# Inland navigation and land use interact to impact European freshwater biodiversity

Aaron Sexton, Jean-Nicolas Beisel, Cybill Staentzel, Christian Wolter, Evelyne Tales, Jérôme Belliard, Anthonie Buijse, Vanesa Martínez Fernández, Karl Wantzen, Sonja Jähnig, et al.

► To cite this version:

Aaron Sexton, Jean-Nicolas Beisel, Cybill Staentzel, Christian Wolter, Evelyne Tales, et al. Inland navigation and land use interact to impact European freshwater biodiversity. Nature Ecology & Evolution, 2024, 10.1038/s41559-024-02414-8. hal-04592917

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1 **Title:**

2 Inland navigation and land use interact to impact European freshwater biodiversity

3

4 **Author list:**

5 Aaron N. Sexton¹, Jean-Nicolas Beisel², Cybill Staentzel², Christian Wolter³, Evelyne Tales⁴,

6 Jérôme Belliard⁴, Anthonie D.Buijse⁵,⁶, Vanesa Martínez Fernández⁷, Karl M. Wantzen⁸, Sonja

7 C. Jähnig⁹,¹⁰, Carlos Garcia de Leaniz¹¹, Astrid Schmidt-Kloiber¹², Peter Haase¹³,¹⁴, Marie Anne

8 Eurie Forio¹⁵, Gait Archambaud¹⁶, Jean-François Fruget¹⁷, Alain Dohet¹⁸, Vesela Evtimova¹⁹,

9 Zoltán Csabai²⁰, ²¹, Mathieu Floury⁴, Peter Goethals¹⁵, Várbiró Gábor²², Miguel Cañedo-

10 Argüelles²³, Aitor Larrañaga²⁴, Anthony Maire²⁵, Ralf B. Schäfer¹³, ²⁶, James S. Sinclair¹³, Rudy

11 Vannevel¹⁵, Ellen Welti²⁷ & Alienor Jeliazkov¹, ⁴

12

13 **Affiliations:**

14 1. Fondation pour la Recherche sur la Biodiversité (FRB), Centre de Synthèse et d'Analyse sur la Biodiversité (CESAB), Montpellier France.

16 2. Université de Strasbourg, ENGEES, CNRS, LIVE UMR 7362, F-67000 Strasbourg, France.

18 3. Department of Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany.

20 4. University of Paris-Saclay, INRAE, HYCAR, Antony, France.

21 5. Deltares, Department of Freshwater Ecology and Water Quality, Delft, the Netherlands.
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6. Wageningen University & Research, Aquaculture and Fisheries group, Wageningen, the Netherlands.

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7. Departamento de Sistemas y Recursos Naturales, E.T.S. Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid.

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8. UNESCO Chair "Fleuves et Patrimoine", CNRS UMRS CITERES, Tours University, 37000 Tours, and CNRS UMR LIVE, Strasbourg University, France.

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9. Department of Community and Ecosystem Ecology Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.

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

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11. Centre for Sustainable Aquatic Research (CSAR), Department of Biosciences, Swansea University, Swansea, UK.

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12. University of Natural Resources and Life Sciences, BOKU Vienna, Institute of Hydrobiology and Aquatic Ecosystem Management, Gregor Mendel Strasse 33, 1180 Vienna, Austria.

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13. Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Clamecystraße 12, 63571, Gelnhausen, Germany.

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14. Faculty of Biology, University of Duisburg, Essen, Germany.

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15. Department of Animal Sciences and Aquatic Ecology, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.

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16. INRAE, Aix Marseille Univ, RECOVER, Aix-en-Provence, France

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17. ARALEP - Ecologie des Eaux Douces, Villeurbanne 69603 France.

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18. Environmental Research and Innovation (ERIN) Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg.
45

19. Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, 1
Tsar Osvoboditel Blvd., Sofia 1000, Bulgaria.

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20. University of Pécs, Department of Hydrobiology, 6 Ifjúság útja, H7624 Pécs, Hungary.

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21. Balaton Limnological Research Institute, 3 Klebelsberg Kuno, H8237, Tihany, Hungary.

49

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

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23. FEHM Lab, Institute of Environmental Assessment and Water Research (IDAEA-CSIC),
Jordi Girona, 18-26, 08034 Barcelona, Spain.

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24. Department of Plant Biology and Ecology, University of the Basque Country, Leioa
48940 Spain.

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25. EDF R&D, LNHE (Laboratoire National d'Hydraulique et Environnement), Chatou,
France.

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26. Institute for Environmental Sciences, RPTU Kaiserslautern-Landau, Fortstrasse 7, 76829
Landau, Germany.

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27. Conservation Ecology Center, Smithsonian's National Zoo and Conservation Biology
Institute, Front Royal, VA, USA.

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Contact Information for corresponding author:

62

Aaron Sexton

63

Fondation sur la Recherché pour la Biodiversité

64

Montpellier, France, 34000

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e-mail: aaron.sexton@fondationbiodiversite.fr

66

ORCID ID: 0000-0002-8926-3872
Inland navigation in Europe is proposed to increase in the coming years, being promoted as a low-carbon form of transport. However, we currently lack knowledge on how this would impact biodiversity at large scales and interact with existing stressors. We addressed this knowledge gap by analyzing fish and macroinvertebrate community time series across large European rivers comprising 19,592 observations from 4,049 sampling sites spanning the last 32 years. We found ship traffic to be associated with biodiversity declines, i.e. loss of fish and macroinvertebrate taxonomic richness, diversity, and trait richness. Ship traffic was also associated with increases in taxonomic evenness, which, in concert with richness decreases, was attributed to losses in rare taxa. Ship traffic was especially harmful for benthic taxa and those preferring slow flows. These effects often depended on local land use and riparian degradation. In fish, negative impacts of shipping were highest in urban and agricultural landscapes. Regarding navigation infrastructure, the negative impact of channelization on macroinvertebrates was only evident when riparian degradation was also high. Our results demonstrate the risk of increasing inland navigation on freshwater biodiversity. Integrative waterway management accounting for riparian habitats and landscape characteristics could help to mitigate these impacts.
more ship traffic, but would also require the modification of navigation infrastructures (e.g.,
heavier embankments, larger locks and ports, additional waterways) that will impact freshwater
ecosystems in ways that are difficult to predict (He et al. 2021; Belletti et al. 2020). Inland
navigation and its infrastructures have largely been investigated as being vectors for the spread of
invasive species, (Leuven et al. 2009; Hanafiah et al. 2013; Nunes et al. 2015) and not nearly as
much for their direct impact on facets of native biodiversity such as richness, evenness and
functional trait responses, as has been done in the case of global change stressors such as climate
change and land use (Dudgeon et al. 2006)..

Additionally, with few exceptions (Zajicek et al. 2018; Zajicek and Wolter 2019), most research
on inland navigation has been conducted at local spatial scales, examining the effects of a specific
lock, river or catchment (e.g., Bergman et al. 2022; Xiong et al. 2021; Jackson and Grey 2013;
Huckstorf et al. 2011). These studies have shown that inland navigation constitutes a significant
pressure on freshwater biodiversity. Specifically, these studies have shown ship traffic to decrease
fish richness, abundances, and reproduction, especially that of limnophilic fish (those with
preference to slow flowing waters) (Xiong et al. 2021; Huckstorf et al. 2011; Wolter 2001), and
modifications of river systems to accommodate for inland navigation to reduce fisheries and other
riverine ecosystem services (Zhang et al. 2018). Declines in biodiversity associated with
navigation are often attributed to the destruction of natural habitats, construction of artificial
embankments, aquatic and acoustic pollution, and ship waves that further degrade habitats
(Huckstorf et al. 2011; Wolter 2001).

Given that most studies have addressed different navigation-related effects, taxa, and local
contexts, (e.g. focused on YOY-fish (Wolter 2001), or on the Rhine river (Leuven et al. 2009) or
Yangtze river (Xiong et al. 2021), or on one canal (Vergeynst et al. 2021)) drawing generalizations
on the overall effect of inland navigation at a large scale is fraught with uncertainty. General conclusions are further challenged by potential context-dependency of navigation-biodiversity relationships. For example, the landscape (e.g. urban vs. agricultural areas) and habitat conditions (channelized rivers, disconnected from floodplains) are both known to be important determinants of freshwater communities, for example decreased habitat connectedness or increases in intense land use leading to reductions in diversity, endangered species, and shifts in community composition (Brink et al. 1996; Décamps 2011; Manfrin et al. 2020). Therefore, analyses of fine-scale observations across multiple land use contexts and habitat conditions are required to assess whether the impact of inland navigation is consistent across catchments, and if this impact interacts with other stressors.

To better understand how inland navigation impacts freshwater biodiversity at a continental scale, and how the impact is moderated by landscape and habitat conditions, we compiled several local biodiversity datasets across Europe and modelled their relationships with navigation pressures. Specifically, we tested the relationship between navigation stressors (ship traffic, density of ports and locks, and degree of river channelization) and the taxonomic and functional diversity, i.e. taxonomic and functional richness, Simpson's Reciprocal diversity, functional dispersion, and taxonomic and functional evenness of freshwater fish and macroinvertebrate communities, as well as the prevalence of invasive taxa. We hypothesized consistently negative effects of ship traffic on taxonomic and functional richness and diversity. Additionally, in ports, especially where ships dock closely, invasive species will be most prevalent, as observed in marine and estuarine systems (Keller et al. 2011). For example, in Canadian marine ports, invasive marine macroinvertebrates are found in ballast tanks, while Finnish ports commonly document invasive species with high eutrophic tolerance in ship ballast waters (Briski et al. 2011; Paavola et al. 2008). However, these
studies and previous reviews have focused mainly on marine invasive species and maritime ports,
and similar investigations in freshwater systems are lacking. In addition, we evaluated how land
use and riparian degradation would interact with the effects of navigation. We hypothesized that
navigation's negative impact would be lower in highly degraded landscapes, because of correlated
tolerances of organisms towards landscape-related and ship traffic-related stress. Thus, although
co-occurring stressors might increase the overall level of impact, this impact would be lower than
if the tolerances to the stressors were either un- or negatively correlated (Vinebrook et al. 2004).

159

160 **Results**

161 Our biodiversity database consisted of 19,592 samples (13,335 fish and 6,257 macroinvertebrate
162 communities) from 4,049 sampling sites (2,381 fish [Fig.1a] and 1,668 macroinvertebrates
163 [Fig.1b]) across Europe from 1990 to 2022. We obtained ship traffic in all large European rivers,
164 which ranged from no recorded vessel to more than one million vessels per month, and the location
165 of 1,215 locks and 433 ports used for inland navigation, and we calculated rates of river
166 channelization at the landscape scale (for further details, see Methods).

167
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168

169 **Fig. 1 – Fish and macroinvertebrate communities across Europe.** *Locations of all biodiversity*
170 data used in our analyses (colored dots). Panel a) Fish (n = 2,381). Panel b) Macroinvertebrates (n =
171 1,668). Large rivers and navigated canals are shown in blue lines.

172

173 Navigation Impact

174 Ship traffic was strongly associated with decreases in taxonomic richness and Simpson's
175 Reciprocal diversity, and functional richness of both fish and macroinvertebrates (Fig.2, p-values,
176 R², model estimates and standard errors for all models are provided in Supplemental Table 1).
177 Additionally, for both fish and macroinvertebrates, ship traffic was positively associated with the
178 prevalence of invasive species and taxonomic evenness (Fig.2, Supplemental Table 1). For all
179 models other than invasive species, functional diversity and evenness, ship traffic was the term in
180 the model with the largest effect size (i.e. the effect causing the greatest change in the response metric), indicating strong impacts throughout (Fig. 2).

182

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183

184 **Fig. 2** — Inland navigation and other stressor effects on freshwater fish and macroinvertebrate biodiversity. Model estimates from the GLMMs. Each column represents a separate model, with the response metric and taxonomic group as the column header, with predictors of the model as rows. Red cells indicate a negative effect and blue cells represent a positive effect, with color intensity representing the magnitude of effect. Non-significant effects are blank cells (p values and standard errors reported in Supplemental Table 1). Ships refers to ship traffic, Ports and Locks represent the density of each, Channelization represents the portion of the river stretch that has been channelized, Agriculture and Urban represent the respective portion of land use at each site, and Riparian Degradation represents the portion of the riparian habitat that has been lost from its potential extent. All predictors were standardized to an average value of 0 and SD of ±1, allowing one to compare predictors.

194
The negative effects of ship traffic on fish communities had important context dependencies regarding urban and agricultural land use. Ship traffic's negative association with fish richness and Simpson's Reciprocal diversity and the positive association with evenness was magnified in areas of high agricultural and urban land use with relatively lower effects in areas of low agricultural and urban cover (Fig.3a, b and e, Supplemental Table 1). Additionally, the positive association between ship traffic and invasive species was magnified in highly urbanized areas (Fig.2, Extended Data Fig. 1, Supplemental Table 1).

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203

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204

**Fig. 3** – Context-dependent effect of ship traffic. Plots a-d show the marginal effect of ship traffic from the GLMMs on taxonomic richness and evenness across varying degrees of land use. The x-axis represents the standardized (average of 0 ± 1 SD) intensity of land use (Urban – blue line or Agriculture – red line), and the corresponding y-axis value the marginal effect of ship
traffic on the biodiversity metric. The marginal effect is the additional effect for a given absolute effect of the other variables, here in particular land use. Negative values indicate ship traffic decreases the metric (e.g. decrease in macroinvertebrate taxonomic richness at low levels of urbanization), zero on the y-axis represents a null effect, and positive values show an increase.

The dotted lines represent the standard error of the marginal value. Plots e and f visualize these changes in the marginal effect of ship traffic at specific levels of urbanization (high, medium and low levels of urban land use – medium representing the average level of urbanization, and high and low representing the max and min values) by showing the predicted values of taxonomic richness as ship traffic increases in across different land use scenarios. The top and bottom graphs show the effects on fish and macroinvertebrate communities, respectively.

219

For macroinvertebrates, urban land use modulated the effect of ship traffic (Fig.3c, d and f). Ship traffic's negative effect on richness was strongly negative in areas of low urban land use, but minimal in areas of high urban land use (Fig.3c and f, Supplemental Table 1). Shipping's positive effect on taxonomic evenness was only observed in areas of low to moderate urban cover, and was also only negative in areas of high riparian degradation (Fig.3d, Supplemental Table 1).

The effects of channelization were also context-dependent. For macroinvertebrates, taxonomic and functional diversity, and functional richness decreased in areas of high channelization, but only when riparian degradation was also high (Fig.2, Extended Data Fig.2, Supplemental Table 1). Additionally, when riparian degradation was high, channelization was associated with significant increases in invasive species prevalence (Fig.2, Extended Data Fig.2, Supplemental Table 1). In areas of low to moderate riparian degradation, channelization had no effect on the studied metrics.
231 The density of locks was weakly associated with increases in fish taxonomic richness and
232 functional diversity, and a decrease in invasive prevalence, and with slight decreases in
233 macroinvertebrates functional diversity (Fig.2, Supplemental Table 1). Port density was associated
234 with slight increases in taxonomic and functional richness for both fish and macroinvertebrates
235 (Fig.2, Supplemental Table 1).

236 *Traits involved in navigation-biodiversity relationships*

237 To better identify the guilds of species responding to navigation, we analyzed the relationships
238 between traits and navigation pressures via an RLQ-Fourth Corner analysis, which quantifies the
239 covariance between species traits and environmental variables, mediated by the species
240 abundances at sites (Dray et al. 2014). We found strong significant relations between the
241 environmental variables and species traits in both fish and macroinvertebrates.

242 In fish, we found increased ship traffic positively associated with traits such as pelagic
243 environment, planktivorous feeding, and diadromous migration, generalist flow preferences
244 (eurytopic) and negatively with stagnant water preference (limnophilic) (Fig.4, Supplemental
245 Table 2). Limnophilic species were more likely to occur in areas around locks. Additionally, we
246 found the presence of ports positively associated with planktivorous and detritivorous feeding
247 traits, and with psammophilic and lithophilic reproduction (gravel spawning) (Fig.4, Supplemental
248 Table 2).

249
250

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251

**Fig. 4** – Fish trait responses to navigation. Ordinations from an RLQ-Fourth corner analysis that represents the relationships between fish traits (colored text) and navigation pressures (bold green boxes). Both ordination axes (traits and navigation pressures) are significantly linked (p = 0.002). Both traits and navigation pressures with highly positive x-axis values (e.g. Pelagic habitat and Ships) are strongly associated, and the same for y-axis relationships.

257
<figure><img src="image_6.png" /><figcaption>**Fig. 5** – **Macroinvertebrate trait responses to navigation.** Ordinations from an RLQ-Fourth corner analysis which represents the relationships between macroinvertebrate traits (colored text) and navigation pressures (bold green boxes). The axes of the two ordinations (traits and navigation pressures) are significantly linked (p = 0.002). Traits with highly positive y-axis values (e.g. Burrower locomotion) are strongly associated with navigation pressures with highly positive y-axis values (e.g. Locks & Ports), and the same for x-axis relationships.</figcaption></figure>

266

For macroinvertebrates, we found increased ship traffic negatively associated with interstitial/crawling species and passive aquatic dispersers, and positively with fliers, aquatic active dispersers and locomotive swimmers. Increases in navigation infrastructures such as ports and locks were associated with increases in active aerial dispersal, free eggs, and those inhabiting gravel, sandy, and silty substrates. (Fig.5, Supplemental Table 2).
272

To identify how land use and riparian degradation mediated navigations effect on traits, we ran additional RLQ-Fourth Corner analyses using subsets of sites with the highest degree of anthropogenic land cover and riparian degradation (top 25% of sites with each land use class). In fish, degradation did not change the effect of navigation on traits (Extended Data Fig.2, Supplemental Table 3). In macroinvertebrates, trait responses also did not change at agricultural sites, but in highly urbanized sites the association of ship traffic with burrowing species and those laying clutches in terrestrial sites turned from positive (full dataset) to strongly negative (Extended Data Fig.2, Supplemental Table 3).

281

## 282 Discussion

Our results suggest that inland navigation has strongly negative impacts on freshwater biodiversity at the European scale. In particular, we found negative responses (decreases in richness and diversity) of both fish and macroinvertebrates to ship traffic, at both taxonomical and trait/functional levels, and increases in the prevalence of invasive species. This confirms previous studies at local scales reporting negative impacts of navigation on freshwater fish and macroinvertebrate communities. This study goes beyond existing work in showing that inland navigation affects the whole 35,000 km European inland waterway network and threatens biodiversity of multiple taxa, building on previous work that focused on only sections of a particular river at a time.

292 Ship traffic
The effects on biodiversity are reflected in trait shifts in both fish and macroinvertebrate communities. Ship traffic had the strongest negative effects on benthic and limnophilic taxa and on fish species that suspend their eggs in the flow. Ship traffic was positively associated with pelagic and eurytopic taxa and those fixing their eggs in clutches to substrates. Together these trait shifts reflect selection pressures by vessel-induced waves and return currents against specialist and towards generalist life history traits (Zajicek and Wolter 2019; Gabel et al. 2017). Ship waves erode banks and re-suspend sediment both degrading littoral habitats for fish and macroinvertebrates (Gabel et al. 2011). Additionally, commonly used heavy embankments to prevent bank erosion considerably impact littoral habitats in all navigated rivers, and have been shown to reduce fish diversity (Wolter, 2001). Reducing ship speed and creating shallow habitats that are protected from ship waves could mitigate some of these wave-induced negative impacts. Additionally, many waterways require dredging of the river bottom to maintain navigable fairway depth (Grygoruk et al. 2015), which has disproportionally negative impacts on benthic habitats. These known effects of ship traffic and fairway maintenance can explain negative association with benthic species.

The community shifts described above are confirmed by the positive association between ship traffic and taxonomic evenness in both fish and macroinvertebrates. Decreasing species richness, the loss of rare taxa and small populations, resulted in ship traffic induced increases in evenness. Correspondingly, in a follow-up analysis we found strong correlations between taxonomic evenness and the abundance and occurrence of rare taxa for both fish and macroinvertebrates, (Supplemental File 2, Extended Data Fig.3) supporting the hypothesis that ship traffic drives losses in populations of rare species. Shipping favors the most tolerant, generalist and abundant/ubiquitous taxa and eliminates rare species, resulting in a more even community. For
example, eurytopic fish tolerating a wide variety of flow rates were positively associated with ship traffic, while negatively so with flow specialists (both limnophilic and rheophilic taxa). This is a concern as it may allow invasive goby fish species, such as *Proterorhinus semilunaris*, or *Neogobius fluviatilis* that are generalists in their flow preferences, to proliferate. Additionally, this might have consequences at the ecosystem level given the role that specialist species play in ecological function and stability (Hooper et al. 2005). For example, Cao et al. (2018) documented significant losses of benthic macroinvertebrate species, especially among sensitive taxa, as a result of habitat degradation in streams leading to losses in ecosystem functionality, especially nutrient cycling.

## 325 *Interaction with land use*

326 Degraded landscapes magnified negative impacts of ship traffic on fish communities, with higher 327 decreases in richness and diversity in urban and agricultural areas. These interactions demonstrate 328 that land use can modify the effects of shipping and create environments where only few taxa will 329 persist. For example, in agricultural systems, in areas with little to no riparian buffer, the dual 330 stressors lead to fish and macroinvertebrate communities dominated by pollution tolerant taxa 331 (Effert-Fanta et al. 2019). Research at large scales has shown that multiple stressors like pollution, 332 hydromorphological modifications, land use, and water use can synergistically interact to create 333 stronger effects in combination on freshwater ecosystem services (Elmhagen et al 2015; Wen et 334 al. 2017). Several freshwater fish species living in large rivers might tolerate some level of 335 degradation, agricultural or urban pollution and habitat modifications, but still decline or disappear 336 when another stressor such as ship traffic is added.

337 For macroinvertebrates, the impact of ship traffic more closely followed our original hypothesis 338 that navigation's impact would be lessened in degraded areas – especially in urban areas. The
339 impact of ship traffic on species richness was minimal in highly urbanized areas as depicted in
340 Figure 3c (i.e. marginal effect of ship traffic close to zero when urban cover is high). However,
341 the effects of ship traffic on species richness were strongly negative in areas with moderate to low
342 levels of urbanization. This suggests that the taxa exhibited a positive co-tolerance to urbanization
343 and ship traffic, where the same sensitive taxa are independently extirpated by each stressor. For
344 example, a weak swimming species that is sensitive to low dissolved oxygen levels is extirpated
345 by both stressors related to urban land use and navigation, e.g. in an urban environment, either by
346 urban runoff or vessel-induced wake wash. Several studies have documented what is described as
347 the "urban stream syndrome" whereby urban freshwater ecosystems have similar hydrology,
348 ecology and pollution (Booth et al. 2004; Halstead et al. 2014, Wantzen et al. 2019). These urban
349 streams are often characterized by increases in pH and heavy metals as well as temperature and
350 suspended solids, which is typically accompanied by decreases in dissolved oxygen (Winter and
351 Duthie 2013; Martins et al. 2017). This combination has been shown to produce negative effects
352 on macroinvertebrate communities (Gal et al. 2019). Similarly, ship traffic has been reported to
353 increase suspended sediment, release heavy metals and other toxins (Fetters et al. 2016; Prygiel et
354 al. 2016; Floehr et al. 2013). Urban waterways are particularly confined, homogeneous and
355 simplified, which might have resulted in little negative impact of ship traffic observed, as their
356 aquatic communities are already depauperated and composed of taxa tolerant to stressors resulting
357 from ship traffic.

358 The contrasting result that urban land use magnified the impact of ship traffic on fish, but lowered
359 it on macroinvertebrates as discussed above might be explained by the particular simplified habitat
360 structure of urban waterways. Steep bank slopes, hard embankments and the lack of shelter
361 structures particularly expose fish to vessel-induced physical forces, like return currents and wake
362 wash, thereby magnifying the negative effect of ship traffic (Wolter 2008). Moreover, fish may be
363 more tolerant than macroinvertebrates with respect to water quality, which means that their
364 tolerances are less correlated than for macroinvertebrates. Correspondingly, a recent study from
365 China found that urban pollutants were a primary driver of macroinvertebrate communities, while
366 urban fish communities were driven by dispersal limitations instead (Xu et al. 2023). Indeed, a
367 recent study found that landscape connectivity was more important than urbanization for fish
368 community characteristics in Hungary (Czeglédi et al. 2020). Future research on how dual
369 stressors such as urbanization and navigation can interact to influence freshwater biodiversity
370 across taxa and functional groups is certainly warranted.

371 The effects of channelization on macroinvertebrate communities were also dependent on local and
372 landscape contexts, such that in areas of high riparian degradation and agricultural cover the
373 negative effect was aggravated. We found that channelization was associated with decreased
374 richness and diversity and increased invasive species only when riparian degradation was also high
375 (Extended Data Fig.1). These interactive effects indicate potential benefits of protecting and
376 restoring riparian habitat to mitigate the impacts of river channelization. For example, in highly
377 channelized river stretches, the macroinvertebrate community could be partially protected by
378 restoring the riparian habitat. Riparian vegetation provides habitat structure, shade, food resources,
379 terrestrial habitat for aquatic insects and can control algal biomass, (Li et al. 1994; Corline et al.
380 2023). In addition, riparian vegetation reduces bank erosion, thereby preserving habitat suitability
381 for macroinvertebrates, in turn lessening navigation/channelization impacts. However, mitigation
382 measures are also required to further improve water quality, which is often insufficient, particularly
383 in many larger European rivers (Lemm et al. 2021).
Fish richness tended to be higher in river segments with a higher number of locks. This is likely an impoundment effect creating more lentic habitats in the river, which then allow limnophilic species to proliferate – a finding that is corroborated by our trait analysis showing these species to be more frequent in areas of high lock densities. This, in concert with the finding of taxonomic evenness decreases in areas of high lock densities indicates a shift in community composition near locks, and not that locks are increasing fish biodiversity. However, locks may also increase connectivity for certain species in highly fragmented rivers, because lock operation at least occasionally allows fish passage (Lin et al. 2013; Vergeynst et al. 2021). This is relevant for management, as oftentimes locks and dams are side-by-side, meaning that fish of different swimming abilities may be able to navigate through such blockages via different avenues – some via fish ladders and others via locks. Further research is needed to elucidate how locks should be best operated to reduce river fragmentation.

Richness values of both fish and macroinvertebrates were slightly higher in areas near ports. However, while statistically significant, the effect sizes were small – an order of magnitude smaller than the negative effect sizes of shipping intensity. Similar to locks, this finding probably results from shifts in community composition due to novel habitats created in ports as well as potential ballast water release therein. Ports alter the physical environment of lowland rivers by increasing depth, providing pools of standing water, and accumulating sediments (Samsami et al. 2022; Keller et al. 2011). These accumulating sediments may explain why macroinvertebrates living in sandy and gravel substrates, and fish reproducing in sandy substrates (psammophils) were positively associated with ports in our trait analysis. Ballast water mixing and release occurring in port areas is known to release species (Costello et al. 2022) and to spread invasive species especially by seagoing vessels (Wan et al. 2021; Molnar et al. 2008). However, here we did not find increases
408 of invasive species in port areas. This may indicate that either the ballast water transport of non-native species by inland navigation is less relevant than in marine shipping, or that the inland waterways are already widely colonized by non-native species, so that the ports did not pop up as hotspots. Further research would be warranted to scrutinize the links between ballast water and freshwater invasive and exotic species.
409
410
411
412

413 Conclusion

414 Europe's large rivers host a wealth of Europe's freshwater biodiversity, and are vital socio-economic systems. Here we show that inland navigation can have negative impacts on this biodiversity – mainly via frequent ship traffic. This impact on biodiversity is highlighted by ship traffic being associated with declines in taxonomic and functional richness and diversity, declines in rare taxa, and increases in invasive species prevalence. These findings are important in regard to both recent biodiversity declines and composition shifts in freshwater communities and global and European initiatives and strategies to hold biodiversity loss. Importantly though, we show that this impact is context-dependent – shifting in relation to land use and riparian degradation.
415
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421
422 Riparian habitats can serve as a potential mitigation tool, especially reducing the negative impacts of river channelization. This may become important if inland navigation, and the river management required to support it, would increase in the coming years. Overall, our results inform global and European initiatives and strategies to halt and reverse biodiversity loss.
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423
424
425

426 In addition to biodiversity declines, we observed shifts in community compositions and functional traits. These findings add to those of recent research, for example showing shifts towards pollution-tolerant taxa in urban and agricultural streams, and climate warming shifting the average body sizes of freshwater fish communities (Rumschlag et al. 2023; Arranz et al. 2023). We add to this by showing ship traffic to be especially negative for benthic taxa, locks to shift communities
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428
429
430
towards slow-flow taxa, and for port areas to benefit macroinvertebrates living in sandy bottom
rivers. Mitigating the negative impacts on benthic taxa could be achieved via simple measures
such as reducing vessel speed to reduce hydraulic forces on shoreline habitats, as well as larger
efforts such as the re-establishment of shallow habitats protected from ship waves. These findings,
in concert with previous work, are important for restoration plans. As a halt in freshwater
biodiversity recoveries in recent years has been found (Haase et al. 2023), it is important to
consider all stressors, including potentially emerging stressors such as navigation that are currently
neglected, to fully restore communities. Finally, it is important to focus restoration/mitigation
efforts not only on small streams, but also large rivers and balance the use of these rivers for socio-
economic goods with the role they play in hosting biodiversity.

441

442 Methods

443 Fish & Macroinvertebrate Biodiversity

We combined data sets of fish (n = 9 datasets) and macroinvertebrate (n = 11) communities in
large European rivers and canals from the NAVIDIV consortium (Inland navigation infrastructures
and biodiversity: impacts and opportunities for waterwayscape management -
https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-
cesab/navidiv/), complemented by macroinvertebrate time series from Haase et al. (2023) relevant
for our study. We defined a "large river" as having a Strahler order of five or greater based on the
HydroSHEDS database (Lehner et al. 2008). This ensured we focused only on navigated and
navigable waterways and omitted smaller rivers from the analysis because of the absence of
stressors associated with navigation. However, large rivers without navigation were considered as
potential reference sites. Moreover, given that larger rivers typically are more degraded than smaller rivers, the inclusion of smaller rivers may have led to an overestimation of the navigation effect because of the collinearity of river size with navigation (Nilsson et al. 2005). We used data from 1992 to 2021, as the opening of the Main-Danube canal in 1992 marked the beginning of the spread of many freshwater invasive species (Leuven et al. 2009). During this time period the total amount of goods transported by commercial navigation, a proxy for ship traffic has remained approximately constant (see next section). One ecological community dataset consisted only of presences-absence data. These data were only used to calculate the taxonomic richness model; all other included datasets featured abundance data for multiple taxa at specific locations with spatial coordinates, years and sampling effort. While our data are pan-European, the bulk of the biodiversity data are in France and Central Europe, which aligns with the main shipping routes in Europe. To ensure that this geographic bias did not influence our results, we re-ran our GLMMs without observations from France. This removed 4,845 fish observations, and 1,263 macroinvertebrate observations. The models resulted in the exact same interpretations (i.e. no major changes in significance of model terms and interactions or R² values). These model outputs are provided in Supplemental Table 5.

From each dataset we calculated the taxonomic richness (as the number of taxa, most commonly at the genus level for macroinvertebrates and species level for fish), Simpson's Reciprocal Diversity Index, using the 'diversity' function in the "vegan" R package (Oksanen et al. 2022), and Simpson's evenness based on relative abundances (diversity divided by species number) of each sampling occasion. We chose to use Simpson's Reciprocal Diversity Index, a Hill number, instead of other common metrics (e.g. Shannon) because our functional metrics, calculated in the "mFD" package, are also Hill number metrics.
Additionally, we calculated the relative abundance of invasive taxa. Taxa were identified as invasive on a dataset-by-dataset basis, and relied on expert opinion from either the data collector/provider, or an expert in the region on fish or macroinvertebrates. Biodiversity metrics (richness, diversity, evenness, invasive prevalence) were calculated for each sampling effort of each database, then combined into one file for running models, such that each row was an individual sample with diversity metrics, the database identifier, sampling effort identifier, and the navigation and landscape predictors used for analyses.

To explore the effects of navigation on the ecological traits of these communities, we compiled ecological trait information on the traits that were expected to play a role in the response of communities. Fish traits of feeding guild, habitat, migration, and reproduction were obtained from the freshwaterecology.com trait database (Schmidt-Kloiber, & Hering 2015). Flow preference traits followed the FAME consortium (Development, Evaluation and Implementation of a Standardized Fish-based Assessment Method for the Ecological Status of European Rivers, EU FP5), sensitivity was obtained from van Treeck et al. (2020), Fish Region Index (FRI) and its variation (S²FRI) were based on Wolter et al. (2021). Macroinvertebrate traits were obtained from the Tachet database (Tachet et al. 2010). Linking these species traits to our community data, we calculated the metrics functional richness, diversity (specifically Functional Dispersion of the functional space of communities [Villéger et al. 2008]), and evenness, using the “mFD” package in R (Magneville et al. 2021). The traits we use here are *ecological* traits, and not strictly *functional* traits according to (Violle et al. 2007). Therefore when we refer to functional diversity, evenness and richness, we use these terms in the broad colloquial sense to represent the diversity of organism attributes that interact with the abiotic and biotic environments (as defined in Magneville et al. 2021).
Ship traffic, i.e. vessel counts were obtained from Marine Traffic (https://www.marinetraffic.com/) and compiled as the number of ships passing through 30 km stretches of river per-month over the course of 2019. This grain was chosen to provide the optimal grain for our European-extent study – generating 4,174 river lengths across Europe, allowing us to compare river stretches at the continental extent. A finer grain would not have been relevant due to the linear flow of ship traffic, but a larger grain would not have allowed us to compare traffic changes between cities/ports, and our choice of grain generally aligns with the Water Framework Directives River Basin Management Plans. A comparison of the ship traffic in 2019 to independent data of ship traffic logs from locks throughout Europe including the rivers Danube, Rhine, Elbe, Spree, Oder, and others provided by Zajicek et al. (2018) for 1992 to 2015 showed that 2019 was similar to this period (Supplemental Figure 1). Channelization was also measured at the grain of 30 km stretches.

Within the same river stretches used for shipping we calculated the total length of channelized river obtained via the Open Street Map database (https://openstreetmap.org/). Channelization represents a metric of riverine modifications that have been made for navigation, though reasons for channelization may go beyond navigation and also include manipulation of floodplain for agriculture. Almost all European navigated rivers have been channelized to some extent (Petts et al. 1989). The location of all inland ports and locks was obtained via personal communication from the United Nations Economic Commission for Europe (UNECE), and is sourced from the UNECE Inventory of Main Standards and Parameters of the Waterway Network. We counted the number of ports and locks within a 5 km buffer (consistent with the grain of land use) around each biodiversity sampling site.
To identify how the surrounding landscape and habitat characteristics moderate navigation's impact, we collected data on the land use and riparian degradation surrounding each biodiversity sampling site. We considered the two major land use types that are associated with the degradation of surface waters in Europe: agriculture and urbanization. We clipped land use (obtained from CORINE Land Cover 2018, [100 m positional accuracy] https://land.copernicus.eu/pan-european/corine-land-cover/clc2018) to 5 km-radius circular buffers around sampling locations.

While additional research is needed to elucidate the spatial grain at which land use influences aquatic taxa, studies have shown that a grain between 1-5 km is appropriate (Wang et al. 2001; Bergerot et al. 2013). We found strong correlations between land use at 1 km and 5 km across our sites (Supplemental Figure 2). Within these buffers, we obtained the surface sizes of surrounding urban and agricultural land uses for each site. To prevent collinearity issues between the two land use variables (significant Pearson's correlation test between urban and agricultural land use), we conducted a Principal Component Analysis (PCA) on these values as the PCA creates two uncorrelated variables. We then used the site scores from each Principle Component (PC) that were strongly associated with each land use class. This was done for the macroinvertebrate sites, and then separately for the fish sites, such that one PCA was run on each set of sites. In macroinvertebrate sites, the first PC (PC1) had a high rotation score for urban land use, and PC2 had a high rotation score for agricultural land use. It was reversed for fish sites, such that PC1 site scores were used to represent agricultural land use, and PC2 represented urban land use. Code for this PCA and the resulting outputs (rotation scores, standard deviation, and proportional and cumulative variance) are provided in Supplemental Table 4 and in the repository for this paper linked in the data availability section.
To obtain finer-grain information on habitat structure, we characterized riparian degradation at the grain of 1 km using the Green Arteries database from CORINE (Weissteiner et al. 2016). Within a 1 km radius buffer around our biodiversity sampling sites, we clipped the “Potential Riparian Extent” (PRZ) as modeled based on hydrologic and geomorphologic characteristics, and the “Actual Riparian Extent” (ARZ) from satellite images. Our metric of Riparian degradation around each site was then calculated as the inverse proportion of ARZ to PRZ: [1 - (ARZ/PRZ)].

To control for large-scale abiotic influences and differences from one region of Europe to the next, we included data on the size of river and subcatchment for each sampling site. River size (Strahler order) and subcatchment size were both obtained from the HydroSHEDS database. We used Pfafstetter level six of subcatchment size (average of size of 7,972.7 m²) because this maximized the prediction (code provided in Supplemental File 1).

## Models

To assess the relationships between navigation and taxonomic and functional diversity metrics, we ran Generalized Linear Mixed Models (GLMMs) with the function 'glmer' of the "lme4" R package (Bates et al. 2015). For the taxonomic richness models, a Poisson distribution was used and for all others a Gaussian distribution was used. The model structure was:

Biodiversity Response ~ Ship Traffic + Ports + Locks + Channelization + Riparian Degradation + Agriculture + Urban + Ship:Riparian + Ship:Agriculture + Ship:Urban + Channelization:Riparian + Channelization:Agriculture + Channelization:Urban + Subcatchment Size + Strahler order + (1|Study/Year).

Here, Ship Traffic was the average number of ships per month in the river stretch of the collection site and Channelization was proportion of the river that had been channelized in the river stretch
of the site. Ports and Locks were the number of ports and locks within 5 km from each site, Riparian Degradation was the proportion of the riparian forest that had been lost within a 1 km buffer, and Agriculture and Urban were the site scores relating to each land use class from the PCA. Subcatchment size was the size of the Pfafstetter level six of each site, and Strahler order is the respective order of the river of a site. The random effect of (1|Study/Year) represents a nested design wherein Study represents the data source, and Year represents the year that the sample was collected.

We used this model to test the effect of navigation and associated infrastructures, and their interactions with local and landscape stressors while controlling for hydrotopographic factors that differ between river basins and size (Strahler order, subcatchment size). Additionally, the inclusion of the random effect (Study/Year) allowed us to remove potential influences of different data sources and of temporal trends, which may, for example, be due to climatic changes.

For macroinvertebrates, we found an effect of year on taxonomic richness – an increase in richness from 2000-2010, followed by a decrease from 2010-2020, whereas fish exhibited no trend (via a Generalized Additive Model [GAM] see Supplemental Figure 3 for both macroinvertebrates and fish). To control for this non-linear pattern in diversity across years, we included a second order polynomial term for year to the macroinvertebrate models of: + (poly(yr, 2). Plot's showing the year effect on macroinvertebrates and fish are visualized in Supplemental Fig. 3. Finally, for the invasive species abundance model for both fish and macroinvertebrates, we included an “offset” term of total community abundance (log transformed) to control for large samples.

Collinearity between our fixed effects was checked via their Variable Inflation Factors (VIF) scores and all were below two, indicating the model was acceptable (e.g. Graham 2003; Supplemental Table 1). All predictor terms were standardized to zero mean and standard deviation
of +/- 1. Scatterplots of pairwise comparisons of predictor values are provided in Supplemental Fig.4. We identified the marginal effects of shipping (a partial derivative from the GLMM equation) at various levels of landscape degradation (Fig. 3a-d), which was obtained using the ‘margins’ and ‘cplot’ functions in the “margins” R package. Fig. 3e-f were produced using the ‘interact_plot’ function in the “interactions” R package, which allowed us to plot regressions lines at specific levels of our moderator variable (urban land use) to explore interactions. (Leeper 2021; Long 2019). Figures were produced using the “ggplot” and “ggpubr” R packages (Wickham 2016; Kassambara 2020). Silhouettes of fish and macroinvertebrates were obtained from the “rphylogic” R package, and belong to Marie Russell (2020-12-29) License: https://creativecommons.org/licenses/by/3.0/ and Guillaume Dera (2022-12-01) License: https://creativecommons.org/publicdomain/zero/1.0/.

## Trait Analysis

To understand the guilds of species responding to navigation, we analyzed the relationships between functional traits and navigation pressures via an RLQ-Fourth Corner analysis, which quantifies the covariance between traits and environmental variables, mediated by the species abundances (Dray et al. 2014). This multivariate method analyzes the links between species traits and environmental variables via an ordination approach. Specifically, we combined the environmental variables (ship traffic, count of ports and locks, and rate of channelization) at sites (creating a matrix R), the species abundances at these sites (matrix L) and the species traits (matrix Q) to create a “fourth corner” (matrix M) that is an environmental-trait relationship matrix. Each of these matrices is turned into ordinations to allow for direct comparability and computational analysis (via a correspondence analysis to the species table and a principal component analysis to the environmental and trait tables), and then the analysis finds linear combinations of
612 environmental variables and linear combinations of traits that maximize their squared cross-covariance. A permutation test (n = 999 permutations) is then conducted to test global significance of the traits-environment relationships (Supplemental Table 2). As this is an ordination approach, this generates suites of traits that commonly co-occur and identifies how they relate to environmental ordination axes. This analysis was conducted using the “ade4” R package (Dray et al. 2007), applying the function ‘rlq’ to join the three matrices and the ‘fourthcorner rlq’, function was used to run the permutation analysis following Dray et al. (2014) (Supplemental Table 2). The same suite of traits used to calculate functional richness, diversity and evenness were used in this RLQ-Fourth Corner analysis for both fish and macroinvertebrates.

621

622 **Data availability**

623 All data and R code used in this study are available in an open-source repository
624 (https://github.com/aaronsexton/Sextonetal_Navigation_Biodiversity_Supplementals).

625

626 **Acknowledgments**

627 We thank Nicolas Casajus for providing continued assistance and recommendations on analyses
628 and data representation. Additionally, we thank the United Nations Economic Commissions of
629 Europe (UNECE) for providing locations of inland ports locks and canals. This research is a
630 product of the NAVIDIV group funded by the synthesis center CESAB of the French Foundation
631 for Research on Biodiversity (FRB; www.fondationbiodiversite.fr) and by the French National
632 Research Program on Transport Infrastructure, Territories, Ecosystems and Landscapes
633 (ITTECOP; https://www.ittecop.fr/en/) of the French Ministry of Ecological Transition (MTE).
S.C.J acknowledges funding through the Leibniz Competition project "Freshwater Megafauna Futures". P.H. received funding from the EU Horizon 2020 project eLTER PLUS (Grant Agreement No. 871128). A.L. received funding from the Spanish Ministry of Science and Innovation (project PID2020-115830GB-100). A.D.B. received funding from EU Horizon 2020 project MERLIN project (Grant agreement No. 101036337).

639

640 **Author Contributions Statement**

A.N.S, A.J. and J.N.B. conceived of the study. C.S., C.W., E.T., J.B., A.D.B, V.M.F., K.M.W., S.C.J., C.G.L., A.S.K. contributed to initial ideas. A.N.S. and A.J. designed and conducted analyses. P.H., M.A.E.F, G.A., J.F.F., A.D., V.E., Z.C., M.F., P.G., V.G., M.C.A., A.L. A.M., R.B.S., J.S.S., R.V., E.W., C.W., E.T., and J.B. provided biodiversity data, and all authors provided significant input on manuscript writing.

646

647 **Competing Interest Statement**

The authors report no competing interests.

649

650 **Figure Captions**

651

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922
**Figure 1.** Macroinvertebrate's response to channelization is dependent on riparian degradation and agricultural cover. The panels show the marginal effect of channelization from the GLMMs on macroinvertebrate communities along a gradient of riparian degradation and agricultural land use. A negative value on the y-axis indicates a negative impact of channelization on said metric (e.g. a decrease in taxonomic richness), and a value at 0 (grey horizontal line) indicates a null effect. Left panel shows the relationships of taxonomic richness and right panel shows the prevalence of invasive taxa. Predictors were standardized to zero mean and a SD of ±1.
Marginal Effect of Channelization

Taxonomic Richness

<figure><img src="image_7.png" /></figure>

Invasive Prevalence

<figure><img src="image_8.png" /></figure>

Taxonomic Diversity

<figure><img src="image_9.png" /></figure>

Invasive Prevalence

<figure><img src="image_10.png" /></figure>
Figure 2. Fish and macroinvertebrate trait relationships with ship traffic across different land use scenarios. Trait scores associated with the environmental axis of heightened ship traffic in the RLQ analysis are shown in the ‘Full Dataset’ column, and are the values shown in the ordination of Figures 4 and 5. Columns two and three of each table show the trait associations with heightened ship traffic when the RLQ is re-run in highly agricultural sites (those with the top 25% of agricultural cover across the datasets) and in highly urbanized sites.

<table><caption>Macroinvertebrate Trait Associations with Ship Traffic Across Land Use Scenarios</caption><thead><tr><th>Trait Class</th><th>Trait</th><th>Full Dataset</th><th>Highly Agricultural Sites</th><th>Highly Urbanized Sites</th></tr></thead><tbody><tr><td>Locomotion</td><td>Flier</td><td>0.337</td><td>0.393</td><td>0.311</td></tr><tr><td>Dispersal</td><td>Aerial Active</td><td>0.290</td><td>0.411</td><td>-0.034</td></tr><tr><td>Locomotion</td><td>Full Water Swimmer</td><td>0.292</td><td>0.261</td><td>0.332</td></tr><tr><td>Reproduction</td><td>Clutches Fixed</td><td>0.255</td><td>0.158</td><td>0.340</td></tr><tr><td>Substrate</td><td>Mud</td><td>0.181</td><td>0.263</td><td>-0.033</td></tr><tr><td>Reproduction</td><td>Clutches Vegetation</td><td>0.141</td><td>0.057</td><td>0.191</td></tr><tr><td>Dispersal</td><td>Aquatic Active</td><td>0.134</td><td>0.085</td><td>0.234</td></tr><tr><td>Saprobity</td><td>Oligosaprobic</td><td>0.095</td><td>0.140</td><td>0.001</td></tr><tr><td>Substrate</td><td>Organic Detritus</td><td>0.088</td><td>0.105</td><td>0.067</td></tr><tr><td>Saprobity</td><td>Xenosaprobic</td><td>0.082</td><td>0.154</td><td>0.016</td></tr><tr><td>Locomotion</td><td>Burrower</td><td>0.067</td><td>0.072</td><td>-0.221</td></tr><tr><td>Reproduction</td><td>Clutches Terrestrial</td><td>0.064</td><td>0.114</td><td>-0.232</td></tr><tr><td>Reproduction</td><td>Eggs Cemented</td><td>0.042</td><td>-0.023</td><td>0.224</td></tr><tr><td>Reproduction</td><td>Clutches Free</td><td>0.039</td><td>0.057</td><td>-0.150</td></tr><tr><td>Locomotion</td><td>Perm. Attached</td><td>0.026</td><td>0.085</td><td>0.011</td></tr><tr><td>Substrate</td><td>Silt</td><td>0.012</td><td>0.095</td><td>-0.106</td></tr><tr><td>Locomotion</td><td>Surface Swimmer</td><td>0.007</td><td>-0.040</td><td>-0.111</td></tr><tr><td>Dispersal</td><td>Aerial Passive</td><td>0.000</td><td>0.012</td><td>-0.162</td></tr><tr><td>Reproduction</td><td>Eggs Free</td><td>-0.006</td><td>-0.069</td><td>0.200</td></tr><tr><td>Saprobity</td><td>Alpha.Mesosaprobic</td><td>-0.041</td><td>-0.077</td><td>0.087</td></tr><tr><td>Reproduction</td><td>Asexual</td><td>-0.056</td><td>-0.026</td><td>-0.022</td></tr><tr><td>Substrate</td><td>Macrophytes</td><td>-0.064</td><td>-0.081</td><td>0.148</td></tr><tr><td>Locomotion</td><td>Crawler</td><td>-0.078</td><td>0.059</td><td>-0.005</td></tr><tr><td>Saprobity</td><td>Polysaprobic</td><td>-0.113</td><td>0.025</td><td>0.269</td></tr><tr><td>Substrate</td><td>Sand</td><td>-0.125</td><td>0.007</td><td>-0.246</td></tr><tr><td>Locomotion</td><td>Temp. Attached</td><td>-0.127</td><td>-0.121</td><td>-0.044</td></tr><tr><td>Saprobity</td><td>Beta.Mesosaprobic</td><td>-0.180</td><td>-0.137</td><td>-0.096</td></tr><tr><td>Substrate</td><td>Boulders Pebbles</td><td>-0.185</td><td>-0.180</td><td>-0.139</td></tr><tr><td>Substrate</td><td>Twigs Roots</td><td>-0.186</td><td>-0.125</td><td>-0.152</td></tr><tr><td>Substrate</td><td>Gravel</td><td>-0.211</td><td>-0.126</td><td>-0.167</td></tr><tr><td>Substrate</td><td>Microphytes</td><td>-0.270</td><td>-0.230</td><td>-0.100</td></tr><tr><td>Reproduction</td><td>Ovoviviparity</td><td>-0.290</td><td>-0.244</td><td>-0.135</td></tr><tr><td>Locomotion</td><td>Interstitial</td><td>-0.295</td><td>-0.244</td><td>-0.123</td></tr><tr><td>Dispersal</td><td>Aquatic Passive</td><td>-0.302</td><td>-0.376</td><td>-0.100</td></tr></tbody></table>

939

940

941

942

<table><caption>Fish Trait Associations with Ship Traffic Across Land Use Scenarios</caption><thead><tr><th>Trait Class</th><th>Trait</th><th>Full Dataset</th><th>Highly Agricultural Sites</th><th>Highly Urbanized Sites</th></tr></thead><tbody><tr><td>Habit</td><td>Pelagic</td><td>2.005</td><td>-0.445</td><td>-0.571</td></tr><tr><td>Feeding</td><td>Planktivorous</td><td>1.895</td><td>-0.500</td><td>-0.732</td></tr><tr><td>Reproduction</td><td>Psammophil</td><td>1.664</td><td>0.500</td><td>0.524</td></tr><tr><td>Migration</td><td>Diadromous</td><td>0.928</td><td>-0.235</td><td>-0.421</td></tr><tr><td>Reproduction</td><td>Pelagophil</td><td>0.730</td><td>-0.302</td><td>0.353</td></tr><tr><td>Feeding</td><td>Piscivorous</td><td>0.528</td><td>0.619</td><td>0.359</td></tr><tr><td>Feeding</td><td>Parasitic</td><td>0.445</td><td>-0.107</td><td>1.121</td></tr><tr><td>Region</td><td>Fri</td><td>0.430</td><td>0.318</td><td>0.274</td></tr><tr><td>Rheophily</td><td>Eurytop</td><td>0.292</td><td>0.291</td><td>0.158</td></tr><tr><td>Region</td><td>Sensitivity</td><td>0.263</td><td>0.117</td><td>0.386</td></tr><tr><td>Reproduction</td><td>Phytolithophil</td><td>0.222</td><td>0.561</td><td>0.362</td></tr><tr><td>Feeding</td><td>Omnivorous</td><td>0.023</td><td>0.263</td><td>0.194</td></tr><tr><td>Habit</td><td>Demersal</td><td>0.011</td><td>-0.031</td><td>0.287</td></tr><tr><td>Migration</td><td>Potamodromous</td><td>-0.067</td><td>0.136</td><td>0.191</td></tr><tr><td>Reproduction</td><td>Speleophil</td><td>-0.069</td><td>0.728</td><td>0.744</td></tr><tr><td>Region</td><td>S2Fri</td><td>-0.070</td><td>0.287</td><td>0.043</td></tr><tr><td>Habit</td><td>Benthic</td><td>-0.123</td><td>0.087</td><td>0.065</td></tr><tr><td>Reproduction</td><td>Lithophil</td><td>-0.131</td><td>-0.627</td><td>-0.722</td></tr><tr><td>Reproduction</td><td>Phytophil</td><td>-0.209</td><td>-0.430</td><td>0.284</td></tr><tr><td>Feeding</td><td>Invertivorous</td><td>-0.409</td><td>-0.580</td><td>-0.179</td></tr><tr><td>Migration</td><td>Nonmigratory</td><td>-0.409</td><td>-0.598</td><td>-0.490</td></tr><tr><td>Feeding</td><td>Detritivorous</td><td>-0.419</td><td>-0.896</td><td>-0.227</td></tr><tr><td>Feeding</td><td>Phytovorous</td><td>-0.493</td><td>-1.006</td><td>-0.287</td></tr><tr><td>Rheophily</td><td>Rheophil</td><td>-0.501</td><td>-0.644</td><td>-0.537</td></tr><tr><td>Reproduction</td><td>Polyphil</td><td>-0.586</td><td>-0.622</td><td>-1.036</td></tr><tr><td>Rheophily</td><td>Limnophil</td><td>-0.588</td><td>-0.774</td><td>-1.509</td></tr><tr><td>Reproduction</td><td>Ostracophil</td><td>-0.657</td><td>-0.796</td><td>-1.695</td></tr><tr><td>Reproduction</td><td>Lithopelagophil</td><td>-0.706</td><td>-0.886</td><td>-1.910</td></tr></tbody></table>
**Figure 3.** Relationships between taxonomic evenness and the abundance and occurrences of rare species in fish and macroinvertebrates. We defined a rare species as a species whose abundance accounting for less than 25% of the total community abundance (see Supplemental file 2 for details).

<figure><img src="image_11.png" /></figure>