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  • 2020-2024  (12)
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  • 1
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    Multi-technique monitoring of climate change signature in Svalbard (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2023-05-30
    Description: The Svalbard archipelago is strongly impacted by past and present-day ice melting. This area offers the benefit that several in situ and space datasets are available at different spatial and temporal resolutions. We perform a multi-technique intercomparison for a better understanding of the different processes and extract common climate-related signals, allowing climate change signature analysis. Space geodetic techniques provide time series of daily crustal deformation and monthly gravity field variations over several years at local and regional scales. Seasonal signals included in these time series are mainly caused by the (visco)elastic response of variable mass load due to ice and snow accumulation. GNSS positioning and GRACE equivalent water height time series are compared to geophysical models based on mass redistributions and snow models. For this, we applied specific data analysis methods to accurately separate the different sources and reveal climate change signature from seasonal signals. In addition, ground gravimetry, field datasets, sediment analysis, Sentinel observations, aerial photography, and laser telescan are used to characterize the evolution of the area. These datasets are used to produce maps representing the marine sedimentary facies and glacial environments of different glaciers. We estimate the footprints of glacier retreat and ice thickness loss by monitoring the evolution of the coastline and follow the hydrological network for different glaciers (Kronebreen and Lovenbreen) and fjords to show the contraction of the glacier's drainage during melting. The joint analysis of the maps and space geodesy time series gives the estimation of crustal uplift due to glacier melt.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 2
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    Evolution of ice shelves in North Greenland during the last 30 years (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2023-05-10
    Description: In north Greenland, which holds more than 2.7 m of sea level equivalent, the ice flows through ice shelves, as in Antarctica. These floating platforms are the most vulnerable parts of the ice sheets as the advection of warm, salty ocean waters increases basal melting, which can trigger an increase in ice flow into the ocean. Here we study the recent dynamic and geometric changes of all present and former ice shelves along the north coast of Greenland. We document the evolution of the surface elevation using data from the GIMP project, from NASA's instruments (ICESat-1/2, ATM, LVIS, GLISTIN-A) and generate DEMs using ASTER imagery between 2000-present. We also monitor changes in surface ice velocity and grounding zone evolution using a combination of optical and radar data. We use the elevation time series to monitor the temporal evolution of the ice shelves volumes and combine them with the surface flow velocity to calculate basal melt rates in a Lagrangian framework at unprecedented level of resolution. Finally, we compare our observations with nearby CTD measurements, the TOPAZ4b reanalysis of Arctic ocean physics provided by Copernicus Marine Service and model outputs from the Modèle Atmosphérique Régoinal. We show that basal melting, grounding line retreat and fracturing are rapidly increasing and is followed by an increase in ice discharge into the ocean. These observations demonstrate that significant changes are occuring in a region that has long been considered stable, which may have dramatic consequences for the ice sheet contribution to sea level rise.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 3
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    Climate change signature extraction from hydrological loading signals in South America (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2023-09-12
    Description: To assess climate change effects, it is important to understand how the water cycle interacts with other Earth system processes. Redistributions of environmental masses - atmosphere, ocean, continental hydrology - induce detectable variations in the Earth's gravity field and crustal deformations, known as loading effects. The GNSS (Global Navigation Satellite System) and GRACE (Gravity Recovery And Climate Experiment) and GRACE Follow-On space gravity missions allow monitoring water mass transfers giving long time series (〉 20 years) highly complementary in terms of both spatial and temporal resolutions. South America, with its large hydrological basins, has the strongest seasonal hydrological signal in the world. The hydrological loading signal exhibits different spectral contributions resulting from the superposition of different phenomena acting at different scales. It contains several markers of climate change including changes in precipitation, water storage and extreme events. However, climate markers are not directly accessible in the signals observed by space geodesy. Reliable extraction of the hydrological part therefore requires the use of efficient signal processing method to separate the different contributions of mass redistribution and to compare the observations with the deformations predicted by geodynamical models. We apply an innovative multivariate analysis method combining MSSA (Multi-Channel Singular Spectrum Analysis) and MICA (Multidimensional Independent Component Analysis) to permanent GNSS sites in South America. We demonstrate how better inferring hydrological loading signal contributes to a better understanding of the water cycle, enhancing global inference of the mass transport at the Earth’s surface and gives significant insights on climate change driven signals.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 4
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    Manual detection of glacier extents on Kilimanjaro, Mount Kenya, and in the Rwenzori Range in 2021/2022 (2024)
    PANGAEA
    Details
    Publication Date: 2024-06-12
    Description: Tropical glaciers are helpful indicators of climatic changes in high-altitude environments. In East Africa, the glaciers in the three high-mountain regions Kilimanjaro, Mount Kenya and the Rwenzori Range have retreated substantially since the late 19th century. However, as there are no recent estimates for all regions, our study updates the time series of tropical glacier extent in East Africa. The methodological approach of the investigation is manual detection of ice body margins based on high-resolution satellite images of the PlanetScope program from 2021/2022. We performed three types of detection that included a minimum, a primary and a maximum extent, to indicate the range of the probable glacier area and account for individual allocation of pixels and influences of shaded or snow-covered areas.
    Keywords: Binary Object; Binary Object (Character Set); Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); East Africa; Kilimanjaro; Mount Kenya; Rwenzori; Satellite imagery; SATI; Tropical Climate; tropical glacier
    Type: Dataset
    Format: text/tab-separated-values, 54 data points
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  • 5
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    Paleo±Dust: Quantifying uncertainty in paleo-dust deposition across archive types (2024)
    PANGAEA
    Details
    Publication Date: 2024-05-21
    Description: Paleo±Dust is an updated compilation of bulk and 〈10-µm paleo-dust deposition rate with quantitative 1-σ uncertainties that are inter-comparable among archive types (lake sediment cores, marine sediment cores, polar ice cores, peat bog cores, loess samples). Paleo±Dust incorporates a total of 285 pre-industrial Holocene (pi-HOL) and 209 Last Glacial Maximum (LGM) dust flux constraints from studies published until December 2022. We also recalculate previously published dust fluxes to exclude data from the last deglaciation and thus obtain more representative constraints for the last pre-industrial interglacial and glacial end-member climate states. Metadata include all components necessary to derive dust deposition rate, including: age range, thickness, density, eolian content. We also include 1-sigma uncertainties on each of these components, and on the final bulk and 〈10-µm dust deposition rates. Specific notes for each site and a list of references are also included.
    Keywords: Dust flux; Holocene; Ice core; Lake sediment core; Last Glacial Maximum; Loess; Marine Sediment Core; Peat bog; Uncertainty
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 6
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    Pre-industrial Holocene (pi-HOL) paleo-dust record (2024)
    PANGAEA
    Details
    Publication Date: 2024-06-25
    Keywords: 0038PG; 0055PG; 0058PG; 0082PG; 11071651 Pistoncore2; 11191756 Piston core 8, TT013-PC72; 12JPC; 138-848B; 138-849A; 138-850A; 138-851E; 138-852A; 138-853B; 145-887; 14MC_13BB; 16MC_sed; 177-1088; 1MC_sed; 21MC_20BB; 26MC-25BB; 29MC-28BB; 33MC_32BB; 342; 39MC-36BB; 74KL_sed; 7MC_sed; 90-593; 9MC_sed; Accumulation rate, dust, per year; Accumulation rate, dust, per year, size fraction 〈 10 µm; Accumulation rate, dust, per year, size fraction 〈 10 µm, standard deviation; Accumulation rate, dust, per year, standard deviation; Accumulation rate, sediment, mean per year; Accumulation rate, sediment, standard deviation; Aeolian components, fractional; Aeolian components, standard deviation; Age, maximum/old; Age, maximum/old, standard deviation; Age, minimum/young; Age, minimum/young, standard deviation; Ahklun-Mountains; Alaska, USA; Amazon Fan; Amsterdam-Island; Antarctica; ANT-XI/2; ANT-XXVI/2; APSARA4; Area/locality; Argentina; Atlantic Ocean; Australia; Baicaoyuan; Baie_Canada; Baimapo; Banshan; Baoji-Lingyuan; Barrhill; Barton-County; Baxie-Dongxiang; BC; Beglitsa; Beiguoyuan; Beiguoyuan-II; Beisel-Steinle; Beiyuan; Beiyuantou; Belgium; Bering Sea; Bignell-Hill-1; Blue-Lake; Borehole-OT-1; Box corer; calculated, 1 sigma; CALYPSO; CALYPSO2; Calypso Corer; Calypso Corer II; Canada; Canterbury-Plains-II; Caocun; CD129; Chagelebulu-1-Cagelebulu; Changwu; Charles Darwin; Chatanika-River; Chena; Chenjiawo-Lantian-1; Chile; China; Chitina; Chumbur-Kosa; Chunhua; Clear; Columbine; COMPCORE; Composite Core; Copper, Alaska, U.S.A., North America; Core; CORE; CRATER; Crater lake, USA; Crvenka; Dadiwan; Davidsmosse; Debrecen-Alfoldi-brickyard; Delta-Junction; Density, dry bulk; Density, dry bulk, standard deviation; Depth, sediment/rock, standard deviation; Dome C; Dome C, Antarctica; Draftinge-Mosse; DRILL; Drilling/drill rig; Duanjiapo-Lantian-2; Duowa; Dust flux; Dust mass fraction 〈 10 µm, fractional; Dust mass fraction 〈 10 µm, standard deviation; E26-1; EDC; Emperor Seamount; EN06601; EN066-21GGC; EN066-29GGC; EN066-38PG; Endeavor; ENV; Environmental investigation; EPICA Dome C; Equatorial East Pacific; Equatorial Pacific; Event label; Finnhojden; Flag; Focun; Fox/Goldstream; Ganzi; Gaobai; Gaolanshan; GC; GeoB1515-1; GeoB1523-1; GGC37-VG19; GISP; GISP2; Global River Discharge; Glomar Challenger; Gorina; Grashojden; Gravity corer; Gravity corer (Kiel type); Greenland; Gulf of Aden; Halfway-House; Hani; Harberton; Hoalin; Hokberg; Holocene; Hongtushan; Hookers-Point_Malvinas-Islands; Huanglong; Huangshan; Huangyanghe; Huanxian; Hungary; Hura-Village; Ice_core_diverse; Ice core; ICEDRILL; Ice drill; Ile-du-Havre; IMAGES III - IPHIS; India; Indian Ocean; INDIEN SUD 2; Indonesia; Iran; Irig; Israel; Jiezicun-Jiezhichun; Jikariya-Lake; Jingbian-I; Jingbian-II; Jingchuan; Jingyuan; Jiuzhoutai-Lanzhou; Joides Resolution; JPC; Jumbo Piston Core; Kajemarum-Oasis; KAL; Kalat-e-Naderi-a; Karukinka; Kasten corer; Kazakhstan; Kenai-1; Kenai-2; Kirpichny; KL; KL11_sed; KL15_sed; KL23_sed; KN11002; Knorr; KNR110-55; KNR110-58; KNR110-82; KNR73-4PC; KS15-5; Kuma; Kurortne; Kyrgyz; La_Grande_coreLG2; Lake sediment core; Landa; Laoguantai; Last Glacial Maximum; LATITUDE; Leg138; Leg145; Leg177; Leg90; Leninsk-I; Le Suroît; LG2; LGG_loess; Lijiayuan; Likhvin; Lingtai; LJW10; Loess; LOESS; Loess profile; LONGITUDE; Lozada; Lozhok; LRC_loess; Lujiaowan; Lynch-Crater; M16/2; Majiayuan; Malvinas-Islands; Marine Sediment Core; Marion Dufresne (1972); Marion Dufresne (1995); Matanuska-Valley; MC1208-17PC; MC1208-31BB; MD03-2705; MD106; MD11-3357; MD134; MD185; MD88-769; MD88-770; MD94-102; MD94-104; MD972138; MD97-2138; ME0005-24JC; Melville; Mengdashan; Meteor (1986); Mfabeni-MF4-12; Misten; ML1208-37BB; MOHOS; Mohos, Romania; Mt-Harif; MUC; Mujiayuan-Wupu; MultiCorer; MV1014-02-17JC; MW91-9-GGC48; Namibia; Naponee; Native-Companion-Lagoon; Neor-Lake; New Zealand; NGRIP2; Nigeria; Nilka; Ningxian; NLT17; Nome; North Atlantic; NorthGRIP; North Pacific Ocean; OC437-07; OC437-07_GC27; OC437-07_GC37; OC437-07_GC49; OC437-07_GC66; OC437-07_GC68; Oceanus; Opuwo_Namibia; OWR; P7; Pacific Ocean; PALEOCINAT; Palouse; Panama Basin; PC; Peat bog; PEATC; Peat corer; Pegwell-Bay; Peters; Phorphyry; PICABIA; Piston corer; Piston corer (BGR type); PLDS-007G; PLDS-1; Pleiades; Polarstern; Primorskoje; PS2498-1; PS28; PS28/304; PS75/059-2; PS75/100-4; PS75 BIPOMAC; Qilian-Shan; Qilian-Shan-section; Qumalai-2; Qumalai-5; Ramat-Beka; Ramnicu-Sarat; RC08; RC08-102; RC13; RC13-140; RC13-189; RC14; RC14-105; RC14-121; RC17; RC17-177; RC24; RC24-1; RC24-12; RC24-7; RC27; RC27-42; Red Sea; Reference/source; Renjiahutong; RGS; Rio-Rubens; River gauging station; RNDB-74P; Robat-e-Khakestari; Robert Conrad; Romania; Romantic; Roxolany; RPC; Rudak; Russia; Russian peat corer; SA6_5; Sagwon; Sampling/drilling ice; Santa-Victoria; SeaLevel; SEDCO; Sediment corer; Semlac; Serbia; Shankerpora; Shaozhuang; Shaw-Creek-Flats; Shiguanzhi; Sihailongwan; SL; Slope-Mt-Brooks-Range; SO136; SO136_038GC-6; SO14-08-05; Sonne; South-Africa; South Atlantic; South Atlantic Ocean; Southern Ocean; South Pacific; South Pacific/Tasman Sea/PLATEAU; South Pacific Ocean; Southwest Pacific Ocean; Spain; Stari-Slankamen; St-Michael-Island-Puyuk-Lake; St-Michael-Island-Zagoskin-Lake; Store_Mosse; SU90-03; Sweden; SW Indian Ocean; Tajik-Basin; Tajikistan; TASQWA; Taul-Muced; TGS; Thickness; Thomas G. Thompson; Tide gauge station; TLD_loess; TLD16; TN057-21; TN057-6-PC4; Tongde; Tortugas-I; TR163-19; TR163-22; TREE; Tree ring sampling; TT013; TT013_18; TT013_72; TT013-MC19; TT013-MC27; Tuxiangdao; Type; Ukraine; Uncertainty; United Kingdom; Upper-Snowy-Core; USA; Uzbekistan; V19; V19-29; V20; V20-122; V20-234; V21; V21-146; V21-29; V22; V22-182; V28; V28-203; V30; V30-40; V32; V32-126; Valikhanov; Veliki-Surduk; Vema; Vostok; WDD; Weinan; Weinan-2; Weinan-Yangguo; WIND; WIND-28K; Wulipu; XEB_loess; Xiadongcun-Jixian; Xiala; Xiangpishan; Xiaoerbulake; Xifeng; Xifeng-II; Xinghai; Xining-Dadunling; Xistral-Mountains; Xueyuan; Xunyi; XY17_loess; XZP; Y69-106P; Y69-71P; Y9; Y9_core; YALOC69; Yanchang; Yaoxian-I; Yaoxian-II-YX; Yaquina; Yellibadragh; Yichuan; Yinwan; Yuanbao; Yuanpu-Yuanbo-Xinzhuangyuan; Yuexi; Zeketai; Zhaitang; Zhangjiayuan; Zhaojiachuan; Zhenbeitai; Zhouqu; ZS_loess
    Type: Dataset
    Format: text/tab-separated-values, 4965 data points
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  • 7
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    Last Glacial Maximum (LGM) paleo-dust record (2024)
    PANGAEA
    Details
    Publication Date: 2024-06-25
    Keywords: 0038PG; 11071651 Pistoncore2; 11191756 Piston core 8, TT013-PC72; 12JPC; 130-806; 138-848B; 138-849A; 138-850A; 138-851E; 138-852A; 138-853B; 14MC-13BB; 21MC_20BB; 26MC-25BB; 29MC-28BB; 33MC_32BB; 39MC-36BB; 74KL_sed; 90-593; Accumulation rate, dust, per year; Accumulation rate, dust, per year, size fraction 〈 10 µm; Accumulation rate, dust, per year, size fraction 〈 10 µm, standard deviation; Accumulation rate, dust, per year, standard deviation; Accumulation rate, sediment, mean per year; Accumulation rate, sediment, standard deviation; Achenheim; Aeolian components, fractional; Aeolian components, standard deviation; Age, maximum/old; Age, maximum/old, standard deviation; Age, minimum/young; Age, minimum/young, standard deviation; Albertirsa; Angola Basin; Antarctica; ANT-XI/2; ANT-XXVI/2; APSARA4; Area/locality; Argentina; Atlantic Ocean; Australia; Austria; BC; BCCPT; Beiguoyuan; Beiyuantou; Belgium; Bellevue; Bignell-Hill; Boeckingen; Boenningheim; Borehole-OT-1; Box corer; Caijiagou; calculated, 1 sigma; CALYPSO; CALYPSO2; Calypso Corer; Calypso Corer II; Calypso Square Core System; Canteen-Creek; Canterbury-Plains; CASQS; CD129; Charles Darwin; China; Chumbur-Kosa; COMPCORE; Composite Core; Core; CORE; Core-G39; Crawford; Crvenka; Czech Republic; Darai-Kalon; Debrecen-Alfoldi-brickyard; Density, dry bulk; Density, dry bulk, standard deviation; Depth, sediment/rock, standard deviation; Dolni-Vestonice; Dome C; Dome C, Antarctica; DRILL; Drilling/drill rig; Dunaszekcso; Dunlap; Dust flux; Dust mass fraction 〈 10 µm, fractional; Dust mass fraction 〈 10 µm, standard deviation; E26-1; EDC; Egg-Lake; EN06601; EN066-21GGC; EN066-29GGC; EN066-38PG; Endeavor; EPICA Dome C; Equatorial East Pacific; Equatorial Pacific; Eustis; Event label; Flag; France; Gaolanshan; GC; GeoB1035-1; GeoB3808-6; Germany; GISP; GISP2; Glomar Challenger; Gorina; Gravity corer; Gravity corer (Kiel type); Greenland; Gulang; Gulf of Aden; Halfway-House; Halyc; Heimugou-1; Hoalin; Holocene; Huangshan; Hummeston; Hungary; Ice_core_diverse; Ice core; ICEDRILL; Ice drill; IMAGES III - IPHIS; IMAGES XV - Pachiderme; India; Indian Ocean; INDIEN SUD 2; Iran; Irig; Iskitim; Israel; Jingyuan; Jingyuan-II; Joides Resolution; JPC; Jumbo Piston Core; KAL; Kalat-e-Naderi-a; Kasten corer; Katymar-brickyard; Kazakhstan; Keller-Farm; Kisiljevo; KL; KL11_sed; KL15_sed; KL23_sed; KNR73-3PC; KNR73-4PC; KS15-5; Lake sediment core; Last Glacial Maximum; LATITUDE; Leg130; Leg138; Leg90; Le Suroît; Lihkvin; Lingezhuang; Liujiapo-1; Loess; LOESS; Loess profile; LONGITUDE; Loveland; Lowland-Point; Lozada; Lozhok; M34/3; M45/5_86; M45/5_90; M45/5a; M6/6; Madaras-brickyard; Majiayuan; Marine Sediment Core; Marion Dufresne (1972); Marion Dufresne (1995); MC1208-17PC; MC1208-31BB; McCook; MD03-2705; MD07-3076; MD07-3076Q; MD106; MD11-3357; MD134; MD159; MD185; MD88-769; MD88-770; MD94-102; MD94-104; MD972138; MD97-2138; ME0005-24JC; Melville; Mende; Meteor (1986); Mid Atlantic Ridge; Mitoc-Malu-Galben; ML1208-37BB; Molodova-V; Morrison; MSN; MtCass-E2a; MUC; MultiCorer; Multiple opening/closing net; MV1014-02-17JC; MW91-9-GGC48; Natchez; Native-Companion-Lagoon; Neka; New Zealand; NGRIP2; Nilka; North Atlantic; NorthGRIP; North Pacific Ocean; Nosak; Now-Deh; Nussloch; OC437-07; OC437-07_GC27; OC437-07_GC49; OCE437-07-GC68; Oceanus; P7; Pacific Ocean; PALEOCINAT; Panama Basin; Panama-Bentley; PC; Peat bog; Pegwell-Bay; PICABIA; Piston corer; Piston corer (BGR type); PLDS-007G; PLDS-1; Pleiades; Polarstern; Primorskoje; PS2498-1; PS28; PS28/304; PS75/059-2; PS75/100-4; PS75 BIPOMAC; Ramat-Beka; Rapids-City; RC08; RC08-102; RC11; RC1112; RC11-210; RC11-238; RC13; RC13-114; RC13-140; RC13-189; RC14; RC14-105; RC17; RC17-177; RC24; RC24-1; RC24-12; RC24-7; RC27; RC27-42; Red Sea; Reference/source; Remicourt; Remizovka; RNDB-PC13; Robert Conrad; Rocourt; Romania; Romont-East; Russia; Salt-Creek; Sampling/drilling ice; SEDCO; Sediment corer; Semlac; Serbia; Shankerpora; Shaozhuang; Sihailongwan; SL; SO136; SO136_038GC-6; SO14-08-05; Sonne; South Atlantic; South Atlantic Ocean; Southern Ocean; South Pacific; South Pacific/Tasman Sea/PLATEAU; South Pacific Ocean; Southwest Pacific Ocean; Stari-Slankamen; St-Michael-Island-Zagoskin-Lake; SU90-03; SU90-08; SU90-09; SU90-11; Surduk-2; SW Indian Ocean; Szeged-Othalom-I; Tajikistan; TASQWA; Thickness; Thomas G. Thompson; Thomas G. Thompson (1964); TLD_loess; TN057-21; TN057-6-PC4; Tortugas-II; Toshan; TR163-19; TR163-22; TR163-31; TT013; TT013_18; TT013_72; TT013-MC112; TT013-MC34; TT013-MC97; TT154-10; TTXXX; Type; Ukraine; Uncertainty; United Kingdom; USA; V19; V19-28; V20; V20-122; V20-234; V21; V21-146; V21-40; V22; V22-182; V28; V28-203; V28-238; V30; V30-40; V32; V32-126; V32-128; Valikhanov; Veliki-Surduk; Vema; Vicksburg_loess; Vostok; VTR01-10GGC; W8709A; W8709A-1; Wecoma; Weinan; West-Helena; Willendorf-Il; WIND; WIND-28K; XEB_loess; Xiaoerbulake; Xifeng; Xifeng-II; Xueyuan; Xunyi; XY17_loess; XZP; Y69-106P; Y69-71P; Y9; Y9_core; YALOC69; Yaoxian-I; Yaquina; Yuanbao; Yuanpu-Yuanbo; Zeketai; Zhaosu-Boma; Zhongjiacai; Zhouqu; ZS_loess
    Type: Dataset
    Format: text/tab-separated-values, 3347 data points
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  • 8
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    Destructive fishing: An expert‐driven definition and exploration of this quasi‐concept (2024)
    Wiley
    In:  EPIC3Conservation Letters, Wiley, ISSN: 1755-263X
    Details
    Publication Date: 2024-04-17
    Description: 〈jats:title〉Abstract〈/jats:title〉〈jats:p〉Numerous policy and international frameworks consider that “destructive fishing” hampers efforts to reach sustainability goals. Though ubiquitous, “destructive fishing” is undefined and therefore currently immeasurable. Here we propose a definition developed through expert consultation: “Destructive fishing is any fishing practice that causes irrecoverable habitat degradation, or which causes significant adverse environmental impacts, results in long‐term declines in target or nontarget species beyond biologically safe limits and has negative livelihood impacts.” We show strong stakeholder support for a definition, consensus on many biological and ecological dimensions, and no clustering of respondents from different sectors. Our consensus definition is a significant step toward defining sustainable fisheries goals and will help interpret and implement global political commitments which utilize the term “destructive fishing.” Our definition and results will help reinforce the Food and Agricultural Organization's Code of Conduct and meaningfully support member countries to prohibit destructive fishing practices.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 9
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    Understanding and addressing shortfalls in European wild bee data (2024)
    In:  Biological Conservation vol. 290 no. 110455
    Details
    Publication Date: 2024-04-18
    Description: Understanding and reversing biodiversity decline in the Anthropocene requires robust data on species taxonomic identity, distribution, ecology, and population trends. Data deficits hinder biodiversity assessments and conservation, and despite major advances over the past few decades, our understanding of bee diversity, decline and distribution in Europe is still hampered by such data shortfalls. Using a unique digital dataset of wild bee occurrence and ecology, we identify seven critical shortfalls which are an absence of knowledge on geographic distributions, (functional) trait variation, population dynamics, evolutionary relationships, biotic interactions, species identity, and tolerance to abiotic conditions. We describe “BeeFall,” an interactive online Shiny app tool, which visualizes these shortfalls and highlights missing data. We also define a new impediment, the Keartonian Impediment, which addresses an absence of high-quality in situ photos and illustrations with diagnostic characteristics and directly affects the outlined shortfalls. Shortfalls are highly correlated at both the provincial and national scales, identifying key areas in Europe where knowledge gaps can be filled. This work provides an important first step towards the long-term goal to mobilize and aggregate European wild bee data into a multiscale, easy access, shareable, and updatable database which can inform research, practice, and policy actions for the conservation of wild bees.
    Keywords: Knowledge gaps ; Big data ; Online tool ; Biodiversity decline ; Citizen science ; Biodiversity monitoring
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 10
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    Characterizing porous microaggregates and soil organic matter sequestered in allophanic paleosols on Holocene tephras using synchrotron-based X-ray microscopy and spectroscopy (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-29
    Description: Allophanic tephra-derived soils can sequester sizable quantities of soil organic matter (SOM). However, no studies have visualized the fine internal porous structure of allophanic soil microaggregates, nor studied the carbon structure preserved in such soils or paleosols. We used synchrotron radiation-based transmission X-ray microscopy (TXM) to perform 3D-tomography of the internal porous structure of dominantly allophanic soil microaggregates, and carbon near-edge X-ray absorption fine-structure (C NEXAFS) spectroscopy to characterize SOM in ≤ 12,000-year-old tephra-derived allophane-rich (with minor ferrihydrite) paleosols. The TXM tomography showed a vast network of internal, tortuous nano-pores within an allophanic microaggregate comprising nanoaggregates. SOM in the allophanic paleosols at four sites was dominated by carboxylic/carbonyl functional groups with subordinate quinonic, aromatic, and aliphatic groups. All samples exhibited similar compositions despite differences between the sites. That the SOM does not comprise specific types of functional groups through time implies that the functional groups are relict. The SOM originated at the land/soil surface: ongoing tephra deposition (intermittently or abruptly) then caused the land-surface to rise so that the once-surface horizons were buried more deeply and hence became increasingly isolated from inputs by the surficial/modern organic cycle. The presence of quinonic carbon, from biological processes but vulnerable to oxygen and light, indicates the exceptional protection of SOM and bio-signals in allophanic paleosols, attributable both to the porous allophane (with ferrihydrite) aggregates that occlude the relict SOM from degradation, and to rapid burial by successive tephra-fallout, as well as strong Al-organic chemical bonding. TXM and C NEXAFS spectroscopy help to unravel the fine structure of soils and SOM and are of great potential for soil science studies.
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Springer Nature
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