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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Khan, Imran; Errounda, Fatima Zahra; Yangui, Sami; Glitho, Roch; +1 Authors

    With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements. Comment: This paper has been accepted in IEEE DCOSS 2015, IoTIP-15 Workshop to be held on 12th June 2015 in Brazil

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    Conference object . 2015 . Peer-reviewed
    License: CC BY
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2017
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    http://arxiv.org/pdf/1709.0648...
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    https://doi.org/10.48550/arxiv...
    Article . 2017
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      Conference object . 2015 . Peer-reviewed
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2017
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      http://arxiv.org/pdf/1709.0648...
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      https://doi.org/10.48550/arxiv...
      Article . 2017
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    Authors: Chaves, Óscar M.; Bicca-Marques, Júlio César; Chapman, Colin A.;

    Seed dispersal is a key process driving the structure, composition, and regeneration of tropical forests. Larger frugivores play a crucial role in community structuring by dispersing large seeds not dispersed by smaller frugivores. We assessed the hypothesis that brown howler monkeys (Alouatta guariba clamitans) provide seed dispersal services for a wide assemblage of plant species in both small and large Atlantic forest fragments. Although fruit availability often decreases in small fragments compared with large ones, we predicted that brown howlers are efficient seed dispersers in quantitative and qualitative terms in both forest types given their high dietary flexibility. After a 36-month study period and 2,962 sampling hours, we found that howlers swallowed and defecated intact the vast majority of seeds (96%-100%) they handled in all study sites. Overall, they defecated ca. 315,600 seeds belonging to 98 species distributed in eight growth forms. We estimated that each individual howler dispersed an average of 143 (SD = 49) seeds >2 mm per day or 52,052 (SD = 17,782) seeds per year. They dispersed seeds of 58% to 93% of the local assemblages of fleshy-fruit trees. In most cases, the richness and abundance of seed species dispersed was similar between small and large fragments. However, groups inhabiting small fragments tended to disperse a higher diversity of seeds from rarely consumed fruits than those living in large fragments. We conclude that brown howlers are legitimate seed dispersers for most fleshy-fruit species of the angiosperm assemblages of their habitats, and that they might favor the regeneration of Atlantic forest fragments with the plentiful amount of intact seeds that they disperse each year. Dataset_seeds_dispersedHere we provided data on seed dispersal by six wild groups of brown howler monkeys (Alouatta guariba clamitans). This research was conducted during a 36-month period in three small (<10 ha: S1, S2, and S3) and three large (>90 ha: L1,L2, and L3) Atlantic forest fragments in Rio Grande do Sul State, southern Brazil.Dataset_seed_handlingHere we provided data on seed/fruit handling by six wild groups of brown howler monkeys (Alouatta guariba clamitans). This research was conducted during a 36-month period in three small (<10 ha: S1, S2, and S3) and three large (>90 ha: L1,L2, and L3) Atlantic forest fragments in Rio Grande do Sul State, southern Brazil.

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    DRYAD; ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2019
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    Authors: Bartels, Samuel F.; James, Ryan S.; Caners, Richard T.; Macdonald, S. Ellen;

    1. Site moisture is an important component of the forest landscape for maintaining biodiversity, including forest-floor bryophytes, but little is known about its role in shaping understory responses to harvesting. 2. We investigated the influence of site wetness, determined using a remotely-sensed, topographic depth-to-water (DTW) index, on responses of bryophyte cover, richness, diversity, and composition to variable retention harvesting (comparing: 2% [clear-cut], 20%, and 50% dispersed green tree retention and uncut controls [100% retention]) in three boreal forest cover-types (broadleaf, mixed, and conifer forests) in western Canada. The DTW index provides an approximation of depth to water at or below the soil surface, and was derived from wet-areas mapping based on discrete Airborne Laser Scanning data acquired over an experimentally harvested landscape located in northwestern Alberta, Canada. 3. The effectiveness of leaving retention (versus clear-cutting) for conserving bryophyte communities depended on site wetness, as indicated by DTW, with the specifics varying among forest types. In broadleaf forests, bryophyte cover and richness were generally low and not much affected by harvesting but drier sites had higher richness and a few more unique species. In mixed and conifer forests, leaving retention (versus clear-cutting) on wetter (versus drier) sites was more effective for conserving bryophyte cover, wetter sites had higher total species richness, and more species were exclusive to wetter sites. 4. Synthesis and applications. Site wetness, as indicated using the remotely-sensed topographic site wetness index "depth-to-water," mediates bryophyte responses to variable-retention harvests. Specifically, our results suggested that in conifer and mixed forests it would be more beneficial to target wetter sites for retention patches or dispersed retention whereas in broadleaf sites there might be a slight advantage to targeting drier sites. Our study demonstrates that this tool could be used to inform management decisions around leaving dispersed or patch retention.28-Jan-2019 Bryophyte species and depth-to-water index valuesBryophyte (mosses and liverworts) species cover data and estimation of depth-to-water index values for retention harvest sites sampled in northwestern Alberta, Canada.Bartels-et-al-2019-deposited data-Dryad.xlsx

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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    Authors: Winkler, Manuela; Plichta, Roman; Buysse, Pauline; Lohila, Annalea; +362 Authors

    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. AIAS-COFUND fellowship programme - Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration 609033 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 Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) PN-III-P2-2.1-PED-2019-4924 PN2019-2022/19270201 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 Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal' Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I) LO1415 International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis Bavarian Ministry of the Environment and Public Health UGV06080204000 German Research Foundation (DFG) 192626868 French National Research Agency (ANR) ANR-19-CE32-0005-01 Centre National de la Recherche Scientifique (CNRS) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PIA APOYO CCTE AFB170008 PIA AFB170008 grant project VEGA of the Ministry of Education of the Slovak Republic Slovak Academy of Sciences 2/0132/18 Portuguese Foundation for Science and Technology CEECIND/02509/2018 CESAM UIDP/50017/2020+UIDB/50017/2020 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11170516 1180205 Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) E26/200.84/2019 German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association Kempe Foundations - research infrastructure ICOS Kempe Foundations - research infrastructure SITES Gobern of Spain PERMAPLANET CTM2009-10165-E ANTARPERMA CTM2011-15565-E PERMASNOW CTM2014-52021-R German Research Foundation (DFG) DFG- FZT 118 202548816 TI 338/14-1 TI 338/14-2 BA 3843/6-1 project for national infrastructure support CzeCOS/ICOS LM2015061 GLORIA-EU EVK2-CT2000-00056 Swedish Research Council Swedish Research Council Formas 2014-00530 2018-00792 2016-01187 Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 112-201501-00609 Biotechnology and Biological Sciences Research Council (BBSRC) 206/D16053 FWO G0H1517N NERC E3 doctoral training partnership grant at the University of Edinburgh NE/L002558/1 UK Natural Environment Research Council through the ShrubTundra Project NE/M016323/1 Scottish Government's Rural and Environment Science and Analytical Services Division National Natural Science Foundation of China (NSFC) 41861134039 41941015 41877458 Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 141513/2017-9 Ministry of Science and Higher Education of the Russian Federation FSRZ-2020-0014 Grant Agency of the Czech Republic 20-28119S 20-05840Y GA17-19376S 21-11487S Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-06691 Federal Ministry of Education & Research (BMBF) 01LL0912 01LG1201M 01LG1201N Polish National Centre for Research and Development Pol-Nor/203258/31/2013 Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE) Krasnoyarsk Territory Krasnoyarsk Regional Fund of Science 20-45-242908 Netherlands Organization for Scientific Research (NWO) 016.VICI.170.072 EU-LEAP-Agri (RAMSES II) EU-DESIRA (CASSECS) EU-H2020 (SustainSahel) Austrian Climate Research Program ACRP7 - CentForCSink - KR14AC7K11960 National Research, Development and Innovation Fund of Hungary K128441 Federal Ministry of Education & Research (BMBF) FKZ 031B0516C SUSALPS project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor) Portuguese Foundation for Science and Technology European Commission European Union's Horizon 2020 research and innovation program 678841 Natural Sciences and Engineering Research Council of Canada (NSERC) Ministry of Education, Youth & Sports - Czech Republic LTAUSA18007 Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137 ALTER-net multi-site grant, River LIFE project LIFE08 NAT/S/000266 Swedish Research Council Formas 2018-01781 2018-02700 2019-00836 Netherlands Organization for Scientific Research (NWO) 863.14.013 Russian Foundation for Basic Research (RFBR) 18-05-60203-Arktika Arctic Challenge for Sustainability II (ArCS II) JPMXD1420318865 BECC - Biodiversity and Ecosystem services in a Changing Climate Smithsonian Institution Smithsonian Tropical Research Institute Ministry of Education and Science of Ukraine 505 550 574 602 Ministry of Science and Higher Education, Poland N N305 304840 iDiv by the German Research Foundation DFG- FZT 118 202548816 University of Helsinki, Faculty of Science (MICROCLIM) 7510145 BiodivERsa 2019-2020 BioDivClim call 'ASICS' ANR-20-EBI5-0004 MoEFCC, Govt of India (AICOPTAX project) 22018/12/2015/RE/Tax Direccion General de Cambio Climatico del Gobierno de Aragon National Science Foundation, Poland UMO-2017/27/B/ST10/02228 Norwegian Research Council (NORKLIMA grants) 184912 244525 Independent Research Fund Denmark 8021-00423B 7027-00133B New Frontiers in Research Fund-Exploration NFRF-2018-02043 ODYSSEE project (PN-II-ID-JRP-RO-FR-2012) ANR-13-ISV7-0004 Global Challenges program at the University of Wollongong Instituto Antartico Chileno INACH_RT-48_16 INACH FR-0418 project LTER-CWN (FFG, F&E Infrastrukturforderung) 858024 Swedish Research Council Formas Swedish Research Council Baden-Wurttemberg Ministry of Science, Research and Arts Natural Environment Research Council (NERC) NE/L002558/1 Bolin Centre for Climate Research, Stockholm University Swedish Forest Society Foundation 2018-485-Steg 2 2017 Swiss National Science Foundation (SNSF) 20FI20_173691 Co-management Board of Torngat Mountains National Park NERC National Capability LTS-S: UK-SCAPE NE/R016429/1 UK NERC Independent Research Fellowship NE/S01537X/1 General Directorate of State Forests, Warsaw, Poland French National Research Agency (ANR) ANR-10-LABX-45 National Science Centre, Poland 2016/21/B/ST10/02271 NSERC Canada Graduate Scholarship (Doctoral) Program Consiliul National al Cercetarii Stiintifice (CNCS) Slovak Research and Development Agency APVV-19-0319 Spanish Research Agency (VULBIMON) CGL2017-90040-R Polish National Science Centre 2017/27/B/NZ8/00316 Zone Atelier CNRS Antarctique et Terres Australes Energy Research Fund NYR-11 - 2019 NYR-18 - 2020 European Commission 172198 193645 31003A_176044 EU Horizon2020 INFRAIA project eLTER-PLUS 871128 Iran National Science Foundation (INSF) 96005914 Carnegie Trust for the Universities of Scotland Programa Operativo FEDER 2018 B1-RNM-163-UGR-18 National Geographic Society 9480-14 WW-240R-17 Research Foundation Flanders (FWO-SBO) S000619N Humboldt Fellowship for Experienced Researchers Swiss Federal Office for the Environment (FOEN) European Research Council (ERC) FORMICA 757833 Department of Industry, Innovation and Science fundacion Ramon Areces grant ELEMENTAL-CLIMATE FEDER, within the PT2020 Partnership Agreement Aarhus University Research Foundation, Denmark Saxon Switzerland National Park Administration National Science Foundation (NSF) DEB 1557094 Arctic Interactions at the University of Oulu Russian Science Foundation (RSF) 21-14-00209. Svalbard Environmental Protection Fund 15/128 Russian Science Foundation (RSF) 18-74-10048 Knut & Alice Wallenberg Foundation 2015.0047 Bavarian Forest National Park administration Russian Foundation for Basic Research (RFBR) National Research Foundation - South Africa Natural Environment Research Council (NERC) National Science Foundation (NSF) 1556772 MEMOIRE project PN-III-P1-1.1-PD2016-0925 Jardin Botanico Atlantico SV-20-GIJON-JBA Swedish National Space Board (SNSB) 95/16 Swiss National Science Foundation (SNSF) Spanish Government PID2019-110521GB-I00 Australian Research Council DE180100570 Australian Research Council DE140101611 Czech Academy of Sciences RVO 67985939 SAD Region Bretagne (Project INFLICT) CASSECS project by the European Union ARC the Australian Antarctic Division Autonomous Province of Bolzano (ITA) Qatar Petroleum QUEX-CAS-QP-RD-18/19 ERDF (CE LignoSilva) ITMS 313011S735 Kempe Foundation JCK-1112 JCK-1822 European Commission CGL2016-78093-R Societas pro Fauna et Flora Fennica Swedish Research Council 2014-04270 project MIUR PON Cluster OT4CLIMA Research Council of Norway 269957 Academy of Finland 318930 337552 European Commission 17841 774124 Tiina and Antti Herlin Foundation National Parks (DYNBIO) 1656/2015 Estonian Research Council PRG609 FWO G018919N W001919N 12P1819N research infrastructure ICOS-SE SRDA APVV-16-0325 APVV-20-0365 UK Research & Innovation (UKRI) Oberfrankenstiftung OFS FP00237 Spanish Government FPU17/05869 Maaja vesitekniikan tuki ry. Catalan government 2017-1005 ETH Zurich FEVER ETH-27 19-1 Australian Research Council Tiroler Wissenschaftsfonds Research Council of Norway ENI CBC BSB PONTOS BSB 889 European Commission 230970 CISSC (program ICRP) 2397 ANID PIA / BASAL FB210006 EU FP6 NitroEurope 17841 Swedish Research Council University of Innsbruck Villum Foundation 17523 MCTI/CNPq/FNDCT 68/2013 25N BIODIVERS 3-BIOSERV Spanish Polar Committee Nordenskiold Samfundet EU Horizon 2020 641918 EU FP7 ECLAIRE 282910 Tyson Research Center Stelvio National Park Universidad Javeriana Australian Government ANID-FONDECYT 1181745 Sime Darby Foundation Inuit of Nunatsiavut CONICYT-PAI 79170119 University of Alcala EU INTERACT program Forestry Commission Spanish Government Giacomi foundation Max Planck Society GEF-UNEP NEC05348 Weston Foundation ANID-MPG 190029 ArcticNet-NCE Compete 2020 DOB Ecology ScotNature ETH Zurich ArcticNet AGROPOLIS Flexpeil Total SA CGIAR INACH

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    https://doi.org/10.32942/osf.i...
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    https://doi.org/10.21256/zhaw-...
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    Authors: Hyjazie, Batoule F.; Forrest, Jessica R. K.;

    Hyjazie and Forrest, 2023 Code repository for Hyjazie and Forrest, 2023: "Supplemental nesting habitat increases bee abundance in apple orchards" This Rmarkdown file is divided into two sections: main manuscript and supporting information. The main manuscript is further divided into two sections: Year 1 and Year 2 (which includes a subsection of Year 1 + Year 2 (i.e., All Years)). Each of these sections has two sub-sections: abundance and apple quality. Supporting information includes all information relating to trap nests. Each of these subsections will report a code for some general summary statistics, models, and/or figures. All files are excel files. Missing values are depicted as N/A. Main Manuscript Variables Common in All Spreadsheets Orchard: Unique two-letter identifier for each orchard, reflecting site pairings used for treatment assignment. For example, Orchard IDs “HM” (with one treatment plot) and “IC” (with one control plot) are paired as Orchard “HC”. N = 16 levels. Orchard ID: Unique two-letter identifier for each orchard, reflecting orchard ownership. Some orchards had only a single plot (either control or treatment) and needed to be paired with a plot in another (nearby) orchard; these pairings are reflected in the “Orchard” variable. N=16 levels. Site: Numerical identifier since each orchard either had 1, 2 or 3 sites. Site ID: Numerical identifier since each orchard either had 1, 2 or 3 sites and accounts for orchards that only have plot (either control or treatment) and needed to be paired with another (n=16). Plot: Control (without trap nests) or Treatment (with trap nests). Shaded TºC: Measured temperature in the shade. Unshaded TºC: Measured temperature out of the shade. % Cloud Cover: Estimated percent of cloud cover (0% = no cloud in sight). Wind (m/s): Measured wind speed. Abundance "Osmia_Abundance_1.xlsx": Excel file with Osmia abundance data (Year 1) "Bee_Abundance_1.xlsx": Excel file with total bee abundance data (Year 1) "Total_Abundance_1.xlsx": Excel file with total pollinator abundance data (all flower visitors including non-bees) (Year 1) "Total_Abundance_Year_2.xlsx": Excel file with total abundance data (Year 2) Variables Round: Sampling period (either during apple bloom, pre-thinning (Round 2), or post-thinning (Round 3)). Transect: 3 transect walks were conducted per sampling period per orchard per site (either 1, 2, or 3). Date: Date of transect walk. Start Time: Start time of transect walk. End Time: End time of transect walk. Taxa: Taxon of floral visitor to nearest genus whenever possible. Count: Count of floral visitor observed during particular transect walk. Bee: Binary column to indicate if the taxon in question is a bee or non-bee flower visitor. Sex: Male or Female or Unknown (N/A). Apple "Apple_1.xsls": Excel file with all apple data (Year 1) "Apple_Year_2.xslx": Excel file with all apple data (Year 2) Variables Tree #: Numerical identifier (1, 2, or 3) for the tree measured in each plot (each plot had 3 trees). Age: Age of tree (either adult or dwarf) Tree Height (cm) N Bud #: Number of apple flower buds on the North-facing branch. N Pre-Thinning Fruit #: Number of apples growing on the North-facing branch pre-thinning. N Post-Thinning Fruit #: Number of apples growing on the North-facing branch post-thinning. S Bud #: Number of apple flower buds on the South-facing branch. S Pre-Thinning Fruit #: Number of apples growing on the South-facing branch pre-thinning. S Post-Thinning Fruit #: Number of apples growing on the South-facing branch post-thinning. N Apple 1 Circumference (cm): Circumference (measure of apple quality) of 1st apple (tip) on the North-facing branch. N Apple 2 Circumference (cm): Circumference (measure of apple quality) of 2nd apple (mid-section) on the North-facing branch. N Apple 3 Circumference (cm): Circumference (measure of apple quality) of 3rd apple (nearest to trunk) on the North-facing branch. S Apple 1 Circumference (cm): Circumference (measure of apple quality) of 1st apple (tip) on the South-facing branch. S Apple 2 Circumference (cm): Circumference (measure of apple quality) of 2nd apple (mid-section) on the South-facing branch. S Apple 3 Circumference (cm): Circumference (measure of apple quality) of 3rd apple (nearest to trunk) on the South-facing branch. Fruits Measured: Count of number of fruits with a circumference value Mean Non-Zero Circumference (cm): Mean of circumference of fruits with a circumference value. Supporting Information Trap Nests "Trap_Nest_Edge_Effects.xlsx": Excel file with distance from orchard edge for each trap nest (Year 1) "Trap_Nest_Final_Count.xlsx": Excel file with final tally trap nest occupants for each trap nest (Year 1). Some values differ from those obtained from summing values in "Trap_Nest_Total.xlsx" because values in this file were adjusted following x-ray examination of nest contents (so, values in this file for cell numbers per nest are more accurate). "Trap_Nest_Total.xlsx": Excel file with running tally (taken for every sampling period) of trap nest occupants for each trap nest (Year 1) "Pollen.xlsx": Excel file with pollen data extracted from trap nests (Year 1) Variables Trap Nest ID: Unique alphanumerical identifier for each of the 10 trap nests present within every orchard and site. Trap Nest Hole # (or Hole #): Numerical identifier indicating the designated location of the hole within a particular trap-nest. Trap Nest Edge Distance (m): Distance of a particular trap-nest from orchard edge. Trap Nest Edge: The type of edge (forest, road, or field) Round: Sampling period of trap nest monitoring (either during apple bloom, pre-thinning (Round 2), or post-thinning (Round 3), Round 4, or Final Count (at thend of summer of Year 1)). Date: Date of trap-nest monitoring. Total Hole #: Total number of possible holes within a particular trap-nest (varies from 10-14). Size: Size of nest (small=S, medium=M, large=L). "# New Cells": Number of new cells produced since the last visit (if no new cells=0). "# Total Cells": Number of total cells including those of the new cells that were produced (if no new cells, this number remains unchanged since the last visit). "# Surviving Osmia": Number of surviving Osmia per nest as determined by X-ray. "# Surviving Osmia": Number of surviving female Osmia per nest as determined by X-ray. Seal: Type of nest seal (material) that was used for nest construction (mud, leaf, or resin). This helps to identify taxa. Taxa: Type of insect that constructed the nest. "Chelostomoides" = resin bees (Megachile: Chelostomoides) and wasps (Passaloecus). "Wasp" = potter wasps (eumenine Vespidae) and Trypoxylon. "Megachile" = leaf-cutting Megachile. Sealed: Binary (yes or no) to indicate if the nest was sealed.

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    Journal of Applied Ecology
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    Authors: Bourgeois, Bérenger; Vanasse, Anne; González, Eduardo; Andersen, Roxane; +1 Authors

    Trajectories of plant communities can be described by different models of plant succession. While a Clementsian (gradual continuum model) or Gleasonian approach (relay floristics model) has traditionally been used to inform restoration outcomes, alternative succession models developed recently may better represent restoration trajectories. The threshold dynamics succession model, which predicts an abrupt species turnover after an environmental threshold is crossed, has never been used in a restoration context. This model might, however, better describe shifts in plant competitive ranking and facilitation interactions during species turnover. Fifty-three riparian zones, planted with trees 3–17 years prior to sampling, and 14 natural riparian forests were studied in two agricultural watersheds of south-eastern Québec (Canada). The cover of vegetation strata was assessed at the site scale, and the cover of plant species was estimated in a total of 784 1-m2 plots. Canopy cover was measured stereoscopically for each plot. As revealed by Principal Response Curves and broken stick models, herbaceous species composition was stable during the first 12–13 years after tree planting, but then abruptly shifted. This two-step pattern in species turnover followed the increase in canopy cover after tree planting. Once canopy cover passed a threshold of ca 40%, plant succession started and led to the re-establishment of forest communities 17 years after planting. Following herbaceous species turnover, the cover of ecological groups changed significantly towards covers of natural riparian forests: shade-tolerant species generally increased, while light-demanding and non-native species decreased. Vegetation structure was also significantly affected by tree planting: tree and shrub cover increased, while monocot cover decreased. Synthesis and applications. Tree planting efficiently restored herbaceous forest communities in riparian zones by inducing a species turnover mediated by light availability corresponding to the threshold dynamics model in plant succession. Fostering and monitoring canopy closure in tree-planted riparian zones should improve restoration success and the design of alternative strategies. The innovative statistical approach of this study aiming to identify succession patterns and their associated theoretical models can guide future restoration in any type of ecosystem around the world to bridge the gap between science and management. Data_Bourgeois_et_al_2016_JApplEcol

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    Dataset . 2016
    Data sources: B2FIND
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      Dataset . 2016
      Data sources: B2FIND
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    Authors: Muhly, Tyler B.; Johnson, Cheryl A.; Hebblewhite, Mark; Neilson, Eric W.; +11 Authors

    Aim: The influence of humans on large carnivores, including wolves, is a worldwide conservation concern. In addition, human‐caused changes in carnivore density and distribution might have impacts on prey and, indirectly, on vegetation. We therefore tested wolf responses to infrastructure related to natural resource development (i.e., human footprint). Location: Our study provides one of the most extensive assessments of how predators like wolves select habitat in response to various degrees of footprint across boreal ecosystems encompassing over a million square kilometers of Canada. Methods: We deployed GPS‐collars on 172 wolves, monitored movements and used a generalized functional response (GFR) model of resource selection. A functional response in habitat selection occurs when selection varies as a function of the availability of that habitat. GFRs can clarify how human‐induced habitat changes are influencing wildlife across large, diverse landscapes. Results: Wolves displayed a functional response to footprint. Wolves were more likely to select forest harvest cutblocks in regions with higher cutblock density (i.e., a positive functional response to high‐quality habitats for ungulate prey) and to select for higher road density in regions where road density was high (i.e., a positive functional response to human‐created travel routes). Wolves were more likely to use cutblocks in habitats with low road densities, and more likely to use roads in habitats with low cutblock densities, except in winter when wolves were more likely to use roads regardless of cutblock density. Main conclusions: These interactions suggest that wolves trade‐off among human‐impacted habitats, and adaptively switch from using roads to facilitate movement (while also risking encounters with humans), to using cutblocks that may have higher ungulate densities. We recommend that conservation managers consider the contextual and interacting effects of footprints when assessing impacts on carnivores. These effects likely have indirect impacts on ecosystems too, including on prey species. Functional response of wolves to human development across boreal North AmericaBoreal wolf RSF raster fileswolf_rasters.zip

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    DRYAD; ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Thomas, Sean C.; Martin, Adam R.;

    Assessing the potential for forest carbon (C) capture and storage requires accurate assessments of C in live tree tissues. In the vast majority of local, regional, and global assessments, C content has been assumed to be 50% of tree biomass; however, recent studies indicate that this assumption is not accurate, with substantial variation in C content among tree species as well as among tissue types. Here we conduct a comprehensive literature review to present a global synthesis of C content in tissues of live trees. We found a total of 253 species-specific stem wood C content records owing to 31 studies, and an additional 34 records of species with C content values of other tissues in addition to stem wood. Stem wood C content varied significantly as a function of biome (tropical, subtropical/ Mediterranean, temperate/ boreal) and species type (conifer, angiosperm). Conifer species exhibited greater wood C content than angiosperm species (50.8 ± 0.7% (95% C.I.) vs. 47.7 ± 0.3%, respectively), a trend that was consistent among all biomes. Although studies have documented differences in C content among plant tissues, interspecific differences in stem wood appear to be of greater importance overall: among species, stem wood C content explained 37, 76, 48, 81, and 63% respectively of the variation in bark, branch, twig, coarse root, and fine root C content values, respectively. In each case, these intraspecific patterns approximated 1:1 linear relationships. Most published stem wood C content values (and all values for other tree tissues) are based on dried wood samples, and so neglect volatile C constituents that constitute on average 1.3 – 2.5% of total C in live wood. Capturing this volatile C fraction is an important methodological consideration for future studies. Our review, and associated data compilation, provides empirically supported wood C fractions that can be easily incorporated into forest C accounting, and may correct systematic errors of ~1.6 – 5.8% in forest C assessments. Thomas_and_Martin_Dryad_global_wood_carbon_database

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    Dataset . 2012
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2012
      Data sources: B2FIND
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    Authors: Zawada, Daniel J.; Rieger, Landon A.; Bourassa, Adam, E.; Degenstein, Douglas A.;

    The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km. Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4. Global coverage

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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    Authors: Beauchesne, David; Jaeger, Jochen A. G.; St-Laurent, Martin-Hugues; Jaeger, Jochen AG.;

    Although prey species typically respond to the most limiting factors at coarse spatiotemporal scales while addressing biological requirements at finer scales, such behaviour may become challenging for species inhabiting human altered landscapes. We investigated how woodland caribou, a threatened species inhabiting North-American boreal forests, modified their fine-scale movements when confronted with forest management features (i.e. clearcuts and roads). We used GPS telemetry data collected between 2004 and 2010 on 49 female caribou in a managed area in Québec, Canada. Movements were studied using a use – availability design contrasting observed steps (i.e. line connecting two consecutive locations) with random steps (i.e. proxy of immediate habitat availability). Although caribou mostly avoided disturbances, individuals nonetheless modulated their fine-scale response to disturbances on a daily and annual basis, potentially compromising between risk avoidance in periods of higher vulnerability (i.e. calving, early and late winter) during the day and foraging activities in periods of higher energy requirements (i.e. spring, summer and rut) during dusk/dawn and at night. The local context in which females moved was shown to influence their decision to cross clearcut edges and roads. Indeed, although females typically avoided crossing clearcut edges and roads at low densities, crossing rates were found to rapidly increase in greater disturbance densities. In some instance, however, females were less likely to cross edges and roads as densities increased. Females may then be trapped and forced to use disturbed habitats, known to be associated with higher predation risk. We believe that further increases in anthropogenic disturbances could exacerbate such behavioural responses and ultimately lead to population level consequences. Beauchesne Jaeger and St-Laurent_PLoS ONE datasetsCharacteristics of observed and random steps of female Woodland caribou inhabiting a highly disturbed landscape in eastern Canada.Beauchesne et al_datasets PLoS ONE.zip

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    DANS-EASY
    Dataset . 2013
    Data sources: B2FIND
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      Dataset . 2013
      Data sources: B2FIND
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    Authors: Khan, Imran; Errounda, Fatima Zahra; Yangui, Sami; Glitho, Roch; +1 Authors

    With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements. Comment: This paper has been accepted in IEEE DCOSS 2015, IoTIP-15 Workshop to be held on 12th June 2015 in Brazil

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    ZENODO
    Conference object . 2015 . Peer-reviewed
    License: CC BY
    Data sources: ZENODO; Crossref
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2017
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    http://arxiv.org/pdf/1709.0648...
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    https://doi.org/10.48550/arxiv...
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      http://arxiv.org/pdf/1709.0648...
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    Authors: Chaves, Óscar M.; Bicca-Marques, Júlio César; Chapman, Colin A.;

    Seed dispersal is a key process driving the structure, composition, and regeneration of tropical forests. Larger frugivores play a crucial role in community structuring by dispersing large seeds not dispersed by smaller frugivores. We assessed the hypothesis that brown howler monkeys (Alouatta guariba clamitans) provide seed dispersal services for a wide assemblage of plant species in both small and large Atlantic forest fragments. Although fruit availability often decreases in small fragments compared with large ones, we predicted that brown howlers are efficient seed dispersers in quantitative and qualitative terms in both forest types given their high dietary flexibility. After a 36-month study period and 2,962 sampling hours, we found that howlers swallowed and defecated intact the vast majority of seeds (96%-100%) they handled in all study sites. Overall, they defecated ca. 315,600 seeds belonging to 98 species distributed in eight growth forms. We estimated that each individual howler dispersed an average of 143 (SD = 49) seeds >2 mm per day or 52,052 (SD = 17,782) seeds per year. They dispersed seeds of 58% to 93% of the local assemblages of fleshy-fruit trees. In most cases, the richness and abundance of seed species dispersed was similar between small and large fragments. However, groups inhabiting small fragments tended to disperse a higher diversity of seeds from rarely consumed fruits than those living in large fragments. We conclude that brown howlers are legitimate seed dispersers for most fleshy-fruit species of the angiosperm assemblages of their habitats, and that they might favor the regeneration of Atlantic forest fragments with the plentiful amount of intact seeds that they disperse each year. Dataset_seeds_dispersedHere we provided data on seed dispersal by six wild groups of brown howler monkeys (Alouatta guariba clamitans). This research was conducted during a 36-month period in three small (<10 ha: S1, S2, and S3) and three large (>90 ha: L1,L2, and L3) Atlantic forest fragments in Rio Grande do Sul State, southern Brazil.Dataset_seed_handlingHere we provided data on seed/fruit handling by six wild groups of brown howler monkeys (Alouatta guariba clamitans). This research was conducted during a 36-month period in three small (<10 ha: S1, S2, and S3) and three large (>90 ha: L1,L2, and L3) Atlantic forest fragments in Rio Grande do Sul State, southern Brazil.

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    DRYAD; ZENODO
    Dataset . 2019
    License: CC 0
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    Authors: Bartels, Samuel F.; James, Ryan S.; Caners, Richard T.; Macdonald, S. Ellen;

    1. Site moisture is an important component of the forest landscape for maintaining biodiversity, including forest-floor bryophytes, but little is known about its role in shaping understory responses to harvesting. 2. We investigated the influence of site wetness, determined using a remotely-sensed, topographic depth-to-water (DTW) index, on responses of bryophyte cover, richness, diversity, and composition to variable retention harvesting (comparing: 2% [clear-cut], 20%, and 50% dispersed green tree retention and uncut controls [100% retention]) in three boreal forest cover-types (broadleaf, mixed, and conifer forests) in western Canada. The DTW index provides an approximation of depth to water at or below the soil surface, and was derived from wet-areas mapping based on discrete Airborne Laser Scanning data acquired over an experimentally harvested landscape located in northwestern Alberta, Canada. 3. The effectiveness of leaving retention (versus clear-cutting) for conserving bryophyte communities depended on site wetness, as indicated by DTW, with the specifics varying among forest types. In broadleaf forests, bryophyte cover and richness were generally low and not much affected by harvesting but drier sites had higher richness and a few more unique species. In mixed and conifer forests, leaving retention (versus clear-cutting) on wetter (versus drier) sites was more effective for conserving bryophyte cover, wetter sites had higher total species richness, and more species were exclusive to wetter sites. 4. Synthesis and applications. Site wetness, as indicated using the remotely-sensed topographic site wetness index "depth-to-water," mediates bryophyte responses to variable-retention harvests. Specifically, our results suggested that in conifer and mixed forests it would be more beneficial to target wetter sites for retention patches or dispersed retention whereas in broadleaf sites there might be a slight advantage to targeting drier sites. Our study demonstrates that this tool could be used to inform management decisions around leaving dispersed or patch retention.28-Jan-2019 Bryophyte species and depth-to-water index valuesBryophyte (mosses and liverworts) species cover data and estimation of depth-to-water index values for retention harvest sites sampled in northwestern Alberta, Canada.Bartels-et-al-2019-deposited data-Dryad.xlsx

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2019
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    Authors: Winkler, Manuela; Plichta, Roman; Buysse, Pauline; Lohila, Annalea; +362 Authors

    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. AIAS-COFUND fellowship programme - Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration 609033 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 Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) PN-III-P2-2.1-PED-2019-4924 PN2019-2022/19270201 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 Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal' Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I) LO1415 International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis Bavarian Ministry of the Environment and Public Health UGV06080204000 German Research Foundation (DFG) 192626868 French National Research Agency (ANR) ANR-19-CE32-0005-01 Centre National de la Recherche Scientifique (CNRS) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PIA APOYO CCTE AFB170008 PIA AFB170008 grant project VEGA of the Ministry of Education of the Slovak Republic Slovak Academy of Sciences 2/0132/18 Portuguese Foundation for Science and Technology CEECIND/02509/2018 CESAM UIDP/50017/2020+UIDB/50017/2020 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11170516 1180205 Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) E26/200.84/2019 German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association Kempe Foundations - research infrastructure ICOS Kempe Foundations - research infrastructure SITES Gobern of Spain PERMAPLANET CTM2009-10165-E ANTARPERMA CTM2011-15565-E PERMASNOW CTM2014-52021-R German Research Foundation (DFG) DFG- FZT 118 202548816 TI 338/14-1 TI 338/14-2 BA 3843/6-1 project for national infrastructure support CzeCOS/ICOS LM2015061 GLORIA-EU EVK2-CT2000-00056 Swedish Research Council Swedish Research Council Formas 2014-00530 2018-00792 2016-01187 Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 112-201501-00609 Biotechnology and Biological Sciences Research Council (BBSRC) 206/D16053 FWO G0H1517N NERC E3 doctoral training partnership grant at the University of Edinburgh NE/L002558/1 UK Natural Environment Research Council through the ShrubTundra Project NE/M016323/1 Scottish Government's Rural and Environment Science and Analytical Services Division National Natural Science Foundation of China (NSFC) 41861134039 41941015 41877458 Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 141513/2017-9 Ministry of Science and Higher Education of the Russian Federation FSRZ-2020-0014 Grant Agency of the Czech Republic 20-28119S 20-05840Y GA17-19376S 21-11487S Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-06691 Federal Ministry of Education & Research (BMBF) 01LL0912 01LG1201M 01LG1201N Polish National Centre for Research and Development Pol-Nor/203258/31/2013 Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE) Krasnoyarsk Territory Krasnoyarsk Regional Fund of Science 20-45-242908 Netherlands Organization for Scientific Research (NWO) 016.VICI.170.072 EU-LEAP-Agri (RAMSES II) EU-DESIRA (CASSECS) EU-H2020 (SustainSahel) Austrian Climate Research Program ACRP7 - CentForCSink - KR14AC7K11960 National Research, Development and Innovation Fund of Hungary K128441 Federal Ministry of Education & Research (BMBF) FKZ 031B0516C SUSALPS project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor) Portuguese Foundation for Science and Technology European Commission European Union's Horizon 2020 research and innovation program 678841 Natural Sciences and Engineering Research Council of Canada (NSERC) Ministry of Education, Youth & Sports - Czech Republic LTAUSA18007 Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137 ALTER-net multi-site grant, River LIFE project LIFE08 NAT/S/000266 Swedish Research Council Formas 2018-01781 2018-02700 2019-00836 Netherlands Organization for Scientific Research (NWO) 863.14.013 Russian Foundation for Basic Research (RFBR) 18-05-60203-Arktika Arctic Challenge for Sustainability II (ArCS II) JPMXD1420318865 BECC - Biodiversity and Ecosystem services in a Changing Climate Smithsonian Institution Smithsonian Tropical Research Institute Ministry of Education and Science of Ukraine 505 550 574 602 Ministry of Science and Higher Education, Poland N N305 304840 iDiv by the German Research Foundation DFG- FZT 118 202548816 University of Helsinki, Faculty of Science (MICROCLIM) 7510145 BiodivERsa 2019-2020 BioDivClim call 'ASICS' ANR-20-EBI5-0004 MoEFCC, Govt of India (AICOPTAX project) 22018/12/2015/RE/Tax Direccion General de Cambio Climatico del Gobierno de Aragon National Science Foundation, Poland UMO-2017/27/B/ST10/02228 Norwegian Research Council (NORKLIMA grants) 184912 244525 Independent Research Fund Denmark 8021-00423B 7027-00133B New Frontiers in Research Fund-Exploration NFRF-2018-02043 ODYSSEE project (PN-II-ID-JRP-RO-FR-2012) ANR-13-ISV7-0004 Global Challenges program at the University of Wollongong Instituto Antartico Chileno INACH_RT-48_16 INACH FR-0418 project LTER-CWN (FFG, F&E Infrastrukturforderung) 858024 Swedish Research Council Formas Swedish Research Council Baden-Wurttemberg Ministry of Science, Research and Arts Natural Environment Research Council (NERC) NE/L002558/1 Bolin Centre for Climate Research, Stockholm University Swedish Forest Society Foundation 2018-485-Steg 2 2017 Swiss National Science Foundation (SNSF) 20FI20_173691 Co-management Board of Torngat Mountains National Park NERC National Capability LTS-S: UK-SCAPE NE/R016429/1 UK NERC Independent Research Fellowship NE/S01537X/1 General Directorate of State Forests, Warsaw, Poland French National Research Agency (ANR) ANR-10-LABX-45 National Science Centre, Poland 2016/21/B/ST10/02271 NSERC Canada Graduate Scholarship (Doctoral) Program Consiliul National al Cercetarii Stiintifice (CNCS) Slovak Research and Development Agency APVV-19-0319 Spanish Research Agency (VULBIMON) CGL2017-90040-R Polish National Science Centre 2017/27/B/NZ8/00316 Zone Atelier CNRS Antarctique et Terres Australes Energy Research Fund NYR-11 - 2019 NYR-18 - 2020 European Commission 172198 193645 31003A_176044 EU Horizon2020 INFRAIA project eLTER-PLUS 871128 Iran National Science Foundation (INSF) 96005914 Carnegie Trust for the Universities of Scotland Programa Operativo FEDER 2018 B1-RNM-163-UGR-18 National Geographic Society 9480-14 WW-240R-17 Research Foundation Flanders (FWO-SBO) S000619N Humboldt Fellowship for Experienced Researchers Swiss Federal Office for the Environment (FOEN) European Research Council (ERC) FORMICA 757833 Department of Industry, Innovation and Science fundacion Ramon Areces grant ELEMENTAL-CLIMATE FEDER, within the PT2020 Partnership Agreement Aarhus University Research Foundation, Denmark Saxon Switzerland National Park Administration National Science Foundation (NSF) DEB 1557094 Arctic Interactions at the University of Oulu Russian Science Foundation (RSF) 21-14-00209. Svalbard Environmental Protection Fund 15/128 Russian Science Foundation (RSF) 18-74-10048 Knut & Alice Wallenberg Foundation 2015.0047 Bavarian Forest National Park administration Russian Foundation for Basic Research (RFBR) National Research Foundation - South Africa Natural Environment Research Council (NERC) National Science Foundation (NSF) 1556772 MEMOIRE project PN-III-P1-1.1-PD2016-0925 Jardin Botanico Atlantico SV-20-GIJON-JBA Swedish National Space Board (SNSB) 95/16 Swiss National Science Foundation (SNSF) Spanish Government PID2019-110521GB-I00 Australian Research Council DE180100570 Australian Research Council DE140101611 Czech Academy of Sciences RVO 67985939 SAD Region Bretagne (Project INFLICT) CASSECS project by the European Union ARC the Australian Antarctic Division Autonomous Province of Bolzano (ITA) Qatar Petroleum QUEX-CAS-QP-RD-18/19 ERDF (CE LignoSilva) ITMS 313011S735 Kempe Foundation JCK-1112 JCK-1822 European Commission CGL2016-78093-R Societas pro Fauna et Flora Fennica Swedish Research Council 2014-04270 project MIUR PON Cluster OT4CLIMA Research Council of Norway 269957 Academy of Finland 318930 337552 European Commission 17841 774124 Tiina and Antti Herlin Foundation National Parks (DYNBIO) 1656/2015 Estonian Research Council PRG609 FWO G018919N W001919N 12P1819N research infrastructure ICOS-SE SRDA APVV-16-0325 APVV-20-0365 UK Research & Innovation (UKRI) Oberfrankenstiftung OFS FP00237 Spanish Government FPU17/05869 Maaja vesitekniikan tuki ry. Catalan government 2017-1005 ETH Zurich FEVER ETH-27 19-1 Australian Research Council Tiroler Wissenschaftsfonds Research Council of Norway ENI CBC BSB PONTOS BSB 889 European Commission 230970 CISSC (program ICRP) 2397 ANID PIA / BASAL FB210006 EU FP6 NitroEurope 17841 Swedish Research Council University of Innsbruck Villum Foundation 17523 MCTI/CNPq/FNDCT 68/2013 25N BIODIVERS 3-BIOSERV Spanish Polar Committee Nordenskiold Samfundet EU Horizon 2020 641918 EU FP7 ECLAIRE 282910 Tyson Research Center Stelvio National Park Universidad Javeriana Australian Government ANID-FONDECYT 1181745 Sime Darby Foundation Inuit of Nunatsiavut CONICYT-PAI 79170119 University of Alcala EU INTERACT program Forestry Commission Spanish Government Giacomi foundation Max Planck Society GEF-UNEP NEC05348 Weston Foundation ANID-MPG 190029 ArcticNet-NCE Compete 2020 DOB Ecology ScotNature ETH Zurich ArcticNet AGROPOLIS Flexpeil Total SA CGIAR INACH

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    NTNU Open
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    Global Change Biology
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    Lirias
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    Serveur académique lausannois
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    MPG.PuRe
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    CNR ExploRA
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    KITopen
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    https://doi.org/10.21256/zhaw-...
    Article . 2022
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    Apollo
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    Authors: Hyjazie, Batoule F.; Forrest, Jessica R. K.;

    Hyjazie and Forrest, 2023 Code repository for Hyjazie and Forrest, 2023: "Supplemental nesting habitat increases bee abundance in apple orchards" This Rmarkdown file is divided into two sections: main manuscript and supporting information. The main manuscript is further divided into two sections: Year 1 and Year 2 (which includes a subsection of Year 1 + Year 2 (i.e., All Years)). Each of these sections has two sub-sections: abundance and apple quality. Supporting information includes all information relating to trap nests. Each of these subsections will report a code for some general summary statistics, models, and/or figures. All files are excel files. Missing values are depicted as N/A. Main Manuscript Variables Common in All Spreadsheets Orchard: Unique two-letter identifier for each orchard, reflecting site pairings used for treatment assignment. For example, Orchard IDs “HM” (with one treatment plot) and “IC” (with one control plot) are paired as Orchard “HC”. N = 16 levels. Orchard ID: Unique two-letter identifier for each orchard, reflecting orchard ownership. Some orchards had only a single plot (either control or treatment) and needed to be paired with a plot in another (nearby) orchard; these pairings are reflected in the “Orchard” variable. N=16 levels. Site: Numerical identifier since each orchard either had 1, 2 or 3 sites. Site ID: Numerical identifier since each orchard either had 1, 2 or 3 sites and accounts for orchards that only have plot (either control or treatment) and needed to be paired with another (n=16). Plot: Control (without trap nests) or Treatment (with trap nests). Shaded TºC: Measured temperature in the shade. Unshaded TºC: Measured temperature out of the shade. % Cloud Cover: Estimated percent of cloud cover (0% = no cloud in sight). Wind (m/s): Measured wind speed. Abundance "Osmia_Abundance_1.xlsx": Excel file with Osmia abundance data (Year 1) "Bee_Abundance_1.xlsx": Excel file with total bee abundance data (Year 1) "Total_Abundance_1.xlsx": Excel file with total pollinator abundance data (all flower visitors including non-bees) (Year 1) "Total_Abundance_Year_2.xlsx": Excel file with total abundance data (Year 2) Variables Round: Sampling period (either during apple bloom, pre-thinning (Round 2), or post-thinning (Round 3)). Transect: 3 transect walks were conducted per sampling period per orchard per site (either 1, 2, or 3). Date: Date of transect walk. Start Time: Start time of transect walk. End Time: End time of transect walk. Taxa: Taxon of floral visitor to nearest genus whenever possible. Count: Count of floral visitor observed during particular transect walk. Bee: Binary column to indicate if the taxon in question is a bee or non-bee flower visitor. Sex: Male or Female or Unknown (N/A). Apple "Apple_1.xsls": Excel file with all apple data (Year 1) "Apple_Year_2.xslx": Excel file with all apple data (Year 2) Variables Tree #: Numerical identifier (1, 2, or 3) for the tree measured in each plot (each plot had 3 trees). Age: Age of tree (either adult or dwarf) Tree Height (cm) N Bud #: Number of apple flower buds on the North-facing branch. N Pre-Thinning Fruit #: Number of apples growing on the North-facing branch pre-thinning. N Post-Thinning Fruit #: Number of apples growing on the North-facing branch post-thinning. S Bud #: Number of apple flower buds on the South-facing branch. S Pre-Thinning Fruit #: Number of apples growing on the South-facing branch pre-thinning. S Post-Thinning Fruit #: Number of apples growing on the South-facing branch post-thinning. N Apple 1 Circumference (cm): Circumference (measure of apple quality) of 1st apple (tip) on the North-facing branch. N Apple 2 Circumference (cm): Circumference (measure of apple quality) of 2nd apple (mid-section) on the North-facing branch. N Apple 3 Circumference (cm): Circumference (measure of apple quality) of 3rd apple (nearest to trunk) on the North-facing branch. S Apple 1 Circumference (cm): Circumference (measure of apple quality) of 1st apple (tip) on the South-facing branch. S Apple 2 Circumference (cm): Circumference (measure of apple quality) of 2nd apple (mid-section) on the South-facing branch. S Apple 3 Circumference (cm): Circumference (measure of apple quality) of 3rd apple (nearest to trunk) on the South-facing branch. Fruits Measured: Count of number of fruits with a circumference value Mean Non-Zero Circumference (cm): Mean of circumference of fruits with a circumference value. Supporting Information Trap Nests "Trap_Nest_Edge_Effects.xlsx": Excel file with distance from orchard edge for each trap nest (Year 1) "Trap_Nest_Final_Count.xlsx": Excel file with final tally trap nest occupants for each trap nest (Year 1). Some values differ from those obtained from summing values in "Trap_Nest_Total.xlsx" because values in this file were adjusted following x-ray examination of nest contents (so, values in this file for cell numbers per nest are more accurate). "Trap_Nest_Total.xlsx": Excel file with running tally (taken for every sampling period) of trap nest occupants for each trap nest (Year 1) "Pollen.xlsx": Excel file with pollen data extracted from trap nests (Year 1) Variables Trap Nest ID: Unique alphanumerical identifier for each of the 10 trap nests present within every orchard and site. Trap Nest Hole # (or Hole #): Numerical identifier indicating the designated location of the hole within a particular trap-nest. Trap Nest Edge Distance (m): Distance of a particular trap-nest from orchard edge. Trap Nest Edge: The type of edge (forest, road, or field) Round: Sampling period of trap nest monitoring (either during apple bloom, pre-thinning (Round 2), or post-thinning (Round 3), Round 4, or Final Count (at thend of summer of Year 1)). Date: Date of trap-nest monitoring. Total Hole #: Total number of possible holes within a particular trap-nest (varies from 10-14). Size: Size of nest (small=S, medium=M, large=L). "# New Cells": Number of new cells produced since the last visit (if no new cells=0). "# Total Cells": Number of total cells including those of the new cells that were produced (if no new cells, this number remains unchanged since the last visit). "# Surviving Osmia": Number of surviving Osmia per nest as determined by X-ray. "# Surviving Osmia": Number of surviving female Osmia per nest as determined by X-ray. Seal: Type of nest seal (material) that was used for nest construction (mud, leaf, or resin). This helps to identify taxa. Taxa: Type of insect that constructed the nest. "Chelostomoides" = resin bees (Megachile: Chelostomoides) and wasps (Passaloecus). "Wasp" = potter wasps (eumenine Vespidae) and Trypoxylon. "Megachile" = leaf-cutting Megachile. Sealed: Binary (yes or no) to indicate if the nest was sealed.

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    Journal of Applied Ecology
    Article . 2024 . Peer-reviewed
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    ZENODO
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      Journal of Applied Ecology
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    Authors: Bourgeois, Bérenger; Vanasse, Anne; González, Eduardo; Andersen, Roxane; +1 Authors

    Trajectories of plant communities can be described by different models of plant succession. While a Clementsian (gradual continuum model) or Gleasonian approach (relay floristics model) has traditionally been used to inform restoration outcomes, alternative succession models developed recently may better represent restoration trajectories. The threshold dynamics succession model, which predicts an abrupt species turnover after an environmental threshold is crossed, has never been used in a restoration context. This model might, however, better describe shifts in plant competitive ranking and facilitation interactions during species turnover. Fifty-three riparian zones, planted with trees 3–17 years prior to sampling, and 14 natural riparian forests were studied in two agricultural watersheds of south-eastern Québec (Canada). The cover of vegetation strata was assessed at the site scale, and the cover of plant species was estimated in a total of 784 1-m2 plots. Canopy cover was measured stereoscopically for each plot. As revealed by Principal Response Curves and broken stick models, herbaceous species composition was stable during the first 12–13 years after tree planting, but then abruptly shifted. This two-step pattern in species turnover followed the increase in canopy cover after tree planting. Once canopy cover passed a threshold of ca 40%, plant succession started and led to the re-establishment of forest communities 17 years after planting. Following herbaceous species turnover, the cover of ecological groups changed significantly towards covers of natural riparian forests: shade-tolerant species generally increased, while light-demanding and non-native species decreased. Vegetation structure was also significantly affected by tree planting: tree and shrub cover increased, while monocot cover decreased. Synthesis and applications. Tree planting efficiently restored herbaceous forest communities in riparian zones by inducing a species turnover mediated by light availability corresponding to the threshold dynamics model in plant succession. Fostering and monitoring canopy closure in tree-planted riparian zones should improve restoration success and the design of alternative strategies. The innovative statistical approach of this study aiming to identify succession patterns and their associated theoretical models can guide future restoration in any type of ecosystem around the world to bridge the gap between science and management. Data_Bourgeois_et_al_2016_JApplEcol

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
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      Dataset . 2016
      Data sources: B2FIND
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    Authors: Muhly, Tyler B.; Johnson, Cheryl A.; Hebblewhite, Mark; Neilson, Eric W.; +11 Authors

    Aim: The influence of humans on large carnivores, including wolves, is a worldwide conservation concern. In addition, human‐caused changes in carnivore density and distribution might have impacts on prey and, indirectly, on vegetation. We therefore tested wolf responses to infrastructure related to natural resource development (i.e., human footprint). Location: Our study provides one of the most extensive assessments of how predators like wolves select habitat in response to various degrees of footprint across boreal ecosystems encompassing over a million square kilometers of Canada. Methods: We deployed GPS‐collars on 172 wolves, monitored movements and used a generalized functional response (GFR) model of resource selection. A functional response in habitat selection occurs when selection varies as a function of the availability of that habitat. GFRs can clarify how human‐induced habitat changes are influencing wildlife across large, diverse landscapes. Results: Wolves displayed a functional response to footprint. Wolves were more likely to select forest harvest cutblocks in regions with higher cutblock density (i.e., a positive functional response to high‐quality habitats for ungulate prey) and to select for higher road density in regions where road density was high (i.e., a positive functional response to human‐created travel routes). Wolves were more likely to use cutblocks in habitats with low road densities, and more likely to use roads in habitats with low cutblock densities, except in winter when wolves were more likely to use roads regardless of cutblock density. Main conclusions: These interactions suggest that wolves trade‐off among human‐impacted habitats, and adaptively switch from using roads to facilitate movement (while also risking encounters with humans), to using cutblocks that may have higher ungulate densities. We recommend that conservation managers consider the contextual and interacting effects of footprints when assessing impacts on carnivores. These effects likely have indirect impacts on ecosystems too, including on prey species. Functional response of wolves to human development across boreal North AmericaBoreal wolf RSF raster fileswolf_rasters.zip

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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    DRYAD; ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Thomas, Sean C.; Martin, Adam R.;

    Assessing the potential for forest carbon (C) capture and storage requires accurate assessments of C in live tree tissues. In the vast majority of local, regional, and global assessments, C content has been assumed to be 50% of tree biomass; however, recent studies indicate that this assumption is not accurate, with substantial variation in C content among tree species as well as among tissue types. Here we conduct a comprehensive literature review to present a global synthesis of C content in tissues of live trees. We found a total of 253 species-specific stem wood C content records owing to 31 studies, and an additional 34 records of species with C content values of other tissues in addition to stem wood. Stem wood C content varied significantly as a function of biome (tropical, subtropical/ Mediterranean, temperate/ boreal) and species type (conifer, angiosperm). Conifer species exhibited greater wood C content than angiosperm species (50.8 ± 0.7% (95% C.I.) vs. 47.7 ± 0.3%, respectively), a trend that was consistent among all biomes. Although studies have documented differences in C content among plant tissues, interspecific differences in stem wood appear to be of greater importance overall: among species, stem wood C content explained 37, 76, 48, 81, and 63% respectively of the variation in bark, branch, twig, coarse root, and fine root C content values, respectively. In each case, these intraspecific patterns approximated 1:1 linear relationships. Most published stem wood C content values (and all values for other tree tissues) are based on dried wood samples, and so neglect volatile C constituents that constitute on average 1.3 – 2.5% of total C in live wood. Capturing this volatile C fraction is an important methodological consideration for future studies. Our review, and associated data compilation, provides empirically supported wood C fractions that can be easily incorporated into forest C accounting, and may correct systematic errors of ~1.6 – 5.8% in forest C assessments. Thomas_and_Martin_Dryad_global_wood_carbon_database

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    DANS-EASY
    Dataset . 2012
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      DANS-EASY
      Dataset . 2012
      Data sources: B2FIND
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    Authors: Zawada, Daniel J.; Rieger, Landon A.; Bourassa, Adam, E.; Degenstein, Douglas A.;

    The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km. Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4. Global coverage

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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    Authors: Beauchesne, David; Jaeger, Jochen A. G.; St-Laurent, Martin-Hugues; Jaeger, Jochen AG.;

    Although prey species typically respond to the most limiting factors at coarse spatiotemporal scales while addressing biological requirements at finer scales, such behaviour may become challenging for species inhabiting human altered landscapes. We investigated how woodland caribou, a threatened species inhabiting North-American boreal forests, modified their fine-scale movements when confronted with forest management features (i.e. clearcuts and roads). We used GPS telemetry data collected between 2004 and 2010 on 49 female caribou in a managed area in Québec, Canada. Movements were studied using a use – availability design contrasting observed steps (i.e. line connecting two consecutive locations) with random steps (i.e. proxy of immediate habitat availability). Although caribou mostly avoided disturbances, individuals nonetheless modulated their fine-scale response to disturbances on a daily and annual basis, potentially compromising between risk avoidance in periods of higher vulnerability (i.e. calving, early and late winter) during the day and foraging activities in periods of higher energy requirements (i.e. spring, summer and rut) during dusk/dawn and at night. The local context in which females moved was shown to influence their decision to cross clearcut edges and roads. Indeed, although females typically avoided crossing clearcut edges and roads at low densities, crossing rates were found to rapidly increase in greater disturbance densities. In some instance, however, females were less likely to cross edges and roads as densities increased. Females may then be trapped and forced to use disturbed habitats, known to be associated with higher predation risk. We believe that further increases in anthropogenic disturbances could exacerbate such behavioural responses and ultimately lead to population level consequences. Beauchesne Jaeger and St-Laurent_PLoS ONE datasetsCharacteristics of observed and random steps of female Woodland caribou inhabiting a highly disturbed landscape in eastern Canada.Beauchesne et al_datasets PLoS ONE.zip

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    DANS-EASY
    Dataset . 2013
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2013
      Data sources: B2FIND
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