Abstract

Often regarded as a potential threat to the native fish fauna worldwide, the Brown Trout (Salmo trutta), has successfully established its population in the majority of the Himalayan rivers post its introduction dating back to the eighteenth century. Over the years, the species has gained infamy as a sport fish and is considered a profitable source of income to the locals ensuing a heightened propagule pressure due to lack of appropriate management actions. No comprehensive study has been conducted to date in order to understand the mechanism by which the Brown Trout poses threat to the native fish populations. Through the present study, we could assess its competition with the native Snow Trout (Schizothorax richardsonii) to understand the spatial assemblage of both the species across space in Tirthan, a pristine high-altitude river of the western Himalaya. River Tirthan is one of the major tributaries of River Beas traversing for most of its stretch within the protected boundaries of the Great Himalayan National Park Conservation Area. A total of 108 sampling points were chosen from confluence to origin of rivers/streams, ranging from 989 to 3677msl. A total of 28 explanatory variables were recorded at each point. Overall, the Brown Trout adults were found to be greater in relative abundance (66.1%) than the Snow Trout adults (33.9%). The fingerlings of Snow Trout on the other hand, were distinctively high in relative abundance (61.9%) than those of the invasive Brown Trout (38.1%). Non-native trout showed higher abundance in the higher stream orders i.e. in the main streams while natives mostly restricted themselves to the lower order streams. Redundancy analysis (RDA) for species and environmental covariates resulted in 40.75% of constrained variance with higher eigen values for Redundancy analysis1 and Redundancy analysis2. Ward’s minimum variance clustering of Hellinger transformed data revealed sites agglomerating into six reasonable distinct subgroups with respect to species abundances. Immature individuals of non-native and native trout used similar habitat conditions, but they differed in using habitats at adult stage. Our results show a competitive dominance of Brown Trout in terms of higher abundance and maximum space utilization that highlight an urgent action for preventing its introductions to new areas. We recommend a national policy of ‘The Indian Invasive Species Act’ and management level interventions to control overstocking in the areas of established population.

Introduction

Brown Trout (Salmo trutta) is one of the top global invaders and has been documented as detrimental for many native fish species (Gerig et al., 2017; Muhlfeld et al., 2017). Brown Trout, which are a native of Europe, have been introduced in over 24 nations worldwide (Elliott, 1989), including in India in 1899 and have been thriving with the native fish populations since then (Sehgal, 1999; Petr, 1999). Any species becomes invasive due to two broad governing factors, favourable environment and favourable physiological traits. In case of Brown Trout, keeping in mind its propensity to invade freshwaters of multiple nations, it is sturdy enough to manage in tough environmental conditions too, and hence the latter factor of physiology would be the key behind its invasiveness. As a strategy to invade, the Brown Trout populations show different growth characteristics and breeding attributes in different environments (Deacon et al., 2011; Fernández-Chacón et al., 2015; Dieterman et al., 2016). This is a sign of phenotypic plasticity, due to local adaptations to the varied environments (Nicola and Almodóvar, 2002). The Snow Trout (Schizothorax richardsonii) belonging to sub-family Schizothoracinae, seemingly appeared during the first interglacial period (Hora, 1955; Menon, 1962) when central Asia underwent the formation of turbulent streams, demanding the reduction of scales, a feature characteristic to the Schizothoracines. The primitive forms though still exist in China, during the second glaciation event, this group dispersed westwards onto Sistan and Kashmir. Diversification of genera and species of sub-family Schizothoracinae occurred in the subsequent glacial and interglacial periods. Although a native of this area, there have been rare studies to understand its actual distributional range, size structure and species-environmental relationships (Mohan, 2006; Sharma and Dhanze, 2010; Rajput et al., 2013; Sharma et al., 2014; Sharma et al., 2019). Brown Trout and the native Schizothoracines belong to the rhithron zone of the Himalaya, characterized by a mean monthly temperature of 17.3°C with high concentrations of dissolved oxygen (10.01 mg l-1) and turbulent waters. Owing to the similarity in habitat preferences, their co-existence and interactions have been hypothesized based on their differing feeding and breeding biology patterns (Sehgal, 1999). In Himalaya, population of Brown Trout is well established in many river systems. One such river is Tirthan, which is abode to the exotic-invasive Brown Trout and native Snow Trout. This river is one of the most pristine rivers and a no-go area for hydropower development due to strong protest by the local communities. As it stands, the river is popular as an “angling reserve” specifically for Brown Trout due to provision of limited day-based licenses by the National Park and State Fisheries authorities for sustainable angling, however, no scientific study is available so far about its distributional range, abundance and its potential impact on native populations which is crucial for sustainable management. The environmental filtering and niche partitioning play greatest role in shaping fish community structure in natural systems (Marsh-Matthews and Matthews, 2000; Grenouillet and Hẽrissẽ, 2004; Kautza and Sullivan, 2012). Understanding fish-habitat relationship at different geographical and temporal scales is essential for effective management and conservation decisions (Allan, 2004; Heino et al., 2007; Buisson et al., 2008). In addition, group of species with similar physiological and behavioural adaptations are often able to colonize in analogous environmental conditions, while species with higher similarity in traits can also lead to greater competition (Matthews, 1998; Jackson et al., 2001). Most studies that have measured the community assemblages are largely based on environmental characteristics, landscape features and anthropogenic stressors while less attention has been given to the presence of non-native species and predation pressures in shaping native species distributions (Gerhard et al., 2004; Fischer and Paukert, 2008; Kautza and Sullivan, 2012; Dubey et al., 2012). To date, knowledge of spatial variations in high altitude stream dwelling fish population and their association with the presence of invasive Brown Trout in natural environmental setups has been rarely investigated. Such studies are critical for strong policy level interventions and management of healthy native fish population in high altitude Himalayan rivers. Therefore, bringing forth the objective of understanding these processes more keenly with the aid of their environmental and riverscape level characterization would provide perspicacity for managing increased proliferation of non-native fishes in Himalaya. We thus initiated this study to understand (1) whether the invasive Brown Trout influences the spatial distribution of the Snow Trout, if so, (2) are the native distributions perturbed in specific stream orders and (3) whether there are possible refuge sites for the natives that can be targeted for conservation.

Study area

Sampling was carried out from the June 2016 to June 2018 across 108 locations in the Tirthan river catchment comprising 21 streams and the main river channel traversing a total river stretch of 182.7 km (Fig. 1). The studied streams constituted an elevational range of 900-4800 m asl, most of which could only be accessed through trekking along the stream beds. A downstream to upstream approach was followed starting at the confluence and moving onto the origin. For the higher order streams (4th and 5th) sampling was conducted at every 500 m asl. However, in case of the lower order streams (1st to 3rd), an interval of 200 m asl was chosen to ensure equivalent representation of lower order tributaries which often covered a stream length of less than 500m. A total of 108 sampling points were covered.

Fig. 1.

Map of the Tirthan river watershed showing the sampled locations and the species recorded across different stream orders.

Fig. 1.

Map of the Tirthan river watershed showing the sampled locations and the species recorded across different stream orders.

Methods

To understand the role of environment in structuring the abundance of the fingerlings and adults of the Snow Trout and Brown Trout, we examined a total of 28 environmental variables at each sampling location (Table 1). All the variables considered were continuous except stream order which was ordinal while glacier, snow and spring-fed stream characteristics were recorded as binomial variables (presence/absence) at each location. Of the total variables, 25 were ground-data collections made on site, while three variables (distance from source, distance from confluence and stream order) were extracted using the geostatistical tools available in ArcGIS ver. 10.8.

Table 1.

Explanatory variables analysed from the 108 sampling locations in the Tirthan river basin.

Explanatory variablesCodeMean (Range)
Altitude (m) alt 1955 (989‒3677) 
Distance from source (m) dfs 15.89 (0.07‒1.10) 
Distance from confluence (m) dfc 12.24 (0.00‒71.00) 
pH pH 8.53 (7.93–8.87) 
Water temperature (°C) temp 15.48 (6.20‒24.80) 
Total dissolved solids (mg l-1tds 29.11 (6.67‒129.33) 
Electrical conductivity (µS cm-1ec 68.29 (16.67‒243.67) 
Dissolved oxygen (mg l-1do 10.76 (8.00‒12.59) 
Total river width (m) trw 16.37 (0.30‒93.33) 
Wet river width (m) wrw 11.91 (0.23‒60.00) 
Flow (m s-1flw 0.85 (0.27‒4.43) 
Depth (m) dep 0.89 (0.03‒36.00) 
Slope (%) slp 12.95 (1.00‒61.00) 
Bedrock (%) bedr 25.02 (0‒83.33) 
Boulders (%) bld 29.00 (3.33‒81.67) 
Cobbles (%) cbl 25.15 (3.33‒61.67) 
Gravel (%) grv 7.88 (0.00‒20.00) 
Sand (%) sand 13.13 (0.00‒76.67) 
Riffles (%) rif 41.78 (0.00‒100.00) 
Run (%) run 3.42 (0.00‒60.00) 
Pool (%) pul 23.52 (0.00‒100.00) 
Cascade (%) cscd 19.14 (0.00‒60.00) 
Rapid (%) rpd 10.19 (0.00‒75.00) 
Waterfall (%) wfl 1.80 (0.00‒70.00) 
Stream order sord 1‒5 (coded) 
Glacier-fed stream (Presence/Absence) glac 0‒1 (coded) 
Spring-fed stream (Presence/Absence) spr 0‒1 (coded) 
Snow-fed stream (Presence/Absence) snw 0‒1 (coded) 
Explanatory variablesCodeMean (Range)
Altitude (m) alt 1955 (989‒3677) 
Distance from source (m) dfs 15.89 (0.07‒1.10) 
Distance from confluence (m) dfc 12.24 (0.00‒71.00) 
pH pH 8.53 (7.93–8.87) 
Water temperature (°C) temp 15.48 (6.20‒24.80) 
Total dissolved solids (mg l-1tds 29.11 (6.67‒129.33) 
Electrical conductivity (µS cm-1ec 68.29 (16.67‒243.67) 
Dissolved oxygen (mg l-1do 10.76 (8.00‒12.59) 
Total river width (m) trw 16.37 (0.30‒93.33) 
Wet river width (m) wrw 11.91 (0.23‒60.00) 
Flow (m s-1flw 0.85 (0.27‒4.43) 
Depth (m) dep 0.89 (0.03‒36.00) 
Slope (%) slp 12.95 (1.00‒61.00) 
Bedrock (%) bedr 25.02 (0‒83.33) 
Boulders (%) bld 29.00 (3.33‒81.67) 
Cobbles (%) cbl 25.15 (3.33‒61.67) 
Gravel (%) grv 7.88 (0.00‒20.00) 
Sand (%) sand 13.13 (0.00‒76.67) 
Riffles (%) rif 41.78 (0.00‒100.00) 
Run (%) run 3.42 (0.00‒60.00) 
Pool (%) pul 23.52 (0.00‒100.00) 
Cascade (%) cscd 19.14 (0.00‒60.00) 
Rapid (%) rpd 10.19 (0.00‒75.00) 
Waterfall (%) wfl 1.80 (0.00‒70.00) 
Stream order sord 1‒5 (coded) 
Glacier-fed stream (Presence/Absence) glac 0‒1 (coded) 
Spring-fed stream (Presence/Absence) spr 0‒1 (coded) 
Snow-fed stream (Presence/Absence) snw 0‒1 (coded) 

We used the Spearman rank correlation method to identify the highly collinear variables and address the multicollinearity in the data. We considered a correlation value of 0.80 as the threshold for selecting the collinear variables. The retained variables were then further assessed for their influence on the complete dataset by utilizing the Principal Component Analysis (PCA). Prior to the PCA, environmental variables were standardized with zero mean and unit variance. Eigenvalues from PCA were used for the multidimensionality reduction and the set of environmental variables which had high loadings on the components of highest variance were then selected for further analysis. The final set of explanatory variables were tested for their effects on structuring the distribution of the species.

As our aim was to decipher the competitive interactions in geographic space between the native Snow Trout and the invasive Brown Trout, we divided the fish occurrences into four groups: (a) Snow Trout fingerlings (b) Snow Trout adults (c) Brown Trout fingerlings and (d) Brown Trout adults. This was done as the fingerlings of both the species were observed to occupy distinctively differing habitats as compared to the adults, thus including them as separate categories further proffered analysis of the spatial competitive interactions across life history stages for the two species. We utilized the constrained redundancy analysis (RDA) with a transformed data approach to understand responses of the fish assemblage structure to the explanatory variables. We used permutation tests to check the significance of the RDA results for the overall model as well as for the individual canonical axes with 1000 randomly permuted associations (Legendre and Gallagher, 2001). The results were explored based on a response-predictor RDA biplot based on the two constrained axes showing highest proportion of explained variance.

To determine if spatial configuration of the study sites reflected the results of species’ preferences in our RDA plot, we utilized the cluster analysis. Group diagnostics for the sites was performed based on the average Silhouette widths representing the degree by which a site typified its cluster. We used the Ward method to explore the hierarchical partitioning of sites by constructing reordered dendrograms with chord distance (Legendre and Legendre, 1998; Oksanen, 2015). We ran 100 iterations to produce the Silhouette widths. All analyses were performed in R ver. 4.0.2 using the packages ‘vegan’, ‘dendextend, ‘gclus’, ‘cluster’ and ‘mvpart’ (Galili, 2015; Oksanen, 2015; Hurley, 2019; Maechler et al., 2019; De’ath et al., 2014; R Core Team, 2020).

Results

Of the 108 sites sampled, fish were encountered at 64 locations. The Brown Trout adults were found to be greater in relative abundance (66.1%) than the Snow Trout adults (33.9%). Conversely, the Snow Trout fingerlings were comparatively higher in relative abundance (61.9%) than the fingerlings of the invasive Brown Trout (38.1%). The Spearman rank correlation resulted in four highly collinear pairs of explanatory variables, of which the biologically meaningful variables were retained a posteriori. This resulted in removal of total river width, distance from source and electrical conductivity. The remaining 25 variables subjected to PCA resulted in 24 principal components, with the first three displaying 48.69% of the cumulative variance, also evident with the scree plot. The PCA resulted in further removal of pH, cobbles and snow-fed streams based on their low loadings on the three principal components with high eigen values. Furthermore, the small angles formed by the vectors of these variables indicated a strong collinearity in environmental space (Fig. 2). We thus retained the remaining 22 explanatory variables for further analysis.

Fig. 2.

The Principal Component analysis (PCA) plot of the 108 sampling locations and 25 explanatory variables selected after the correlation test projected on the axes of maximum inertia. The vector lengths for each variable correspond to the contribution it makes in determination of the two principal components. The projections are made in type-2 scaling where the angles formed by two vectors represent the degree of correlation between variables, small angles representing higher correlations and vice-versa.

Fig. 2.

The Principal Component analysis (PCA) plot of the 108 sampling locations and 25 explanatory variables selected after the correlation test projected on the axes of maximum inertia. The vector lengths for each variable correspond to the contribution it makes in determination of the two principal components. The projections are made in type-2 scaling where the angles formed by two vectors represent the degree of correlation between variables, small angles representing higher correlations and vice-versa.

The RDA of the fish assemblage structure constrained with the explanatory variables resulted in a total of eight axes with four constrained and four unconstrained axes. The constrained axes explained 40.75 % of the total variance (adjusted R2=0.25), of which two important components expressed 90.96 % of the total explained variance (variance proportion explained: RDA1=59.48 %, RDA2=31.47%). The RDA biplot scores of the constraining variables indicated altitude to be the highest loaded variable on the 2nd RDA axis (loading= -0.90) which is also indicated by the longest vector on the RDA plot (Fig. 3). Further, temperature (RDA2 loading= 0.77) and stream order (RDA 2 loading= 0.73) also seem to play a major role in dictating the habitat preferences of the fishes. Spring-fed (RDA 1 loading= 0.70) and glacier-fed (RDA 1 loading= -0.72) stream variables also seemed to influence the habitat preferences of the species. The Brown Trout adults were found to be strongly and positively correlated to the glacier-fed stream segments with higher stream orders and a microhabitat composed largely of rapids (Fig. 3). The adults of the invasive Brown Trout were also found to have distinctively high loadings on the RDA axes (RDA1= -0.96, RDA2=0.60). The adults of the native Snow Trout on the other hand did not show a very significant loading on either axis (RDA1=-0.07, RDA2=0.24) and showed a strong inclination towards habitats with higher sand characterised by runs and higher stream orders. The RDA results revealed strong collinearity between the fingerlings of the natives and invasives as is evident by the angle their vectors share in the RDA plot (Fig. 3). Both the groups of fingerlings showed a strong preference towards habitats with high total dissolved solids and pools with strong inclination towards higher temperatures and spring-fed stream segments. The fingerlings also showed a moderate inclination towards increasing stream orders indicating a higher preference of stream orders that are neither too small nor large. All the adults as well as fingerlings showed negative correlation with increasing altitude, as evident with the absence of fishes observed in the higher altitude river stretches during our field surveys (Fig. 1). The silhouette widths estimated 6 clusters optimally representing the distribution of fish groups across sites. The invasive Brown Trout adults solely occupied the 4th or 5th order streams with no other fish group overlapping as indicated by the blue cluster in the reordered dendrogram (Fig. 4). On the contrary, only four sites of the 108 sampled sites, showed isolated presence of Snow Trout distributed majorly on the 5th order stream segments. Sites with competitive interactions between the adults of both the species formed another small cluster equally represented by the 3rd and 5th order streams. A separate cluster defined the presence of adults and fingerlings of Snow Trout restricted majorly to the 3rd order streams. A large number of sites formed the cluster of maximal overlap between all the four fish groups, specifically the fingerlings of both the species which were found to overlap in almost all the sites of this cluster. This cluster formed primarily of the sites on 3rd and 4th order streams. The largest cluster was formed by the sites where no fish occurrences were recorded.

Fig. 3.

The Redundancy analysis (RDA) biplot of the responses of the fish assemblage (red) constrained with the environment (dark cyan). The length of vectors represents the strong loadings on the axes while the angles between the vectors represent the correlation between and within the environmental variables and fish assemblage where bta: Brown Trout adult, btf: Brown Trout fingerling, sta: Snow Trout adult, stf: Snow Trout fingerling. [Explanatory variable codes: see Table 1].

Fig. 3.

The Redundancy analysis (RDA) biplot of the responses of the fish assemblage (red) constrained with the environment (dark cyan). The length of vectors represents the strong loadings on the axes while the angles between the vectors represent the correlation between and within the environmental variables and fish assemblage where bta: Brown Trout adult, btf: Brown Trout fingerling, sta: Snow Trout adult, stf: Snow Trout fingerling. [Explanatory variable codes: see Table 1].

Fig. 4.

Reordered dendrogram of fish assemblage with ward clustering of chord distance among sites using squared dissimilarities. A total of 6 clusters were selected based on the Silhouette widths where the sites are labelled with initials representing the stream orders, where FR= 1st, SC=2nd, TH=3rd, FO=4th and FV=5th order stream segments. The clusters from left to right are indicative of sites with all the four groups (turquoise), both the adults and fingerlings of the native Snow Trout (magenta), no fish (red), Snow Trout adults (yellow), adults of the Snow Trout and the invasive Brown Trout (green), and solely the adults of Brown Trout (blue). Silhouette figures of the four fish groups are provided with snouts facing towards the cluster they represent.

Fig. 4.

Reordered dendrogram of fish assemblage with ward clustering of chord distance among sites using squared dissimilarities. A total of 6 clusters were selected based on the Silhouette widths where the sites are labelled with initials representing the stream orders, where FR= 1st, SC=2nd, TH=3rd, FO=4th and FV=5th order stream segments. The clusters from left to right are indicative of sites with all the four groups (turquoise), both the adults and fingerlings of the native Snow Trout (magenta), no fish (red), Snow Trout adults (yellow), adults of the Snow Trout and the invasive Brown Trout (green), and solely the adults of Brown Trout (blue). Silhouette figures of the four fish groups are provided with snouts facing towards the cluster they represent.

Discussion and conclusions

Our study indicates a disruption in the spatial distribution of native Snow Trout across river stretch, with significantly isolated records in the 5th order streams that were predominantly occupied by the invasive Brown Trout. The strikingly lower numbers of the Snow Trout adults further heighten concerns on the sustenance of the native population. We highlight the 3rd and 4th order tributaries as potential refuge sites for the native Snow Trout owing to a much higher abundance of their fingerlings regardless of the overlap with the invasive Brown Trout.

Our results of the RDA and cluster analysis distinctly indicate common ecological preferences of the fingerling life history of both the native Snow Trout and the non-native Brown Trout. This is in congruence with our field observations where the fingerlings of both the species occupied the same pools, most of which being restricted to the 3rd and 4th order spring-fed tributaries with moderate temperature. Interestingly the Brown Trout has been documented to share overlapping ecological preferences with the early life histories of its competitors in other basins of the world (Fausch and White, 1986; McIntosh et al., 1992).

The results of our study are in congruence with the other documented observations of the impacts of the invasive trout on the distribution of the native species across the globe (Larson and Moore, 1985; Fausch 2008; Hoxmeier and Dieterman, 2013). Owing to its aggressive territoriality, the Brown Trout has been found to exclude the native fishes, distinctively relegating them to the headwater or the smaller order tributaries (Fausch and White, 1986; Kirk et al., 2018). Owing to higher numbers of the Brown Trout adults in the downstream segments, the Snow Trout seemingly maintains higher numbers in the upstream segments. Additionally, the lower abundance of the Snow Trout adults can be most likely attributed to the recruitment failure owing to Brown Trout predation and competition pressures.

Although Brown Trout has been documented with an increased piscivory outside its native range (Budy et al., 2013), yet we observed rare piscivory during the biological investigations we made in our previous study in the basin (Sharma et al., 2021). This does indicate a predation impact, but is nevertheless not quite significant. Furthermore, the unequivocally contrasting feeding strategies of herbivory in the Snow Trout and carnivory in the Brown Trout seem to dismiss the competition for food affecting the distributional disruption of the Snow Trout in our study (Sharma, 1984; Froese and Pauly, 2011). Brown Trout has been widely reported as a strong interference competitor where exclusion of the natives is a commonplace owing to its aggressive territoriality (Budy et al., 2013; Lobón-Cerviá and Sanz, 2017). In the same vein, the native Snow Trout seem to be negatively affected by the interference (competition for space) rather than the exploitative (predator-prey interaction) competition.

Our previous study on the same river basin revealed the inherent plastic traits of the Snow Trout with a raised fecundity as compared to its populations in the other basins. This definitely highlights the ability of the Snow Trout to cope up with the invasion impacts, provided the river is undammed. The headwater lower order tributaries of the Himalayan riverscape which are spring-fed especially hold the key conservation areas for the natives. The headwater lower streams provide a heterogenous environment as compared to the main river stretch (Vannote et al., 1980; Gooderham et al., 2007; Biggs et al., 2017), thereby proffering opportunities to ameliorate the invasion impacts on the natives and acting as potential refuges (Kauffman et al., 2007; Woodford and McIntosh, 2010). However, the continuous stocking of Brown Trout by the local authorities in Tirthan surpasses the plastic abilities of the natives to cope with the invasion pressures, evident with the significantly lower relative abundance of the Snow Trout adults in our study.

Through our results, we provide a strong basis to understand the community level organization of native and non-native populations in Himalayan headwater networks and also identify the role of different environmental factors governing the distribution of individuals in their young and adult stages. The Brown Trout in river Tirthan provides a suitable recreational opportunity to the local communities, though its overstocking can be a potential threat to the native populations of Snow Trout. Considering the strong territorial nature of Brown Trout, our results suggest that the substantial occupied ranges of the Brown Trout in Tirthan can pose population and community level risk for the natives. Though evidence of Brown Trout predation on the fingerlings of Snow Trout was recorded intermittently during our surveys, the greater overlap in their suitable habitats can increase the chances of predation. We recommend regular monitoring of abundance and size structure of both the populations to better facilitate informed scientific decision about level and frequency of stocking. Based on our findings we suggest to have a ‘Invasive Species Act’ to prevent and manage invasive species in India. Further, stocking of non-native Brown Trout needs to be discouraged in the Himalaya.

Acknowledgements

This study was funded by the Department of Science and Technology (DST), Govt. of India under the National Mission for Sustaining the Himalayan Ecosystem project (DST Grant Number: DST/SPLICE/CCP/NMSHE/TF-2/WII/2014[G]). The authors are grateful to Director and Dean, WII for their encouragement. Special thanks are due to Dr. S. Sathyakumar, Nodal Scientist, NMSHE, WII for his advice and guidance throughout this work. We thank the Head, Department of Zoology, Panjab University for support. Authors also thank Himachal Pradesh Forest and Fisheries departments for their permission and support during fieldwork.

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