# Understanding the complex dynamics of zebra mussel invasions over several decades in European rivers: drivers, impacts and predictions

Phillip Haubrock, Ismael Soto, Melina Kourantidou, Danish Ahmed, Ali Serhan Tarkan, Paride Balzani, Kristi Bego, Antonín Kouba, Sadi Aksu, Elizabeta Briski, et al.

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## To cite this version:

Phillip Haubrock, Ismael Soto, Melina Kourantidou, Danish Ahmed, Ali Serhan Tarkan, et al. Understanding the complex dynamics of zebra mussel invasions over several decades in European rivers: drivers, impacts and predictions. Oikos, 2024, 10.1111/oik.10283. hal-04410363

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OIKOS

Research article

# Understanding the complex dynamics of zebra mussel invasions over several decades in European rivers: drivers, impacts and predictions

Phillip J. Haubrock<sup>1,2,3</sup>, Ismael Soto<sup>1,2</sup>, Melina Kourantidou<sup>4,5</sup>, Danish A. Ahmed<sup>3</sup>, Ali Serhan Tarkan<sup>6</sup>, Paride Balzani<sup>1</sup>, Kristi Bego<sup>7</sup>, Antonín Kouba<sup>2</sup>, Sadi Aksu<sup>8</sup>, Elizabeta Briski<sup>9</sup>, Francisco Sylvester<sup>10</sup>, Vanessa De Santis<sup>11</sup>, Gaït Archambaud-Suard<sup>12</sup>, Núria Bonada<sup>13</sup>, Miguel Cañedo-Argüelles<sup>14</sup>, Zoltán Csabai<sup>15,16</sup>, Thibault Datry<sup>17</sup>, Mathieu Floury<sup>18</sup>, Jean-François Fruget<sup>19</sup>, John Iwan Jones<sup>20</sup>, Marie-Helene Lizee<sup>12</sup>, Anthony Maire<sup>21</sup>, John F. Murphy<sup>20</sup>, Davis Ozolins<sup>22</sup>, Jes Jessen Rasmussen<sup>10,23</sup>, Agnija Skuja<sup>22</sup>, Gábor Várbíró<sup>24</sup>, Piet Verdonschot<sup>25</sup>, Ralf C. M. Verdonschot<sup>25</sup>, Peter Wiberg-Larsen<sup>26</sup> and Ross N. Cuthbert<sup>10,27</sup>

Oikos

2024: e10283

doi: 10.1111/oik.10283

Subject Editor: Gregor Kalinkat

Editor-in-Chief: Dries Bonte

Accepted 10 December 2023

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<footnote n="1"><sup>1</sup>Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany</footnote>

<footnote n="2"><sup>2</sup>Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, University of South Bohemia in České Budějovice, Zátiší, Vodňany, Czech Republic</footnote>

<footnote n="3"><sup>3</sup>Department of Mathematics and Natural Sciences, Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally, Kuwait</footnote>

<footnote n="4"><sup>4</sup>Université de Bretagne Occidentale, UMR 6308 AMURE, IUEM, Plouzané, France</footnote>

<footnote n="5"><sup>5</sup>Department of Sociology, Environmental and Business Economics, University of Southern Denmark, Esbjerg, Denmark</footnote>

<footnote n="6"><sup>6</sup>Department of Ecology and Vertebrate Zoology, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland</footnote>

<footnote n="7"><sup>7</sup>Senckenberg Gesellschaft für Naturforschung (SNG) Senckenberganlage, Frankfurt, Germany</footnote>

<footnote n="8"><sup>8</sup>Vocational School of Health Services, Eskişehir Osmangazi University, Eskişehir, Turkey</footnote>

<footnote n="9"><sup>9</sup>GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany</footnote>

<footnote n="10"><sup>10</sup>Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Salta, Argentina</footnote>

<footnote n="11"><sup>11</sup>Water Research Institute of the National Research Council of Italy (IRSA-CNR), Largo Tonolli, Verbania-Pallanza, Italy</footnote>

<footnote n="12"><sup>12</sup>INRAE, Aix Marseille Univ, RECOVER, Aix-en-Provence, France</footnote>

<footnote n="13"><sup>13</sup>Departament de Biologia Evolutiva, FEHM-Lab (Freshwater Ecology, Hydrology and Management), Ecologia i Ciències Ambientals, Facultat de Biologia, Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona (UB), Barcelona, Spain</footnote>

<footnote n="14"><sup>14</sup>FEHM-Lab (Freshwater Ecology, Hydrology and Management), Institute of Environmental Assessment and Water Research (IDAEA), CSIC, Barcelona, Spain</footnote>

<footnote n="15"><sup>15</sup>Department of Hydrobiology, University of Pécs, Pécs, Hungary</footnote>

<footnote n="16"><sup>16</sup>Balaton Limnological Research Institute, Tihany, Hungary</footnote>

<footnote n="17"><sup>17</sup>INRAE, UR RiverLy, Centre de Lyon-Villeurbanne, Villeurbanne, France</footnote>

<footnote n="18"><sup>18</sup>University of Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Villeurbanne, France</footnote>

<footnote n="19"><sup>19</sup>ARALEP, Ecologie des Eaux Douces, Campus LyonTech-La Doua, Villeurbanne, France</footnote>

<footnote n="20"><sup>20</sup>School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK</footnote>

<footnote n="21"><sup>21</sup>EDF R and D, Laboratoire National d'Hydraulique et Environnement (LNHE), Chatou, France</footnote>

<footnote n="22"><sup>22</sup>Institute of Biology, University of Latvia, Riga, Latvia</footnote>

<footnote n="23"><sup>23</sup>Norwegian Institute for Water Research (NIVA Denmark), Copenhagen, Denmark</footnote>
**Correspondence: Phillip J. Haubrock (phillip.haubrock@Senckenberg.de) and Ismael Soto (isma-sa@hotmail.com)**

The zebra mussel *Dreissena polymorpha* is one of the most successful, notorious, and detrimental aquatic invasive non-native species worldwide, having invaded Europe and North America while causing substantial ecological and socio-economic impacts. Here, we investigated the spatiotemporal trends in this species' invasion success using 178 macroinvertebrate abundance time series, containing 1451 records of *D. polymorpha* collected across nine European countries between 1972–2019. Using these raw (absolute) abundance data, we examined trends and drivers of occurrences and relative abundances of *D. polymorpha* within invaded communities. Meta-regression models revealed non-significant trends both at the European level and for the majority of the invaded countries, except for France (significant decreasing trend) and Hungary (marginally positive trend). At the European level, the number of *D. polymorpha* occurrences over time followed a flat-top bell-shaped distribution, with a steep increase between 1973–1989 followed by a plateau phase prior to significantly declining post-1998. Using a series of climatic and hydromorphological site-specific characteristics of invaded and uninvaded sites from two periods (1998–2002; 2011–2015), we found that native richness, non-native abundance, distance to the next barrier, and elevation were associated with the occurrence of *D. polymorpha*. We also found that higher native richness and lower latitude were related to lower relative abundances. Using Cohen's D as a measure of *D. polymorpha* impact, we found that biodiversity within the invaded sites was initially higher than in uninvaded ones, but then declined, suggesting differences in biodiversity trends across invaded and uninvaded sites. While our results emphasise the high invasion success of *D. polymorpha*, increasing stressors within the context of global change – particularly ongoing climate change – are likely to enhance invasion rates and the impact of *D. polymorpha* in the near future, exacerbated by the lack of timely and effective management actions.

**Keywords:** biodiversity impact, climatic and hydromorphological characteristics, global and climate change, macroinvertebrate abundance, spatiotemporal trends

## Introduction

Freshwater ecosystems are critical to biodiversity conservation as well as to human and social well-being (Grizzetti et al. 2016, Vollmer et al. 2018). However, freshwater environments have been globally impacted by multiple anthropogenic stressors (e.g. hydrological and morphological modification; diffuse and point source pollution; Heino et al. 2009, Darwall et al. 2018, Reid et al. 2019, Balzani et al. 2021) for centuries, resulting in diverse changes to their ecological communities (Jackson et al. 2016, Carrier-Belleau et al. 2022). Furthermore, ongoing global changes have substantially altered these environments (Heino et al. 2009), leading to abrupt changes in the structure of biological communities, including the invasion of non-native species (Jarić et al. 2019, Tickner et al. 2020).

Invasive non-native species are arguably among the main stressors in freshwater ecosystems (Sala et al. 2000), and one of the major drivers of species extinctions (Blackburn et al. 2014, Ricciardi et al. 2021), costing tens of billions of dollars per year in damages and management expenditure (Cuthbert et al. 2021). Introductions of aquatic non-native species can be accidental (e.g. as ballast water stowaways in commercial vessels; Bailey 2015) or intentional (e.g. for aquaculture purposes; Haubrock et al. 2021a), and have been occurring for centuries (Ricciardi et al. 2017, Simberloff et al. 2020, Seebens et al. 2021). Introduction rates of non-native species are expected to increase in the near future (Seebens et al. 2018, 2020a, b) and are directly linked to globalisation (e.g. with increasing

numbers of pathways and invasion vectors; Capinha et al. 2015, Bellard et al. 2016) and climate change (e.g. decreasing the environmental resilience to invasions and creating more suitable conditions for invaders; Hellmann et al. 2008, Mainka and Howard 2010, Jackson et al. 2015, Hui et al. 2016). Aquatic non-native species, in particular, tend to thrive and succeed in altered or artificial environments (Strayer 2010, Sara et al. 2018), which can favour biological traits prevalent among non-native species (Byers 2002, Tyrrell and Byers 2007, Hufbauer et al. 2012).

The success of non-native species can be partitioned into primary (i.e. arrival) and secondary components (i.e. establishing and maintaining high population densities, promoting spread; Lockwood et al. 2009), and is dependent on multiple factors (Crooks and Rilov 2009). These include the receiving communities' invasibility, which is predetermined by factors such as species composition in the receiving ecosystem, habitat quality, resource availability and climatic conditions (Rejmánek et al. 2013, Barney et al. 2016), as well as the invaders' traits (defining their invasiveness; Mathakutha et al. 2019, van Kuijk et al. 2021), and propagule pressure (Barney et al. 2016).

Most assessments of the progression of biological invasions (Bij de Vaate et al. 2002) or invasion dynamics (Pyšek and Prach 1995, Thresher et al. 2003) have been done at small spatial and temporal scales. Therefore, there is little information on the invasion dynamics and their associated drivers at large spatio-temporal scales (i.e. across regions or internationally, or over several decades). This knowledge gap

<footnote n="24"><sup>24</sup>Department of Tisza River Research, Centre for Ecological Research, Institute of Aquatic Ecology, Debrecen, Hungary</footnote>

<footnote n="25"><sup>25</sup>Wageningen Environmental Research, Wageningen University and Research, Wageningen, the Netherlands</footnote>

<footnote n="26"><sup>26</sup>Department of Ecoscience, Aarhus University, Silkeborg, Denmark</footnote>

<footnote n="27"><sup>27</sup>Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK</footnote>
(Ricciardi et al. 2021), compounded with a lack of timely management interventions (Caffrey et al. 2014), has limited our ability to prevent invasions and secondary spread (Hulme 2009, Vilà et al. 2009, Haubrock et al. 2021b). Moreover, the often delayed responses have resulted in data/information being conserved to the period after the invader's establishment and subsequent impact, obscuring potential inferences of conditions and variables that facilitated the invasion initially. Nevertheless, some studies have attempted to analyse early invasion dynamics based on the back-projection of occurrences (Soto et al. 2022).

Recently, Soto et al. (2023a) investigated invasion dynamics of Ponto-Caspian species in European freshwater streams. One of the most notorious Ponto-Caspian invaders is the zebra mussel *Dreissena polymorpha*, which is listed among the ‘100 worst’ invaders in Europe (Nentwig et al. 2018) and recognized for its impact on native biodiversity (Burlakova et al. 2000, 2006) while being listed as the most severe biofouling aquatic non-native species (Ward and Ricciardi 2007, Sousa et al. 2011). The species is also widespread in North America and parts of Asia, while other regions (e.g. South America, Australia or the Pacific Islands) remain uninvaded (Seebens et al. 2020a, b, Briski unpubl.). *Dreissena polymorpha* represents the first mass invasion of a Ponto-Caspian species (Strayer and Smith 1993, Ricciardi and MacIsaac 2000), that started to spread across aquatic systems in mainland Europe prior to the 19th century (Aldridge et al. 2004), and is now present in most of Europe (Fig. 1a, Dölle and Kurzmann 2020, Karatayev et al. 2011, Karatayev and Burlakova 2022).

*Dreissena polymorpha* invasion success in different water bodies has been underpinned by its functional characteristics (i.e. biological traits and environmental tolerance; Strayer 2009, Rolla et al. 2020a, Carvalho et al. 2021, Karatayev and Burlakova 2022). Particularly, its ‘ecosystem engineering’ ability (Sousa et al. 2009, Emery-Butcher et al. 2020) can cause environmental changes (Effler et al. 1996, Ricciardi 2003, Higgins and Vander Zanden 2010), altering nutrient concentrations and increasing water transparency (Holland 1993, Fahlenstiel et al. 1995), but also changing the benthic habitat (Karatayev and Burlakova 2022). Its presence can thus influence entire community composition (Bastviken et al. 1998, Ricciardi et al. 1998, Crane et al. 2020, 2022), outcompete indigenous mussels (Ricciardi et al. 1995, Schloesser 1996, Pilotto et al. 2016), while also facilitating the establishment of other non-native species by creating habitats that can be used as refuge, e.g. in the case of *Dikerogammarus villosus* (Soto et al. 2022). This species’ effects on the community can also impact food webs. Some fish species can be negatively affected due to the reduction of their prey, while some waterfowl species can dramatically increase their presence and change their migration patterns due to a high dietary reliance on *D. polymorpha* (Karatayev and Burlakova 2022). A high tolerance to pollution, ability to be entrained in human vectors (e.g. boating equipment), and a substrate preference for rocks as well as artificial surfaces (cement, steel, rope, water pipes) and structures (such as weirs and dams; ZMIS 2001), also promote their success in a variety of habitats (Karatayev and Burlakova 2022).

While the European distribution and local impacts of *D. polymorpha* are relatively well-known (Padilla 1997, Hallstan et al. 2010, Burlakova et al. 2022), large spatio-temporal assessments (including differences at the national level) of invasion dynamics, trends and trajectories, and factors that could explain its establishment success are lacking (Strayer et al. 2011, Haubrock et al. 2022a). To this end, using a comprehensive database of 1816 European freshwater macroinvertebrate time series and the widely distributed invasive zebra mussel *D. polymorpha* as a model species, we aimed to: 1) investigate differences in spatio-temporal trends in this invader’s European distribution, invasion dynamic and ecological impact, by comparing changes in the biodiversity of invaded communities; 2) identify site-specific variables that predict its invasion success; and 3) develop ecological niche models based on *D. polymorpha* occurrences in Europe, as well as hydroclimatic preferences, to determine the present suitable areas for *D. polymorpha* and to forecast the future potential invasion range under different climate change scenarios.

<figure><img src="image_5.png" /><figcaption>Figure 1. (a) Distribution, establishment, and introduction year of *Dreissena polymorpha* in Europe, indicating its invasion corridors (general movement directions; Lockwood et al. 2013) and invasion pathways (canalised systems; Bij de Vaate 2002 and Karateyev and Burlakova 2022), as well as an approximation of its native region (Son 2007, Brown and Stepien 2010, Stepien et al. 2013) and (b) the distribution of time series (i.e. sites) invaded by *D. polymorpha* indicated by black dots and the availability of occurrences recorded in GBIF as the gradient (GBIF.org, accessed on 10 September 2022).</figcaption></figure>
## Meta-regression modelling

To identify the overall trend in the raw abundance of *D. polymorpha* within invaded European communities, we employed a meta-analytical approach (meta-regression). The term meta-regression refers to a meta-analysis that uses separate regressions from multiple studies (e.g. time series). In particular, we employed a multivariate meta-regression model using the *rma.mv* function of the 'metafor' R package (www.r-project.org, Viechtbauer 2010), using the Mann-Kendall test trend (S-statistics) and its respective variance as an effect size for the models (Hamed and Rao 1998, Pilotto et al. 2020). We specified the coordinates (i.e. latitude and longitude) as a random effect to account for spatial variation and used a Gaussian structure (Cressie 1993, Maire et al. 2019). These models allow us to control the weight of each time series using the inverse-variance method, which is roughly proportional to sample size (i.e. sampled years). First, we extracted the S-statistic (i.e. slope) for each time series using the abundance of *D. polymorpha* over sampled years. Subsequently, we grouped the S-Statistics and their variance in the model, obtaining an overall trend and confidence interval (CI). Lastly, we calculated the heterogeneity of the meta-regression model (*I*²), which is a descriptive statistic that reflects the percentage of variation across studies that is due to heterogeneity rather than randomness (Borenstein et al. 2017, 2021). The time series with less than three sampled years with the presence of *D. polymorpha* were removed, given that it has not been possible to obtain S-statistics for those (i.e. due to a sporadic record or insufficient yearly coverage). In addition, four time series were removed due to a lack of variance (variance=0). We repeated the meta-regression modelling for each country with records of *D. polymorpha* (Fig. 1b) to identify spatial differences within country-specific trends. From the aforementioned nine countries included, time series for Latvia and Switzerland were unavailable for the modelling as they did not meet the minimum requirements for meta-regressions (i.e. multiple time series), but were included in the overall (European) model.

## Extraction of response and explanatory variables

We extracted two response variables to capture primary and secondary invasion success from our initial dataset of raw abundances: occurrence and proportional abundance, respectively. We used the occurrences of *D. polymorpha* over time within all time series, defined as the presence of this species in a given sample and time. Due to the differences in the sampling methods across time series, the raw abundance measured was not always comparable (Soto et al. 2023b). To account for this, and to compare trends over time in a consistent and systematic manner, we calculated the proportions of *D. polymorpha* in each year within every individual community's overall abundance (i.e. % of sampled individuals: relative abundance).

We collated climatic and spatial data to investigate their relationship with *D. polymorpha* trends (Supporting information). Following the work flow for SMDs of Naimi and Araújo

(2016), we extracted mean daily temperature and precipitation 12 months prior to the sampling event from the gridded European-scale observation-based dataset (E-OBS; spatial resolution: 0.1°; Cornes et al. 2018) to infer the effects of climatic variables. To investigate the potential effect of instream barriers and hydromorphological characteristics (Havel et al. 2005), we extracted the distance to the next weir and the elevation of the stream from the Global Reservoir and Dam Database (GRanD) ver. 1.3 and the MERIT Hydro digital elevation model (Yamazaki et al. 2019) at 90 m spatial resolution. We also obtained stream slope estimates by applying the *r.stream.slope* function in GRASS Geographic Information System (Jasiewicz and Metz 2011, GRASS Development Team 2017) to the Hydrography90m resource (Amatulli et al. 2022).

## Spatio-temporal trends

To investigate temporal trends in the occurrence of *D. polymorpha* in Europe and in each country, as well as relative abundance trends (as a measurement of secondary success) over time, we used two generalised additive models (GAMs) using the *gam* function of the 'mgcv' R package (www.r-project.org, Wood and Wood 2015). For these models, we used all 178 time series covering 178 sites with records of *D. polymorpha* (including those time series excluded in the meta-regression model). Both the occurrence and relative abundance models contained only the factor 'year' to infer trends over time and the number of sites within the respective year to control for sampling efforts affecting the respective trends (Soto et al. 2022).

The residual distribution of all models was inspected visually through histograms to determine the best family for each model, from which we selected a negative binomial distribution with log-link for both models. From these GAMs, we then fit an autoregressive AR(1) process (Nathan et al. 1999) for the residuals to produce diagnostic plots to infer changes in the relative abundance of *D. polymorpha*. This allowed the computation of derivatives of fitted splines using the method of measurable differences to estimate the rate of change (slope) in the fitted smoother. With these, we identified and superimposed periods of significant changes within the respective sites upon their trends over time (Simpson 2018), which were then visually inspected.

## Determining drivers of success

To identify variables that can predict the success of *D. polymorpha* invasions, we performed a series of spatial analyses using the European records of *D. polymorpha*. First, we identified two five-year periods containing the highest numbers of invaded sites (1998–2002: n=89; 2011–2015: n=54; Supporting information). Using all available time series from both periods, we 1) distinguished time series (and hence sites) with [1] and without [0] *D. polymorpha* and 2) averaged the relative abundance of *D. polymorpha* and all predictors over each five-year period. In total, this resulted in 89 invaded and 878 uninvaded sites in 1998–2002 and 54 invaded and 1293 uninvaded sites in 2011–2015.
# Ecological niche modelling (ENM)

## Distribution data

For species presence data, we combined observational records from 1816 macroinvertebrate time series (Haase et al. 2023) and human observation data (15 091 points) from the Global Biodiversity Information Facility (GBIF.org, accessed on 10 September 2022) and the Spanish Catalogue of Invasive Alien Species (www.miteco.gob.es/eu/biodiversidad/temas/conser-vacion-de-especies/dreissena_polymorpha_tcm35-441987.zip) (n=144 points). In the modelling of species distribution, data from all water systems were used and estimated. The records from GBIF were rarefied spatially at 1 km intervals using SDMtoolbox to eliminate duplicate points and prevent sampling bias (Warren et al. 2014) in ArcGIS ver. 10.8.1. Following this spatial rarefying, 2325 occurrence records remained in the records data (Supporting information).

## Environmental variables and model selection

Climate data for the period 1970–2000 encompassed 19 bioclimatic variables downloaded from the WorldClim ver. 2.1 at a spatial resolution of 30 arc seconds (~ 1 km at the equator; Fick and Hijmans 2017). Hydrology, physiography, landcover and soil data were rasterized from HYDROATLAS (Linke et al. 2019, Lehner et al. 2022) using the ArcGIS ver. 10.8.1 conversion toll feature (Supporting information). These data were then clipped to the study area (ArcGIS ver. 10.8.1), and tested for correlations to reduce multicollinearity using the variance-inflation factor (VIF; Naimi et al. 2014) exclud-ing those variables with VIF > 10. Of the environmental and climate variables, 50 were correlated. Highly correlated vari-ables were excluded from the model, and ultimately 32 vari-ables were used in the final model (Supporting information).

Future projections of climate change for the 20-year inter-vals 2021–2040, 2041–2060, 2060–2081 and 2081–2100 were conducted using the Shared Socioeconomic Pathways: low greenhouse gas emissions (SSPs126), intermediate green-house gas emissions (SSPs245), high greenhouse gas emissions (SSPs370) and very high greenhouse gas emissions (SSPs585; IPCC 2021) that were obtained from the CanESM5 (Swart et al. 2019) and Miroc6 (Hajima et al. 2020) global climate models. The data existing at www.worldclim.org were obtained from Coupled Model Intercomparison Project Phase 6 (CMIP 6) (Eyring et al. 2016). We calculated the percent cover of urban areas (%-urban) to infer the effects of settlements and water bodies (%-waterbodies) to infer the effects of secondary introductions within each sub-catch-ment in the year of sampling, using the *r.univar* function (Neteler et al. 2012).

The distribution potential of zebra mussel was predicted using MAXENT ver. 3.4.4 (Phillips et al. 2017) with auto features 10 replicate, random test, a maximum iteration of 1000 replicates, replicate run type cross-validation method, and threshold rule maximum training sensitivity plus speci-ficity. Evaluation of the models used the area under the ROC curve (AUC). Area under the ROC curve values range between 0 and 1, where 1 indicates 100 % accuracy, mod-els that show values above 0.7 are considered good models (Alexandre da Silva et al. 2019) and values ≤ 0.5 indicate

To identify predictors that facilitate the establishment of *D. polymorpha* (i.e. occurrence as a proxy of primary success) and respectively form abundant populations (i.e. relative abundance as a proxy of secondary success), we examined changes in the occurrence and relative abundances of the species following the methodological frame-work proposed by Feld et al. (2016). To identify the most important variables for predicting changes in the occurrence and relative abundances of *D. polymorpha* and to remove redundant predictors, we used a random forest (i.e. variable selection) using the function *rfsrc* in the R package ‘randomForestSRC’ (Ishwaran and Kogalur 2022). We selected variables according to their variable importance until the latter decreased considerably. Variable importance in a random forest (RF) is determined by measuring the decrease in prediction accuracy when a particular variable is randomly permuted while keeping other variables unchanged (i.e. the higher the decrease in accuracy, the more important the variable is considered), and we used a total of 2000 decision trees. Additionally, no collinearity was detected among the most important predictors identified by the RF (VIF < 5; Naimi et al. 2014).

Then, we used a series of GAMs, modelling the occur-rence (primary success; two levels: 0 vs 1) as well as the averaged relative abundance (secondary success) of the two periods (1998–2002; 2011–2015) as a function of the highly important predictors identified by the RF (all aver-aged across the five-year periods to create heterogeneous periods). Significant non-linear predictors were classified as positive, negative, or bell-shaped (initially positively affected before declining). All analyses were performed in R ver. 4.1.3 (www.r-project.org).

## Determining impact of *D. polymorpha*

To determine differences in the effects of *D. polymorpha* on recipient biodiversity through space and time, we com-pared invaded and uninvaded sites by *D. polymorpha* across Europe and respectively invaded countries using Cohen's *D* (Cohen 1988), which quantifies differences between the standardised means of biodiversity, measured in units of SD (small difference: d=0.2; medium: d=0.5; large: d=0.8; based on benchmarks suggested by Cohen 1988). A large Cohen's *D*, for instance, indicates that the mean difference is large compared to the variability. To this end, we considered only invaded time series with a minimum of three occurrences of *D. polymorpha* and averaged each species' abundance in each respective year across available time series. These were then compared with the average abundances per species in the respective years of unin-vaded time series. Each analysis was conducted for the time series within a European country assessed. Cohen's *D* is equated by the formula: Cohen's *D* = (xᵢ - xᵢₙ) / √(sᵢ + sᵢₙ) / 2 where xᵢ and xᵢₙ are the mean abundances of each species in invaded and uninvaded years, respectively, and sᵢ and sᵢₙ are the respective SDs. Cohen's *D* was calculated using the *cohensD* function of the ‘lsr’ R package (Navarro 2021) for each year independently.
predictive discrimination that is no better than a random guess (Ruiz-Navarro et al. 2016).

Two models, climate and hydroclimate, were developed in the study. In the climatic model, the current and future habitat suitability of the species was determined using only climatic variables. In the hydroclimatic model, the habitat suitability of the species was determined by using climate, hydrology, physiography, landcover and soil data.

The distribution change and centroid shift were calculated using ver. 2.5 of the SDMtoolbox (Brown et al. 2017). The

'Centroid Changes (Line)' tool was used to calculate the geo-metric center of the predicted distribution under different scenarios, thus determining the distribution direction of *D. polymorpha* on the European continent.

## Results

Our data contained a total of 178 time-series with at least one record of *D. polymorpha* spanning seven countries over the period 1968–2019, for a total of 1451 records (i.e. annual

<figure><img src="image_6.png" /><figcaption>Figure 3. Slopes of each time series according to their geographic origin. The number of respective time series (K) included in each trend is indicated above each line, accompanied by the heterogeneity of the data (I²). Precision refers to the inverse of standard error (1/SE). Each dot represents one time series and its size reflects the precision (i.e. weight) of each time series with regard to the overall trend. The horizontal bars of each trend reflect the 95% confidence intervals (thick bar) and prediction intervals (thin bar).</figcaption></figure>
occurrence in a time series) of the species. The first records of *D. polymorpha* in our dataset occurred in 1968 in 12 sites (i.e. time series) in Germany. Among all records, raw abundances of *D. polymorpha* ranged from 1 to 11 400 individual m⁻² (Fig. 3). One hundred thirty-two time series were of sufficient length (n ≥ 3 sampled years with *D. polymorpha*) for the performance of meta-regression models. We did not identify an overall 'European' trend in the slope of the raw abundance for *D. polymorpha* (0.48; CI: -0.75 to 1.71). We detected that 60 time series showed positive trends (significant: n = 5), 68 negative (significant: n = 8), and 4 remained constant (i.e. with a slope = 0). Across invaded countries individually, only France indicated a significant negative trend in the *D. polymorpha* raw abundance slope (-4.57; -8.75 to -0.39). The remaining countries (i.e. Germany, England, Hungary and the Netherlands) showed no significant trend over time. Each meta-regression model's I² varied between < 0.01 and 50 % (Fig. 3).

## Spatio-temporal trends

Length of *D. polymorpha* time-series differed across invaded countries. The AR-model identified a steep, significant increase in *D. polymorpha* occurrences (i.e. invaded sites) across Europe between 1973–1989 (Fig. 4a), before significantly declining post-1998, which was predominantly driven by changing occurrences in Germany (Supporting information). The relative abundances averaged 12.55 ± 18.18% (mean ± SD) of all sampled individuals across all time series, from an average of 0.75% in France to 15.76% in Germany. The AR-model further identified a significant decline in *D.*

*polymorpha* relative abundance to have occurred between 1995 and 2008, concomitant to a decline in Latvia (based on n = 1 time series) between 1991–2009, and remained constant for the other countries (Fig. 4b, Supporting information).

## Determining drivers of success

The out-of-bag prediction error was 0.36 and 0.26 for the occurrence and relative abundance of *D. polymorpha*, respectively. The variables that best explained changes in the occurrence of *D. polymorpha* based on the random forest, and were thus used in the GAM, were: latitude, abundance of non-native species, elevation, distance to the next barrier and native richness species (Fig. 5a, Supporting information). For the relative abundance of *D. polymorpha*, the most important predictors were: native richness and latitude (Fig. 5b).

Using two periods (1998–2002; 2011–2015; Fig. 5) to infer spatial trends and drivers for the success of *D. polymorpha* invasions, we found considerable dissimilarity between predictors of occurrence and predictors of relative abundance, which also differed depending on the period considered. In 1998–2002, the occurrence of *D. polymorpha* (adjusted R²=0.796) was negatively associated with the abundance of non-native species present at the invaded site, richness of native species, as well as the distance to the next weir (p < 0.05), while we observed a bell-shape for the effect of latitude (p < 0.01). Elevation showed a non-significant effect on the occurrence of *D. polymorpha* (p=0.14). Concomitantly, a higher native richness negatively affected *D. polymorpha* relative abundance (p < 0.05), whereas an increase in latitude positively affected the relative abundance of *D. polymorpha*

<figure><img src="image_7.png" /><figcaption>Figure 4. Generalised additive models for the occurrence (a) and relative abundance (b) of *Dreissena polymorpha* across Europe as estimated based on AR(1) errors, showing a 99% pointwise confidence interval (shaded area) with blue sections indicating periods of significant positive change and red sections indicating periods of significant decline). Dashed sections indicate periods without significant changes. Black arrows indicate the directionality of significant temporal trends over the entire period.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 5. Drivers affecting the occurrences and relative abundance of *Dreissena polymorpha* based on generalised additive models within the periods 1998–2002 (period 1) and 2011–2015 (period 2).</figcaption></figure>

(p < 0.01). In 2011–2015, the occurrence model (adjusted R² = 0.239) identified a positive relationship with high native richness and a negative relationship with elevation, non-native species abundance, and distance to the outlet (p < 0.05), while we observed a bell-shape response for latitude (p < 0.01). The relative abundance model (adjusted R² = 0.040) only showed a positive relationship with latitude (p < 0.0, Supporting information).

Determining the impact of *D. polymorpha*

Comparisons of Cohen's D, estimated between the invaded and uninvaded sites of *D. polymorpha*, revealed a significant decrease across Europe, France, Germany, Latvia and England (Supporting information). However, values remained positive, indicating that the diversity of macroinvertebrates within the invaded sites was generally higher than in uninvaded sites (Fig. 6), even after excluding all other non-native species (Supporting information). Trends in macroinvertebrate diversity were similar among invaded and uninvaded sites in all countries except for Latvia (where diversity increased significantly

only in invaded sites), France and Germany (where diversity increased significantly only in uninvaded sites).

Focusing on specific taxonomic groups (molluscs, crustaceans, and insects), diversity trends differed across countries and between invaded and uninvaded sites, but showed a comparable pattern across taxa. In Latvia, invaded sites showed a decrease in the diversity of insects and molluscs (although the latter not significantly), and the same result for insects was found in Hungary. On the other hand, a decrease in crustacean diversity in uninvaded sites was observed in the Netherlands, while Denmark showed a general decrease in both invaded and uninvaded sites in mollusc and insect diversity, similarly to the crustacean diversity in Germany (Supporting information).

Ecological niche modelling (ENM)

Habitat suitability model

Variables that contributed the most to the model (> 1) are presented in the Supporting information. Climate Zone
<figure><img src="image_9.png" /><figcaption>Figure 6. Trend in Cohen's D, comparing the effect size between time series (i.e. sites) invaded by *D. polymorpha* and uninvaded sites within Europe. Periods of no (dashed-black), positive (blue), and negative (red) significant changes, estimated based on AR(1) errors, are displayed on the trend. Inset plot: Mann–Kendall trends in invaded and uninvaded macroinvertebrates communities.</figcaption></figure>

(clz_cl_smj) was the most contributing variable, with cool temperatures and moisture being the most influential, followed by the mean temperature of the coldest quarter. Wetland classes in which the species was mostly found were (in that order) lakes, reservoirs, rivers, and coastal wetlands (estuary, delta and lagoon). Unconsolidated sediments (SU) and carbonate sedimentary rocks (SC) were the dominating lithological classes, whereas metamorphic rocks, xeric freshwaters, and endorheic pressures contributed negatively to the habitat suitability. The freshwater ecoregions significantly contributing to the model were temperate coastal rivers, temperate upland rivers, and temperate floodplain rivers and wetlands (Fig. 7, Supporting information). The AUC of the ENM model was 0.868, indicating an adequate model performance and allowing the prediction of the distribution of *D. polymorpha*.

## Future distributions

Among the environmental variables, mean diurnal range, mean temperature of the warmest and coldest quarter, and precipitation seasonality (i.e. the coefficient of variation) were the most influential variables. The environmental variable with the highest gain, when used in isolation, was mean temperature of the driest quarter (Bio9), which therefore appears to have the most useful contribution. Modelling results suggested that *D. polymorpha* would enlarge its currently suitable habitat (Supporting information) in the future (Fig. 8a–b). The distribution of the species was predicted to expand its range from central Europe to the east and north in the future (2021–2040, 2041–2060, 2061–2080 and 2081–2100; Supporting information).

## Discussion

Using a collection of 178 time-series containing *D. polymorpha* in Europe since 1968, we found invasion dynamics to differ spatially and temporally, with abundance snapshots ranging from 1 to 11 400 individuals m⁻². Occurrence data at the European level followed a bell-shaped distribution, with occurrences peaking in the early 1990s, and declining thereafter. Relative abundance trends generally lacked statistical clarity at the European scale, driven by country-level differences. Furthermore, we found that *D. polymorpha* occurrence and relative abundance were driven by environmental determinants, such as latitude, distance to physical barriers or outlets, and native richness. Ecological niche modelling also highlighted the role of temperature and precipitation in affecting the present distribution of *D. polymorpha* in Europe, and future forecasting pointed to potential further expansion

<figure><img src="image_10.png" /><figcaption>Figure 7. Ecological niche modelling showing the habitat suitability of the hydroclimatic model based on suitability of the area estimated using a combination of human observations listed in GBIF (10 September 2022), the Spanish catalogue of invasive alien species, and our data (a; Supporting information).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 8. Ecological niche modelling showing the future distribution and probability of the occurrences of *Dreissena polymorpha* based on suitability of the area (Supporting information) estimated based on human observations listed in GBIF, Spanish catalogue of invasive alien species and our data. Current and future potential distribution prepared according to the Maximum Entropy Model (Maxent) method under four global climate models: CanESM5 (a) and MIROC 6 (b), and four possible Shared Socioeconomic Pathways (SSPs) (SSP 126, SSPs245, SSPs 370 and SSPs 585 scenario for the years 2021–2040, 2041–2060, 2061–2080 and 2080–2100 based on habitat suitability (Supporting information).</figcaption></figure>
of this invader's range towards the European northeast due to
changes in climatic conditions.

Spatio-temporal trends

Comparing time series at the national level, we found that although the majority of data originated from Germany (73.48%), significant trends in the slope of *D. polymorpha* raw abundance were only found for France and Hungary. While the decrease of *D. polymorpha* abundance in France suggested that the growth rate of the French population was declining, the opposite was found for the Hungarian populations. These differences among countries are likely unrelated to the introduction year of the species (present in Hungary since ~ 1794; Grossinger 1797) through the Central corridor, which opened in 1784, encompassing the Dnieper and Vistula River (Fig. 1). In other countries, we found raw abundance trends to be mostly constant. Such latency is common for non-native species prior to 'boom-bust' dynamics, or afterwards within a late-stage plateau (Strayer et al. 2017, Haubrock et al. 2022b), but also indicates that populations were steadily but weakly increasing at low rates, albeit not significantly (Fig. 1, Supporting information), with local populations showing varying dynamics in raw and relative abundances.

Although several countries expressed significant trends
in both occurrences and relative abundances, the use of
AR-models highlighted that significant changes in one
country's trend (e.g. declines in occurrences in Germany
and relative abundances in Latvia) were sufficient to affect
the overall European trend, giving the misguided impression
that the overall European population was decreasing. While
we are not aware of any adherent management-related rea-
son that could have led to the indicated decline in its rela-
tive abundance, ecological reasons, such as the competition
with the congener *D. rostriformis bugensis*, might have played
a role (Wawrzyniak-Wydrowska et al. 2019). This could have
affected the decline of occurrences, while simultaneously
indicating that *D. polymorpha* is still present in those sites.
It is impossible to infer whether its occurrences in our data
go back to initial introductions following e.g. the Central or
Northern invasion corridor (Supporting information). It is
therefore possible that several – if not the majority of – occur-
rences in smaller streams and higher elevations, which were
observed years after the onset of monitoring, are relatively
recent introductions.

However, the observed differences across national trends
could also be affected by the timing in terms of at what
point along the invasion process populations are, making
the extraction of overall patterns (i.e. whether we are seeing
an actual range contraction or a plateau of saturation) dif-
ficult. Considering spatial differences in temporal trends in
both occurrences and relative abundances, we identified that
both were relatively continuous over time, possibly indicat-
ing unabated invasions despite potential for preventive and
post-invasion management actions (Coughlan et al. 2020).
Our results are likely to provide a conservative estimate of *D*.

*polymorpha* populations, as there is a possibility that many
invaded sites were not included in our study. This situation
particularly applies to 45 time series, where information was
available only at the genus level, making it impossible to dif-
ferentiate *D. polymorpha* from other dreissenid mussels, such
as *D. bugensis*. As a result, these 45 time series were excluded
from our analyses.

Determining drivers of success

Invasion success and its determinants may vary spatially and temporally, especially as changes in the environment can facilitate an invader's establishment or induce sudden population shifts, often resembling 'boom-bust' dynamics (Haubrock et al. 2022b). Using the occurrence of an invader as a direct proxy of primary success and differences in the relative abundance as secondary success (being also a modifier of impact on the invaded ecosystem; Jackson et al. 2015), we identified predictors that are of high spatio-temporal relevance. A commonly identified predictor that affected the primary and secondary success of *D. polymorpha* was the richness of native species, albeit with differing sign and strength depending on the time period. It has often been suggested that previous invasions into a receiving community can lower native richness thereby reducing its biotic resistance and leading to 'invasion meltdown' processes (Simberloff and Von Holle 1999, Simberloff 2006) where the chances of successful future invasions are increased (Alofs and Jackson 2014, Beaury et al. 2020). Moreover, higher non-native species abundance and richness are indicative of disturbances in the environment, as substantiated by the importance of anthropogenic disturbances for the occurrence of other invasive non-native bivalves (Linares et al. 2020). Although *D. polymorpha* is generally preceding other invasions (Soto et al. 2023a), it is additionally possible that its introduction or secondary spread either directly or indirectly suffers from other non-native species, as suggested by the negative effects of non-native abundances on *D. polymorpha* occurrences observed here. This may be indicative of *D. polymorpha* not being facilitated by other non-native species (possibly due to increased competition), but rather *D. polymorpha* facilitating the establishment of other non-native species, as in the case of *D. villosus* (Harrow-Lyle and Kirkwood 2020, Rolla et al. 2020b, Soto et al. 2023a), which was present in 164 of the 178 time series invaded by *D. polymorpha*. Invaded sites also generally tend to be more degraded with weaker trophic structures (Godoy 2019) and thus, often contain higher numbers of introduced species (Leung and Mandrak 2007). There was also a negative effect of native richness on the relative abundance of *D. polymorpha*, which however increased with an increase in latitude, likely reflecting a relationship with temperature, thereby aligning with previous studies (MaMahon and Ussary 1995). Shorter distances to the next barriers facilitated *D. polymorpha* establishment as well. Artificial barriers, such as weirs, small dams, and other waterwork structures could provide protection from predators (i.e. reduced predation pressure), creating a suitable refuge for *D. polymorpha*, and act as stepping-stones for dispersal
(Havel et al. 2005). This also corresponds to the opening of new shipping canals and successive invasion corridors such as the Rhine–Main–Danube Canal in 1992 (Fig. 1), thereby increasing the propagule pressure and thus, the likely invasion success of *D. polymorpha* in recent years (Lockwood et al. 2009, Lorencová et al. 2015, Soto et al. 2023a). Nevertheless, it is possible that other variables (Hünicken et al. 2022) were also important but unavailable to us. Propagule pressure, in particular from highly infested lakes connected to running water, is considered highly relevant in the case of intentionally introduced species, but arguably more difficult to evaluate in the case of accidental introductions (Blackburn et al. 2013).

Determining impacts of *D. polymorpha*

Considering the magnitude of impacts this invader can have on receiving ecosystems (Sousa et al. 2014), it is likely that *D. polymorpha* had already contributed to past declines in biodiversity baselines (Emery-Butcher et al. 2020), making temporal inferences based on the invaded communities’ bio-diversity difficult considering the relatively late start dates of time series. Yet, while biodiversity within the invaded time series remained higher than in the uninvaded time series, the visible decline in Cohen’s D at certain time points was driven by an increase in the biodiversity of uninvaded time series. Although biodiversity in sites uninvaded by *D. polymorpha* could have been affected by different stressors than in invaded sites, the presence of *D. polymorpha* has been shown to boost the population density of epifaunal and infan- nal macroinvertebrates such as Gastropoda, Hirudinea and Turbellaria (Ward and Ricciardi 2007). Yet, while promot- ing some taxonomic groups, *D. polymorpha* negatively affects other taxonomic groups and can cause substantial ecological disruption and socio-economic costs (Ricciardi et al. 1997, Haubrock et al. 2022a). While we found that biodiversity trends differed across countries, crustaceans, insects, and mol- luscs showed similar patterns, characterised by positive trends across most countries and invasion status (Supporting infor- mation). Inferring identified trends to dreissenid impacts can also be difficult, considering the magnitude of additional stressors and ecosystem complexities that can affect macroin- vertebrate communities (Jurca et al. 2021), especially as tem- poral trends of Cohen’s D showed similar signals to relative abundances (%) of *D. polymorpha*.

Ecological niche modelling

The ENM predicted an increase in the occupied area of
*D. polymorpha* outgoing from central Europe to the north
towards 2030 (2011–2040) and then to the northeast towards
the year 2100 (2071–2100). This was facilitated by the mean
diurnal range, mean temperature respectively of the warmest
and coldest quarters, and precipitation seasonality (i.e. the
coefficient of variation), highlighting the importance of the
mean temperature and precipitation variables and the inter-
twined effect of climate change on the progression of large
scale invasions (Hellmann et al. 2008). While illustrating

a likely harmful range expansion with possible detrimen-
tal effects on native communities and regional economies
(Haubrock et al. 2022a), it has to be noted that the com-
puted range invaded in the future is likely a very conservative
estimate. This is because no observational data points from
Mediterranean areas such as Spain and Italy were present, and
only a few were available from eastern Europe in the consid-
ered datasets. Especially in Spain, the Pyrenees served as a
barrier to prevent invasion for some time, but eventually, one
invasion event from France succeeded (Rajagopal et al. 2009,
Peñarrubia et al. 2016). Since the first record of *D. polymorpha*
in 2001 in the lower Ebro (Seebens et al. 2017, 2018), its fast
expansion due to unintentional introductions through e.g.
hitchhiking at fishing gear and high tolerance towards pollu-
tion has led to its successful establishment in most parts the
country (Navarro et al. 2013, Banha et al. 2016) – using the
numerous lakes and dams as ‘stepping stones’. Other uncer-
tainties around our results may arise from the fact that we
did not account for biotic interactions (i.e. biotic resistance
via predation/competition) or commonly reported replace-
ment dynamics among invasive bivalves, such as between *D.
polymorpha* and the more recent invader *D. r. bugensis* under
certain environmental conditions (Karatayev and Burlakova
2022). In these cases, unpredicted declines in *D. polymor-
pha* may occur, reflecting secondary invasions from similar
taxa. In addition, it has often been argued that GBIF data
is inaccurate or incomplete (Nekola et al. 2019, Shirey et al.
2019). Here, by considering only observational data, we tried
to add a certain degree of scrutiny, but nevertheless, acknowl-
edge the potential shortcoming of GBIF (Nekola et al. 2019,
Shirey et al. 2019).

Conclusions

Biological invasions are among the most pervasive stressors for freshwater and especially riverine ecosystems, interacting with hydromorphological alterations (Sinnatamby et al. 2020), climate change (Woodward et al. 2010, Pham et al. 2019), changes in the land use (Wijesiri et al. 2018, Trolle et al. 2019, Hasan et al. 2020), and pollution (Häder et al. 2020). Using *D. polymorpha* time series, which has a very long and relatively well-documented invasion history in Europe, we found indications that both primary invasion success (occurrences) and relative abundances (i.e. secondary success) differed spatiotemporally and were predicted by different external drivers, underlining the complexity of invasion dynamics (Haubrock et al. 2022b). To date, despite attempts and experiments for population control of *D. polymorpha* (Aldridge et al. 2006, Luoma et al. 2018, Coughlan et al. 2020), effective management tools are either not available or not applied to degrees that allow for extensive control (Wimbush et al. 2009). Yet, an improved understanding of invasion dynamics and environmental drivers of success remains important to inform prevention and management options to avoid large-scale infestations and high costs derived from this species (Adams and Lee 2012).
*Funding* – PJH received funding from the EU Horizon 2020 project eLTER PLUS (Grand agreement no. 871128). RNC is funded by the Leverhulme Trust (ECF-2021-001). This study was supported by the Grant Agency of the University of South Bohemia, project no. 065/2022/Z. MC was supported by a Ramón y Cajal contract funded by the Spanish Ministry of Science and Innovation (RYC2020-029829-I).

## Author contributions

**Phillip J. Haubrock:** Conceptualization (equal); Formal analysis (equal); Supervision (equal); Writing – original draft (equal); Writing – review and editing (equal).

**Ismael Soto:** Almena Data curation (equal); Formal analysis (equal); Methodology (equal); Writing – original draft (equal).

**Melina Kourantidou:** Validation (equal); Writing – original draft (equal); Writing – review and editing (equal).

**Ali Serhan Tarkan:** Supervision (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal).

**Paride Balzani:** Supervision (equal); Writing – original draft (equal); Writing – review and editing (equal).

**Antonín Kouba:** Visualization (equal); Writing – review and editing (equal).

**Sadi Aksu:** Data curation (equal); Formal analysis (equal); Writing – original draft (equal).

**Elizabeta Briski:** Conceptualization (equal); Visualization (equal); Writing – review and editing (equal).

**Francisco Sylvester:** Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal).

**Vanessa De Santis:** Supervision (equal); Writing – review and editing (equal).

**Gaït Archambaud-Suard:** Data curation (equal); Writing – review and editing (equal).

**Núria Bonada:** Supervision (equal); Validation (equal); Writing – original draft (equal).

**Miguel Cañedo-Argüelles:** Formal analysis (equal); Visualization (equal); Writing – review and editing (equal).

**Zoltán Csabai:** Data curation (equal); Writing – review and editing (equal).

**Thibault Datry:** Writing – review and editing (equal).

**Mathieu Floury:** Writing – review and editing (equal).

**Jean-François Fruget:** Writing – review and editing (equal).

**John Iwan Jones:** Writing – review and editing (equal).

**Marie-Helene Lizee:** Writing – review and editing (equal).

**Anthony Maire:** Formal analysis (equal); Writing – review and editing (equal).

**John F. Murphy:** Writing – review and editing (equal).

**Davis Ozolins:** Writing – review and editing (equal).

**Jes Jessen Rasmussen:** Supervision (equal); Writing – review and editing (equal).

**Agnija Skuja:** Writing – review and editing (equal).

**Gábor Várbíro:** Writing – review and editing (equal).

**Piet Verdonschot:** Writing – review and editing (equal).

**Ralf C. M. Verdonschot:** Writing – review and editing (equal).

**Peter Wiberg-Larsen:** Supervision (equal); Writing – review and editing (equal).

**Ross N. Cuthbert:** Supervision (equal); Validation (equal); Writing – original draft (equal); Writing – review and editing (equal).

## Data availability statement

The data and R code can be found at Github: https://github.com/IsmaSA/Dreissena-polymorpha (Haubrock et al. 2023).

## Supporting information

The Supporting information associated with this article is available with the online version.

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