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More than one million barriers fragment Europe’s rivers

Abstract

Rivers support some of Earth’s richest biodiversity1 and provide essential ecosystem services to society2, but they are often fragmented by barriers to free flow3. In Europe, attempts to quantify river connectivity have been hampered by the absence of a harmonized barrier database. Here we show that there are at least 1.2 million instream barriers in 36 European countries (with a mean density of 0.74 barriers per kilometre), 68 per cent of which are structures less than two metres in height that are often overlooked. Standardized walkover surveys along 2,715 kilometres of stream length for 147 rivers indicate that existing records underestimate barrier numbers by about 61 per cent. The highest barrier densities occur in the heavily modified rivers of central Europe and the lowest barrier densities occur in the most remote, sparsely populated alpine areas. Across Europe, the main predictors of barrier density are agricultural pressure, density of river-road crossings, extent of surface water and elevation. Relatively unfragmented rivers are still found in the Balkans, the Baltic states and parts of Scandinavia and southern Europe, but these require urgent protection from proposed dam developments. Our findings could inform the implementation of the EU Biodiversity Strategy, which aims to reconnect 25,000 kilometres of Europe’s rivers by 2030, but achieving this will require a paradigm shift in river restoration that recognizes the widespread impacts caused by small barriers.

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Fig. 1: Artificial instream barriers in Europe (from the AMBER Barrier Atlas).
Fig. 2: Extent of river fragmentation in Europe.
Fig. 3: Extent of barrier under-reporting.

Data availability

Data for the AMBER Barrier Atlas (Fig. 1), observed barrier densities (Fig. 2a), ground-truthed barrier densities (Fig. 2b) and modelled barrier densities (Fig. 2c) are freely available at https://amber.international/european-barrier-atlas/ as well as at https://doi.org/10.6084/m9.figshare.12629051 under a CC-BY-4.0 license. Data for ground-truthed surveyed reaches (Extended Data Table 1, Extended Data Fig. 3) are also available at https://doi.org/10.6084/m9.figshare.12629051 under a CC-BY-4.0 license. Results of walkover surveys in test rivers (Extended Data Table 1), and barrier database sources (Table 1) are also available at https://doi.org/10.6084/m9.figshare.12629051Source data are provided with this paper.

Code availability

The Python code used for modelling of barrier abundance, with links to GIS files for visualization, is available under a GNU (https://en.wikipedia.org/wiki/GNU_Project) General Public License at https://github.com/AMBER-data/atlas-model. Protocols used for barrier database management, duplicate exclusion and processing were done manually in SQL and QGIS using ad hoc procedures and are not deposited in a repository.

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Acknowledgements

This study was funded by the EC Horizon 2020 Research and Innovation Programme, AMBER (Adaptive Management of Barriers in European Rivers) Project, grant agreement number 689682, led by C.G.d.L. B.B. was partially supported by EUR H2O’Lyon (ANR-17-EURE-0018) at Université de Lyon. We are indebted to the following institutions, which facilitated barrier data gathering: the International Commission for the Protection of the Danube River (Albania, Croatia, North Macedonia, Serbia, Slovenia); the Ministeri de Medi Ambient, Agricultura i Sostenibilitat. Govern d’Andorra (Andorra); the Bundesministerium für Nachhaltigkeit und Tourismus (Austria); the Service Public de Wallonie, Secrétariat Général (Belgium); the Vlaamse Milieumaatschappij (Belgium); the Agencija za vodno područje Jadranskog mora (Bosnia and Herzegovina); the Balkanka Association (Bulgaria); The Cyprus Conservation Foundation, Terra Cypria (Cyprus); the Ministerstvo Zemědělství (Czech Republic); the Danish Environmental Protection Agency (Denmark); the Keskkonnaministeerium (Estonia); Suomen ympäristökeskus (Finland); the Office national de l’eau et des milieux aquatiques (ONEMA) (France); Landesanstalt für Umwelt Baden-Württemberg (Germany); Bayerisches Landesamt für Umwelt (Germany); Senatsverwaltung für Umwelt, Verkehr und Klimaschutz Berlin (Germany); Landesamt für Umwelt Brandenburg (Germany); Der Senator für Umwelt, Bau und Verkehr Bremen (Germany); Amt für Umweltschutz Hamburg (Germany); Hessisches Landesamt für Naturschutz, Umwelt und Geologie (Germany); Landesamt für Umwelt, Naturschutz und Geologie Mecklenburg-Vorpommern (Germany); Ministerium für Umwelt, Energie, Bauen und Klimaschutz Niedersachsen (Germany); Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (Germany); Landesamt für Umwelt Rheinland-Pfalz (Germany); Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (Germany); Landesbetrieb für Hochwasserschutz und Wasserwirtschaft Sachsen-Anhalt (Germany); Landesamt für Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein (Germany); Thüringer Landesamt für Umwelt und Geology (Germany); Bundesanstalt für Gewässerkunde (Germany); the Greek Committee on Large Dams (Greece); the WWF Greece; Országos Vízügyi Főigazgatóság (Hungary); the Marine and Freshwater Research Institute (Iceland); Inland Fisheries Ireland (Ireland); Registro Italiano Dighe (Italy); Regione Emilia-Romagna, Lombardia, Piemonte, Toscana (Italy), Latvijas Vides, ģeoloģijas un meteoroloģijas centrs (Latvia); Aplinkos apsaugos agentūra (Lithuania); the Ministère du Développement durable et des Infrastructures (Luxembourg); the Ministarstvo poljoprivrede i ruralnog razvoja (Montenegro); PDOK (The Netherlands); VISpas (The Netherlands); Norges vassdrags-og energidirektorat (Norway); Gospodarstwo Wody Polskie, National Water Management Board (Poland); the Stanisław Sakowicz Inland Fisheries Institute (Poland); the Universidade de Lisboa, Centro de Estudos Florestais (Portugal); Agência Portuguesa do Ambiente (Portugal); Ministerul Apelor și Pădurilor (Romania); WWF Danube-Carpathian (Romania); Ministerstvo životného prostredia (Slovakia); Confederación Hidrográfica del Júcar (Spain); Confederación Hidrográfica del Cantábrico (Spain); Confederación Hidrográfica del Duero (Spain); Confederación Hidrográfica del Tajo (Spain); Confederación Hidrográfica del Guadiana (Spain); Confederación Hidrográfica del Ebro (Spain); Uraren Euskal Agentzia (Spain); Confederación Hidrográfica del Guadalquivir (Spain); Confederación Hidrográfica del Segura (Spain); Universidad de Murcia (Spain); Universidad de Córdoba (Spain); Universidad de Cantabria (Spain); Ministerio para la Transición Ecológica y el Reto Demográfico (Spain); Agència Catalana de l’Aigua (Spain); SUDOANG (Spain); LST Biotopkartering vandringshinder (Sweden); Nationella Biotopkarteringsdatabasen (Sweden); Federal Office for the Environment (FOEN) (Switzerland); Geocat.ch (Switzerland); the Environment Agency (UK); Natural Resources Wales (UK); and the Scottish Environment Protection Agency (UK). We thank G. Luoni for help in developing the random forest regression model, and many students and colleagues who helped with field surveys across Europe.

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Contributions

B.B., S.B., W.v.d.B. and C.G.d.L. designed the study. B.B., S.B., G.S. and W.v.d.B. led the work and organized the collection of barrier data; B.B., S.B., L.B., A.C. and C.G.d.L. carried out the analysis; C.G.d.L. and B.B. wrote the initial drafts of the manuscript with essential input from S.B., L.B, J.J., A.C., S.C. and W.v.d.B.; G.S. and J.J. designed and curated the barrier database; K.M.W. helped to obtain unpublished barrier records from German Länder; B.B., P.F.G., R.O.d.A., G.R. and G.S. cleaned existing barrier inventories. Walkover river surveys were conducted/organized by G.S. and P.M. (Portugal, France); E.D., E.G.-V, C.R., S.F. and G.L. (Spain); B.B. and S.B. (Italy, Lithuania); J.J. and P.E.J. (Wales); K.A., K.B. and N.J. (Denmark), J.B. and J.K. (Ireland), M.C. and M.P. (Balkans, Danube, Estonia, Germany, Scandinavia); T.F., C.T.S. (Germany); P.K., A.V., J.K., M.C.L., S.V. and J.S.T. (England); E.V. and L.M. (Scotland); P.P., M.L. and M.Z. (Poland); H.W. and A.B. (The Netherlands). G.G., J.R., L.W., M.B. and P.G. advised on the development of the Atlas and the policy implications. All co-authors critically revised and approved the edited manuscript.

Corresponding authors

Correspondence to Carlos Garcia de Leaniz or Wouter van de Bund.

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The authors declare no competing interests.

Additional information

Peer review information Nature thanks Arnout van Soesbergen, Christiane Zarfl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Approach used to estimate river fragmentation in Europe.

To correct for under-reporting and derive more accurate estimates of barrier density we used a four-step approach: (1) compilation of georeferenced barrier records from local, regional and national barrier databases (the AMBER Barrier Atlas), (2) data cleaning and removal of duplicate records, (3) ground-truthing barrier densities from walkover river surveys, and (4) statistical barrier modelling via random forest regression.

Extended Data Fig. 2 Cumulative height distribution of artificial barriers found in European rivers.

The figure shows (log10 scale) that most barriers (68% of n = 117,371 built structures equal to or greater than 10 cm in height) are low-head structures (such as fords, culverts and sluice gates) smaller than 2 m in height; these are ubiquitous but typically unreported in existing barrier inventories.

Source data

Extended Data Fig. 3 Location of test reaches used to ground-truth the AMBER Barrier Atlas during walkover surveys.

We walked 147 test reaches totalling 2,715 km that were representative of river types found in Europe in terms of altitude, slope, stream order, biogeography and land use. River network and country boundaries were sourced from the European Environment Agency35.

Extended Data Fig. 4 Variation in areal barrier density and main drivers of barrier abundance modelled by random forest regression.

a, The predicted barrier density at ECRINS sub-catchments (barriers per km2; number of sub-catchments 8,467). b, Agricultural pressure (proportion of agricultural area, Corine Land Cover 2, level 1). c, Road crossing density (crossings per km2), d, Mean altitude (m.a.s.l., metres above sea level). e, Extent of surface water (proportion of area occupied by surface water, Corine Land Cover 5, level 1). f, The relative weight (MDI) of the 11 predictors used to model barrier density (detailed in Extended Data Table 4). Country and sub-basin boundaries, CORINE Land Cover and mean altitude were sourced from the European Environment Agency35,61,63 and road density was sourced from the GRIP database64.

Source data

Extended Data Fig. 5 Performance of the barrier density model.

The maps show the distribution of modelling residuals (predicted minus observed barrier density, in units of barriers per km2) for the model calibration dataset (number of sub-catchments 2,306) (a), and the whole AMBER Barrier Atlas dataset (number of sub-catchments 8,467) (b). Country and sub-basin boundaries were sourced from the European Environment Agency35.

Extended Data Table 1 Results of river walkover surveys used to ground-truth barrier records
Extended Data Table 2 Comparisons of barrier densities (barriers per kilometre) in Europe and elsewhere using a common river network (HydroSHEDS71)
Extended Data Table 3 Incidence of barrier duplicates and duplicate exclusion criteria
Extended Data Table 4 Variables used to model barrier density
Extended Data Table 5 Sensitivity analysis for barrier density modelling

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Belletti, B., Garcia de Leaniz, C., Jones, J. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020). https://doi.org/10.1038/s41586-020-3005-2

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