dc.creatorLembrechts, Jonas J.
dc.creatorAalto, Juha
dc.creatorAshcroft, Michael B.
dc.creatorDe Frenne, Pieter
dc.creatorKopecký, Martin
dc.creatorLenoir, Jonathan
dc.creatorLuoto, Miska
dc.creatorMaclean, Ilya M. D.
dc.creatorRoupsard, Olivier
dc.creatorFuentes Lillo, Eduardo
dc.creatorGarcía, Rafael A.
dc.creatorPellissier, Loïc
dc.creatorPitteloud, Camille
dc.creatorAlatalo, Juha M.
dc.creatorSmith, Stuart W.
dc.creatorBjörk, Robert G.
dc.creatorMuffler, Lena
dc.creatorCesarz, Simone
dc.creatorGottschall, Felix
dc.creatorOkello, Joseph
dc.creatorUrban, Josef
dc.creatorPlichta, Roman
dc.creatorSvátek, Martin
dc.creatorPhartyal, Shyam S.
dc.creatorWipf, Sonja
dc.creatorEisenhauer, Nico
dc.creatorPuscas, Mihai
dc.creatorTurtureanu, Pavel D.
dc.creatorVarlagin, Andrej
dc.creatorDimarco, Romina Daniela
dc.creatorBarros, Ana Agustina
dc.creatorMazzolari, Ana Clara
dc.date.accessioned2022-04-29T10:47:37Z
dc.date.accessioned2022-10-15T05:20:13Z
dc.date.available2022-04-29T10:47:37Z
dc.date.available2022-10-15T05:20:13Z
dc.date.created2022-04-29T10:47:37Z
dc.date.issued2020-04
dc.identifierLembrechts, Jonas J.; Aalto, Juha; Ashcroft, Michael B.; De Frenne, Pieter; Kopecký, Martin; et al.; SoilTemp: A global database of near‐surface temperature; Wiley Blackwell Publishing, Inc; Global Change Biology; 26; 11; 4-2020; 6616-6629
dc.identifier1354-1013
dc.identifierhttp://hdl.handle.net/11336/156085
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4349082
dc.description.abstractCurrent analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
dc.languageeng
dc.publisherWiley Blackwell Publishing, Inc
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15123
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1111/gcb.15123
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCLIMATE CHANGE
dc.subjectDATABASE
dc.subjectECOSYSTEM PROCESSES
dc.subjectMICROCLIMATE
dc.subjectSOIL CLIMATE
dc.subjectSPECIES DISTRIBUTIONS
dc.subjectTEMPERATURE
dc.subjectTOPOCLIMATE
dc.titleSoilTemp: A global database of near‐surface temperature
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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