ForestTemp - Sub-canopy microclimate temperatures of European forests

. 2021 Dec ; 27 (23) : 6307-6319. [epub] 20211003

Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid34605132

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

Centre for Sustainable Ecosystem Solutions School of Biological Sciences University of Wollongong Wollongong Australia

CREAF Catalonia Spain

CSIC Global Ecology Unit CREAF CSIC UAB Catalonia Spain

Department of Earth and Environmental Sciences KU Leuven Leuven Belgium

Department of Ecology and Evolution University of Lausanne Lausanne Switzerland

Department of Ecology Environment and Plant Sciences and Bolin Centre for Climate Research Stockholm University Stockholm Sweden

Department of Ecology Swedish University of Agricultural Sciences Uppsala Sweden

Department of Environmental Systems Science ETH Zurich Zurich Switzerland

Department of Forest Botany Dendrology and Geobiocoenology Mendel University in Brno Brno Czech Republic

Department of Geosciences and Geography Helsinki Finland

Department of Plant Biology and Ecology University of Seville Seville Spain

Environment and Sustainability Institute University of Exeter Penryn Campus Penryn UK

European Commission Joint Research Centre Ispra Italy

Experimental Plant Ecology Institute of Botany and Landscape Ecology University of Greifswald Greifswald Germany

Faculty of Ecology and Environmental Sciences Technical University in Zvolen Zvolen Slovakia

Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Prague Czech Republic

Faculty of Forestry Technical University in Zvolen Zvolen Slovakia

Faculty of Science and Technology Free University of Bolzano Bolzano Italy

Finnish Meteorological Inst Helsinki Finland

Forest and Nature Lab Department of Environment Ghent University Melle Gontrode Belgium

Forest Services Bolzano Italy

Institute for Agriculture and Forestry Systems in the Mediterranean National Research Council of Italy Perugia Italy

Institute of Botany of the Czech Academy of Sciences Průhonice Czech Republic

Musée et Jardins botaniques Cantonaux Lausanne Switzerland

Plant Ecology Albrecht von Haller Institute for Plant Science Georg August University of Goettingen Goettingen Germany

Research Group PLECO University of Antwerp Wilrijk Belgium

Swiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland

UMR CNRS 7058 'Ecologie et Dynamique des Systèmes Anthropisés' Université de Picardie Jules Verne Amiens France

Wageningen University and Research Wageningen The Netherlands

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Aalto, J., Scherrer, D., Lenoir, J., Guisan, A., & Luoto, M. (2018). Biogeophysical controls on soil-atmosphere thermal differences: Implications on warming Arctic ecosystems. Environmental Research Letters, 13(7), 074003. https://doi.org/10.1088/1748-9326/aac83e

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5(1), 1958-2015. https://doi.org/10.1038/sdata.2017.191

Appelhans, T., Mwangomo, E., Hardy, D. R., Hemp, A., & Nauss, T. (2015). Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania. Spatial Statistics, 14, 91-113. https://doi.org/10.1016/j.spasta.2015.05.008

Ashcroft, M. B., & Gollan, J. R. (2013). The sensitivity of topoclimatic models to fine-scale microclimatic variability and the relevance for ecological studies. Theoretical and Applied Climatology, 114(1-2), 281-289. https://doi.org/10.1007/s00704-013-0841-0

Bennie, J., Huntley, B., Wiltshire, A., Hill, M. O., & Baxter, R. (2008). Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecological Modelling, 216(1), 47-59. https://doi.org/10.1016/j.ecolmodel.2008.04.010

Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), 43-69. https://doi.org/10.1080/02626667909491834

Bramer, I., Anderson, B. J., Bennie, J., Bladon, A. J., De Frenne, P., Hemming, D., & Gillingham, P. K. (2018). Advances in monitoring and modelling climate at ecologically relevant scales. Advances in Ecological Research, 58, 101-161. https://doi.org/10.1016/BS.AECR.2017.12.005

Bütikofer, L., Anderson, K., Bebber, D. P., Bennie, J. J., Early, R. I., & Maclean, I. M. D. (2020). The problem of scale in predicting biological responses to climate. Global Change Biology, 26(12), 6657-6666. https://doi.org/10.1111/gcb.15358

Cervellini, M., Zannini, P., Di Musciano, M., Fattorini, S., Jiménez-Alfaro, B., Rocchini, D., Field, R., R. Vetaas, O., Irl, S. D. H., Beierkuhnlein, C., Hoffmann, S., Fischer, J.-C., Casella, L., Angelini, P., Genovesi, P., Nascimbene, J., & Chiarucci, A. (2020). A grid-based map for the Biogeographical Regions of Europe. Biodiversity Data Journal, 8. https://doi.org/10.3897/BDJ.8.e53720

Davis, K. T., Dobrowski, S. Z., Holden, Z. A., Higuera, P. E., & Abatzoglou, J. T. (2019). Microclimatic buffering in forests of the future: The role of local water balance. Ecography, 42(1), 1-11. https://doi.org/10.1111/ecog.03836

De Frenne, P., Lenoir, J., Luoto, M., Scheffers, B. R., Zellweger, F., Aalto, J., Ashcroft, M. B., Christiansen, D. M., Decocq, G., De Pauw, K., Govaert, S., Greiser, C., Gril, E., Hampe, A., Jucker, T., Klinges, D. H., Koelemeijer, I. A., Lembrechts, J. J., Marrec, R., … Hylander, K. (2021). Forest microclimates and climate change: Importance, drivers and future research agenda. Global Change Biology, 27(11), 2279-2297. https://doi.org/10.1111/gcb.15569

De Frenne, P., Zellweger, F., Rodríguez-Sánchez, F., Scheffers, B. R., Hylander, K., Luoto, M., Vellend, M., Verheyen, K., & Lenoir, J. (2019). Global buffering of temperatures under forest canopies. Nature Ecology & Evolution, 3(5), 744-749. https://doi.org/10.1038/s41559-019-0842-1

De Kort, H., Panis, B., Helsen, K., Douzet, R., Janssens, S. B., & Honnay, O. (2020). Pre-adaptation to climate change through topography-driven phenotypic plasticity. Journal of Ecology, 108(4), 1465-1474. https://doi.org/10.1111/1365-2745.13365

Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802-813. https://doi.org/10.1111/j.1365-2656.2008.01390.x

European Union. (2020). Copernicus land monitoring service. European Environment Agency (EEA).

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086

Frey, S. J. K., Hadley, A. S., Johnson, S. L., Schulze, M., Jones, J. A., & Betts, M. G. (2016). Spatial models reveal the microclimatic buffering capacity of old-growth forests. Science Advances, 2(4), e1501392. https://doi.org/10.1126/sciadv.1501392

Geiger, R. (1950). The climate near the ground. Harvard University Press, 482 pp.

George, A. D., Thompson, F. R., & Faaborg, J. (2015). Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies. Remote Sensing Letters, 6(12), 924-932. https://doi.org/10.1080/2150704X.2015.1088671

Gisnås, K., Westermann, S., Schuler, T. V., Melvold, K., & Etzelmüller, B. (2016). Small-scale variation of snow in a regional permafrost model. The Cryosphere, 10(3), 1201-1215. https://doi.org/10.5194/tc-10-1201-2016

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031

Govaert, S., Meeussen, C., Vanneste, T., Bollmann, K., Brunet, J., Cousins, S. A. O., Diekmann, M., Graae, B. J., Hedwall, P.-O., Heinken, T., Iacopetti, G., Lenoir, J., Lindmo, S., Orczewska, A., Perring, M. P., Ponette, Q., Plue, J., Selvi, F., Spicher, F., … De Frenne, P. (2020). Edge influence on understorey plant communities depends on forest management. Journal of Vegetation Science, 31(2), 281-292. https://doi.org/10.1111/jvs.12844

Graae, B. J., Vandvik, V., Armbruster, W. S., Eiserhardt, W. L., Svenning, J.-C., Hylander, K., Ehrlén, J., Speed, J. D. M., Klanderud, K., Bråthen, K. A., Milbau, A., Opedal, Ø. H., Alsos, I. G., Ejrnaes, R., Bruun, H. H., Birks, H. J. B., Westergaard, K. B., Birks, H. H., & Lenoir, J. (2018). Stay or go-How topographic complexity influences alpine plant population and community responses to climate change. Perspectives in Plant Ecology, Evolution and Systematics, 30, 41-50. https://doi.org/10.1016/j.ppees.2017.09.008

Greenwell, B., Boehmke, B., Cunningham, J., & GBM Developers. (2020). gbm: Generalized boosted regression models. R package version 2.1.8. https://CRAN.R-project.org/package=gbm

Greiser, C., Meineri, E., Luoto, M., Ehrlén, J., & Hylander, K. (2018). Monthly microclimate models in a managed boreal forest landscape. Agricultural and Forest Meteorology, 250-251, 147-158. https://doi.org/10.1016/j.agrformet.2017.12.252

Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., & Bayr, K. J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83(1-2), 181-194. https://doi.org/10.1016/S0034-4257(02)00095-0

Jarraud, M. (2008). Guide to meteorological instruments and methods of observation (WMO-No. 8). World Meteorological Organisation.

Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122

Kašpar, V., Hederová, L., Macek, M., Müllerová, J., Prošek, J., Surový, P., Wild, J., & Kopecký, M. (2021). Temperature buffering in temperate forests: Comparing microclimate models based on ground measurements with active and passive remote sensing. Remote Sensing of Environment, 263, 112522. https://doi.org/10.1016/j.rse.2021.112522

Kearney, M. R., & Porter, W. P. (2017). NicheMapR-An R package for biophysical modelling: The microclimate model. Ecography, 40(5), 664-674. https://doi.org/10.1111/ecog.02360

Kearney, M. R., Shamakhy, A., Tingley, R., Karoly, D. J., Hoffmann, A. A., Briggs, P. R., & Porter, W. P. (2014). Microclimate modelling at macro scales: A test of a general microclimate model integrated with gridded continental-scale soil and weather data. Methods in Ecology and Evolution, 5(3), 273-286. https://doi.org/10.1111/2041-210X.12148

Kopecký, M., Macek, M., & Wild, J. (2021). Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition. Science of the Total Environment, 757, 143785. https://doi.org/10.1016/j.scitotenv.2020.143785

Körner, C., & Hiltbrunner, E. (2018). The 90 ways to describe plant temperature. Perspectives in Plant Ecology, Evolution and Systematics, 30, 16-21. https://doi.org/10.1016/j.ppees.2017.04.004

Kuhn, M. (2012). The caret package. Journal of Statistical Software, 28.

Landuyt, D., Perring, M. P., Seidl, R., Taubert, F., Verbeeck, H., & Verheyen, K. (2018). Modelling understorey dynamics in temperate forests under global change-Challenges and perspectives. Perspectives in Plant Ecology, Evolution and Systematics, 31, 44-54. https://doi.org/10.1016/j.ppees.2018.01.002

Lembrechts, J. J., Aalto, J., Ashcroft, M. B., De Frenne, P., Kopecký, M., Lenoir, J., Luoto, M., Maclean, I. M. D., Roupsard, O., Fuentes-Lillo, E., García, R. A., Pellissier, L., Pitteloud, C., Alatalo, J. M., Smith, S. W., Björk, R. G., Muffler, L., Ratier Backes, A., Cesarz, S., … Nijs, I. (2020). SoilTemp: A global database of near-surface temperature. Global Change Biology, 26(11), 6616-6629. https://doi.org/10.1111/gcb.15123

Lembrechts, J. J., & Lenoir, J. (2020). Microclimatic conditions anywhere at any time! Global Change Biology, 26(2), 337-339. https://doi.org/10.1111/gcb.14942

Lembrechts, J. J., Lenoir, J., Roth, N., Hattab, T., Milbau, A., Haider, S., Pellissier, L., Pauchard, A., Ratier Backes, A., Dimarco, R. D., Nuñez, M. A., Aalto, J., & Nijs, I. (2019). Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography, 28(11), 1578-1596. https://doi.org/10.1111/geb.12974

Lembrechts, J. J., Nijs, I., & Lenoir, J. (2018). Incorporating microclimate into species distribution models. Ecography, 42(7), 1267-1279. https://doi.org/10.1111/ecog.03947

Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J., & Grenouillet, G. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution, 4(8), 1044-1059. https://doi.org/10.1038/s41559-020-1198-2

Lenoir, J., Graae, B. J., Aarrestad, P. A., Alsos, I. G., Armbruster, W. S., Austrheim, G., Bergendorff, C., Birks, H. J. B., Bråthen, K. A., Brunet, J., Bruun, H. H., Dahlberg, C. J., Decocq, G., Diekmann, M., Dynesius, M., Ejrnaes, R., Grytnes, J.-A., Hylander, K., Klanderud, K., … Svenning, J.-C. (2013). Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Global Change Biology, 19(5), 1470-1481. https://doi.org/10.1111/gcb.12129

Lenoir, J., Hattab, T., & Pierre, G. (2017). Climatic microrefugia under anthropogenic climate change: Implications for species redistribution. Ecography, 40(2), 253-266. https://doi.org/10.1111/ecog.02788

Lenoir, J., & Svenning, J.-C. (2013). Latitudinal and elevational range shifts under contemporary climate change. In Encyclopedia of biodiversity (pp. 599-611). https://doi.org/10.1016/B978-0-12-384719-5.00375-0

Macek, M., Kopecký, M., & Wild, J. (2019). Maximum air temperature controlled by landscape topography affects plant species composition in temperate forests. Landscape Ecology, 34(11), 2541-2556. https://doi.org/10.1007/s10980-019-00903-x

Maclean, I. M. D. (2019). Predicting future climate at high spatial and temporal resolution. Global Change Biology, 26(2), 1003-1011. https://doi.org/10.1111/gcb.14876

Maclean, I. M. D., Duffy, J. P., Haesen, S., Govaert, S., De Frenne, P., Vanneste, T., Lenoir, J., Lembrechts, J. J., Rhodes, M. W., & Van Meerbeek, K. (2021). On the measurement of microclimate. Methods in Ecology and Evolution, 12(8), 1397-1410. https://doi.org/10.1111/2041-210X.13627

Maclean, I. M. D., Mosedale, J. R., & Bennie, J. J. (2019). Microclima: An r package for modelling meso- and microclimate. Methods in Ecology and Evolution, 10(2), 280-290. https://doi.org/10.1111/2041-210X.13093

McLaughlin, B. C., Ackerly, D. D., Klos, P. Z., Natali, J., Dawson, T. E., & Thompson, S. E. (2017). Hydrologic refugia, plants, and climate change. Global Change Biology, 23(8), 2941-2961. https://doi.org/10.1111/gcb.13629

Meeussen, C., Govaert, S., Vanneste, T., Haesen, S., Van Meerbeek, K., Bollmann, K., Brunet, J., Calders, K., Cousins, S. A. O., Diekmann, M., Graae, B. J., Iacopetti, G., Lenoir, J., Orczewska, A., Ponette, Q., Plue, J., Selvi, F., Spicher, F., Sørensen, M. V., … De Frenne, P. (2021). Drivers of carbon stocks in forest edges across Europe. Science of the Total Environment, 759, 143497. https://doi.org/10.1016/j.scitotenv.2020.143497

Meineri, E., Dahlberg, C. J., & Hylander, K. (2015). Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution. Ecological Modelling, 313, 127-136. https://doi.org/10.1016/j.ecolmodel.2015.06.028

Meineri, E., & Hylander, K. (2017). Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection. Ecography, 40(8), 1003-1013. https://doi.org/10.1111/ecog.02494

Monin, A. S., & Obukhov, A. M. (1954). Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR, 151(163), e187.

Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., & Thépaut, J.-N. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data Discussions, 2021, 1-50. https://doi.org/10.5194/essd-2021-82

Niittynen, P., & Luoto, M. (2018). The importance of snow in species distribution models of arctic vegetation. Ecography, 41(6), 1024-1037. https://doi.org/10.1111/ecog.03348

Nilsson, M. C., & Wardle, D. A. (2005). Understory vegetation as a forest ecosystem driver: Evidence from the northern Swedish boreal forest. Frontiers in Ecology and the Environment, 3(8), 421-428.

Pacifici, M., Foden, W. B., Visconti, P., Watson, J. E. M., Butchart, S. H. M., Kovacs, K. M., Scheffers, B. R., Hole, D. G., Martin, T. G., Akçakaya, H. R., Corlett, R. T., Huntley, B., Bickford, D., Carr, J. A., Hoffmann, A. A., Midgley, G. F., Pearce-Kelly, P., Pearson, R. G., Williams, S. E., … Rondinini, C. (2015). Assessing species vulnerability to climate change. Nature Climate Change, 5(3), 215-224. https://doi.org/10.1038/nclimate2448

Pecl, G. T., Araújo, M. B., Bell, J. D., Blanchard, J., Bonebrake, T. C., Chen, I.-C., Clark, T. D., Colwell, R. K., Danielsen, F., Evengård, B., Falconi, L., Ferrier, S., Frusher, S., Garcia, R. A., Griffis, R. B., Hobday, A. J., Janion-Scheepers, C., Jarzyna, M. A., Jennings, S., … Williams, S. E. (2017). Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355(6332), eaai9214. https://doi.org/10.1126/science.aai9214

Perry, D. A. (1994). Forest ecosystems. Johns Hopkins University Press.

Pincebourde, S., & Woods, H. A. (2020). There is plenty of room at the bottom: Microclimates drive insect vulnerability to climate change. Current Opinion in Insect Science, 41, 63-70. https://doi.org/10.1016/j.cois.2020.07.001

Potter, K. A., Arthur Woods, H., & Pincebourde, S. (2013). Microclimatic challenges in global change biology. Global Change Biology, 19(10), 2932-2939. https://doi.org/10.1111/gcb.12257

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Richardson, L. F. (1922). Weather prediction by numerical process. Cambridge University Press.

Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913-929. https://doi.org/10.1111/ecog.02881

Scheffers, B. R., De Meester, L., Bridge, T. C. L., Hoffmann, A. A., Pandolfi, J. M., Corlett, R. T., Butchart, S. H. M., Pearce-Kelly, P., Kovacs, K. M., Dudgeon, D., Pacifici, M., Rondinini, C., Foden, W. B., Martin, T. G., Mora, C., Bickford, D., & Watson, J. E. M. (2016). The broad footprint of climate change from genes to biomes to people. Science, 354(6313), aaf7671. https://doi.org/10.1126/science.aaf7671

Senf, C., & Seidl, R. (2021). Mapping the forest disturbance regimes of Europe. Nature Sustainability, 4(1), 63-70. https://doi.org/10.1038/s41893-020-00609-y

van den Hoogen, J., Geisen, S., Routh, D., Ferris, H., Traunspurger, W., Wardle, D. A., de Goede, R. G. M., Adams, B. J., Ahmad, W., Andriuzzi, W. S., Bardgett, R. D., Bonkowski, M., Campos-Herrera, R., Cares, J. E., Caruso, T., de Brito Caixeta, L., Chen, X., Costa, S. R., Creamer, R., … Crowther, T. W. (2019). Soil nematode abundance and functional group composition at a global scale. Nature, 572(7768), 194-198. https://doi.org/10.1038/s41586-019-1418-6

van den Hoogen, J., Robmann, N., Routh, D., Lauber, T., van Tiel, N., Danylo, O., & Crowther, T. W. (2021). A geospatial mapping pipeline for ecologists. BioRxiv, 1-9. https://doi.org/10.1101/2021.07.07.451145

Vercauteren, N., Destouni, G., Dahlberg, C. J., & Hylander, K. (2013). Fine-resolved, near-coastal spatiotemporal variation of temperature in response to insolation. Journal of Applied Meteorology and Climatology, 52(5), 1208-1220. https://doi.org/10.1175/JAMC-D-12-0115.1

Willis, K. J., & Bhagwat, S. A. (2009). Biodiversity and climate change. Science, 326(5954), 806-807. https://doi.org/10.1126/science.1178838

Wilson, A. M., & Jetz, W. (2016). Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLOS Biology, 14(3), e1002415. https://doi.org/10.1371/journal.pbio.1002415

Zellweger, F., Coomes, D., Lenoir, J., Depauw, L., Maes, S. L., Wulf, M., Kirby, K. J., Brunet, J., Kopecký, M., Máliš, F., Schmidt, W., Heinrichs, S., den Ouden, J., Jaroszewicz, B., Buyse, G., Spicher, F., Verheyen, K., & De Frenne, P. (2019). Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Global Ecology and Biogeography, 28(12), 1774-1786. https://doi.org/10.1111/geb.12991

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