freshwater ecosystem research paper

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• Published: 09 August 2023

The recovery of European freshwater biodiversity has come to a halt

• Peter Haase   ORCID: orcid.org/0000-0002-9340-0438 1 , 2   na1 ,
• Diana E. Bowler   ORCID: orcid.org/0000-0002-7775-1668 3 , 4 , 5 ,
• Nathan J. Baker   ORCID: orcid.org/0000-0001-7948-106X 1 , 6 ,
• Núria Bonada 7 ,
• Sami Domisch   ORCID: orcid.org/0000-0002-8127-9335 8 ,
• Jaime R. Garcia Marquez 8 ,
• Jani Heino   ORCID: orcid.org/0000-0003-1235-6613 9 ,
• Daniel Hering 2 ,
• Sonja C. Jähnig   ORCID: orcid.org/0000-0002-6349-9561 8 , 10 ,
• Astrid Schmidt-Kloiber   ORCID: orcid.org/0000-0001-8839-5913 11 ,
• Rachel Stubbington 12 ,
• Florian Altermatt   ORCID: orcid.org/0000-0002-4831-6958 13 , 14 ,
• Mario Álvarez-Cabria   ORCID: orcid.org/0000-0003-2709-5030 15 ,
• Giuseppe Amatulli 16 ,
• David G. Angeler   ORCID: orcid.org/0000-0003-2197-7470 17 , 18 , 19 , 20 ,
• Gaït Archambaud-Suard 21 ,
• Iñaki Arrate Jorrín 22 ,
• Thomas Aspin 23 ,
• Iker Azpiroz 24 ,
• Iñaki Bañares 25 ,
• José Barquín Ortiz   ORCID: orcid.org/0000-0003-1897-2636 15 ,
• Christian L. Bodin 26 ,
• Luca Bonacina   ORCID: orcid.org/0000-0002-4695-5932 27 ,
• Roberta Bottarin   ORCID: orcid.org/0000-0002-6352-3699 28 ,
• Miguel Cañedo-Argüelles   ORCID: orcid.org/0000-0003-3864-7451 7 , 29 ,
• Zoltán Csabai   ORCID: orcid.org/0000-0003-1700-2574 30 , 31 ,
• Thibault Datry 32 ,
• Elvira de Eyto   ORCID: orcid.org/0000-0003-2281-2491 33 ,
• Alain Dohet   ORCID: orcid.org/0000-0003-2962-7387 34 ,
• Gerald Dörflinger   ORCID: orcid.org/0000-0003-0209-4648 35 ,
• Emma Drohan   ORCID: orcid.org/0000-0002-3391-3100 36 ,
• Knut A. Eikland   ORCID: orcid.org/0000-0001-5037-7509 37 ,
• Judy England   ORCID: orcid.org/0000-0001-5247-4812 38 ,
• Tor E. Eriksen   ORCID: orcid.org/0000-0003-2033-6399 39 ,
• Vesela Evtimova   ORCID: orcid.org/0000-0002-6358-8011 40 ,
• Maria J. Feio   ORCID: orcid.org/0000-0003-0362-6802 41 ,
• Martial Ferréol   ORCID: orcid.org/0000-0001-6740-3654 32 ,
• Mathieu Floury   ORCID: orcid.org/0000-0002-4952-5807 8 , 42 ,
• Maxence Forcellini   ORCID: orcid.org/0000-0003-4921-2189 32 ,
• Marie Anne Eurie Forio 43 ,
• Riccardo Fornaroli   ORCID: orcid.org/0000-0001-6326-5653 27 ,
• Nikolai Friberg 39 , 44 , 45 ,
• Jean-François Fruget 46 ,
• Galia Georgieva   ORCID: orcid.org/0000-0003-3367-3802 40 ,
• Peter Goethals 43 ,
• Manuel A. S. Graça   ORCID: orcid.org/0000-0002-6470-8919 41 ,
• Wolfram Graf 11 ,
• Andy House 23 ,
• Kaisa-Leena Huttunen   ORCID: orcid.org/0000-0003-0488-1274 47 ,
• Thomas C. Jensen   ORCID: orcid.org/0000-0003-2777-2759 37 ,
• Richard K. Johnson 17 ,
• J. Iwan Jones   ORCID: orcid.org/0000-0002-7238-2509 48 ,
• Jens Kiesel   ORCID: orcid.org/0000-0002-4371-6434 8 , 49 ,
• Lenka Kuglerová 50 ,
• Aitor Larrañaga 51 ,
• Patrick Leitner 11 ,
• Lionel L’Hoste   ORCID: orcid.org/0000-0003-0239-9468 34 ,
• Marie-Helène Lizée 21 ,
• Armin W. Lorenz   ORCID: orcid.org/0000-0002-3262-6396 2 ,
• Anthony Maire   ORCID: orcid.org/0000-0003-0920-773X 52 ,
• Jesús Alberto Manzanos Arnaiz 22 ,
• Brendan G. McKie 17 ,
• Andrés Millán 53 ,
• Don Monteith   ORCID: orcid.org/0000-0003-3219-1772 54 ,
• Timo Muotka 47 ,
• John F. Murphy   ORCID: orcid.org/0000-0003-2102-7686 48 ,
• Davis Ozolins   ORCID: orcid.org/0000-0003-3752-2040 55 ,
• Riku Paavola   ORCID: orcid.org/0000-0002-4708-1413 56 ,
• Petr Paril   ORCID: orcid.org/0000-0002-7471-997X 31 ,
• Francisco J. Peñas 15 ,
• Francesca Pilotto   ORCID: orcid.org/0000-0003-1848-3154 37 ,
• Marek Polášek   ORCID: orcid.org/0000-0003-3213-7135 31 ,
• Jes Jessen Rasmussen   ORCID: orcid.org/0000-0002-5932-3125 39 ,
• Manu Rubio   ORCID: orcid.org/0000-0001-5225-9557 24 ,
• David Sánchez-Fernández   ORCID: orcid.org/0000-0003-1766-0761 53 ,
• Leonard Sandin 37 ,
• Ralf B. Schäfer   ORCID: orcid.org/0000-0003-3510-1701 57 ,
• Alberto Scotti 28 , 58 ,
• Longzhu Q. Shen 8 , 59 ,
• Agnija Skuja 55 ,
• Stefan Stoll   ORCID: orcid.org/0000-0002-3656-417X 2 , 60 ,
• Michal Straka 31 , 61 ,
• Henn Timm 62 ,
• Violeta G. Tyufekchieva   ORCID: orcid.org/0000-0001-9324-0430 40 ,
• Iakovos Tziortzis   ORCID: orcid.org/0000-0002-9315-7773 35 ,
• Yordan Uzunov 40 ,
• Gea H. van der Lee   ORCID: orcid.org/0000-0002-8731-682X 63 ,
• Rudy Vannevel 43 , 64 ,
• Emilia Varadinova   ORCID: orcid.org/0000-0002-2382-9191 40 , 65 ,
• Gábor Várbíró   ORCID: orcid.org/0000-0001-5907-3472 66 ,
• Gaute Velle   ORCID: orcid.org/0000-0002-5603-271X 26 , 67 ,
• Piet F. M. Verdonschot   ORCID: orcid.org/0000-0002-4126-7452 63 , 68 ,
• Ralf C. M. Verdonschot   ORCID: orcid.org/0000-0002-0977-5975 63 ,
• Yanka Vidinova   ORCID: orcid.org/0000-0002-6438-2349 40 ,
• Peter Wiberg-Larsen 69 &
• Ellen A. R. Welti   ORCID: orcid.org/0000-0001-6944-3422 1 , 70   na1  

Nature volume  620 ,  pages 582–588 ( 2023 ) Cite this article

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• Freshwater ecology

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss 1 . Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity 2 . Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.

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Time series of freshwater macroinvertebrate abundances and site characteristics of European streams and rivers

Freshwater ecosystems are biodiversity hotspots and provide vital ecosystem services, including drinking water, food, energy and recreation. However, humans have degraded freshwaters for centuries, with impacts sharply increasing after World War II during the great acceleration 3 . Freshwaters are exposed to anthropogenic pressures from agricultural and urban land uses over whole catchments, accumulating pollutants, including phosphorus, organic-rich effluents, fine sediments, pesticides and emergent pollutants (such as nanoplastics and pharmaceuticals) 4 , 5 . Furthermore, freshwaters have been degraded by hydromorphological alterations, water extraction, invasive species and climate change 6 , 7 . In response to legislation such as the US Clean Water Act (1972) and the EU Water Framework Directive (2000), key countermeasures designed to improve water quality and restore freshwater habitats were implemented, including better wastewater treatment and controls on the emission of airborne pollutants. These actions resulted in considerable declines in organic pollution and acidification beginning around 1980 8 . Over the past 50 years, such mitigation measures have resulted in quantifiable improvements in freshwater biodiversity in some locations 9 , yet the number and impacts of stressors threatening freshwater ecosystems continues to increase worldwide and the biological quality of rivers remains poor globally 10 , 11 .

Freshwater invertebrates are a phylogenetically and ecologically diverse group that contribute to critical ecosystem processes, including decomposing organic matter, filtering water, providing energy to higher trophic levels, and transporting nutrients and energy between aquatic and terrestrial ecosystems 12 , 13 . Moreover, freshwater invertebrates have long been a cornerstone of water-quality monitoring. The biological traits of freshwater invertebrates are well characterized, enabling the assessment of functional diversity—the range of functional traits of the organisms in a given ecosystem 14 —an important facet of biodiversity that can be used as a proxy for ecosystem functioning 15 , 16 . However, trajectories of taxonomic and functional diversity have rarely been investigated simultaneously at larger spatial and temporal scales. Determining the trajectories of taxonomic and functional change could inform the development of evidence-based management strategies that address stressors through mitigation, restoration and conservation. Furthermore, how temporal changes in biodiversity manifest across large spatial scales and vary among taxonomic groups remains equivocal 17 , 18 , 19 . Examining whole ecological groups representative of a particular ecosystem (for example, freshwater invertebrate communities in river ecosystems) may help to clarify discrepancies across studies and identify key drivers of temporal change.

Here we analysed pan-European patterns and drivers of multidecadal trends in abundance and taxonomic and functional diversity of invertebrate communities using a comprehensive dataset of 1,816 time series collected in riverine systems in 22 European countries between 1968 and 2020 (Fig. 1 ). The dataset comprises 714,698 observations of 2,648 taxa in 26,668 samples. The time series span a mean of 19.2 years with an average of 14.9 sampling years (minimum 8 years, maximum 32 years). We address two research questions: (1) how abundance, taxonomic diversity and functional diversity of freshwater invertebrate communities have changed over the past five decades in European streams and rivers; and (2) what environmental factors have driven these changes. Given that Europe-wide management has resulted in improvements in water quality 2 , 20 , we hypothesize that abundance, taxonomic diversity and functional diversity have increased, consistent with a recovery. We further hypothesize that freshwater invertebrate community recovery was strongest around the end of the previous century after the onset of concerted efforts to mitigate stressor impacts and restore ecosystems, but has slowed in recent years owing to diminishing returns on these actions in addition to remaining and new pressures including climate change, land-use intensification and emerging pollutants. We assessed evidence for negative impacts of multiple human pressures, including dams, urban areas and cropland, and increasing temperatures, while accounting for subcatchment characteristics (such as elevation and stream size). We used hierarchical Bayesian models to estimate trends and identify drivers of change in abundance and taxonomic and functional diversity of Europe’s freshwater invertebrate communities, while accounting for temporal autocorrelation, sampling date and sampling variation across studies and countries.

a , A timeline of major stressors (above the line) and environmental legislation (below the line) affecting Europe’s freshwater ecosystems (citations are provided in Supplementary Table 1 ). UN/ECE LTRAP,  United Nations Economic Commission for Europe Long-Range Transboundary Air Pollution. b , The sampling sites (points) and the rate of temporal change in taxon richness of freshwater invertebrate communities (colour of points) across 22 European countries (black). c , The distribution of sampling sites over time and countries. ‘Other’ includes countries with fewer than 50 sampling sites.

Recovery of Europe’s freshwater invertebrate communities

Across all time series, taxon richness increased by 0.73% per year, whereas abundance increased by 1.17% per year between 1968 and 2020 (Fig. 2a,b ), substantiating previous documentation of a recovery process 18 , 21 , 22 . The probabilities of trends derived from posterior distributions (that is, the probability of the mean trend being above or below zero) revealed 0.99 and 0.91 probabilities of a mean increase in taxon richness and abundance, respectively. Despite these net-positive trends, taxon richness declined at 30% of sites and abundance declined at 39% of sites. Abundance trends for EPT taxa (mayflies, stoneflies and caddisflies—an indicator group of water quality 23 ) and insects increased (EPT, +2.38% per year, 0.97 probability; insects, +1.53% per year, 0.95 probability) at higher net rates than the overall trends. EPT richness (+0.45% per year, 0.82 probability) and insect richness (+0.71% per year, 0.99 probability) trends increased, but at net rates lower than the overall trends (Extended Data Fig. 1 ).

a – h , Overall meta-analysis estimates and distributions of site-level trends for taxonomic metrics of taxon richness ( a ), abundance ( b ), Shannon’s evenness ( c ) and turnover ( d ), and functional metrics of richness ( e ), redundancy ( f ), evenness ( g ) and turnover ( h ) across all 1,816 sites. The black error bars and text on each panel show the mean estimates (percentage change per year). The error bars indicate the 80%, 90% and 95% CIs.

Freshwater ecosystems are frequently invaded by non-native species 7 . We therefore examined whether changes in abundance and richness were driven by these taxa. Non-native species comprised an average of 4.9% of the species and 8.9% of the individuals at the 1,299 sites for which the taxonomic resolution allowed detection. Thus, native species dominated most communities (with 99.9% of sites comprising >50% native species). When considering only native taxa, trends in richness (+0.64% per year, 0.98 probability) and abundance (+0.26% per year, 0.61 probability) remained positive, but less so than overall net trends (Fig. 2 and Extended Data Fig. 1 ). For sites at which non-native species were detected (898 out of 1,299 sites), non-native species richness (+3.97% per year, 0.99 probability) and abundance (+3.9% per year, 0.95 probability) increased sharply (Extended Data Fig. 1 ).

Functional diversity, which describes the value and range of functional traits of the organisms in a given ecosystem 14 (Supplementary Table 4 ), also increased over the 53-year study period. Functional richness, which quantifies the functional space filled by a community, increased on average by 2.4% per year (0.99 probability of increase; Fig. 2e ). Functional redundancy—a measure of overlap in functional trait space—had no strong trend (+0.03% per year, 0.64 probability of increase; Fig. 2f ). By contrast, functional evenness declined (−0.22% per year, 0.96 probability of decrease; Fig. 2g ), as did taxonomic evenness (−0.54% per year, 0.99 probability; Fig. 2c ). Similarly, functional temporal turnover (−0.32% per year, 0.97 probability; Fig. 2h ) and taxonomic temporal turnover declined (−0.2% per year, 0.87 probability; Fig. 2d ). Together, these results suggest that functional diversity trends largely paralleled those of taxonomic diversity. Model estimates and raw distributions of trends for additional taxonomic and functional metrics are shown in Extended Data Fig. 2 .

Gains in species richness have come to a halt

While overall net trends provide an overview across the entire study period and enable comparison with other long-term biodiversity studies 17 , 19 , 24 , they may mask important shorter-term temporal fluctuations in trends. Thus, to provide more nuanced, complementary trend information, we used a ten-year moving-window approach to examine the trajectories of freshwater invertebrate community change over time. Nonlinear trajectories were expected due to temporal variation in pressures and the implementation of mitigation measures 25 . To improve spatial representativity and comparability across years, only years with at least 250 sites from at least 8 countries were included, corresponding to the period of 1990 to 2020.

Although trends in taxon richness were generally positive, indicating increases in local richness through time, this effect became weaker over the decades (mean change in trends = −8.8% per year, 95% credible interval (CI) range: −13.6% to −3.8% per year). Trends in taxon richness started declining around 2010 and then levelled off, reaching an average of net zero around 2013 (Fig. 3a ), indicating an end to the preceding recovery period. When considering only the dominant pattern as measured by the proportion of positive trends, the proportion of sites with increasing taxon richness declined after windows centred on the early 2000s (Fig. 3e ). Functional richness trends were more variable, with the highest trends evident for windows centred on 2000 and 2010, and near net zero trends after 2010 (Fig. 3c ). Functional richness trends had an overall tendency to decline (mean change in trends of functional richness = −5.9% per year, 95% CI range: −12% to +0.1% per year). Temporal changes in the proportion of sites with positive functional richness trends were similar to those reported for taxon richness (Fig. 3e,g ). Trends in abundance (Fig. 3b,f ) and functional redundancy (Fig. 3d,h ) changed little over time (that is, CIs overlapped with zero in an analysis of the change in trend estimates over time), although abundance trends tended to decline from windows centred on 2010 until the end of the study period.

a – h , Modelled trend estimates from moving windows of taxon richness ( a ), abundance ( b ), functional richness ( c ) and functional redundancy ( d ), and the proportion of sites with positive trend estimates of taxon richness ( e ), abundance ( f ), functional richness ( g ) and functional redundancy ( h ). Trend estimates were calculated from Bayesian mixed-effects models of trends from at least 250 time series with at least 6 years of data from at least 8 countries within 10-year moving windows (totalling 21,495 time-series segments). The proportions are based on whether site-level trend estimates of these time-series were above zero or not. For trend estimates in a – d , blue and red areas indicate the overall positive (>0) and negative (<0) mean trend estimates for the given 10-year window, respectively, and the grey polygons indicate the 80%, 90% and 95% CIs. For site proportions in e – h , blue and red areas indicate a larger proportion of positive (>50% of sites) and negative (<50% of sites) site-level trend estimates for the given 10-year window, respectively, and the grey polygons indicate 80%, 90% and 95% CIs.

Although similar trends in taxonomic and functional metrics were expected due to functional variation being constrained by taxon richness, functional diversity can be more responsive to environmental gradients 26 . However, changes in functional diversity have rarely been quantified in large-scale investigations of temporal change in biodiversity 27 , 28 . A switch from primarily positive trends in functional richness in the late 1990s and early 2000s to near-zero trends starting around 2012 (Fig. 3c ) may suggest no further improvements in ecosystem functioning. The concurrent limited change in functional redundancy (Fig. 3d ) indicates that the increase in functional richness provided new traits to these communities rather than adding traits that were already present. Both taxonomic and functional trends in evenness and turnover remained near zero or slightly negative over time (Extended Data Fig. 3 ).

Environmental drivers of biodiversity change

Identifying the natural and anthropogenic drivers of biotic change is critical to inform effective management strategies. Here we show that climate, dam impacts, and the percentage of upstream urban areas and cropland (both sources of pollution and causes of habitat degradation) can all be linked to trends in taxonomic and functional metrics representing Europe’s freshwater invertebrate communities (Fig. 4 and Extended Data Figs. 4 and 5 ).

a – h , Estimated effects of the mean ( t max mean) and trend ( t max slope (sl.)) of annual maximum monthly mean temperatures, mean (ppt mean) and trend (ppt sl.) of the annual cumulative precipitation, the dam impact score (dam) and the percentage of the upstream catchment covered by urban areas and cropland on site-level long-term trend estimates for taxon richness ( a ), abundance ( b ), evenness ( c ) and turnover ( d ), and functional richness ( e ), redundancy ( f ), evenness ( g ) and turnover ( h ). n  = 1,816 biologically independent sites for all metrics. Positive and negative estimates are shown in blue and red, respectively. For climatic drivers, mean values refer to mean long-term values at each site and represent geographical variation; trends were calculated by regressing annual mean values against year, using the coefficient as an estimate of climatic trend and represent temporal variation. All response variables are site-level trends (that is, change in biodiversity metric over time) and all covariates were standardized to units of s.d. before analysis. A positive coefficient means that sites with higher values of the driver tended to have higher trends, although not necessarily positive trends, compared with sites with lower values of the driver. For example, trends in taxon richness were higher at sites with higher maximum mean temperatures ( t max mean) but lower at sites with higher rates of temperature increase ( t max sl.; b ). The bars around the estimates indicate 80%, 90% and 95% CIs. The grey horizontal lines separate the three environmental driver groups: climate, dams and land use. Estimates of stream characteristics (stream order, flow accumulation, elevation and slope) are shown in Extended Data Fig. 6 .

Climate strongly influenced freshwater invertebrate communities (Fig. 4 ). Overall, sites experienced a net increase in air temperature of +0.037 °C per year ± 0.0007 s.e.m. (with 94% of sites warming) and a slight net increase in precipitation of +0.49 mm per year ± 0.12 s.e.m. (with 57% of sites getting wetter) over the studied intervals. Sites in areas with higher mean air temperatures were more likely to gain taxa (Fig. 4 ) compared with those in cooler areas. This may indicate that climate warming has not yet reached critical values for many European freshwater invertebrates, consistent with previous predictions for ectotherms in temperate regions 29 , 30 . Alternatively, lower recovery rates for biotic communities in cooler areas could reflect the less severe degradation of northern sites before recovery started. By contrast, more warming over time had negative biodiversity outcomes, with negative effects on long-term trends of taxon richness, abundance and functional richness (Fig. 4 ). Mean precipitation had a positive effect on long-term trends of functional richness but a negative effect on long-term trends of abundance and functional redundancy, indicating the addition of functionally unique taxa at wet sites. However, greater increases in precipitation over time had a negative effect on long-term trends of both taxonomic and functional richness (Fig. 4 ). Precipitation can influence invertebrate communities and their functioning by altering flow regimes (and therefore water quality and temperature through changes in runoff, discharge and dilution) and food availability 6 .

Biodiversity trends were generally lower at sites downstream of dams and in catchments with a high percentage of urban areas or cropland. High dam impacts (that is, those in systems connected to more dams and/or closer to dams) had negative effects on long-term trends in taxon richness, abundance, functional richness and functional redundancy (Fig. 4 ). Dams increase sediment loads, reduce longitudinal connectivity, and change river flow and temperature regimes 31 , 32 , 33 . By contrast, high dam impacts had a positive effect on long-term trends of both taxonomic and functional evenness, suggesting that dominant species declined in abundance in communities downstream of dams, whereas richness losses were more pronounced for rare species. Furthermore, increases in functional evenness, accompanied by decreases in functional richness and redundancy, could reflect selection for a subset of traits that confer tolerance of the conditions downstream of dams, including altered resource availability and hydromorphological homogenization. A greater percentage of upstream cropland had a negative effect on long-term trends in taxonomic and functional richness and abundance. Cropland frequently contributes to nutrient-enriched runoff, leaving primarily tolerant taxa 34 . A greater percentage of upstream urban areas had negative effects on taxon richness long-term trends (Fig. 4 ), but positive effects on non-native richness long-term trends (Extended Data Fig. 5a ), suggesting losses of rare and sensitive native species. Biodiversity trends varied little with stream characteristics, although sites at higher elevations had lower gains in functional richness, potentially due to rising temperatures (as evidenced by a weak positive correlation between temperature trends and elevation; r  = 0.15) 35 . Larger rivers became relatively more prone to invasion by non-native species 36 (Extended Data Figs. 6 – 8 ).

Reviving the recovery

Using a comprehensive Europe-wide dataset, we document the recovery of freshwater invertebrate communities over the past 53 years. The taxon richness gains observed across 70% (1,269 out of 1,816) of time series are concurrent with widespread implementation of mitigation measures 8 , particularly improvements in wastewater treatment motivated by the EU Urban Waste Water Directive from 1991. However, gains in taxon richness started to decelerate around 2010, which may indicate that progress towards recovery has come to a halt at many sites, while remaining sites may reflect either predominant recovery or ongoing degradation towards the end of the study period. Most of our sites are monitored under the EU Water Framework Directive (WFD) and 60% of WFD-monitored rivers still do not reach ‘good ecological status’ 37 . Even at ‘good’ sites, considerable recovery could be needed to reach ‘high ecological status’, suggesting that improvements documented here represent only a partial recovery of European freshwater ecosystems.

Regardless of the reason for the deceleration, the impacts to Europe’s rivers caused by ongoing pressures remain extensive and severe 37 , 38 . Although our observational data prevent confirmation of the underlying causal processes, our interpretation of the overall recovery being a response to improving water quality aligns with the conclusions of other studies of European freshwater invertebrate time series 9 , 39 . Negative effects of poor water quality on biodiversity are supported by our findings that freshwater invertebrate communities downstream of dams, urban areas and cropland were less likely to experience biodiversity recovery. Urban areas produce the majority of micropollutants, are hubs of non-native species invasions (Extended Data Fig. 5a ) and generate high-nutrient inputs, whereas croplands are sources of fine sediment 40 , pesticides and nutrient-laden runoff 41 , and greatly contribute to river salinization 42 . Most European rivers bear a substantial legacy of human impacts on their hydromorphology 8 , 38 , with urban areas being the most affected, despite considerable river restoration in recent decades 43 . The positive effects of higher mean temperatures on long-term trends in invertebrate richness probably reflect the lower initial degradation in northern European countries. This may also reflect the relatively cool temperatures in European countries, whereas decreases in invertebrate richness are currently expected in freshwaters of warmer bioregions, such as tropical regions, which are not represented in our study 44 . However, the negative effects on long-term trends of taxon richness, abundance and functional richness in communities experiencing greater rates of warming are worrying. These effects are likely to worsen as temperatures continue to rise and as climatic extremes including summer droughts and heatwaves become more common 45 .

Considering that environmental legislation and policy have insufficiently addressed ongoing and emerging stressors 8 , the stalled recovery is unsurprising. Further management actions to revive the recovery should target sites at greater risk of biodiversity decline, such as those downstream of urban areas, cropland and dams, while maintaining and strengthening protection of the least impacted systems that are refuges of biodiversity. Specifically, substantial, catchment-scale changes in land management must go beyond current legislative requirements and achieve greater reductions in water extraction and inputs of pollutants including fine sediments, pesticides and fertilizers. Substantial investment is needed to upgrade sewage networks and improve wastewater treatment plants to better manage stormwater overflow and more effectively remove micropollutants, nutrients, salts and other contaminants 46 . Adopting a catchment-scale approach that considers barriers to dispersal 47 can further enhance the effectiveness of management, conservation and restoration practices 32 , 48 . Additional hydromorphological restoration efforts are required to reconnect rivers and floodplains to improve ecosystem functioning, prevent destructive floods, and adapt riverine systems to future climatic and hydrological regimes. Finally, standardized, large-scale and long-term biodiversity monitoring, paired with parallel environmental data collection 49 , 50 , should be prioritized to effectively characterize temporal changes in biodiversity and environmental drivers and identify sites at high risk 51 .

Current large-scale measures to address biodiversity loss remain rare, especially for invertebrates. This in part reflects our understanding of biodiversity change, which is limited by unknown historical baseline conditions and complex variation in interacting anthropogenic stressors. Insufficient baseline data present challenges both for characterization of biodiversity trends and ecological status of communities, and evaluation of tolerable levels and effects of stressors 52 . Data on the state of freshwater communities both before and during the great acceleration are largely lacking, making it unclear when freshwater degradation peaked. Long-term data from the UK suggest freshwater invertebrate biodiversity was lowest at the start of the 1990s 53 , but our pre-1990s data are insufficient to determine whether this pattern is Europe-wide (Fig. 1c ). Moreover, comparison with unimpacted ‘reference’ communities, a standard practice in freshwater ecology, is becoming increasingly challenging due to the emergence of new communities 54 resulting from climate change, non-native species invasions and other pressures 55 . Progress towards biodiversity goals needs to recognize these changing pressures through flexible strategies to protect and foster Earth’s remaining biodiversity. We call for adaptive environmental management that recognizes conservation and restoration objectives as shifting targets that can be modified to adapt to global change and maximize the protection of biodiversity.

Time series

We assembled a database of time series of riverine invertebrate communities following a data call targeting European ecologists and environmental managers. We included only time series that (1) included abundance estimates; (2) documented whole freshwater invertebrate communities (including all sampled macroinvertebrates, for example, Coleoptera, Crustacea, Diptera, Ephemeroptera, Hirudinea, Mollusca, Odonata, Oligochaeta, Plecoptera, Trichoptera, Tricladida); (3) identified most taxa to family, genus or species; (4) had ≥8 sampling years (not necessarily consecutive); (5) used the same sampling method and taxonomic resolution throughout the sampling period; and (6) had consistent sampling effort per site (for example, the number of samples or area sampled) in all years.

Only one sampling event per year was included for each time series, where a sampling event was defined as the sample or samples collected within a single day. For time series with multiple sampling seasons within or among years, we included only one sampling season (defined as three consecutive months), preferentially using the season with the longest time series. No time series had multiple sampling events per season. Sensitivity analyses indicated limited effects of season on trend estimates (Extended Data Fig. 10 ). We removed taxa that are not freshwater invertebrates, including terrestrial and semi-aquatic taxa, and vertebrates, in addition to freshwater invertebrates that were recorded inconsistently owing to their small size (such as mites, copepods and cladocerans).

Between 13 and 516 taxa were sampled per site across all sampling years. Communities from 42% of sites were identified to species, 30% were identified to mixed (species-to-family) taxonomic levels and 28% were identified primarily to family. In total, 2,648 taxa from 959 genera, 212 families and 47 groups (primarily orders) were recorded. We list time-series locations, durations and characteristics in Supplementary Table 2 and list the number of sites sampled per year and country in Supplementary Table 3 .

Our compiled time series represent different stream types and stream orders from a large geographical area of Europe. Data were collected for purposes including research projects and regulatory biomonitoring, although detailed information on the purpose is unavailable for some time series. These data were not selected randomly but were collected from available studies that met our six criteria. As these data were collected from sites exposed to varying and unquantified levels of anthropogenic impacts, we cannot rule out biases arising from unequal representation of sites exposed to different impact levels from severely impacted to least impacted.

Community metrics

We calculated taxonomic and functional diversity metrics representing freshwater invertebrate communities across sites and over time. We also examined different community subsets: native and non-native species, and insects and EPT taxa (Ephemeroptera, Plecoptera, Trichoptera, that is, mayflies, stoneflies, caddisflies, grouped as an indicator of water quality 56 ).

Taxonomic diversity

We calculated total abundance, taxon richness, Shannon’s diversity, Shannon’s evenness, rarefied richness (calculated on the basis of standardized numbers of individuals) and temporal turnover for each site and year. As sampling effort was standardized within time series before metric calculation, individual-based rarefied richness was used to estimate the number of taxa per given number of individuals, based on the lowest number of individuals per sampling year in each time series 17 . We calculated temporal turnover as the ratio of taxa gained or lost to the total number of taxa present between two timepoints using the R package codyn 57 . All other taxonomic metrics were calculated using the R package vegan 58 .

Functional diversity

Traits were extracted from the European databases freshwaterecology.info (v.7.0) 59 and DISPERSE 60 . First, we downloaded trait data for all taxa. We considered biological traits that influence both a taxon’s response to and its effects on its environment 61 , 62 . Specifically, we compiled data on 10 biological traits (with 53 trait modalities): respiration type, resistance form, dispersal type, aquatic stage, life cycle duration, reproduction type, maximum potential body size, wing form, propensity to drift and feeding type 60 , 63 . For taxa with multiple aquatic life stages (primarily beetles), whenever available from the trait databases, functional roles were assigned for each life stage, otherwise adult traits were used. We included only traits for which information was available for >85% of all taxa. All traits were fuzzy coded across multiple modalities depending on the information available; for example, the trait ‘maximum potential body size’ contains seven modalities ranging from ≤0.25 cm to >8 cm. Within each trait, we scaled affinities to different component modalities between 0 and 1 (summing to 1 across modalities for each taxon), so that each taxon was assigned an affinity score for each modality 64 , to recognize potential trait plasticity.

We took the following steps to fill in gaps due to missing trait data. First, when trait data were not available at the original identification level (15.9% trait coverage across taxa), we used genus-level trait data, resulting in 48.2% coverage. Genus-level trait data are generally sufficient to represent most interspecific variation among freshwater invertebrates and thus taxon responses to environmental variability 61 . Next, when genus-level trait data were not available for taxa identified to genus, we replaced missing values in trait modalities with the median of trait profiles of all species within a genus from the full taxon list, resulting in 61.3% coverage. For taxa identified to family level with no available data for a given trait, we replaced missing values in trait modalities with the median value of trait profiles of all genera within a family, resulting in 90.5% coverage across all taxa. The lack of accurate phylogenies for many invertebrate taxa, low trait coverage at the species level and mixed taxonomic resolution across sampling sites prevented the use of other gap-filling approaches, but taxonomic aggregation generally aligns well with expert trait assignments 65 .

We analysed functional diversity separately for each site by calculating six distance-based metrics chosen to describe multiple facets of community niche space and to align with taxonomic diversity metrics: functional richness, functional redundancy, functional evenness, functional turnover, functional divergence and Rao’s quadratic entropy (definitions and citations are provided in Supplementary Table 4 ). All functional metrics except for functional redundancy and turnover were calculated using the dbFD function in the R package FD 66 . In calculations of functional richness and divergence, we used six principal coordinate analysis axes (the dbFD ‘m’ argument), according to current recommendations 67 . To enable calculation of functional turnover, we calculated community-weighted means of each functional trait category weighted by taxa abundance, then calculated turnover of the community-weighted means using the R package codyn, as for taxonomic temporal turnover 57 , 68 . We calculated abundance-weighted functional redundancy using the uniqueness function in the R package adiv 69 . We calculated redundancy according to a previous report 70 : community uniqueness ( U ) was calculated as quadratic diversity divided by Simpson diversity and functional redundancy was calculated as 1 −  U . The trait input matrix was based on Euclidean distances bound between 0 and 1 and the tolerance threshold was 10 −8 .

Non-native species

Non-native species were defined as introduced species (that is, those present due to human activities, not natural range expansion) at the country level (for example, a species native to Bulgaria could be non-native in the UK). To identify non-native species, we used two databases: DAISIE 71 and the Global Alien First Record Database (GAFRD) (v.2) 72 , 73 . DAISIE contains non-native species in addition to native species defined as invasive because they cause economic loss (that is, pest species). GAFRD includes only non-native species but is limited to species and countries for which the approximate year of introduction is known. From each database, we first extracted all species listed for each European country in our dataset. We determined each species’ country of origin using the Global Biodiversity Information Facility 74 or peer-reviewed publications, both to eliminate native species listed in DAISIE and to check whether species listed as non-native in one European country were also non-native elsewhere (for example, a North American species marked as non-native in Germany in GAFRD would be non-native in all European countries in which it occurred).

In total, we identified 61 non-native species. The initial analysis of native and non-native species was restricted to the 1,299 sites at which taxa were identified to species or a mixed taxonomic resolution; we excluded the remaining 517 sites due to the coarse (primarily family level) taxonomic resolution, which does not allow for reliable identification of non-native species. Estimates of trends in non-native species richness and abundance were restricted to the 898 (of 1,299) sites at which non-native species were recorded. The two most abundant non-native species were the New Zealand mud snail, Potamopyrgus antipodarum (≥1 individual present in ≥1 year at 81% of sites) and the North American bladder snail, Physella acuta (34% of sites).

Stream characteristics and environmental predictors

Stream network.

We used the MERIT Hydro 75 digital elevation model (DEM) to delineate the high-resolution Hydrography90m stream network 76 . To achieve a high spatial accuracy, we used an upstream contributing area of 0.05 km 2 as the stream channel initialization threshold using the r.watershed and r.stream.extract modules in GRASS GIS 77 . We next calculated the subcatchments for each segment of the stream network, that is, the area contributing laterally to a given stream reach between two nodes, using the r.basins module. Coordinates indicating a site’s location did not always occur in the delineated stream network due to spatial inaccuracy of either the DEM or the coordinates. To ensure that point occurrences matched the DEM-derived stream network and therefore the network topology, we first identified the subcatchment in which each point occurrence was located, then moved all points to the corresponding stream segments using the v.net module within the given subcatchment. From each point, we calculated the network (as the fish swims) distance (km) using the v.net.distance module, and the Euclidean (as the crow flies) distance to all other point occurrences using the v.distance module. The distance was set to NA when sites were located in different drainage basins, and therefore not connected through the network.

Environmental predictors

We calculated stream topographical and topological predictors using the MERIT Hydro DEM 76 . Using the r.univar module in GRASS GIS, we computed the average elevation (m), elevation difference between the site and the upstream subcatchment (m), slope and the upstream contributing area (or flow accumulation, km 2 ) for each subcatchment. To create a proxy for dam impacts, we calculated the network distance between each site and each upstream dam using the Global Reservoir and Dam Database (v.1.3) 78 . For dam impact score calculations, see Supplementary equation ( 1 ).

We extracted monthly climatic predictors from the TerraClimate dataset 79 for 1967–2020, which covered all sites and years. For each site, we identified the sampling month and computed the mean monthly climatic value for the corresponding subcatchment. We calculated climatic predictors of cumulative annual precipitation (mm) and maximum monthly temperature (°C) for each 12 month period preceding the mean sampling month at each site. Trend values in precipitation and maximum temperature over the period covered by each time series were calculated using Bayesian models fitted using the R package brms 80 . These models were similar to those used to calculate site-level biodiversity metric trends, in which a trend was estimated as the coefficient of a continuous year effect. The TerraClimate dataset is associated with uncertainties in areas of complex terrain, but our large number of sampling sites, relatively good station coverage and the low physiographical complexity of most site locations should have minimized error in our analyses.

We calculated the proportion of land cover categories in each subcatchment using the ESA CCI Land Cover time series 81 for each year from 1992 to 2018. Land cover data were available for 92% of analysed site and year combinations and for 99% of sites. We computed the entire upstream catchment for each point occurrence using the r.water.outlet module and calculated the percentage cover of each land cover category within this area. The areas of cropland and urban land were calculated as the percentage of the upstream area averaged across the sampled years at each site.

A list of the stream characteristics and environmental drivers, their units and sources is provided in Supplementary Table 5 .

Statistical analysis

Trend analysis.

Temporal trends in each taxonomic (abundance, richness, Shannon’s diversity, Shannon’s evenness, individual-based rarefied richness and temporal turnover), functional (redundancy, richness, evenness, turnover, divergence and Rao’s quadratic entropy) and community subset (taxon richness and abundance of native species, non-native species, EPT taxa and insects only) metric were assessed using a two-step approach. First, we calculated site-level trends for each metric using Bayesian linear models fitted using the R package brms 80 . In these models, a biodiversity metric was the response variable and year was the continuous predictor variable of which the coefficient represented the temporal trend estimate.

The form of the model was: bf(BiodiversityMetric ~ cYear + ar(time = iYear,  p  = 1, cov = TRUE)).

Fixed-year variables were centred to improve model convergence (cYear) and year in the temporal autocorrelation term was included as a count with the first year of sampling considered year 1 (iYear). The models accounted for any residual temporal autocorrelation using an ar(1) term 82 and included day of year as an additional predictor when variation in sampling dates at a site was >30 days.

The form of the model was: bf(BiodiversityMetric ~ cday_of_year + cYear + ar(time = iYear, p  = 1, cov = TRUE)).

The models assumed normally distributed errors, which were checked visually using histograms. Taxonomic evenness, functional richness, total abundance and subset abundance (non-native, native, EPT and insect abundance) were log 10 -transformed, and functional divergence was squared to meet the normality assumption.

We ran linear mixed-effects models (LMM) in the brms package to synthesize site-level data and estimate overall mean trends. The LMM included site-level trend estimates as the response, and an overall intercept and two random effects (country and study identity) as predictors. These random effects accounted for data heterogeneity due to unequal numbers of sites among studies and countries. Site-level trends were normally distributed; we therefore assumed normal errors. Site-level trends were combined in a meta-analysis model to estimate the mean trend across studies, including the uncertainty (represented by the s.d.) of the trend estimates, using brms 80 .

The form of the model was: brm(estimate|se(sd_trend_estimate) ~ 1 + (1|study_id) + (1|country), data = response_stan, iter = 5000, inits = 0, chains = 4, prior = c(set_prior(“normal(0,3)”, class = “Intercept”)), control = list(adapt_delta = 0.90, max_treedepth = 12)).

For each response metric, we calculated the proportion of the posterior distribution of the mean trend estimate (that is, the overall LMM intercept) above or below zero, that is, the probability of an increasing or decreasing mean trend.

In Bayesian models, we mostly used default brms settings, including four chains, which were run for 5,000 iterations (50% burn-in). We used default priors except for trend estimates, for which we selected a narrower prior to diminish the influence of biologically unrealistic trend estimates. Specifically, we used normally distributed priors with a mean of zero and an s.d. of 10 (for site-level trends) or 3 (for mean site-level trends). We compared our meta-analysis model of trends with and without including the uncertainty of site-level trend estimates. To optimize model fit, unweighted models were used for non-native and EPT abundance, and for EPT taxon richness. Functional turnover was fitted using beta models as values were bound between 0 and 1. The percentage change per year was calculated by back-transforming model estimates. Back-transformation calculations varied according to the originally modelled transformations of response variables (see the ‘equationsToPercChangePerYr.xlsx’ file in the ‘plots/Fig2_DensityPlots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ). We further tested a one-stage synthesis approach in which mean trends were estimated in one large mixed-effect model of the observed data, including random intercepts and slopes. Overall, these models produced similar trend results (see figure 16 in the ‘Online Figures.docx’ file in the ‘plots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ).

Moving-window analysis

To assess how estimates of trends in abundance and taxonomic and functional diversity changed over time, we used a moving-window approach. We used a similar two-stage process as described above. For each year of the analysis, we calculated trends within a ten-year window in which all time series with ≥6 sampling years and from ≥8 countries were included. A ten-year window was chosen according to current recommendations regarding times-series length 83 , 84 and six was chosen as the number of sampling years covering >50% of each ten-year period. This analysis was restricted to the period between a first moving window from 1990 to 1999, in which any time series with ≥6 sampling years was included, to a final window from 2011 to 2020. After estimating site-level trends centred on each year of the moving window, we ran a Bayesian LMM for each year to estimate the overall mean trends across sites in that time period. These models followed the same form as used to calculate trend estimates, containing the predictor variables of trends including an error term to account for uncertainty, an overall intercept, and study identity and country as random effects (see the equation in the ‘Trend analysis’ section).

To test for an overall linear change in the trajectory of moving-window trends, we modelled the effect of year on moving-window trend estimates using brms 80 .

The form of the model was: brm(MovingWindowTrend|se(sd_trend_estimate) ~ year, data = moving_window_trends, iter = 5000, inits = 0, chains = 4, prior = c(set_prior(“normal(0,3)”, class = “Intercept”)), control = list(adapt_delta = 0.90, max_treedepth = 12)).

These models identified a linear decline in trends in taxon richness and a tendency for decline in functional richness trends over time (see figure 21 in the ‘Online Figures.docx’ file in the ‘plots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ).

We examined the proportion of sites with positive trends and how this proportion changed through time for our key biodiversity metrics of taxon richness, abundance, functional richness and functional redundancy. To do this, we used site-level moving-window trends and estimated the proportion of sites with positive trends in each year. We repeated this calculation for each posterior draw to propagate through site-level uncertainty to the overall mean proportion and estimated 80%, 90% and 95% CIs. To ensure this proportion was not driven by studies with especially large numbers of sampling sites, we weighted each site by the inverse of the number of sites in each study. This complements the moving-window analysis by examining whether the emerging mean trends are typical of site-level patterns. This analysis was based only on trend direction and not trend magnitude and was therefore less affected by any noise contributed by studies with trends at the extremes.

An important caveat of the moving-window analysis is that different sites are included in different moving windows. Supplementary Table 6 lists the number of sites per window in each country. Although we accounted for the heterogeneity of site distribution across studies and countries within years, models cannot correct for the changing number of sampled sites across years. We cannot fully discount the possibility that biases in the characteristics of sites sampled across time affected trajectory results. We therefore conducted two additional moving-window analyses to investigate this, the first limited to sites with long-term data and the second limited to sites with species-level taxonomic resolution. The first additional analysis initially included only sites with ≥20 sampling years between 1990–2020, although moving windows with start years of 1990 and 1991 were excluded as they included <200 sites. This analysis included 308 sites from 8 countries. The second analysis included sites with species-level taxonomic data and windows covering 1990–2020 with >200 sites, resulting in windows from 1994–2003 to 2011–2020. The species-level moving-window analysis included 717 sites from 14 countries. Apart from the sites included, models were identical to our original moving-window analyses described above. These alternative moving-window analyses found similar declines in the trend of taxon richness over time (see figures 22–25 in the ‘Online Figures.docx’ file in the ‘plots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ).

Analysis of environmental predictors

We assessed responses of biodiversity metrics to climate (both the mean and the trend over the time series’ durations) and upstream land cover (as the annual mean cover value during the sampling period), dam impact score and subcatchment characteristics (Supplementary Table 5 ). We did not include upstream land-use trends as most sites exhibited low variation: cropland cover changed by a mean of −0.002% per year ± 0.11 s.e.m., with no change detected at 634 sites; urban cover changed by 2.48% per year ± 0.14 s.e.m., but with no change detected at 803 sites. To examine relationships between environmental drivers and biodiversity trends, we modelled trend estimates using an LMM, incorporating trend errors as for the calculation of the overall trend, including all predictor variables as fixed effects, and study identity and country as random effects.

The form of the model was: brm(estimate|se(sd) ~ PrecipTrend + TempTrend + PrecipMean + TempMean + StreamOrder + Accumulation + Elevation + Slope + Urban + Crop + DamScore + (1|study_id) + (1|country), = response_stan, iter = 5000, chains = 4, prior = prior1, control = list(adapt_delta = 0.90, max_treedepth = 12)).

We ran models using the R package brms 80 . We standardized predictor variables to unit s.d. to facilitate comparison of their relative importance. We used regularizing horseshoe priors on environmental covariates that pull unimportant covariate effects towards zero to avoid overfitting. Our analysis of drivers focused on site-level variation in long-term trends, and not temporal variation in short-term trends examined in the moving-window analysis. Thus, our driver analysis cannot be used to understand recent changes in trends. To further examine whether biodiversity trends were positive or negative across the range of driver values, we used R package marginaleffects 85 to visualize responses to drivers while holding other driver covariates at their median. Predicted trends complement the effects on trends shown in Fig. 4 (see figures 28–34 in the ‘Online Figures.docx’ file in the ‘plots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ).

Model checking

All models run to quantify biodiversity trends and responses to drivers were evaluated by plotting the posterior samples to confirm chain convergence, examining R -hat values (<1.1) 86 and estimating Pareto shape parameters using the argument pareto_k_table in the R package loo 87 . For trend models and across the 20 examined biodiversity metrics, an average of 99.5% of the 1,816 sites had shape parameter estimates of k  < 0.7 (a threshold for good model performance). For environmental driver models, an average of 99% of the 1,816 sites had shape parameter estimates of k  < 0.7.

Sensitivity analysis

To check the robustness of our results to analytical decisions, we ran multiple sensitivity analyses for all biodiversity metrics. We tested the effects on trend estimates of (1) taxonomic resolution, by rerunning meta-analysis models with resolution (family, mixed, and species) as an additional fixed factor; (2) sampling season, by rerunning meta-analysis models (described in the ‘Trend analysis’ section) with season (winter, spring, summer and fall) as an additional fixed factor; and (3) country, using a jackknife resampling analysis in which the meta-analysis was rerun after sequentially removing countries. Models were otherwise similar to those presented above. Scripts for sensitivity analyses are available at GitHub ( https://github.com/Ewelti/EuroAquaticMacroInverts (HPC_Sensitivity_analysis.R and HPC_Meta_analysis_country_jackknife).

Some caution is advised when inferring conclusions from a dataset including different levels of taxonomic resolution or different seasons. However, intra-site sampling was consistently within one season or taxonomic resolution, so intra-site trends were not affected by these differences. Neither taxonomic resolution nor season had strong directional effects on trend estimates, with error bars generally overlapping. Patterns across taxonomic resolutions and sampling seasons were generally similar to those presented in Fig. 2 (Extended Data Figs. 9 and 10 ). Trends of taxonomic richness were robust to one-country removal but abundance trends became more strongly positive on removal of data from some countries, suggesting geographical variability in abundance trends (see figure 17 in the ‘Online Figures.docx’ file in the ‘plots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ).

We analysed the effect of the number of sampling years in a time series on observed trends using simple linear regression. The number of sampling years did not affect trend estimates of taxon richness ( R 2  < 0.001), abundance ( R 2  < 0.001), functional richness ( R 2  = 0.004) or functional redundancy ( R 2  < 0.001) (see figure 14 in the ‘Online Figures.docx’ file in the ‘plots’ folder at https://github.com/Ewelti/EuroAquaticMacroInverts ).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data needed to reproduce analyses including metadata, site characteristics and values of each metric (for example, species richness, functional richness) for each site and year are available at Figshare ( https://doi.org/10.6084/m9.figshare.22227841 ). Biodiversity composition data are available at GitHub ( https://github.com/Ewelti/EuroAquaticMacroInverts/raw-data ).

Code availability

Annotated R code is available at GitHub ( https://github.com/Ewelti/EuroAquaticMacroInverts ).

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Acknowledgements

N. Kaffenberger helped with initial data compilation. Funding for authors and data collection and processing was provided by the EU Horizon 2020 project eLTER PLUS (grant agreement no. 871128); the German Federal Ministry of Education and Research (BMBF; 033W034A); the German Research Foundation (DFG FZT 118, 202548816); Czech Republic project no. P505-20-17305S; the Leibniz Competition (J45/2018, P74/2018); the Spanish Ministerio de Economía, Industria y Competitividad—Agencia Estatal de Investigación and the European Regional Development Fund (MECODISPER project CTM 2017-89295-P); Ramón y Cajal contracts and the project funded by the Spanish Ministry of Science and Innovation (RYC2019-027446-I, RYC2020-029829-I, PID2020-115830GB-100); the Danish Environment Agency; the Norwegian Environment Agency; SOMINCOR—Lundin mining & FCT—Fundação para a Ciência e Tecnologia, Portugal; the Swedish University of Agricultural Sciences; the Swiss National Science Foundation (grant PP00P3_179089); the EU LIFE programme (DIVAQUA project, LIFE18 NAT/ES/000121); the UK Natural Environment Research Council (GLiTRS project NE/V006886/1 and NE/R016429/1 as part of the UK-SCAPE programme); the Autonomous Province of Bolzano (Italy); and the Estonian Research Council (grant no. PRG1266), Estonian National Program ‘Humanitarian and natural science collections’. The Environment Agency of England, the Scottish Environmental Protection Agency and Natural Resources Wales provided publicly available data. We acknowledge the members of the Flanders Environment Agency for providing data. This article is a contribution of the Alliance for Freshwater Life ( www.allianceforfreshwaterlife.org ).

Author information

These authors contributed equally: Peter Haase, Ellen A. R. Welti

Authors and Affiliations

Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany

Peter Haase, Nathan J. Baker & Ellen A. R. Welti

Faculty of Biology, University of Duisburg-Essen, Essen, Germany

Peter Haase, Daniel Hering, Armin W. Lorenz & Stefan Stoll

Department of Ecosystem Services, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

Diana E. Bowler

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

Department of Ecosystem Services, Helmholtz Center for Environmental Research—UFZ, Leipzig, Germany

Laboratory of Evolutionary Ecology of Hydrobionts, Nature Research Centre, Vilnius, Lithuania

Nathan J. Baker

FEHM-Lab (Freshwater Ecology, Hydrology and Management), Department of Evolutionary Biology, Ecology and Environmental Sciences, Facultat de Biologia, Institut de Recerca de la Biodiversitat (IRBio), University of Barcelona, Barcelona, Spain

Núria Bonada & Miguel Cañedo-Argüelles

Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany

Sami Domisch, Jaime R. Garcia Marquez, Sonja C. Jähnig, Mathieu Floury, Jens Kiesel & Longzhu Q. Shen

Geography Research Unit, University of Oulu, Oulu, Finland

Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany

Sonja C. Jähnig

Department of Water, Atmosphere and Environment, Institute of Hydrobiology and Aquatic Ecosystem Management, University of Natural Resources and Life Sciences, Vienna, Austria

Astrid Schmidt-Kloiber, Wolfram Graf & Patrick Leitner

School of Science and Technology, Nottingham Trent University, Nottingham, UK

Rachel Stubbington

Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

Florian Altermatt

Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

IHCantabria—Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, Spain

Mario Álvarez-Cabria, José Barquín Ortiz & Francisco J. Peñas

School of the Environment, Yale University, New Haven, CT, USA

Giuseppe Amatulli

Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden

David G. Angeler, Richard K. Johnson & Brendan G. McKie

IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, Victoria, Australia

David G. Angeler

Brain Capital Alliance, San Francisco, CA, USA

School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA

INRAE, UMR RECOVER Aix Marseille Univ, Centre d’Aix-en-Provence, Aix-en-Provence, France

Gaït Archambaud-Suard & Marie-Helène Lizée

Agencia Vasca del Agua, Vitoria-Gasteiz, Spain

Iñaki Arrate Jorrín & Jesús Alberto Manzanos Arnaiz

Wessex Water, Bath, UK

Thomas Aspin & Andy House

Ekolur Asesoría Ambiental SLL, Oiartzun, Spain

Iker Azpiroz & Manu Rubio

Departamento de Medio Ambiente y Obras Hidráulicas, Diputación Foral de Gipuzkoa, Donostia-San Sebastián, Spain

Iñaki Bañares

LFI—The Laboratory for Freshwater Ecology and Inland Fisheries, NORCE Norwegian Research Centre, Bergen, Norway

Christian L. Bodin & Gaute Velle

Department of Earth and Environmental Sciences—DISAT, University of Milano-Bicocca, Milan, Italy

Luca Bonacina & Riccardo Fornaroli

Institute for Alpine Environment, Eurac Research, Bolzano, Italy

Roberta Bottarin & Alberto Scotti

FEHM-Lab, Institute of Environmental Assessment and Water Research (IDAEA), CSIC, Barcelona, Spain

Miguel Cañedo-Argüelles

Department of Hydrobiology, University of Pécs, Pécs, Hungary

Zoltán Csabai

Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic

Zoltán Csabai, Petr Paril, Marek Polášek & Michal Straka

INRAE, UR RiverLy, Centre de Lyon-Villeurbanne, Villeurbanne, France

Thibault Datry, Martial Ferréol & Maxence Forcellini

Fisheries Ecosystems Advisory Services, Marine Institute, Newport, Ireland

Elvira de Eyto

Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

Alain Dohet & Lionel L’Hoste

Water Development Department, Ministry of Agriculture, Rural Development and Environment, Nicosia, Cyprus

Gerald Dörflinger & Iakovos Tziortzis

Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Ireland

Emma Drohan

Norwegian Institute for Nature Research (NINA), Oslo, Norway

Knut A. Eikland, Thomas C. Jensen, Francesca Pilotto & Leonard Sandin

Environment Agency, Wallingford, UK

Judy England

Norwegian Institute for Water Research, Oslo, Norway

Tor E. Eriksen, Nikolai Friberg & Jes Jessen Rasmussen

Department of Aquatic Ecosystems, Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria

Vesela Evtimova, Galia Georgieva, Violeta G. Tyufekchieva, Yordan Uzunov, Emilia Varadinova & Yanka Vidinova

Department of Life Sciences, University of Coimbra, Marine and Environmental Sciences Centre, ARNET, Coimbra, Portugal

Maria J. Feio & Manuel A. S. Graça

Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Villeurbanne, France

Mathieu Floury

Department of Animal Sciences and Aquatic Ecology, Ghent University, Ghent, Belgium

Marie Anne Eurie Forio, Peter Goethals & Rudy Vannevel

Freshwater Biological Section, University of Copenhagen, Copenhagen, Denmark

Nikolai Friberg

water@leeds, School of Geography, University of Leeds, Leeds, UK

ARALEP—Ecologie des Eaux Douces, Villeurbanne, France

Jean-François Fruget

Department of Ecology and Genetics, University of Oulu, Oulu, Finland

Kaisa-Leena Huttunen & Timo Muotka

School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK

J. Iwan Jones & John F. Murphy

Department of Hydrology and Water Resources Management, Christian-Albrechts-University Kiel, Institute for Natural Resource Conservation, Kiel, Germany

Jens Kiesel

Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden

Lenka Kuglerová

Department of Plant Biology and Ecology, University of the Basque Country, Leioa, Spain

Aitor Larrañaga

Laboratoire National d’Hydraulique et Environnement, EDF Recherche et Développement, Chatou, France

Anthony Maire

Department of Ecology and Hydrology, University of Murcia, Murcia, Spain

Andrés Millán & David Sánchez-Fernández

UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Lancaster, UK

Don Monteith

Institute of Biology, University of Latvia, Riga, Latvia

Davis Ozolins & Agnija Skuja

Oulanka Research Station, University of Oulu Infrastructure Platform, Kuusamo, Finland

Riku Paavola

Institute for Environmental Science, RPTU Kaiserslautern-Landau, Landau, Germany

Ralf B. Schäfer

APEM, Stockport, UK

Alberto Scotti

Institute for Green Science, Carnegie Mellon University, Pittsburgh, PA, USA

Longzhu Q. Shen

Department of Environmental Planning / Environmental Technology, University of Applied Sciences Trier, Birkenfeld, Germany

Stefan Stoll

T.G. Masaryk Water Research Institute, Brno, Czech Republic

Michal Straka

Chair of Hydrobiology and Fishery, Centre for Limnology, Estonian University of Life Sciences, Elva vald, Estonia

Wageningen Environmental Research, Wageningen University and Research, Wageningen, The Netherlands

Gea H. van der Lee, Piet F. M. Verdonschot & Ralf C. M. Verdonschot

Flanders Environment Agency, Aalst, Belgium

Rudy Vannevel

Department of Geography, Ecology and Environment Protection, Faculty of Mathematics and Natural Sciences, South-West University ‘Neofit Rilski’, Blagoevgrad, Bulgaria

Emilia Varadinova

Department of Tisza River Research, Centre for Ecological Research, Institute of Aquatic Ecology, Debrecen, Hungary

Gábor Várbíró

Department of Biological Sciences, University of Bergen, Bergen, Norway

Gaute Velle

Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands

Piet F. M. Verdonschot

Department of Ecoscience, Aarhus University, Aarhus, Denmark

Peter Wiberg-Larsen

Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, USA

Ellen A. R. Welti

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Contributions

P.H. conceived the study. N.B., J.H., D.H., S.C.J. and A.S.-K. contributed to initial ideas. E.A.R.W. performed data cleaning. E.A.R.W., D.E.B. and N.J.B. designed and conducted analyses. S.D. and J.R.G.M. provided environmental data with assistance from G.A., J.K. and L.Q.S. E.A.R.W. and P.H. wrote the first draft with assistance from D.E.B., and all of the authors contributed to additional versions. F.A., M.Á.-C., D.G.A., G.A., I.A.J., T.A., I.A., I.B., J.B.O., C.L.B., L.B., N.B., R.B., M.C.-A., N.J.B., Z.C., T.D., E.d.E., A.D., G.D., E.D., K.A.E., J.E., T.E.E., V.E., M.J.F., M. Ferréol, M. Floury, M. Forcellini, M.A.E.F., R.F., N.F., J.-F.F., G.G., P.G., M.A.S.G., W.G., A.H., P.H., K.-L.H., T.C.J., R.K.J., J.I.J., L.K., A.L., P.L., L.L. M.-H.L., A.W.L., A. Maire, J.A.M.A., B.G.M., A. Millán, D.M., T.M., J.F.M., D.O., R.P., F.J.P., F.P., M.P., P.P., J.J.R., M.R., D.S.-F., L.S., R.B.S., A.S.-K., A. Scotti, A. Skuja, S.S., M.S., R. Stubbington, H.T., V.G.T., I.T., Y.U., G.H.v.d.L., R.V., E.V., G. Várbíró, G. Velle, P.F.M.V., R.C.M.V., Y.V. and P.W.-L. provided invertebrate data.

Corresponding authors

Correspondence to Peter Haase or Ellen A. R. Welti .

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Extended data figures and tables

Extended data fig. 1 trend estimates for community subsets..

Overall estimates and distributions of trends in a , non-native species richness, b , non-native abundance, c , native taxon richness, d , native abundance, e , Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxon richness, f , EPT abundance, g , insect taxon richness, and h , insect abundance. Bars around estimates indicate 80%, 90%, and 95% credible intervals. Trend estimates for native taxa (c, d) are restricted to the 1,299 sites at which taxa were identified to species or a mixed taxonomic resolution. Trend estimates for non-native species (a, b) are restricted to the 898 (of 1,299) sites at which non-native species were detected. Incorporating the remaining 394 (30.1%) of the 1,299 sites (i.e. those with no detected non-native species) as having trends = 0 resulted in an average increase of 2.75% y −1 in richness and 2.79% y −1 in abundance.

Extended Data Fig. 2 Trend estimates for additional biodiversity metrics.

Overall estimates and distributions of trends in a , Shannon’s diversity, b , rarefied taxon richness, c , functional divergence, and d , Rao’s quadratic entropy (n = 1,816 biologically independent sites for all metrics). Bars around estimates indicate 80%, 90%, and 95% credible intervals.

Extended Data Fig. 3 Moving window trends for additional biodiversity metrics.

Estimated trends in a , Shannon’s evenness, b , taxonomic turnover, c , functional evenness, and d , functional temporal turnover. Estimates were calculated from Bayesian mixed-effects models of trends from ≥250 time series with ≥6 years of data from ≥8 countries within 10-year moving windows. Grey polygons indicate 80, 90, and 95% credible intervals.

Extended Data Fig. 4 Estimated effects of environmental drivers on temporal trends in additional biodiversity metrics.

Estimated effects of the mean (tmax mean) and trend (tmax sl. [slope]) of annual maximum temperature, mean (ppt mean) and trend (ppt sl.) of annual precipitation, dam impacts (dam), and the percentage of the upstream catchment covered by urban areas and cropland on temporal trends in a , Shannon’s diversity, b , rarefied taxon richness, c , functional (func.) divergence, and d , Rao’s quadratic entropy (Q) (n = 1,816 biologically independent sites for all metrics). Bars around estimates indicate 80%, 90%, and 95% credible intervals. Grey, horizontal lines separate the three environmental driver groups: climate, dams, and land use.

Extended Data Fig. 5 Estimated effects of environmental drivers on biodiversity metrics representing community subsets.

Estimated effects of the mean (tmax mean) and trend (tmax sl. [slope]) of annual maximum temperature, mean (ppt mean) and trend (ppt sl. [slope]) of annual precipitation, dam impacts (dam), and the percentage of the upstream catchment covered by urban areas and cropland on temporal trends in a , non-native species richness, b , native taxon richness, c , EPT richness, d , insect richness, e , non-native abundance, f , native abundance, g , EPT abundance, and h , insect abundance. Trend estimates for native taxa (b, f) are restricted to 1,299 sites at which taxa were identified to species or a mixed taxonomic resolution. Trend estimates for non-native species (a, e) are restricted to the 898 (of 1,299) sites at which non-native species were detected. Bars around estimates indicate 80%, 90%, and 95% credible intervals. Bars around estimates indicate 80%, 90%, and 95% credible intervals. Grey, horizontal lines separate the three environmental driver groups: climate, hydrology, and land use.

Extended Data Fig. 6 Estimated effects of stream characteristics on biodiversity metrics.

Estimated effects of slope, elevation, flow accumulation (accum.) and Strahler stream order (str. order) on temporal trends in a , taxon richness, b , abundance, c , evenness, d , turnover, and functional (func.) e , richness, f , redundancy, g , evenness, and h , turnover (n = 1,816 biologically independent sites for all metrics). Bars around estimates indicate 80%, 90%, and 95% credible intervals.

Extended Data Fig. 7 Estimated effects of stream characteristics on additional biodiversity metrics.

Estimated effects of stream characteristics of slope, elevation, flow accumulation (accum.) and Strahler stream order (str. order) on temporal trends in a , Shannon’s diversity, b , rarefied taxon richness, c , functional (func.) divergence, and d , Rao’s quadratic entropy (n = 1,816 biologically independent sites for all metrics). Bars around estimates indicate 80%, 90%, and 95% credible intervals.

Extended Data Fig. 8 Estimated effects of stream characteristics on taxon richness and abundance of taxa subsets.

Estimated effects of slope, elevation, flow accumulation (accum.) and Strahler stream order (str. order) on temporal trends in a , non-native species richness, b , native taxon richness, c , EPT richness, d , insect richness, e , non-native abundance, f , native abundance, g , EPT abundance, and h , insect abundance. Trend estimates for native taxa (b, f) are restricted to 1,299 sites at which taxa were identified to species or a mixed taxonomic resolution. Trend estimates for non-native species (a, e) are restricted to the 898 (of 1,299) sites at which non-native species were detected. Bars around estimates indicate 80%, 90%, and 95% credible intervals.

Extended Data Fig. 9 Sensitivity of biodiversity metric responses to taxonomic identification level.

Error bars represent 95% credible intervals. Overlapping error bars indicate comparable trend estimates for analyses at species (n = 762), genus/mixed (n = 537) and family (n = 517) taxonomic levels; Func., functional; Est., estimated trend.

Extended Data Fig. 10 Sensitivity of biodiversity metric responses to sampling season.

Error bars represent 95% credible intervals. The largest differences between seasons were found for winter, which likely reflects the low number of sites sampled in this season (winter n = 5, spring n = 623, summer n = 473, fall n = 715). Func. refers to functional; Est. refers to trend estimates.

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Haase, P., Bowler, D.E., Baker, N.J. et al. The recovery of European freshwater biodiversity has come to a halt. Nature 620 , 582–588 (2023). https://doi.org/10.1038/s41586-023-06400-1

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Ecological monitoring, assessment, and management in freshwater systems.

1. Introduction

2. contributions, 3. conclusions, acknowledgments, author contributions, conflicts of interest.

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© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

Share and Cite

Park, Y.-S.; Hwang, S.-J. Ecological Monitoring, Assessment, and Management in Freshwater Systems. Water 2016 , 8 , 324. https://doi.org/10.3390/w8080324

Park Y-S, Hwang S-J. Ecological Monitoring, Assessment, and Management in Freshwater Systems. Water . 2016; 8(8):324. https://doi.org/10.3390/w8080324

Park, Young-Seuk, and Soon-Jin Hwang. 2016. "Ecological Monitoring, Assessment, and Management in Freshwater Systems" Water 8, no. 8: 324. https://doi.org/10.3390/w8080324

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The Global Trend of Microplastic Research in Freshwater Ecosystems

Yaochun wang.

1 Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

2 Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, China

Naicheng Wu

Associated data.

Not applicable.

The study of microplastics and their impact on aquatic ecosystems has received increasing attention in recent years. Drawing from an analysis of 814 papers related to microplastics published between 2013 and 2022 in the Web of Science Core Repository, this paper explores trends, focal points, and national collaborations in freshwater microplastics research, providing valuable insights for future studies. The findings reveal three distinct stages of microplastics: nascent development (2013–2015), slow rise (2016–2018), and rapid development (2019–2022). Over time, the focus of research has shifted from “surface”, “effect”, “microplastic pollution”, and “tributary” to “toxicity”, “species”, “organism”, “threat”, “risk”, and “ingestion”. While international cooperation has become more prevalent, the extent of collaboration remains limited, mostly concentrated among English-speaking countries or English and Spanish/Portuguese-speaking countries. Future research directions should encompass the bi-directional relationship between microplastics and watershed ecosystems, incorporating chemical and toxicological approaches. Long-term monitoring efforts are crucial to assessing the sustained impacts of microplastics.

1. Introduction

The low decomposition rate and high productivity (300 million tons in 2020 [ 1 ]) of plastic have led to its increased presence in ecosystems, resulting in negative impacts on freshwater bodies [ 2 ]. Plastic pollution was listed as one of the “top 10 urgent environmental issues” by the United Nations Environment Assembly in 2014, and in 2018, World Environment Day was celebrated under the theme of “Plastic Wars Plastic Decisions” [ 3 ]. The occurrence of plastic in water has a toxic effect on organisms and poses significant ecological risks [ 4 ]. It is evident that, amidst the dramatic increase in demand for plastics, finding a balanced solution between environmental friendliness and plastic consumption is of utmost importance.

Plastic particles with a size smaller than 5 mm are commonly referred to as “microplastics” [ 5 , 6 ]. In the aquatic environment, plastic waste undergoes breakdown through mechanical overuse, photodegradation, oxidation, and weathering, resulting in the formation of small particles (<5 mm) known as secondary microplastics. On the other hand, plastic particles present in cosmetics and fibers in clothing are considered primary microplastics. Due to their small size, microplastics can be transported over long distances by wind, rivers, and ocean currents. Numerous studies have confirmed the impact of microplastics on organisms and ecosystems. Available information indicates that microplastic pollution is found in varying abundances across different locations, ranging from the equator [ 7 , 8 ] to the north and south Poles [ 9 , 10 ]. Previous studies have shown significant findings: Wright and Kelly found that the human excretory system eliminates 90% of microplastics through feces, while the remaining 10% is absorbed into the bloodstream [ 11 ]; the same study also revealed that nanoscale microplastics can pass through the brains of carp, leading to behavioral disorders. Additionally, Martins and Guilhermino discovered that microplastics can induce transgenerational effects in Daphnia, resulting in birth disorders and reduced growth rates in their offspring [ 12 ]. However, it is important to note that most toxicity studies have been conducted at high concentrations that are not environmentally relevant and often involve pristine polymers. In summary, global microplastic pollution has the potential to significantly impact ecosystems and human health. Therefore, ecological and environmental studies on microplastics are of great significance [ 13 , 14 ].

The sourcing, storage, pollution, and utilization of freshwater, a vital resource for irrigation and sustaining life on land, constitute a significant global concern [ 15 , 16 , 17 , 18 , 19 ]. Microplastics have been found in rivers and lakes [ 1 ] as well as wetlands [ 20 ]. Several highly cited review articles [ 1 , 20 , 21 ] have made valuable contributions by providing comprehensive insights into the methodologies, characteristics, distribution, and pollution associated with microplastics. However, these reviews primarily rely on traditional approaches such as summarizing the existing literature, which may result in limited coverage of the available research. To address this limitation, an analysis of trends, focal points, and national collaborations in microplastic research specifically focused on freshwater environments can offer a macro-level summary of the current state of knowledge. This approach, based on a comprehensive examination of numerous publications, provides a valuable resource for future research endeavors in the field of microplastics in freshwater environments.

With the increasing number of publications, publishing institutions, and international collaborations, there is a growing need for an objective and macroscopic research methodology. Bibliometrics plays a crucial role in assessing and analyzing researchers’ productivity [ 22 ], inter-institutional collaborations [ 23 ], the impact of national research investments on research and development [ 24 ], and the quality of scholarly work [ 25 ]. By analyzing key information from a large number of publications and utilizing visual methods to represent the intersection, derivation, and interaction between research objects, bibliometrics can help predict trends, explore interrelationships, and uncover hotspots and frontiers in research [ 26 ]. However, few articles have combined bibliometrics and the literature reviews to explore the progress of microplastic research in freshwater ecosystems. Therefore, this paper employs bibliometric analysis to summarize research trends, focal points, and international collaborations in the field of freshwater microplastics. Based on the selected keywords (TI = (microplastic*) AND TI = (fresh water or freshwater or plain water or lake or river or canal or stream or wetland or wetland or glacier or ice or groundwater or groundwater or underground water)) and the defined selection criteria, articles published since 2013 are included in this study. The objectives of this research are as follows: (i) to identify research trends in publications on microplastics in freshwater ecosystems from the period 2013 to 2022; (ii) to identify the focal points of publications on microplastics in freshwater ecosystems from 2013 to 2022; and (iii) to explore the trends of international cooperation on microplastics in freshwater ecosystems from 2013 to 2022.

2. Materials and Methods

2.1. data sources.

Based on the Web of Science Core Collection database (including SCI source journals, SSCI source journals, CCR source journals and CI source journals), publications were searched using the following search term: TI = (microplastic*) AND TI = (freshwater or plain water or lake or river or canal or stream or wetland or glacier or ice or groundwater or underground water) to filter the articles in January 2023. After filtering and eliminating irrelevant literature, 814 articles related to the topic were selected. Since the articles were mainly distributed after 2013, this article focuses on the articles from 2013 to 2022 for analysis. It should be noted that this paper only analyzes articles from the core database and therefore may not include all studies on microplastics in freshwater environments.

2.2. Research Methodology

In this paper, we analyze the research focal points on microplastics in freshwater ecosystems from different periods by combining bibliometrics and the literature reviews. We used R software [ 27 , 28 ] to analyze trends in publication and international cooperation. Additionally, we utilized VOSviewer software [ 29 ] to analyze focal points at different periods. Specifically, we identified high-frequency keywords (focal points) based on the number of occurrences in the article titles and considered collaboration between authors from different countries as international collaboration. Notably, in the analysis of focal points with VOSviewer software, we selected keywords with more than five occurrences in 2013–2017 to be displayed in the figure, and keywords with more than ten occurrences in 2018–2022 to be shown. This selection was necessary due to the large number of keywords, and we focused on displaying high-frequency words. Regarding the analysis of country cooperation, we chose to display collaborations with more than two occurrences in 2013–2017 in the figure, and collaborations with more than five occurrences in 2018–2022.

3.1. Analysis of Publication Numbers

Based on the search results, we analyzed the publication trends of microplastics in freshwater ecosystems ( Figure 1 ). Although the first article on microplastics was published in 1977 [ 30 ], there was limited attention paid to freshwater microplastics before 2013. However, our results demonstrate a steady increase in the number of published articles each year from 2013 to 2022. Based on the annual publication numbers, we divided the research development process into three stages. The first stage, from 2013 to 2015, can be characterized as the nascent development stage, with a small number of papers and a slow rate of increase. The average annual number of publications during this stage was below 5. The second stage, from 2016 to 2018, marked the slow rise stage, with an average of 25 publications per year, resulting in a total of 75 papers. Finally, the period from 2019 to 2022 represented the rapid development stage, with a total of 725 articles published, accounting for 90% of the total literature. The annual rate of increase during this stage exceeded 50%. Overall, there has been a consistent and gradual increase in the number of publications related to microplastics in freshwater ecosystems.

Publications in the field of microplastics in freshwater ecosystems from 2013 to 2022 (data from the Web of Science Core Collection). The horizontal axis represents the different years from 2013-2022 and the vertical axis represents the number of publications. The numbers on the line represent the number of publications corresponding to each year.

3.2. Research Trends

Figure 2 illustrates the most frequently occurring keywords in the titles of the 814 articles and their associations with other keywords. Figure 2 a provides an overview of the overall research focal point and trends from 2013 to 2022. Additionally, Figure 2 b presents the focal points specifically from 2013 to 2017, while Figure 2 c focuses on the period between 2018 and 2022. In the earlier years (2013–2017), the research focal point, as shown in Figure 2 b, exhibited a certain level of repetition. The main areas of focus were “polyethylene”, “surface”, “effect”, “microplastic pollution”, and “tributary”. These 21 keywords can be categorized into three topics: sources of microplastics (green), distribution patterns and methods (blue), and environmental impacts (red).

Research focal points and trends of microplastics in freshwater ecosystems over different periods. ( a ): 2013–2022; ( b ): 2013–2017; ( c ): 2018–2022. The size of the circles indicates the frequency of the keywords; the larger the circle, the higher the frequency of occurrence; the different colors symbolize the hot topics in which the keywords are found, with the keywords at the edges of the hot topics being transitional words; and the lines between the keywords indicate their connections to other keywords.

However, as depicted in Figure 2 c for the years 2018 to 2022, the research focus on microplastics expanded significantly. The emphasis shifted towards “toxicity”, “species”, “organism”, “threat”, “risk”, and “ingestion”. The 42 keywords in this period are grouped into two prominent topics: sources and distribution patterns (green) and environmental impacts (red). Notably, the proportion of environmental impacts increased compared to the earlier years. Overall, the research conducted between 2018 and 2022 exhibited a wider range of focal points and research areas than that of 2013–2017. The research focal point gradually shifted from the characteristics of microplastics to the environmental impacts they pose.

3.3. Trends in International Cooperation

The number of articles published by countries has shown an overall increase, as depicted in Figure 3 and Table 1 (and Supplementary Table S1 and S2 ). International cooperation in microplastics research has also witnessed strengthening, but there are still several countries that have not actively participated in such collaborations, including Sudan, Central Africa, Syria, Myanmar, Afghanistan, and Venezuela. The collaborations that have occurred primarily involve English-speaking countries or English and Spanish/Portuguese-speaking countries. Currently, China, the UK, the US, and Germany exert more influence in microplastic research. Notably, Czechia, France, and Slovenia have produced higher-quality articles, as indicated by their higher average number of citations, during the period of 2013–2017. Similarly, Singapore has shown a similar trend from 2018 to 2022.

Temporal trend changes in international cooperation on microplastics in aquatic ecosystems. ( a ): 2013–2022; ( b ): 2013–2017; ( c ): 2018–2022. The shade of color indicates the number of articles published by countries; the darker the color, the more articles are published; red lines indicate international collaborations; the higher the volume of collaborative publications between two countries, the thicker the red line between them and the closer the relationship (We specify that the display starts after more than five international collaborations in 2013–2017 and after more than ten international collaborations in 2017–2022).

Top 10 cited countries and publications from 2013 to 2022 (publications, number of citations—the total number of citations for articles published in this country).

Figure 3 a shows the international cooperation in freshwater microplastics research from 2013–2022. Figure 3 b and Supplementary Table S1 highlight that international cooperation in microplastic-related research was minimal from 2013 to 2017. However, over the past five years, international collaboration has increased notably between the Americas and Europe, as depicted in Figure 3 c and Supplementary Table S2 . During the period of 2018–2022, there has been a significant rise in connections between China and the USA, Australia, and Canada. Notably, the US, UK, Australia, and Canada have exhibited greater international activity. Overall, research on freshwater microplastics is increasingly establishing connections across continents, with the most closely linked collaborations involving China and the USA, China and Australia, China and Canada, the USA and Canada, and the USA and the UK.

Table 1 lists the top ten countries with the most cited publications under freshwater microplastics research. From 2013–2017, the United States ranked first in citations (4053), while Czechia had a smaller number of publications but ranked tenth (350) in citations and first in average citations, above the United States. There is little difference in the ranking of the UK, China, Canada, and Germany regarding the number of articles published and citations. Overall, the US had a more substantial influence during this period.

From 2018 to 2022, China has a clear advantage in terms of the number of publications (748) and citations (10,571), with more than four times the number of publications than the second place (2161), but the average citation of publications is relatively low. Singapore has the highest average number of citations (822), a large difference from second place. During the same period, the US ranked second in terms of the number of articles published but third in terms of citations, indicating a decline in the overall influence of the US during this period.

4. Discussion

4.1. sources and distribution patterns of microplastics from 2013 to 2017.

The inclusion of terms such as “polypropylene (PP)” and “polyethylene (PE)”, as well as “polymer type”, from 2013 to 2017 suggests that the distribution of microplastics by polymer type was a major focus during this time period. PP and PE have been identified as primary sources of microplastics in extensive research and have remained significant concerns throughout the history of microplastic studies. PE is widely used in various industrial applications, such as packaging films for industrial and food products, shopping bags, garbage bags, and wrap-around films. Additionally, PP is mainly used in automobiles, appliances, and single-use packaging, with an average of 20% of the plastic parts used in every car worldwide being made of it, making it the most common source of microplastics [ 31 ]. The combined analysis demonstrates a high level of concern for both microplastics and their prevalence in freshwater between 2013 and 2017.

The presence of terms such as “tributary” and “surface” indicates the exploration of microplastic distribution areas from 2013 to 2017. Scholars during this stage have focused on discussing tributaries, rivers [ 32 ], estuaries [ 33 ], lakes [ 34 ], and streams [ 35 ]. Based on our literature summary, many studies during this period (2013–2017) have analyzed the distribution of microplastics in terms of surface water microplastic pollution [ 36 , 37 , 38 ] in relation to their quantity, abundance, shape, and color [ 35 ]. However, the distribution of microplastics can vary in different environments [ 36 ], and it is important to enhance the study of microplastics in different habitats. Zbyszewski and Corcoran suggested that the number and shape of microplastics can indicate whether they have undergone fragmentation, such as being subjected to wave action, sedimentation, or oxidation [ 39 ]. During 2013–2017, the focus was on the morphological characteristics of microplastics and their presence in the water column, although most attention was given to surface waters. Microplastics of different materials have different densities, and therefore, they can be distributed throughout the water column, with low-density microplastics typically occupying the surface water environment while high-density microplastics are often found in sediments and benthic organisms [ 40 ]. However, physical or chemical breakdowns can alter the size and structure of microplastics, thereby changing their position in the water column as a secondary effect [ 40 ].

In summary, the focal point for sources and distribution patterns of microplastics in 2013–2017 is the microplastic distribution and physical characteristics (e.g., number, size, distribution), while studies of environmental impacts are lacking.

4.2. Environmental Impacts from 2013 to 2017

The study of the sources and distribution patterns of microplastics eventually led to a discussion of their impact on water bodies. The inclusion of critical words such as “influence”, “concern”, and “organism” demonstrates the scholarly focus on the effects of microplastics on freshwater. However, the focus on these topics during this stage was not comprehensive and primarily relied on studying their interaction with the environment and organisms based on particle size, material, and shape [ 35 , 41 ] and their sorption effects.

Microplastics in the aquatic environment can directly or indirectly affect the quality of the abiotic environment. Plastics can impact the scattering of light in the aquatic environment, thereby affecting chemical cycling. Different concentrations of microplastics have different effects on the environment [ 42 ]. For example, estuaries with high concentrations of microplastic pollutants have more significant pollution effects [ 42 ]. In addition to interactions with the abiotic environment, microplastics have broader impacts by indirectly or directly affecting biological communities [ 43 ]. Although this topic has attracted some attention since 2013, few scholars focused on it before 2018. Some studies have demonstrated the uptake of microplastics by crabs, amphibians, and invertebrates [ 44 , 45 ]. However, there is uncertainty regarding whether ingestion of uncontaminated microplastics will impact the health of organisms [ 46 ]. Research has primarily focused on whether organisms ingest microplastics, while there is still limited research on the effects after ingestion.

4.3. Sources and Distribution Patterns of Microplastics from 2018 to 2022

More attention has been given to the freshwater environment since 2018, likely due to a greater awareness of its direct connection to human health. Studies conducted in freshwater environments such as the Kelvin River in the UK, Poyang Lake in China, the Lagoon of Bizerte in Tunisia, the Flemish Rivers in Belgium, and Vembanad Lake in India have revealed that microplastic concentrations in the freshwater range from 0.01 to 3 g/L [ 47 , 48 , 49 , 50 ].

The sources of microplastics include “polyvinyl chloride (PVC)”, “polyester”, “pellet”, and “fiber”, with fibrous microplastics being of particular concern from 2018 to 2022. Numerous studies have found high rates of fibrous microplastics in laboratory simulations of laundry processes, as well as in microplastic emissions from wool textiles during home laundering [ 51 , 52 , 53 , 54 ]. Freshwater microplastic samples also exhibited a significant amount of fibrous microplastics, up to 59% [ 55 ]. Polyvinyl chloride (PVC), a highly carcinogenic material capable of adsorbing and accumulating triclosan and affecting zebrafish, has been identified as a source of microplastics in the aquatic environment [ 56 ], leading to increased interest among scholars. PVC and polyester, due to their high densities, tend to sink to the bottom of rivers, while PP and PP are floating microplastics due to their low densities.

The final distribution of microplastics in the water column is also influenced by their degree of weathering, sorption, and rate of aging [ 57 ]. Denser microplastics may cause more lasting damage to the environment. Various environmental factors can affect the rate and mode of microplastic decomposition [ 58 ], and physical breakdown can be slower in freshwater compared to marine environments [ 55 ]. Some studies have shown that certain lake environments may experience more significant weathering of microplastic particles due to enhanced UV penetration [ 38 ]. Additionally, research has started to focus on domestic wastewater. Studying microplastics in domestic wastewater provides insights into the quantities and characteristics of microplastics released through household discharge. Overall, microplastic enrichment is more prominent in areas with intense human activity, and research is beginning to shift toward freshwater and urban environments that are in closer proximity to humans.

4.4. Environmental Impacts from 2018 to 2022

The largest proportion of the environmental impact of microplastics occurred between 2018 and 2022, with a greater focus on topics such as “species”, “transport”, “risk”, “adsorption”, “ingestion”, “interaction”, “threat”, “toxicity”, and “fish” compared to the period of 2013–2017. Toxicological and adsorption effects of microplastics: research on the toxicology of microplastics has primarily concentrated on aquatic organisms, with limited investigation into their effects on humans. It has been discovered that the breakdown of untreated plastic waste in water into microplastics can result in more complex ecological threats as they migrate and adsorb substances [ 59 ]. Numerous scholars have also observed that microplastics combine with harmful environmental pollutants such as additives, pesticides, heavy metals, and pharmaceuticals, forming more complex mixed pollutants. Consequently, the complex contaminants associated with microplastics are linked to various human diseases, including obesity, endocrine disorders, cancer, and cardiovascular problems, indicating that microplastic pollution poses a toxicological threat to human health [ 60 , 61 ].

Overall, the environmental impact of microplastics can pose a more serious toxicological threat, depending on the combination of contaminants. While microplastics tend to absorb pollutants from the environment [ 62 ], their sorption capacity varies among different types and environments [ 63 ]. This variation can be attributed to different microplastics having rubbery domains, functional groups, and polarity. Research suggests that polyethylene (PE) has a more flexible structure compared to other materials, making it more prone to adsorb organic compounds [ 64 , 65 ]. Older microplastics are more likely to adsorb contaminants compared to newly introduced ones, and the polarity of the microplastic and the contaminant during adsorption affects the process. For example, the amide-based polar polymer polyamide (PA) exhibits a higher adsorption capacity for polar antibiotics [ 66 ], while polar polystyrene (PS) has a higher affinity for polar nitrobenzene, and non-polar perfluorooctane sulfonamide (FOSA) has a higher adsorption capacity for non-polar PE [ 67 ].

Additionally, environmental factors such as pH, salinity, and the concentration of contaminants in the surroundings influence the sorption process. Studies have demonstrated that increasing salinity and pH both enhance the sorption capacity of PE [ 68 ]. Interestingly, microplastics sampled closer to the pollution source generally exhibit higher sorption capacities than those collected farther away. In summary, the sorption behavior of microplastics is influenced by both environmental factors and their physical properties. However, the ingestion of such contaminated microplastics by aquatic organisms can result in more severe and widespread harm and may even pose a risk to humans through trophic transfer.

Microplastics have an impact on aquatic organisms and the food web. The inclusion of keywords such as “fish” among the topics indicates that more scholars are beginning to investigate the actual effects of microplastics on organisms. In recent years, there have been an increasing number of experiments simulating the ingestion of microplastics by freshwater organisms to study their presence and effects [ 43 , 69 , 70 ]. These studies have demonstrated that once aquatic organisms ingest microplastics, their digestive systems are the first to be affected. For instance, here’s a study discovered that microplastics accumulate in the digestive tract, causing intestinal blockage, pseudo-satiation, and reduced food capacity [ 71 ]. Studies on crayfish have shown that their feeding rate and body weight decrease with increased ingestion of microplastics [ 72 ], while fish experience intestinal damage, such as rupture of intestinal villi and splitting of intestinal epithelial cells, due to microplastic ingestion [ 73 ]. Persistent damage to the digestive system can lead to more severe consequences, including growth inhibition, damage to the reproductive system, and reduced mobility [ 46 ]. Excessive ingestion of microplastics can also cause other adverse effects, including oxidative stress, altered enzyme levels, and physical organ damage [ 72 , 73 ]. It is important to note that these effects are based on currently observable levels, and microplastics that go unnoticed may gradually break down into smaller particles within the organism, penetrating the cell membranes and participating in the body’s circulation.

As previously discussed, chemically contaminated microplastics will carry toxic substances into organisms and the food chain, damaging the entire food web [ 62 , 74 ]. As early as 2014, some scholars have found that microplastics moved along the food web from lower to higher trophic levels [ 75 ]. In 2016, in the well-known experiment by Batel, an artificial food chain including Artemia to zebrafish was set up and found that microplastics can eventually accumulate in zebrafish via Artemia [ 76 ]. Following this, some scholars have discovered microplastics in salt, a finding that brought microplastics and humans closer together [ 77 ]. Subsequently, Karami et al. found microplastics in commercial salt from different countries, including Australia, France, Iran, Japan, Malaysia, New Zealand, and Portugal [ 78 ]. In summary, microplastics threaten freshwater organisms and food webs, and more understanding is needed to predict the future impact of microplastics.

5. Conclusions and Future Research

The main conclusions drawn from the visual analysis of publications, focal points, and international cooperation on microplastics in aquatic ecosystems are as follows:

• (1) Number of literature phases: Between 2013 and 2022, publications on microplastics went through three stages: budding development (2013–2015), slow rise (2016–2018), and rapid development (2019–2022). The number of publications increased over time.
• (2) Trends in focal points: The research focus shifted from the basic morphological characteristics, distribution, and fundamental impacts of microplastics (2013–2017) to the complex impacts of microplastics (2018–2022). In the earlier period (2013–2017), the effects of microplastics were primarily studied in terms of their shape and polymer type. In the later period (2018–2022), the focus expanded to include species, organisms, transport, toxicity, and other related factors.
• (3) Trends in international cooperation: International cooperation in microplastics research has increased over time, with strong representation from countries such as China, the USA, Australia, and Europe. Collaborations often occur between English-speaking countries or between English and Spanish/Portuguese-speaking countries, indicating that language barriers may limit broader international collaboration. There are still many countries and regions globally, particularly in Africa, the Middle East, and South America, that are not extensively involved in international cooperation on microplastics research.

Future research should include the following:

• (1) Strengthening the cross-analysis of microplastics with chemistry and toxicology to study their sorption-pollution effects with different pollutants. Currently, research on the sorption contamination of microplastics is not extensive enough. While there is a focus on sorption contamination with antibiotics, heavy metals, and other substances, the contamination effects and toxicological impacts of microplastics combined with microorganisms, bacteria, pesticides, new pollutants, and other substances have been less studied. Therefore, future studies should expand the classification of the sorption-pollution effects of microplastics for different contaminants.
• (2) Strengthening the long-term monitoring of microplastics to explore the actual pollution process over extended periods. Most current studies on the effects of microplastics are conducted in laboratory settings, where the microplastics retain their original qualities, shapes, and materials [ 79 ]. However, microplastics undergo changes in the environment over time. Currently, there is a lack of long-term monitoring data in the field of microplastics research, which can hinder scholars’ understanding of the actual contamination process of microplastics. Therefore, future research should focus on strengthening long-term monitoring studies to investigate the evolving relationship between microplastics and the environment.

Acknowledgments

We would like to express our gratitude to Sangar Khan for his valuable input and review of the language used in this manuscript. We would also like to thank Shaojing Jiang for revising the article.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics11060539/s1 , Table S1: Countries involved in microplastics-related research and the volume of their publications, 2013–2017; Table S2: Countries involved in microplastics-related research and the volume of their publications, 2018-2022.

Funding Statement

This study was supported financially by the National Natural Science Foundation of China (Nos. 52279068, 42001014) and Natural Science Foundation of Zhejiang Province (No. Q21D010016).

Author Contributions

Conceptualization, Y.W. (Yaochun Wang). and N.W.; methodology, G.L.; software, Y.W. (Yixia Wang); validation, H.M. and X.S.; formal analysis, Y.W. (Yaochun Wang) and N.W.; investigation, C.W.; resources, C.W.; data curation, X.S.; writing—original draft preparation, Y.W. (Yaochun Wang); writing—review and editing, Y.W. (Yaochun Wang); visualization, N.W.; supervision, N.W.; project administration, N.W.; funding acquisition, N.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

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Scientific literature on freshwater ecosystem services: trends, biases, and future directions

• AQUATIC ECOSYSTEM SERVICES
• Review Paper
• Published: 23 September 2022
• Volume 850 , pages 2485–2499, ( 2023 )

Cite this article

• João Carlos Nabout   ORCID: orcid.org/0000-0001-9102-3627 1 ,
• Karine Borges Machado 1 ,
• Ana Clara Maciel David 1 ,
• Laura Beatriz Gomes Mendonça 2 ,
• Samiris Pereira da Silva 1 &
• Priscilla Carvalho 2  

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The aim of this study was to conduct a systematic review of the scientific literature on aquatic ecosystem services. We used the Web of Science database for our research. We found a total of 1,367 articles, with 837 papers that studied ecosystem services provided through the interaction between terrestrial and aquatic environments (terrestrial–aquatic papers) and 530 papers relating only to aquatic environments. Despite the growing number of articles, there is still less research on aquatic environments compared to terrestrial environments. We found a correlation between types of services and taxonomic groups, such that supporting ecosystem services were investigated using mainly macroinvertebrate, microorganisms and aquatic macrophytes communities, while provisioning and cultural ecosystem services were frequently studied with fishes. The analysis of keywords revealed that there was a temporal shift in the terms studied, and some words presented a higher frequency in aquatic research. Nonetheless, other words appeared in aquatic and terrestrial–aquatic studies with similar frequencies. The trends and biases detected in the present paper can help us to understand the role of aquatic biodiversity in ecosystem services more fully in the future as well as assist in the creation of strategies for future studies on the conservation of these services.

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Acknowledgements

We are grateful to the funding agencies that provided scholarships and grants to the authors. JCN received Productivity Grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). KBM received Post-doc Scholarship from Fundação de Amparo a Pesquisa do Estado de Goiás (FAPEG). ACMD received scholarship from FAPEG. LBGM received scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES—code 001). SPS received scholarship from CNPq. Our work on Aquatic Ecology has been continuously supported by different grants: CNPq (Process 400870/2019-2), FAPEG (Process 201710267000519), and National Institutes for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation (MCTI/CNPq/FAPEG/465610/2014-5), and Brazilian Network on Global Climate Change Research (Rede CLIMA).

FAPEG (Fundação de Amparo à Pesquisa do Estado de Goiás), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for scholarships and productivity grants. This study was financed in part by National Institutes for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation (MCTI/CNPq/FAPEG/465610/2014-5), and Brazilian Network on Global Climate Change Research (Rede CLIMA).

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JCN: Conception and design, analysis and interpretation of data, acquisition of data, and drafting of the article. KBM: Conception and design, analysis and interpretation of data, acquisition of data, drafting of the article and revising critically for important intellectual content. ACMD: Analysis and interpretation of data, acquisition of data and drafting of the article. LBGM: Analysis and interpretation of data, acquisition of data and drafting of the article. SPS: Analysis and interpretation of data, acquisition of data and drafting of the article. PC: Conception and design, analysis and interpretation of data, acquisition of data, drafting of the article, and revising critically for important intellectual content.

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Nabout, J.C., Machado, K.B., David, A.C.M. et al. Scientific literature on freshwater ecosystem services: trends, biases, and future directions. Hydrobiologia 850 , 2485–2499 (2023). https://doi.org/10.1007/s10750-022-05012-6

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Received : 17 March 2022

Revised : 23 August 2022

Accepted : 02 September 2022

Published : 23 September 2022

Issue Date : July 2023

DOI : https://doi.org/10.1007/s10750-022-05012-6

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