8 research outputs found
Global fine-resolution data on springtail abundance and community structure
No full textSpringtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.</p
Globally invariant metabolism but density-diversity mismatch in springtails.
No full textSoil life supports the functioning and biodiversity of terrestrial ecosystems. Springtails (Collembola) are among the most abundant soil arthropods regulating soil fertility and flow of energy through above- and belowground food webs. However, the global distribution of springtail diversity and density, and how these relate to energy fluxes remains unknown. Here, using a global dataset representing 2470 sites, we estimate the total soil springtail biomass at 27.5 megatons carbon, which is threefold higher than wild terrestrial vertebrates, and record peak densities up to 2 million individuals per square meter in the tundra. Despite a 20-fold biomass difference between the tundra and the tropics, springtail energy use (community metabolism) remains similar across the latitudinal gradient, owing to the changes in temperature with latitude. Neither springtail density nor community metabolism is predicted by local species richness, which is high in the tropics, but comparably high in some temperate forests and even tundra. Changes in springtail activity may emerge from latitudinal gradients in temperature, predation and resource limitation in soil communities. Contrasting relationships of biomass, diversity and activity of springtail communities with temperature suggest that climate warming will alter fundamental soil biodiversity metrics in different directions, potentially restructuring terrestrial food webs and affecting soil functioning
Globally invariant metabolism but density-diversity mismatch in springtails
No full textDATA AVAILABILITY : The data that support the findings of this study have been deposited in
the Figshare database64 under CC-BY 4.0 license and accession code:
https://doi.org/10.6084/m9.figshare.16850419; high-resolutionmaps85
can be accessed at https://doi.org/10.6084/m9.figshare.
16850446. Source data are provided with this paper.CODE AVAILABILITY : Programming code for the path analysis and the geospatial modelling
is available under CC-BY 4.0 from Figshare64: https://doi.org/10.6084/
m9.figshare.16850419.Soil life supports the functioning and biodiversity of terrestrial ecosystems.
Springtails (Collembola) are among the most abundant soil arthropods regulating
soil fertility and flow of energy through above- and belowground food
webs. However, the global distribution of springtail diversity and density, and
how these relate to energy fluxes remains unknown. Here, using a global
dataset representing 2470 sites, we estimate the total soil springtail biomass at
27.5 megatons carbon, which is threefold higher than wild terrestrial vertebrates,
and record peak densities up to 2 million individuals per square meter
in the tundra.Despite a 20-fold biomass difference between the tundra and the
tropics, springtail energy use (community metabolism) remains similar across
the latitudinal gradient, owing to the changes in temperature with latitude.
Neither springtail density nor community metabolism is predicted by local
species richness, which is high in the tropics, but comparably high in some
temperate forests and even tundra. Changes in springtail activity may emerge
from latitudinal gradients in temperature, predation and resource limitation in
soil communities. Contrasting relationships of biomass, diversity and activity
of springtail communities with temperature suggest that climate warming will
alter fundamental soil biodiversity metrics in different directions, potentially
restructuring terrestrial food webs and affecting soil functioning.FUNDING : Open Access funding enabled and organized by Projekt DEAL.The article is an outcome of the #GlobalCollembola community initiative that is voluntarily supported by researchers around the world. Data collection and analysis was supported by the Russian Science Foundation and by Deutsche Forschungsgemeinschaft. We acknowledge support by the Open Access Publication Funds of the Göttingen University. The following funding bodies provided support for individual contributors: ARC SRIEAS Securing Antarctica’s Environmental Future., Slovak Scientific Grant Agency, RFBR 19-516- 60002 to N.A.K., Carl Tryggers Stiftelse för Vetenskaplig Forskning and Qatar Petroleum to J.M.A., BIO 27 (2013-2014)-MAGyP and PICTO 2084 (2012)-ANPCyT to V.B., DAAD-19-10 and MSM200962001 to T.C., grant TE, PN-III-P1-1.1-TE-2019-0358 to C.F., NWO grant 821.01.015 to O.F., National Natural Sciences Foundation of China No 41471037 and 41871042 to M.G., BIO 27 (2013-2014), MAGyP; PICT 2084 (2012), FONCyT to D.F.G., NRF South African National Antarctic Programme grant 110734 to M.G., Natural Resources Canada (NRCan), EcoEnergy Innovation Initiative under the Office of Energy Research and Development, and the Natural Sciences and Engineering Research Council of Canada (NSERC) to I.T.H., L.A.V. and L.R., Independent Research Fund Denmark grant no. DFF-4002-00384 to M.H., Estonian Science Foundation G9145 to M.I., SA-France bilateral grant to C.J., SA (NRF)/Russia (RFBR) Joint Science and Technology Research Collaboration project no. 19-516-60002 (FRBR) and no. 118904 (NRF) to M.P. and C.J., European Research Council (ERC), European Union’s Horizon 2020 research and innovation programme (grant agreement no. 677232; to N.E.); iDiv, German Research Foundation (DFG–FZT 118, 202548816) to M.J. and N.E., French National Agency of Research (ANR) (JASSUR research project; ANR-12-VBDU- 0011), «Ministère de l’Agriculture et de la Pêche» and «Ministère de l’Education Nationale de la Recherche et de la Technologie» (ACTA programme), «Ministère de l’Aménagement du Territoire et de l’Environnement » (Pnetox programme), EU-funded project, ECOGEN QLK5-CT-2002-01666 (www.ecogen.dk), “Agence de l’Environnement et de la Maîtrise de l'Énergie” (BIOINDICATEUR 2, BIOTECHNOSOL), ANDRA and GISFI (www.gisfi.fr) to S.J., GRR SERBIODIV (Région Normandie, France) to MCha, ESF9258, B02 to A.K., Fundamental Research Funds for the Central Universities (grant no. 2018CDXYCH0014) to D.L., DFG 316045089 to J.L., Massey University Research Fund grant to M.A.M., DFG SCHE 376/38-2 to M.M.P., grant fromthe Austria Academy of Science: Heritage_2020-043_Modeling- Museum to P.Q., Slovak Scientific Grant Agency: VEGA Nos. 1/0441/ 03 and 1/3267/06 to N.R., Higher Education Commission of Pakistan to M.I.R., RSF 21-74-00126 to R.A.S., Austrian Federal Government and European Union (Rural Development 2014-2020) to J.S., АААА- А17-122040600025-2 to A.A.T., Brazilian Council for Scientific and Technological Development—CNPq (grant no. 152717/2016-1) to B.R.W., 309030/2018-8 to D.Z. and 305426/2018-4 to B.C.B., National Natural Science Foundation of China (31970434, 31772491) to N.N.G., Research and Innovation Support Foundation of Santa Catarina (FAPESC) (6.309/2011-6/FAPESC) and the CNPq (563251/2010-7/ CNPq) to L.C.I.O.F., O.K.-F., the Latvian Council of Science Grants no. 90.108, 93.140, 96.0110, 01.0344 to E.J., CNPq for the Research Productivity Grant (305939/2018-1) to D.B., FPI-MICINN grant in the project INTERCAPA (CGL2014-56739-R) to P.H, the Natural Sciences and Engineering Research Council of Canada (NSERC) to Z.L., Ministry of Innovation and Technology of Hungary TKP2021-NKTA-43 to D.W.https://www.nature.com/ncomms/am2024Plant Production and Soil ScienceSDG-13:Climate actionSDG-15:Life on lan
Global fine-resolution data on springtail abundance and community structure
No full textCODE AVAILABILITY : Programming R code is openly available together with the database from Figshare.SUPPLEMENTARY MATERIAL 1 : Template for data collectionSUPPLEMENTARY MATERIAL 2 : Data Descriptor WorksheetSpringtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.Open Access funding enabled and organized by Projekt DEAL.http://www.nature.com/sdatahj2024Plant Production and Soil ScienceSDG-15:Life on lan
BioTIME 2.0 : Expanding and Improving a Database of Biodiversity Time Series
No full textPeer reviewe
BioTIME 2.0 : expanding and improving a database of biodiversity time series
No full textMotivation.
Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.
Main Types of Variables Included.
The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years.
Spatial Location and Grain.
Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size.
Time Period and Grain.
The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric.
Major Taxa and Level of Measurement.
The database includes any eukaryotic taxa, with a combined total of 56,400 taxa.
Software Format.
csv and. SQL
BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series
No full textABSTRACTMotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years.Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size.Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric.Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa.Software Formatcsv and. SQL
BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series
No full textABSTRACTMotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years.Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size.Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric.Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa.Software Formatcsv and. SQL
