Host tree availability shapes potential distribution of a target epiphytic moss species more than direct climate effects.

Biotic interactions Bryophytes Climate change Forest management Nature conservation Species distribution model

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
08 Aug 2024
Historique:
received: 02 03 2024
accepted: 30 07 2024
medline: 9 8 2024
pubmed: 9 8 2024
entrez: 8 8 2024
Statut: epublish

Résumé

Climate change significantly impacts the distribution of woody plants, indirectly influencing the dynamics of entire ecosystems. Understanding species' varied responses to the environment and their reliance on biotic interactions is crucial for predicting the global changes' impact on woodland biodiversity. Our study focusses on Dicranum viride, a moss of conservation priority, and its dependence on specific phorophytes (host trees). Using species distribution modelling (SDM) techniques, we initially modelled its distribution using climate-only variables. As a novel approach, we also modelled the distribution of the main phorophyte species and incorporated them into D. viride SDM alongside climate data. Finally, we analysed the overlap of climatic and geographic niches between the epiphyte and the phorophytes. Inclusion of biotic interactions significantly improved model performance, with phorophyte availability emerging as the primary predictor. This underscores the significance of epiphyte-phorophyte interactions, supported by substantial niche overlap. Predictions indicate a potential decline in the suitability of most of the current areas for D. viride, with noticeable shifts towards the northern regions of Europe. Our study underscores the importance of incorporating biotic interactions into SDMs, especially for dependent organisms. Understanding such connections is essential to implement successful conservation strategies and adapt forest management practices to environmental changes.

Identifiants

pubmed: 39117663
doi: 10.1038/s41598-024-69041-y
pii: 10.1038/s41598-024-69041-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

18388

Informations de copyright

© 2024. The Author(s).

Références

Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Global Change Biol. 24, 1150–1163. https://doi.org/10.1111/gcb.13925 (2018).
doi: 10.1111/gcb.13925
Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103. https://doi.org/10.1016/j.baae.2020.04.003 (2020).
doi: 10.1016/j.baae.2020.04.003
Bärmann, L., Kaufmann, S., Weimann, S. & Hauck, M. Future forests and biodiversity: Effects of Douglas fir introduction into temperate beech forests on plant diversity. For. Ecol. Manage. 545, 121286. https://doi.org/10.1016/j.foreco.2023.121286 (2023).
doi: 10.1016/j.foreco.2023.121286
Salesa, D., Baeza, M. J., Pérez-Ferrándiz, E. & Santana, V. M. Longer summer seasons after fire induce permanent drought legacy effects on Mediterranean plant communities dominated by obligate seeders. Sci. Total Environ. 822, 153655. https://doi.org/10.1016/j.scitotenv.2022.153655 (2022).
doi: 10.1016/j.scitotenv.2022.153655 pubmed: 35124057
Hanel, M. et al. Revisiting the recent European droughts from a long-term perspective. Sci. Rep. 8, 9499. https://doi.org/10.1038/s41598-018-27464-4 (2018).
doi: 10.1038/s41598-018-27464-4 pubmed: 29934591 pmcid: 6015036
Barkman, J. J. Phytosociology and ecology of cryptogamic epiphytes: including a taxonomic survey and description of their vegetation units in Europe. (Van Gorcum, 1958).
Wierzcholska, S. et al. Natural forest remnants as refugia for bryophyte diversity in a transformed mountain river valley landscape. Sci. Total Environ. 640–641, 954–964. https://doi.org/10.1016/j.scitotenv.2018.05.340 (2018).
doi: 10.1016/j.scitotenv.2018.05.340 pubmed: 30021328
Łubek, A., Kukwa, M., Jaroszewicz, B. & Czortek, P. Identifying mechanisms shaping lichen functional diversity in a primeval forest. For. Ecol. Manage. 475, 118434. https://doi.org/10.1016/j.foreco.2020.118434 (2020).
doi: 10.1016/j.foreco.2020.118434
Karpińska, O., Kamionka-Kanclerska, K., Czortek, P., Dyderski, M. K. & Czeszczewik, D. Spatial niche segregation between bird species in the Białowieża primeval forest (NE Poland). For. Ecosyst. 10, 100129. https://doi.org/10.1016/j.fecs.2023.100129 (2023).
doi: 10.1016/j.fecs.2023.100129
Wysocki, A., Czortek, P., Konowalik, K., Proćków, J. & Wierzcholska, S. Opposite effects of host tree species on the realised niche of Dicranum viride—A model species belonging to the group of endangered epiphytes. For. Ecol. Manage. 545, e121303. https://doi.org/10.1016/j.foreco.2023.121303 (2023).
doi: 10.1016/j.foreco.2023.121303
Oakes, L. E., Hennon, P. E., O’Hara, K. L. & Dirzo, R. Long-term vegetation changes in a temperate forest impacted by climate change. Ecosphere 5, 1–28. https://doi.org/10.1890/ES14-00225.1 (2014).
doi: 10.1890/ES14-00225.1
Hartmann, H. et al. Climate change risks to global forest health: emergence of unexpected events of elevated tree mortality worldwide. Annu. Rev. Plant Biol. 73, 673–702. https://doi.org/10.1146/annurev-arplant-102820-012804 (2022).
doi: 10.1146/annurev-arplant-102820-012804 pubmed: 35231182
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426. https://doi.org/10.1038/s41467-017-02690-y (2018).
doi: 10.1038/s41467-017-02690-y pubmed: 29382833 pmcid: 5789858
Sangüesa-Barreda, G. et al. Warmer springs have increased the frequency and extension of late-frost defoliations in southern European beech forests. Sci. Total Environ. 775, 145860. https://doi.org/10.1016/j.scitotenv.2021.145860 (2021).
doi: 10.1016/j.scitotenv.2021.145860 pubmed: 33631566
Martinez del Castillo, E. et al. Climate-change-driven growth decline of European beech forests. Commun. Biol. 5, 163. https://doi.org/10.1038/s42003-022-03107-3 (2022).
Bosela, M. et al. Climate warming induced synchronous growth decline in Norway spruce populations across biogeographical gradients since 2000. Sci. Total Environ. 752, 141794. https://doi.org/10.1016/j.scitotenv.2020.141794 (2021).
doi: 10.1016/j.scitotenv.2020.141794 pubmed: 32898800
Doležal, J., Mazůrek, P. & Klimešová, J. Oak decline in southern moravia: The association between climate change and early and late wood formation in oaks. Preslia 82, 289–306 (2010).
Taccoen, A., Piedallu, C., Seynave, I., Gégout-Petit, A. & Gégout, J.-C. Climate change-induced background tree mortality is exacerbated towards the warm limits of the species ranges. Ann. For. Sci. 79, 23. https://doi.org/10.1186/s13595-022-01142-y (2022).
doi: 10.1186/s13595-022-01142-y
Pecchi, M. et al. Species distribution modelling to support forest management. A literature review. Ecol. Model. 411, 108817. https://doi.org/10.1016/j.ecolmodel.2019.108817 (2019).
doi: 10.1016/j.ecolmodel.2019.108817
Dymytrova, L., Stofer, S., Ginzler, C., Breiner, F. T. & Scheidegger, C. Forest-structure data improve distribution models of threatened habitat specialists: Implications for conservation of epiphytic lichens in forest landscapes. Biol. Conserv. 196, 31–38. https://doi.org/10.1016/j.biocon.2016.01.030 (2016).
doi: 10.1016/j.biocon.2016.01.030
Puchałka, R. et al. Predicted range shifts of alien tree species in Europe. Agric. For. Meteorol. 341, 109650. https://doi.org/10.1016/j.agrformet.2023.109650 (2023).
doi: 10.1016/j.agrformet.2023.109650
Nascimbene, J., Benesperi, R., Casazza, G., Chiarucci, A. & Giordani, P. Range shifts of native and invasive trees exacerbate the impact of climate change on epiphyte distribution: The case of lung lichen and black locust in Italy. Sci. Total Environ. 735, 139537. https://doi.org/10.1016/j.scitotenv.2020.139537 (2020).
doi: 10.1016/j.scitotenv.2020.139537 pubmed: 32485454
Cieśliński, S. et al. Relicts of the primeval (virgin) forest. Relict phenomena. Phytocenosis 6, 197–216 (1996).
Mölder, A., Schmidt, M., Engel, F., Schönfelder, E. & Schulz, F. Bryophytes as indicators of ancient woodlands in Schleswig-Holstein (Northern Germany). Ecol. Indicators 54, 12–30. https://doi.org/10.1016/j.ecolind.2015.01.044 (2015).
doi: 10.1016/j.ecolind.2015.01.044
Frego, K. A. Bryophytes as potential indicators of forest integrity. For. Ecol. Manage. 242, 65–75. https://doi.org/10.1016/j.foreco.2007.01.030 (2007).
doi: 10.1016/j.foreco.2007.01.030
Gignac, L. D. Bryophytes as indicators of climate change. The Bryologist 104, 410–420 (2001).
doi: 10.1639/0007-2745(2001)104[0410:BAIOCC]2.0.CO;2
Proctor, M. C. F. in Bryophyte Biology (eds A. Jonathan Shaw & Bernard Goffinet) 237–268 (Cambridge University Press, 2008).
Norby, R. J., Childs, J., Hanson, P. J. & Warren, J. M. Rapid loss of an ecosystem engineer: Sphagnum decline in an experimentally warmed bog. Ecol. Evol. 9, 12571–12585. https://doi.org/10.1002/ece3.5722 (2019).
doi: 10.1002/ece3.5722 pubmed: 31788198 pmcid: 6875578
Zanatta, F. et al. Bryophytes are predicted to lag behind future climate change despite their high dispersal capacities. Nat. Commun. 11, 5601. https://doi.org/10.1038/s41467-020-19410-8 (2020).
doi: 10.1038/s41467-020-19410-8 pubmed: 33154374 pmcid: 7645420
Fialová, L., Plášek, V., Klichowska, E., Guo, S. & Nobis, M. Temperature and precipitation more than tree cover affect the distribution patterns of epiphytic mosses within the Orthotrichaceae family in China and adjacent areas. Plants 12, 222. https://doi.org/10.3390/plants12010222 (2023).
doi: 10.3390/plants12010222 pubmed: 36616349 pmcid: 9824502
Patiño, J. et al. Climate threat on the Macaronesian endemic bryophyte flora. Sci. Rep. 6, 29156. https://doi.org/10.1038/srep29156 (2016).
doi: 10.1038/srep29156 pubmed: 27377592 pmcid: 4932530
He, X., He, K. S. & Hyvönen, J. Will bryophytes survive in a warming world?. Perspect. Plant Ecol. Evol. Syst. 19, 49–60. https://doi.org/10.1016/j.ppees.2016.02.005 (2016).
doi: 10.1016/j.ppees.2016.02.005
Chawengkul, P., Tiwutanon, P., Sanevas, N. & Kraichak, E. Predicting the future distribution of Leucobryum aduncum under climate change. Diversity 16, 1–12. https://doi.org/10.3390/d16020125 (2024).
doi: 10.3390/d16020125
Callaghan, D. A. et al. Global geographical range and population size of the habitat specialist Codonoblepharon forsteri (Dicks.) Goffinet in a changing climate. J. Bryol. 44, 35–50. https://doi.org/10.1080/03736687.2022.2032541 (2022).
Plášek, V. et al. Quo Vadis, Orthotrichum pulchellum? A journey of epiphytic moss across the European continent. Plants 11, 2669. https://doi.org/10.3390/plants11202669 (2022).
doi: 10.3390/plants11202669 pubmed: 36297693 pmcid: 9610992
Poncet, R., Hugonnot, V. & Vergne, T. Modelling the distribution of the epiphytic moss Orthotrichum rogeri to assess target areas for protected status. Cryptogam. Bryol. 36, 3–17. https://doi.org/10.7872/cryb.v36.iss1.2015.3 (2015).
doi: 10.7872/cryb.v36.iss1.2015.3
Hsu, R.C.-C. et al. Simulating climate change impacts on forests and associated vascular epiphytes in a subtropical island of East Asia. Divers. Distrib. 18, 334–347. https://doi.org/10.1111/j.1472-4642.2011.00819.x (2012).
doi: 10.1111/j.1472-4642.2011.00819.x
Mitchell, R. J., Hewison, R. L., Hester, A. J., Broome, A. & Kirby, K. J. Potential impacts of the loss of Fraxinus excelsior (Oleaceae) due to ash dieback on woodland vegetation in Great Britain. N. J. Bot. 6, 2–15. https://doi.org/10.1080/20423489.2016.1171454 (2016).
doi: 10.1080/20423489.2016.1171454
da Cunha, H. F., Ferreira, É. D., Tessarolo, G. & Nabout, J. C. Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite. Biotropica 50, 625–632. https://doi.org/10.1111/btp.12555 (2018).
doi: 10.1111/btp.12555
Giannini, T. C., Chapman, D. S., Saraiva, A. M., Alves-dos-Santos, I. & Biesmeijer, J. C. Improving species distribution models using biotic interactions: A case study of parasites, pollinators and plants. Ecography 36, 649–656. https://doi.org/10.1111/j.1600-0587.2012.07191.x (2013).
doi: 10.1111/j.1600-0587.2012.07191.x
Trainor, A. M., Schmitz, O. J., Ivan, J. S. & Shenk, T. M. Enhancing species distribution modeling by characterizing predator–prey interactions. Ecol. Appl. 24, 204–216. https://doi.org/10.1890/13-0336.1 (2014).
doi: 10.1890/13-0336.1 pubmed: 24640545
Palacio, F. X. & Girini, J. M. Biotic interactions in species distribution models enhance model performance and shed light on natural history of rare birds: A case study using the straight-billed reedhaunter Limnoctites rectirostris. J. Avian Biol. 49, e01743. https://doi.org/10.1111/jav.01743 (2018).
doi: 10.1111/jav.01743
Piwowarczyk, R. & Kolanowska, M. Effect of global warming on the potential distribution of a holoparasitic plant (Phelypaea tournefortii): both climate and host distribution matter. Sci. Rep. 13, 10741. https://doi.org/10.1038/s41598-023-37897-1 (2023).
doi: 10.1038/s41598-023-37897-1 pubmed: 37400559 pmcid: 10318063
Han, L. et al. Preferred prey reduce species realized niche shift and improve range expansion prediction. Sci. Total Environ. 859, 160370. https://doi.org/10.1016/j.scitotenv.2022.160370 (2023).
doi: 10.1016/j.scitotenv.2022.160370 pubmed: 36414055
Kiehl, K., Kirmer, A., Donath, T. W., Rasran, L. & Hölzel, N. Species introduction in restoration projects—Evaluation of different techniques for the establishment of semi-natural grasslands in Central and Northwestern Europe. Basic Appl. Ecol. 11, 285–299. https://doi.org/10.1016/j.baae.2009.12.004 (2010).
doi: 10.1016/j.baae.2009.12.004
Schumacker, R. & Martiny, P. in Red data book of European bryophytes. Part 2 29–193 (European Committee for Conservation of Bryophytes, 1995).
Dierßen, K. Distribution, ecological amplitude and phytosociological characterization of European bryophytes. (Cramer in der Gebrüder Borntraeger Verlagsbuchhandlung, 2001).
Stebel, A. in Gatunki roślin. Poradniki ochrony siedlisk i gatunków Natura 2000—podręcznik metodyczny [Plant species. Natura 2000 Habitats and Species Conservation Manuals - methodological handbook] Vol. 4 (eds B. Sudnik-Wójcikowska & H. Werblan-Jakubiec) 36–38 (Ministerstwo Środowiska, 2004).
Ignatova, E. A. & Fedosov, V. E. Species of Dicranum (Dicranaceae, Bryophyta) with fragile leaves in Russia. Arctoa 17, 63–84. https://doi.org/10.15298/arctoa.17.05 (2008).
Ellenberg, H. et al. Zeigerwerte von Pflanzen in Mitteleuropa. Scr. Geobot. 18, 175–214 (1992).
Bardat, J. & Hugonnot, V. Les Communautés à Dicranum viride (Sull. & Lesq.) Lindb. En France Métropolitaine. Cryptogam. Bryol. 23, 123–147 (2002).
Stebel, A., Cykowska-Marzencka, B. & Żarnowiec, J. in Chorological studies on Polish Carpathian bryophytes (eds Adam Stebel & Ryszard Ochyra) 99–110 (SORUS SC, 2011).
Ignatov, M. S. & Ignatova, E. A. Sphagnaceae-Hedwigiaceae [Moss Flora of the Middle European Russia, Vol. 1: Sphagnaceae-Hedwigiaceae]. (KMK Scientific Press Ltd., 2003).
Baisheva, E., Mežaka, A., Shirokikh, P. & Martynenko, V. Ecology and distribution of Dicranum viride (Sull. Lesq.) Lindb. (Bryophyta) in the Southern Ural Mts. Arctoa 22, 41–50. https://doi.org/10.15298/arctoa.22.07 (2013).
Mežaka, A. et al. Rare epiphytic bryophyte Dicranum viride (Sull. & Lesq.) Lindb. (Dicranaceae, Bryophyta) spatial patterns in boreo-nemoral forest landscape. Nova Hedwig. 116, 283–297. https://doi.org/10.1127/nova_hedwigia/2023/0837 (2023).
Percel, G., Bouget, C., Gosselin, M., Dumas, Y. & Laroche, F. Disentangling fine- and large-scale colonization processes in metapopulation dynamics: A case study on a threatened epiphytic bryophyte. Oikos 2024, e10052. https://doi.org/10.1111/oik.10052 (2024).
doi: 10.1111/oik.10052
Mežaka, A., Pošiva-Bunkovska, A., Oļehnoviča, E., Nitcis, M. & Bambe, B. EU habitat directive bryophyte species distribution and conservation in Latvia. Biologia 79, 1193–1207. https://doi.org/10.1007/s11756-023-01571-8 (2024).
doi: 10.1007/s11756-023-01571-8
Wierzcholska, S., Dyderski, M. K. & Jagodziński, A. M. Potential distribution of an epiphytic bryophyte depends on climate and forest continuity. Global Planet. Change 193, 103270. https://doi.org/10.1016/j.gloplacha.2020.103270 (2020).
doi: 10.1016/j.gloplacha.2020.103270
Düll, R. & Meinunger, L. Deutschlands Moose: die Verbreitung der deutschen Moose in der BR Deutschland und in der DDR, ihre Höhenverbreitung, ihre Arealtypen, sowie Angaben zum Rückgang der Arten. (IDH-Verlag, 1989).
Hedenäs, L. & Bisang, I. Key to European Dicranum species. Herzogia 17, 179–197 (2004).
European Environment Information and Observation Network. Reporting under Article 17 of the Habitats Directive: Dicranum viride. https://nature-art17.eionet.europa.eu/article17/ (2024).
Konowalik, K. Phylogeography and colonization pattern of subendemic round-leaved oxeye daisy from the Dinarides to the Carpathians. Sci. Rep. 12, 16443. https://doi.org/10.1038/s41598-022-19619-1 (2022).
doi: 10.1038/s41598-022-19619-1 pubmed: 36180475 pmcid: 9525303
de Oliveira, G., Rangel, T. F., Lima-Ribeiro, M. S., Terribile, L. C. & Diniz-Filho, J. A. F. Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: A new approach based on environmentally equidistant records. Ecography 37, 637–647. https://doi.org/10.1111/j.1600-0587.2013.00564.x (2014).
doi: 10.1111/j.1600-0587.2013.00564.x
Sobral-Souza, T., Lima-Ribeiro, M. S. & Solferini, V. N. Biogeography of Neotropical Rainforests: Past connections between Amazon and Atlantic Forest detected by ecological niche modeling. Evol. Ecol. 29, 643–655. https://doi.org/10.1007/s10682-015-9780-9 (2015).
doi: 10.1007/s10682-015-9780-9
Varela, S., Anderson, R. P., García-Valdés, R. & Fernández-González, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37, 1084–1091. https://doi.org/10.1111/j.1600-0587.2013.00441.x (2014).
doi: 10.1111/j.1600-0587.2013.00441.x
QGIS Development Team. QGIS geographic information system v. 3.22.6 Białowieża (2021).
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.228.v2.1 (2021).
Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J. J. & Elith, J. Predictive performance of presence-only species distribution models: A benchmark study with reproducible code. Ecol. Monogr. 92, e01486. https://doi.org/10.1002/ecm.1486 (2022).
doi: 10.1002/ecm.1486
Fang, Y. et al. Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae. Sci. Total Environ. 756, 143841. https://doi.org/10.1016/j.scitotenv.2020.143841 (2021).
doi: 10.1016/j.scitotenv.2020.143841 pubmed: 33248784
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. https://doi.org/10.1371/journal.pone.0055648 (2013).
doi: 10.1371/journal.pone.0055648 pubmed: 23409015 pmcid: 3569459
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x (2013).
doi: 10.1111/j.1600-0587.2012.07348.x
Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1–12. https://doi.org/10.1007/s00035-014-0124-0 (2014).
doi: 10.1007/s00035-014-0124-0
IPCC. Climate Change 2021—The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2021).
Dunne, J. P. et al. The GFDL earth system model version 4.1 (GFDL-ESM 4.1): Overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst. 12, e2019MS002015. https://doi.org/10.1029/2019MS002015 (2020).
Lange, S. & Büchner, M. ISIMIP3b bias-adjusted atmospheric climate input data (v1.1). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.842396.1 (2021).
Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble platform for species distribution modeling. R package version 3.4.6 (2020).
Mallen-Cooper, M. et al. Towards an understanding of future range shifts in lichens and mosses under climate change. J. Biogeogr. 50, 406–417. https://doi.org/10.1111/jbi.14542 (2023).
doi: 10.1111/jbi.14542
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).
doi: 10.1111/j.1365-2664.2006.01214.x
McCullagh, P. & Nelder, J. A. Generalized Linear Models. 2nd edition. (Chapman & Hall, 1989).
Hastie, T. J. & Tibshirani, R. Generalized Additive Models. (Chapman and Hall, 1990).
Ridgeway, G. The state of boosting. Comput. Sci. Stat. 31, 172–181 (1999).
Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and regression trees (Chapman and Hall, 1984).
Hastie, T., Tibshirani, R. & Buja, A. flexible discriminant analysis by optimal scoring. J. Am. Stat. Assoc. 89, 1255–1270. https://doi.org/10.1080/01621459.1994.10476866 (1994).
doi: 10.1080/01621459.1994.10476866
Ripley, B. D. Pattern Recognition and Neural Networks. (Cambridge University Press, 1996).
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).
doi: 10.1016/j.ecolmodel.2005.03.026
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).
doi: 10.1023/A:1010933404324
Busby, J. R. in Nature Conservation: Cost Effective Biological Surveys and Data Analysis (eds C. R. Margules & M. P. Austin) 64–68 (CSIRO, 1991).
Farzin, S. Assessing accuracy methods of species distribution models: AUC, Specificity, sensitivity and the true skill statistic. Glob. J. Hum. Soc. Sci. 18, 7–18 (2018).
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446. https://doi.org/10.1038/s41467-019-09519-w (2019).
doi: 10.1038/s41467-019-09519-w pubmed: 30926936 pmcid: 6441032
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69. https://doi.org/10.1111/j.1472-4642.2008.00491.x (2009).
doi: 10.1111/j.1472-4642.2008.00491.x
Hao, T., Elith, J., Guillera-Arroita, G. & Lahoz-Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25, 839–852. https://doi.org/10.1111/ddi.12892 (2019).
doi: 10.1111/ddi.12892
Kassambara, A. ggpubr: 'ggplot2' Based Publication Ready Plots. R package version 0.6.0 (2023).
Perpiñán, O. & Hijmans, R. rasterVis. R package version 0.51.5 (2023).
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecol. Biogeogr. 21, 481–497. https://doi.org/10.1111/j.1466-8238.2011.00698.x (2012).
doi: 10.1111/j.1466-8238.2011.00698.x
Warren, D., Glor, R. & Turelli, M. Environmental Niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x (2008).
doi: 10.1111/j.1558-5646.2008.00482.x pubmed: 18752605
Hijmans, R. raster: Geographic data analysis and modeling. R package version 3.6–14 (2023).
Warren, D. & Dinnage, R. ENMTools: Analysis of Niche Evolution using Niche and Distribution Models. R package version 1.0.7 (2022).
Tang, Y., Horikoshi, M. & Li, W. ggfortify: Unified interface to visualize statistical results of popular R packages. R J. 8, 478–489. https://doi.org/10.32614/RJ-2016-060 (2016).
Hijmans, R., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.3–14 (2023).
R Core Team. R: A language and environment for statistical computing version 4.2.1 "Funny-Looking Kid" (R Foundation for Statistical Computing, Vienna, Austria, 2022).
Désamoré, A. et al. How do temperate bryophytes face the challenge of a changing environment? Lessons from the past and predictions for the future. Global Change Biol. 18, 2915–2924. https://doi.org/10.1111/j.1365-2486.2012.02752.x (2012).
doi: 10.1111/j.1365-2486.2012.02752.x
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234. https://doi.org/10.1038/s41586-018-0005-6 (2018).
doi: 10.1038/s41586-018-0005-6 pubmed: 29618821
Tuba, Z., Slack, N. G. & Stark, L. R. Bryophyte ecology and climate change (Cambridge University Press, 2011).
Raggio, J., Green, A., Pintado, A., Sancho, L. G. & Büdel, B. Functional performance of biocrusts across Europe and its implications for drylands. J. Arid Environ. 186, 104402. https://doi.org/10.1016/j.jaridenv.2020.104402 (2021).
doi: 10.1016/j.jaridenv.2020.104402
Furness, S. B. & Grime, J. P. Growth rate and temperature responses in bryophytes: II. A comparative study of species of contrasted ecology. J. Ecol. 70, 525–536. https://doi.org/10.2307/2259920 (1982).
McKinney, M. L. Extinction vulnerability and selectivity: Combining ecological and paleontological views. Annu. Rev. Ecol. Syst. 28, 495–516. https://doi.org/10.1146/annurev.ecolsys.28.1.495 (1997).
doi: 10.1146/annurev.ecolsys.28.1.495
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354. https://doi.org/10.1126/science.1127863 (2006).
doi: 10.1126/science.1127863 pubmed: 16857940
Walker, K. J. & Preston, C. D. Ecological predictors of extinction risk in the flora of Lowland England UK. Biodivers. Conserv. 15, 1913–1942. https://doi.org/10.1007/s10531-005-4313-4 (2006).
doi: 10.1007/s10531-005-4313-4
Julliard, R., Jiguet, F. & Couvet, D. Common birds facing global changes: what makes a species at risk?. Global Change Biol. 10, 148–154. https://doi.org/10.1111/j.1365-2486.2003.00723.x (2004).
doi: 10.1111/j.1365-2486.2003.00723.x
Munday, P. L. Habitat loss, resource specialization, and extinction on coral reefs. Global Change Biol. 10, 1642–1647. https://doi.org/10.1111/j.1365-2486.2004.00839.x (2004).
doi: 10.1111/j.1365-2486.2004.00839.x
Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization?. Front. Ecol. Environ. 9, 222–228. https://doi.org/10.1890/080216 (2011).
doi: 10.1890/080216
Mežaka, A., Brūmelis, G. & Piterāns, A. The distribution of epiphytic bryophyte and lichen species in relation to phorophyte characters in Latvian natural old-growth broad leaved forests. Folia Cryptogam. Est. 44, 89–99 (2008).
Berg, Å., Gärdenfors, U., Hallingbäck, T. & Norén, M. Habitat preferences of red-listed fungi and bryophytes in woodland key habitats in southern Sweden—analyses of data from a national survey. Biodivers. Conserv. 11, 1479–1503. https://doi.org/10.1023/A:1016271823892 (2002).
doi: 10.1023/A:1016271823892
Király, I. & Ódor, P. The effect of stand structure and tree species composition on epiphytic bryophytes in mixed deciduous–coniferous forests of Western Hungary. Biol. Conserv. 143, 2063–2069. https://doi.org/10.1016/j.biocon.2010.05.014 (2010).
doi: 10.1016/j.biocon.2010.05.014
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).
Putna, S. & Mežaka, A. Preferences of epiphytic bryophytes for forest stand and substrate in North-East Latvia. Folia Cryptogam. Est. 51, 75–83. https://doi.org/10.12697/fce.2014.51.08 (2014).
Gábor, L. et al. Habitats as predictors in species distribution models: Shall we use continuous or binary data?. Ecography 2022, e06022. https://doi.org/10.1111/ecog.06022 (2022).
doi: 10.1111/ecog.06022
Ferretto, A. et al. Modelling the future distribution of rare bryophytes in Scotland: The importance of the inclusion of habitat loss. Plant Ecol. Divers. https://doi.org/10.1080/17550874.2023.2274839 (2023).
doi: 10.1080/17550874.2023.2274839
Gülçin, D., Arslan, E. S. & Örücü, Ö. K. Effects of climate change on the ecological niche of common hornbeam (Carpinus betulus L.). Ecol. Inform. 66, 101478. https://doi.org/10.1016/j.ecoinf.2021.101478 (2021).
Ellison, A. M. et al. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Front. Ecol. Environ. 3, 479–486. https://doi.org/10.1890/1540-9295(2005)003[0479:LOFSCF]2.0.CO;2 (2005).
doi: 10.1890/1540-9295(2005)003[0479:LOFSCF]2.0.CO;2
Millar, C. I., Stephenson, N. L. & Stephens, S. L. Climate change and forests of the future: Managing in the face of uncertainty. Ecol. Appl. 17, 2145–2151. https://doi.org/10.1890/06-1715.1 (2007).
doi: 10.1890/06-1715.1 pubmed: 18213958
Sykes, M. T., Prentice, I. C. & Cramer, W. A bioclimatic model for the potential distributions of North European tree species under present and future climates. J. Biogeogr. 23, 203–233 (1996).
doi: 10.1046/j.1365-2699.1996.d01-221.x
Thurm, E. A. et al. Alternative tree species under climate warming in managed European forests. For. Ecol. Manage. 430, 485–497. https://doi.org/10.1016/j.foreco.2018.08.028 (2018).
doi: 10.1016/j.foreco.2018.08.028
Hickler, T. et al. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Global Ecol. Biogeogr. 21, 50–63. https://doi.org/10.1111/j.1466-8238.2010.00613.x (2012).
doi: 10.1111/j.1466-8238.2010.00613.x
Mežaka, A., Brūmelis, G. & Piterāns, A. Tree and stand-scale factors affecting richness and composition of epiphytic bryophytes and lichens in deciduous woodland key habitats. Biodivers. Conserv. 21, 3221–3241. https://doi.org/10.1007/s10531-012-0361-8 (2012).
doi: 10.1007/s10531-012-0361-8
Barbé, M., Bouchard, M. & Fenton, N. J. Examining boreal forest resilience to temperature variability using bryophytes: forest type matters. Ecosphere 11, e03232. https://doi.org/10.1002/ecs2.3232 (2020).
doi: 10.1002/ecs2.3232
Spitale, D. Forest and substrate type drive bryophyte distribution in the Alps. J. Bryol. 39, 128–140. https://doi.org/10.1080/03736687.2016.1274090 (2017).
doi: 10.1080/03736687.2016.1274090
Yang, Z. et al. Spatio-temporal variation in potential habitats for rare and endangered plants and habitat conservation based on the maximum entropy model. Sci. Total Environ. 784, 147080. https://doi.org/10.1016/j.scitotenv.2021.147080 (2021).
doi: 10.1016/j.scitotenv.2021.147080 pubmed: 33905926
Oliver, T. H., Smithers, R. J., Beale, C. M. & Watts, K. Are existing biodiversity conservation strategies appropriate in a changing climate?. Biol. Conserv. 193, 17–26. https://doi.org/10.1016/j.biocon.2015.10.024 (2016).
doi: 10.1016/j.biocon.2015.10.024
Booth, T. H. Species distribution modelling tools and databases to assist managing forests under climate change. For. Ecol. Manage. 430, 196–203. https://doi.org/10.1016/j.foreco.2018.08.019 (2018).
doi: 10.1016/j.foreco.2018.08.019
Snäll, T., Pennanen, J., Kivistö, L. & Hanski, I. Modelling epiphyte metapopulation dynamics in a dynamic forest landscape. Oikos 109, 209–222. https://doi.org/10.1111/j.0030-1299.2005.13616.x (2005).
doi: 10.1111/j.0030-1299.2005.13616.x
Snäll, T., Hagström, A., Rudolphi, J. & Rydin, H. Distribution pattern of the epiphyte Neckera pennata on three spatial scales: Importance of past landscape structure connectivity and local conditions. . Ecography 27, 757–766 (2004).
doi: 10.1111/j.0906-7590.2004.04026.x
Löbel, S., Snäll, T. & Rydin, H. Species richness patterns and metapopulation processes—evidence from epiphyte communities in boreo-nemoral forests. Ecography 29, 169–182. https://doi.org/10.1111/j.2006.0906-7590.04348.x (2006).
doi: 10.1111/j.2006.0906-7590.04348.x
Robillard, C. M., Coristine, L. E., Soares, R. N. & Kerr, J. T. Facilitating climate-change-induced range shifts across continental land-use barriers. Conserv. Biol. 29, 1586–1595. https://doi.org/10.1111/cobi.12556 (2015).
doi: 10.1111/cobi.12556 pubmed: 26193759

Auteurs

Adrian Wysocki (A)

Department of Plant Biology, Institute of Environmental Biology, Wrocław University of Environmental and Life Sciences, Kożuchowska 7a, 51-631, Wrocław, Poland. adrian.wysocki@upwr.edu.pl.

Sylwia Wierzcholska (S)

Department of Plant Biology, Institute of Environmental Biology, Wrocław University of Environmental and Life Sciences, Kożuchowska 7a, 51-631, Wrocław, Poland.

Jarosław Proćków (J)

Department of Plant Biology, Institute of Environmental Biology, Wrocław University of Environmental and Life Sciences, Kożuchowska 7a, 51-631, Wrocław, Poland.

Kamil Konowalik (K)

Department of Botany and Plant Ecology, Wrocław University of Environmental and Life Sciences, Pl. Grunwaldzki 24a, 50-363, Wrocław, Poland.

Articles similaires

Glycogen Storage Disease Type II Humans Critical Pathways Europe
India Carbon Sequestration Environmental Monitoring Carbon Biomass
Humans Climate Change Health Personnel Surveys and Questionnaires Medical Oncology
Lakes Salinity Archaea Bacteria Microbiota

Classifications MeSH