Non-native freshwater fishes in India: existing evidence and knowledge gaps

419 Non-native freshwater fishes in India: existing evidence and knowledge gaps Lohith Kumar1,2,3 , Florian Ruland1,2,4 , Jonathan M. Jeschke1,2 1 Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Müggelseedamm 310, 12587 Berlin, Germany 2 Institute of Biology, Freie Universität Berlin, Königin-Luise-Str. 1-3, 14195 Berlin, Germany 3 ICAR-Central Inland Fisheries Research Institute, Barrackpore, India 4 West Iceland Nature Research Centre, Hafnargotu 3, 340 Stykkisholmur, Iceland Corresponding author: Lohith Kumar ([email protected]) Copyright: © Lohith Kumar 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). Review Article Abstract India has experienced the introduction of numerous non-native fish species (NNF), some of which have caused ecological and economic impacts. This systematic review provides a currently lacking overview of NNF research in India, potential biases, available evidence, and knowledge gaps. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identified a total of 332 records, documenting the presence of 58 NNF distributed across 17 basins, and 19 translocated species in India. The Ganga was the most studied basin (113 studies), followed by the West Flowing Rivers Tadri to Kanyakumari basin (37 studies), however, with 30 NNF reported from each of these basins. We demonstrate how these results can be due to saturated sampling in the Ganga and identify which basins might be currently understudied. We also illustrate how extreme floods precipitated an increase in NNF into rivers and lakes from confinement in the West Flowing Rivers Tadri to Kanyakumari basin. The common carp, Cyprinus carpio, was the most frequently reported NNF (160 times), while Mozambique tilapia, Oreochromis mossambicus, was the most widely distributed NNF (13 basins). We found there is a growing number of publications in the field, but that up to 40% of studies have appeared in potentially predatory journals. A minority of studies (44%) investigated NNF impacts, most of which used data from the literature (58%) and reported only qualitative impacts (69%). Most documented impacts were ecological (79%), while some were socio-economic (11%) or both (10%). Only 18% of the studies addressed NNF management. The knowledge synthesized and the gaps identified in this study might serve as a basis for future studies and be useful for efficiently allocating limited resources for investigating NNF in India. Key words: Asia, freshwater ecosystems, invader impacts, invasive alien fishes, management of invasive alien species, spatial distributions Introduction While the research field of invasion biology has largely focused on terrestrial taxonomic groups (Pyšek et al. 2008; Jeschke and Heger 2018), freshwater fishes are one of the most extensively introduced and impactful groups (Leprieur et al. 2008; Cucherousset and Olden 2011; Marcolin et al. 2025). A total of 1451 non-native fish species (NNF) are known to have established globally in novel environments (Seebens et al. 2023), and many of these have become invasive, i.e., have caused Academic editor: Filipe Ribeiro Received: 10 January 2025 Accepted: 25 April 2025 Published: 7 October 2025 Citation: Kumar L, Ruland F, Jeschke JM (2025) Non-native freshwater fishes in India: existing evidence and knowledge gaps. In: Anastácio P, Ribeiro F, Chainho P (Eds) Invasions in Aquatic Systems. NeoBiota 102: 419–440. https://doi.org/10.3897/ neobiota.102.146421 NeoBiota 102: 419–440 (2025) DOI: 10.3897/neobiota.102.146421 Advancing research on alien species and biological invasions A peer-reviewed open-access journal NeoBiota 420 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India deleterious impacts on native biodiversity, ecosystem services or human economy and well-being (following the definition of the International Union for Conservation of Nature; IUCN 2000). For instance, the invasive Nile perch, Lates niloticus, caused the extinction of 200 native endemic fish species in Lake Victoria, Africa, resulting in irreversible damage to the lake’s ecosystem and affecting the livelihoods of fishermen (this example and others were reviewed by Bernery et al. 2022; Bacher et al. 2023). Further, an estimated 37 billion USD in economic losses have been caused globally by 27 invasive non-native fish species (Haubrock et al. 2022). While most research on NNF has focused on wealthy regions like North America, Europe and Australia (Olden et al. 2008; Bernery et al. 2022; Marcolin et al. 2025), we here focus on India, which has also experienced many NNF introductions. It is a vast country with 4% of the earth’s freshwater resources spread across 25 river basins (India-WRIS 2012) and hosting about 936 native freshwater fish species (AqGRISI 2024), many of which are endemic (Dahanukar et al. 2004; Singh and Sarma 2015). The first documented NNF in India was the brown trout, Salmo trutta, which was introduced in 1863 as a sport fish for angling (Singh and Lakra 2011). Subsequently, there were intentional and unintentional introductions of many NNF through various pathways, such as ornamental trade, aquaculture and biological control (introduction pathways to India were reviewed by Singh and Lakra 2011), whose estimated numbers today vary between 288 and 633 (Ghosh et al. 2003; Singh 2021a; Sandilyan 2022). Fourteen NNF have been classified as invasive in India, following the criteria suggested by the Convention on Biological Diversity 2002 (Sandilyan et al. 2019). Various aspects of freshwater NNF have been extensively studied in India, encompassing their presence (Kurup et al. 2006; Das et al. 2013; Gurumayum 2021), population trends (Kumar 2000; Raghavan et al. 2008), biology (Raj et al. 2021b; Singh et al. 2021), impacts (e.g. decline in native fish catch in river Ganga; Singh et al. 2013), and strategies to manage them (e.g. declaring Himalayan rivers’ headwater as invasion refugia for native fishes; Sharma et al. 2021). There has also been research on NNF as potential disease vectors (Tripathi et al. 2022) and on heavy metals in NNF including contamination, bioaccumulation and use of NNF in toxicological studies (Singh et al. 2012). Other studies examined the performance of NNF in aquaculture (e.g. common carp, Cyprinus carpio; Manna and Som 1982; Das et al. 2021) and for biological control (e.g. western mosquito fish, Gambusia affinis; Chakraborty et al. 2008; Ghosh et al. 2011). Moreover, several reviews have provided insights into various aspects of NNF (Sarkar et al. 2012; Singh 2021a; Sandilyan 2022). Despite the extensive research conducted in these areas, a comprehensive synthesis integrating these studies is currently lacking which can support evidence-based decision-making and help prioritize management actions (Cook et al. 2013). This study aims to provide such a review on NNF in India, compiling studies on NNF across all freshwater basins in India. Specifically, we provide an overview of the available types of studies that directly or indirectly investigated NNF in India, their development over time and in which scientific journals (non-predatory vs. potentially predatory) they were published. Further, we identify the NNF for which most research studies were carried out in India and provide a map showing the distribution of NNF corrected for basin area and research effort in India. We also analyze determinants of the number of NNF and show how these numbers have changed through time for different basins and India as a whole. Additionally, 421 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India we report on the proportion of studies addressing different types of NNF impacts and the level of collaboration between executing agencies, as well as the influence of funding on the assessment of potential impacts. Finally, we compare recommended management strategies and their comprehensiveness. Methods Data collection We gathered records of NNF in India up to the end of 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for ecology and evolutionary biology (O’Dea et al. 2021). We scanned literature from the Google Scholar and Web of Science databases using the English search string: “Invasive” OR “Exotic” OR “Alien” OR “Introduced” OR “Non-native” OR “Nonnative” OR “Non-indigenous” OR “Nonindigenous”) AND “India” AND “Fish*”. A total of 2305 studies were identified of which 332 studies were considered relevant for our study (see Suppl. material 1: table S1 for details). A study was considered relevant if it dealt with freshwater NNF (excluding studies on marine and estuarine NNF) in the geographical extent of India. For each of the 332 identified relevant studies, we compiled the following information: (1) author name(s); (2) year of publication; (3) name of journal or other type of publication; (4) type of article (peer-reviewed or potentially predatory articles in scientific journal, book/book chapter, seminar proceedings or magazine article); (5) the focal study area (“checklist”: documented diversity of fish, “biology”: biology of NNF, “fish health”: disease/pathogens and their spread, “aquaculture”: fish production and enhancement including nutrition, “review”: review papers, “impact”: impact of NNF in recipient ecosystem, “management”: management of NNF, “fishery”: fishery of NNF, “toxicity”: bioaccumulation and effect of pesticides and heavy metals, “reports”: reporting of presence or range extension of NNF, “status”: population trend of NNF; “biocontrol efficiency”: testing efficiency of NNF for disease control; and “others”: ornamental trade/database/supply chain management/ use of DNA in taxonomy and post-harvest/processing); (6) in which river basin the study was carried out (field studies) or the fishes were collected (laboratory studies); (7) the focal NNF and/or translocated species; (8) qualitative or quantitative impact; (9) type of impact (experimental, observational or inferred); (10) direction of impact; (11) any acknowledged funding; (12) author affiliations; and (13) any suggested management measures (see below and Suppl. materials 3, 4 for details). We consulted several sources to determine if an article was published in a potentially predatory journal. Beall’s list contains potentially predatory journals or publishers (https://beallslist.net/), whereas Clarivate’s Journal Citation Report (https://jcr.clarivate.com/jcr/browse-journals) and National Academy of Agricultural Science (NAAS), India (https://naas.org.in/NJS/journals2022.pdf), publish lists of peer-reviewed journals every year. Therefore, we categorized a study as potentially predatory if the respective journal was listed in Beall’s list and if it was not listed in the reports of Clarivate’s Journal Citation Report or NAAS. As river basins (also known as catchment areas or watersheds) form the natural biogeographical barriers for freshwater fishes (Leprieur et al. 2008), we noted in which river basins each study was carried out. To do so, we extracted boundaries of 25 river basins provided by HydroBASINS (Lehner and Grill 2013) based on the 422 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India river basin boundaries established by the Government of India (India-WRIS 2012) (https://indiawris.gov.in/wris/#/atlas). Depending on the geographic location that was investigated in a study, it was assigned to the respective river basin. Laboratory studies were assigned to river basins if the information on the location of sample collection was available; if not, records were classified as laboratory studies without information on river basin. Records where the study areas were mentioned as political boundaries (states or country) or eco-regions (Indian Northeastern Region, Indian Himalaya, Western Ghats) were classified separately, and the same was true for studies covering multiple river basins. We noted which non-native or translocated fish species were mentioned in each study. We defined non-native fish species (NNF) as those that have been introduced to India by human action, either intentionally or unintentionally (following Blackburn et al. 2011; Jeschke et al. 2022; synonyms to “non-native” are “alien”, “exotic” or “non-indigenous”); and we defined translocated fish species as those that are native to India but have been introduced within India by human action, either intentionally or unintentionally, to one or more river basins outside of their native range. Since both Latin and common English species names change over time and synonyms exist, we standardized species names using Eschmeyer’s Catalog of Fishes (Fricke et al. 2024). Regarding NNF impacts, we discriminated qualitative from quantitative impact assessments. Studies that reported impacts non-numerically or descriptively (for example, non-native fishes gradually replacing native fish fauna in the middle stretch of the Cauvery river; Roshith et al. 2022) were labelled as qualitative impacts; whereas studies that reported impacts numerically (for example, estimates of annual fish landings of non-native common carp, Cyprinus carpio and native fish species in the River Ganga, and its comparison with historical fish landings; Ray et al. 2021) were considered as quantitative impacts. Impacts were described as ecological, socio-economic, or both, and the direction of impact as deleterious, beneficial, or both (following Jeschke et al. 2014). Finally, we classified the mode of impact assessment as either (i) observational, impacts were observed by the authors (for example, burrows dug by the Amazon sailfin catfish, Pterygoplicthys pardalis, in canal margins; (Raj et al. 2021b) or temporal variation in fish catch composition (Sarkar et al. 2012); (ii) experimental, impacts were assessed by conducting experiments as in comparing the efficacy of two non-native larvivorous fish (Ghosh et al. 2011); or (iii) inferred, impact deduced from the literature, based on the species known to have impacts elsewhere (Raj et al. 2020). We also gathered information on suggested management measures. Robertson et al. (2020) proposed a framework for the management of biological invasions with ten key management terms that broadly cover all active management objectives including the support of active management (monitoring, risk assessment etc.). Using these terms, we categorized the management measures suggested in our studies (see Suppl. material 4 for details). With the resulting dataset, we investigated trends in NNF research, proportion of potentially predatory articles, numbers of NNF, most studied NNF, most studied river basins, river basin with most NNF. We also map the spatial distribution of NNF across basins (corrected for basin area) and for the basins Ganga, Brahmaputra, EFR (East Flowing Rivers) Pennar to Kanyakumari, and WFR (West Flowing Rivers) Tadri to Kanyakumari, as well as collectively for India, we chronologically analyzed the relationship between the cumulative number of NNF species and the 423 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India number of studies conducted there. Further, we examined patterns in impact assessments, proportion of studies in different focal areas, collaboration in research, and suggested management measures. Statistical analyses We analyzed differences in research effort among river basins. Some basins have not been studied at all. Therefore, we first used a binomial Generalised Linear Model (GLM) to test if the probability of at least one study being conducted in a basin is dependent on its size. We used the natural logarithm of area (in km2) of each basin in the model: [1] studyConducted ~ log(basinArea) Then we used a linear model to see if, among the studied basins, larger basins were prioritized. To ensure normal distribution of residuals, both the number of studies and the basin area were log-transformed. The data were scanned for outliers and the following model was run with all basins as well as without the Ganga: [2] log(nStudies) ~ log(basinArea) A third model was used to detect any possible relationship between basin area, number of studies and the number of detected NNF in a basin. Here again, the reduced dataset was used covering only studied basins and area and number of studies were log-transformed. The full model consequently is: [3] nNonNativeFishSpecies ~ log(nStudies) + log(area) +log(nStudies):log(area) All models with all parameter combinations were compared using the dredge function of the MuMIn package (Bartoń 2023). All parameters that appeared in the best models with and accumulated model weight of > 0.95 were considered. The resulting most parsimonious model is given in the Results section. We also investigated other potential biases in our studies. For this, we performed pairwise Chi-square tests with 100’000 bootstrap simulations to test if the funding source (public or private) has an influence on the predicted impact of the invader (deleterious or beneficial). Where not explicitly mentioned otherwise, all analyses were conducted using the R base package (R Core Team 2020). Results There has been an exponential growth in the number of publications on NNF in India over time. The first of the 332 identified studies is from 1957; however, there were no publications in the next decade (1961–70) and fewer than 10 publications per decade until 2000. In the following decades, the number of studies accelerated, with around 50 publications from 2001 to 2010 and almost 190 from 2011 to 2020. There were 73 publications in the first two years of the current decade (2021–2022). Projecting this number to all 10 years of the current decade means there will be an estimated 365 publications from 2021 to 2030 (Fig. 1). This projection is conservative, as it does not assume a further increase in the numbers of publications per year. 424 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India Most records are publications in scientific journals (309 records or 93%), but 41% of those were published in potentially predatory journals. For studies published by authors affiliated with universities, even the majority (54%) of publications in scientific journals were published in potentially predatory journals, whereas it was about a quarter (24%) for publications from non-university research institutes (see Suppl. material 2: fig. S1). For studies with both universities and non-university research institutes as affiliations, the proportion of publications in potentially predatory journals was in between (35%). About 38% of the records examined NNF directly, investigating either their biology, impact, management, fishery, report, status or bio-control (see Suppl. material 2: fig. S2). A high number of studies (31% or 103 records) focused on documenting fish diversity (checklist) where NNF were opportunistic detections, while only 3% of the studies primarily reported NNF (11 records). Fish health (9%) and aquaculture (7%) are two other important topics of the studies in our dataset, which also includes several reviews (6%). From our records, we detected 58 NNF distributed across 17 basins and 19 translocated fishes in India (Tables 1, 2). Of these, 55 NNF were reported from open waters (natural rivers and lakes as well as man-made reservoirs) in at least one basin. We were able to assign 250 studies (75%) to 17 river basins, whereas no studies were found for the remaining eight river basins (Table 3). The other 25% of the studies referred to political boundaries rather than river basins as study area (13%), were laboratory studies with no information on where the fish sampling location (10%) or focused on eco-regions or multiple river basins (3% combined). About half of the 250 studies were conducted in the Ganga basin (46% or 113 records), making it by far the most studied river basin in India followed distantly by the basin West Flowing Rivers Tadri to Kanyakumari (15% or 37 records). Significantly, despite this research bias there were 30 NNF reported for each of these Figure 1. Decadal trend of publications in research about non-native fishes in India. The gray box for the current decade indicates the conservatively estimated number of publications from 2021 to 2030, assuming that the publication rate observed in this decade’s first two years (73 publications in 2021 and 2022) will be constant until the end of the decade. 425 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India Table 1. List of non-native fish species (NNF) in India reported in the studies included in our dataset. Non-native fish species Non-native fish species Osteoglossiformes: Arapaimidae Siluriformes: Pangasiidae Arapaima gigas Pangasianodon hypophthalmus Osteoglossiformes: Osteoglossidae Characiformes: Serrasalmidae Osteoglossum bicirrhosum Colossoma macropomum Lepisosteiformes: Lepisosteidae Piaractus brachypomus Atractosteus spatula Piaractus mesopotamicus Salmoniformes: Salmonidae Pygocentrus nattereri* Oncorhynchus mykiss † Beloniformes: Adrianichthyidae Oncorhynchus nerka ‡‡ Oryzias javanicus Salmo salar ‡‡ Cyprinodontiformes: Poeciliidae Salmo trutta † Gambusia affinis* † Salvelinus fontinalis ‡‡ Gambusia holbrooki* † Salvelinus namaycush ‡‡ Poecilia mexicana Cypriniformes: Cyprinidae Poecilia reticulata Barbonymus altus Xiphophorus hellerii Barbonymus gonionotus Xiphophorus maculatus Carassius auratus Cichliformes: Cichlidae Carassius carassius Amphilophus trimaculatus Ctenopharyngodon idella Andinoacara rivulatus § Cyprinus carpio* † Astronotus ocellatus Cyprinus rubrofuscus Coptodon zillii Puntius brevis Hemichromis bimaculatus Cypriniformes: Xenocyprididae Mayaheros urophthalmus Hypophthalmichthys molitrix Oreochromis aureus Hypophthalmichthys nobilis* Oreochromis mossambicus* † Mylopharyngodon piceus* Oreochromis niloticus* Cypriniformes: Tincidae Oreochromis urolepis § Tinca tinca Thorichthys meeki § Siluriformes: Clariidae Anabantiformes: Channidae Clarias gariepinus* Channa lucius Siluriformes: Ictaluridae Anabantiformes: Helostomatidae Ictalurus punctatus Helostoma temminckii Siluriformes: Loricariidae Anabantiformes: Osphronemidae Hypostomus plecostomus Macropodus opercularis Pterygoplichthys ambrosettii* Osphronemus goramy Pterygoplichthys disjunctivus* Trichopodus microlepis Pterygoplichthys multiradiatus* Trichopodus trichopterus Pterygoplichthys pardalis* Trichopsis vittata * Invasive in India based on Sandilyan et al. (2019) who followed the definition of the Convention of Biological Diversity (2002). † Among the ‘100 of the World’s Worst Invasive Alien Species’ (GISD 2024). § Reported from fish farms but no record from the wild in our dataset. ‡‡ Non-native population potentially extinct (Joshi and Lal 2017). Table 2. List of translocated fish species in India reported in the studies included in our dataset. Basin(s) Reported translocated fish species 5, 7, 9, 12, 19, 24, 25 Cirrhinus mrigala, Labeo catla, Labeo rohita 1Anabas cobojius, Anabas testudineus, Channa punctata, Channa striata, Cirrhinus mrigala, Clarias magur, Danio rerio, Esomus danrica, Heteropneustes fossilis, Labeo catla, Labeo rohita, Notopterus notopterus, Puntius sophore 7Badis badis, Lepidocephalichthys guntea, Osteobrama cotio, Pethia gelius, Pethia phutunio 5, 9, 17, 24, 25 Tor khudree Basins: (1) Andaman Nicobar Islands, (5) Cauvery, (7) EFR Pennar and Kanyakumari, (9) Godavari, (12) Krishna, (17) Narmada, (19) Pennar, (24) WFR Tadri to Kanyakumari, (25) WFR Tapi to Tadri. 426 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India two basins (Fig. 2A, B). The common carp, Cyprinus carpio, was the most frequently reported NNF (160 times) followed by the Mozambique tilapia, Oreochromis mossambicus (108 times) (see Suppl. material 2: fig. S3A). However, the latter was the most widely distributed NNF (13 basins) followed by Cyprinus carpio (12 basins) (see Suppl. material 2: fig. S3B). Additionally, there were 24 NNF that were reported from only one basin each (see Suppl. material 5 for details). The log(n) of the basin area had no significant relationship with the probability of at least one study being conducted in a basin (binomial GLM with p = 0.15). Further, the number of studies for studied basins was correlated to the basin area when using log-log transformation (linear model with adj. R2 = 0.18, p = 0.049), but this relationship was exclusively driven by the largest and most studied river basin – the Ganga – and disappears when it is removed (linear model with adj. R2 = 0.02, p = 0.27). Results for the full model (model [3] in the Material and Methods section) show that the number of detected non-native fish species is mainly related to the number of studies conducted in the respective basin, not to its area or the interaction of studies and area (see Suppl. material 1: table S6). Results for the most parsimonious model – the number of fish species in relation to the log of the number of studies (adjusted R2 = 0.69, p < 0.001) – are shown in Fig. 2C, D. This model is robust to removing the Ganga basin (R2 = 0.56, p < 0.001). The river basins in India displayed a varying extent of (apparent) saturation in terms of NNF detections. The saturation curve almost flattened in the WFR Tadri to Table 3. List of river basins in India with the number of non-native fish species (NNF) reported for each basin and the number of studies. Number Basin name Area (km2) # NNF # Studies 1 Andaman Nicobar Islands 8249 12 4 2 Barak and Others 41723 0 0 3 Brahmani and Baitarani 51822 0 0 4 Brahmaputra 194413 12 17 5 Cauvery 81155 11 10 6 EFR Mahanadi and Pennar 86643 0 0 7 EFR Pennar and Kanyakumari 100139 19 11 8 Ganga 861452 30 113 9 Godavari 312812 10 7 10 Indus 321289 7 16 11 Inland Drainage in Rajasthan 139917 0 0 12 Krishna 258948 11 16 13 Lakshadweep Islands 32 0 0 14 Mahanadi 141589 7 5 15 Mahi 34842 1 4 16 Minor Rivers Draining into Myanmar and Bangladesh 36202 0 0 17 Narmada 98796 4 2 18 North Ladakh not draining into Indus 26018 0 0 19 Pennar 55213 6 1 20 Sabarmati 21674 2 1 21 Subernarekha 29196 0 0 22 Tapi 65145 1 1 23 WFR Kutch and Saurashtra 321851 2 1 24 WFR Tadri to Kanyakumari 55177 30 37 25 WFR Tapi to Tadri 55940 8 4 NNF: Non-native fish species, EFR: East flowing river, WFR: West flowing river. 427 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India Kanyakumari basin until 2021, but there was an abrupt rise in the number of detected NNF in 2022, which is also evident for India as a whole (Fig. 3A, E). With fewer additions to the NNF detections in the Brahmaputra basin in the last 50% of studies, it appears that most NNF in this basin have been detected (Fig. 3B). The detection of new NNF continues to increase almost linearly in the EFR Pennar to Kanyakumari basin (Fig. 3C). In the Ganga basin, the detection of NNF increased gradually and then almost plateaued with fewer detections for every additional study conducted (Fig. 3D). Figure 2. A number of studies on non-native fish species (NNF) for each river basin in India B number of reported NNF for each basin C relationship between the number of reported NNF and the number of studies for each basin (log-transformed) (adjusted R2 = 0.69, p < 0.001) D map of residuals from plot C: red and blue colors indicate higher and lower numbers of reported NNF for each basin considering the number of studies, respectively. Grey color indicates basins with no studies (A), with no NNF reported (B) and that were not considered for analysis due to the absence of studies (D). Basins: (1) Andaman Nicobar Islands, (2) Barak and Others, (3) Brahmani and Baitarani, (4) Brahmaputra, (5) Cauvery, (6) East Flowing River (EFR) Mahanadi and Pennar, (7) EFR Pennar and Kanyakumari, (8) Ganga, (9) Godavari, (10) Indus, (11) Inland Drainage in Rajasthan, (12) Krishna, (13) Lakshadweep, (14) Mahanadi, (15) Mahi, (16) Minor Rivers Draining into Myanmar and Bangladesh, (17) Narmada, (18) North Ladakh not draining into Indus, (19) Pennar, (20) Sabarmati, (21) Subernarekha, (22) Tapi, (23) West Flowing River (WFR) Kutch and Saurashtra, (24) WFR Tadri to Kanyakumari, (25) WFR Tapi to Tadri. 434 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India Data availability All of the data that support the findings of this study are available in the main text or Supplementary Information. 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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.102.146421.suppl1 Supplementary material 2 Supplementary images Authors: Lohith Kumar, Florian Ruland, Jonathan M. Jeschke Data type: zip 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.102.146421.suppl2 440 NeoBiota 102: 419–440 (2025), DOI: 10.3897/neobiota.102.146421 Lohith Kumar et al.: Non-native fishes in India Supplementary material 3 Records used and information extracted Authors: Lohith Kumar, Florian Ruland, Jonathan M. Jeschke 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.102.146421.suppl3 Supplementary material 4 Management measures suggested in records Authors: Lohith Kumar, Florian Ruland, Jonathan M. Jeschke 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.102.146421.suppl4 Supplementary material 5 Spatial distribution of NNF Authors: Lohith Kumar, Florian Ruland, Jonathan M. Jeschke 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.102.146421.suppl5