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  • 2015-2019  (9)
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  • 1
  • 2
    Publication Date: 2016-09-23
    Print ISSN: 0939-6314
    Electronic ISSN: 1617-6278
    Topics: Archaeology , Biology
    Published by Springer
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  • 3
    Publication Date: 2019-03-27
    Description: Arctic environments may respond very sensitively to ongoing global change, as observed during the past decades for Arctic vegetation. Only little is known about the broad-scale impacts of early and mid 20th-century industrialization and climate change on remote Arctic environments. Palynological analyses of Greenland ice cores may provide invaluable insights into the long-term vegetation, fire, and pollution dynamics in the Arctic region. We present the first palynological record from a Central Greenland ice core (Summit Eurocore ’89, 72°35’N, 37°38’W; the location of Greenland Ice Core Project GRIP) that provides novel high-resolution microfossil data on Arctic environments spanning AD 1730–1989. Our data suggest an expansion of birch woodlands after AD 1850 that was abruptly interrupted at the onset of the 20th century despite favorable climatic conditions. We therefore attribute this Betula woodland decline during the 20th century to anthropogenic activities such as sheep herding and wood collection in the sub-Arctic. First signs of coal burning activities around AD 1900 coincide with the onset of Arctic coal mining. The use of coal and fire activity increased steadily until AD 1989 resulting in microscopic-size pollution of the ice sheet. We conclude that human impact during the 20th century strongly affected (sub)-Arctic environments. Moreover, ecosystems have changed through the spread of adventive plant species (e.g. Ranunculus acris, Rumex) and the destruction of sparse native woodlands. We show for the first time that optical palynology allows paleoecological reconstructions in extremely remote sites 〉500 km from potential sources, if adequate methods are used.
    Print ISSN: 0959-6836
    Electronic ISSN: 1477-0911
    Topics: Geography , Geosciences
    Published by Sage Publications
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  • 4
  • 5
    Publication Date: 2018-06-06
    Description: High-resolution sediment chronologies with the best possible time control are essential for comparing palaeoecological studies with independent high-precision climatic, archaeological or historic data in order to disentangle causes and effects of past environmental, ecological and societal change. We present two varved lake sediment sequences from Moossee and Burgäschisee (Swiss Plateau) that have chronologies developed with Bayesian models and radiocarbon (14C) dating of terrestrial plant macrofossils extracted from sediment samples with constant age ranges. We illustrate the potential of high-resolution 14C dating for the construction of robust, high-precision sediment chronologies. The mean 2σ age uncertainties were reduced to±19 cal yr for Moossee and to±54 cal yr for Burgäschisee over the entire period of 3000 cal yr, while 2σ uncertainties of only±13 cal yr and±18 cal yr respectively, were achieved for shorter time intervals. These precisions are better than or comparable to those of previous varve studies. Our results imply that a sophisticated subsampling strategy and a careful selection of short-lived and well-defined terrestrial plant remains are crucial to avoid outlying 14C ages. A direct linkage between palaeoeological studies with dendrochronologically dated, local archaeological sites as well as a precise comparison with high-resolution climate proxy data have become feasible.
    Print ISSN: 0033-8222
    Electronic ISSN: 1945-5755
    Topics: Archaeology , Energy, Environment Protection, Nuclear Power Engineering , Geosciences
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  • 6
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    Unknown
    PANGAEA
    In:  Supplement to: Finsinger, Walter; Heiri, Oliver; Valsecchi, Verushka; Tinner, Willy; Lotter, André F (2007): Modern pollen assemblages as climate indicators in southern Europe. Global Ecology and Biogeography, 16(5), 567-582, https://doi.org/10.1111/j.1466-8238.2007.00313.x
    Publication Date: 2024-03-06
    Description: Aim and Location: Our aim is to develop pollen-climate inference models for southern Europe and to test their performance and inference power by cross-validation with modern climate data. Surface sediments collected from lakes along a climate gradient from the winter-cold/summer-wet Alps to winter-wet/summer-dry Sicily were analysed for modern pollen assemblages. Methods: For each lake, mean monthly temperatures, seasonal precipitation and site-specific climate uncertainties have been estimated. Pollen-climate relationships were studied using numerical analyses, and inference models were derived by partial least squares (PLS) and weighted-averaging PLS (WA-PLS) regressions for January and July temperatures (T ), and for winter, spring and summer precipitation (P). In order to assess whether these variables are also of ecological importance for vegetation in the subregions, we split the data set into an Alpine and a Mediterranean subset. Results: Low bootstrap cross-validated root mean square errors of prediction (RMSEP) for January T (1.7 °C), July T (2.1 °C) and summer P (38 mm), as well as low RMSEPs expressed as a percentage of the gradient length (8 -9%), indicate a good inference power. Models revealed excellent to good performance statistics for January T, July T and summer P (r2 = 0.8), and for winter and spring P (r 2 = c. 0.5). We show that the variables with the highest explanatory power differ between the two subregions. These are summer T and P for the Alpine set, and January T, winter P and July T for the Mediterranean set. Main conclusions: The study reveals the influence of climatic conditions during the growing season on modern pollen assemblages and indicates the potential of pollen data for long-term climate reconstructions of parameters such as winter precipitation and temperature, which seem to be the main factors having an influence on the variability of Mediterranean climate. These models may therefore provide important information on past regional climate variability in southern Europe.
    Keywords: Abies; Acer; Achillea-type; Aconitum; Aesculus; Aethusa cynapium-type; Alchemilla-type; Allium-type; Alnus glutinosa-type; Alnus viridis; Ambrosia-type; Amorpha fruticosa; Anemone hortensis; Anemone nemorosa-type; Anthericum-type; Anthriscus sylvestris-type; Apium inundatum-type; Arbutus-type; Artemisia; Asplenium-type; Asteroideae; Athyrium filix-femina; Betula; Biviere_Cesaro; Biviere Cesaro, BC; Botrychium; Bupleurum-type; Buxus; Calluna vulgaris; Caltha-type; Campanula; Carpinus; Caryophyllaceae; Castanea sativa; Cedrus; Celtis; Centaurea nigra-type; Centaurea solstitialis; Cerastium cerastioides-type; Cerastium fontanum-type; Cerealia; Chaerophyllum hirsutum-type; Chamaerops humilis; Chenopodiaceae-type; Cichorioideae; Cirsium; Cistus crispus-type; Cistus salvifolius; Clematis group; Cornus mas; Cornus sanguinea; Corylus avellana; Crassula; Cruciferae; Cryptogramma crispa; Cryptomeria-type; Cystopteris fragilis; Date/Time of event; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Dianthus-type; Dryopteris filix-mas-type; Echium; Elaeagnus; Empetrum-type; Ephedra distachya-type; Ephedra fragilis-type; Erica arborea-type; Ericaceae; Eucalyptus; Euphorbia; Event label; Fagus; Falcaria vulgaris-type; Fallopia convolvulus-type; Filipendula; Frangula alnus; Fraxinus excelsior; Fraxinus ornus; GC; GCUWI; Gentiana pneumonanthe-type; Geranium; Gramineae; Gravity corer; Gravity corer, UWITEC; Gypsophila repens-type; Hedera-type; Helianthemum; Helleborus viridis; Heracleum-type; HONK; HON-Kajak sediment corer; Humulus lupulus; Humulus-type; Huperzia selago; Hypericum perforatum-type; Identification; Italy; Juglans; Juniperus; Lac_des_Grenouilles; Lac des Grenouilles, GREN; Lago_Albano; Lago_Buse; Lago_Calamone; Lago_Campagna; Lago_Cece; Lago_Colbricon_inferiore; Lago_Colbricon_superiore; Lago_Cornisello_inferiore; Lago_Cornisello_superiore; Lago_del_Segrino; Lago_dellAccesa; Lago_dellAquilente; Lago_delle_Lame; Lago_delle_Trote; Lago_dellOrgials; Lago_di_Alice_Superiore; Lago_di_Alserio; Lago_di_Ballone; Lago_di_Bertignano; Lago_di_Bolsena; Lago_di_Bracciano; Lago_di_Burano; Lago_di_Caldaro; Lago_di_Caldonazzo; Lago_di_Campo; Lago_di_Candia; Lago_di_Caselette; Lago_di_Cei; Lago_di_Comabbio; Lago_di_Fibreno; Lago_di_Fraturno; Lago_di_Ganna; Lago_di_Lases; Lago_di_Lavarone; Lago_di_Martignano; Lago_di_Meugliano; Lago_di_Mezzano; Lago_di_Monate; Lago_di_Moncrivello; Lago_di_Montorfano; Lago_di_Nemi; Lago_di_Paterno; Lago_di_Pojala; Lago_di_Rascino; Lago_di_San_Floriano; Lago_di_San_Michele; Lago_di_Santa_Colomba; Lago_di_SantAnna_di_Vinadio; Lago_di_Sibolla; Lago_di_Tovel; Lago_di_Valdurna; Lago_di_Varese; Lago_di_Varsi; Lago_di_Ventina; Lago_di_Vico; Lago_Fedaia; Lago_Gemini_inferiore; Lago_Ghirla; Lago_Giulianello; Lago_Grande_di_Avigliana; Lago_Grande_di_Monticchio-2; Lago_Idro; Lago_Lamar; Lago_Laudemio; Lago_Lauson; Lago_Lungo; Lago_Lusia_III; Lago_Madrano; Lago_Monticolo_Grande; Lago_Moregna; Lago_Padule_Cerretano; Lago_Pusiano; Lago_San_Puoto; Lago_Santo; Lago_Santo_Parmense; Lago_Santo_Terlago; Lago_Scuro_Cerretano; Lago_Scuro_delle_Agoraie; Lago_Scuro-Polverosa; Lago_Sfondato; Lago_Sirino; Lago_Sirio; Lago_Stellune; Lago_Trearie; Lago_Verde_del_Passo_del_Brattello; Lago_Viverone; Lago Albano, ALB; Lago Buse, BUSE; Lago Calamone, CAL; Lago Campagna, CAM; Lago Cece, CECE; Lago Colbricon inferiore, COLI; Lago Colbricon superiore, COLS; Lago Cornisello inferiore, COIN; Lago Cornisello superiore, COSU; Lago dellAccesa, ACC; Lago dellAquilente, AQUI; Lago delle Lame, LAM; Lago delle Trote, TROT; Lago dellOrgials, ORG; Lago del Segrino, SEG; Lago di Alice Superiore, ALI; Lago di Alserio, ALS; Lago di Ballone, BAL; Lago di Bertignano, BERT; Lago di Bolsena, BOLS; Lago di Bracciano, BRA; Lago di Burano, BUR; Lago di Caldaro, CALD; Lago di Caldonazzo, CA; Lago di Campo, PT 16; Lago di Candia, CAN; Lago di Caselette, CAS; Lago di Cei, CEI; Lago di Comabbio, COM; Lago di Fibreno, FIBR; Lago di Fraturno, FRAT; Lago di Ganna, GAN; Lago di Lases, LAS; Lago di Lavarone, LA; Lago di Martignano, MART; Lago di Meugliano, MEU; Lago di Mezzano, MEZ; Lago di Monate, MONA; Lago di Moncrivello, MOC; Lago di Montorfano, MON; Lago di Nemi, NEMI; Lago di Paterno, PAT; Lago di Pojala, PT 41; Lago di Rascino, RASC; Lago di San Floriano, SFLO; Lago di San Michele, LSM; Lago di Santa Colomba, SCOL; Lago di SantAnna di Vinadio, SANN; Lago di Sibolla, SIB; Lago di Tovel, TOV; Lago di Valdurna, VALD; Lago di Varese, VARE; Lago di Varsi, VAR; Lago di Ventina, VEN; Lago di Vico, VICO; Lago Fedaia, FED; Lago Gemini inferiore, GEMNF; Lago Ghirla, GHI; Lago Giulianello, GIUL; Lago Grande di Avigliana, AVG; Lago Grande di Monticchio, MONT 05/3A; Lago Idro, IDR; Lago Lamar, LAMR; Lago Laudemio, LL; Lago Lauson, LAUZ; Lagolo; Lagolo, LAG; Lago Lungo, LU; Lago Lusia III, LUSI; Lago Madrano, MADR; Lago Monticolo Grande, MGRA; Lago Moregna, MOR; Lago Padule Cerretano, PADC; Lago Pusiano, PUS; Lago San Puoto, SAP; Lago Santo (Cembra), SCEM; Lago Santo Parmense, SPA; Lago Santo Terlago, TERL; Lago Scuro Cerretano, SCER; Lago Scuro delle Agoraie (Lame), SCLA; Lago Scuro - Polverosa, SCPO; Lago Sfondato, PT 09; Lago Sirino, SI; Lago Sirio, SIR; Lago Stellune, STE; Lago Trearie, LT; Lago Verde del Passo del Brattello, VERD; Lago Viverone, VIV; Laguna_del_Faro; Laguna del Faro (Pantano Grande), LF; Lake; Larix; Latitude of event; Ligustrum; Lilium martagon-type; Linum austriacum-type; Liquidambar; Loiseleuria procumbens-type; Longitude of event; Lotus-type; Lythrum salicaria; Melampyrum; Mentha-type; Mercurialis; Minuartia rubra-type; Morus alba; Odontites-type; Olea; Onobrychis-type; Ononis-type; Ostrya-type; Pantano_Cuba; Pantano Cuba, PC; Papilionaceae; Peucedanum-type; Phillyrea; Picea; Pimpinella major-type; Pinus cembra; Pinus sylvestris-type; Pistacia; Plantago alpina-type; Plantago coronopus; Plantago lanceolata-type; Plantago major/media; Platanus; Polygonum amphibium; Polygonum aviculare; Polygonum viviparum; Polypodium vulgare-type; Populus; Potentilla-type; Primula farinosa; Primula veris-type; Prunus-type; Pteridium aquilinum; Pulsatilla; Quercus cerris-type; Quercus ilex-type; Quercus robur/Q. pubescens-type; Ranunculus acris-type; Rhamnus-type; Rhinanthus-type; Rhus group; Robinia pseudoacacia; Rosaceae; Rubiaceae; Rumex acetosa-type; Rumex acetosella-type; Sagina; Salix; Sambucus; Sample code/label; Sanguisorba minor ssp. minor; Sanguisorba officinalis; Saxifraga oppositifolia-type; Scrofulariaceae; Secale; Sedum; Senecio-type; Silene dioica-type; Silene vulgaris-type; Site; Solanum dulcamara; Solanum nigrum-type; Soldanella; Sorbus-type; Spores, monolete; Symphytum; Tamarix; Taxus; Thalictrum; Thesium humifusum; Tilia; Trifolium pratense-type; Trifolium repens-type; Trinia glauca-type; Trollius; Tsuga; Ulex-type; Ulmus; Umbelliferae; Urio_Quattrocchi; Urio Quattrocchi, UQ; Urtica pilulifera; Urtica urens; Vaccinium-type; Valeriana dioica-type; Valeriana officinalis-type; Veronica-type; Viburnum lantana; Vicia-type; Viscum; Vitis; Zea mays
    Type: Dataset
    Format: text/tab-separated-values, 17388 data points
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  • 7
    facet.materialart.
    Unknown
    PANGAEA
    In:  European Pollen Database (EPD)
    Publication Date: 2024-02-16
    Keywords: Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; EPD; KULC; KULLENBERG corer; Lithology/composition/facies; SO8923; Soppensee
    Type: Dataset
    Format: text/tab-separated-values, 3 data points
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  • 8
    facet.materialart.
    Unknown
    PANGAEA
    In:  European Pollen Database (EPD)
    Publication Date: 2024-02-16
    Keywords: Age, 14C calibrated; Calendar age; DEPTH, sediment/rock; EPD; KULC; KULLENBERG corer; SO8923; Soppensee
    Type: Dataset
    Format: text/tab-separated-values, 2 data points
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  • 9
    facet.materialart.
    Unknown
    PANGAEA
    In:  European Pollen Database (EPD)
    Publication Date: 2024-02-16
    Keywords: Abies; Acer; AGE; Alnus; Apiaceae; Artemisia; Asteraceae; Betula; Botrychium; Brassicaceae; Buxus; Campanulaceae; Cannabaceae; Carpinus betulus; Caryophyllaceae; Castanea; Centaurea cyanus; Centaurea nigra-type; Ceratophyllum: spines; Cerealia; Chenopodiaceae; Cichorioideae; Corylus; Counting, palynology; Cyperaceae; DEPTH, sediment/rock; Dryopteris filix-mas; EPD; Ephedra distachya; Ephedra fragilis; Equisetum; Ericaceae; Fabaceae; Fagus; Filipendula; Frangula alnus; Fraxinus excelsior; Gramineae; Hedera helix; Helianthemum; Hippophae rhamnoides; Ilex aquifolium; Indeterminable: unknown; Juglans; Juniperus; KULC; KULLENBERG corer; Lamiaceae; Liliaceae; Lotus; Lycopodium (added); Lycopodium (counted); Lycopodium tablets; Lygeum spartum; Lythrum; Malvaceae; Mentha-type; Monolete spore(s); Myriophyllum; Nuphar; Nymphaea; Pediastrum; Picea; Pinus; Pinus, stomata; Plantago; Plantago alpina-type; Plantago lanceolata; Plantago major/media; Polygonum amphibium; Polypodium; Populus; Potamogeton; Quercus; Ranunculaceae; Ranunculus subgen. Batrachium; Rosaceae; Rosaceae trees; Rubiaceae; Rumex; Salix; Sample volume; Sanguisorba minor; Sanguisorba officinalis; Saxifraga oppositifolia-type; Secale; Selaginella selaginoides; SO8923; Soppensee; Sparganium; Sphagnum; Spores, trilete; Taxus; Thalictrum; Thelypteris palustris; Tilia; Trapa; Trifolium; Typha; Ulmus; Urtica; Varia; Viburnum; Viscum; Zea mays
    Type: Dataset
    Format: text/tab-separated-values, 23571 data points
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