Species distributions are conventionally modelled using coarse-grained macroclimate data measured in open areas, potentially leading to biased predictions since most terrestrial species reside in the shade of trees. For forest plant species across Europe, we compared conventional macroclimate-based species distribution models (SDMs) with models corrected for forest microclimate buffering. We show that microclimate-based SDMs at high spatial resolution outperformed models using macroclimate and microclimate data at coarser resolution. Additionally, macroclimate-based models introduced a systematic bias in modelled species response curves, which could result in erroneous range shift predictions. Critically important for conservation science, these models were unable to identify warm and cold refugia at the range edges of species distributions. Our study emphasizes the crucial role of microclimate data when SDMs are used to gain insights into biodiversity conservation in the face of climate change, particularly given the growing policy and management focus on the conservation of refugia worldwide.

Department of Botany Faculty of Science Charles University Prague 2 Czech Republic

Department of Earth and Environmental Sciences Celestijnenlaan 200E Leuven Belgium

Faculty of Environmental Sciences Czech University of Life Sciences Prague Prague 6 Suchdol Czech Republic

Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Prague 6 Suchdol Czech Republic

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

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

KU Leuven Plant Institute KU Leuven Leuven Belgium

Research Group PLECO University of Antwerp Wilrijk Belgium

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

Zobrazit více v PubMed

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), 170191. Available from: https://doi.org/10.1038/sdata.2017.191

Beauregard, F. & de Blois, S. (2014) Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models. PLoS One, 9(3), e92642. Available from: https://doi.org/10.1371/journal.pone.0092642

Benito, B.M., Cayuela, L. & Albuquerque, F.S. (2013) The impact of modelling choices in the predictive performance of richness maps derived from species-distribution models: guidelines to build better diversity models. Methods in Ecology and Evolution, 4(4), 327-335. Available from: https://doi.org/10.1111/2041-210x.12022

Bertrand, R., Perez, V. & Gégout, J.-C. (2012) Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France. Global Change Biology, 18(8), 2648-2660. Available from: https://doi.org/10.1111/j.1365-2486.2012.02679.x

Booth, T.H. (2022) Checking bioclimatic variables that combine temperature and precipitation data before their use in species distribution models. Austral Ecology, 47(7), 1506-1514. Available from: https://doi.org/10.1111/aec.13234

Booth, T.H., Nix, H.A., Busby, J.R. & Hutchinson, M.F. (2014) Bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 20(1), 1-9. Available from: https://doi.org/10.1111/ddi.12144

Burnham, K.P. & Anderson, D.R. (2004) Multimodel inference. Sociological Methods & Research, 33(2), 261-304. Available from: https://doi.org/10.1177/0049124104268644

Burrows, M.T., Schoeman, D.S., Buckley, L.B., Moore, P., Poloczanska, E.S., Brander, K.M. et al. (2011) The pace of shifting climate in marine and terrestrial ecosystems. Science, 334(6056), 652-655. Available from: https://doi.org/10.1126/science.1210288

Caron, M.M., Zellweger, F., Verheyen, K., Baeten, L., Hédl, R., Bernhardt-Römermann, M. et al. (2021) Thermal differences between juveniles and adults increased over time in European forest trees. Journal of Ecology, 109(11), 3944-3957. Available from: https://doi.org/10.1111/1365-2745.13773

Chauvier, Y., Descombes, P., Guéguen, M., Boulangeat, L., Thuiller, W. & Zimmermann, N.E. (2022) Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity. Ecography, 2022(10). Available from: https://doi.org/10.1111/ecog.05973

Cheng, Y., Tjaden, N.B., Jaeschke, A., Thomas, S.M. & Beierkuhnlein, C. (2021) Using centroids of spatial units in ecological niche modelling: effects on model performance in the context of environmental data grain size. Global Ecology and Biogeography, 30(3), 611-621. Available from: https://doi.org/10.1111/geb.13240

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

De Lombaerde, E., Vangansbeke, P., Lenoir, J., Van Meerbeek, K., Lembrechts, J., Rodríguez-Sánchez, F. et al. (2022) Maintaining forest cover to enhance temperature buffering under future climate change. Science of the Total Environment, 810, 151338. Available from: https://doi.org/10.1016/j.scitotenv.2021.151338

Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G. et al. (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27-46. Available from: https://doi.org/10.1111/j.1600-0587.2012.07348.x

Elith, J. & Leathwick, J.R. (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677-697. Available from: https://doi.org/10.1146/annurev.ecolsys.110308.120159

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. Available from: https://doi.org/10.1002/joc.5086

Fourcade, Y., Besnard, A.G. & Secondi, J. (2018) Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography, 27(2), 245-256. Available from: https://doi.org/10.1111/geb.12684

Gábor, L., Jetz, W., Lu, M., Rocchini, D., Cord, A., Malavasi, M. et al. (2022) Positional errors in species distribution modelling are not overcome by the coarser grains of analysis. Methods in Ecology and Evolution, 13(10), 2289-2302. Available from: https://doi.org/10.1111/2041-210X.13956

Geiger, R. (1950) The climate near the ground. Cambridge, Mass: Harvard University Press, p. 482.

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. Available from: https://doi.org/10.1016/j.agrformet.2017.12.252

Guisan, A., Graham, C.H., Elith, J. & Huettmann, F. (2007) Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13(3), 332-340. Available from: https://doi.org/10.1111/j.1472-4642.2007.00342.x

Haesen, S., Lembrechts, J.J., De Frenne, P., Lenoir, J., Aalto, J., Ashcroft, M.B. et al. (2021) ForestTemp-sub-canopy microclimate temperatures of European forests. Global Change Biology, 27(23), 6307-6319. Available from: https://doi.org/10.1111/gcb.15892

Haesen, S., Lembrechts, J.J., De Frenne, P., Lenoir, J., Aalto, J., Ashcroft, M.B. et al. (2023) ForestClim-bioclimatic variables for microclimate temperatures of European forests. Global Change Biology, 29(11), 2886-2892. Available from: https://doi.org/10.1111/gcb.16678

Hageer, Y., Esperón-Rodríguez, M., Baumgartner, J.B. & Beaumont, L.J. (2017) Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? PeerJ, 5(6), e3446. Available from: https://doi.org/10.7717/peerj.3446

Harwood, T.D., Mokany, K. & Paini, D.R. (2014) Microclimate is integral to the modeling of plant responses to macroclimate. Proceedings of the National Academy of Sciences, 111(13), E1164-E1165. Available from: https://doi.org/10.1073/pnas.1400069111

Heinken, T., Diekmann, M., Liira, J., Orczewska, A., Schmidt, M., Brunet, J. et al. (2022) The European Forest Plant species list (EuForPlant): concept and applications. Journal of Vegetation Science, 33(3). Available from: https://doi.org/10.1111/jvs.13132

Hermy, M., Honnay, O., Firbank, L., Grashof-Bokdam, C. & Lawesson, J.E. (1999) An ecological comparison between ancient and other forest plant species of Europe, and the implications for forest conservation. Biological Conservation, 91(1), 9-22. Available from: https://doi.org/10.1016/S0006-3207(99)00045-2

Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2), 142-152. Available from: https://doi.org/10.1016/j.ecolmodel.2006.05.017

Hudson, J., Castilla, J.C., Teske, P.R., Beheregaray, L.B., Haigh, I.D., McQuaid, C.D. et al. (2021) Genomics-informed models reveal extensive stretches of coastline under threat by an ecologically dominant invasive species. Proceedings of the National Academy of Sciences, 118(23), e2022169118. Available from: https://doi.org/10.1073/pnas.2022169118

Hylander, K., Greiser, C., Christiansen, D.M. & Koelemeijer, I.A. (2022) Climate adaptation of biodiversity conservation in managed forest landscapes. Conservation Biology, 36(3), e13847. Available from: https://doi.org/10.1111/cobi.13847

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

Jiménez, L. & Soberón, J. (2020) Leaving the area under the receiving operating characteristic curve behind: an evaluation method for species distribution modelling applications based on presence-only data. Methods in Ecology and Evolution, 11(12), 1571-1586. Available from: https://doi.org/10.1111/2041-210X.13479

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. et al. (2017) Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. Available from: https://doi.org/10.1038/sdata.2017.122

Kass, J.M., Muscarella, R., Galante, P.J., Bohl, C.L., Pinilla-Buitrago, G.E., Boria, R.A. et al. (2021) ENMeval 2.0: redesigned for customizable and reproducible modeling of species' niches and distributions. Methods in Ecology and Evolution, 12(9), 1602-1608. Available from: https://doi.org/10.1111/2041-210X.13628

Kearney, M.R., Gillingham, P.K., Bramer, I., Duffy, J.P. & Maclean, I.M.D. (2020) A method for computing hourly, historical, terrain-corrected microclimate anywhere on earth. Methods in Ecology and Evolution, 11(1), 38-43. Available from: https://doi.org/10.1111/2041-210X.13330

Kopáček, J., Bače, R., Hejzlar, J., Kaňa, J., Kučera, T., Matějka, K. et al. (2020) Changes in microclimate and hydrology in an unmanaged mountain forest catchment after insect-induced tree dieback. Science of the Total Environment, 720, 137518. Available from: https://doi.org/10.1016/j.scitotenv.2020.137518

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

Lake, T.A., Briscoe Runquist, R.D. & Moeller, D.A. (2020) Predicting range expansion of invasive species: pitfalls and best practices for obtaining biologically realistic projections. Diversity and Distributions, 26(12), 1767-1779. Available from: https://doi.org/10.1111/ddi.13161

Lancaster, L.T. & Humphreys, A.M. (2020) Global variation in the thermal tolerances of plants. Proceedings of the National Academy of Sciences, 117(24), 13580-13587. Available from: https://doi.org/10.1073/pnas.1918162117

Lembrechts, J.J. (2023) Microclimate alters the picture. Nature Climate Change, 13(5), 423-424. Available from: https://doi.org/10.1038/s41558-023-01632-5

Lembrechts, J.J., Nijs, I. & Lenoir, J. (2019) Incorporating microclimate into species distribution models. Ecography, 42(7), 1267-1279. Available from: https://doi.org/10.1111/ecog.03947

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

Lenth, R.V. (2021) Emmeans: estimated marginal means, aka least-squares means.

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. Available from: https://doi.org/10.1007/s10980-019-00903-x

Maclean, I.M.D. (2020) Predicting future climate at high spatial and temporal resolution. Global Change Biology, 26(2), 1003-1011. Available from: https://doi.org/10.1111/gcb.14876

Maclean, I.M.D., Duffy, J.P., Haesen, S., Govaert, S., De Frenne, P., Vanneste, T. et al. (2021) On the measurement of microclimate. Methods in Ecology and Evolution, 12(8), 1397-1410. Available from: https://doi.org/10.1111/2041-210X.13627

Maclean, I.M.D. & Early, R. (2023) Macroclimate data overestimate range shifts of plants in response to climate change. Nature Climate Change, 13(5), 484-490. Available from: https://doi.org/10.1038/s41558-023-01650-3

Man, M., Wild, J., Macek, M. & Kopecký, M. (2022) Can high-resolution topography and forest canopy structure substitute microclimate measurements? Bryophytes say no. Science of the Total Environment, 821, 153377. Available from: https://doi.org/10.1016/j.scitotenv.2022.153377

Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J.M., Uriarte, M. et al. (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5(11), 1198-1205. Available from: https://doi.org/10.1111/2041-210X.12261

Nadeau, C.P., Giacomazzo, A. & Urban, M.C. (2022) Cool microrefugia accumulate and conserve biodiversity under climate change. Global Change Biology, 28(10), 3222-3235. Available from: https://doi.org/10.1111/gcb.16143

Norberg, A., Abrego, N., Blanchet, F.G., Adler, F.R., Anderson, B.J., Anttila, J. et al. (2019) A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs, 89(3), e01370. Available from: https://doi.org/10.1002/ecm.1370

Ovaskainen, O. & Abrego, N. (2020) Joint species distribution modelling. Cambridge University Press. Available from: https://doi.org/10.1017/9781108591720

Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E. & Blair, M.E. (2017) Opening the black box: an open-source release of Maxent. Ecography, 40(7), 887-893. Available from: https://doi.org/10.1111/ecog.03049

R Core Team. (2021) R: A Language and Environment for Statistical Computing.

Radosavljevic, A. & Anderson, R.P. (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4), 629-643. Available from: https://doi.org/10.1111/jbi.12227

Randin, C.F., Engler, R., Normand, S., Zappa, M., Zimmermann, N.E., Pearman, P.B. et al. (2009) Climate change and plant distribution: local models predict high-elevation persistence. Global Change Biology, 15(6), 1557-1569. Available from: https://doi.org/10.1111/j.1365-2486.2008.01766.x

Roe, N.A., Ducey, M.J., Lee, T.D., Fraser, O.L., Colter, R.A. & Hallett, R.A. (2022) Soil chemical variables improve models of understorey plant species distributions. Journal of Biogeography, 49(4), 753-766. Available from: https://doi.org/10.1111/jbi.14344

Sanczuk, P., De Pauw, K., De Lombaerde, E., Luoto, M., Meeussen, C., Govaert, S. et al. (2023) Microclimate and forest density drive plant population dynamics under climate change. Nature Climate Change, 13(8), 840-847. Available from: https://doi.org/10.1038/s41558-023-01744-y

Sentinella, A.T., Warton, D.I., Sherwin, W.B., Offord, C.A. & Moles, A.T. (2020) Tropical plants do not have narrower temperature tolerances, but are more at risk from warming because they are close to their upper thermal limits. Global Ecology and Biogeography, 29(8), 1387-1398. Available from: https://doi.org/10.1111/geb.13117

Slavich, E., Warton, D.I., Ashcroft, M.B., Gollan, J.R. & Ramp, D. (2014) Topoclimate versus macroclimate: how does climate mapping methodology affect species distribution models and climate change projections? Diversity and Distributions, 20(8), 952-963. Available from: https://doi.org/10.1111/ddi.12216

Stark, J.R. & Fridley, J.D. (2022) Microclimate-based species distribution models in complex forested terrain indicate widespread cryptic refugia under climate change. Global Ecology and Biogeography, 31(3), 562-575. Available from: https://doi.org/10.1111/geb.13447

Sunday, J.M., Bates, A.E. & Dulvy, N.K. (2012) Thermal tolerance and the global redistribution of animals. Nature Climate Change, 2(9), 686-690. Available from: https://doi.org/10.1038/nclimate1539

Svenning, J.-C., Normand, S. & Skov, F. (2008) Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography, 31(3), 316-326. Available from: https://doi.org/10.1111/j.0906-7590.2008.05206.x

Thom, D., Sommerfeld, A., Sebald, J., Hagge, J., Müller, J. & Seidl, R. (2020) Effects of disturbance patterns and deadwood on the microclimate in European beech forests. Agricultural and Forest Meteorology, 291, 108066. Available from: https://doi.org/10.1016/j.agrformet.2020.108066

van Proosdij, A.S.J., Sosef, M.S.M., Wieringa, J.J. & Raes, N. (2016) Minimum required number of specimen records to develop accurate species distribution models. Ecography, 39(6), 542-552. Available from: https://doi.org/10.1111/ecog.01509

Venables, W.N. & Ripley, B.D. (2002) Modern applied statistics with S, Fourth edition. New York: Springer.

Wüest, R.O., Zimmermann, N.E., Zurell, D., Alexander, J.M., Fritz, S.A., Hof, C. et al. (2020) Macroecology in the age of big data-where to go from here? Journal of Biogeography, 47(1), 1-12. Available from: https://doi.org/10.1111/jbi.13633

Zellweger, F., Coomes, D., Lenoir, J., Depauw, L., Maes, S.L., Wulf, M. et al. (2019) Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Global Ecology and Biogeography, 28(12), 1774-1786. Available from: https://doi.org/10.1111/geb.12991

Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J. et al. (2020) A standard protocol for reporting species distribution models. Ecography, 43(9), 1261-1277. Available from: https://doi.org/10.1111/ecog.04960

Zurell, D., Thuiller, W., Pagel, J., Cabral, J.S., Münkemüller, T., Gravel, D. et al. (2016) Benchmarking novel approaches for modelling species range dynamics. Global Change Biology, 22(8), 2651-2664. Available from: https://doi.org/10.1111/gcb.13251