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More losses than gains during one century of plant biodiversity change in Germany

Nature volume 611, pages 512–518 (2022)Cite this article

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Abstract

Long-term analyses of biodiversity data highlight a ‘biodiversity conservation paradox’: biological communities show substantial species turnover over the past century1,2, but changes in species richness are marginal1,3,4,5. Most studies, however, have focused only on the incidence of species, and have not considered changes in local abundance. Here we asked whether analysing changes in the cover of plant species could reveal previously unrecognized patterns of biodiversity change and provide insights into the underlying mechanisms. We compiled and analysed a dataset of 7,738 permanent and semi-permanent vegetation plots from Germany that were surveyed between 2 and 54 times from 1927 to 2020, in total comprising 1,794 species of vascular plants. We found that decrements in cover, averaged across all species and plots, occurred more often than increments; that the number of species that decreased in cover was higher than the number of species that increased; and that decrements were more equally distributed among losers than were gains among winners. Null model simulations confirmed that these trends do not emerge by chance, but are the consequence of species-specific negative effects of environmental changes. In the long run, these trends might result in substantial losses of species at both local and regional scales. Summarizing the changes by decade shows that the inequality in the mean change in species cover of losers and winners diverged as early as the 1960s. We conclude that changes in species cover in communities represent an important but understudied dimension of biodiversity change that should more routinely be considered in time-series analyses.

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Fig. 1: Patterns of change in plant diversity over one century.
Fig. 2: Inequality of losses and gains.
Fig. 3: Null model simulations of changes in species cover.
Fig. 4: Losers and winners across one century in Germany.
Fig. 5: Temporal course of the inequality of species losses and gains.

Data availability

All data are available as a data paper50 and available at https://doi.org/10.25829/idiv.3514-0qsq70 under the terms specified by CC BY 4.0.

Code availability

The R code for retrieving resurvey ID × species × time interval combinations and that was used to calculate the results presented in this paper is provided in Supplementary Code 1 and is available at https://github.com/idiv-biodiversity/ReSurveyGermany_Analysis. The R code that was used to produce the null models in Supplementary Code 2 is available at https://github.com/idiv-biodiversity/ReSurveyGermany_null_models.

References

  1. Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).

    Article  PubMed  Google Scholar 

  6. Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Environ. Resour. 28, 137–167 (2003).

    Article  Google Scholar 

  7. Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  8. Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).

    Article  PubMed  Google Scholar 

  9. Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Primack, R. B. et al. Biodiversity gains? The debate on changes in local- vs global-scale species richness. Biol. Conserv. 219, A1–A3 (2018).

    Article  Google Scholar 

  12. Vellend, M. The biodiversity conservation paradox. Am. Sci. 105, 94 (2017).

    Article  Google Scholar 

  13. Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).

    Article  Google Scholar 

  14. Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).

    Article  Google Scholar 

  15. Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).

  16. Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).

  17. Staude, I. R. et al. Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nat. Ecol. Evol. 4, 802–808 (2020).

    Article  PubMed  Google Scholar 

  18. Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).

    Article  PubMed  Google Scholar 

  20. Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2021).

    Article  ADS  CAS  Google Scholar 

  21. Beck, J. J., Larget, B. & Waller, D. M. Phantom species: adjusting estimates of colonization and extinction for pseudo-turnover. Oikos 127, 1605–1618 (2018).

    Article  Google Scholar 

  22. Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).

    Article  Google Scholar 

  23. Avolio, M. L. et al. A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere 10, e02881 (2019).

    Article  Google Scholar 

  24. Diekmann, M. et al. Patterns of long‐term vegetation change vary between different types of semi‐natural grasslands in Western and Central Europe. J. Veg. Sci. 30, 187–202 (2019).

    Article  Google Scholar 

  25. Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gini, C. Il diverso accrescimento delle classi sociali e la concentrazione della ricchezza. Giornale degli Economisti38, 27–83 (1909).

  27. Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).

    Article  PubMed  Google Scholar 

  29. Hundt, R. Ökologisch‐geobotanische Untersuchungen an den mitteldeutschen Wiesengesellschaften unter besonderer Berücksichtigung ihres Wasserhaushaltes und ihrer Veränderung durch die Intensivbewirtschaftung (Wehry-Druck OHG, 2001).

  30. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Jansen, F., Bonn, A., Bowler, D. E., Bruelheide, H. & Eichenberg, D. Moderately common plants show highest relative losses. Conserv. Lett. 13, e12674 (2020).

    Article  Google Scholar 

  32. Bruelheide, H. et al. Using incomplete floristic monitoring data from habitat mapping programmes to detect species trends. Divers. Distrib. 26, 782–794 (2020).

    Article  Google Scholar 

  33. Sperle, T. & Bruelheide, H. Climate change aggravates bog species extinctions in the Black Forest (Germany). Divers. Distrib. 27, 282–295 (2020).

    Article  Google Scholar 

  34. McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).

    Article  CAS  PubMed  Google Scholar 

  35. Timmermann, A., Damgaard, C., Strandberg, M. T. & Svenning, J.-C. Pervasive early 21st-century vegetation changes across Danish semi-natural ecosystems: more losers than winners and a shift towards competitive, tall-growing species. J. Appl. Ecol. 52, 21–30 (2015).

    Article  Google Scholar 

  36. Milligan, G., Rose, R. J. & Marrs, R. H. Winners and losers in a long-term study of vegetation change at Moor House NNR: effects of sheep-grazing and its removal on British upland vegetation. Ecol. Indic. 68, 89–101 (2016).

  37. Baskin, Y. Winners and losers in a changing world. BioScience 48, 788–792 (1998).

    Article  Google Scholar 

  38. Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).

    Article  Google Scholar 

  39. Naaf, T. & Wulf, M. Habitat specialists and generalists drive homogenization and differentiation of temperate forest plant communities at the regional scale. Biol. Conserv. 143, 848–855 (2010).

    Article  Google Scholar 

  40. Heinrichs, S. & Schmidt, W. Biotic homogenization of herb layer composition between two contrasting beech forest communities on limestone over 50 years. Appl. Veg. Sci. 20, 271–281 (2017).

    Article  Google Scholar 

  41. Reinecke, J., Klemm, G. & Heinken, T. Vegetation change and homogenization of species composition in temperate nutrient deficient Scots pine forests after 45 yr. J. Veg. Sci. 25, 113–121 (2014).

    Article  Google Scholar 

  42. Metzing, D. et al. Rote Liste und Gesamtartenliste der Farn- und Blütenpflanzen (Trachaeophyta) Deutschlands (Landwirtschaftsverlag, 2018).

  43. Poschlod, P. Geschichte der Kulturlandschaft (Ulmer, 2017).

  44. Sukopp, H. ‘Rote Liste’ der in der Bundesrepublik Deutschland gefährdeten Arten von Farn- und Blütenpflanzen. (1. Fassung). Nat. Landsch. 49, 315–322 (1974).

    Google Scholar 

  45. Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).

    Article  PubMed  Google Scholar 

  46. Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Jandt, U., von Wehrden, H. & Bruelheide, H. Exploring large vegetation databases to detect temporal trends in species occurrences. J. Veg. Sci. 22, 957–972 (2011).

    Article  Google Scholar 

  48. Jones, F. A. M. & Magurran, A. E. Dominance structure of assemblages is regulated over a period of rapid environmental change. Biol. Lett. 14, 20180187 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Chytrý, M., Tichý, L., Hennekens, S. M. & Schaminée, J. H. J. Assessing vegetation change using vegetation-plot databases: a risky business. Appl. Veg. Sci. 17, 32–41 (2014).

    Article  Google Scholar 

  50. Jandt, U. et al. ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany. Sci. Data, https://doi.org/10.1038/s41597-022-01688-6 (2022)

  51. Bohn, U. & Schniotalle, S. Hochmoor-, Grünland- und Waldrenaturierung im Naturschutzgebiet ‘Rotes Moor’/Hohe Rhön 1981–2001 (Landwirtschaftsverlag, 2008).

  52. Rosenthal, G. Erhaltung und Regeneration von Feuchtwiesen. Vegetationsökologische Untersuchungen auf Dauerflächen. Diss. Bot. 182, 1–283 (1992).

    Google Scholar 

  53. Schwabe, A. & Kratochwil, A. Pflanzensoziologische Dauerflächen-Untersuchungen im Bannwald ‘Flüh’ (Südschwarzwald) unter besonderer Berücksichtigung der Weidfeld-Sukzession. Standort Wald 49, 5–49 (2015).

    Google Scholar 

  54. Poschlod, P., Schreiber, K.-F., Mitlacher, K., Römermann, C. & Bernhardt-Römermann, M. in Landschaftspflege und Naturschutz im Extensivgrünland. 30 Jahre Offenhaltungsversuche Baden-Württemberg Vol. 97 (eds. Schreiber, K.-F. et al.) 243–288 (2009).

  55. Hennekens, S. M. & Schaminée, J. H. J. TURBOVEG, a comprehensive data base management system for vegetation data. J. Veg. Sci. 12, 589–591 (2001).

    Article  Google Scholar 

  56. Chytrý, M. et al. EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Appl. Veg. Sci. 23, 648–675 (2020).

    Article  Google Scholar 

  57. Bruelheide, H., Tichý, L., Chytrý, M. & Jansen, F. Implementing the formal language of the vegetation classification expert systems (ESy) in the statistical computing environment R. Appl. Veg. Sci. 12, e12562 (2021).

  58. Jansen, F. & Dengler, J. GermanSL—eine universelle taxonomische Referenzliste für Vegetationsdatenbanken. Tuexenia 28, 239–253 (2008).

    Google Scholar 

  59. Wisskirchen, R. & Haeupler, H. Standardliste der Farn-und Blütenpflanzen Deutschlands (Ulmer, 1998).

  60. Jansen, F. & Dengler, J. Plant names in vegetation databases–a neglected source of bias. J. Veg. Sci. 21, 1179–1186 (2010).

    Article  Google Scholar 

  61. Wegener, U. Vegetationswandel des Berggrünlands nach Untersuchungen von 1954 bis 2016—Wege zur Erhaltung der Bergwiesen (Mountain grasslands vegetation change after research from 1954 to 2016—ways to preserve mountain meadows). Abh. Berichte Aus Dem Mus. Heine. 11, 35–101 (2018).

    Google Scholar 

  62. Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).

    Article  ADS  Google Scholar 

  63. Weiner, J. & Solbrig, O. T. The meaning and measurement of size hierarchies in plant populations. Oecologia 61, 334–336 (1984).

    Article  ADS  PubMed  Google Scholar 

  64. Signorell, A. et al. DescTools: tools for descriptive statistics. R version 0.99.32 https://CRAN.R-project.org/package=DescTools (2020).

  65. BiolFlor—a new plant-trait database as a tool for plant invasion ecology. Divers. Distrib. 10, 363–365 (2004).

  66. INSPIRE. D2.8.III.18 Data Specification on Habitats and Biotopes—Technical Guidelines https://inspire.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_HB_v3.0rc2.pdf (2013).

  67. Jandt, U. & Bruelheide, H. German Vegetation Reference Database (GVRD). Biodivers. Ecol. 4, 355–355 (2012).

    Article  Google Scholar 

  68. Sokal, R. R. & Rohlf, F. J. Biometry (Freeman, 1995).

  69. Chytrý, M., Tichý, L., Holt, J. & Botta‐Dukát, Z. Determination of diagnostic species with statistical fidelity measures. J. Veg. Sci. 13, 79–90 (2002).

    Article  Google Scholar 

  70. Gotelli, N. J. Null model analysis of species co‐occurrence patterns. Ecology 81, 2606–2621 (2000).

    Article  Google Scholar 

  71. Pillar, V. D., Sabatini, F. M., Jandt, U., Camiz, S. & Bruelheide, H. Revealing the functional traits linked to hidden environmental factors in community assembly. J. Veg. Sci. 32, e12976 (2021).

  72. Sabatini, F. M., Jiménez‐Alfaro, B., Burrascano, S., Lora, A. & Chytrý, M. Beta‐diversity of central European forests decreases along an elevational gradient due to the variation in local community assembly processes. Ecography 41, 1038–1048 (2018).

    Article  Google Scholar 

  73. MacArthur, R. On the relative abundance of species. Am. Nat. 94, 25–36 (1960).

    Article  Google Scholar 

  74. Prado, P. I., Miranda, M. D. & Chalom, A. sads: maximum likelihood models for species abundance distributions. R version 0.4.2. https://CRAN.R-project.org/package=sads (2018).

  75. Kuhn, G., Heinz, S. & Mayer, F. Grünlandmonitoring Bayern. Ersterhebung der Vegetation 2002–2008. Schriftenreihe LfL Bayer. Landesanst. Für Landwirtsch. 3, 1–161 (2011).

    Google Scholar 

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Acknowledgements

We are grateful to the surveyors who recorded vegetation in the field and provided these data. We acknowledge the data contributors who made their data available to us or helped in recording these data: T. Dittmann, A. Erfmeier, B. Gerken, K. Günther, S. Heinz, W. Hakes, H. Heklau, A. Henrichfreise, E. Hüllbusch, A. Huwer, A. Immoor, S. L. Kühn, B. Krause, S. Leonhardt, J. Reinecke, U. Scheidel, I. Vollmer and E. Wagner. We thank D. Bowler for her analysis of spatial representativeness; V. Hahn and S. Bernhard for their advice on Figs. 2 and 3; and T. Muer, the Regensburgische Botanische Gesellschaft and the Haupt Verlag for the permit to use the photographs from Floraweb.de for Fig. 4. We appreciate the support for the strategic project sMon by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (DFG-FZT 118, 202548816).

Author information

Author notes

  1. These authors contributed equally: Ute Jandt, Helge Bruelheide

Authors and Affiliations

  1. Institute of Biology, Department of Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany

    Ute Jandt, Helge Bruelheide, Reinhard A. Klenke, Francesco Maria Sabatini & Sylvia Haider

  2. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Ute Jandt, Helge Bruelheide, Aletta Bonn, Volker Grescho, Reinhard A. Klenke, Francesco Maria Sabatini, Markus Bernhardt-Römermann, Jürgen Dengler, Sylvia Haider, Martin Lindner, Christiane Roscher & Karsten Wesche

  3. Faculty of Agricultural and Environmental Sciences, Rostock University, Rostock, Germany

    Florian Jansen

  4. Department of Ecosystem Services, Helmhotz Centre for Environmental Research – UFZ, Leipzig, Germany

    Aletta Bonn & Volker Grescho

  5. Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany

    Aletta Bonn

  6. BIOME Lab, Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum University of Bologna, Bologna, Italy

    Francesco Maria Sabatini

  7. Institute of Ecology and Evolution, Friedrich Schiller University Jena, Jena, Germany

    Markus Bernhardt-Römermann

  8. BMS – Umweltplanung, Osnabrück, Germany

    Volker Blüml

  9. Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland

    Jürgen Dengler

  10. Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), Bayreuth, Germany

    Jürgen Dengler

  11. Vegetation Ecology and Conservation Biology, Institute of Ecology, University of Bremen, Bremen, Germany

    Martin Diekmann & Burghard Wittig

  12. Vegetation Science and Nature Conservation Group, Institute for Biology and Environmental Sciences, University of Oldenburg, Oldenburg, Germany

    Inken Doerfler

  13. Independent researcher, Göttingen, Germany

    Ute Döring

  14. Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria

    Stefan Dullinger & Sabine B. Rumpf

  15. General Botany, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Thilo Heinken

  16. Department of Vegetation Studies and Landscape Management, Federal Institute of Hydrology, Koblenz, Germany

    Peter Horchler

  17. Institut für Agrarökologie und Biologischen Landbau, AG Vegetationsökologie und -monitoring, Bayerische Landesanstalt für Landwirtschaft, Freising, Germany

    Gisbert Kuhn

  18. Institute of Biology, Martin Luther University Halle-Wittenberg, Halle, Germany

    Martin Lindner

  19. Ministerium für Wissenschaft, Energie, Klimaschutz und Umwelt des Landes Sachsen-Anhalt, Magdeburg, Germany

    Katrin Metze

  20. Department of Landscape Management & Restoration Ecology, Fachhochschule Erfurt, Erfurt, Germany

    Norbert Müller

  21. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

    Tobias Naaf & Monika Wulf

  22. Landscape Ecology Group, Institute for Biology and Environmental Sciences, University of Oldenburg, Oldenburg, Germany

    Cord Peppler-Lisbach

  23. Ecology and Conservation Biology, Institute of Plant Sciences, Faculty of Biology and Preclinical Medicine, University of Regensburg, Regensburg, Germany

    Peter Poschlod

  24. Department of Physiological Diversity, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

    Christiane Roscher

  25. Department of Landscape and Vegetation Ecology, University of Kassel, Kassel, Germany

    Gert Rosenthal & Nils Stanik

  26. Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

    Sabine B. Rumpf

  27. Department of Environmental Sciences, University of Basel, Basel, Switzerland

    Sabine B. Rumpf

  28. Department of Silviculture and Forest Ecology of the Temperate Zones, Georg-August-University Göttingen, Göttingen, Germany

    Wolfgang Schmidt

  29. Institute for Ecosystem Research, Kiel University, Kiel, Germany

    Joachim Schrautzer

  30. Faculty of Biology, Technical University Darmstadt, Darmstadt, Germany

    Angelika Schwabe

  31. Biologische Station Kreis Steinfurt e.V., Tecklenburg, Germany

    Peter Schwartze

  32. Independent researcher, Waldkirch, Germany

    Thomas Sperle

  33. Fachgebiet Chemische Pflanzenökologie, Fachbereich Biologie, Technische Universität Darmstadt, Darmstadt, Germany

    Christian Storm

  34. Institute of Ecology and Evolution, University of Jena, Jena, Germany

    Winfried Voigt

  35. Independent researcher, Halberstadt, Germany

    Uwe Wegener

  36. Botany Department, Senckenberg Museum of Natural History Görlitz, Görlitz, Germany

    Karsten Wesche

  37. International Institute Zittau, Technische Universität Dresden, Zittau, Germany

    Karsten Wesche

  38. Lower Saxony Water Management, Coastal Protection and Nature Conservation Agency, Betriebsstelle Lüneburg, Standort Verden, Verden, Germany

    Burghard Wittig

  39. Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Monika Wulf

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  1. Ute Jandt
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Contributions

U.J. and H.B. conceived the idea for the project. All authors were involved in collecting datasets, developing the conceptual framework and interpreting the results. H.B. performed the statistical analyses and developed the null model. U.J. and H.B. wrote the first draft of the manuscript. All authors commented on and agreed with the final version of the manuscript.

Corresponding author

Correspondence to Helge Bruelheide.

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Extended data figures and tables

Extended Data Fig. 1 Temporal coverage of the 92 projects included in the study.

The coloured lines indicate the start and the end of a project, black diamonds show in which years surveys were made. Resurvey type refers to either studies that were repeated within a particular community across a site without attempts to match plots (community comparison), or were carried out on matched plots, which were either permanently marked or relocated from exact descriptions (semi-permanent). The lower graph shows the number of times a particular year was included in any of the time series.

Extended Data Fig. 2 Effect of the length of the observation interval on plant diversity change.

The temporal change of species richness (SR) in plot records (a–c) and mean cover change of species (d–f) is shown separately for short (≤ 2 years), medium (> 2 and ≤ 10 years) and long observation intervals (> 10 years). The black dashed line shows zero change, while the red solid line in a)–c) shows the mean change of richness and in d)–f) the species’ median change in cover in percentage points. According to a mixed effects model estimated mean overall effect size was in a) +0.025 (p = 3.9 x 10−9, df = 4,142), b) +0.007 (p = 0.093, df = 3,903) and c) −0.150 (p < 2 x 10−16, df = 8,612). In d)–f) plot Interval comparisons of the mean of all cover changes per species between time points Y1 and Y2 of the start and end year, respectively, are shown on an axis with a sign*square root-transformation. According to an exact binomial test estimated overall median of cover change was in d) 0 (95 per cent confidence interval 0 and 0.007), e) −0.02 (CI −0.02 and 0) and f) −0.26 (CI −0.53 and 0.002).

Extended Data Fig. 3 Temporal change of plant richness expressed per decade.

Interval comparisons of species richness (SR) in plot records between time points Y1 and Y2 of the start and end year, respectively, and divided by the length of the interval in decades ((Y2-Y1)*10) (n = 13,987). Estimated overall effect size was +0.062 according to a mixed effects model (p = 1.8 x 10−7) with a 95% confidence interval between +0.039 and +0.086.

Extended Data Fig. 4 Effect of plot surface area on plant diversity change.

The temporal change of species richness (SR) in plot records (a–c) and mean cover change of species (d–f) is shown shown separately for small (> 25 m2), medium-size (25 m2) and large plots (>25 m2). The black dashed line shows zero change, while the red solid line in a)–c) shows the mean change of richness and in d)–f) the species’ median change in cover in percentage points. According to a mixed effects model estimated mean overall effect size was in a) −0.03 (p = 0.064, df = 487), b) −0.031 (p = 1.55 x 10−13, df = 4,204) and c) −0.095 (p < 2 x 10−16, df = 9,124). In d)–f) plot Interval comparisons of the mean of all cover changes per species between time points Y1 and Y2 of the start and end year, respectively, are shown on an axis with a sign*square root-transformation. According to an exact binomial test estimated overall median of cover change was in d) −0.017 (95 per cent confidence interval −0.065 and −0.001), e) −0.019 (CI −0.043 and −0.006) and f) −0.26 (CI −0.134 and −0.050).

Extended Data Fig. 5 Different measures of temporal change of plant diversity.

The histograms show the interval comparisons of plot records between time points Y1 and Y2 of the start and end year, respectively. The black dashed line shows the zero change, while the red solid line shows the mean change as predicted from a mixed effects model. a) Change in Shannon’s index of diversity (H). Estimated mean effect size for H −0.055 (p = 2.2 x 10−16, df = 5,462, 95% confidence interval −0.064 and −0.047). b) Change in Pielou’s index of evenness (E). Estimated mean effect size for E −0.019 (p = 2.6 x 10−16, 95% confidence interval −0.024 and −0.015). c) Difference in the area under the rank abundance curves. Estimated mean difference −0.143 (p = 0.00211, 95% confidence interval −0.194 and −0.091). d) Difference in the number of cover gains and losses. Estimated mean difference −0.407 (p = 7.9 x 10−7, 95% confidence interval −0.569 and −0.246). e) Change in mean cover of all the species in a plot (in per cent covered ground). Estimated mean effect size for mean cover +0.025 (p = 1.0 x 10−10, 95% confidence interval +0.018 and +0.033). f) Change in median cover of all the species in a plot (per cent of covered ground). Estimated mean effect size for median cover −0.007 (p = 0.2984, 95% confidence interval −0.021 and +0.007).

Extended Data Fig. 6 Temporal change in mean cover change of all species.

Plot Interval comparisons of the mean of all cover changes per species in percentage points between time points Y1 and Y2 of the start and end year, respectively, shown on an axis with a sign*square root-transformation. The black dashed line shows the zero change, while the red solid line shows the median change in cover across all species. All species in the dataset were included (n = 1,794). Estimated overall median of cover change was −0.0625 (95 per cent confidence interval −0.089 and −0.035) and significantly different from zero according to an exact binomial test (p < 0.001).

Extended Data Fig. 7 Map of plot locations of all plots of all projects.

One or several of the total of n = 23,641 plot records are summarized under the same plot resurvey ID (n = 7,738). Note that the more complete coverage of Bavaria resulted from including the grassland monitoring Bavaria which started in 200275. The map was produced using rnaturalearthdata (free vector and raster map data at naturalearthdata.com).

Extended Data Fig. 8 Assignment of time-series plot records to EUNIS habitat types.

Each time series was assigned to the habitat type by using the earliest plot record that matched with the level 3 EUNIS classification. The classification was based on the EUNIS-ESy expert system56 using the R code implementation57. ?: plots not assigned to any level 3 EUNIS habitat type, +: assigned to more than one level 3 EUNIS habitat type, A: Marine habitats, C: Inland surface waters, H: Inland sparsely vegetated habitats or devoid of vegetation, N: Coastal habitats, Q: Wetlands, R: Grasslands and lands dominated by forbs, mosses or lichens, S: Heathlands, scrub and tundra, T: Forests and other wooded land, V: Vegetated man-made habitats, including arable land. Labels for EUNIS habitats were only printed at the top of the corresponding bar section when the number of assigned records was ≥ 150.

Supplementary information

Supplementary Methods

Supplementary Methods 1 and 2. Supplementary Methods 1 shows the steps of data preparation and analysis and Supplementary Methods 2 contains an illustration of the null model scenarios.

Reporting Summary

Supplementary Table 1

A list of all projects included in the study.

Supplementary Table 2

A list of all taxa that were harmonized across all projects.

Supplementary Table 3

A list of all taxon names that were adapted within projects.

Supplementary Code 1

The R code to retrieve resurvey ID x species x time interval combinations and to calculate the results.

Supplementary Code 2

The R code to produce the null models.

Peer Review File

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Jandt, U., Bruelheide, H., Jansen, F. et al. More losses than gains during one century of plant biodiversity change in Germany. Nature 611, 512–518 (2022). https://doi.org/10.1038/s41586-022-05320-w

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