAEA
    In:  Supplement to: Heinzelmann, Sandra M; Bale, Nicole Jane; Villanueva, Laura; Sinke-Schoen, Daniëlle; Philippart, Catharina J M; Sinninghe Damsté, Jaap S; Schouten, Stefan; van der Meer, Marcel T J (2016): Seasonal changes in the D/H ratio of fatty acids of pelagic microorganisms in the coastal North Sea. Biogeosciences, 13(19), 5527-5539, https://doi.org/10.5194/bg-13-5527-2016
    Details
    Publication Date: 2023-01-13
    Description: Culture studies of microorganisms have shown that the hydrogen isotopic composition of fatty acids depends on their metabolism, but there are only few environmental studies available to confirm this observation. Here we studied the seasonal variability of the deuterium-to-hydrogen (D / H) ratio of fatty acids in the coastal Dutch North Sea and compared this with the diversity of the phyto- and bacterioplankton. Over the year, the stable hydrogen isotopic fractionation factor epsilon between fatty acids and water (epsilon lipid/water) ranged between -172 and -237 per mil, the algal-derived polyunsaturated fatty acid nC20:5 generally being the most D-depleted (-177 to -235 per mil) and nC18:0 the least D-depleted fatty acid (-172 to -210 per mil). The in general highly D-depleted nC20:5 is in agreement with culture studies, which indicates that photoautotrophic microorganisms produce fatty acids which are significantly depleted in D relative to water. The epsilon lipid/water of all fatty acids showed a transient shift towards increased fractionation during the spring phytoplankton bloom, indicated by increasing chlorophyll a concentrations and relative abundance of the nC20:5 polyunsaturated fatty acids, suggesting increased contributions of photoautotrophy. Time periods with decreased fractionation (less negative epsilon lipid/water values) can potentially be explained by an increased contribution of heterotrophy to the fatty acid pool. Our results show that the hydrogen isotopic composition of fatty acids is a promising tool to assess the community metabolism of coastal plankton potentially in combination with the isotopic analysis of more specific biomarker lipids.
    Keywords: Coastal_North-Sea; North Sea
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 7
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    Seagrass meadows of the Solomon Islands, derived from field surveys conducted 13 May and 16 June 2004 (2016)
    PANGAEA
    In:  Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Townsville
    Details
    Publication Date: 2023-01-13
    Description: Approximately 6,633 ±1,446 hectares (ha) of predominately intertidal and shallow subtidal seagrass meadows were mapped during the Solomon Islands Rapid Ecological Assessment (SIREA) between 13 May and 16 June 2004. This was the first comprehensive survey of the Solomon Islands and it focused on the main island group from Choiseul Island in the northwest to Makira in the southeast. The survey involved examination of 1,426 field validation sites/points and identified 486 individual meadows. 10 species of seagrass were identified throughout the Solomon Islands. Mapping survey methodologies followed standardised global seagrass research methods (McKenzie et al. 2001, doi:10.1016/B978-044450891-1/50006-2) where observers walked or free-dived to assess survey points. At each survey site/point seagrass % cover and/or above ground biomass (standing crop, grams dry weight (g DW/m**2)) was determined within quadrats (50cm x 50cm) using a non-destructive visual estimates of biomass technique and the seagrass species present identified. Water depth and visual/tactile description of sediment were also recorded at each survey site/point. A differential handheld global positioning system (GPS) was used to locate each survey site/point (accuracy ±5m). Seagrass meadow boundaries were determined based on the positions of survey sites and the presence of seagrass, coupled with depth contours and remote sensing (e.g. aerial photography) where available. The meadow boundary accuracy varied from 7.5m to 500m. The resulting data of each survey point and each seagrass meadow was saved as an ArcMap shapefile and projected to WGS84. Most Solomon Islands seagrasses were found in water shallower than 10m and meadows were monospecific or consisted of multispecies communities; up to 6 species present at a single location. The dominant species encountered were Enhalus acoroides and Thalassia hemprichii. 54% of all seagrass meadows (per hectare basis) were found in Malaita Province. All other provinces each included less than 12% of the seagrass meadows. Seagrass distribution appears to be primarily influenced by the degree of wave action (exposure) and nutrient availability. Solomon Islands' seagrass habitats can be generally categorised into four broad habitats: estuaries (incl. large shallow lagoons), coastal (incl. fringing reef), deep-water and reef (e.g., barrier or isolated).
    Keywords: File content; File format; File name; File size; Solomon_Islands; Solomon Islands; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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  • 8
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    Fiji mapped mangrove ecosystem service rankings in GeoJSON format (2016)
    PANGAEA
    In:  Supplement to: Atkinson, Scott C; Jupiter, Stacy D; Adams, Vanessa M; Ingram, J Carter; Narayan, Siddharth; Klein, Carissa J; Possingham, Hugh P (2016): Prioritising Mangrove Ecosystem Services Results in Spatially Variable Management Priorities. PLoS ONE, 11(3), e0151992, https://doi.org/10.1371/journal.pone.0151992
    Details
    Publication Date: 2023-01-13
    Description: Incorporating the values of the services that ecosystems provide into decision making is becoming increasingly common in nature conservation and resource management policies, both locally and globally. Yet with limited funds for conservation of threatened species and ecosystems there is a desire to identify priority areas where investment efficiently conserves multiple ecosystem services. We mapped four mangrove ecosystems services (coastal protection, fisheries, biodiversity, and carbon storage) across Fiji. Using a cost-effectiveness analysis, we prioritised mangrove areas for each service, where the effectiveness was a function of the benefits provided to the local communities, and the costs were associated with restricting specific uses of mangroves. We demonstrate that, although priority mangrove areas (top 20%) for each service can be managed at relatively low opportunity costs (ranging from 4.5 to 11.3% of overall opportunity costs), prioritising for a single service yields relatively low co-benefits due to limited geographical overlap with priority areas for other services. None-the-less, prioritisation of mangrove areas provides greater overlap of benefits than if sites were selected randomly for most ecosystem services. We discuss deficiencies in the mapping of ecosystems services in data poor regions and how this may impact upon the equity of managing mangroves for particular services across the urban-rural divide in developing countries. Finally we discuss how our maps may aid decision-makers to direct funding for mangrove management from various sources to localities that best meet funding objectives, as well as how this knowledge can aid in creating a national mangrove zoning scheme.
    Type: Dataset
    Format: application/zip, 2.1 MBytes
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    DATA PANGAEA
  • 9
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    Salinity vs. delta Deuterium in the coastal North Sea (2016)
    PANGAEA
    Details
    Publication Date: 2023-01-13
    Keywords: Coastal_North-Sea; DEPTH, water; North Sea; Salinity; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 106 data points
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  • 10
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    Fatty acids in the coastal North Sea (2016)
    PANGAEA
    Details
    Publication Date: 2023-01-13
    Keywords: Coastal_North-Sea; DATE/TIME; DEPTH, water; n-fatty acid C14:0; n-fatty acid C14:0 (peak area); n-fatty acid C16:0; n-fatty acid C16:0 (peak area); n-fatty acid C16:1; n-fatty acid C16:1 (peak area); n-fatty acid C18:0; n-fatty acid C18:0 (peak area); n-fatty acid C18:unsaturated; n-fatty acid C18:unsaturated (peak area); North Sea; Polyunsaturated fatty acids, C20; Polyunsaturated fatty acids, C20 (peak area); Sum fatty acids (peak area)
    Type: Dataset
    Format: text/tab-separated-values, 536 data points
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