Abstract

In order to assess the distribution pattern and understand the prevailing factors for predicting further expansion of an exotic fish Oreochromis niloticus, this study was undertaken in the Ganga river flowing through the state of Uttar Pradesh using MaxEnt model. The authors report the distribution pattern of O. niloticus and prevailing causative factors mounting the expansion of O. niloticus in the Ganges based on MaxEnt modeling technique. The presence only occurrence data-set for this invasive species was prepared from the field data and also from data collated from the authenticated publications of different fisheries researchers. The data-set was analyzed with environmental and topographical variables typically incorporating seasonal and temporal variability using MaxEnt, a maximum entropy algorithm which showed that the area under curve was much closer to 1 ( 0.999). The model predicted elevation as the most influential predictor variable with permutation importance of 69.2% followed by slope_steepness (10.1%), Tmax_1 (7.3%) and Srad_5 (6.8%). The findings from the results suggest that invasive O. niloticus tend to spread in rivers where elevation is lower as well as slope_steepness of the river is higher and thus indicated that invasion might be higher in the downstream of the river. The model suggests that topography and its derived variable are the most significant predictors for distribution of invasive O. niloticus. The results of this study also confirm that the water qualities of the Ganga river are suitable for O. niloticus and if the model is supplemented with water quality variables data, the influential predictor variable in water quality can be well investigated with permutation importance.

Introduction

The Ganga river system exhibits great species diversity in terms of composition, abundance and phylogeny (Sarkar et al., 2012; Das et al., 2013). As per the recent database of the ICAR- National Bureau of Fish Genetic Resources (NBFGR), Lucknow, India (Anon., 2019) there are 3535 finfishes of which 3035 are native and over 500 are non-native to India representing 46 orders, 252 families and 1,018 genera. Out of 3035 native fishes, 1016 are freshwater, 113 brackish and 1906 marine fish species.). The overall species pool of the Gangetic fish assemblage is estimated at around 300 species comprising of 53+ families, 150+ genera; and 250 species (Sarkar et al., 2012). The floodplain fishery is dominated by the presence of major and minor carp (Cyprinidae), Catfish (Siluriformes: 6-7 families), Clupeidae, Notopteridae and a mix of many other families. Major carp and the Clupeid fish, Hilsa (Tenualosa ilisha) and some large catfish form the most valued catches across most parts of the Gangetic floodplains (Sarkar et al., 2017; Pathak, 2018).

In recent years, many exotic fish species have gravitated into the Ganga River and the distribution of the invaded fish population has greatly differed in tropical conditions of the river (Singh et al., 2013; Das et al., 2013). Exotic Tilapia (O. nolitcus) has been reported to contribute substantially to the catches of the Gangetic plains (Singh et al., 2013). The expanding distribution and abundance of Tilapia O. niloticus is now playing major role in affecting the existing fish habitats of the region, which is an important step toward understanding the changing scenario of fish invasion for conserving the endemic species in the freshwater ecosystem. Worldwide, researchers are interested in knowing the extent of spread and distribution of the exotic species due to growing adverse impacts of the exotic species in the natural river waters (Rodriguez-Rey et al., 2019).

Species distribution modeling in spite of few limitations such as violating the equilibrium assumption during the invasion process are used widely for assessing potential distributions of invasive species (Maloney et al., 2013). Distributions of species vary according to an array of biological and physical conditions underlying the fundamental niche and correlative species distribution that enhances our understanding of the niches in geographic space (West et al., 2016). Species distribution models (SDMs) are the efficient tools to predict the geographic and environmental range of a species. SDMs estimate the relationship between species records at sites and the environmental and/or spatial characteristics of those sites (Franklin, 2010). SDMs are widely used for stream bioassessment, estimating changes in the habitat suitability and identifying conservation priorities. In the last two decades, many developments have been made in the field of SDM and multiple methods are now available (Elith et al., 2006; Austin, 2007) of which many have been used to predict the distribution of fishes by predicting the suitable habitat (Mcnyset, 2005; Oakes et al., 2005; Buisson et al., 2008). Moreover, a recent comprehensive comparison of presence-only data modeling techniques (Elith et al., 2006) highlight that some new methods have better predictive accuracy than the established methods and among new methods, Maximum Entropy Species Distribution Modeling (MaxEnt) (Phillips et al., 2006; Elith et al., 2006; Rose et al., 2016) is better to optimize predictive accuracy. Recent survey of literatures suggests that species distribution model could be better if more ecological theory is included by taking species-environment relationship in consideration (Austin, 2007; Guisan et al., 2013; Xiao et al., 2018.). The maximum entropy model (Maxent; Phillips et al., 2006) is one of the most widely used presence-only SDMs. The objective of this study was to generate expansion extent of exotic Tilapia exploring environmental and topographical variables as well as potential habitat suitability using Maxent fit with presence-only data, and used field sampling to test the predictions. The spatial distribution pattern, as well as ecological niche of invaded Tilapia into the Ganga river, was assessed deducing the environmental factors affecting its distribution. The generated information on changing geographical and environmental range attributed to the distribution expansion of invasive Tilapia will be helpful to conservation scientists for developing containment plan and management strategies.

Materials and methods

Data on fishing activities in the Ganga river mainly centred in the middle stretch of the Ganges passing through the states of Uttar Pradesh was generated. The commercial fishing activities are active in the middle stretch of the Ganga in the State of Uttar Pradesh.

Species occurrence and environmental records

The presence only occurrence data-set for Tilapia was prepared from the field data and data collated from the authenticated publications of different fisheries researchers. As many as 192 occurrence records of O.niloticus created by a continuous field information were generated between 2009 and 2020. The environmental dataset comprised of topography, hydrology and climate which were downloaded and used to develop the probability spatial distribution model for the ecological environment for the O. niloticus.

Climatic variables

World Clim Version 2.0 that holds the data with a spatial resolution of 30 seconds (~1 km2) on different climatic variables was used (Fick and Hijmans, 2017). This is the new release, which has a set of global climate layers (gridded climate data) with a spatial resolution of about 1 km2.

Data evaluation model derived hydrological and topographical variables

Global Multi-resolution Terrain Elevation Data (GMTED 2010 database) was used to derive data for the state of Uttar Pradesh with a spatial resolution of 30 m on different hydrological and topographical variables. GMTED was based on data derived from 11 raster-based elevation sources and the model provided data at three separate resolutions (horizontal post spacing) of 30 arc-seconds (about 1 kilometer), 15 arc-seconds (about 500 meters), and 7.5 arc seconds (about 250 meters). This new product suite provides global coverage of all land areas. In the present study, only the mean dataset available in GMTED was used in deriving the values of different hydrological parameters. Table 1 lists the hydrological parameters whose values were derived by applying the AT Search Algorithm developed by Ehlschlaeger (1989) using the GRASS GIS software. AT Search Algorithm also known as least cost search is a method which identifies drainage flow directly from the original elevation data. From the prepared dataset topographical variables like slope and climate were derived using the GRASS GIS (Version 7.8.1) software.

Table 1.

Hydrological and topographic data variables details in the MaxEnt model.

Hydrological variablesTopographic Variables
max_slope_length = Maximum length of surface flow Landscape slope
Landscape aspect 
accumulation = Number of cells that drain through each cell 
log_accumulation = Absolute logarthmic value of accumulation 
tci = Topographic index ln(a / tan(b)) (Quinn et al., 1991
spi = Stream power index a * tan(b) (Moore et al., 1991
drainage = Drainage direction (numbered from 1 to 8) 
                                                                    basin 
                                                                    stream 
length_slope = Slope length (Weltz et al., 1987
slope_steepness = Slope_steepness (McCool et al., 1987
Hydrological variablesTopographic Variables
max_slope_length = Maximum length of surface flow Landscape slope
Landscape aspect 
accumulation = Number of cells that drain through each cell 
log_accumulation = Absolute logarthmic value of accumulation 
tci = Topographic index ln(a / tan(b)) (Quinn et al., 1991
spi = Stream power index a * tan(b) (Moore et al., 1991
drainage = Drainage direction (numbered from 1 to 8) 
                                                                    basin 
                                                                    stream 
length_slope = Slope length (Weltz et al., 1987
slope_steepness = Slope_steepness (McCool et al., 1987

Model construction

We used MaxEnt model, a maximum-entropy based machine learning (presence/pseudo-absence) algorithm to model the distribution of O. niloticus (L.) in the middle stretch of the Ganga river. MaxEnt predicts the probability distribution of invasive Tilapia on the basis of a given set of predictor variables and presence-only species occurrence data (Elith et al., 2010). The collated climatic data was resampled using Lanczos interpolation to transform the data to a finer grid of spatial resolution of 30m. All the GIS analysis was done using GRASS GIS (Version 7.8.1) software. MaxEnt, a maximum entropy-based machine learning program that estimated the probability distribution for Tilapia occurrence, was based on environmental constraints (Phillips et al., 2006). The freely available MaxEnt software, version 3.1 was used to estimate the probability of presence of Tilapia varying from 0 to 1, where 0 is the lowest and 1 the highest probability (100%).

Results

In the Ganga River basin, the predictive distribution map for Tilapia created through MaxEnt model showed occurrence probability of 75 to 80%; 85 to 95% and above 95% as displayed in different colours for various river streams (Fig 1). MaxEnt model predicted the potential niches and probability distribution for O. noloticus based on the known occurrence records (Fig. 1). The map of Tilapia distribution in the Ganga river was prepared at threshold of >=0.5 because at this threshold and above, the species was more likely to be present. The red training line showed the ‘fit’ of the model to the training data. The blue testing line indicated the ‘fit’ of the model to the testing data and was the real test of the model’s predictive power (Fig. 2). The Area Under Curve (AUC) for the training points was 0.999 and for test points was 0.969, with a standard deviation of 0.007. Furthermore, the obtained distribution model showed that the predicted distribution range of O. niloticus was the total predicted distribution range size where AUC was 0.999 (Fig. 2). Mean AUC value was close to 1 for the Tilapia species, which meant that the model was a good classifier. The model predicted elevation as the most influential predictor variable with permutation importance of 69.2% followed by slope_steepness (10.1%), temperature Tmax_1 (7.3%) and solar radiation Srad_5 (6.8%) (Fig 3 A-D). Values shown on the y-axis were the predicted probability of suitable conditions, as given by the logistic output format with all other variables set to their average value over the set of presence localities. Tmax_1 (7.3%) showed maximum temperature in January and solar radiation Srad_5 (6.8%) in May (Fig. 3C). Most important climatic variables were maximum temperature in January (mean 21.663±6.6 degree Celsius) and maximum solar radiation in May (mean 26605.145±1468.33 kj m-2 per day). This makes sense because O. niloticus is a tropical cold-blooded species which cannot tolerate lower temperature for long period of time hence prefer not to live where temperature stay low for long. O. niloticus establishment in newer areas was mainly attributed to the suitable receiving environment in terms of minimum temperature range of 15–19 degree Celsius. In general, a decrease in mean temperature and an increase in the amplitude of temperature fluctuations with increasing altitude were observed to be associated with the dispersal of the invasive O. niloticus. Further, solar radiation was single most influential factor for primary productivity of the ecosystem. Dependency of the species on solar radiation in May also indicated that this period of the year was most important for its growth and represented feeding phase of Tilapia. It was obvious that monthly variables were helping MaxEnt to obtain a good ‘fit’ to the training data, but the topographical variable generalized better giving comparatively better results on the set-aside test data.

Fig. 1.

Predictive distribution map for Tilapia in the Ganga River basin.

Fig. 1.

Predictive distribution map for Tilapia in the Ganga River basin.

Fig. 2.

Area under curve (AUC) between average sensitivity and specificity for the O. niloticus. Training data (AUC = 0.999) and test data (AUC = 0.969) compared to random prediction (AUC = 0.5) in the receiver operating characteristic (ROC) curve for representation of the Maxent model for Oreochromis niloticus.

Fig. 2.

Area under curve (AUC) between average sensitivity and specificity for the O. niloticus. Training data (AUC = 0.999) and test data (AUC = 0.969) compared to random prediction (AUC = 0.5) in the receiver operating characteristic (ROC) curve for representation of the Maxent model for Oreochromis niloticus.

Fig. 3

(A-D) Important predictor variables and their response curve for O. niloticus with their permutation importance in % (average of 4 replicate runs). In each graph X-axis represent prediction probabilities between 0 (Absent) and 1 (100% present). Elevation 69.2%, slope_steepness (10.1%), Tmax_1 (7.3%) and Srad_5 (6.8%).

Fig. 3

(A-D) Important predictor variables and their response curve for O. niloticus with their permutation importance in % (average of 4 replicate runs). In each graph X-axis represent prediction probabilities between 0 (Absent) and 1 (100% present). Elevation 69.2%, slope_steepness (10.1%), Tmax_1 (7.3%) and Srad_5 (6.8%).

Discussion

The results of this study delineate that Tilapia tend to thrive in the Ganga river where elevation is lower as well as slope_steepness of the river is higher and thus indicate that invasion may be higher in the downstream. The areas where environmental temperature varied from 15 to 27°C, the occurrence probability in the river was above 95% while its occurrence varied when the temperature range fluctuated below 15 °C. This condition was well observed in the Sone river where occurrence probability of Tilapia is over 95% as per developed Maxent distribution map. In River Ganges, January is the month with lowest temperature and the probability of presence of Tilapia increased with temperature elevation. This delineated that its invasion will more likely be downstream where temperature in January is higher than upstream. Such findings corroborate with previous reports where invasion of Tilapia has been shown progressing downstream (Singh et al., 2013; Singh et al., 2014). Further, solar radiation has been considered another fundamental ecosystem modulator. The results of this study pointed out that species composition and population structure are influenced by solar radiation since the occurrence probability of Tilapia has been found over 95% in large radiation. Thus, solar radiation has been found to be a very important factor in spread of invasive Tilapia, as it is associated with the community structure in the aquatic or riverine system (Deng et al., 2018). The analysis of the results clearly show that the accuracy and resolution of the input DEM have significant implications on the values of the hydrologically important spatial indices derived from the DEM. Topographic complexity has been obviously recognised as an important predictor of fish species distributions, supporting higher abundance of invasive Tilapia O. niloticus. However, other predictors such as slope_steepness (10.1%), Tmax_1 (7.3%) and Srad_5 (6.8%) each have been found to be indicator predictors of distribution of Tilapia in the Ganga river system. The model further indicate that topography and its derived variable are the most significant predictors for distribution of O. niloticus. These variables are present in the higher gain i.e. contained most information as compared to other variables. The presence of Tilapia only with recorded occurrence along with environmental variables derived from topography, hydrology and climate analysis for modeling distribution using the MaxEnt produced high accuracy information showing ‘outstanding’ prediction model as AUC was found much closed to 1 (AUC = 0.999). Our observations suggest that the model will be highly useful in order to estimate future distribution of invasive fish under climate change. Using a suite of model validation tests, this study suggests that a MaxEnt model fit with limited presence-only data can provide forceful estimates of habitat suitability for invasive species on the landscape. MaxEnt model ‘fit’ with a small number of presence points resulted in predictions that were strong to an independent test dataset collected during the period from 2009 to 2018. MaxEnt model analyzed the relative habitat suitability outputs providing strong evidence that there was minimal difference in overall spatial predictions. Other SDM comparisons have highlighted the importance of independent model validation (Gaston and Garcia-Vinas, 2011; Gies et al., 2015). Our results suggest that a Maxent presence-only model can accurately forecast the habitat suitability of the generalist invasive Tilapia (Long et al., 2009). The logistic growth regression model in reference to Tilapia population prediction for future did not differ significantly when compared from MaxEnt findings tested with presence-absence data (Singh and Srivastava, 2020).

The ever-increasing distribution of Tilapia majorly depends on interaction with the local species and the environment. Over the years, ecosystem has always been a main focus of research by the ecologists for conserving and protecting the local fish population in response to the reported loss of biodiversity (Singh et al., 2013; Milardi et al., 2018). The information on increasing geographic distribution of Tilapia as presented by the predictive MaxEnt model is important for a variety of applications in ecology and conservation (Graham et al., 2004; Radwan et al., 2019). The increasing diversity, abundance and range of the invasive exotic Tilapia in the Ganges have become one of biggest threat to the native biodiversity (Singh et al., 2013; Singh and Srivastava, 2020). The extending range of exotic Tilapia and reduction in native fish species is undoubtedly a serious concern for conservation of fishes in the Ganges. To overcome the issues arising from spread and distribution of invasive exotic Tilapia in the Ganga river, the knowledge generated on the spread, distribution pattern and ecological niche using MaxEnt modeling will be highly useful to the conservation scientists for effective action. Other uses of species distribution model involves ‘‘transferral’’: producing the model over one study area and then applying it to another area, or to changed environmental conditions in the same area. Example applications may include predicting the effect of climate change on species distributions (Thuiller et al., 2005; Araujo et al., 2005) and predicting areas at risk for fish invasions (Peterson, 2003; Thuiller et al., 2005). Such applications may require different choices of feature types and regularization parameters from those defined here in this study. It is also proposed here that MaxEnt modelling can make predictions of species range size even under climate change (Pearson et al., 2007).

Suggestions and conclusions

The applied MaxEnt model provides a baseline regarding the spatial and temporal distribution patterns of invasive Tilapia in a vast area of the Ganga river ecosystem. However, it lacks some essential information about species size and naturally populated abundance. In future, efforts should be made to regularly collect data under conditions that will enable the sophisticated modelling techniques to be more accurate and applicable for a larger knowledge base. Data for seasons are also lacking and efforts should be made to fill that gap. The present work has shown that MaxEnt model is a cost-efficient way of developing robust SDMs. In the future, we should plan to explore the possibility of applying novel field data to provide reliable estimates of Tilapia or any other invasive species abundance and density (Williams et al., 2006). It is an important avenue of future research to determine guidelines for using MaxEnt modelling methods to create reliability for predicting spread of invasive fish.

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