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Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome

Nature Ecology & Evolution volume 4, pages 802–808 (2020)Cite this article

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Abstract

Biodiversity time series reveal global losses and accelerated redistributions of species, but no net loss in local species richness. To better understand how these patterns are linked, we quantify how individual species trajectories scale up to diversity changes using data from 68 vegetation resurvey studies of seminatural forests in Europe. Herb-layer species with small geographic ranges are being replaced by more widely distributed species, and our results suggest that this is due less to species abundances than to species nitrogen niches. Nitrogen deposition accelerates the extinctions of small-ranged, nitrogen-efficient plants and colonization by broadly distributed, nitrogen-demanding plants (including non-natives). Despite no net change in species richness at the spatial scale of a study site, the losses of small-ranged species reduce biome-scale (gamma) diversity. These results provide one mechanism to explain the directional replacement of small-ranged species within sites and thus explain patterns of biodiversity change across spatial scales.

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Fig. 1: Spatial distribution of resurvey studies in Europe.
Fig. 2: Species that go extinct from a study site have smaller ranges than persisting and colonizing ones.
Fig. 3: Small-ranged species drive the increase in the average extinction risk from high N deposition.

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Data availability

The community change and environmental site-level data are available on figshare at https://figshare.com/s/45d71eb77c23c11bc857. The species composition data are available from forestreplot.ugent.be, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. These data are, however, available from the authors upon request and with the permission of the forestREplot consortium.

Code availability

The R code for all analyses is available on figshare at https://doi.org/10.6084/m9.figshare.10110713.v1.

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Acknowledgements

This paper is an outcome of the sREplot working group supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118). P.D.F. and P.V. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Starting Grant FORMICA 757833). K.V. received funding through ERC Consolidator Grant PASTFORWARD 614839. M.K. and M. Macek were supported by the Czech Academy of Sciences (grant no. RVO 67985939). F.M. was supported by the Slovak Research and Development Agency (grant no. APVV-15-0270). R.H, M.C. and O.V. were supported by the grant agency of the Czech Republic (grant no. 17-09283S) and Czech Academy of Sciences (grant no. RVO 67985939). T.N. was supported by the Slovenian Research Agency (grant no. J4-1765). I.B. was supported by grant no. EFOP-3.6.1-16-2016-00018. R.P. was supported by a grant from the National Science Centre, Poland (no. 2016/20/S/NZ800428). B.T. was financed by the Higher Education Institutional Excellence Program of the Ministry for Innovation and Technology in Hungary, within the framework of the third thematic programme of the University of Pécs.

Author information

Authors and Affiliations

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

    Ingmar R. Staude, Ute Jandt & Henrique M. Pereira

  2. Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Ingmar R. Staude, Ute Jandt & Henrique M. Pereira

  3. Department of Botany, University of Wisconsin, Madison, WI, USA

    Donald M. Waller

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

    Markus Bernhardt-Römermann

  5. Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden

    Anne D. Bjorkman

  6. Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, Sweden

    Jörg Brunet

  7. Forest & Nature Lab, Ghent University, Gontrode, Belgium

    Pieter De Frenne, Kris Verheyen, Pieter Vangansbeke & Lander Baeten

  8. Institute of Botany, Czech Academy of Sciences, Brno, Czech Republic

    Radim Hédl, Markéta Chudomelová & Ondřej Vild

  9. Department of Botany, Faculty of Science, Palacký University in Olomouc, Olomouc, Czech Republic

    Radim Hédl

  10. UMR 7058 CNRS, EDYSAN—Ecologie et Dynamique des Systèmes Anthropisés, Jules Verne University of Picardie, Amiens, France

    Jonathan Lenoir & Guillaume Decocq

  11. Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovakia

    František Máliš

  12. National Forest Centre, Zvolen, Slovakia

    František Máliš

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

    Monika Wulf & Tobias Naaf

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

    Monika Wulf & Thilo Heinken

  15. Department of Hidrobiology, University of Pécs, Szekszárd, Hungary

    Adrienne Ortmann-Ajkai

  16. Department of Forest Biodiversity, Faculty of Forestry, University of Agriculture in Kraków, Kraków, Poland

    Remigiusz Pielech

  17. Faculty of Forestry, Institute of Environmental and Earth Sciences, University of Sopron, Sopron, Hungary

    Imre Berki

  18. Environment Agency Austria, Vienna, Austria

    Thomas Dirnböck

  19. Department of Plant Physiology and Ecology, University of Rzeszów, Rzeszów, Poland

    Tomasz Durak

  20. Białowieża Geobotanical Station, Faculty of Biology, University of Warsaw, Warszawa, Poland

    Bogdan Jaroszewicz

  21. Institute of Botany of the Czech Academy of Sciences, Průhonice, Czech Republic

    Martin Kopecký, Martin Macek & Petr Petřík

  22. Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká, Czech Republic

    Martin Kopecký

  23. Department of Botany, Faculty of Biological Sciences, University of Wrocław, Wrocław, Poland

    Marek Malicki & Kamila Reczyńska

  24. Department of Forestry and Renewable Forest Resources, University of Ljubljana, Ljubljana, Slovenia

    Thomas A. Nagel

  25. Norwegian Institute of Bioeconomy Research, Division of Forest and Forest Resources, Bergen, Norway

    Fride Høistad Schei

  26. Department of Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany

    Wolfgang Schmidt

  27. Department of Plant Systematics, Ecology and Theoretical Biology, Institute of Biology, L. Eötvös University, Budapest, Hungary

    Tibor Standovár

  28. Museum of Natural History, University of Wrocław, Wrocław, Poland

    Krzysztof Świerkosz

  29. Institute for Regional Development, University of Pécs, Szekszárd, Hungary

    Balázs Teleki

  30. MTA-DE Lendület Functional and Restoration Ecology Research Group, University of Debrecen, Debrecen, Hungary

    Balázs Teleki

  31. Research Institute for Nature and Forest, Brussels, Belgium

    Hans Van Calster

Authors
  1. Ingmar R. Staude
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  2. Donald M. Waller
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  3. Markus Bernhardt-Römermann
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  4. Anne D. Bjorkman
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  5. Jörg Brunet
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  6. Pieter De Frenne
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  7. Radim Hédl
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  8. Ute Jandt
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  9. Jonathan Lenoir
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  10. František Máliš
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  11. Kris Verheyen
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  12. Monika Wulf
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  13. Henrique M. Pereira
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  14. Pieter Vangansbeke
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  15. Adrienne Ortmann-Ajkai
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  16. Remigiusz Pielech
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  17. Imre Berki
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  18. Markéta Chudomelová
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  19. Guillaume Decocq
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  20. Thomas Dirnböck
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  21. Tomasz Durak
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  22. Thilo Heinken
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  23. Bogdan Jaroszewicz
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  24. Martin Kopecký
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  25. Martin Macek
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  26. Marek Malicki
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  27. Tobias Naaf
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  28. Thomas A. Nagel
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  29. Petr Petřík
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  30. Kamila Reczyńska
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  31. Fride Høistad Schei
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  32. Wolfgang Schmidt
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  33. Tibor Standovár
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  34. Krzysztof Świerkosz
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  35. Balázs Teleki
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  36. Hans Van Calster
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  37. Ondřej Vild
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  38. Lander Baeten
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Contributions

I.R.S., D.M.W. and L.B. conceived the study, with input from the sREplot working group (M.B.-R., A.D.B., J.B., P.D.F., R.H., U.J., J.L., F.M., K.V. and M.W.). I.R.S. performed the analyses, with input from D.M.W. and L.B. I.R.S., D.M.W. and L.B. wrote the manuscript, with input and contributions from M.B.-R., A.D.B., J.B., P.D.F., R.H., U.J., J.L., F.M., K.V., M.W., H.M.P., P.V., A.O.-A., R.P., I.B., M.C., G.D., T. Dirnböck, T. Durak, W.S., T.H., F.H.S., B.J., M.K., M. Macek, M. Malicki, T.N., T.A.N., P.P., K.R., T.S., K.Ś., B.T., H.V.C. and O.V. The authorship order was determined as follows: (1) core authors, (2) sREplot participants (alphabetical) and other major contributors and (3) authors contributing community composition data and to an advanced version of the manuscript (alphabetical).

Corresponding author

Correspondence to Ingmar R. Staude.

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Competing interests

The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Change in species numbers.

Frequency distribution of the difference in species numbers between the resurvey and baseline survey.

Extended Data Fig. 2 Change in nitrophilous species.

a, Frequency distribution of Ellenberg indicator values for nitrogen (eivN) across species. b, Frequency distribution of the mean eivN of extinct (dark green) and colonizing (light green) species.

Extended Data Fig. 3 Range sizes.

a, Frequency distribution of range sizes measured as area of occupancy (AOO) from GBIF point occurrence records. b, Spearman correlation plot of AOO and extent of occurrence (EOO) range sizes from digitized range maps. Points colored in magenta identify continental species. Correlation coefficient with and without continental species is: ρ = .71 and ρ = .74, respectively.

Extended Data Fig. 4 Correlations between predictor variables.

the year of the baseline survey (t1), time between surveys (∆t), cumulative N deposition between 1900 and the year of the baseline survey (Nt1) and intercensus cumulative nitrogen deposition (∆N).

Extended Data Fig. 5 Directed acyclic graph.

Directed acyclic graph of hypothesized causal links between predictor and response variables. Cumulative N deposition at the year of the baseline (Nt1), intercensus cumulative N-deposition (∆N) and time between surveys (∆t) directly influence the outcome (extinction probability, E). Year of the baseline survey (t1) directly influences ∆t and Nt1: the earlier the baseline survey, the longer the time between surveys; the earlier the baseline survey, the lower the cumulative N deposition at the year of the baseline survey. To estimate the direct effect of ∆N, it is sufficient to include ∆t as a covariate. This closes the backdoor81 through t1 (t1 →Nt1 → E) and as a result differences in baseline year do not confound the effect of ∆N.

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Supplementary Figs. 1–3 and Tables 1–7.

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Staude, I.R., Waller, D.M., Bernhardt-Römermann, M. 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). https://doi.org/10.1038/s41559-020-1176-8

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