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opencc-zeroJan 2020View details →

Effects of past and present-day landscape structure on forest soil microorganisms

Principles of landscape ecology have been built on birds and plant species distribution, but the number of clues is now growing on below-ground organisms, whose dispersal may also be affected by above-ground landscape structure. For communities of microorganisms, the question remains if and how they answer to landscape structure, with or without time lag, and if some groups of microorganisms may react more than others. Here, we investigated if fungi or bacteria diversity is driven by the amount of forest cover in the current or the past landscape. We tested the Habitat Amount Hypothesis (HAH) on ancient forests of Cevennes national park, that were particularly fragmented 150 years ago, and are today surrounded by recent forests. As ancient forests are often more diverse in plant species, we hypothesized that the higher quantity of ancient forests in the landscape, the richer fungal and bacterial communities would be locally. More precisely, we expected that ectomycorrhizal fungi, and pathotrophic fungi, often indicators of mature forests, would be also more sensitive to forest history and therefore to the quantity of ancient forests than bacteria and saprotrophic fungi. We sampled 40 soil cores per 0.5 ha, pooled in 8 composite samples per plot in 27 landscapes and sequenced ITS and 16S marker by Illumina-Mi seq. To identify functional groups of fungi, we relied on their taxonomy and the use of public databases. Our results partly follow the HAH, as fungi richness was positively related with the quantity of ancient forests in the landscape and not by the focal patch size. Ectomycorrhizal and pathotrophic fungi were positively affected by the ancient forest cover, and so were saprotrophic ones, but not bacteria. Local factors also shaped the communities such as soil composition and elevation, confirming classical patterns in soil ecology. Interestingly, past landscape structure better explained fungi communities richness than contemporary landscape, suggesting a time lag in the response of communities to landscape modification and a potential extinction debt. Our results invite to consider below-ground communities in landscape studies and historical ecology, as their structure and functions might be intimately linked with soil and landscape history.

opencc-by-4.0Jun 2017View details →

Causes and importance of new particle formation in the present-day and pre-industrial atmospheres: supporting data

Data presented in the manuscript "Causes and importance of new particle formation in the present-day and pre-industrial atmospheres" currently in review. Particle number concentrations (files with initial word "CCN" or "N3") have units of particles per cubic centimetre, calculated at ambient temperature and pressure. Files with initial word "solar" have units of percent. Ion production rates have units ion pairs per cubic centimetre per second. The simulation data presented here was generated with the GLOMAP aerosol model, https://www.see.leeds.ac.uk/research/icas/research-themes/atmospheric-chemistry-and-aerosols/groups/aerosols-and-climate/the-glomap-model/ running on a T42 grid. The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.

opencc-zeroOct 2023View details →

Vertical land motion due to present-day ice loss from Greenland's and Canada's peripheral glaciers

Greenland's bedrock responds to the ongoing loss of ice mass with an elastic vertical land motion (VLM) that is measured by Greenland's GNSS Network (GNET). The measured VLM also contains other contributions, including the long-term viscoelastic response of the Earth to previous deglaciation. Greenland's ice sheet (GrIS) is producing the most significant contribution to the total VLM. The contribution of peripheral glaciers (PGs) from both Greenland (GrPGs) and Arctic Canada (CanPGs) has not been carefully accounted for in the GNSS time series analysis. This is a significant concern, since GNET stations are often closer to PGs than to the ice sheet. We find that PGs produce significant elastic rebound, especially in North and East Greenland. Across these regions, the PGs result in up to 37% of the elastic rebound. For a few stations in the North, the VLM from PGs is larger than the GrIS one.

opencc-by-4.0Dec 2021View details →

Present-day surface deformation of Sicily: Insights from Sentinel-1 data processed by a PS-InSAR approach

The directory DATASET.zip provides PS-InSAR data used in Henriquet et al., (2022). The data set contains for each Sentinel-1 track (44, 117, 22, 124) the mean PS velocities along the LOS, before (ps_mean_v.xy.v-dos) and after (ps_mean_v-dos_adjusted2GPS.xy) their adjustment to the 3D-GNSS velocity field, as well as the disparities of the PS velocities (ps_mean_disp.xy). The data set also includes the East- and Up-component of the reconstructed mean PS velocity field (East.grd and Up.grd) used in the Figures 7 to 12 in the paper.

opencc-by-4.0Sep 2022View details →

LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling

This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476

opencc-by-4.0Oct 2022View details →

Present‑day crustal deformation across the Daliang Shan, southeastern Tibetan Plateau: constrained by a dense GPS network

1. Intensive observations    In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas. 2. Data processing We employed the GAMIT and GLOBK software (Herring et al., 2015a, 2015b) to process the raw GPS data and derived the GPS positioning time series with respect to the international terrestrial reference frame for 2014 (ITRF2014) (Altamimi et al., 2017). We utilized the GAMIT software to process the double-differenced carrier-phase observations and acquired regional daily loosely constrained solutions for the site coordinates and satellite orbits. The geophysical models used have been described by Hao et al. (2021). In addition, we employed the same strategy to process ~70 evenly distributed ITRF core GPS sites to acquire global daily loosely constrained solutions. Then, we employed the GLOBK software to combine the same regional and global daily solutions to obtain a GPS time series. Three large earthquakes occurred in the study area: the 2004 M 9.1 Sumatra earthquake, the 2008 M 8.0 Sichuan Wenchuan earthquake, and the 2013 M 7.0 Sichuan Lushan earthquake. For the GPS time series for the campaign sites, we utilized the coseismic slip model of the 2004 Sumatra earthquake (Chlieh et al., 2007). We interpolated the coseismic displacements of the 2008 Wenchuan earthquake (Shen et al., 2009) to correct the coseismic offsets. We only used the data observed before 2008 for those GPS sites contaminated by significant postseismic deformation related to the 2008 Wenchuan earthquake (Wang & Shen, 2020). For the GPS sites affected by the coseismic deformation caused by the 2013 Lushan earthquake (Jiang et al., 2014), we also used data observed before the mainshock to mitigate the coseismic and postseismic deformation. After removing the transient deformation caused by the earthquakes, we used the weighted least-squares adjustment method to estimate linear trends of the velocities. We used the linear trend, seasonal variations, coseismic offset, and color noise model for the continuous GPS sites to fit the time series. We utilized the maximum likelihood estimation (MLE) technique and the CATS software (Williams et al., 2004; Williams., 2008) to estimate the characteristics of the noise in the residuals of the GPS time series after removing the linear trend and seasonal variations (Hao et al., 2016). Then, we obtained the GPS velocities with respect to the ITRF2014 and applied Euler rotation to transfer it to the Eurasia-fixed frame (Altamimi et al., 2017). The reference frames of the GPS velocities reported in previous studies are different from ours. Therefore, to transfer the latter to our selected frame, we employed the Helmert transformation with four parameters through common sites for our velocities and the published velocities. We only chose spatially uniformly distributed common sites with post-fit residuals of less than 1.0 mm/yr in the north-ward and east-ward components. Finally, we derived the geodetically consistent GPS crustal movement in the Daliang Shan and its adjacent areas with respect to the stable Eurasian Plate. Additionally, in order to reduce the residual rigid motion caused by the far-field reference of the Eurasian Plate, we chose the stable South China block as the near-field reference frame. Subsequently, our derived GPS velocities were translated into the South China block reference frame using the published Euler rotation vectors (Hao et al., 2019).   References                                                                 Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model. Geophys. J. Int. 209:1906–1912 Chlieh, M., Avouac, J. P. , Hjorleifsdottir, V. , Song, T. , Ji, C. , Sieh, K., Sladen, A., Hebert, H., Prawirodirdjo, L., Bock, Y., Galetzka, J., 2007. Coseismic slip and afterslip of the great Mw 9.15 Sumatra-Andaman earthquake of 2004. Bulletin of the Seismological Society of America, 97(1A), 152–173. Hao, M., Freymueller, J. T., Wang, Q. L., Cui, D. X., Qin, S. L. 2016. Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data. Earth and Planetary Science Letters, 437, 1-8. http://dx.doi.org/10.1016/j.epsl.2015.12.038. Hao, M., Li, Y., Zhuang, W., 2019. Crustal movement and strain distribution in east Asia revealed by GPS observations. Scientific Reports, https://doi.org/10.1038/s41598-019-53306-y, 16797. Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021. “Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008. Herring, T.A., King, R.W., McClusky, S.C., 2015a. GAMIT reference manual, GPS analysis at MIT, Release 10.6. Massachusetts Institute of Technology, Cambridge. Herring, T.A., King, R.W., McClusky, S.C., 2015b. GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6. Massachusetts Institute of Technology, Cambridge. Jiang, Z., Wang, M., Wang, Y., Wu, Y., Che, S., Shen, Z.K., Bürgmann, R., Sun, J., Yang, Y., Liao, H., Li, Q., 2014. GPS constrained coseismic source and slip distribution of the 2013 Mw6.6 Lushan, China, earthquake and its tectonic implications. Geophysical Research Letters 41, 407–413, doi:10.1002/2013GL058812. Shen, Z.K., Sun, J., Zhang, P., Wan, Y., Wang, M., Bürgmann, R., Zeng, Y.H., Gan, W.J., Wang, Q.L., 2009. Slip maxima at fault junctions and rupturing of barriers during the 2008 Wenchuan earthquake. Nat Geosci 2:718–724. Wang, M., Shen, Z.K., 2020. Present-day crustal deformation of continental China derived from GPS and its tectonic implications. J. Geophys. Res. 125 (2) https://doi. org/10.1029/2019JB018774. Williams, S.D.P., 2008. CATS: GPS coordinate time series analysis software. GPS Solutions, 12, 147–153. <a href="http://dx.doi.org/10.1007/s10291-007-0086-4">http://dx.doi.org/10.1007/s10291-007-0086-4</a>. Williams, S.D.P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R.M., Prawirodirdjo, L., Miller, M., Johnson, D.J. 2004. Error analysis of continuous GPS position time series. J. Geophys. Res. 109 (B03412). http://dx.doi.org/10.1029/2003JB002741.

opencc-by-4.0Mar 2024View details →

Helperfiles for using hmmix for calling archaic introgression into present day humans (both hg19 and hg38)

These files are: 1) Strict callability masks from 1000 genomes project in hg19 and hg38 coordinates 2) Outgroup files: hg38_Outgroup_1000g_HGDP.txt: Frequencies of derived alleles in 490 present individuals with Sub-Saharan related ancestry: 426 from 1000genomes project and 64 from HGDP (total=490) Only first two columns are used by hmmix. The remaining columns what the reference base (hg38 refgenome), ancestral base, derived bases and the frequency of the derived bases in HGDP and 1000genomes hg19_Outgroup_1000g.txt: Frequencies of derived alleles in 292 present individuals with Sub-Saharan related ancestry from 1000genomes 3) The mutation rate files are based on the outgroup files. They report the mutation rate in 1 Mb window scaled by the genomewide mutation rate  4) The reference genome for hg19 and hg38 5) The ancestral allele calls for hg19 and hg38

opencc-by-4.0Aug 2024View details →

The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'

The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated  by four CMIP6 models'

opencc-by-4.0Mar 2023View details →

The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback

Supporting data for "The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback", by K. Furtado, Y. Tsushima and P. R. Field.

opencc-by-4.0Jul 2023View details →

Data and code for publication "The stability of present-day Antarctic grounding lines - Part B"

Data and code for the publication <a href="https://tc.copernicus.org/preprints/tc-2022-105/">"The stability of present-day Antarctic grounding lines – Part B: Onset of irreversible retreat of Amundsen Sea glaciers under current climate on centennial timescales cannot be excluded"</a> in The Cryosphere. Zip files contain data, python notebooks for analysis and PISM code. Please contact ronja.reese@northumbria.ac.uk if you have any further questions.

openother-openSep 2023View details →

TC25/Chicago_COMPASS: Present-day heat-related estimates for Chicago community areas [COMPASS-GLM]

Chicago_COMPASS Community area summaries for Chicago and related scripts. Specifics below: Data Each CSV file provides summaries for 77 community areas in Chicago from WRF simulations, satellites, and socioeconomic surveys. The spatial polygons for the community areas and the socioeconomic data were accessed through the Chicago Data Portal: <a href="https://data.cityofchicago.org/">https://data.cityofchicago.org/</a> The WRF code is open source and can be found at: <a href="https://github.com/wrf-model/WRF">https://github.com/wrf-model/WRF</a> Chicago_control, Chicago_no_urb, and Chicago_no_lake have the maximum and minimum average variables of interest for the control, no urban, and no lake simulations. These are for the BEM/BEP runs with the YSU boundary layer scheme and are used for the main results of the paper. The WRF_BEM_MYJ files are for the BEM/BEP control runs with the MYJ boundary layer scheme. The WRF_Noah files are the control runs using just the Unified Noah land surface model (no urban canopy). The WRF_nested file is for a control run using 3-way nested domains, with the inner domain over Chicago at 1.333 km using BEM/BEP and the YSU boundary layer scheme. Chicago_perc_control, Chicago_perc_no_urb, and Chicago_perc_no_lake have the 95th and 98th percentiles of hourly variables of interest for the control, no urban, and no lake simulations. Chicago_MODIStime_control has the daytime and nighttime variables of interest (corresponding to MODIS Aqua overpass) for the control simulations. en01, en02, en03, and so on represent the ensembles for each model configuration. Chicago_geo_socioeconomic includes the socioeconomic variables (median income per capita and Hardship Index), spatial metrics (area and distance from the coast), and satellite-derived estimates (daytime and nighttime land surface temperature (LST), and normalized different vegetation index (NDVI). Scripts WRF_to_tabular.R converts the WRF simulations into tabular data to be injested into Google Earth Engine. Rasterize.js converts the tabular WRF results into a raster with separate bands for each variable on Google Earth Engine. Summarize.js processeses satellite observations and summarizes the satellite and WRF outputs into regions of interest on Google Earth Engine.

publicOct 2023View details →

Vertical land motion due to present-day ice loss from Greenland’s and Canada’s peripheral glaciers

Open the record for dataset details and reuse information.

publicJan 2020View details →

Effects of past and present-day landscape structure on forest soil microorganisms

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publicDec 2025View details →

Neandertal ancestry through time: Insights from genomes of ancient and present-day humans

Open the record for dataset details and reuse information.

FIGURE 5 in The Cainozoic to present-day record of Circum-Mediterranean, NE Atlantic and North Sea Cantharidinae and Trochinae (Trochoidea, Gastropoda)-a synopsis

FIGURE 5. Cainozoic species diversity of Cantharidinae and Trochinae (red bars) compared with the stable isotope record (δ18O) of Zachos et al. (2001), which can roughly be read as climate record. Important geodynamic and climatic events are given in yellow (MMCO = Middle Miocene Climate Optimum, MSC = Messinian Salinity Crisis). No clear relation between global climate, geodynamic events and trochid evolution can be deduced form the data. Note that the endemic Sarmatian radiation is not shown in the figure.

FIGURE 3 in The Cainozoic to present-day record of Circum-Mediterranean, NE Atlantic and North Sea Cantharidinae and Trochinae (Trochoidea, Gastropoda)-a synopsis

FIGURE 3. Contribution of species per genus in stratigraphic intervals and geographic regions (based on table 2); abbreviations: Anc. = Anceps, Cal. = Callumbonella, Clanc. = Clanculus, Clel. = Clelandella, Gib. = Gibbula, Gibuli. = Gibbuliculus, Gib. s.l, = Gibbula s.l., Gib. s.l. (Sarm) = Sarmatian Gibbula s.l., Jujub. = Jujubinus, Kish. = Kishinewia, Lesp. = Lesperonia, Pago. = Pagodatrochus, Paro. = Paroxystele, Phor. = Phorcus, Phorcul. = Phorculus, Roll. = Rollandiana, Sarm. = Sarmatigibbula, Sinz. = Sinzowia, Stero. = Steromphala, Timi. = Timisia.

FIGURE 2 in The Cainozoic to present-day record of Circum-Mediterranean, NE Atlantic and North Sea Cantharidinae and Trochinae (Trochoidea, Gastropoda)-a synopsis

FIGURE 2. Stratigraphic occurrence and species numbers for Cantharidinae and Trochinae for the entire geographic regions discussed herein. Insert shows Eastern Paratethyan endemic genera.

FIGURE 1 in The Cainozoic to present-day record of Circum-Mediterranean, NE Atlantic and North Sea Cantharidinae and Trochinae (Trochoidea, Gastropoda)-a synopsis

FIGURE 1. Chronostratigraphy after Gradstein et al. (2012) with regional Paratethys stages after Popov et al. (2004)

FIGURE 4 in The Cainozoic to present-day record of Circum-Mediterranean, NE Atlantic and North Sea Cantharidinae and Trochinae (Trochoidea, Gastropoda)-a synopsis

FIGURE 4. Comparison of the molecular phylogeny of Uribe et al. (2017a) (grey bars are 95% confidence intervals) and assumed serigraphic distributions based on the fossil record (in orange).

FIGURE 7 in The Cainozoic to present-day record of Circum-Mediterranean, NE Atlantic and North Sea Cantharidinae and Trochinae (Trochoidea, Gastropoda)-a synopsis

FIGURE 7. Eocene to present-day Cantharidinae: 7A1–A2. Amonilea parnensis (Bayan, 1870), Eocene, Lutetian, FontenaySaint-Père (France), MNHN.F.J02025; 7B1–B2. Anceps anceps (Eichwald, 1850), late Miocene, Bessarabian, Kertsch (Russia), NHMW 2020/0050/0001; 7C1–C2. Anceps papilla (Eichwald, 1850), late Miocene, Bessarabian, Jenikale Kertsch (Russia), NHMW 2020/0050/0004; 7D1–D2. Callumbonella suturalis (Philippi, 1836), present-day, Alboran Sea, reproduced from Go-