[en] The extent to which species can balance out the loss of suitable habitats due to climate warming by shifting their ranges is an area of controversy. Here, we assess whether highly efficient wind-dispersed organisms like bryophytes can keep-up with projected shifts in their areas of suitable climate. Using a hybrid statistical-mechanistic approach accounting for spatial and temporal variations in both climatic and wind conditions, we simulate future migrations across Europe for 40 bryophyte species until 2050. The median ratios between predicted range loss vs expansion by 2050 across species and climate change scenarios range from 1.6 to 3.3 when only shifts in climatic suitability were considered, but increase to 34.7–96.8 when species dispersal abilities are added to our models. This highlights the importance of accounting for dispersal restrictions when projecting future distribution ranges and suggests that even highly dispersive organisms like bryophytes are not equipped to fully track the rates of ongoing climate change in the course of the next decades.
Collart, Flavien ✱; Université de Liège - ULiège > Département de Biologie, Ecologie et Evolution > Biologie de l'évolution et de la conservation - aCREA-Ulg
Broennimann, Olivier
Mateo, Ruben G.
Papp, Beata
Muñoz, Jesus
Baurain, Denis ; Université de Liège - ULiège > Département des sciences de la vie > Phylogénomique des eucaryotes
Guisan, Antoine ✱
Vanderpoorten, Alain ✱; Université de Liège - ULiège > Département de Biologie, Ecologie et Evolution > Biologie de l'évolution et de la conservation - aCREA-Ulg
✱ These authors have contributed equally to this work.
Language :
English
Title :
Bryophytes are predicted to lag behind future climate change despite their high dispersal capacities
Publication date :
2020
Journal title :
Nature Communications
eISSN :
2041-1723
Publisher :
Nature Publishing Group, United Kingdom
Volume :
11
Pages :
5601
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
Tier-1 supercomputer CÉCI : Consortium des Équipements de Calcul Intensif
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture ULiège - Université de Liège F.R.S.-FNRS - Fonds de la Recherche Scientifique
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
IPCC. Global Warming of 1.5 °C (World Meteorological Organization, Geneva, 2018).
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018). DOI: 10.1038/s41586-018-0301-1
Rosenzweig, C. et al. Attributing physical and biological impacts to anthropogenic climate change. Nature 453, 353–357 (2008). DOI: 10.1038/nature06937
Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Change 3, 678–682 (2013). DOI: 10.1038/nclimate1887
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012). DOI: 10.1111/j.1461-0248.2011.01736.x
Pearson, R. G. Climate change and the migration capacity of species. Trends Ecol. Evol. 21, 111–113 (2006). DOI: 10.1016/j.tree.2005.11.022
Tzedakis, P. C., Emerson, B. C. & Hewitt, G. M. Cryptic or mystic? Glacial tree refugia in northern Europe. Trends Ecol. Evol. 28, 696–704 (2013). DOI: 10.1016/j.tree.2013.09.001
Nogués-Bravo, D. et al. Cracking the code of biodiversity responses to past climate change. Trends Ecol. Evol. 33, 765–776 (2018). DOI: 10.1016/j.tree.2018.07.005
Corlett, R. T. & Westcott, D. A. Will plant movements keep up with climate change? Trends Ecol. Evol. 28, 482–488 (2013). DOI: 10.1016/j.tree.2013.04.003
Chen, I. C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011). DOI: 10.1126/science.1206432
Dullinger, S. et al. Modelling the effect of habitat fragmentation on climate-driven migration of European forest understorey plants. Divers. Distrib. 21, 1375–1387 (2015). DOI: 10.1111/ddi.12370
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018). DOI: 10.1038/s41586-018-0005-6
Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first century climate change. Nat. Clim. Change 2, 619–622 (2012). DOI: 10.1038/nclimate1514
Rumpf, S. B. et al. Extinction debts and colonization credits of non-forest plants in the European Alps. Nat. Comm. 10, 4293 (2019). DOI: 10.1038/s41467-019-12343-x
Qiao, H., Saupe, E. E., Soberon, J., Peterson, A. T. & Myers, C. E. Impacts of niche breadth and dispersal ability on Macroevolutionary patterns. Am. Nat. 188, 149–162 (2016). DOI: 10.1086/687201
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019). DOI: 10.1126/sciadv.aat4858
Keil, P. et al. Patterns of beta diversity in Europe: the role of climate, land cover and distance across scales. J. Biogeogr. 39, 1473–1486 (2012). DOI: 10.1111/j.1365-2699.2012.02701.x
Svenning, J. C., Normand, S. & Skov, F. Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography 31, 316–326 (2008). DOI: 10.1111/j.0906-7590.2008.05206.x
Saladin, B. et al. Environment and evolutionary history shape phylogenetic turnover in European tetrapods. Nat. Comm. 10, 249 (2019). DOI: 10.1038/s41467-018-08232-4
Schurr, F. M. et al. How to understand species niches and range dynamics: a demographic research agenda for biogeography. J. Biogeogr. 39, 2146–2162 (2012). DOI: 10.1111/j.1365-2699.2012.02737.x
Engler, R. et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter? Ecography 32, 34–45 (2009). DOI: 10.1111/j.1600-0587.2009.05789.x
Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013). DOI: 10.1111/j.1600-0706.2013.00399.x
Zurell, D. et al. Benchmarking novel approaches for modelling species range dynamics. Glob. Change Biol. 22, 2651–2664 (2016). DOI: 10.1111/gcb.13251
Fordham, D. A. et al. How complex should models be? Comparing correlative and mechanistic range dynamics models. Glob. Change Biol. 24, 1357–1370 (2018). DOI: 10.1111/gcb.13935
Shaw, A. J., Szövényi, P. & Shaw, B. Bryophyte diversity and evolution: windows into the early evolution of land plants. Am. J. Bot. 98, 352–369 (2011). DOI: 10.3732/ajb.1000316
Shaw, A. J., Carter, B. E., Aguero, B., Pinheiro da Costa, D. P. & Crowl, A. A. Range change evolution of peat mosses (Sphagnum) within and between climate zones. Glob. Change Biol. 25, 108–120 (2019). DOI: 10.1111/gcb.14485
Kostka, J. E. et al. The Sphagnum microbiome: new insights from an ancient plant lineage. N. Phytol. 211, 57–64 (2016). DOI: 10.1111/nph.13993
He, X., He, K. S. & Hyvönen, J. Will bryophytes survive in a warming world? Persp. Plant Ecol. Evol. Syst. 19, 49–60 (2016). DOI: 10.1016/j.ppees.2016.02.005
Perera-Castro, A. V. et al. It is hot in the sun: Antarctic mosses have high temperature optima for photosynthesis despite cold climate. Front. Plant Sci. 11, 1178 (2020). DOI: 10.3389/fpls.2020.01178
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models, With Applications in R (Cambridge University Press, Cambridge, 2017).
Robledo-Arnuncio, J. J., Klein, E. K., Muller-Landau, H. C. & Santamaría, L. Space, time and complexity in plant dispersal ecology. Mov. Ecol. 2, 16 (2014). DOI: 10.1186/s40462-014-0016-3
Meier, E. S., Lischke, H., Schmatz, D. R. & Zimmermann, N. E. Climate, competition and connectivity affect future migration and ranges of European trees. Glob. Ecol. Biogeogr. 21, 164–178 (2012). DOI: 10.1111/j.1466-8238.2011.00669.x
Prasad, A. M., Gardiner, J. D., Iverson, L. R., Matthews, S. N. & Matthew, P. Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change. Glob. Change Biol. 19, 2196–2208 (2013). DOI: 10.1111/gcb.12204
Bullock, J. M. et al. Modelling spread of British wind-dispersed plants under future wind speeds in a changing climate. J. Ecol. 100, 104–115 (2012). DOI: 10.1111/j.1365-2745.2011.01910.x
Nathan, R. et al. Spread of North American wind-dispersed trees in future environments. Ecol. Lett. 14, 211–219 (2011). DOI: 10.1111/j.1461-0248.2010.01573.x
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, C. I. Climate change threats to plant diversity in Europe. Proc. Natl Acad. Sci. USA 102, 8245–8250 (2005). DOI: 10.1073/pnas.0409902102
Thompson, J. D. Plant Evolution in the Mediterranean (Oxford University Press, Oxford, 2005).
Patiño, J. & Vanderpoorten, A. Bryophyte biogeography. Crit. Rev. Plant Sci. 37, 175–209 (2018). DOI: 10.1080/07352689.2018.1482444
Ledent, A. et al. No borders during the post-glacial assembly of European bryophytes. Ecol. Lett. 22, 973–986 (2019). DOI: 10.1111/ele.13254
Ofori, B. Y., Stow, A. J., Baumgartner, J. B. & Beaumont, L. J. Combining dispersal, landscape connectivity and habitat suitability to assess climate-induced changes in the distribution of Cunningham’s skink, Egernia cunninghami. PLoS ONE 12, e0184193 (2017). DOI: 10.1371/journal.pone.0184193
Hodgetts, N. G. et al. A Miniature World in Decline: European Red List of Mosses, Liverworts and Hornworts (IUCN, Brussels, 2019).
Mateo, R. G., Vanderpoorten, A., Muñoz, J., Laenen, B. & Désamoré, A. Modeling species distributions from heterogeneous data for the biogeographic regionalization of the European bryophyte flora. PloS ONE 8, e55648 (2013). DOI: 10.1371/journal.pone.0055648
Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017). DOI: 10.1111/ecog.02671
Zanatta, F. et al. Measuring spore settling velocity for an improved assessment of dispersal rates in mosses. Ann. Bot. 118, 197–206 (2016). DOI: 10.1093/aob/mcw092
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005). DOI: 10.1002/joc.1276
Niittynen, P. & Luoto, M. The importance of snow in species distribution models of arctic vegetation. Ecography 41, 1024–1037 (2018). DOI: 10.1111/ecog.03348
Lembrechts, J. J. & Lenoir, J. Microclimatic conditions anywhere at any time! Glob. Change Biol. 26, 337–339 (2020). DOI: 10.1111/gcb.14942
Maclean, I. M. Predicting future climate at high spatial and temporal resolution. Glob. Change Biol. 26, 1003–1011 (2020). DOI: 10.1111/gcb.14876
Steen, V., Sofaer, H. R., Skagen, S. K., Ray, A. J. & Noon, B. R. Projecting species’ vulnerability to climate change: which uncertainty sources matter most and extrapolate best? Ecol. Evol. 7, 8841–8851 (2017). DOI: 10.1002/ece3.3403
Goberville, E., Beaugrand, G., Hautekèete, N. C., Piquot, Y. & Luczak, C. Uncertainties in the projection of species distributions related to general circulation models. Ecol. Evol. 5, 1100–1116 (2015). DOI: 10.1002/ece3.1411
Didersky, M. K., Paz, S., Frelich, L. E. & Jagodzinski, A. M. How much does climate change threaten European forest tree species distributions? Glob. Change Biol. 24, 1150–1163 (2017). DOI: 10.1111/gcb.13925
Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013). DOI: 10.1002/jame.20038
Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosc. Model Dev. Discuss 4, 689–763 (2011). DOI: 10.5194/gmdd-4-689-2011
Harris, R. M. B. et al. Climate projections for ecologists. Wires Clim. Change 5, 621–637 (2014). DOI: 10.1002/wcc.291
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophysic. Res. Biogeosci. 116, 1–12 (2011). DOI: 10.1029/2011JG001708
Acevedo, P., Jiménez-Valverde, A., Lobo, J. M. & Real, R. Delimiting the geographical background in species distribution modelling. J. Biogeogr. 39, 1383–1390 (2012). DOI: 10.1111/j.1365-2699.2012.02713.x
Mateo, R. G. et al. From climatic niche conservatism to spatial predictions: what can invasive mosses tell us? Ecography 38, 480–487 (2015). DOI: 10.1111/ecog.01014
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007). DOI: 10.1016/j.tree.2006.09.010
McCullagh, P. & Nelder, J. A. Generalized Linear Models 2nd edn (Chapman & Hall, London, 1989).
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001). DOI: 10.1214/aos/1013203451
Thuiller, W., Georges, D., Engler, R. & Breiner, F. T. Biomod2: ensemble platform for species distribution modeling (The R Foundation, Austria, 2016).
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo‐absences for species distribution models: how, where and how many? Meth. Ecol. Ecol. 3, 327–338 (2012). DOI: 10.1111/j.2041-210X.2011.00172.x
Briscoe, N. J. et al. Forecasting species range dynamics with process‐explicit models: matching methods to applications. Ecol. Lett. 22, 1940–1956 (2019). DOI: 10.1111/ele.13348
Peterson, A. T. Ecological niche conservatism: a time structured of evidence. J. Biogeogr. 38, 817–827 (2011). DOI: 10.1111/j.1365-2699.2010.02456.x
Werkowska, W., Marquez, A. L., Real, R. & Acevedo, P. A practical overview of transferability in species distribution modelling. Environ. Rev. 25, 1–7 (2016).
Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802 (2018). DOI: 10.1016/j.tree.2018.08.001
Engler, R. & Guisan, A. MigClim: predicting plant distribution and dispersal in a changing climate. Divers. Distrib. 15, 590–601 (2009). DOI: 10.1111/j.1472-4642.2009.00566.x
Engler, R., Hordijk, W. & Guisan, A. The MIGCLIM R package— seamless integration of dispersal constraints into projections of species distribution models. Ecography 35, 872–878 (2012). DOI: 10.1111/j.1600-0587.2012.07608.x
Collart, F. & Engler, R. BryophyteDispersion v1.0. (Zenodo, 2020).
Katul, G. G. et al. Mechanistic analytical models for long-distance seed dispersal by wind. Am. Nat. 166, 368–381 (2005). DOI: 10.1086/432589
Lönnell, N., Hylander, K., Jonsson, B. G. & Sundberg, S. The fate of the missing spores—patterns of realized dispersal beyond the closest vicinity of a sporulating moss. PLoS ONE 7, e41987 (2012). DOI: 10.1371/journal.pone.0041987
Vanderpoorten, A. et al. To what extent are bryophytes efficient dispersers? J. Ecol. 107, 2149–2154 (2019). DOI: 10.1111/1365-2745.13161
Rieux, A. et al. Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment. PLoS ONE 9, e103225 (2014). DOI: 10.1371/journal.pone.0103225
Wang, L. & Lu, B.-R. Model-based calculating tool for pollen-mediated gene flow frequencies in plants. AoB Plants. 9, plw086 (2017).
Nathan, R., Horn, H. S., Chave, J. & Levin, S. A. in Seed Dispersal and Frugivory: Ecology, Evolution and Conservation (eds Levey, D. J., Silva, W. R. & Galetti, M.) 69–82 (CAB International, Wallingford, 2002).
Gualtieri, G. & Secci, S. Comparing methods to calculate atmospheric stability-dependent wind speed profiles: a case study on coastal location. Renew. Energy 36, 2189–2204 (2011). DOI: 10.1016/j.renene.2011.01.023
Patiño, J. et al. Approximate Bayesian Computation reveals the crucial role of oceanic islands for the assembly of continental biodiversity. Syst. Biol. 64, 579–589 (2015). DOI: 10.1093/sysbio/syv013
Ingenloff, K. et al. 2017 Predictable invasion dynamics in North American populations of the Eurasian collared dove Streptopelia decaocto. Proc. R. Soc. B 284, 20171157 (2017). DOI: 10.1098/rspb.2017.1157
Shortlidge, E. E. et al. Passive warming reduces stress and shifts reproductive effort in the Antarctic moss, Polytrichastrum alpinum. Ann. Bot. 119, 27–38 (2017). DOI: 10.1093/aob/mcw201
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
