Climate-driven invasion risk and ecological niche overlap between non-native round goby and native European fishes

107 Climate-driven invasion risk and ecological niche overlap between non-native round goby and native European fishes Dagmara Błońska1,2 , Sadi Aksu3, Phillip J. Haubrock2,4 , Ali Serhan Tarkan1,5 1 University of Lodz, Faculty of Biology and Environmental Protection, Department of Ecology and Vertebrate Zoology, Banacha 12/16, 90-237 Lodz, Poland 2 Department of Life and Environmental Sciences, Faculty of Science and Technology, Bournemouth University, Poole, BH12 5BB, Dorset, UK 3 VocationalSchoolofHealthServices,EskişehirOsmangaziUniversity,26040,Eskişehir,Turkiye 4 UniversityofSouthBohemiainČeskéBudějovice,FacultyofFisheriesandProtectionofWaters,SouthBohemianResearchCentreofAquacultureand BiodiversityofHydrocenoses,Zátiší728/II,38925Vodňany,CzechRepublic 5 DepartmentofBasicSciences,FacultyofFisheries,MuğlaSıtkıKoçmanUniversity,Muğla,Turkiye Correspondingauthor:DagmaraBłońska(dagmar[email protected]) Copyright: © Dagmara Błońska et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract Biological invasions and climate change are two of the most prominent drivers of freshwater biodiversity loss. In this study, we assessed the climate-driven invasion risk and ecological niche overlap between the non-native round goby Neogobius melanostomus and several native European freshwater fishes (Barbatula barbatula, Cottus gobio, Cottus perifretum, Cobitis taenia, Gobio gobio, Gymnocephalus cernua, and Perca fluviatilis) to forecast shifts in their potential distributions using ecological niche modeling combined with future climate projections. Our models projected a substantial northward and westward expansion of N. melanostomus under future climate scenarios, with suitable habitats increasing especially under high-emission pathways. Native species also exhibited shifts in their distributions, often resulting in increased geographic overlap with N. melanostomus. Habitat overlap was most pronounced between N. melanostomus and C. taenia, with future projections identifying over 10% of the landscape as high-suitability. Overlap hotspots were concentrated in northern Europe, particularly along the coasts of the Netherlands, Germany and the UK, highlighting the potential for escalating biotic interactions such as competition, habitat displacement, and trophic disruption, particularly in vulnerable benthic communities. Alongside negative interactions, our results highlight potential climate refugia with limited projected overlap, pointing to priority areas for conservation. Our findings thus emphasize the urgent need for regionally targeted monitoring and conservation strategies that account for both climate-driven range dynamics and ecological interactions between invasive and native species. Integrating spatial forecasting with ecological risk assessment offers critical insights that can support efforts to mitigate the impacts of biological invasions in a warming world. Key words: Biological invasions, climate change, ecological niche modelling, Europe, freshwater fish, invasive species, Neogobius melanostomus, niche overlap, species distribution Introduction Biological invasions, i.e. the phenomenon describing the introduction, establishment, spread, causes, and consequences of human-mediated non-native species introductions outside their native range (Soto et al. 2024), present a considerable threat for nature and human well-being (Shackleton et al. 2019). This threat is exacerbated for freshwater ecosystems due to the close association with human activities (Dudgeon et al. 2006) as human settlements have historically been estabAcademic editor: Tammy Robinson-Smythe Received: 27 June 2025 Accepted: 19 September 2025 Published: 17 October 2025 Citation: Błońska D, Aksu S, Haubrock PJ, Tarkan AS (2025) Climate-driven invasion risk and ecological niche overlap between non-native round goby and native European fishes. NeoBiota 103: 107–127. https://doi. org/10.3897/neobiota.103.163409 NeoBiota 103: 107–127 (2025) DOI: 10.3897/neobiota.103.163409 Advancing research on alien species and biological invasions A peer-reviewed open-access journal NeoBiota 108 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change lished around freshwater, serving as critical sources of drinking water, food, energy, transportation, and recreation (Postel and Carpenter 1997). This long-standing reliance on freshwater systems has also increased the frequency and diversity of non-native species introductions, primarily via human-mediated pathways such as aquaculture, commercial shipping, recreational angling, the aquarium trade, and the construction of artificial canals connecting previously isolated catchments (Nunes et al. 2015; Bernery et al. 2024). The visual inaccessibility of non-native species beneath the surface makes freshwater ecosystems also more susceptible to impacts of biological invasions occurring without being noticed (Reid et al. 2019). This includes, for instance, changes to trophic webs, habitat structure, community structure and ecosystem functioning (Ricciardi and MacIsaac 2011). The nature of freshwater ecosystems exacerbates these consequences as rivers form linear, dendritic networks that can facilitate the upstream and downstream dispersal of non-native species, thereby increasing the potential for successive establishments and thus, an expansion of the area affected by invasion impacts (Perrin et al. 2020). Moreover, lakes, ponds, and wetlands function as relatively isolated ecological islands, rendering them and their high degree of endemism susceptible to invasion impacts becoming irreversible (Havel et al. 2005; Leuven et al. 2009). Moreover, these insular bodies of water can paradoxically serve as stepping-stones for overland dispersal, especially when facilitated via flood events, canal systems, or deliberate releases (Ficetola et al. 2007; Keller et al. 2008), but also when the non-native invader is capable of overland movement (Rachalewski et al. 2013). The round goby Neogobius melanostomus, native to the Black and Caspian Sea basins, is a non-native species known for its rapid spread across European and North American freshwater ecosystems (Kornis et al. 2012; Cerwenka et al. 2023). Following its rapid expansion, it has emerged as one of the most impactful non-native fish species in Europe, largely attributed to its high ecological plasticity and aggressive territorial behavior (Kornis et al. 2012; Cerwenka et al. 2023). In Europe, the round goby is invasive in major river systems such as the Danube, Rhine, and Elbe, as well as their tributaries, where its spread has been facilitated by canals and shipping routes linking previously isolated drainages (Cerwenka et al. 2023). Over the past two decades, its rapid range expansion has correlated with rising water temperatures, particularly within the Danube basin, where river regulation and other anthropogenic pressures have altered hydro-morphological and thermal regimes, reshaping fish communities (Harka and Bíró 2007; Bănăduc et al. 2020, 2023). The species’ high phenotypic plasticity further enhances its capacity to exploit new habitats, driving both northward shifts and unpredictable westward colonization. Notably, round goby invasions can progress with exceptional speed, with populations becoming dominant in less than two years after introduction (Jůza et al. 2018). Numerous small-bodied benthic species native to Europe’s freshwater ecosystems (such as Barbatula barbatula (Linnaeus, 1758), Cottus gobio Linnaeus, 1758, Cobitis taenia (Linnaeus, 1758), and Gobio gobio (Linnaeus, 1758)) occupy similar habitats and trophic niches as N. melanostomus, leading to significant ecological overlap (e.g., Piria et al. 2016; Janáč et al. 2018). Among the benthic species most threatened by the expansion of the round goby are bullheads (Cottus gobio and C. perifretum Freyhof, Kottelat & Nolte, 2005). Reports of their decline were published two decades ago (Jurajda et al. 2005) and have since been confirmed (e.g. van Kessel et al. 2016; Baer et al. 2017). In addition to the competitive advantage 109 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change of gobies over native bullheads, anthropogenic pressures such as increased siltation, channel modification, and water pollution also have adverse effects on these vulnerable populations (Knaepkens et al. 2002). Direct impact of round goby competitive superiority was observed in the Netherlands, where its rapid expansion led to severe decline in a G. cernua (Linnaeus, 1758) population (van Kessel et al. 2016). The presence of Ponto-Caspian gobies also affects B. barbatula, C. taenia, and G. gobio, and given that the round goby is the most expansive and efficient competitor among them, its impact on native species can be expected to be particularly strong (Grabowska et al. 2023). Invasive gobies have moreover been observed to monopolize shelter sites, preying on the eggs and juveniles of native fish, and altering benthic community structures through both direct interactions and broader ecosystem effects (Kornis et al. 2012; Grabowska et al. 2023). As climate change alters the environmental suitability of the European landscape, non-native species such as N. melanostomus, may expand their ranges into previously unsuitable regions (Harka and Bíró 2007; Kornis and Vander Zanden 2010; Błońska et al. 2024b). The combination of N. melanostomus’ wide distribution, rapid range expansion, and strong ecological impacts makes it a priority invader for assessing climate-driven invasion risks across Europe. However, while several studies have assessed the ecological impacts of N. melanostomus, few have integrated climate projections with spatial overlap analysis to evaluate risk to multiple native species at the continental scale (Reid and Ricciardi 2022; Le Hen et al. 2023), making modeling of potential shifts and quantifying overlap with native species crucial for anticipating future conflict zones and guiding conservation action (Emiroğlu et al. 2023). To this end, we use ecological niche modeling and future climate scenarios to assess the invasion risk posed by N. melanostomus to a suite of ecologically vulnerable native European fish species. Specifically, we (i) project range shifts under current and future climate conditions, (ii) quantify habitat overlap with native species across different climate scenarios, and (iii) discuss the ecological consequences and conservation implications of following interactions with native species in light of climate-driven invasion dynamics. Methods Data preparation Species occurrence data Among invasive Ponto-Caspian gobies, we focused on Neogobius melanostomus because it is the most widespread and ecologically impactful fish species in Europe, with stronger competitive dominance and broader distribution than other gobiids such as Neogobius fluviatilis or Babka gymnotrachelus (Cerwenka et al. 2023; Grabowska et al. 2023). A set of native European fish species considered ecologically vulnerable to invasions by Ponto-Caspian gobies were identified based on the existing literature– including the review by Grabowska et al. (2023) as well as other relevant studies such as Jůza et al. (2018) and Błońska et al. (2025). This resulted in the inclusion of bullheads Cottus gobio and Cottus perifretum, stone loach Barbatula barbatula, gudgeon Gobio gobio, spined loach Cobitis taenia, ruffe Gymnocephalus cernua, and European perch Perca fluviatilis Linnaeus, 1758. 110 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change Species occurrence records for N. melanostomus and native fish species were retrieved from the “Global Biodiversity Information Facility” (GBIF; www.gbif.org). To ensure data quality, raw datasets underwent a multi-step cleaning process. Initial filtering using the CoordinateCleanerR package (Zizka et al. 2019) removed records with common spatial errors such as institutional coordinates (e.g. museums), zero coordinates, and capital city centroids. Additional manual cleaning eliminated records with missing coordinates, geographic outliers, and implausible locations (e.g. terrestrial points or duplicated records). To reduce spatial autocorrelation and sampling bias in species distribution models, spatial thinning was applied to each dataset using a rarefaction threshold of 5 km, implemented with thespThinR package (Aiello‐Lammens et al. 2015). Environmental data Environmental predictors were obtained from the “WorldClim” 2.1 database (Fick and Hijmans 2017) at a spatial resolution of 2.5 arc-minutes (~5 km). This dataset included 19 bioclimatic variables and an elevation raster. Raster layers were cropped to the study area (longitude: -20° to 80°; latitude: 30° to 80°) and aligned to the elevation raster. Cells with missing values were removed to ensure consistency across layers. Future climate data were sourced from Coupled Model Intercomparison Project Phase 6 (CMIP6) models for two time periods (2041–2060 and 2081–2100), considering two potential trajectories of shared socioeconomic pathways: SSP12.6 (low emissions) and SSP5-8.5 (high emissions) (Eyring et al. 2016). Then, five models were selected based on their performance in simulating hydrological and climatic variables relevant to freshwater ecosystems: 1. MPI-ESM1-2-HR: High spatial resolution; strong performance in modeling freshwater temperature and river dynamics (Gutjahr et al. 2019). 2. CNRM-CM6-1: Accurate in simulating precipitation variability and hydrological extremes (Voldoire et al. 2019). 3. IPSL-CM6A-LR: Reliable for simulating temperature and precipitation changes across Europe (Boucher et al. 2020). 4. HadGEM3-GC31-LL: High skill in modeling extreme climatic events and terrestrial hydrology (Williams et al. 2018). 5. EC-Earth3-Veg: Integrates vegetation, soil moisture, and hydrological processes (Döscher et al. 2022). Variable Selection A two-stage filtering process was used to reduce multicollinearity between environmental variables. First, buffer zones were created around each point to represent the environmental conditions around the species’ geographical distribution points. Buffer zones were defined using the buffer function of theterra R package with a radius of 1 degree (~111 km) in WGS84 (EPSG:4326). This radius was chosen to characterize the large-scale environmental conditions of the species and to compensate for spatial uncertainties. Overlapping buffer zones were merged into a single geometric object. Climate and elevation raster data were then extracted from the merged buffer zone. Missing values (NA) were removed from the dataset, resulting in a clean data- 111 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change set for analysis. At the second stage, Pearson Correlation Analysis identified highly correlated pairs of variables (|r| > 0.90). For each correlated pair, the variable considered more ecologically relevant to freshwater fish distributions (e.g., temperature or precipitation variables over derived ratios) was retained, while the less interpretable variable was excluded (Dormann et al. 2013). Variance Inflation Factor (VIF) scores were calculated iteratively using the usdm package (Naimi et al. 2014) and variables with VIF > 10 were excluded. VIF measures the degree of multicollinearity among predictors, with higher values indicating redundancy; removing variables above the threshold reduces inflation of regression coefficients and improves model stability. This process resulted in an optimized set of three environmental variables for each native species distribution modeling, with the most influential predictors varying among species. It should be noted that TSS and ROC metrics were applied only for model performance evaluation and were not used during variable selection. Although species were parameterized with different subsets of variables, this does not bias interspecific comparisons because all models were calibrated under the same filtering framework and variable selection criteria, ensuring comparability in how climate constraints were represented. Temperatureand precipitation-related variables–such as BIO1, BIO3, BIO15, and elevation–weremost frequently selected (Table 1; Suppl. material 1: fig. S2a–h). Although the final set of predictors differed slightly among species, all were selected under the same filtering criteria, ensuring that the models remain directly comparable across species. Species distribution modelling Range shifts Species distribution models (SDMs) were constructed using the BIOMOD2 R package (Thuiller et al. 2009; Thuiller 2023). This platform supports multiple modeling algorithms and ensemble forecasting. A total of 12 algorithms were used: Artificial Neural Networks (ANN; Haykin 1999); Classification Tree Analysis (CTA; Breiman et al. 1984); Flexible Discriminant Analysis (FDA; Hastie and Tibshirani 1996); Generalized Additive Models (GAM; Hastie and Tibshirani 1990); Generalized Boosted Models (GBM; Friedman 2001); Generalized Linear Models (GLM; McCullagh and Nelder 1989); Multivariate Adaptive Regression Splines (MARS; Friedman 1991); Maximum Entropy (MAXENT; Phillips et al. 2006); Maximum Entropy with R (MAXNET; Phillips and Dudík 2008); Random Forest (RF; Breiman 2001); Surface Range Envelope (SRE; Busby 1991); Extreme Gradient Boosting (XGBOOST; Chen and Guestrin 2016). Table 1. Top three environmental variables contributing to each native fish species distribution models for each native fish species. Species Top Variable 1 Top Variable 2 Top Variable 3 Neogobius melanostomus Elevation BIO1 (Annual Mean Temperature) BIO11 (Mean Temperature of Coldest Quarter) Barbatula barbatula BIO3 (Isothermality) BIO15 (Precipitation Seasonality) BIO18 (Precipitation of Warmest Quarter) Cottus gobio BIO15 (Precipitation Seasonality) BIO7 (Temperature Annual Range) BIO3 (Isothermality) Cottus perifretum BIO4 (Temperature Seasonality) BIO15 (Precipitation Seasonality) BIO1 (Annual Mean Temperature) Cobitis taenia BIO1 (Annual Mean Temperature) BIO18 (Precipitation of Warmest Quarter) Elevation Gobio gobio BIO1 (Annual Mean Temperature) BIO15 (Precipitation Seasonality) BIO3 (Isothermality) Gymnocephalus cernua BIO5 (Max Temperature of Warmest Month) Elevation BIO15 (Precipitation Seasonality) Perca fluviatilis BIO18 (Precipitation of Warmest Quarter) BIO9 (Mean Temperature of Driest Quarter) BIO15 (Precipitation Seasonality) 112 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change Model performance was evaluated using five-fold cross-validation. Predictive power was assessed using the True Skill Statistic (TSS) and the Area Under the Receiver Operating Characteristic Curve (ROC). Models with TSS and ROC values > 0.7 were retained for ensemble forecasting. Projections were made under current climate conditions and both future SSP scenarios. Raster processing and environmental data extraction were performed using the dismo R package (Hijmans et al. 2017). The ensemble approach enabled robust prediction of distributional shifts and identification of high-risk areas under climate change. Habitat and niche overlap analysis To assess potential interactions between N. melanostomus and native fish species, pixel-wise multiplication of habitat suitability raster was performed for all species pairs. The resulting overlap rasters were normalized to a 0–100 scale and classified into five categories: 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. For each overlap class, the proportion of pixels was calculated. These were visualized using histograms to represent current and future scenarios. This approach allowed identification of high-overlap zones, potential conflict areas, and anticipated shifts under projected climate change (e.g. Emiroğlu et al. 2023). All overlap analyses were calculated in R using the terraR package (Hijmans 2025). Results Current scenario Under current climate conditions, model projections closely matched the known distribution of Neogobius melanostomus, with high suitability concentrated in the Ponto-Caspian region and major connected river basins across central Europe (Fig. 2). Native species also showed distributions largely consistent with their known ranges (Suppl. material 1: fig. S1). For some species, notably Cottus gobio and Barbatula barbatula, the models indicated relatively constrained suitable habitats under current conditions, suggesting narrower realized niches compared to more widespread species such as Perca fluviatilis or Cobitis taenia. These baseline distributions provide a reference for evaluating future shifts, as summarised in Suppl. material 2, which presents habitat stability, loss, and gain under SSP1-2.6 and SSP5-8.5 scenarios. Range shifts Ecological niche modeling predicted a noticeable northward shift in the potential distribution of N. melanostomus under both low (SSP1-2.6) and high (SSP5-8.5) emission scenarios. These shifts intensified in the late-century projections (2081– 2100), especially under SSP5-8.5, indicating increasing climatic suitability across northern Europe (Fig. 1). Native species such as C. taenia and P. fluviatilis were also projected to expand their ranges westward or northward (Suppl. material 1: fig. S1). Notably, sympatric zones–where N. melanostomus overlapped with native species–became more pronounced in northern coastal regions, particularly the UK, the Netherlands, and northern Germany (Fig. 2). Among the environmental predictors, isothermality (BIO3)–which reflects the ratio of diurnal to annual temperature variability–was one of the most influential 113 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change Figure 1. Distribution of Neogobius melanostomus and native species in the assessment area. Figure 2. a. Present distribution of Neogobius melanostomus and predicted distributions under current conditions (SSP1-2.6) for b. 2041– 2060 and c. 2081–2100, and future conditions (SSP5-8.5) for d. 2041–2060 and e. 2081–2100. Values are shown as a colour gradient, with blue (0) indicating minimum habitat suitability and red (1000) indicating maximum suitability. 114 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change variables shaping species distributions (Suppl. material 1: fig. S2a–h). It was particularly relevant for N. melanostomus, which favored areas with high isothermality, indicating a tolerance for environments with high daily thermal variability but relatively stable seasonal regimes (Suppl. material 1: fig. S2a). In contrast, several native species, including B. barbatula, G. gobio, Gymnocephalus cernua, and P. fluviatilis, were associated with low isothermality, indicating a preference for more thermally stable habitats (Suppl. material 1: fig. S2b–h). Cobitis taenia showed a broad tolerance, occurring across both high and low isothermality regions (Suppl. material 1: fig. S2e), while Cottus perifretum appeared largely insensitive to this variable (Suppl. material 1: fig. S2d). Elevation also played a significant role, generally exhibiting a negative relationship with habitat suitability across all species. This effect was strongest in N. melanostomus, which consistently avoided higher elevations, likely due to its adaptation to warmer, lowland environments (Suppl. material 1: fig. S2a). Other species showed similar, albeit less pronounced, patterns in response to elevation, reinforcing its broad importance in shaping freshwater fish distributions under current climate conditions. With regard to future drivers, these patterns were largely consistent under future climate scenarios, with isothermality (BIO3) and elevation remaining the strongest predictors of habitat suitability for most species. However, for several natives such as P. fluviatilis and G. cernua, the relative importance of precipitation seasonality (BIO15) increased in future projections, suggesting that hydrological variability may play a greater role under warming conditions (Suppl. material 1: fig. S2). These relationships were broadly consistent across current and future scenarios, with isothermality and temperature-related variables remaining the dominant predictors of habitat suitability under climate change. However, the relative influence of precipitation seasonality (BIO15) increased for several native species in future projections (Suppl. material 1: fig. S2), indicating that hydrological variability may become a stronger driver of distributional shifts under warming climates. Niche overlap analysis We first quantified current levels of spatial overlap between N. melanostomus and native fishes before comparing shifts under future climate scenarios. This approach allows us to distinguish increases in overlap caused by niche expansion of the invader, niche contraction of natives, or both. Under current climate conditions, the average spatial overlap between Neogobius melanostomus and native species was relatively low (<3%). The lowest overlap was observed with Cottus perifretum (0.6%), while the highest occurred with Cobitis taenia (4.7%) (Table 2, Fig. 3; Suppl. material 1: fig. S4). Under future climate scenarios–particularly SSP5-8.5 for 2081– 2100–the average overlap increased modestly to around 5%. The highest projected overlap remained between N. melanostomus and C. taenia (7.9%), while C. perifretum continued to exhibit the lowest overlap, with over 96% of shared area falling into low suitability zones (0–20%) (Table 2; Fig. 4; Suppl. material 1: fig. S3). Under current climate conditions, overlap values were generally low (<3%), ranging from 0.6% for C. perifretum to 4.7% for C. taenia (Table 2; Fig. 3). By mid-century (2041–2060), overlap increased modestly under both scenarios. The strongest increases were projected under SSP5-8.5, with C. taenia and P. fluviatilis exceeding 5% overlap, while under SSP1-2.6, increases were more moderate and restricted mainly to C. taenia and G. cernua (Table 2; Fig. 4). By late century (2081–2100), 115 NeoBiota 103: 107–127 (2025), DOI: 10.3897/neobiota.103.163409 Dagmara Błońska et al.: Forecasting round goby invasion under climate change Figure 3. Prediction of overall niche overlap between Neogobius melanostomus and native species in the assessment area under current climate conditions. overlaps became more pronounced, especially under SSP5-8.5, with C. taenia reaching nearly 8% and moderate increases observed for G. cernua and P. fluviatilis. In contrast, B. barbatula, C. gobio, and G. gobio consistently maintained minimal overlap with N. melanostomus across all scenarios (Table 2; Figs 3, 4). 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Methods in Ecology and Evolution 10: 744–751. https://doi.org/10.1111/2041-210X.13152 Supplementary material 1 Supplementary figures and table Authors: Dagmara Błońska, Sadi Aksu, Phillip J. Haubrock, Ali Serhan Tarkan Data type: pdf Explanation note: fig. S1. Projected distribution of native species under current and future conditions for a) Barbatula barbatula; b) Cottus gobio; c) Cottus perifretum; d) Cobitis taenia; e) Gobio gobio; f) Gymnocephalus cernua; g) Perca fluviatilis. fig. S2. Response curves of environmental variables for a) Neogobius melanostomus; b) Barbatula barbatula; c) Cottus gobio; d) Cottus perifretum; e) Cobitis taenia; f) Gobio gobio; g) Gymnocephalus cernua; h) Perca fluviatilis. fig. S3. Contact zone probabilities of Neogobius melamostomus and other native species under current and future climate scenarios. fig. S4. Prediction of overall niche overlap between Neogobius melanostomus and native species (a: Cobitis taenia; b: Barbatula barbatula, c: Cottus perifretum, d: Gobio gobio, e: Cottus gobio, f: Gymnocephalus cernua, g: Perca fluviatilis) in different climate models. table S1. Habitat overlap rates (%) between Neogobius melanostomus and selected native species across different suitability classes under current and future climate scenarios. Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/neobiota.103.163409.suppl1 Supplementary material 2 Future SSP126 and SSP585 Climate Scenarios (2041–2060, 2081–2100): Habitat Status Categories (No Habitat, Stable, Gained, Lost). Authors: Dagmara Błońska, Sadi Aksu, Phillip J. Haubrock, Ali Serhan Tarkan Data type: xlsx Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/neobiota.103.163409.suppl2