2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Agricultural and Animal Husbandry College of Tibet University, Linzhi 860000, China;
4 Xilinhot No.6 Middle School, Xilinhot 026000, China;
5 Fujian Marine Products Technical Promotion Station, Fuzhou 350003, China;
6 UMR 5174 EDB, CNRS-University Paul Sabatier, 118 route de Narbonne, Toulouse, France
There is abundant evidence that the uplift of the Tibetan Plateau can be a positive driving force for the biological evolution of plateau rivers and lakes (Qin and Chen, 2005a). The unique niches and scientific value of plateau water systems are being increasingly highlighted, since the effects of global warming are amplified with elevation, and high-mountain environments tend to experience more rapid changes than at lower places (Pepin et al., 2015). Presently, plateau rivers and lakes have received increasing attention. The social, economic, environmental protection, and general function of lakes in the Tibetan Plateau in general are potentially more sensitive to climate change than that of lakes in other regions (He, 2007; Ju et al., 2012), and moreover, sediments in plateau lakes can provide useful information of mineralogy (Wu et al., 2011), isotopes geochemistry (Wu et al., 2012), and biology (Bai et al., 2009). More pessimistic estimates suggest that average temperature in the Tibetan Plateau will rise by 2-3.6℃ by the year 2100 (Shen et al., 2002; Zhang et al., 2011; Xie, 2012), which can be accelerated by additional human activities (Huang, 2011). This will cause significant impacts on the plateau river ecosystem and water resource vulnerability, including surface runoff reduction, shrinkage in lake area, and wetland degradation (Qin and Chen, 2005b). Elevationdependent warming can also accelerate the rate of change in mountain ecosystems, cryospheric systems, in hydrology and biodiversity (Pepin et al., 2015). Moreover, development of hydropower will probably be inevitable due to the huge hydropower potential of plateau rivers. These factors all pose a great challenge to the integrity of the river ecosystems (Zhai, 2009). Maintaining the integrity of these river ecosystems requires properly regulated development by human society, as they have a significant impact on the bearing capacity for water resources, regional economy, social development, and environmental protection in the Tibetan Plateau (Duan et al., 2011). It is therefore urgent to pay close attention to the ecological safety of plateau rivers (Luosang, 2005; Hu and Zheng, 2011).
Many previous studies show that protozoan community is an ideal tool for evaluating water quality for maintaining river ecosystem safety (Shen, 1990; Tan et al., 2010). On one hand, protozoa can influence ecosystem health as a link in food chain through top-down effects, as exemplified by protozoa grazers who suppress algal bloom (Xu et al., 2010), regulate bacterial abundance by predation (Zhang et al., 2014), and improve the circulation of matter of water ecosystems (Zhou et al., 2003; Wey et al., 2012), On the other hand, protozoan communities are closely related to the physico-chemical properties of water. For example, high chlorine concentrations can reduce the presence of free-living protozoa (FLP) compared to those in low chlorine concentrations, while FLP detected in high-temperature samples are fewer than those in low-temperature samples (Canals et al., 2015). In addition, chlorophyll a and nitrate are potential causative factors responsible for the seasonal variations of tintinnids (Rakshit et al., 2014), which was strongly influenced by food availability (Yang et al., 2015).
Periphytic protozoa are a part of periphyton—a community that varies between different substrates in water (Shen, 1980; Liu et al., 1999; Ma et al., 2005; Vaerewijck et al., 2011). For instance, periphytic ciliates were significantly correlated with the changes in environmental variables, especially temperature, pH, dissolved oxygen (Zhang and Xu, 2015); Gong et al. (2005) studied biofilm ciliates from scallop farming waters of the Jiaozhou Bay (China) and found 12 dominant species that presented a clear successional pattern over the year; .protozoan abundance is also affected by temperature, the plant inputs of organic matter, and dissolved oxygen (Puigagut et al., 2012). It can function as an outstanding barometer for plateau water environments due to climate warming (Pepin et al., 2015), leading to a stronger matched growth between protozoa grazers and their prey in the transfer of matter and energy toward higher trophic levels (Aberle et al., 2012), and due to human activities that affect water ecosystems as well (Duan et al., 2011). Hence, investigating periphytic protozoa communities is important for the sustainable development of plateau water ecosystems.
Tibet, known as the "Water Tower of Asia", possesses 20 rivers with drainage areas larger than 10 000 km2, a number only second to that of Sichuan Province in China (Guan et al., 1984). The largest river in Tibet is the Yarlung Zangbo River, a part of an international river called Brahmaputra River that flows into Indian Ocean. It is the fifth largest river in China, with five first tributaries, one of which is the Niyang River (Guan et al., 1984). Rapid social and economic development, including the construction of the Duobu Hydropower Station, Lhasa-Nyingchi highway and Lhasa-Nyingchi railway, has led to the emergence of many sandpits and cement mills in the Niyang River region. In addition, the relatively simple structure of bearing fish community makes the river more vulnerable to environmental changes (Shen and Guo, 2008). Under this circumstance, maintaining the sustainable development and water ecosystem function of the Niyang River has become a tough challenge. Since the year 2008, we have been focused on the water ecology of the Niyang River, and its relevant data, including phytoplankton (Liu et al., 2013a), periphytic algae (Liu et al., 2013b), macrozoobenthos (Liu et al., 2014), zooplankton (Liu et al., 2016) and their environment (Liu et al., 2015). These data have been reported successively, all of which reveals that the Niyang River deserves continuous attention, especially during the construction of Duobu Hydropower Station, LhasaNyingchi highway and Lhasa-Nyingchi railway. Therefore, we chose periphytic protozoa assemblages to study the relationship between its variability and key physicochemical factors, in order to (1) document the taxonomic composition of periphytic protozoa, (2) describe the population dynamics of numerically dominant species, and (3) analyze possible correlation between periphytic protozoa assemblages and water environment parameters.2 MATERIAL AND METHOD 2.1 Study area and sampling procedure
The Niyang River (92°22'-94°27'E, 29°26'-29°55'N) is located at the western Nyingchi area in Tibet, with a total length of 286 km, a drainage area of 17 535 km2, an elevation drop of 2 080 m, and an average slope of 7.27‰. It generally flows from northwest to southeast. Specifically, it flows from east to west in the upper course, and turns from south to east near the Nyingchi County (Guan et al., 1984).
A total of four sampling sites based on river morphology were selected for data collection, with three parallel samples taken for each site. The four sites are located at: S1: 29°53'N, 93°16'E, and elevation 3 391 m, standing for middle-upper course of the Niyang River, with sandstone riverbed, water flow comparatively fast; S2: 30°00'N, 93°54'E, elevation 3 225 m, located at the confluence of the Niyang River and Bahe River which is a tributary of the Niyang River, standing for the middle course of the Niyang River, with sandstone riverbed, water flow fast; S3: 29°31'N, 94°27'E, elevation 2 946 m, standing for lower course of the Niyang River, with clay riverbed, water flow slow; S4: 29°26'N, 94°27'E, elevation 2 919 m, located at the confluence of Niyang River and Yarlung Zangbo River, standing for the lower course of the Niyang River, with sandstone riverbed, water flow comparatively slow (Fig. 1).
Periphytic protozoa and physicochemical water samples were collected seasonally in winter (December, 2008) and spring (March 2009), both of seasons being called dry season; summer (June 2009), also called rain season; autumn (September 2009) also called medium season; from 2008 to 2009. The physicochemical conditions of water in the Niyang River varied with seasons and sampling sites, generally with an average water temperature of below 10℃, pH of around 7.8, hardness of around 2° DH, an average mineralization of below 100 mg/L, an average chemical oxygen demand (COD) of below 6.5 mg/L, an average dissolved oxygen of above 10 mg/L, and an average total alkalinity of < 35 mg/L, generally, higher water temperature, displayed in S2 or in Autumn, higher COD (Table 1).2.2 Periphytic protozoa collection and analysis
Protozoa adhering to regular river stones were collected from watercourse by scraping and washing stones in situ at a depth of nearly 0.5 m, and protozoa samples were collected through 25# plankton net. The stone area was drawn on grid paper and then calculated by using the gridiron method, a sample with cumulative areas of 0.25 m2 approximately, and preserved samples with Lugol's solution, with the samples being taken back to lab for quantitative analysis. Two slices per sample and 30 visions per slice were observed from microscope and identified based on reference data (Shen, 1990). Classification system mainly based on Adl et al. (2012), who talked about precise classification based on molecular phylogenetic.
Quantitative analysis of periphytic protozoa was based on the formula below:
in which, PP: total abundance of periphytic protozoa, unit: ind./m2; CCA: counting cell area; MVA: microscope vision area; SSV: saving sample volume; ASV: analyzed sample volume; NPPSA: number of periphytic protozoa samples analyzed; and CSA: collecting sample area.
Six diversity indices were used to analyze periphytic protozoa communities in the Niyang River, including: Shannon-Wiener diversity index (SH), Pielou's evenness index (PI), species richness (SR), total abundance (TA), occurrence frequency and relative abundance.
1. Shannon-Wiener diversity index (Shannon and Wiener, 1949).
where s is the number of periphytic protozoa genera; pi is the proportion of a certain periphytic protozoa genera i to all periphytic protozoa genera.
2. Pielou evenness index (Pielou, 1975).
where H' is the Shannon-Wiener diversity index; H'max is the theoretical maximum Shannon-Wiener diversity index; and S is the number of periphytic protozoa genera.
3. Species richness is the number of periphytic protozoa genera appearing in a sample site or a season.
4. Total abundance is the total number of periphytic protozoa appearing in a sample site or a season, (unit: ind./m2).
5. Occurrence frequency is the ratio of occurrence of a certain periphytic protozoa genera in four sample sites or four seasons (unit: %).
6. Relative abundance is the ratio of the number of a certain periphytic protozoa to that of all periphytic protozoa (unit: %).2.3 Data processing
Principal component analysis (PCA) was employed to study the variability characteristics of periphytic protozoa communities, and Duncan test was employed to distinguish relevant parameters among sampling sites or seasons. Relationship between periphytic protozoa and environmental factors was tested by using classification and regression trees (CART) and canonical correlation analysis (CCA), before which the data were transformed by lg (x+1) (Ye et al., 2015). All statistical analyses were done by using version 2.13.1 of R (R Development Core Team 2011). The "ade4" package (Dray and Dufour, 2007) was applied for the PCA and CCA, and the "rpart" package (Therneau et al., 2010) was applied for the regression tree models.3 RESULT 3.1 Species composition of periphytic protozoa in the Niyang River
In total, 15 genera of periphytic protozoa were identified in Niyang River, according to Adl et al. (2012) and Shen (1990), belonged to Tubulinea, Alveolata, Discosea and Rhizaria. Among them, Alveolata possessed nine genera, most genera among four kingdom, such as Holophrya, Glaucoma, Cyclidium, Vorticella, Chilodonella, Colpoda, Bursellopsis, Enchelys and Enchelydium, with total relative abundance up to 56.08%, Glaucoma with highest occurrence frequency, up to 37.50%; Tubulinea possessed three genera, such as Arcella, Difflugia and Centropyxis, with total relative abundance up to 26.73%, Difflugia with highest occurrence frequency, up to 81.25%; Discosea only possessed one genera, which was Thecamoeba, with total relative abundance up to 15.11%; Rhizaria possessed two genera, such as Euglypha and Cyphoderia, with total relative abundance up to 2.09%. Generally, Alveolata possessed most genera, with most total relative abundance, exceeding 50%, Difflugia and Glaucoma were dominant genera, with occurrence frequencies 81.25% and 37.50% respectively (Table 2).3.2 Spatial-temporal patterns of periphytic protozoa
Result from Duncan test revealed that there was no significant difference (P>0.05) for the Niyang River from the upper course to its confluence with Yarlung Zangbo River, where the four indices each displayed a "V" curve tendency. The lowest values appeared in the middle course of the Niyang River, with species richness being below 2, total abundance being around 1 000 ind./m2, the Shannon-Wiener diversity index being around 0.25 nits/ind. and, Pielou's evenness index being below 0.4. The maximum values for species richness and total abundance appeared around the confluence of the Niyang River and Yarlung Zangbo River, at 3 and 15 000 ind./m2 respectively, and the maximum value of the Shannon-Wiener diversity index and Pielou's evenness index appeared in the upper middle course of Niyang River, both being about 0.8 (Fig. 2).
Across seasons, a significant difference occurred in the Shannon-Wiener diversity index only between winter and summer (or autumn) shown using Duncan test (P < 0.05), the same result was shown for species richness between seasons, and no significant seasonal difference for the rest two indices were found. The four indices all declined in the order of winter, spring, summer and autumn, with minimum values of around 2, 1 000 ind./m2, 0.2 nits/ind., 0.3, and maximum values of around 6 ind., 16 000 ind./m2, 1.3 nits/ind., 0.7 for species richness, total abundance, the ShannonWiener diversity index and Pielou's evenness index respectively (Fig. 2).
The spatial-temporal characteristics of periphytic protozoa were analyzed by using PCA, showing that: most periphytic protozoa communities along the Niyang River were similar except for some minor differences; periphytic protozoa communities in winter were distinguished from those in other seasons; periphytic protozoa in the sandstone substrate surpassed the clay substrate (Fig. 3).3.3 Relationship between periphytic protozoa communities and key environment factors
CCA was applied to analyze the relationship between periphytic protozoa and environmental factors including surface water temperature, pH, hardness, mineralization, chemical oxygen demand (COD), dissolved oxygen, total nitrogen, total phosphorus, total alkalinity, and ammoniacal nitrogen. The densities of Difflugia, Glaucomais, Enchelydium, Cyphoderia, and Enchelys were related to water temperature, alkalinity, hardness, pH value, and dissolved oxygen, respectively. The relationship between periphytic protozoa and environmental factors had no strong connection to the locations of sample sites (Fig. 4).
The CART model was employed to predict the relationship between periphytic protozoa communities and environmental factors (Fig. 5). (1) For the total abundance of periphytic protozoa, the season was the most important predictive variable. The total abundance in autumn was lower than in other seasons (Fig. 5a). Water temperature and elevation were of secondary importance in the abundance prediction, with their thresholds at 12.29℃ and 3 308 m respectively. Particularly, the total abundance was much higher with an elevation of above 3 308 m in summer and winter. (2) Prediction of Shannon-Wiener diversity index and Pielou's evenness index are highly similar, with pH playing the first important role in the prediction, followed by NH4-N; if pH is higher and NH4-N is lower, no periphytic protozoa would survive. Elevation, with a threshold at 3 308 m, was the third most important roles in the prediction of Shannon-Wiener diversity index, where lower elevation would result in a higher Shannon-Wiener diversity index. The third most important role in the prediction of Pielou's evenness index is the season, which is lower in spring than in the other three seasons.4 DISCUSSION 4.1 Influence of water temperature on protozoa distribution
Water temperature is a critical parameter of aquatic ecosystems (Segura et al., 2015). It is affected by many factors. Watershed geomorphology and snowmelt control stream thermal sensitivity to air temperature (Lisi et al., 2015). Elevation and mean basin slope are related negatively to mean water temperature (Segura et al., 2015). Water temperature influences the distribution, abundance, and aquatic organism health of stream ecosystems (Sarah et al., 2013). Generally, air temperature rise will lead to water temperature rise (Sarah et al., 2013). Warming might lead to a stronger match between protist grazers and their prey altering in turn the transfer of matter and energy toward higher trophic levels (Aberle et al., 2012). Yang et al. (2015) had pointed out that ciliate community was positively correlated with water temperature, higher numbers of free-living protozoa (FLP) taxa were observed in enrichment cultures incubated at 20℃ instead of 7℃ (Vaerewijck et al., 2011). On the contrary, under some specific circumstance, FLP detected in high-temperatures samples ((53.1±5.7)℃) was 38% lower than in lowtemperature samples ((27.8±5.8)℃) (Canals et al., 2015), terrific high temperature might be the right reason. For tropical streams, the higher values of species richness, density and biomass were recorded during the winter, when the lower temperatures were registered (Camargo and Velho, 2011). In fact, temperature, nutrients and salinity might best explain the changes in ciliates assemblage during annual cycle (Gong et al., 2005). The results of our experiment showed that the abundance of Difflugia adhering to sandstone was related to water temperature based on CCA. The total abundance of periphytic protozoa was higher when the water temperature was above 12.29℃ based on CART model. Generally, higher water temperature, displayed in S2 or in Autumn, higher COD, which showed in our research, higher COD can indicate more organic matter, leading to more bacteria, and more food for bacteriovorus taxa.
The results from both univariate and multivariate analyses indicate that water temperature might not directly control the temporal dynamics of the ciliate community but indirectly influence it via the food availability (Yang et al., 2015). To be emphasized, water flow could be a crucial factor affecting periphytic protozoa population structure through food availbility. A case in point is the results of our experiment, four indices including species richness, total abundance, Shannon-Wiener diversity index and Pielou's evenness index appeared in the "V" between the middle-upper of Niyang River and the confluence of the Niyang River and Yarlung Zangbo River, and the lowest value appeared at the middle of the Niyang River where water flow was comparatively fast, under this circumstance, phytoplankton could not stay on the substrate of riverbed for a long period as it was a disadvantage for multiplication, as a result, the total abundance of phytoplankton in the Niyang River displayed lowest value (Liu et al., 2013a), so did periphytic protozoa assemblages.4.2 Influence of water stability on protozoa communities
The Niyang River is supplemented by rainwater and meltwater (Guan et al., 1984). Melt water contains plentiful ion and mineral elements (Wang et al., 1988), which might merge into a stream or a river, as a result, there are distinct spatial and temporal characteristics for high elevation catchments of the Sierra Nevada, especially for nitrate and sulfate (Sickman and Melack, 1998). Generally, melt water can affect habitats by changing the physical and chemical features of aquatic ecosystems, which can drive the overall distribution, diversity and behavior of primary producers, triggering cascading effects throughout the food web (Slemmons et al., 2013). Shi et al. (2012) had pointed out that the spatial patterns of protozoa communities were correlated significantly with the changes of chemical variables, especially COD, either alone or in combination with TP and/or TN. The seasonal variations of tintinnids are affected by chlorophyll a, nitrate (Rakshit et al., 2014) and salinity (Biswas et al., 2013); the ciliate community was positively correlated with chlorophyll a and negatively correlated with various inorganic nutrients (Yang et al., 2015); biomass-based Shannon-Wiener index and species richness of testate amoebae were significantly unimodal related to trophic status (P < 0.05) (Ju et al., 2014). Coincidentally, the result based on CCA shows that the densities of Difflugia, Glaucomais, Enchelydium, Cyphoderia, and Enchelys are related to water temperature, alkalinity, hardness, pH, and dissolved oxygen, respectively. Using the CART method, it is suggested that periphytic protozoa's total abundance and Shannon-Wiener diversity index are relatively higher when the watercourse elevation is above 3 308 m; otherwise, the Shannon-Wiener diversity index and Pielou's evenness index were lower when pH value and ammoniacal nitrogen were lower or higher.
Rainfall regime can disturb habitat structural complexity and stability which would influence protozoa's species richness (Camargo et al., 2012). As rainfall regime varies with seasons and locations, the biomass of testate amoebae decreased significantly along the latitudinal gradient, while Shannon-Wiener indices and species richness presented an opposite trend (P < 0.05) (Ju et al., 2014). The protozoa community showed variations in abundance or richness, with high values appearing in spring (dry season) or in summer (monsoon) (Yang et al., 2015), owing to temperature rise and rainfall increase. Spring was the most productive season for protozoa (Dorgham et al., 2013). The abundance, biomass, and production rate of tintinnids were at the highest during pre-monsoon followed by post-monsoon and monsoon (Biswas et al., 2013). The species richness and abundance of periphytic Centropyxis showed the highest values during the dry season, and the genera Centropyxis was common in all sampling stations throughout the year (Corrêa et al., 2015). Contrary to the foregoing result, in our experiment, the genera Centropyxis was comparatively rare, with an occurrence frequency at 6.25%, and a relative abundance of 1.85%, while the dominant genera were Difflugia and Glaucoma, with occurrence frequencies at 81.25% and 37.50%, respectively, and relative abundances at 19.60% and 20.33%. Difflugiidae are widely known to be unable to adhere to artificial surfaces, as examples, Difflugia gramen and Difflugia lucida have rounded shells, impeding permanence in the periphyton of reaches with higher runoff velocities (Corrêa et al., 2015). Though the genus Difflugia was found to be better adapted to environmental conditions during the dry season not only in the periphyton but as well in planktonic communities river ahead of Cuiabá city (Neto, 2001), as there is evidence that Difflugia species occurs mainly due to differences in river discharge, this genus was unlikely to serve as a potential as water quality indicator (Corrêa et al., 2015).
The species richness, total abundance, ShannonWiener diversity index and Pielou's evenness index of periphytic protozoa are descending in the order of winter, spring, summer and autumn. Meanwhile, winter was totally different with summer or autumn for Shannon-Wiener diversity index and species richness, , mostly because the supplemented water for Niyang River gradually increases from winter to summer, except for autumn, where supplemented water decreases, riverbed shrinks, and water flow, another factor could inhibited the growth of phytoplankton (Zhang et al., 2015), slows down in substrates submerged in fast water flow before, which condition is suitable for periphytic protozoa survival, but requires an adaptation period. Thus, when winter comes, the riverbed would become stabilized, and the peak of periphytic protozoa communities would come.5 CONCLUSION
From field survey for the Niyang River between 2008 and 2009, 15 genera, belonged to five kingdom, among them, Alveolata possessed most genera, with most total relative abundance, nearly 50%, Difflugia and Glaucoma were dominant genera. Significant difference occurred only between winter and summer (or autumn) for Shannon-Wiener diversity index. However, no significant difference was found through the Niyang River (P>0.05). A significant descending trend in season for the four diversity indices (species richness, total abundance, Shannon-Wiener diversity index and Pielou's evenness index) was revealed, in the order of winter, spring, summer, and autumn. The four diversity indices displayed a V-shaped pattern from the upper-middle stream to the confluence of the Niyang River and Yarlung Zangbo River, with the lowest values appearing at the middle of the Niyang River. The densities of Difflugia, Glaucoma, Enchelydium, Cyphoderia, and Enchelys were related to water temperature, alkalinity, hardness, pH and dissolved oxygen, respectively. In the near future, large scale investigation, e.g. Yarlung Zangbo River, should be carried out to testify through quantitative analysis how water flow and water supplement affect periphytic protozoa assemblage.6 ACKNOWLEDGEMENT
Thanks are given to YAN Hongwei, YIXI Quyun, LIU Jinfeng, DANZENG Pingcuo for their field sampling. Thanks also go to the Editor Roger Z. YU and anonymous reviewers for their helpful comments and constructive suggestions.
|Aberle N, Bauer B, Lewandowska A, Gaedke U, Sommer U, 2012. Warming induces shifts in microzooplankton phenology and reduces time-lags between phytoplankton and protozoan production. Marine Biology, 159(11): 2441–2453. Doi: 10.1007/s00227-012-1947-0|
|Adl S M, Simpson A G B, Lane C E, Lukeš J, Bass D, Bowser S S, Brown M W, Burki F, Dunthorn M, Hampl V, Heiss A, Hoppenrath M, Lara E, le Gall L, Lynn D H, McManus H, Mitchell E A D, Mozley-Stanridge S E, Parfrey L W, Pawlowski J, Rueckert S, Shadwick L, Schoch C L, Smirnov A, Spiegel F W, 2012. The revised classification of eukaryotes. Journal of Eukaryotic Microbiology, 59(5): 429–514. Doi: 10.1111/jeu.2012.59.issue-5|
|Bai J H, Cui B S, Chen B, Zhang K J, Deng W, Gao H F, Xiao R, 2009. Spatial distribution and ecological risk assessment of heavy metals in surface sediments from a typical plateau lake wetland, China. Ecological Modelling, 222(2): 301–306.|
|Biswas S N, Godhantaraman N, Rakshit D, Sarkar S K, 2013. Community composition, abundance, biomass and production rates of Tintinnids (Ciliata: Protozoa) in the coastal regions of Sundarban Mangrove wetland, India. Indian Journal of Geo-Marine Sciences, 42(2): 163–173.|
|Camargo J C, Velho L F M, 2011. Longitudinal variation of attributes from flagellate protozoan community in tropical streams. Acta Scientiarum Biological Sciences, 33(2): 161–169.|
|Camargo J C, Vieira L C G, Velho L F M, 2012. The role of limnological variables and habitat complexity in impacted tropical streams as reágulatory factors on the flagellate protozoa community. Acta Limnologica Brasiliensia, 24(2): 193–206. Doi: 10.1590/S2179-975X2012005000038|
|Canals O, Serrano-Suárez A, Salvadó H, et al, 2015. Effect of chlorine and temperature on free-living protozoa in operational man-made water systems (cooling towers and hot sanitary water systems) in Catalonia. Environmental Science and Pollution Research, 22(9): 6610–6618. Doi: 10.1007/s11356-014-3839-y|
|Corrêa L V A, Hardoim E L, Zeilhofer P, 2015. Is the periphytic structure of testaceans (protozoa: rhizopoda) related to water quality: a case study in the Cuiabá River, Brazil. Applied Ecology and Environmental Research, 13(1): 85–97.|
|Dorgham M M, El-Tohamy W S, Aziz N E A, El-Ghobashi A, Qin J G, 2013. Protozoa in a stressed area of the Egyptian Mediterranean coast of Damietta, Egypt. Oceanologia, 55(3): 733–750. Doi: 10.5697/oc.55-3.733|
|Dray S, Dufour A B, 2007. The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software, 22(4): 1–20.|
|Duan S Q, Wang J, Feng S H, Li J, Han S F, 2011. Research on the assessment of water resources vulnerability of lakes in Yunnan Plateau. China Rural Water and Hydropower(9): 55–59.|
|Gong J, Song W B, Warren A, 2005. Periphytic ciliate colonization: annual cycle and responses to environmental conditions. Aquatic Microbial Ecology, 39(2): 159–170.|
|Guan Z H, Chen C Y, Qu Y X, et al, 1984. Rivers and Lakes in Tibet. Science Press, Beijing.|
|He G Q. 2007. Alpine Wetland Ecosystem Service Valuation and Wetland Protection Countermeasures in Tibet. Northwest Agriculture & Forestry University, Xi'an, China. (in Chinese)|
|Hu Y L, Zheng W, 2011. Research on sustainable development in plateau lake basins. Ecological Economy, (3): 168-171(3): 168-171–183.|
|Huang Q. 2012. The influence about Human Activities and Ecological Environment Change around Sanjiangyuan Region based on GIS. Minzu University of China, Beijing, China. (in Chinese with English abstract)|
|Ju J T, Zhu L P, Feng J L, Wang J B, Wang Y, Xie M P, Peng P, Zhen X L, Lü X M, 2012. Hydrodynamic process of Tibetan Plateau lake revealed by grain size: case study of Pumayum Co. Chinese Science Bulletin, 57(19): 2433–2441. Doi: 10.1007/s11434-012-5083-5|
|Ju L H, Yang J, Liu L M, Wilkinson D M, 2014. Diversity and distribution of freshwater testate amoebae (protozoa) along latitudinal and trophic gradients in China. Microbial Ecology, 68(4): 657–670. Doi: 10.1007/s00248-014-0442-1|
|Lisi P J, Schindler D E, Cline T J, Scheuerell M D, Walsh P B, 2015. Watershed geomorphology and snowmelt control stream thermal sensitivity to air temperature. Geophysical Research Letters, 42(9): 3380–3388. Doi: 10.1002/2015GL064083|
|Liu H P, Ye S W, Yang X F, Zhang L S, Zhang H J, Fan L Q, Li Z J, 2016. Spatio-temporal characteristics of aquatic organism community and their relationship to environment in Niyang River, the branch of Yarlung Zangbo River, Tibet: 4. zooplankton. Journal of Lake Sciences, 28(2): 370–378. Doi: 10.18307/2016.0217|
|Liu H P, Ye S W, Yang X F, Zhang L S, Zhong G H, He Y P, Basang, Li Z J, 2014. Spatio-temporal dynamics of aquatic organism community and their relationships to environment in Niyang River, Tibet: 3. macrozoobenthos. Journal of Lake Sciences, 26(1): 154–160. Doi: 10.18307/2014.0119|
|Liu H P, Ye S W, Yang X F, Zhang L S, Zhong G H, He Y P, Li Z J, 2013b. Spatio-temporal dynamics of aquatic organism community and their relationships to environment in Niyang River, Tibet: 2. Periphytic algae. Journal of Lake Sciences, 25(6): 907–915. Doi: 10.18307/2013.0615|
|Liu H P, Ye S W, Yang X F, Zhang L S, Zhong G H, Li Z J, 2013a. Spatial-temporal dynamics of aquatic organism community and their relationships to environment in Niyang River, Tibet: 1. Phytoplankton. Journal of Lake Sciences, 25(5): 695–706. Doi: 10.18307/2013.0511|
|Liu H P, Zhong G H, Ye S W, Zhang L S, Zhang H J, Fan L Q, Basang, Li Z J, 2015. Multivariate statistical analysis of water environment for Niyang River, the branch of the Yarlung Zangbo River, Tibet. Journal of Lake Sciences, 27(6): 1187–1196. Doi: 10.18307/2015.0625|
|Liu J K, 1999. Advanced Hydrobiology. Science Press, Beijing.|
|Luosang L Z D J, 2005. Utilization and protection of water resources in the Qinghai-Tibet Plateau. Resources Science, 27(2): 23–27.|
|Ma X F, Liu D Q, Xiong B X, Wang W M, 2005. Studies on the attached protozoan community structure in the Daoguanhe reservoir. Reservoir Fisheries, 25(5): 61–64.|
|Ministry of Environmental Protection of the People's Republic of China, 2002. Standard methods for the examination of water and wastewater. Environmental Science Press, Beijing.|
|Neto E V S. 2001. Índices ecológicos de comunidades de Testacea (Protozoa: Rhizopoda) no Rio Cuiabá-perímetro urbano de Rosário Oeste, Mato Grosso. Universidade Federal do Mato Grosso, Cuiabá.|
|Pepin N, Bradley R S, Diaz H F, et al, 2001. Elevationdependent warming in mountain regions of the world. Nature Climate Change, 5(5): 424–430.|
|Pielou E C. 1975. Ecological Diversity. Wiley, New York. 165p.|
|Puigagut J, Maltais-Landry G, Gagnon V, Brisson J, 2012. Are ciliated protozoa communities affected by macrophyte species, date of sampling and location in horizontal subsurface flow constructed wetlands?. Water Research, 46(9): 3005–3013. Doi: 10.1016/j.watres.2012.03.001|
|Qin D H, Chen Y Y, 2005a. China's Climate and Environmental Evolution (Volume 1): The Evolution and Forecast of China's Climate and Environment. Science Press, Beijing389-390.|
|Qin D H, Chen Y Y, 2005b. China's Climate and Environmental Evolution (Volume 2): The Influence, Adaptation and Mitigation Strategy of Climate and Environment Changes. Science Press, Beijing98-109.|
|Rakshit D, Biswas S N, Sarkar S K, Bhattacharya B D, Godhantaraman N, Satpathy K K, 2014. Seasonal variations in species composition, abundance, biomass and production rate of tintinnids (Ciliata: Protozoa) along the Hooghly (Ganges) River Estuary, India: a multivariate approach. Environmental Monitoring and Assessment, 186(5): 3063–3078. Doi: 10.1007/s10661-013-3601-9|
|Sarah E N, Joshua H V, Michael L D, et al, 2013. Stream temperature sensitivity to climate warming in California's Sierra Nevada: impacts to coldwater habitat. Climatic Change, 116(S1): 149–170.|
|Segura C, Caldwell P, Sun G, McNulty S, Zhang Y, 2015. A model to predict stream water temperature across the conterminous USA. Hydrological Processes, 29(9): 2178–2195. Doi: 10.1002/hyp.v29.9|
|Shannon E E, Wiener W, 1949. The Mathematical Theory of Communication. University Illinois Press, London125.|
|Shen H B, Guo L, 2008. Survey and analysis on fish composition of Niyang River in Tibet. Hebei Fisheries(5): 51-54–60.|
|Shen Y F, 1980. Ecological studies on the periphytic protozoa in Lake Dong Hu, Wuhan. Acta Hydrobiologica Sinica, 7(1): 18–40.|
|Shen Y F, 1990. New technology for microbial monitoring. China Building Industry Press, Beijing.|
|Shen Y P, Wang G X, Wu Q B, Liu S Y, 2002. The Impact of future climate change on ecology and environments in the Changjiang -Yellow Rivers source region. Journal of Glaciology and Geocryology, 24(3): 308–314.|
|Shi X L, Liu X J, Liu G J, Sun Z Q, Xu H L, 2012. An approach to analyzing spatial patterns of protozoan communities for assessing water quality in the Hangzhou section of Jing-Hang Grand Canal in China. Environmental Science and Pollution Research, 19(3): 739–747. Doi: 10.1007/s11356-011-0615-0|
|Sickman J O, Melack J M, 1998. Nitrogen and sulfate export from high elevation catchments of the Sierra Nevada, California. Water, Air, and Soil Pollution, 105(1-2): 217–226.|
|Slemmons K E H, Saros J E, Simon K, 2013. The influence of glacial meltwater on alpine aquatic ecosystems: a review. Environmental Science: Processes & Impacts, 15(10): 1794–1806.|
|Tan X L, Shi X L, Liu G J, Xu H L, Nie P, 2009. An approach to analyzing taxonomic patterns of protozoan communities for monitoring water quality in Songhua River, northeast China. Hydrobiologia, 638(1): 193–201.|
|Therneau T M, Atkinson B, Ripley B. 2010. rpart: Recursive Partitioning. R Package Version 3. p.1-46.|
|Vaerewijck M J M, Sabbe K, Baré J, Houf K, 2011. Occurrence and diversity of free-living protozoa on butterhead lettuce. International Journal of Food Microbiology, 147(2): 105–111. Doi: 10.1016/j.ijfoodmicro.2011.03.015|
|Wang P, Huang C L, Liu Z D, 1988. Snow and ice chemical characteristic for Xixiabangma Mountain area, Tibet. Environmental Science, 9(1): 23–26.|
|Wey J K, Jürgens K, Weitere M, 2012. Seasonal and successional influences on bacterial community composition exceed that of protozoan grazing in river biofilms. Applied and Environmental Microbiology, 78(6): 2013–2024. Doi: 10.1128/AEM.06517-11|
|Wu W H, Xu S J, Lu H Y, Yang J D, Yin H W, Liu W, 2011. Mineralogy, major and trace element geochemistry of riverbed sediments in the headwaters of the Yangtze, Tongtian River and Jinsha River. Journal of Asian Earth Sciences, 40(2): 611–621. Doi: 10.1016/j.jseaes.2010.10.013|
|Wu W H, Zheng H B, Xu S J, Yang J D, Yin H W, 2012. Geochemistry and provenance of bed sediments of the large rivers in the Tibetan Plateau and Himalayan region. International Journal of Earth Sciences, 101(5): 1357–1370. Doi: 10.1007/s00531-011-0719-2|
|Xie H. 2012. The Evapotranspiation and Its Response to Climate Change on the Tibetan Plateau (1970-2010). Lanzhou University, Lanzhou, China. (in Chinese with English abstract)|
|Xu H L, Min G S, Choi J K, Zhu M Z, Jiang Y, Al-Rasheid K A S, 2010. Temporal population dynamics of dinoflagellate Prorocentrum minimum in a semi-enclosed mariculture pond and its relationship to environmental factors and protozoan grazers. Chinese Journal of Oceanology and Limnology, 28(1): 75–81. Doi: 10.1007/s00343-010-9249-1|
|Yang J P, Löder M G J, Gerdts G, Wiltshire K H, 2015. Structural composition and temporal variation of the ciliate community in relation to environmental factors at Helgoland Roads, North Sea. Journal of Sea Research, 101: 19–30. Doi: 10.1016/j.seares.2014.07.010|
|Ye S W, Lin M L, Li L, Liu J S, Song L R, Li Z J, 2015. Abundance and spatial variability of invasive fishes related to environmental factors in a eutrophic Yunnan Plateau lake, Lake Dianchi, southwestern China. Environmental Biology of Fishes, 98(1): 209–224. Doi: 10.1007/s10641-014-0252-9|
|Zhai H J. 2009. Study on the River Ecological Integrity under the Threat of Hydropower Development in LRGR. Beijing Normal University, Beijing, China. (in Chinese with English abstract)|
|Zhang H P, Chen R H, Li F P, Chen L, 2015. Effect of flow rate on environmental variables and phytoplankton dynamics: results from field enclosures. Chinese Journal of Oceanology and Limnology, 33(2): 430–438. Doi: 10.1007/s00343-015-4063-4|
|Zhang J, Örmälä-Odegrip A M, Mappes J, et al, 2014. Topdown effects of a lytic bacteriophage and protozoa on bacteria in aqueous and biofilm phases. Ecology and Evolution, 4(23): 4444–4453. Doi: 10.1002/ece3.2014.4.issue-23|
|Zhang W, Xu H L, 2015. Seasonal shift in community pattern of periphytic ciliates and its environmental drivers in coastal waters of the Yellow Sea, northern China. Journal of the Marine Biological Association of the United Kingdom, 95(2): 277–288. Doi: 10.1017/S002531541400143X|
|Zhang Y H, Xu C J, Qi Q, Hou G L, 2011. Climate change and ecological effect on the Tibetan plateau. Journal of Qinghai University (Natural Science), 29(4): 18–22.|
|Zhou K X, Xu M Q, Cao H, 2003. Influence of protozoan grazing on aquatic bacteria. Acta Hydrobiologica Sinica, 27(2): 190–195.|