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JJL received funding from the Research Foundation Flanders (grant nr. 12P1819N). The project received funding from the Research Foundation Flanders (grants nrs, G018919N, W001919N). JVDH and TWC received funding from DOB Ecology. JA received funding from the University of Helsinki, Faculty of Science (MICROCLIM, grant nr. 7510145) and Academy of Finland Flagship (grant no. 337552). PDF, CM and PV received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Starting Grant FORMICA 757833). JK received funding from the Arctic Interactions at the University of Oulu and Academy of Finland (318930, Profi 4), Maaja vesitekniikan tuki ry., Tiina and Antti Herlin Foundation, Nordenskiold Samfundet and Societas pro Fauna et Flora Fennica. MK received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). TWC received funding from National Geographic Society grant no. 9480-14 and WW-240R-17. MA received funding from CISSC (program ICRP (grant nr:2397) and INSF (grant nr: 96005914). The Royal Botanic Garden Edinburgh is supported by the Scottish Government's Rural and Environment Science and Analytical Services Division. JMA received funding from the Funding Org. Qatar Petroleum (grant nr. QUEX-CAS-QP-RD-18/19). JMA received funding from the European Union's Horizon 2020 research and innovation program (grant no. 678841) and from the Swiss National Science Foundation (grant no. 31003A_176044). JA was supported by research grants LTAUSA19137 (program INTER-EXCELLENCE, subprogram INTER-ACTION) provided by Czech Ministry of Education, Youth and Sports and 20-05840Y of the Czech Science Foundation. AA was supported by the Ministry of Science and Higher Education of the Russian Federation (grant FSRZ-2020-0014). SN, UAT, JJA, and JvO received funding from the Independent Research Fund Denmark (7027-00133B). LvdB, KT, MYB and RC acknowledge funding from the German Research Foundation within the Priority Program SPP-1803 'EarthShape: Earth Surface Shaping by Biota' (grant TI 338/14-1&2 and BA 3843/6-1). PB was supported by grant project VEGA of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences No. 2/0132/18. Forest Research received funding from the Forestry Commission (climate change research programme). JCB acknowledges the support of Universidad Javeriana. JLBA received funding from the Direccion General de Cambio Climatico del Gobierno de Aragon; JLBA acknowledges fieldwork assistance by Ana Acin, the Ordesa y Monte Perdido National Park, and the Servicio de Medio Ambiente de Soria de la Junta de Castilla y Leon. RGB and MPB received funding from BECC - Biodiversity and Ecosystem services in a Changing Climate. MPB received funding from The European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement No. 657627 and The Swedish Research Council FORMAS - future research leaders No. 2016-01187. JB received funding from the Czech Academy of Sciences (grant nr. RVO 67985939). NB received funding from the SNF (grant numbers 40FA40_154245, 20FI21_148992, 20FI20_173691, 407340_172433) and from the EU (contract no. 774124). ICOS EU research infrastructure. EU FP7 NitroEurope. EU FP7 ECLAIRE. The authors from Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazonia, Brazil were supported by the MCTI/CNPq/FNDCT - AcAo Transversal no68/2013 - Programa de Grande Escala da Biosfera-Atmosfera na Amazonia - LBA; Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal'. This is the study 829 of the BDFFP Technical Series. to The EUCFLUX Cooperative Research Program and Forest Science and Research Institute-IPEF. NC acknowledges funding by Stelvio National Park. JC was funded by the Spanish government grant CGL2016-78093-R. ANID-FONDECYT 1181745 AND INSTITUTO ANTARTICO CHILENO (INACH FR-0418). SC received funding from the German Research Foundation (grant no. DFG- FZT 118, 202548816). The National Science Foundation, Poland (grant no. UMO-2017/27/B/ST10/02228), within the framework of the 'Carbon dioxide uptake potential of sphagnum peatlands in the context of atmospheric optical parameters and climate changes' (KUSCO2) project. SLC received funding from the South African National Research Foundation and the Australian Research Council. FM, M, KU and MU received funding from Slovak Research and Development Agency (no. APVV-19-0319). Instituto Antartico Chileno (INACH_RT-48_16), Iniciativa Cientifica Milenio Nucleo Milenio de Salmonidos Invasores INVASAL, Institute of Ecology and Biodiversity (IEB), CONICYT PIA APOYO CCTE AFB170008. PC is supported by NERC core funding to the BAS 'Biodiversity, Evolution and Adaptation Team. EJC received funding from the Norwegian Research Council (grant number 230970). GND was supported by NERC E3 doctoral training partnership grant (NE/L002558/1) at the University of Edinburgh and the Carnegie Trust for the Universities of Scotland. Monitoring stations on Livingston Island, Antarctica, were funded by different research projects of the Gobern of Spain (PERMAPLANET CTM2009-10165-E; ANTARPERMA CTM2011-15565-E; PERMASNOW CTM2014-52021-R), and the PERMATHERMAL arrangement between the University of Alcala and the Spanish Polar Committee. GN received funding from the Autonomous Province of Bolzano (ITA). The infrastructure, part of the UK Environmental Change Network, was funded historically in part by ScotNature and NERC National Capability LTS-S: UK-SCAPE; NE/R016429/1). JD was supported by the Czech Science Foundation (GA17-19376S) and MSMT (LTAUSA18007). ED received funding from the Kempe Foundation (JCK-1112 and JCK-1822). The infrastructure was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415 and by the project for national infrastructure support CzeCOS/ICOS Reg. No. LM2015061. NE received funding from the German Research Foundation (DFG- FZT 118, 202548816). BE received funding from the GLORIA-EU project no EVK2-CT2000-00056, the Autonomous Province of Bolzano (ITA), from the Tiroler Wissenschaftsfonds and from the University of Innsbruck. RME was supported by funding to the SAFE Project from the Sime Darby Foundation. OF received funding from the German Research Foundation (DFG- FZT 118, 202548816). EFP was supported by the Jardin Botanico Atlantico (SV-20-GIJON-JBA). MF was funded by the German Federal Ministry of Education and Research (BMBF) in the context of The Future Okavango (Grant No. 01LL0912) and SASSCAL (01LG1201M; 01LG1201N) projects. EFL received funding from ANID PIA / BASAL FB210006. RAG received funding from Fondecyt 11170516, CONICYT PIA AFB170008 and ANID PIA / BASAL FB210006. MBG received funding from National Parks (DYNBIO, #1656/2015) and The Spanish Research Agency (VULBIMON, #CGL2017-90040-R). MG received funding from the Swiss National Science Foundation (ICOS-CH Phase 2 20FI20_173691). FG received funding from the German Research Foundation (DFG- FZT 118, 202548816). KG and TS received funding from the UK Biotechnology and Biological Research Council (grant = 206/D16053). SG was supported by the Research Foundation Flanders (FWO) (project G0H1517N). KJ and PH received funding from the EU Horizon2020 INFRAIA project eLTER-PLUS (871128), the project LTER-CWN (FFG, F&E Infrastrukturforderung, project number 858024) and the Austrian Climate Research Program (ACRP7 - CentForCSink - KR14AC7K11960). SH and ARB received funding through iDiv funded by the German Research Foundation (DFG- FZT 118, 202548816). LH received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). MH received funding from the Baden-Wurttemberg Ministry of Science, Research and Arts via the project DRIeR (Drought impacts, processes and resilience: making the in-visible visible). LH received funding from International Polar Year, Weston Foundation, and ArcticNet. DH received funding from Natural Sciences and Engineering Council (Canada) (RGPIN-06691). TTH received funding from Independent Research Fund Denmark (grant no. 8021-00423B) and Villum Foundation (grant no. 17523). Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015078, VAN2020/01 and CZ.02.1.01/0.0/0.0/16_013/0001708). KH, CG and CJD received funding from Bolin Centre for Climate Research, Stockholm University and from the Swedish research council Formas [grant n:o 2014-00530 to KH]. JJ received funding from the Funding Org. Swedish Forest Society Foundation (grant nr. 2018-485-Steg 2 2017) and Swedish Research Council FORMAS (grant nr. 2018-00792). AJ received funding from the German Federal Ministry of Education and Research BMBF (Grant Nr. FKZ 031B0516C SUSALPS) and the Oberfrankenstiftung (Grant Nr. OFS FP00237). ISJ received funding from the Energy Research Fund (NYR-11 - 2019, NYR-18 - 2020). TJ was supported by a UK NERC Independent Research Fellowship (grant number: NE/S01537X/1). RJ received funding from National Science Centre of Poland (grant number: 2016/21/B/ST10/02271) and Polish National Centre for Research and Development (grant number: Pol-Nor/203258/31/2013). VK received funding from the Czech Academy of Sciences (grant nr. RVO 67985939). AAK received funding from MoEFCC, Govt of India (AICOPTAX project F. No. 22018/12/2015/RE/Tax). NK received funding from FORMAS (grants nr. 2018-01781, 2018-02700, 2019-00836), VR, support from the research infrastructure ICOS-SE. BK received funding from the National Research, Development and Innovation Fund of Hungary (grant nr. K128441). Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015078 and CZ.02.1.01/0.0/0.0/16_013/0001708). Project B1-RNM-163-UGR-18-Programa Operativo FEDER 2018, partially funded data collection. Norwegian Research Council (NORKLIMA grants #184912 and #244525) awarded to Vigdis Vandvik. MM received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). Project CONICYT-PAI 79170119 and ANID-MPG 190029 awarded to Roy Mackenzie. This work was partly funded by project MIUR PON Cluster OT4CLIMA. RM received funding from the SNF project number 407340_172433. FM received funding from the Stelvio National Park. PM received funding from AIAS-COFUND fellowship programme supported by the Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration (grant agreement no 609033) and the Aarhus University Research Foundation, Denmark. RM received funding from the Ministry of Education, Youth and Sports of the Czech Republic (project LTT17033). SM and VM received funding from EU FP6 NitroEurope (grant nr. 17841), EU FP7 ECLAIRE (grant nr. 282910), the Ministry of Education and Science of Ukraine (projects nr. 505, 550, 574, 602), GEF-UNEP funded "Toward INMS" project (grant nr. NEC05348) and ENI CBC BSB PONTOS (grant nr. BSB 889). The authors from Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazonia, Brazil were supported by the MCTI/CNPq/FNDCT - AcAo Transversal no68/2013 - Programa de Grande Escala da Biosfera-Atmosfera na Amazonia - LBA; Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal'. FJRM was financially supported by the Netherlands Organization for Scientific Research (VICI grant 016.VICI.170.072) and Research Foundation Flanders (FWO-SBO grant S000619N). STM received funding from New Frontiers in Research Fund-Exploration (grant nr. NFRF-2018-02043) and NSERC Discovery. MMR received funding from the Australian Research Council Discovery Early Career Research Award (grant nr. DE180100570). JAM received funding from the National Science Foundation (DEB 1557094), International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, ForestGEO, and Tyson Research Center. IM-S was funded by the UK Natural Environment Research Council through the ShrubTundra Project (NE/M016323/1). MBN received funding from FORMAS, VR, Kempe Foundations support from the research infrastructures ICOS and SITES. MDN received funding from CONICET (grant nr. PIP 112-201501-00609). Spanish Ministry of Science grant PID2019-110521GB-I00 and Catalan government grant 2017-1005. French National Research Agency (ANR) in the frame of the Cluster of Excellence COTE (project HydroBeech, ANR-10-LABX-45). VLIR-OUS, under the Institutional University Coorperation programme (IUC) with Mountains of the Moon University. Project LAS III 77/2017/B entitled: \"Estimation of net carbon dioxide fluxes exchanged between the forest ecosystem on post-agricultural land and between the tornado-damaged forest area and the atmosphere using spectroscopic and numerical methods\", source of funding: General Directorate of State Forests, Warsaw, Poland. Max Planck Society (Germany), RFBR, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 20-45-242908. Estonian Research Council (PRG609), and the European Regional Development Fund (Centre of Excellence EcolChange). Canada-Denmark Arctic Research Station Early Career Scientist Exchange Program, from Polar knowledge Canada (POLAR) and the Danish Agency for Science and Higher Education. AP received funding from Fondecyt 1180205, CONICYT PIA AFB170008 and ANID PIA / BASAL FB210006. MP received funding from the Funding Org. Knut and Alice Wallenberg Foundation (grant nr. 2015.0047), and acknowledges funding from the Swedish Research Council (VR) with contributing research institutes to both the SITES and ICOS Sweden infrastructures. JP and RO were funded by the Spanish Ministry of Science grant PID2019-110521GB-I00, the fundacion Ramon Areces grant ELEMENTAL-CLIMATE, and the Catalan government grant 2017-1005. MPB received funding from the Svalbard Environmental Protection Fund (grant project number 15/128) and the Research Council of Norway (Arctic Field Grant, project number 269957). RP received funding from the Ministry of Education, Youth and Sports of the Czech Republic (grant INTER-TRANSFER nr. LTT20017). LTSER Zone Atelier Alpes; Federation FREE-Alpes. RP received funding from a Humboldt Fellowship for Experienced Researchers. Prokushkin AS and Zyryanov VI contribution has been supported by the RFBR grant #18-05-60203-Arktika. RPu received founding from the Polish National Science Centre (grant project number 2017/27/B/NZ8/00316). ODYSSEE project (ANR-13-ISV7-0004, PN-II-ID-JRP-RO-FR-2012). KR was supported through an Australian Government Research Training Program Scholarship. Fieldwork was supported by the Global Challenges program at the University of Wollongong, the ARC the Australian Antarctic Division and INACH. DR was funded by the project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor), Zone Atelier CNRS Antarctique et Terres Australes, SAD Region Bretagne (Project INFLICT), BiodivERsa 2019-2020 BioDivClim call 'ASICS' (ANR-20-EBI5-0004). SAR received funding from the Australian Research Council. NSF grant #1556772 to the University of Notre Dame. Pavia University (Italy). OR received funding from EU-LEAP-Agri (RAMSES II), EU-DESIRA (CASSECS), EU-H2020 (SustainSahel), AGROPOLIS and TOTAL Foundations (DSCATT), CGIAR (GLDC). AR was supported by the Russian Science Foundation (Grant 18-74-10048). Parc national des Ecrins. JS received funding from Vetenskapsradet grant nr (No: 2014-04270), ALTER-net multi-site grant, River LIFE project (LIFE08 NAT/S/000266), Flexpeil. Helmholtz Association long-term research program TERENO (Terrestrial Environmental Observatories). PS received funding from the Polish Ministry of Science and Higher Education (grant nr. N N305 304840). AS acknowledges funding by ETH Zurich project FEVER ETH-27 19-1. LSC received funding from NSERC Canada Graduate Scholarship (Doctoral) Program; LSC was also supported by ArcticNet-NCE (insert grant #). Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (141513/2017-9); FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (E26/200.84/2019). ZS received funding from the SRDA (grants nos. APVV-16-0325 and APVV-20-0365) and from the ERDF (grant no. ITMS 313011S735, CE LignoSilva). JS, MB and CA received funding from core budget of ETH Zurich. State excellence Program M-V \"WETSCAPES\". AfricanBioServices project funded by the EU Horizon 2020 grant number 641918. The authors from KIT/IMK-IFU acknowledge the funding received within the German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association and from the Bavarian Ministry of the Environment and Public Health (UGV06080204000). Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 192626868, in the framework of the collaborative German-Indonesian research project CRC 990 (SFB): 'EFForTS, Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (Sumatra, Indonesia)'. MS received funding from the Ministry of Education, Youth and Sports of the Czech Republic (grant nr. INTER-TRANSFER LTT19018). TT received funding from the Swedish National Space Board (SNSB Dnr 95/16) and the CASSECS project supported by the European Union. HJDT received funding from the UK Natural Environment Research Council (NERC doctoral training partnership grant NE/L002558/1). German Science Foundation (DFG) GraKo 2010 \"Response\". PDT received funding from the MEMOIRE project (PN-III-P1-1.1-PD2016-0925). Arctic Challenge for Sustainability II (ArCS II; JPMXD1420318865). JU received funding from Czech Science Foundation (grant nr. 21-11487S). TU received funding from the Romanian Ministry of Education and Research (CCCDI - UEFISCDI -project PN-III-P2-2.1-PED-2019-4924 and PN2019-2022/19270201-Ctr. 25N BIODIVERS 3-BIOSERV). AV acknowledge funding from RSF, project 21-14-00209. GFV received funding from the Dutch Research Council NWO (Veni grant, no. 863.14.013). Australian Research Council Discovery Early Career Research Award DE140101611. FGAV received funding from the Portuguese Science Foundation (FCT) under CEECIND/02509/2018, CESAM (UIDP/50017/2020+UIDB/50017/2020), FCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. Ordesa y Monte Perdido National Park. MVI received funding from the Spanish Ministry of Science and Innovation through a doctoral grant (FPU17/05869). JW received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). CR and SW received funding from the Swiss Federal Office for the Environment (FOEN) and the de Giacomi foundation. YY received funding from the National Natural Science Foundation of China (Grant no. 41861134039 and 41941015). ZY received funding from the National Natural Science Foundation of China (grant nr. 41877458). FZ received funding from the Swiss National Science Foundation (grant nr. 172198 and 193645). PZ received funding from the Funding Org. Knut and Alice Wallenberg Foundation (grant no. 2015.0047). JL received funding from (i) the Agence Nationale de la Recherche (ANR), under the framework of the young investigators (JCJC) funding instrument (ANR JCJC Grant project NoANR-19-CE32-0005-01: IMPRINT) (ii) the Centre National de la Recherche Scientifique (CNRS) (Defi INFINITI 2018: MORFO); and the Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE). Fieldwork in the Arctic got facilitated by funding from the EU INTERACT program. SN, UAT, JJA and JvO would like to thank the field team of the Vegetation Dynamics group for their efforts and hard work. We acknowledge Dominique Tristan for letting access to the field. For the logistic support the crew of INACH and Gabriel de Castilla Station team on Deception Island. We thank the Inuvialuit and Kluane First Nations for the opportunity to work on their land. MAdP acknowledges fieldwork assistance and logistics support to Unidad de Tecnologia Marina CSIC, and the crew of Juan Carlos I and Gabriel de Castilla Spanish Antarctic Stations, as well as to the different colleagues from UAH that helped on the instrument maintenance. ERF acknowledges fieldwork assistance by Martin Heggli. MBG acknowledges fieldwork and technical assistance by P Abadia, C Benede, P Bravo, J Gomez, M Grasa, R Jimenez, H Miranda, B Ponz, J Revilla and P Tejero and the Ordesa and Monte Perdido National Park staff. LH acknowledges field assistance by John Jacobs, Andrew Trant, Robert Way, Darroch Whitaker; we acknowledge the Inuit of Nunatsiavut, and the Co-management Board of Torngat Mountains National Park for their support of this project and acknowledge that the field research was conducted on their traditional lands. We thank our many bear guides, especially Boonie, Eli, Herman, John and Maria Merkuratsuk. AAK acknowledges field support of Akhtar Malik, Rameez Ahmad. Part of microclimatic records from Saxony was funded by the Saxon Switzerland National Park Administration. Tyson Research Center. JP acknowledges field support of Emmanuel Malet (Edytem) and Rangers of Reserves Naturelles de Haute-Savoie (ASTERS). Practical help: Roel H. Janssen, N. Huig, E. Bakker, Schools in the tepaseforsoket, Forskar fredag, Erik Herberg. The support by the Bavarian Forest National Park administration is highly appreciated. LvdB acknowledges CONAF and onsite support from the park rangers from PN Pan de Azucar, PN La Campana, PN Nahuelbuta and from communidad agricola Quebrada de Talca. JL and FS acknowledge Manuel Nicolas and all forest officers from the Office National des Forets (ONF) who are in charge of the RENECOFOR network and who provided help and local support for the installation and maintenance of temperature loggers in the field., Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 p ixels ( summarized f rom 8 519 u nique t emperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications., FWO G018919N W001919N 12P1819N, DOB Ecology, University of Helsinki, Faculty of Science (MICROCLIM) 7510145, European Research Council (ERC) FORMICA 757833, Arctic Interactions at the University of Oulu, Academy of Finland 318930 337552, Maaja vesitekniikan tuki ry., Tiina and Antti Herlin Foundation, Nordenskiold Samfundet, Societas pro Fauna et Flora Fennica, Grant Agency of the Czech Republic 20-28119S 20-05840Y GA17-19376S 21-11487S, Czech Academy of Sciences RVO 67985939, National Geographic Society 9480-14 WW-240R-17, CISSC (program ICRP) 2397, Iran National Science Foundation (INSF) 96005914, Scottish Government's Rural and Environment Science and Analytical Services Division, Qatar Petroleum QUEX-CAS-QP-RD-18/19, European Union's Horizon 2020 research and innovation program 678841, Swiss National Science Foundation (SNSF), European Commission 172198 193645 31003A_176044, Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137, Ministry of Science and Higher Education of the Russian Federation FSRZ-2020-0014, Independent Research Fund Denmark 8021-00423B 7027-00133B, German Research Foundation (DFG) DFG- FZT 118 202548816 TI 338/14-1 TI 338/14-2 BA 3843/6-1, grant project VEGA of the Ministry of Education of the Slovak Republic Slovak Academy of Sciences 2/0132/18, Forestry Commission, Universidad Javeriana, Direccion General de Cambio Climatico del Gobierno de Aragon, European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant 657627 SNF 407340_172433 40FA40_154245 20FI21_148992 20FI20_173691, European Commission 17841 774124, MCTI/CNPq/FNDCT 68/2013, Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal', Spanish Government, European Commission CGL2016-78093-R, ANID-FONDECYT 1181745, National Science Foundation, Poland UMO-2017/27/B/ST10/02228, National Research Foundation - South Africa, Australian Research Council, Slovak Research and Development Agency APVV-19-0319, Instituto Antartico Chileno INACH_RT-48_16 INACH FR-0418, Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PIA APOYO CCTE AFB170008 PIA AFB170008, UK Research & Innovation (UKRI), Natural Environment Research Council (NERC), Research Council of Norway, European Commission 230970, NERC E3 doctoral training partnership grant at the University of Edinburgh NE/L002558/1, Carnegie Trust for the Universities of Scotland, Gobern of Spain PERMAPLANET CTM2009-10165-E ANTARPERMA CTM2011-15565-E PERMASNOW CTM2014-52021-R, University of Alcala, Spanish Polar Committee, Autonomous Province of Bolzano (ITA), ScotNature, NERC National Capability LTS-S: UK-SCAPE NE/R016429/1, Ministry of Education, Youth & Sports - Czech Republic LTAUSA18007, Kempe Foundation JCK-1112 JCK-1822, Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I) LO1415, project for national infrastructure support CzeCOS/ICOS LM2015061 GLORIA-EU EVK2-CT2000-00056, Tiroler Wissenschaftsfonds, University of Innsbruck, Sime Darby Foundation, Jardin Botanico Atlantico SV-20-GIJON-JBA, Federal Ministry of Education & Research (BMBF) 01LL0912 01LG1201M 01LG1201N, Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11170516 1180205, ANID PIA / BASAL FB210006, National Parks (DYNBIO) 1656/2015, Spanish Research Agency (VULBIMON) CGL2017-90040-R, Swiss National Science Foundation (SNSF) 20FI20_173691, Biotechnology and Biological Sciences Research Council (BBSRC) 206/D16053 FWO G0H1517N, EU Horizon2020 INFRAIA project eLTER-PLUS 871128, project LTER-CWN (FFG, F&E Infrastrukturforderung) 858024, Austrian Climate Research Program ACRP7 - CentForCSink - KR14AC7K11960, iDiv by the German Research Foundation DFG- FZT 118 202548816, Baden-Wurttemberg Ministry of Science, Research and Arts, Weston Foundation, ArcticNet, Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-06691, Villum Foundation 17523, Ministry of Education, Youth & Sports - Czech Republic LM2015078 VAN2020/01 CZ.02.1.01/0.0/0.0/16_013/0001708 LTT17033 LTT20017 INTER-TRANSFER LTT19018, Bolin Centre for Climate Research, Stockholm University, Swedish Research Council Swedish Research Council Formas 2014-00530 2018-00792 2016-01187, Swedish Forest Society Foundation 2018-485-Steg 2 2017, Federal Ministry of Education & Research (BMBF) FKZ 031B0516C SUSALPS, Oberfrankenstiftung OFS FP00237, Energy Research Fund NYR-11 - 2019 NYR-18 - 2020, UK NERC Independent Research Fellowship NE/S01537X/1, National Science Centre, Poland 2016/21/B/ST10/02271, Polish National Centre for Research and Development Pol-Nor/203258/31/2013, MoEFCC, Govt of India (AICOPTAX project) 22018/12/2015/RE/Tax, Swedish Research Council Formas 2018-01781 2018-02700 2019-00836, research infrastructure ICOS-SE, National Research, Development and Innovation Fund of Hungary K128441, Programa Operativo FEDER 2018 B1-RNM-163-UGR-18, Norwegian Research Council (NORKLIMA grants) 184912 244525, CONICYT-PAI 79170119, ANID-MPG 190029, project MIUR PON Cluster OT4CLIMA, Stelvio National Park, AIAS-COFUND fellowship programme - Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration 609033, Aarhus University Research Foundation, Denmark, EU FP6 NitroEurope 17841, EU FP7 ECLAIRE 282910, Ministry of Education and Science of Ukraine 505 550 574 602, GEF-UNEP NEC05348, ENI CBC BSB PONTOS BSB 889, Netherlands Organization for Scientific Research (NWO) 016.VICI.170.072, New Frontiers in Research Fund-Exploration NFRF-2018-02043, Natural Sciences and Engineering Research Council of Canada (NSERC), Australian Research Council DE180100570, National Science Foundation (NSF) DEB 1557094, International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, Smithsonian Institution Smithsonian Tropical Research Institute, Tyson Research Center, UK Natural Environment Research Council through the ShrubTundra Project NE/M016323/1, Swedish Research Council Formas Swedish Research Council, Kempe Foundations - research infrastructure ICOS Kempe Foundations - research infrastructure SITES, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 112-201501-00609, Spanish Government PID2019-110521GB-I00, Catalan government 2017-1005, French National Research Agency (ANR) ANR-10-LABX-45, General Directorate of State Forests, Warsaw, Poland, Max Planck Society, Russian Foundation for Basic Research (RFBR), Krasnoyarsk Territory Krasnoyarsk Regional Fund of Science 20-45-242908, Estonian Research Council PRG609, Knut & Alice Wallenberg Foundation 2015.0047, Swedish Research Council, fundacion Ramon Areces grant ELEMENTAL-CLIMATE, Svalbard Environmental Protection Fund 15/128, Research Council of Norway 269957, Humboldt Fellowship for Experienced Researchers, Russian Foundation for Basic Research (RFBR) 18-05-60203-Arktika, Polish National Science Centre 2017/27/B/NZ8/00316, ODYSSEE project (PN-II-ID-JRP-RO-FR-2012) ANR-13-ISV7-0004, Australian Government, Department of Industry, Innovation and Science, Global Challenges program at the University of Wollongong, ARC the Australian Antarctic Division, INACH, project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor), Zone Atelier CNRS Antarctique et Terres Australes, SAD Region Bretagne (Project INFLICT), BiodivERsa 2019-2020 BioDivClim call 'ASICS' ANR-20-EBI5-0004, National Science Foundation (NSF) 1556772, EU-LEAP-Agri (RAMSES II) EU-DESIRA (CASSECS) EU-H2020 (SustainSahel), AGROPOLIS, Total SA, CGIAR, Russian Science Foundation (RSF) 18-74-10048, Swedish Research Council 2014-04270, ALTER-net multi-site grant, River LIFE project LIFE08 NAT/S/000266, Flexpeil, Ministry of Science and Higher Education, Poland N N305 304840, ETH Zurich FEVER ETH-27 19-1, NSERC Canada Graduate Scholarship (Doctoral) Program, ArcticNet-NCE, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 141513/2017-9, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) E26/200.84/2019, SRDA APVV-16-0325 APVV-20-0365, ERDF (CE LignoSilva) ITMS 313011S735, ETH Zurich, EU Horizon 2020 641918, German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association, Bavarian Ministry of the Environment and Public Health UGV06080204000 German Research Foundation (DFG) 192626868, Swedish National Space Board (SNSB) 95/16, CASSECS project by the European Union, Natural Environment Research Council (NERC) NE/L002558/1, MEMOIRE project PN-III-P1-1.1-PD2016-0925, Arctic Challenge for Sustainability II (ArCS II) JPMXD1420318865, Consiliul National al Cercetarii Stiintifice (CNCS), Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) PN-III-P2-2.1-PED-2019-4924 PN2019-2022/19270201, 25N BIODIVERS 3-BIOSERV, Russian Science Foundation (RSF) 21-14-00209., Netherlands Organization for Scientific Research (NWO) 863.14.013, Australian Research Council DE140101611, Portuguese Foundation for Science and Technology CEECIND/02509/2018 CESAM UIDP/50017/2020+UIDB/50017/2020, Portuguese Foundation for Science and Technology European Commission, FEDER, within the PT2020 Partnership Agreement, Compete 2020, Spanish Government FPU17/05869, Swiss Federal Office for the Environment (FOEN), Giacomi foundation, National Natural Science Foundation of China (NSFC) 41861134039 41941015 41877458, French National Research Agency (ANR) ANR-19-CE32-0005-01 Centre National de la Recherche Scientifique (CNRS), Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE), EU INTERACT program, Inuit of Nunatsiavut, Co-management Board of Torngat Mountains National Park, Saxon Switzerland National Park Administration, Bavarian Forest National Park administration, BECC - Biodiversity and Ecosystem services in a Changing Climate, Research Foundation Flanders (FWO-SBO) S000619N

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1 degrees C (standard deviation +/- 1.6 degrees C) lower in summer and 2.0 degrees C higher (+/- 0.7 degrees C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately., Global Change Biology, 27 (23), ISSN:1354-1013, ISSN:1365-2486

Livestock farmers rely on a high and stable grassland productivity for fodder production to sustain their livelihoods. Future drought events related to climate change, however, threaten grassland functionality in many regions across the globe. The introduction of sustainable grassland management could buffer these negative effects. According to the biodiversity–productivity hypothesis, productivity positively associates with local biodiversity. The biodiversity–insurance hypothesis states that higher biodiversity enhances the temporal stability of productivity. To date, these hypotheses have mostly been tested through experimental studies under restricted environmental conditions, hereby neglecting climatic variations at a landscape-scale. Here, we provide a landscape-scale assessment of the contribution of species richness, functional composition, temperature, and precipitation on grassland productivity. We found that the variation in grassland productivity during the growing season was best explained by functional trait composition. The community mean of plant preference for nutrients explained 24.8% of the variation in productivity and the community mean of specific leaf area explained 18.6%, while species richness explained only 2.4%. Temperature and precipitation explained an additional 22.1% of the variation in productivity. Our results indicate that functional trait composition is an important predictor of landscape-scale grassland productivity.

Current 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., Global Change Biology, ISSN:1354-1013, ISSN:1365-2486

International audience; Aim: The importance of quantifying the contribution of historical processes in shaping current biodiversity patterns is now recognized, but quantitative approaches that explicitly link speciation, extinction and dispersal processes to palaeo-environmental changes are currently lacking. Here, we propose a spatial diversification model of lineages through time (SPLIT) based on the reconstruction of palaeo-environments. We illustrate our approach using mangroves as a case study and evaluate whether habitat changes caused by plate tectonics explain the current biodiversity patterns of this group. Innovations: The SPLIT model allows one to simulate the evolutionary dynamics of species ranges by spatially linking speciation, extinction and dispersal processes to habitat changes over geological time periods. The SPLIT model provides a mechanistic expectation of speciation and extinction assuming that species are ecologically identical and not interacting. The likelihood of speciation and extinction is equivalent across species and depends on two dispersal parameters interacting with habitat dynamics (d a maximum dispersal distance and ds a distance threshold beyond which gene flow is absent). Beyond classical correlative approaches, this model tracks biodiversity dynamics under palaeo-environmental changes and provides multiple expectations (i.e., alpha-, beta-diversity, phylogenies) that can be compared to empirical patterns. Main conclusions: The SPLIT model allows a better understanding of the origin of biodiversity by explicitly accounting for habitat changes over geological times. The simulations applied to the mangrove case study reproduced the observed longitudinal gradient in species richness, the empirical pattern of beta-diversity and also provided inference on diversification rates. Future developments may include niche evolution and species interactions to evaluate the importance of non-neutral mechanisms. The method is fully implemented in the InsideDNA platform for bioinformatics analyses, and all modelling results can be accessed via interactive web links.

Species currently track suitable abiotic and biotic conditions under ongoing climate change. Adjustments of trophic interactions may provide a mechanism for population persistence, an option that is rarely included in model projections. Here, we model the future distribution, of butterflies in the western Alps of Switzerland under climate change, simulating potential diet expansion resulting from adaptive behavior or new host opportunities. We projected the distribution of 60 butterfly and 298 plant species with species distribution models (SDMs) under three climate change scenarios. From known host plants, we allowed a potential diet expansion based on phylogenetic constraints. We assessed whether diet expansion could reduce the rate of expected regional species extinction under climate change. We found that the risk of species extinctions decreased with a concave upward decreasing shape when expanding the host plant range. A diet expansion to even a few phylogenetically closely related host plants would significantly decrease extinction rates. Yet, even when considering expansion toward all plant species available in the study area, the overall regional extinction risk would remain high. Ecological or evolutionary shifts to new host plants may attenuate extinction risk, but the severe decline of suitable abiotic conditions is still expected to drive many species to local extinction.

High intra-specific genetic diversity is necessary for species adaptation to novel environments under climate change, but species tracking suitable conditions are losing alleles through successive founder events during range shift. Here, we investigated the relationship between range shift since the Last Glacial Maximum (LGM) and extant population genetic diversity across multiple plant species to understand variability in species responses.Location: The circumpolar Arctic and northern temperate alpine ranges.Methods: We estimated the climatic niches of 30 cold-adapted plant species using range maps coupled with species distribution models and hindcasted species suitable areas to reconstructions of the mid-Holocene and LGM climates. We computed the species-specific migration distances from the species glacial refugia to their current distribution and correlated distances to extant genetic diversity in 1295 populations. Differential responses among species were related to life-history traits.Results: We found a negative association between inferred migration distances from refugia and genetic diversities in 25 species, but only 11 had statistically significant negative slopes. The relationships between inferred distance and population genetic diversity were steeper for insect-pollinated species than wind-pollinated species, but the difference among pollination system was marginally independent from phylogenetic autocorrelation.Main conclusion: The relationships between inferred migration distances and genetic diversities in 11 species, independent from current isolation, indicate that past range shifts were associated with a genetic bottleneck effect with an average of 21% loss of genetic diversity per 1000 km−1. In contrast, the absence of relationship in many species also indicates that the response is species specific and may be modulated by plant pollination strategies or result from more complex historical contingencies than those modelled here.

The Cretaceous breakup of Gondwana strongly modified the global distribution of shallow tropical seas reshaping the geographic configuration of marine basins. However, the links between tropical reef availability, plate tectonic processes and marine biodiversity distribution patterns are still unknown. Here, we show that a spatial diversification model constrained by absolute plate motions for the past 140 million years predicts the emergence and movement of diversity hotspots on tropical reefs. The spatial dynamics of tropical reefs explains marine fauna diversification in the Tethyan Ocean during the Cretaceous and early Cenozoic, and identifies an eastward movement of ancestral marine lineages towards the Indo-Australian Archipelago in the Miocene. A mechanistic model based only on habitat-driven diversification and dispersal yields realistic predictions of current biodiversity patterns for both corals and fishes. As in terrestrial systems, we demonstrate that plate tectonics played a major role in driving tropical marine shallow reef biodiversity dynamics., Nature Communications, 7, ISSN:2041-1723

Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns., Royal Society Open Science, 5 (3), ISSN:2054-5703

Coral reefs and their associated fauna are largely impacted by ongoing climate change. Unravelling species responses to past climatic variations might provide clues on the consequence of ongoing changes. Here, we tested the relationship between changes in sea surface temperature and sea levels during the Quaternary and present-day distributions of coral reef fish species. We investigated whether species-specific responses are associated with life-history traits. We collected a database of coral reef fish distribution together with life-history traits for the Indo-Pacific Ocean. We ran species distribution models (SDMs) on 3,725 tropical reef fish species using contemporary environmental factors together with a variable describing isolation from stable coral reef areas during the Quaternary. We quantified the variance explained independently by isolation from stable areas in the SDMs and related it to a set of species traits including body size and mobility. The variance purely explained by isolation from stable coral reef areas on the distribution of extant coral reef fish species largely varied across species. We observed a triangular relationship between the contribution of isolation from stable areas in the SDMs and body size. Species, whose distribution is more associated with historical changes, occurred predominantly in the Indo-Australian archipelago, where the mean size of fish assemblages is the lowest. Our results suggest that the legacy of habitat changes of the Quaternary is still detectable in the extant distribution of many fish species, especially those with small body size and the most sedentary. Because they were the least able to colonize distant habitats in the past, fish species with smaller body size might have the most pronounced lags in tracking ongoing climate change., Ecology and Evolution, 7 (6), ISSN:2045-7758

The Cretaceous breakup of Gondwana strongly modified the global distribution of shallow tropical seas reshaping the geographic configuration of marine basins. However, the links between tropical reef availability, plate tectonic processes and marine biodiversity distribution patterns are still unknown. Here, we show that a spatial diversification model constrained by absolute plate motions for the past 140 million years predicts the emergence and movement of diversity hotspots on tropical reefs. The spatial dynamics of tropical reefs explains marine fauna diversification in the Tethyan Ocean during the Cretaceous and early Cenozoic, and identifies an eastward movement of ancestral marine lineages towards the Indo-Australian Archipelago in the Miocene. A mechanistic model based only on habitat-driven diversification and dispersal yields realistic predictions of current biodiversity patterns for both corals and fishes. As in terrestrial systems, we demonstrate that plate tectonics played a major role in driving tropical marine shallow reef biodiversity dynamics.

Above- and below-ground herbivory are key ecosystem processes that can be substantially altered by environmental changes. However, direct comparisons of the coupled variations of above- and below-ground herbivore communities along elevation gradients remain sparse. Here, we studied the variation in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes, in grasslands along six elevation gradients in the Swiss Alps. By examining variations of community properties of herbivores and their food plants along montane clines, we sought to determine whether the structure and functional properties of these taxonomic groups change with elevation. We found that orthoptera decreased in both species richness and abundance with elevation. In contrast with aboveground herbivores, the taxonomic richness and the total abundance of nematode did not covary with elevation. We further found a stronger shift in above- than below-ground functional properties along elevation, where the mandibular strength of orthoptera matched a shift in leaf toughness. Nematodes showed a weaker pattern of declined sedentary behavior and increased mobility with elevation. In contrast to the direct exposal of aboveground organisms to the surface climate, conditions may be buffered belowground, which together with the influence of edaphic factors on the biodiversity of soil biota, may explain the differences between elevational patterns of above- and below-ground communities. Our study emphasizes the necessity to consider both the above- and below-ground compartments to understand the impact of current and future climatic variation on ecosystems, from a functional perspective of species interactions., Oecologia, 194 (3), ISSN:0029-8549, ISSN:1432-1939