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  • 1
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    Unknown
    NCL20: A global GNSS velocity field for estimating tectonic plate motion and testing GIA models (2021)
    PANGAEA
    Details
    Publication Date: 2023-03-27
    Description: We created a 3D GNSS surface velocity field to estimate tectonic plate motion and test the effect of a set of 1D and 3D Glacial Isostatic Adjustment (GIA) models on tectonic plate motion estimates. The main motivation for creating a bespoke 3D velocity field is to include a larger number of GNSS sites in the GIA-affected areas of investigation, namely North America, Europe, and Antarctica. We created the GNSS surface velocity field using the daily network solutions submitted to the International GNSS Service (IGS) “repro2” data processing campaign, and other similarly processed GNSS solutions. We combined multiple epoch solutions into unique global epoch solutions of high stability. The GNSS solutions we used were processed with the latest available methods and models at the time: all the global and regional solutions adhere to IGS repro2 standards. Every network solution gives standard deviations of site position coordinates and the correlations between the network sites. We deconstrained and combined the global networks and aligned them to the most recent ITRF2014 reference frame on a daily level. Additionally, several regional network solutions were deconstrained and aligned to the unique global solutions. The process was performed using the Tanya reference frame combination software (Davies & Blewitt, 1997; doi:10.1029/2000JB900004) which we updated to facilitate changes in network combination method and ITRF realisation. This resulted in 57% reduction of the WRMS of the alignment post-fit residuals compared to the alignment to the previous ITRF2008 reference frame for an overlapping period. We estimated linear velocities from the time series of GNSS coordinates using the MIDAS trend estimator (Blewitt et al., 2016; doi:10.1002/2015JB012552). The sites selected through multiple steps of quality control constitute a final GNSS surface velocity field which we denote NCL20. This velocity field has horizontal uncertainties mostly within 0.5 mm/yr, and vertical uncertainties mostly within 1 mm/yr, which make it suitable for testing GIA models and estimating plate motion models.
    Keywords: 1LSU_GNSS; 1NSU_GNSS; 1ULM_GNSS; AB04_GNSS; AB08_GNSS; AB12_GNSS; AC58_GNSS; ACOR_GNSS; ACP1_GNSS; ACP6_GNSS; ACSO_GNSS; ACUM_GNSS; ADE1_GNSS; ADIS_GNSS; ADRI_GNSS; AJAC_GNSS; AL30_GNSS; AL40_GNSS; AL50_GNSS; AL60_GNSS; AL70_GNSS; AL90_GNSS; ALCI_GNSS; ALES_GNSS; ALGO_GNSS; ALIC_GNSS; ALRT_GNSS; AMC2_GNSS; ANDO_GNSS; ANG1_GNSS; ANP5_GNSS; ANTO_GNSS; AOML_GNSS; AOPR_GNSS; ARBT_GNSS; ARCM_GNSS; ARFY_GNSS; ARGI_GNSS; ARHP_GNSS; ARHR_GNSS; ARJM_GNSS; ARP3_GNSS; ARPG_GNSS; ARTU_GNSS; ASC1_GNSS; ASCG_GNSS; ASHV_GNSS; ASUB_GNSS; AUCK_GNSS; AUDR_GNSS; AUS5_GNSS; AUTN_GNSS; AVCA_GNSS; AXPV_GNSS; BACA_GNSS; BACK_GNSS; BACO_GNSS; BADH_GNSS; BAHR_GNSS; BAIA_GNSS; BAIE_GNSS; BAKE_GNSS; BAN2_GNSS; BARH_GNSS; BARN_GNSS; BAUS_GNSS; BAYR_GNSS; BBYS_GNSS; BCLN_GNSS; BELE_GNSS; BELF_GNSS; BELL_GNSS; BENN_GNSS; BET1_GNSS; BIAZ_GNSS; BIL5_GNSS; BISK_GNSS; BJCO_GNSS; BJU0_GNSS; BLA1_GNSS; BNDY_GNSS; BNFY_GNSS; BOD3_GNSS; BOGI_GNSS; BOMJ_GNSS; BOR1_GNSS; BORJ_GNSS; BORK_GNSS; BORR_GNSS; BPDL_GNSS; BRAZ_GNSS; BRFT_GNSS; BRGS_GNSS; BRIP_GNSS; BRMF_GNSS; BRMU_GNSS; BRST_GNSS; BRTW_GNSS; BRU5_GNSS; BRUS_GNSS; BSCN_GNSS; BSMK_GNSS; BUDP_GNSS; BUE1_GNSS; BUMS_GNSS; BURI_GNSS; BVHS_GNSS; BYDG_GNSS; CACE_GNSS; CAEN_GNSS; CAGL_GNSS; CAGS_GNSS; CALU_GNSS; CANT_GNSS; CAPF_GNSS; CARM_GNSS; CAS1_GNSS; CASB_GNSS; CASC_GNSS; CASP_GNSS; CAYU_GNSS; CBMD_GNSS; CBSB_GNSS; CCV5_GNSS; CEBR_GNSS; CEDU_GNSS; CEFE_GNSS; CFRM_GNSS; CGGN_GNSS; CHA1_GNSS; CHAN_GNSS; CHAT_GNSS; CHB5_GNSS; CHIZ_GNSS; CHL1_GNSS; CHPI_GNSS; CHR1_GNSS; CHT1_GNSS; CHTI_GNSS; CHUR_GNSS; CJTR_GNSS; CKIS_GNSS; CLIB_GNSS; CLK5_GNSS; CLRK_GNSS; CN13_GNSS; CN14_GNSS; CN15_GNSS; CN16_GNSS; CN23_GNSS; CN24_GNSS; CN28_GNSS; CN29_GNSS; CN33_GNSS; CN34_GNSS; CN35_GNSS; CN41_GNSS; CN46_GNSS; CN53_GNSS; CNC0_GNSS; CNIV_GNSS; CNMR_GNSS; COLA_GNSS; CONO_GNSS; CORB_GNSS; CORC_GNSS; COTE_GNSS; COVG_GNSS; COVX_GNSS; CPAR_GNSS; CRAK_GNSS; CRAO_GNSS; CRDI_GNSS; CRST_GNSS; CTAB_GNSS; CTBR_GNSS; CTGU_GNSS; CTPU_GNSS; CTWN_GNSS; CUIB_GNSS; CUSV_GNSS; CVMS_GNSS; DAKR_GNSS; DANE_GNSS; DARE_GNSS; DAVM_GNSS; DEAR_GNSS; DEFI_GNSS; DEGE_GNSS; DELM_GNSS; DENE_GNSS; DENT_GNSS; DEVI_GNSS; DGLS_GNSS; DNRC_GNSS; DOBS_GNSS; DOMS_GNSS; DOUR_GNSS; DREM_GNSS; DRV5_GNSS; DSL1_GNSS; DUBO_GNSS; DUM1_GNSS; DUPT_GNSS; EBRE_GNSS; ECSD_GNSS; EDOC_GNSS; EGLT_GNSS; EIJS_GNSS; ELEN_GNSS; ENG1_GNSS; ENIS_GNSS; ENTZ_GNSS; EPRT_GNSS; ESCO1_GNSS; ESCU_GNSS; EUR2_GNSS; EUSK_GNSS; EVPA_GNSS; EXU0_GNSS; FALL_GNSS; FFMJ_GNSS; FIE0_GNSS; FLIN_GNSS; FLIU_GNSS; FLM5_GNSS; FLRS_GNSS; FONP_GNSS; FOYL_GNSS; FREE_GNSS; FREI_GNSS; FRKN_GNSS; FTP4_GNSS; FUNC_GNSS; GAAT_GNSS; GABR_GNSS; GACC_GNSS; GACL_GNSS; GACR_GNSS; GAIA_GNSS; GAIT_GNSS; GAL1_GNSS; GANP_GNSS; GARF_GNSS; GAST_GNSS; GCEA_GNSS; GDMA_GNSS; Glacial Isostatic Adjustment (GIA) model; GLPM_GNSS; GLPS_GNSS; GLSV_GNSS; GMSD_GNSS; GNSS; GNSS Receiver; GNVL_GNSS; GODE_GNSS; GOGA_GNSS; GOPM_GNSS; GOUG_GNSS; GRAS_GNSS; GRE0_GNSS; GRIS_GNSS; GRN0_GNSS; GRTN_GNSS; GTK0_GNSS; GUAM_GNSS; GUAX_GNSS; GUIP_GNSS; GUUG_GNSS; GWWL_GNSS; HAAG_GNSS; HAC6_GNSS; HAG6_GNSS; HALY_GNSS; HAMM_GNSS; HAMP_GNSS; HARK_GNSS; HASM_GNSS; HBCH_GNSS; HBRK_GNSS; HCES_GNSS; HDIL_GNSS; HELG_GNSS; HERS_GNSS; HILB_GNSS; HILO_GNSS; HIPT_GNSS; HJOR_GNSS; HKLO_GNSS; HLFX_GNSS; HNLC_GNSS; HNPT_GNSS; HNUS_GNSS; HOB2_GNSS; HOBU_GNSS; HOE2_GNSS; HOLM_GNSS; HONS_GNSS; horizontal GIA; HOS0_GNSS; HOUM_GNSS; HOUS_GNSS; HOWE_GNSS; HOWN_GNSS; HRMM_GNSS; HRST_GNSS; HUGO_GNSS; HYDE_GNSS; IBIZ_GNSS; ICT1_GNSS; IGEO_GNSS; IGGY_GNSS; IISC_GNSS; ILDX_GNSS; ILHA_GNSS; ILSA_GNSS; ILUC_GNSS; IMBT_GNSS; IMPZ_GNSS; INAB_GNSS; INES1_GNSS; INGG_GNSS; INVM_GNSS; INWN_GNSS; IQAL_GNSS; IQUI_GNSS; IRBE_GNSS; IRKM_GNSS; ISCO_GNSS; ISPA_GNSS; IZAN_GNSS; JAB2_GNSS; JCT1_GNSS; JFNG_GNSS; JFWS_GNSS; JOEN_GNSS; JONM_GNSS; JOZE_GNSS; JXVL_GNSS; KAR0_GNSS; KARL_GNSS; KARR_GNSS; KAT1_GNSS; KAUS_GNSS; KELY_GNSS; KERM_GNSS; KEVO_GNSS; KEW5_GNSS; KHAJ_GNSS; KHAR_GNSS; KIRI_GNSS; KIRM_GNSS; KIRU_GNSS; KIVE_GNSS; KJUN_GNSS; KLOP_GNSS; KMOR_GNSS; KNGS_GNSS; KNS5_GNSS; KNTN_GNSS; KOK1_GNSS; KOKM_GNSS; KOSG_GNSS; KOUC_GNSS; KOUG_GNSS; KOUR_GNSS; KRA0_GNSS; KRSS_GNSS; KRTV_GNSS; KST5_GNSS; KSTU_GNSS; KSU1_GNSS; KULU_GNSS; KUN0_GNSS; KUNZ_GNSS; KURE_GNSS; KUUJ_GNSS; KUUS_GNSS; KUWT_GNSS; KVTX_GNSS; KWJ1_GNSS; KWST_GNSS; KYBO_GNSS; KYMH_GNSS; KYTB_GNSS; KYTC_GNSS; KYTD_GNSS; KYTE_GNSS; KYTG_GNSS; KYTH_GNSS; KYTK_GNSS; KYTL_GNSS; KYW1_GNSS; KZN2_GNSS; LAMA_GNSS; LAMT_GNSS; LANS_GNSS; LATITUDE; LCDT_GNSS; LCHS_GNSS; LCKM_GNSS; LCSB_GNSS; LEBA_GNSS; LEES_GNSS; LEIJ_GNSS; LEK0_GNSS; LEON_GNSS; LESV_GNSS; LHCL_GNSS; LHUE_GNSS; LIL2_GNSS; LKHU_GNSS; LLIV_GNSS; LMNO_GNSS; LODZ_GNSS; LOFS_GNSS; LONGITUDE; LOVM_GNSS; LPAL_GNSS; LPGS_GNSS; LPIL_GNSS; LPLY_GNSS; LROC_GNSS; LSBN_GNSS; LSUA_GNSS; LWN0_GNSS; LWX1_GNSS; LYCO_GNSS; LYNS_GNSS; LYRS_GNSS; MACC_GNSS; MADM_GNSS; MADO_GNSS; MAG0_GNSS; MAIR_GNSS; MAJU_GNSS; MALD_GNSS; MALL_GNSS; MAN2_GNSS; MAPA_GNSS; MAR6_GNSS; MARJ_GNSS; MARN_GNSS; MARS_GNSS; MAS1_GNSS; MAUI_GNSS; MAW1_GNSS; MAYZ_GNSS; MCAR_GNSS; MCD5_GNSS; MCIL_GNSS; MCM4_GNSS; MCN1_GNSS; MCNE_GNSS; MCTY_GNSS; MDOR_GNSS; MDR6_GNSS; MDVJ_GNSS; MET6_GNSS; MET7_GNSS; METG_GNSS; MFLD_GNSS; MIAR_GNSS; MICW_GNSS; MIDS_GNSS; MIGD_GNSS; MIHO_GNSS; MIHT_GNSS; MIIR_GNSS; MIKL_GNSS; MIL1_GNSS; MIMN_GNSS; MIMQ_GNSS; MIN0_GNSS; MINI_GNSS; MIPR_GNSS; MIST_GNSS; MKEA_GNSS; MLF1_GNSS; MLVL_GNSS; MNBD_GNSS; MNBE_GNSS; MNCA_GNSS; MNDN_GNSS; MNGR_GNSS; MNJC_GNSS; MNP1_GNSS; MNPL_GNSS; MNRM_GNSS; MNRT_GNSS; MNRV_GNSS; MNSC_GNSS; MNTF_GNSS; MNVI_GNSS; MOAL_GNSS; MOB1_GNSS; MOBS_GNSS; MOED_GNSS; MOEL_GNSS; MOGF_GNSS; MOPN_GNSS; MORP_GNSS; MOVB_GNSS; MPLA_GNSS; MPLE_GNSS; MRO1_GNSS; MRRN_GNSS; MSB5_GNSS; MSHT_GNSS; MSKU_GNSS; MSNA_GNSS; MSPK_GNSS; MSSC_GNSS; MSYZ_GNSS; MTMS_GNSS; MTNT_GNSS; MTY2_GNSS; NAIN_GNSS; NAMA_GNSS; NAPL_GNSS; NAS0_GNSS; NAUR_GNSS; NAUS_GNSS; NBR6_GNSS; NCDU_GNSS; NCGO_GNSS; NCJA_GNSS; NCPO_GNSS; NCSW_GNSS; NCWH_GNSS; NCWI_GNSS; NDMB_GNSS; NEDR_GNSS; NEGI_GNSS; NEIA_GNSS; NESC_GNSS; NEWL_GNSS; NHUN_GNSS; NIST_GNSS; NIUM_GNSS; NJCM_GNSS; NJHC_GNSS; NJI2_GNSS; NJOC_GNSS; NJTW_GNSS; NKLG_GNSS; NLIB_GNSS; NMKM_GNSS; NNOR_GNSS; NOR0_GNSS; NOR1_GNSS; NOR3_GNSS; NOUM_GNSS; NPLD_GNSS; NPRI_GNSS; NRCM_GNSS; NRIL_GNSS; NRL1_GNSS; NRMD_GNSS; NTUS_GNSS; NYBH_GNSS; NYBT_GNSS; NYCL_GNSS; NYCP_GNSS; NYDV_GNSS; NYFD_GNSS; NYFS_GNSS; NYFV_GNSS; NYHC_GNSS; NYHM_GNSS; NYHS_GNSS; NYIR_GNSS; NYLV_GNSS; NYMD_GNSS; NYML_GNSS; NYNS_GNSS; NYON_GNSS; NYPD_GNSS; NYPF_GNSS; NYRB_GNSS; NYST_GNSS; NYWL_GNSS; NYWT_GNSS; OAKH_GNSS; ODS5_GNSS; OHAS_GNSS; OHFA_GNSS; OHHU_GNSS; OHLI_GNSS; OHMO_GNSS; OHMR_GNSS; OHPR_GNSS; OKAN_GNSS; OKAR_GNSS; OKBF_GNSS; OKCB_GNSS; OKCL_GNSS; OKDT_GNSS; OKGM_GNSS; OKHV_GNSS; OKMA_GNSS; OKOM_GNSS; OLKI_GNSS; OMH5_GNSS; ONSM_GNSS; OPMT_GNSS; ORMD_GNSS; OSKM_GNSS; OSLS_GNSS; OSPA_GNSS; OST0_GNSS; OUAG_GNSS; OULU_GNSS; OVE0_GNSS; P032_GNSS; P033_GNSS; P037_GNSS; P038_GNSS; P039_GNSS; P040_GNSS; P042_GNSS; P043_GNSS; P044_GNSS; P049_GNSS; P050_GNSS; P051_GNSS; P052_GNSS; P053_GNSS; P054_GNSS; P055_GNSS; P070_GNSS; P728_GNSS; P775_GNSS; P776_GNSS; P777_GNSS; P778_GNSS; P779_GNSS; P780_GNSS; P802_GNSS; P803_GNSS; P807_GNSS; P817_GNSS; PAAP_GNSS; PAFU_GNSS; PALK_GNSS; PAMS_GNSS; PAPC_GNSS; PARK_GNSS; PARY_GNSS; PASA_GNSS; PASS_GNSS; PATN_GNSS; PATT_GNSS; PBCH_GNSS; PBRM_GNSS; PECE_GNSS; PICL_GNSS; PIGT_GNSS; PIRT_GNSS; PKTN_GNSS; plate motion model; PLTC_GNSS; PNBM_GNSS; PNGM_GNSS; PNR6_GNSS; POAL_GNSS; POHN_GNSS; POLV_GNSS; POR2_GNSS; POTS_GNSS; POUS_GNSS; POVE_GNSS; PRCO_GNSS; PRDS_GNSS; PREI_GNSS; PREM_GNSS; PRPT_GNSS; PSU1_GNSS; PTBB_GNSS; PTGV_GNSS; PTIR_GNSS; PUB5_GNSS; PUIN_GNSS; PULK_GNSS; PUO1_GNSS; PUYV_GNSS; PWEL_GNSS; QAQ1_GNSS; QIKI_GNSS; RAMG_GNSS; RAMO_GNSS; RANT_GNSS; RAT0_GNSS; RBAY_GNSS; RCMV_GNSS; RECF_GNSS; REDU_GNSS; REDZ_GNSS; Reference frame; RESO_GNSS; REUN_GNSS; RG13_GNSS; RG15_GNSS; RG16_GNSS; RG17_GNSS; RG18_GNSS; RG19_GNSS; RG23_GNSS; RG24_GNSS; RIC1_GNSS; RIGA_GNSS; RIO1_GNSS; RIOJ_GNSS; RIS5_GNSS; RLAP_GNSS; RMBO_GNSS; ROB4_GNSS; ROBN_GNSS; ROMU_GNSS; ROSS_GNSS; ROTH_GNSS; RWSN_GNSS;
    Type: Dataset
    Format: text/tab-separated-values, 6755 data points
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  • 2
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    Unknown
    Altimetry, gravimetry, GPS and viscoelastic modelling data for the joint inversion for glacial isostatic adjustment in Antarctica (ESA STSE Project REGINA), links to data files (2017)
    PANGAEA
    In:  Supplement to: Sasgen, Ingo; Martín-Español, Alba; Horvath, Alexander; Klemann, Volker; Petrie, Elizabeth J; Wouters, Bert; Horwath, Martin; Pail, Roland; Bamber, Jonathan L; Clarke, Peter J; Konrad, Hannes; Wilson, Terry; Drinkwater, Mark R (2017): Altimetry, gravimetry, GPS and viscoelastic modelling data for the joint inversion for glacial isostatic adjustment in Antarctica (ESA STSE Project REGINA). Earth System Science Data Discussions, 72 pp, https://doi.org/10.5194/essd-2017-46
    Details
    Publication Date: 2023-11-24
    Description: A major uncertainty in determining the mass balance of the Antarctic ice sheet from measurements of satellite gravimetry, and to a lesser extent satellite altimetry, is the poorly known correction for the ongoing deformation of the solid Earth caused by glacial isostatic adjustment (GIA). In the past decade, much progress has been made in consistently modelling the ice sheet and solid Earth interactions; however, forward-modelling solutions of GIA in Antarctica remain uncertain due to the sparsity of constraints on the ice sheet evolution, as well as the Earth's rheological properties. An alternative approach towards estimating GIA is the joint inversion of multiple satellite data - namely, satellite gravimetry, satellite altimetry and GPS, which reflect, with different sensitivities, trends of recent glacial changes and GIA. Crucial to the success of this approach is the accuracy of the space-geodetic data sets. Here, we present reprocessed rates of surface-ice elevation change (Envisat/ICESat; 2003-2009), gravity field change (GRACE; 2003-2009) and bedrock uplift (GPS; 1995-2013). The data analysis is complemented by the forward-modelling of viscoelastic response functions to disc load forcing, allowing us to relate GIA-induced surface displacements with gravity changes for different rheological parameters of the solid Earth. The data and modelling results presented here form the basis for the joint inversion estimate of present-day ice-mass change and GIA in Antarctica. This paper presents the first of two contributions summarizing the work carried out within a European Space Agency funded study, REGINA, (http://www.regina-science.eu).
    Keywords: File content; File name; File size; pan-Antarctica; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 16 data points
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  • 3
    Electronic Resource
    Electronic Resource
    Crystal and molecular structures of trans-tetrakis(pyridine)dichloroiron(II), -nickel(II), and -cobalt(II) and trans-tetrakis(pyridine)dichloroiron(II) monohydrate (1978)
    s.l. : American Chemical Society
    Inorganic chemistry 17 (1978), S. 1394-1401 
    Details
    ISSN: 1520-510X
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/ic50184a002
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  • 4
    Electronic Resource
    Electronic Resource
    Business Troubles in the Republic of Ireland (1992)
    Oxford, UK : Blackwell Publishing Ltd
    Business ethics 1 (1992), S. 0 
    Details
    ISSN: 1467-8608
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Philosophy , Economics
    Notes: Perspectives on recent business scandals and the current debate.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8608.1992.tb00195.x
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  • 5
    Electronic Resource
    Electronic Resource
    Emergence and survival of herbaceous seedlings in temperate grassy woodlands: Recruitment limitations and regeneration niche (2004)
    Oxford, UK : Blackwell Science Pty
    Austral ecology 29 (2004), S. 0 
    Details
    ISSN: 1442-9993
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract  Field experiments examined herbaceous seedling emergence and survival in temperate grassy woodlands on the New England Tablelands of New South Wales. Effects of intensity of previous grazing, removal of ground cover by fire or clearing, burial of seeds, grazing and seed theft by ants on seedling emergence and survival were studied in two field experiments. Thirteen species with a range of traits were used in the experiments and their cumulative emergence was compared with laboratory germination studies. Field emergence correlated to laboratory germination but all species had lower emergence in the field. Little natural emergence of native species was observed in the field in unsown treatments. Short-lived forbs had the highest emergence, followed by perennial grasses; rhizomatous graminoids and perennial forbs had the lowest emergence. Soil surface and cover treatments did not markedly enhance emergence suggesting that intertussock spaces were not prerequisites for forb emergence. No consistent pattern of enhanced emergence was found for any treatment combination across all species. Seedling survival varied among species, with perennial grasses and short-lived forbs having the highest seedling mortality. Low mortality rates in the graminoids and rhizomatous forbs appeared partially to compensate for lower seedling emergence. All perennial grasses and some short-lived forbs showed increased risk of mortality with grazing. Differences in emergence and survival of species were related to ground cover heterogeneity, soil surfaces and, to some extent, herbivory. The complexity of these patterns when superimposed on temporal variability suggests that no generalizations can be made about the regeneration niche of herbaceous species groups. Strong recruitment limitation and partitioning of resources in the regeneration niche may reduce competition among native species and explain the high species richness of the herbaceous layer in the temperate grassy communities of eastern Australia.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1442-9993.2004.01369.x
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  • 6
    Electronic Resource
    Electronic Resource
    Germinable soil seed banks in a tropical savanna: seasonal dynamics and effects of fire. (2005)
    Oxford, UK : Blackwell Science Pty
    Austral ecology 30 (2005), S. 0 
    Details
    ISSN: 1442-9993
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract  The germinable soil seed bank of a tropical eucalypt savanna of north-eastern Australia was found to be dominated by grasses and forbs, with seed bank density ranging from 58 to 792 seeds per square metre, from a total of 53 species. Late dry season fires and the fire-related cues, heat shock and smoke, broke the seed dormancy of a range of tropical savanna species. Heat shock promoted the germination of the species groups natives, exotics, subshrubs, ephemeral and twining perennial forbs, and the common species Indigofera hirsuta, Pycnospora lutescens and Triumfetta rhomboidea. Exposure to smoke at ambient temperature promoted germination from the soil seed bank of the species groups combined natives, upright perennial forbs and grasses, as well as the common grasses Digitaria breviglumis and Heteropogon triticeus. The germinable soil seed bank varied seasonally, increasing from the mid wet season (February) and early dry season (May) to a maximum in the late dry season (October). The effect of recent fire history on soil seed bank dynamics was limited to the immediate release of some seed from dormancy; a reduction in seed densities of subshrubs and monocots, other than grasses, in recently burnt savanna; and enhanced seed density of the ephemeral I. hirsuta in the year following fire. The seed banks of most savanna species were replenished in the year following burning.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1442-9993.2004.01426.x
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  • 7
    Electronic Resource
    Electronic Resource
    Effect of fire regime on plant abundance in a tropical eucalypt savanna of north-eastern Australia (2003)
    Oxford, UK : Blackwell Science Pty
    Austral ecology 28 (2003), S. 0 
    Details
    ISSN: 1442-9993
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract  Changes in plant abundance within a eucalypt savanna of north-eastern Australia were studied using a manipulative fire experiment. Three fire regimes were compared between 1997 and 2001: (i) control, savanna burnt in the mid-dry season (July) 1997 only; (ii) early burnt, savanna burnt in the mid-dry season 1997 and early dry season (May) 1999; and (iii) late burnt, savanna burnt in the mid-dry season 1997 and late dry season (October) 1999. Five annual surveys of permanent plots detected stability in the abundance of most species, irrespective of fire regime. However, a significant increase in the abundance of several subshrubs, ephemeral and twining perennial forbs, and grasses occurred in the first year after fire, particularly after late dry season fires. The abundance of these species declined toward prefire levels in the second year after fire. The dominant grass Heteropogon triticeus significantly declined in abundance with fire intervals of 4 years. The density of trees (〉2 m tall) significantly increased in the absence of fire for 4 years, because of the growth of saplings; and the basal area of the dominant tree Corymbia clarksoniana significantly increased over the 5-year study, irrespective of fire regime. Conservation management of these savannas will need to balance the role of regular fires in maintaining the diversity of herbaceous species with the requirement of fire intervals of at least 4-years for allowing the growth of saplings 〉2 m in height. Whereas late dry season fires may cause some tree mortality, the use of occasional late fires may help maintain sustainable populations of many grasses and forbs.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1442-9993.2003.01292.x
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  • 8
    Electronic Resource
    Electronic Resource
    Fire-related cues break seed dormancy of six legumes of tropical eucalypt savannas in north-eastern Australia (2003)
    Oxford, UK : Blackwell Science Pty
    Austral ecology 28 (2003), S. 0 
    Details
    ISSN: 1442-9993
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract This paper describes an assessment of the effect of exposure to fire-related cues (heat shock, smoke and nitrate) and the interactions between the cues on seed dormancy release of tropical savanna legumes in north-eastern Australia. Ten legume species were tested, comprising both native and exotic species. The ten species responded variously to the treatments. Brief exposure to temperatures between 80 and 100°C was found to break the seed dormancy of the native ephemeral herbs Chamaecrista mimosoides, Crotalaria calycina, Crotalaria montana, Indigofera hirsuta and Tephrosia juncea, as well as the exotic ephemeral herb Crotalaria lanceolata. Exposure to 80°C combined with treatment with a nitrate solution produced an additive effect on the germination of Chamaecrista mimosoides and Crotalaria lanceolata. However, the four species with the heaviest seeds, two exotic ephemeral herbs (Chamaecrista absus and Crotalaria pallida) and two native perennials (Galactia tenuiflora and Glycine tomentella) displayed no significant increase in germination with exposure to fire-related cues. Exposure to 120°C for 5 min produced seed mortality in all species tested. Two of the largest seeded species, Crotalaria pallida and Galactia tenuiflora, displayed the lowest tolerance to heat shock, with seed mortality after exposure to 100°C for 5 min. These data indicate that fire can promote the germination of some tropical savanna legumes. As a proportion of seeds of each species displayed no innate dormancy, some germination may occur in the absence of fire, especially of exotic species.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1442-9993.2003.01307.x
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  • 9
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    Experiments on tree and shrub establishment in temperate grassy woodlands: Seedling survival (2002)
    Oxford, UK : Blackwell Science Pty
    Austral ecology 27 (2002), S. 0 
    Details
    ISSN: 1442-9993
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract  Experimental studies of the emergence of shrubs and trees in grassy woodlands on the New England Tablelands, New South Wales, Australia, showed that emergence of seedlings was determined by seed supply, seed predators and seed burial. The survival of these seedlings was then observed in an experiment to test the effects of previous land use, grazing by stock and grazing by other vertebrates. The fate of four eucalypts and six shrub species was followed over 5 years. Across all species more than 50% mortality occurred in the first 6 months prior to the imposition of grazing treatments. These deaths were attributed to the combined effects of insect defoliation, cold, and low soil moisture. Average mortality over all treatments showed two distinct trends: eucalypts and one unpalatable shrub (Leptospermum) had greater than 1% survival over 5 years, whereas Acacia, Cassinia, Indigophera, Lomatia and Xanthorrhoea either had very low or no survival after 5 years. The effect of livestock grazing on seedling numbers was rarely detected because of patchy emergence and mortality due to other causes. However, proportional hazard regression models showed that there was often an increased hazard associated with grazing or grazed landscapes. Overall, those species with high hazard coefficients associated with stock are rare in the landscape, whereas those with lesser risk are more common. Recruitment is likely to be an extremely rare event because the highest proportion of germinable seed sown that survived to a juvenile stage was 0.42% and the mean across all species was 0.12%. No natural recruitment of shrub species was observed over 5 years of observation, suggesting that recruitment is episodic and disturbance driven. Enhancing natural ‘regeneration’ of woody plants under these circumstances may be more challenging than simply fencing off remnants.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1442-9993.2002.01221.x
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    Effect of season of burning and removal of herbaceous cover on seedling emergence in a eucalypt savanna of north-eastern Australia (2005)
    Melbourne, Australia : Blackwell Science Pty
    Austral ecology 30 (2005), S. 0 
    Details
    ISSN: 1442-9993
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract  Seedling emergence in a eucalypt savanna of north-eastern Australia was documented over a 12-month period, between May 1999 and May 2000. Seedling emergence for grasses, forbs and subshrubs was found to mainly occur in a brief pulse at the start of the wet season following fire or the removal of grass biomass. Only a minor number of tree and shrub seedlings were detected overall. Burning, or cutting away the grass layer in unburnt savanna, in both the early (i.e. May) and the late (i.e. October) dry seasons significantly increased seedling emergence over undisturbed savanna that had been unburnt for 3 years. Removing the grass layer in unburnt savanna, during either the early or the late dry season, triggered similar seedling densities to savanna burnt in the early dry season. Late dry season fires promoted the greatest seedling density. We attribute this to the higher intensity, late dry season fires releasing a greater proportion of seed from dormancy, coupled with the higher density of soil seed reserves present in the late dry season.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1442-9993.2005.01484.x
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