The authors have declared that no competing interests exist.
Conceived and designed the experiments: MKP JPB. Performed the experiments: JPB MKP. Analyzed the data: KM JPB. Wrote the paper: JPB MKP KM.
Studying diversity and distribution patterns of species along elevational gradients and understanding drivers behind these patterns is central to macroecology and conservation biology. A number of studies on biogeographic gradients are available for terrestrial ecosystems, but freshwater ecosystems remain largely neglected. In particular, we know very little about the species richness gradients and their drivers in the Himalaya, a global biodiversity hotspot.
We collated taxonomic and distribution data of fish species from 16 freshwater Himalayan rivers and carried out empirical studies on environmental drivers and fish diversity and distribution in the Teesta river (Eastern Himalaya). We examined patterns of fish species richness along the Himalayan elevational gradients (50–3800 m) and sought to understand the drivers behind the emerging patterns. We used generalized linear models (GLM) and generalized additive models (GAM) to examine the richness patterns; GLM was used to investigate relationship between fish species richness and various environmental variables. Regression modelling involved stepwise procedures, including elimination of collinear variables, best model selection, based on the least Akaike’s information criterion (AIC) and the highest percentage of deviance explained (D2). This maiden study on the Himalayan fishes revealed that total and non-endemic fish species richness monotonously decrease with increasing elevation, while endemics peaked around mid elevations (700–1500 m). The best explanatory model (synthetic model) indicated that water discharge is the best predictor of fish species richness patterns in the Himalayan rivers.
This study, carried out along one of the longest bioclimatic elevation gradients of the world, lends support to Rapoport’s elevational rule as opposed to mid domain effect hypothesis. We propose a species-discharge model and contradict species-area model in predicting fish species richness. We suggest that drivers of richness gradients in terrestrial and aquatic ecosystems are likely to be different. These studies are crucial in context of the impacts of unprecedented on-going river regulation on fish diversity and distribution in the Himalaya.
Understanding species richness patterns and factors influencing the species distribution is central to ecology and biogeography
While numerous studies have been carried out on species richness patterns in plants
In this study we examine the relationship between elevation and fish species distributions in freshwater Himalayan rivers in the Indian subcontinent. To our knowledge, this is the first attempt to document fish species richness pattern for the entire Himalayan region and understand the drivers behind these patterns. We collated data from primary and secondary sources to examine the relationship between elevation, species richness, and species richness and environmental variables. With the help of analytical methods and modelling, we figured out the importance and influence of various environmental variables in driving fish species richness. Modelling has proven valuable in revealing biodiversity patterns
In order to investigate which parameters control fish species richness in the Himalayan rivers, we quantified the richness of fish species in 16 Himalayan rivers as well as specifically in the Teesta river along with various sets of environmental variables across elevational gradients. Teesta river basin is a suitable study area for conducting ecological and taxonomic studies due to its wide elevational gradient extending from tropical to alpine ecosystems covered within a small geographical extent
Specifically, this study investigates the patterns of fish species richness in the Himalayan rivers and attempts to understand the factor/s and their level of influence in governing these patterns. In a wider sense, this contribution addresses three broad questions: (i) do species richness gradients in aquatic ecosystems follow same patterns as in terrestrial ecosystems? (ii) are the drivers of species richness same in terrestrial and aquatic ecosystems? and (iii) is there a case for realigning species-area theory to deal with aquatic ecosystems?
Upper left: The Indian Himalaya (in grey shade), and Nepal and Bhutan Himalaya. Lower left: Geographical coordinates of the Himalaya (26°30′ –37° N latitude and 72°–97°30′ E longitude). Right: Geographical coordinates of Sikkim Himalaya and location of sampling sites along Teesta river that constituted our study area.
Himalayan mountain ranges, located between 26°30′–37° N and 72°– 97°30′ E, stretch for about 2500 km between Nanga Parbat (8126 m) in the west and Namcha Barwa (7756 m) in the east, covering a geographic area of 594,400 km2 (
Variable root | Description | Derivation | Model variables |
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Tu | Turbidity (ntu) | Primary sampling | Tu.avg |
T | Water temperature (°C) | T.avg | |
TDS | Total dissolved solids (mg L−1) | TDS.avg | |
C | Electrical conductivity (µS cm−1) | C.avg | |
pH | pH | pH.avg | |
DO | Dissolved oxygen (mg L−1) | DO.avg | |
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P | Phytoplankton (cells L−1) | Primary sampling | P.avg |
B | Phytobenthos (cells cm−2) | B.avg | |
M | Macro-invertebrates (individuals m−2) | M.avg | |
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D | River discharge (m3 s−1) | Primary sampling | D.avg |
V | Water current velocity (m s−1) | V.avg | |
G | Gradient (m km−1) | Topographical map | G |
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A | Basin area (km2) | A | |
S | Slope | DEM | MS, S, VS |
15–30% = moderately steep (MS), | |||
30–50% = steep (S), | |||
50–70% = very steep (VS) | |||
DD | Drainage density (km km−2) | Topographical map | DD |
We collated data on fishes of the Himalayan rivers from published sources, documents, checklists and augmented this with primary data from our regular field surveys undertaken during the last six to eight years (see
(A) total species richness (n = 179), non endemic species richness (n = 150) and endemic species richness (n = 29). (B) Species richness plots along the elevational gradient in the Teesta river. The fitted lines for total richness, non endemic richness in the Himalayan rivers and total fish species richness in the Teesta river represent a GAM model. However, for the endemic species of the Himalayan rivers the fitted line represents GLM model.
Categories | Null deviance | GLM |
GLM residual deviance | GAM |
GAM residual deviance |
Total fishes | 2732.394 | 2 (<0.001) | 91.426 | 5 (<0.001) | 48.043 |
Endemic fishes | 478.4367 | 3 (<0.01) | 35.632 | n.s. (>0.05) | − |
Non endemic fishes | 2322.981 | 2 (<0.05) | 73.496 | 5 (<0.001) | 47.064 |
Total Teesta river fishes | 667.247 | 2 (<0.001) | 16.351 | 5 (<0.001) | 7.888 |
For GLM the respective best-fit polynomial order refers to a test against no relationship and with each other.
For GAM the respective degrees of freedom are given and refer to a test against the given GLM model.
n.s. - not significant.
The various environmental variables considered for this study were divided into four categories, viz. physico-chemical, biological, physiographic and topographic (
The details of physico-chemical data used in the analysis are given in
Variables used for themodels | Range | Correlationcoefficient (r) |
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Tu.avg | 6.1–23.7 | −0.905 |
T.avg | 9.3–18.6 | −0.852 |
TDS.avg | 13.3–23.3 | 0.188 |
C.avg | 23.3–33.3 | 0.226 |
pH.avg | 7.2–7.6 | −0.793 |
DO.avg | 7.8–9.4 | −0.142 |
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P.avg | 349–2782 | 0.946 |
B.avg | 4029–9262 | 0.925 |
M.avg | 247–727 | 0.349 |
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D.avg | 30.3–290.7 | −0.922 |
V.avg | 1.0–1.6 | 0.685 |
G | 5.0–55.0 | 0.831 |
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A | 700–5394.5 | −0.976 |
S | n.a | n.a. |
DD | 1.0–2.0 | −0.809 |
n.a. - not applicable.
P<0.01;
P<0.05.
Biological data set included estimation of densities of phytoplankton (suspended algae), phytobenthos and macro-invertebrates of Teesta river (
For the collection of phytoplankton samples, 50 liters of water at each sampling site were filtered using plankton net made up of fine silk cloth (mesh size 25 µm). The residues were transferred to sampling vials and distilled water was added to these so that the total volume was made up to 100 ml. The samples thus obtained was preserved in Lugol’s solution and brought to the laboratory for further analysis. Each sample was thoroughly mixed and 1 ml from the sample was transferred to a Sedgewick-Rafter cell (SR cell) for analysis. Phytoplankton individuals were counted randomly in 100 chambers of the SR cell. The density of phytoplankton was estimated by the following equation:
where A is the average number of individuals per chamber; B, volume of the sample (ml); and L, the total volume of filtered water (liter).
Epilithic phytobenthos were sampled by scraping submerged surfaces of stones and boulders (substrate, measuring 3 cm2 area) with the help of a hard brush. The scrapings were transferred to sampling vials and distilled water was added to these so that the total volume was made up to 100 ml. The samples thus obtained were preserved in Lugol’s solution and brought to the laboratory for further analysis. Each sample was thoroughly mixed and 1 ml from the sample was transferred to a Sedgewick-Rafter cell (SR cell) for analysis. Phytobenthos were counted randomly in 100 chambers of SR cell. The density of phytobenthos was computed as follows (see
where N is the number of individuals counted; At, the total area (cm2) of chambers of SR cell; Vt, total volume (ml) of the sample; Ac, the area (cm2) of total chambers of SR cell counted; Vs, volume of the analyzed sample (ml) in SR cell; and As, the surface area of the substrate scrapped.
Name of the variable | AIC | Residual deviance | D2 | Percentage change in D2 |
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T.avg | 67.07 | 19.29 | 0.783 | – |
T.avg+DO.avg | 62.77 | 12.99 | 0.854 | 9.07 |
T.avg+DO.avg+C.avg | 57.26 | 5.474 | 0.938 | 9.84 |
Stepwise regression (AIC; backward elimination & forward selection) | 56.00 | 4.224 | 0.952 | 1.49 |
All variables | 62.36 | 4.577 | 0.948 | – |
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P.avg | 59.33 | 11.55 | 0.870 | – |
P.avg+B.avg | 56.23 | 6.447 | 0.927 | 6.55 |
Stepwise regression (AIC; backward elimination & forward selection) | 56.21 | 2.425 | 0.973 | 4.96 |
All variables | 57.02 | 5.24 | 0.941 | – |
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D.avg | 66.37 | 18.58 | 0.791 | – |
Stepwise regression (AIC; backward elimination & forward selection) | 50.98 | 1.202 | 0.986 | 24.65 |
All variables | 66.58 | 16.80 | 0.811 | – |
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A | 55.53 | 7.745 | 0.913 | – |
A+DD | 54.77 | 4.992 | 0.944 | 3.40 |
Stepwise regression (AIC; backward elimination & forward selection) | 51.24 | 1.459 | 0.984 | 4.24 |
All variables | 58.34 | 4.563 | 0.949 | – |
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A | 55.53 | 7.745 | 0.913 | – |
A+D.avg | 54.30 | 4.522 | 0.949 | 3.94 |
Stepwise regression (AIC; backward elimination & forward selection) | 50.98 | 1.202 | 0.986 | 3.90 |
All variables | 60.55 | 0.771 | 0.991 | – |
Null deviance = 88.77; d.f. = 8.
Macro-invertebrates attached to the substrate (mainly stones) were collected at random in the net of a square-foot Surber’s sampler. The substrate was disturbed and stirred thoroughly in order to dislodge all the attached macro-invertebrates. The individuals retained in the net were collected in a sampling vial. Samples were preserved in 70% alcohol and brought to the laboratory for further analysis. The macro-invertebrates were counted after identifying them under a compound microscope up to family level
We calculated average, maximum, minimum, range and standard deviation of densities for each of the taxonomic groups sampled and analyzed.
Physiographic data set included estimating water discharge, water current velocity and river gradient (
Topographic data set included surface area of the drainage basin (basin area), slope, and drainage density (
(A) Water temperature represents physico-chemical model. (B) Phytoplankton density represents biological model. (C) Water discharge represents the physiographic model. (D) Basin area represents the topographic model. Discharge was the most important determining factor of fish species richness pattern followed by basin area and water temperature in decreasing order.
Variable | Linear parameter ( |
Quadratic parameter ( |
Percentage change in D2 | ||||||
Estimate | SE | P-value | Estimate | SE | P-value |
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(Intercept) | – | – | – | −4.328 | 1.801 |
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– | – | – |
T.avg | n.a. | n.a. | n.s. | 0.009 | 0.001 |
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n.a. | 89.91 | n.a. |
DO.avg | n.a. | n.a. | n.s. | 0.052 | 0.014 |
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n.a. | 17.69 | n.a. |
C.avg | n.a. | n.a. | n.s. | 0.002 | 0.001 |
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n.a. | 7.38 | n.a. |
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(Intercept) | 5.390E-01 | 2.553E+00 | n.s. | – | – | – | – | – | – |
P.avg | −1.321E-03 | 5.549E-04 |
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4.763E-07 | 2.981E-07 |
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6.13 | 2.70 | 8.83 |
B.avg | 1.637E-03 | 9.598E-04 |
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−1.759E-07 | 9.179E-08 |
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3.37 | 4.26 | 7.63 |
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(Intercept) | 1.303E-01 | 5.667E-01 | n.s. | – | – | – | – | – | – |
D.avg | 2.852E-02 | 5.878E-03 |
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−5.589E-05 | 1.464E-05 |
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36.19 | 19.59 | n.a. |
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(Intercept) | −1.264E-01 | 7.337E-01 | n.s. | – | – | – | – | – | – |
A | 1.349E-03 | 3.854E-04 |
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−1.166E-07 | 4.955E-08 |
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18.1 | 7.08 | n.a. |
DD | n.a. | n.a. | n.s. | n.a. | n.a. | n.s. | n.a. | n.a. | n.a. |
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(Intercept) | 1.303E-01 | 5.667E-01 | n.s. | – | – | – | – | – | – |
D.avg | 2.852E-02 | 5.878E-03 |
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−5.589E-05 | 1.464E-05 |
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36.19 | 19.59 | n.a. |
A | n.a. | n.a. | n.s. | n.a. | n.a. | n.s. | n.a. | n.a. | n.a. |
SE - Standard error;
n.s. - not significant.
P<0.001;
P<0.01;
P<0.05;
P<0.1.
We collated fish species richness of 16 Himalayan rivers along elevational gradient between 50 and 3800 m (see
In order to test the drivers of the observed species richness patterns, an empirical study was carried out in the Teesta river along elevational gradient between 50 and 3000 m. As in case of the Himalayan rivers, 50 m elevation represents the lowermost limit and 3000 m the uppermost limit beyond which no fishes have been reported in the Teesta river. We divided the Teesta river elevational gradient into 60 vertical bands of 50 m each. We followed interpolation method to estimate the number of fish species in each elevation band, which represents species richness at that band (see
The relationship between the species richness and elevation was assessed using generalized linear model (GLM;
We used GLM to analyze the relationship between species richness in Teesta river and various sets of environmental variables using Poisson error distribution and logarithmic link
In the first step the best variable, with least Akaike’s information criterion (AIC)
Finally, a synthetic model was generated using the combination of best performing variables, selected from each environmental variable set, after stepwise regression analyses. Using the best performing variables we repeated model-building exercise to obtain the best predictor of fish species richness pattern.
We calculated relative importance of variables for the model performance, by removing them separately and in combination from GLM models in order to characterize the models. The variables and their terms, which accounted for maximum lowering of percentage D2 were selected as the most important ones for the model. The strength of the model was evaluated with 9-fold cross-validations. For robustness of results, the mean of 90 internal cross-validations was used. All the statistical analyses were performed using R 2.14.0 software
Model | Number of | D2 | MAE | |||
Variables | Parameters | D2 | 9-foldCV* | MAE | 9-foldCV* | |
Physico-chemical | 3 | 3 | 0.952 | 0.712 | 3.300 | 5.275 |
Biological | 2 | 4 | 0.973 | 0.755 | 2.264 | 5.986 |
Physiographic | 1 | 2 | 0.986 | 0.636 | 1.514 | 2.313 |
Topographic | 1 | 2 | 0.984 | 0.850 | 1.460 | 2.057 |
Synthetic | 1 | 2 | 0.986 | 0.636 | 1.514 | 2.313 |
CV*, mean of 90 internal cross-validations (9-fold).
The total and non-endemic species richness in the Himalayan rivers showed monotonic decrease with increasing elevation (
Ranges of different environmental model variables and their Pearson’s correlation coefficient values for the Teesta are given in
In the model building procedure the first selected variable was the one with the least AIC and the highest D2 values as compared to other variables in its respective data set (
In the physico-chemical model, we observed that linear combination of water temperature, dissolved oxygen and electrical conductivity had lower AIC value (57.26) and higher D2 value (0.938). Likewise, linear combination of phytoplankton and phytobenthos served as better predictors in the biological model (AIC = 56.23; D2 = 0.927). In the topographic model linear combination of basin area and drainage density was better predictor of fish species richness (AIC = 54.77; D2 = 0.944).
Stepwise regression analyses further improved each model by achieving the least AIC and the highest D2 values as compared to individual and/or linear combinations of various environmental variables (
In the synthetic model, basin area was found to be the best driver of species richness with least AIC value (55.53) and the highest D2 value (0.913) among the best predictor combination of variables (
The relative importance and effect of the variable removals on model performance were estimated (
All the five models were found to be quite robust after being subjected to a 9-fold cross-validation as they were able to achieve a minimum D2 value of 0.50 (
In the ultimate analysis synthetic and physiographic models emerged as the best predictors of fish species richness in the Teesta (AIC = 50.98; D2 = 0.986) followed by topographic model (AIC = 51.24; D2 = 0.984), physico-chemical model (AIC = 56.00; D2 = 0.952) and biological model (AIC = 56.21; D2 = 0.973). Clearly, synthetic and physiographic models included water discharge in its linear and quadratic terms, it was considered to be the most important driver for fish species richness pattern.
Total fish species richness and non-endemic richness in the Himalayan rivers showed a gradual decline with the increasing elevation, supporting Rapoport’s rule, but a mid domain effect was evident in the endemic fish species in this study. Our results are in contrast to a recent study on tree and bird elevational gradients of the Sikkim Himalaya where the authors reported that the species richness in these taxonomic groups peaked at intermediate elevations
We show that the majority of the Himalayan fish endemics (58.6%) are clustered between 700 and 1500 m. These results, therefore, support the conclusion that endemic species peak towards the middle of an elevational gradient
Various environmental, geographic and topographic features are often described as determinants of species richness patterns along elevational gradients
Our studies indicate that various environmental factors influence distribution of fish species richness differently and the relationship varies in magnitude. Generalized linear model identified water discharge, basin area, temperature and phytoplankton as the most important factors influencing species richness. However, the synthetic model, which represents close to the natural ecosystem state, showed a strong linear relationship of species richness with water discharge and basin area, in that order. This study, therefore, adds a new dimension to current macroecological theories dealing with drivers of species richness. Even as there is some support for species area theory, as indicated by the influence of basin area on fish species richness in the Himalayan rivers (see
Besides the size of river (a function more of water discharge than basin area due to role of precipitation), water temperature plays an important role in influencing fish diversity in the fresh water rivers. The Himalayan rivers at lower elevations are marked by slight annual fluctuations in water temperature, therefore, are inhabited by species assemblages with low physiological tolerance ranges (see
The low predictive performance of GLM models for species richness patterns with respect to other parameters of water chemistry (turbidity, TDS, Electrical Conductivity, pH, dissolved oxygen), biological (phytobenthos, macro-invertebrates), physiographic (water current velocity and gradient) and topography (slope, drainage density), confirm that these factors do not influence or at best contribute only a little to fish species richness in the Himalayan rivers.
Even though this study concerns essentially with diversity and distribution of fish species and drivers of their richness patterns, it potentially posits an important challenge to the unprecedented river regulation for hydropower generation in the Himalayan basins (see
To sum up, we found a greater applicability of Rapoport’s elevational rule in explaining the fish species richness pattern for the Himalayan rivers. Water discharge emerged as the best predictor for fish species richness pattern among 15 different environmental variables in the case study of Teesta river. The results of this study strongly advocate that the drivers of richness gradients in terrestrial and aquatic ecosystems are likely to be different. We recommend greater care to be exercised in design and execution of the ongoing dams being constructed across the Himalayan rivers.
Fish fauna of Himalayan rivers and their distribution and conservation status.
(DOC)
Detailed sources of data.
(DOC)
We thank Thomas Wohlgemuth and Michael P. Nobis for assistance with model formulation and R. Mehta for map preparation.