Invasive carps vs. native fish: a first-pass trait-based index for assessing competition threats

Introduction

Invasive species are recognized as one of the most significant hazards to biodiversity (Elton, 1958; Su et al., 2021) and managing them presents a substantial challenge (Lipták et al., 2024). Globally, hundreds of fish species have successfully established populations outside their native ranges, resulting in diverse negative costly impacts (Diagne et al., 2020; Haubrock et al., 2022). Freshwater systems are particularly vulnerable to invasive species (Kinlock et al., 2020). The changes in hydrology caused by dam construction, land-use modifications both nearshore and in catchments, and increased nutrient runoff from agricultural practices disrupt the trophic interactions in these ecosystems, facilitating invasions (Havel et al., 2015, 2005; Kovalenko, 2019). Further, the connectivity sometimes enhanced by hydrological alterations can facilitate the spread of non-native species, which when combined with late detection, may result in multiple adverse effects on aquatic assemblages (Júlio Jr et al., 2009). Invasive freshwater fish can impact natural ecosystems by interacting with native species through competition, predation, spread of diseases and parasites, hybridization, and others (Bernery et al., 2022; Chakraborty, 2024; ISSG, 2025). Additionally, invasive fish can alter native habitats through behaviors such as substrate disturbance, grazing, and browsing, all of which may disrupt physical and chemical habitat characteristics (Bernery et al., 2022; Chakraborty, 2024; ISSG, 2025).

Bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix), collectively referred to as bigheaded carp, are native to east Asia. They were introduced to the United States in the 1970s to consume and regulate plankton in aquaculture and wastewater treatment facilities (Kelly et al., 2011; Kolar et al., 2007). However, by the 1990s, they had escaped confinement and began expanding into the Mississippi River basin, where they now have established populations (Chick and Pegg, 2001; Kolar et al., 2007). Since then, they have been slowly extending their range and can now be found in most tributaries of the Mississippi River (Miranda, 2023).

Bigheaded carp have specific traits and plasticity that contribute to their successful introduction into non-native habitats (Birdsall, 2023). They migrate long distances to spawn, have high fecundity and growth rates, and have high tolerances to environmental variations (Kolar et al., 2007; Lenaerts et al., 2023). Silver carp primarily feed on phytoplankton, while bighead carp mainly consume zooplankton (Cremer and Smitherman, 1980; Dong and Li, 1994; Ivan et al., 2020). However, bighead carp can also consume detritus and algae when other food sources are scarce (Cremer and Smitherman, 1980; Jennings, 1988; Kolar et al., 2007; Sass et al., 2014). These planktivorous species have the potential to disrupt food webs by competing for plankton with native filter-feeding and planktivorous fish, which rely on plankton for part of their life cycle (Eagles-Smith et al., 2008; Harris et al., 2022; Sass et al., 2014). Several studies have indicated that bigheaded carp exhibit some degree of dietary overlap with native planktivores such as bigmouth buffalo (Ictiobus cyprinellus), gizzard shad (Dorosoma cepedianum), and emerald shiner (Notropis atherinoides), with declines in the abundance and condition of these species (Haupt and Phelps, 2016; Irons et al., 2007; Pendleton et al., 2017; Phelps et al., 2017; Wang et al., 2018). Additionally, juvenile American paddlefish (Polyodon spathula), an iconic recreational and commercial filter-feeder, has been shown to be outcompeted by bighead carp in ponds (Schrank et al., 2003). Similar effects may also be impacting many other species, though these have yet to be studied (Wang et al., 2018).

Estimating competition between bigheaded carps and native species in a multispecies assemblage presents challenges due to the complexity and multidimensional nature of ecological competition, which is seldom direct or easily observable (Ross, 1986; Schoener, 1974). Fish frequently employ resources that are overlapping but not identical. For instance, they may consume distinct species within the same taxonomic group or graze for the same species in distinct habitats (Ríha et al., 2025; Ross, 1986). Therefore, in diverse assemblages, a fish species may compete weakly with many other fishes rather than strongly with one or a few (Ross, 1986). Moreover, many fish exhibit dietary flexibility, ontogenetic shifts, or habitat switching as they develop (Sánchez-Hernández et al., 2019; Winemiller, 1989). These adaptations allow fish to minimize resource overlaps under scarcity, hence altering competition intensity (Polis, 1984; Wisheu, 1998). Competition can vary seasonally, annually, and among habitats within the same water body (Bloomfield et al., 2022). Short-term studies may miss these dynamics; however, long-term or whole-system studies are often logistically challenging. Thus, because direct estimation of competition among species in fish assemblages is impractical, fish ecologists often turn to indices or proxies that may reflect the potential for competition rather than measure competition directly (Wallace Jr., 1981). These indices may integrate dietary and habitat utilization to estimate tentative and multifaceted competitive overlap.

The Tennessee River basin has the highest fish biodiversity in North America and is currently experiencing an increase in bigheaded carp populations within its system of impoundments (Ridgway and Bettoli, 2017; Vallazza et al., 2025). This expansion has the potential to restructure food webs and thereby threaten native biodiversity by disrupting the equilibrium of interactions that support ecological communities (Altenritter et al., 2022; Chick et al., 2020; DeBoer et al., 2018; Harris et al., 2022; Sampson et al., 2009; Yallaly et al., 2014). Some studies have explicitly linked the rise in bigheaded carp densities to a decline in the physical condition of a handful of native planktivores (Haupt and Phelps, 2016; Irons et al., 2007; Pendleton et al., 2017; Phelps et al., 2017), but this may only be the tip of the iceberg. This threat has mobilized natural resource conservation sectors to invest hundreds of millions of dollars to address observed and anticipated detriment (Miranda, 2023). In this context, the aim of this study is to identify the species most vulnerable to competition based on their dietary and habitat overlaps. Our objective is to provide a first-pass view of the functional overlap in dietary and habitat use between bigheaded carp and the diverse fish species assemblage in impounded segments of the Tennessee River basin.

Methods

Study area

The Tennessee River basin spans approximately 106,000 km² across seven U.S. states. With its main stem and tributaries flowing 1,499 km from its headwaters in the Appalachian Mountains to its confluence with the Ohio River. It is the largest tributary of the Ohio River and one of the largest rivers in North America (Delong et al., 2023). Elevation across the basin ranges from 95 m at its mouth to 2,037 m at the headwaters, creating a steep gradient of climates and aquatic habitats (Hampson et al., 2000). The basin averages a discharge of nearly 2,000 m3/s at its mouth (White et al., 2005), with a humid subtropical climate (Beck et al., 2018) characterized by average air temperatures of 2 °C in January and 23 °C in July, winter lows frequently below 0 °C, and summer highs often exceeding 38 °C. The average annual precipitation is about 130 cm.

The basin was heavily impounded beginning in 1933 under the Tennessee Valley Authority (TVA) and today contains 49 reservoirs, with a mean age of about 75 years as of 2020 (Miranda et al., 2025). TVA manages these reservoirs for hydropower generation, flood control, navigation, water supply, and recreation. Connectivity of rivers in the basin has been severely compromised by the dams, which fragment river segments and impede fish passage (Granstaff, 2015). The Tennessee River mainstem includes a cascade of nine major dams (Figure 1) connected by navigation locks that partially disrupt the longitudinal movement of aquatic species (Post van der Burg et al., 2021). Outside of the mainstem, Melton Hill Reservoir also includes a lock and Tellico Reservoir is connected by a canal to Fort Loudoun Reservoir. The remaining 38 impoundments in the Tennessee River basin lack fish passage facilities, which prevents upstream movements and limits downstream movements. The fragmentation caused by dams and the extensive lentic waters they create is harmful to biodiversity (Miranda et al., 2025), however it may also hinder the spread of invasive species.

The Tennessee River basin harbors extraordinary biodiversity, including over 250 native fish species and the highest number of endemic freshwater fish species in North America (White et al., 2005). The basin is recognized as a global hotspot for temperate aquatic biodiversity, showcasing high levels of species richness and endemism among freshwater fish (Smith et al., 2002; Starnes and Etnier, 1986; Thieme et al., 2016). The basin also ranks among the freshwater ecoregions with the highest numbers of imperiled taxa in North America (Jelks et al., 2008; Thieme et al., 2016).

Data sources

We relied on existing databases and published literature to create a broad overview of the dietary and habitat overlap between bigheaded carp and native fish species in reservoirs of the Tennessee River basin. The four-step procedure applied to assess diet and habitat overlap between invasive bigheaded carps and native fish assemblages is detailed below.

First, we used an up-to-date species list that is maintained by the Southeastern Fishes Council (SFC, 2025) to establish the fish assemblage occupying the Tennessee River basin. This list draws from a variety of sources including Etnier and Starnes (1991), Warren et al. (2000), Page and Burr (2011), and Page et al. (2013) to provide a comprehensive inventory of freshwater fish in southeastern North America. Only species native to the Tennessee River basin were included in our analysis. Species not native to the Tennessee River basin, species with pending or unresolved taxonomic status, and hybrids were excluded. These were excluded because of the lack of clear information regarding their dietary and habitat traits.

Second, we compiled a list of species collected in reservoirs of the Tennessee River basin with complementary fish sampling methods, including daytime boat electrofishing that targeted fish in the shallow nearshore zone, and overnight gillnetting that targeted fish in the open-water benthic zone. Sampling methods were like those described by Miranda (2024). These gears reflect different aspects of existing fish assemblages, but in the Tennessee River basin they each adequately tracked spatial differences among reservoirs, suggesting that they adequately reflect standing fish assemblages (Miranda et al., 2021). Extensive surveys were conducted by TVA in 1990-2022 (Miranda et al., 2025), although not all reservoirs were sampled every year. Because occurrence data were drawn from reservoir monitoring programs, our results most directly represent impounded portions of the basin. Nevertheless, the species involved in our analysis are not reservoir specialists but are broadly distributed across both lentic and lotic habitats in the basin and commonly occur in adjacent riverine reaches (Etnier and Starnes, 1993; Boschung and Mayden, 2004).

Third, we matched the SFC species list with the FishTraits database developed by (Frimpong and Angermeier, 2009). The FishTraits database includes >100 traits consisting of categorical, binary, and continuous data representing over 800 freshwater fish species in North America. We focused on 9 trophic traits and 12 habitat traits accessible in the database (Table 1).

Table 1. Dietary and habitat traits included in this evaluation of niche overlap between bigheaded carp and other fish species inhabiting reservoirs within the Tennessee River basin, USA.

Trait codes and their corresponding definitions are those used by Frimpong and Angermeier (2009). N represents the number of species exhibiting the specified characteristic.

Fourth, we categorized species in the Tennessee River basin based on rarity. Often, rare species have specific ecological needs, including consuming particular prey and living in limited habitats (Gaston, 2011). Because of their specialization, they might have fewer resources or habitats in common with other species, including bigheaded carp; nevertheless, specialists are more likely to be extirpated when requirements overlap. Conversely, common species tend to have larger niches that enable them to use a range of environments and resources, potentially increasing overlaps with other species; nevertheless, species with broad niches may persist via niche partitioning. We used a classification derived by Miranda et al. (2025) for the Tennessee River basin. This classification assigns species to one of four levels of rarity, including absent, rare and restricted, rare and spread, and common. Absent indicates occurrence in the Tennessee River basin but not detected by sampling with electrofishing or gillnetting in the reservoirs. Rare and restricted indicates detection in < 50% of the reservoirs and < 50% of the basin (i.e., in a stretch of less than half of the length of the basin). Rare and spread indicates detection in < 50% of the reservoirs and >50% of the basin. Lastly, common indicates detection in >50% of the reservoirs and >50% of the basin. We differentiated between rare and restricted species and rare and spread species, as we anticipated that rare fish confined to sections of the basin inaccessible to bigheaded carps would be unaffected by any diet or habitat overlap. Thus, species classified by Miranda et al. (2025) as either absent from reservoirs or rare and restricted to the upper reaches of the basin—areas inaccessible to bigheaded carps due to dams—would not be impacted by dietary or habitat overlaps and were therefore excluded from all analyses.

Data analysis

We applied an index of resource overlap to assess the risk of competition between bigheaded carp and native fish species. Overlap was estimated with a similarity coefficient, given that our diet and habitat data were binary (e.g., detritus feeder, 1, or not, 0). We specifically cared about shared uses (1–1), i.e., about prey or habitat types that both species used, or that one species used (1–0, 0–1), i.e., prey or habitat types that one species used but not the other. However, shared nonuse (0–0), i.e., neither species used, were not informative about overlap. Thus, we selected the Sørensen similarity index because it focuses on presences and ignores shared absences:

Sorensen′s similarity index= 2a2a+b+c(1)

where a = number of shared uses (1–1), b = number of uses by bigheaded carp but not by a native species (1–0), and c = number of uses by a native species but not by bigheaded carp (0–1). By doubling a, this index weights shared use between bigheaded carp and another species more heavily than mismatches. The index differs narrowly from the Jaccard similarity index that does not doubles a; by doubling a, the Sørensen coefficient tends to give higher similarity scores than the Jaccard coefficient. Index values were computed using the vegan package (Oksanen et al., 2019) in R software (R Core Team, 2022).

The Sørensen index was applied to dietary traits (D) and habitat traits (H), independently, and to bighead carp and silver carp, independently. Then, for each dietary and habitat trait, and for each species comparison, we selected either the bighead carp or silver carp Sørensen index, whichever was higher, and designated it as the bigheaded carp similarity coefficient. We rationalized this approach because silver carp and bighead carp have very similar traits, differing only in one of the traits considered. This strategy was more cautious than using the average of bighead carp and silver carp. We evaluated the hypothesis that dietary and habitat similarities did not differ using a permutation analysis of variance (PERMANOVA). The Euclidean coefficient was used to construct a similarity matrix from a dataset that contained the species name, Sørensen similarity coefficient, and whether the coefficient was for diet or habitat. The PERMANOVA was applied to this matrix.

The resulting Sørensen similarities were combined as a weighted mean into a competition index (CI):

CI=0.7D+(1-0.7)H(2)

In Equation 2, a high CI indicates reduced ecological distance and thus more severe competition. Dietary overlaps reflect potential for exploitative competition for food, whereas habitat overlaps reflect the potential for interference competition by exploiting food at the same sites. We caution that CI is an indicator of potential competition, not evidence of realized competition, as the Sørensen index does not consider aspects such as resource abundance, temporal variation, ontogenetic shifts, or behavioral avoidance. Because the relative importance of dietary versus habitat overlap is uncertain a priori, we used a transparent, hypothesis-based baseline weighting that emphasizes diet (0.7) while retaining habitat information (0.3), consistent with the expectation that exploitative competition for shared food resources is a primary pathway by which bigheaded carp may affect native fishes. Thus, CI is intentionally diet-influenced under this baseline weighting, but still considers habitat overlap.

We explored the sensitivity of our competition index to the weight values applied. This analysis was intended to assess the stability of the rank order under alternative weightings, not to validate CI. To assess the influence of alternative weighting schemes, in Equation 2, we systematically varied the dietary weight from 0 to 1 and recalculated a new CI for each species analyzed. Subsequently, we computed the Spearman rank correlation between each alternate weighting and our baseline 0.7/0.3 scheme. We considered the index robust against alternative weightings if rank correlations exceeded 0.9.

Furthermore, we evaluated the hypothesis that rarity status correlates with dietary and habitat overlap by comparing the Sørensen similarity coefficients for both diets and habitats independently across the rarity classes using a PERMANOVA. The Euclidean coefficient was used to construct a similarity matrix from a dataset that contained the species name, corresponding dietary or habitat Sørensen similarity scores, and respective rarity class. The PERMANOVA was applied to each of these matrices. If differences were detected among rarity classes, we used pairwise comparisons to sort them out.

Results

Overall, 255 fish species were reported by the SFC database in the Tennessee River basin. Between 1990 and 2022, 150 of these species were collected in reservoirs through TVA electrofishing and gill-net sampling. Of the 150 species captured in reservoirs, 127 were native and 23 were non-native (i.e., from outside the Tennessee River basin) and were excluded from analysis. An additional 12 native species were excluded because trait data were unavailable or taxonomically uncertain in the FishTraits database, and 15 rare and restricted native species were removed because they were confined to upper-basin reservoirs inaccessible to bigheaded carp. Of the remaining 100 native species collected in reservoirs, 41 were classified as common, 31 as rare and widespread, and 28 as rare and restricted (Table 2).

Table 2. Native fish species present in Tennessee River reservoirs that are accessible to bigheaded carp, and for which dietary and habitat traits have been documented.

The Sørensen similarity coefficients between bigheaded carp and native species deviated relative to dietary and habitat traits (Figure 2). Mean similarities were 0.67 (Min-Max, 0.29–1; SD, 0.16) for dietary traits and 0.53 (Min-Max, 0–0.8; SD, 0.13) for habitat traits. Dietary and habitat traits were significantly different (pseudo-F = 49.0, P = 0.001). This distinction suggests that native species and bigheaded carp differed less in dietary traits and more in habitat traits. Moreover, there was no significant correlation between the dietary and habitat similarity coefficients (Spearman r = 0.083, P = 0.413), indicating that when native species and bigheaded carp overlapped in diet often did not overlap in habitat, and vice versa.

The competition index was correlated with the dietary component (Spearman r = 0.94, P > 0.01) and habitat component (Spearman r = 0.37, P > 0.01). As intended by the differential weighting assigned to the competition index components, CI best represents dietary overlap. Moreover, the dietary overlap exhibited slightly greater variability than the habitat overlaps, as evidenced by their standard deviations, hence further amplifying the dominance of diet in the CI. Overall, 19 species demonstrated high competition index (i.e., CI > 0.75; Figure 2). The highest CIs included bigmouth buffalo, threadfin shad (Dorosoma petenense), black redhorse (Moxostoma duquesnii), bluntnose minnow (Pimephales notatus), highfin carpsucker (Carpiodes velifer), and gizzard shad. Most species (N = 69) exhibited low to moderate competition indices (i.e., CI < 0.75). Prominent among these were all black basses (Micropterus), temperate basses (Morone), sunfishes (Lepomis), crappies (Pomoxis), gars (Lepisosteus), silversides (Atherinopsidae), and paddlefish. Darter species, sculpins, and pickerels exhibited the lowest CIs. We note that there was no species with a CI of 0, and that the minimum CI estimated was 0.35, suggesting that perhaps our CI estimations may be conservative.

The sensitivity analysis revealed that CI remained consistent regardless of the weighting scheme. Spearman rank correlations between CI values derived from the baseline (0.7/0.3) and CI values derived from alternative weightings remained high (i.e., Spearman r > 0.9) for weightings of 0.5/0.5 (Figure 3). Thus, although the CI absolute values change depending on the weighting scheme, the rank order of CI values remains largely stable. This indicates that our competition index exhibits limited sensitivity to reasonable weighting variations and can be regarded as robust with respect to weighting choice.

The mean CIs were 0.66 (0.49–0.90), 0.64 (0.38–0.90), and 0.58 (0.38–0.90) for the common, rare and spread, and rare and restricted species, respectively (Figure 4). These CIs differed statistically among rarity status (pseudo-F = 3.4, P = 0.042), with pairwise tests indicating a significant difference between the common and the rare and restricted species (pseudo-t = 2.6, P = 0.013), but no difference between the common and the rare and spread species (pseudo-t = 0.6, P = 0.524). Differences between the rare and spread and rare and restricted species were not statistically significant, though the comparison indicated a modest separation (pseudo-t = 1.7, P = 0.088). We emphasize that these comparisons are exploratory and depending on how they are applied, may require further investigation with additional data. From an alternative point of view, out of the 100 species studied, 41% were common, yet 58% of the 19 high CI species were common. These findings, while ambiguous due to heavy overlaps, may point to a gradient of potentially stronger competition between bigheaded carp and common species, but lesser competition with the rarest of species.

Discussion

Our assessment of dietary and habitat overlaps between bigheaded carp and native species revealed a key pattern: dietary similarities generally exceeded habitat similarities, and these two axes of overlap were uncorrelated. This decoupling is consistent with classic niche theory, which recognizes food and space as largely independent dimensions along which species may partition resources to facilitate coexistence (Schoener, 1974; Chase and Leibold, 2003). Accordingly, high trophic similarity does not necessarily imply strong interaction if spatial segregation limits encounter rates (Holt, 1987; Coulter et al., 2019). At the same time, trait-based indices quantify potential interactions, and strong competitive effects may still emerge for some species or life stages when resources become limiting or when seasonal hydrology and temperature compress habitat use and increase co-occurrence. Habitat management that preserves spatial heterogeneity and structural complexity may therefore help maintain segregation and dampen competitive interactions (Almany, 2004). However, such separation may erode in heavily invaded systems where bigheaded carp expand across habitats and reorganize food webs (Coulter et al., 2018; DeBoer et al., 2018; Hochstrasser and Collins, 2024).

The lack of correlation between dietary and habitat overlaps also bears on long-standing suggestions that bigheaded carp exploit a relatively underused pelagic–planktonic foraging pathway in North America (Kolar et al., 2007; Irons et al., 2007). Their tendency to occupy the water column and efficiently filter plankton has sometimes been framed as use of an “empty” or weakly exploited niche relative to benthic-oriented native assemblages. Our results are consistent with this hypothesis in the limited sense that trophic similarity need not translate into spatial overlap, potentially reducing encounter rates with many native species. Nevertheless, multiple lines of evidence caution against interpreting this pattern as proof of a truly vacant trophic niche. Many native fishes depend on plankton during early life stages or seasonally, and isotope-based studies have documented substantial trophic overlap and reorganization of native niches across carp density gradients (DeBoer et al., 2018; Harris et al., 2022). Moreover, experimental syntheses indicate that carp can generate both direct and indirect effects across habitats, including pelagic–benthic coupling via recycling and shunting of consumed organic matter to benthic environments (Hochstrasser and Collins, 2024). Together, these studies suggest that rather than occupying a wholly empty niche, bigheaded carp may be functionally specialized along a trophic axis while remaining partially segregated along a habitat axis. Thus, invasion-driven food-web alteration can redistribute native resource use and habitat associations and potentially intensify competition in habitats not typically associated with carp.

The CIs aligned with empirically documented interactions in field investigations and with rarity classifications, as common species are expected to have broader niches. Species such as bigmouth buffalo, gizzard shad, and emerald shiner have consistently shown evidence consistent with competition and food-web impacts in invaded rivers, including declines in body condition and shifts in diets following changes in zooplankton community structure (Harris et al., 2022; Hayer et al., 2014; Irons et al., 2007; Minder and Pyron, 2018; Pendleton et al., 2017; Shields et al., 2021; Wang et al., 2018). Our analysis listed these three species as having high competition risk (Figure 2). Similarly, paddlefish has been reported to have only modest diet overlap with bigheaded carp (Wang et al., 2018). Accordingly, our analysis listed paddlefish as moderate competition risk. Chick et al. (2020), Fletcher et al. (2019), and Tillotson et al. (2023) provided empirical evidence of a negative effect of invasive silver carp on native fish (e.g., bluegill Lepomis macrochirus, yellow perch Perca flavescens, freshwater drum Aplodinotus grunniens, crappies) that they attributed to competition for zooplankton during early life stages. Our index indicated that few gamefish species as high competition risk since the Frimpong and Angermeier (2009) dataset does not distinguish between seasons and life stages, instead presents aggregated traits that incorporate multiple ontogenetic stages, but they do not precisely distinguish among them. This discrepancy underscores a limitation of our estimated CIs but also highlights the need for re-evaluating our findings with juvenile fish traits. Furthermore, our index offers a basis for expanding the species to monitor in field surveys and empirical research. Lastly, common species are predominantly generalists, particularly in reservoirs (Miranda et al., 2025). Consistent with Gaston (2011), common species are likely to overlap with bigheaded carp across a broader range of their dietary and habitat niches, resulting in higher average CIs among common species.

Because competition requires both shared resources and opportunities for encounter, overlap along dietary or habitat axes does not translate directly into ecological interaction when resources are abundant or species remain spatially segregated. Resource availability varies longitudinally in river systems (Vannote et al., 1980), including the impounded Tennessee River (Miranda et al., 2008), but reservoirs modify these gradients through serial discontinuities (Ward and Stanford, 1995), replacing smooth upstream–downstream transitions with a chain of semi-lentic systems separated by riverine reaches whose dimensions depend on local physiography. Each impoundment establishes its own riverine–transitional–lacustrine gradient, resetting conditions downstream of dams (Miranda and Dembkowski, 2016) and altering retention time, light penetration, and primary productivity (Kimmel and Groeger, 1984; Soballe and Kimmel, 1987; USGS, 2018; Naymik et al., 2023; Kortmann, 2025), collectively transforming the Tennessee River into a sequence of hydrologically connected but ecologically semi-isolated reservoirs. Impoundments also create lentic–lotic “edge effects” at reservoir–tributary interfaces, where reservoir-associated generalists interact with rheophilic tributary fauna; seasonal movements of generalists into tributaries can intensify competition and predation and influence fish communities far upstream of impounded reaches (Šmejkal et al., 2023). Upstream reservoirs tend to be smaller, whereas downstream reservoirs are larger and more connected to floodplains and embayments that support substantial plankton production, generating testable predictions within our trait framework: overlap potential should be greatest where residence time is long and water-column habitat and plankton production are highest—typically in transitional and lacustrine zones and embayments—and where low-velocity off-channel habitats increase co-occurrence with pelagic planktivores (USGS, 2018; DeGrandchamp et al., 2008). At the same time, bigheaded carp recruitment depends on turbulent riverine reaches that keep semi-buoyant eggs suspended during downstream drift, linking ontogenetic requirements to the availability of flowing habitat within regulated systems (Chapman and George, 2011; Garcia et al., 2013). In the Tennessee River, this lentic–lotic juxtaposition is most evident in downstream reservoirs where silver carp show tailwater residency and where removal programs are concentrated (Budnick et al., 2025).

Nevertheless, the methods we applied have several limitations that necessitate cautious interpretation of our findings. First, we relied on a database that measures dietary and habitat traits at coarse resolution such as detritus, phytoplankton, pelagic, or benthic but did not capture finer scales such as temporal or ontogenetic shifts (Sanchez-Hernandez et al., 2022). Because traits are aggregated across life stages, our index cannot resolve early-life plankton dependence or indirect food-web effects. Also, the traits database we applied was developed for more natural environments and may not transfer exactly to anthropogenically modified rivers. In addition, trait uncertainty is higher for some taxa (especially rare species), so overlap estimates should be interpreted cautiously and validated. Consequently, using coarse traits could have overestimated or underestimated competition. Second, overlap indices, such as Sørensen similarity index applied in our study, are also coarse approximations. Because they are binary, they give equal weight to all utilizations, disregarding extent of resource use and resource availability, and offer a static depiction of a species, overlooking ontogeny and adaptation responses to changing environmental conditions. Furthermore, since our traits are binary and some dietary categories are broad (e.g., general invertebrate groupings), CI can overestimate overlap among species that partition resources at finer taxonomic or life-stage scales. Thus, our CIs should be interpreted as a preliminary metric of functional proximity rather than a direct measure of competitive strength. Third, 12 native species were excluded from this analysis because traits data were unavailable. These species included mostly uncommon cyprinids, catostomids, and two marine species that migrate up the Mississippi River and into the Tennessee River's lower reservoirs. Fourth, the competition index constructed from the dietary and habitat similarity coefficients required subjective weighting influenced by a static scheme that is likely spatially and temporally dynamic. Nonetheless, our sensitivity analysis mitigated this concern by indicating that the ordinal ranking of competition indices remained relatively stable across different weighting schemes. Overall, because our competition index aggregates trait information across seasons and life stages, it captures broad trophic similarity but may underestimate episodic spatial overlap during critical periods such as larval feeding or low-flow habitat compression. Given these limitations, our competition index is best viewed as a rudimentary indicator appropriate for first-pass estimations, not a precise measure of the intensity of ecological interaction.

Implications for conservation management

Dietary overlaps, habitat overlaps, and the derived competition indices, although first pass, may offer direct and valuable decision-support implications for managing competition between bigheaded carp and native species. Below we outline 10 concrete, practical ways to turn the competition index into conservation and management action. First, the 19 species identified to have high competition index (i.e., CI > 0.75) may require special monitoring emphasis to track clear indicators such as shifts in population densities, condition factors, or growth rates, particularly for those species classified by Miranda et al. (2025) as rare and spread or rare and restricted. Attention may be directed toward the lowest reservoirs in the basin, as the invasion is advancing upwards but has not yet posed a problem upstream. For this monitoring, it would be advantageous to define clear numeric thresholds that trigger escalation of conservation efforts. Second, develop maps of hotspots where many high-CI, uncommon natives occur or are likely to co-occur with invasive carp. Third, follow up with additional research such as stable isotope analyses and DNA metabarcoding to detect food competition effects that our index based on traits alone will not detect. These studies may focus initially on high-CI taxa. Fourth, seek opportunities to focus ongoing management actions, such as invasive carp harvest programs, where high-CI natives tend to concentrate (e.g., rearing areas); or conversely where carp tend to concentrate. These recommendations complement ongoing suppression and monitoring programs already being implemented across the Mississippi River basin, including targeted commercial harvest and telemetry-informed identification of aggregation areas to direct removals, and spatially explicit/metapopulation models developed to evaluate where harvest is most effective for reducing densities and slowing spread (Kallis et al., 2023; Bouska et al., 2020; Altenritter et al., 2022). Fifth, protect and restore refugia by enhancing or restoring microhabitats that minimize overlap (e.g., aquatic vegetation, key substrate compositions, structurally complex areas) thereby enabling high-CI natives to use habitats invasive carp are unlikely to exploit. Sixth, offer legal protection measures to applicable and uncommon high-CI taxa, such as harvest restrictions, designated protected areas, or seasonal closures. Seventh, where invasive carp are abundant, control measures are unlikely to be effective, and high-CI species exhibit signs of impact, consider augmenting native populations with stockings at strategic locations. Eighth, where possible, use before-after-control-impact (BACI) designs or similar approaches to reliably relate actions to the reactions of high-CI species and thus accelerate knowledge acquisition. BACI-style evaluations and community indicators (e.g., size structure and condition responses of carp and native assemblages) have been used to assess invasion and management outcomes (Phelps et al., 2017; Coulter et al., 2018). Ninth, promote and facilitate research and monitoring high-CI species to refine our preliminary indices and conservation recommendations. Tenth, through public outreach channels, disseminate high-CI predictions to cultivate public support for any forthcoming actions. Overall, these recommendations show how trait-based CI rankings can be used as a practical first-pass tool to prioritize monitoring, target interventions, and guide follow-up studies as the invasion advances. Although formal scenario analysis (Roura-Pascual et al., 2021) is beyond the scope of this study, CI rankings could be incorporated into future scenario-based planning (e.g., invasion expansion, harvest intensity, or habitat-change scenarios) to evaluate how priorities may shift under alternative futures.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Cristina Coccia, Roma Tre University, Italy

Reviewed by: Marek Šmejkal, Academy of Sciences of the Czech Republic (ASCR), Czechia

Jim Long, United States Department of the Interior, United States

James E. Garvey, Southern Illinois University Carbondale, United States