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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Regional occupancy increases for widespread species but decreases for narrowly distributed species in metacommunity time series
Nature Communications Open Access 16 March 2023
ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany
Scientific Data Open Access 19 October 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
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.
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.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
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).
Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).
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).
Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Environ. Resour. 28, 137–167 (2003).
Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).
Primack, R. B. et al. Biodiversity gains? The debate on changes in local- vs global-scale species richness. Biol. Conserv. 219, A1–A3 (2018).
Vellend, M. The biodiversity conservation paradox. Am. Sci. 105, 94 (2017).
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).
Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).
Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).
Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).
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).
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
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).
Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2021).
Beck, J. J., Larget, B. & Waller, D. M. Phantom species: adjusting estimates of colonization and extinction for pseudo-turnover. Oikos 127, 1605–1618 (2018).
Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).
Avolio, M. L. et al. A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere 10, e02881 (2019).
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).
Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).
Gini, C. Il diverso accrescimento delle classi sociali e la concentrazione della ricchezza. Giornale degli Economisti38, 27–83 (1909).
Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).
Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).
Hundt, R. Ökologisch‐geobotanische Untersuchungen an den mitteldeutschen Wiesengesellschaften unter besonderer Berücksichtigung ihres Wasserhaushaltes und ihrer Veränderung durch die Intensivbewirtschaftung (Wehry-Druck OHG, 2001).
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Jansen, F., Bonn, A., Bowler, D. E., Bruelheide, H. & Eichenberg, D. Moderately common plants show highest relative losses. Conserv. Lett. 13, e12674 (2020).
Bruelheide, H. et al. Using incomplete floristic monitoring data from habitat mapping programmes to detect species trends. Divers. Distrib. 26, 782–794 (2020).
Sperle, T. & Bruelheide, H. Climate change aggravates bog species extinctions in the Black Forest (Germany). Divers. Distrib. 27, 282–295 (2020).
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).
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).
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).
Baskin, Y. Winners and losers in a changing world. BioScience 48, 788–792 (1998).
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).
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).
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).
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).
Metzing, D. et al. Rote Liste und Gesamtartenliste der Farn- und Blütenpflanzen (Trachaeophyta) Deutschlands (Landwirtschaftsverlag, 2018).
Poschlod, P. Geschichte der Kulturlandschaft (Ulmer, 2017).
Sukopp, H. ‘Rote Liste’ der in der Bundesrepublik Deutschland gefährdeten Arten von Farn- und Blütenpflanzen. (1. Fassung). Nat. Landsch. 49, 315–322 (1974).
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).
Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).
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).
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).
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).
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)
Bohn, U. & Schniotalle, S. Hochmoor-, Grünland- und Waldrenaturierung im Naturschutzgebiet ‘Rotes Moor’/Hohe Rhön 1981–2001 (Landwirtschaftsverlag, 2008).
Rosenthal, G. Erhaltung und Regeneration von Feuchtwiesen. Vegetationsökologische Untersuchungen auf Dauerflächen. Diss. Bot. 182, 1–283 (1992).
Schwabe, A. & Kratochwil, A. Pflanzensoziologische Dauerflächen-Untersuchungen im Bannwald ‘Flüh’ (Südschwarzwald) unter besonderer Berücksichtigung der Weidfeld-Sukzession. Standort Wald 49, 5–49 (2015).
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).
Hennekens, S. M. & Schaminée, J. H. J. TURBOVEG, a comprehensive data base management system for vegetation data. J. Veg. Sci. 12, 589–591 (2001).
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).
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).
Jansen, F. & Dengler, J. GermanSL—eine universelle taxonomische Referenzliste für Vegetationsdatenbanken. Tuexenia 28, 239–253 (2008).
Wisskirchen, R. & Haeupler, H. Standardliste der Farn-und Blütenpflanzen Deutschlands (Ulmer, 1998).
Jansen, F. & Dengler, J. Plant names in vegetation databases–a neglected source of bias. J. Veg. Sci. 21, 1179–1186 (2010).
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).
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).
Weiner, J. & Solbrig, O. T. The meaning and measurement of size hierarchies in plant populations. Oecologia 61, 334–336 (1984).
Signorell, A. et al. DescTools: tools for descriptive statistics. R version 0.99.32 https://CRAN.R-project.org/package=DescTools (2020).
BiolFlor—a new plant-trait database as a tool for plant invasion ecology. Divers. Distrib. 10, 363–365 (2004).
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).
Jandt, U. & Bruelheide, H. German Vegetation Reference Database (GVRD). Biodivers. Ecol. 4, 355–355 (2012).
Sokal, R. R. & Rohlf, F. J. Biometry (Freeman, 1995).
Chytrý, M., Tichý, L., Holt, J. & Botta‐Dukát, Z. Determination of diagnostic species with statistical fidelity measures. J. Veg. Sci. 13, 79–90 (2002).
Gotelli, N. J. Null model analysis of species co‐occurrence patterns. Ecology 81, 2606–2621 (2000).
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).
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).
MacArthur, R. On the relative abundance of species. Am. Nat. 94, 25–36 (1960).
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).
Kuhn, G., Heinz, S. & Mayer, F. Grünlandmonitoring Bayern. Ersterhebung der Vegetation 2002–2008. Schriftenreihe LfL Bayer. Landesanst. Für Landwirtsch. 3, 1–161 (2011).
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).
The authors declare no competing interests.
Peer review information
Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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 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.
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.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
This article is cited by
Regional occupancy increases for widespread species but decreases for narrowly distributed species in metacommunity time series
Nature Communications (2023)
Biodiversity loss and climate extremes — study the feedbacks
ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany
Scientific Data (2022)
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.