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An approach to bioassessment of water quality using diversity measures based on species accumulative curves
Marine Pollution Bulletin xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
An approach to bioassessment of water quality using diversity measures based on species accumulative curves Guangjian Xu a, Wei Zhang b, Henglong Xu a,⇑ a b
College of Marine Life Science, Ocean University of China, Qingdao 266003, China College of Fisheries, Ocean University of China, Qingdao 266003, China
a r t i c l e
i n f o
Article history: Available online xxxx Keywords: Species diversity Bioassessment Microperiphyton Species-accumulative curves Marine ecosystems
a b s t r a c t Traditional community-based bioassessment is time-consuming because they rely on full species-abundance data of a community. To improve bioassessment efficiency, the feasibility of the diversity measures based on species accumulative curves for bioassessment of water quality status was studied based on a dataset of microperiphyton fauna. The results showed that: (1) the species accumulative curves well fitted the Michaelis–Menten equation; (2) the b- and c-diversity, as well as the number of samples to 50% of the maximum species number (Michaelis–Menten constant K), can be statistically estimated based on the formulation; (3) the rarefied a-diversity represented a significant negative correlation with the changes in the nutrient NH4–N; and (4) the estimated b-diversity and the K constant were significantly positively related to the concentration of NH4–N. The results suggest that the diversity measures based on species accumulative curves might be used as a potential bioindicator of water quality in marine ecosystems. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Microperiphyton is a primary component of periphytic microfauna, and plays an important role in mediating the flux of both matter and energy from plankton to benthos in most aquatic ecosystems (Fischer et al., 2002; Kathol et al., 2009; Norf et al., 2009; Xu et al., 2011, 2014a, b, c; Zhong et al., 2014). Because they respond rapidly to environmental changes, are easily sampled and allow standardization for spatial and/or temporal comparisons, the microperiphyton has successfully be used as a useful bioindicator of water quality status in aquatic environments (Gold et al., 2002; Khatoon et al., 2007; Risse-Buhl and Küsel, 2009; Morin et al., 2010; Zhang et al., 2012; Xu et al., 2014a, b). However, traditional community-based monitoring programs are time-consuming because they rely on the both identification and enumeration of all species within the community. In order to improve bioassessment efficiency, the potential of species diversity measures based on the species accumulative curve (SAC) was explored to confirm the feasibility as a useful bioindicator of water quality status. In this study, the feasibility of the SAC-based diversity measures for evaluating water quality status was studied, based a dataset of microperiphyton communities. Our aim was to estab-
⇑ Corresponding author. Tel./fax: +86 532 8203 2082. E-mail address: [email protected] (H. Xu).
lish a methodology based on the SACs. The main points for this objective were: (1) to extract the a-, b- and c-diversity measures and the relevant parameter based on the SACs; (2) to reveal their relationships with the environmental conditions; and (3) to develop a method allows removing the influence of sampling effort on species diversity information in marine ecosystems. 2. Materials and methods 2.1. Data collection Microperiphyton communities were collected from four sampling stations in coastal waters, near Qingdao, northern China, each with different levels of water quality (Fig. 1: Xu et al., 2014a). A total of 40 samples were collected monthly at the four stations from August 2011 to July 2012 (Fig. 1). Microscopy glass slides, each with an area of 2.5 7.5 cm, were used as artificial substrates for collecting the microperiphyton. For each sampling at each station, 20 glass slides, as two replicates of the sample with 10 slides each, were immersed at a depth of 1 m below the water surface and exposed for 14 days in order to allow colonization by microperiphyton species. Thus, a total of 960 glass slides were examined during the study period. The slides were then retrieved, transferred into Petri dishes with the in-situ sea water from the station, placed in a cooling box, and transported to the laboratory for identification (Xu et al., 2009).
http://dx.doi.org/10.1016/j.marpolbul.2014.11.041 0025-326X/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
2
G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
Species identification was conducted following the methods described by Xu et al. (2014a). Protargol staining was carried out for species identification where necessary (Song et al., 2009). Identifications were performed using keys and guides references such as Song et al. (2009). Although the glass slide substrates used in this study were colonized by a range of microperiphyton organisms, including bacteria, fungi, algae, and micrometazoa, we have restricted our taxonomic analyses to ciliated protozoa. The environmental factors, nitrate nitrogen (NO3–N), nitrite nitrogen (NO2–N), ammonium nitrogen (NH3–N), soluble reactive phosphate (SRP) and COD were measured according to the ‘‘Standard Methods for the Examination of Water and Wastewater’’ (APHA, 1992). The measurements of water temperature (T), pH, salinity (Sal) and dissolved oxygen (DO) were recorded using WTW Multi 3500i sensor. Transparency (Tra) was measured in situ using a transparent scale.
where a is a-diversity, b is b-diversity and c is c-diversity (Ricotta, 2008). Thus, the b-diversity and the sampling effort influence can be predicted through the equation that was derived from the Eqs. (1) and (2):
2.2. Data analyses
The average values of each environmental factor at the four sampling stations are summarized in Table S1. Salinity ranged from 28.7 to 29.3 psu, with the lowest at station A and the highest value at station B. The pH ranged from 8.13 to 8.28 among the four stations. The values of transparency ranged from 2.0 to 3.6 m, with the highest at station D and the lowest at station A. The values of DO were generally >7.5 mg L1 at all stations, with the highest at station D and the lowest value at station A. Nutrients NO3–N and SPR were usually lowest at station C and highest at station A, and the NH4–N decreased from station A to D in concentration (Table S1). The lowest were measured at station C, and the highest values of COD and NO2–N at stations B and D. Note that the transparency and the values of DO had an increasing trend, while NH4– N had a generally decreasing trend, from the more polluted areas (A and B) to the less polluted ones (C and D).
The SACs were modeled by the asymptotic Michaelis–Menten equation:
cn ¼ cmax =ð1 þ K=nÞ
ð1Þ
where cn is the accumulative species number at nth sample; cmax is the predicted maximum accumulative species number; n is the number of samples; K is a constant, i.e., the number of samples to 50% of the predicted maximum accumulative species number (Flather, 1996; Xu et al., 2014b). The fitness of the equation was tested using the SIGMAPLOT (Xu et al., 2012). Before analysis, the SACs were rarefied using the software package EstimateS v8.2 (Colwell et al., 2004; Mao et al., 2005; Clarke et al., 2011). The a-, b- and c-diversities were extracted from SACs: the adiversity equates to the average number of species, which is mathematically identical to the first point on the SAC, and the final point on the SAC is the total number of species recorded, or the c-diversity (Crist and Veech, 2006). The b-diversity, as the difference in species richness between the first and last points, was calculated according to the equation developed by Ricotta (2008):
b ¼ 1 a=c
ð2Þ
bn ¼ 1 a=cmax ð1 þ K=nÞ
ð3Þ
where bn is b-diversity and at nth sample. The correlation analyses between species diversity measures and environmental variables were carried out using the statistical program SPSS v16 at the 0.05 level (Xu et al., 2011). Data were logtransformed before analysis. 3. Results 3.1. Environmental variables
3.2. Observed species richness and SACs The average and accumulative species number recorded in the microperiphyton communities at each sampling station are summarized in Table 1. A total of 144 species were recorded, which occurred in low average number at station A and in high richness at the other three stations (Table 1). However, in terms of accumulative species number, the high values occurred at stations B and C and low at the other two stations (Table 1). The SACs for all communities at each station were shown in Fig. 2. The curves indicated that the number of accumulative species number (i.e., c-diversity) in the communities at each station increased with the increase of sampling effort (number of samples) from each initial value (i.e. a-diversity) (Fig. 2). Linear regression revealed that all four SACs were well fitted to the Michaelis–Menten equation (R2 > 0.99; P < 0.05) (Fig. 3). 3.3. Diversity based on SACs The rarefied a-diversities in microperiphyton communities at each station were summarized in Table 2. The values of both adiversity showed an increasing trend from station A to D (Table 2).
Fig. 1. Sampling stations in coastal waters of the Yellow Sea, near Qingdao, northern China. A: station A, heavily stressed area in Jiaozhou Bay, the pollution being mainly in the form of organic pollutants and nutrients from domestic sewage and industrial discharges from several rivers; B: station B, moderately polluted area Jiaozhou Bay by minor discharges from a small river entering the bay; C: station C, slightly polluted area near the mouth of Jiaozhou Bay and relatively distant from the rivers entering the bay; D: station D, relatively clean area which was outside the bay and distant from the river discharges.
Table 1 Observed average species number and accumulative species number of microperiphyton fauna at four stations in coastal waters of the Yellow Sea during the study period. S
St. A
St. B
St. C
St. D
AvS AcS
31 103
36 115
35 111
35 101
AcS: accumulative species number; AvS: average species number. St. A–D: stations A–D.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
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Fig. 2. Sample-based rarefaction curves of the communities at the sampling stations A (a), B (b), C (c) and d (d) in coastal waters of Yellow Sea, near Qingdao, northern China, during the study period. Dotted lines show the values of a-diversity.
Fig. 3. Estimated c-diversity and the K constant of the communities at the sampling stations A (a), B (b), C (c) and d (d) in coastal waters of Yellow Sea, near Qingdao, northern China, during the study period. K constant, the number of samples to 50% maximum accumulative species number.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
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G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
Table 2 Species biodiversity (a-, b- and c-diversity) and K constant of microperiphyton fauna at four stations in coastal waters of the Yellow Sea during the study period. Parameters
St. A
St. B
St. C
St. D
a-diversity
31 0.78 139 3.6
35 0.77 152 3.4
35 0.76 146 3.3
36 0.72 128 2.6
b-diversity c-diversity K constant
Table 3 Spearman correlations between species biodiversity (a-, b- and c-diversity) and K constant and average of environmental variables with spatial variations at four sampling stations in coastal waters of the Yellow Sea, near Qingdao, northern China during the study period.
a
Parameters
a-diversity
b-diversity
c-diversity
K constant
Sal pH DO COD Tra NO2–N NO3–N NH4–N SRP
0.400 0.800 0.949a 0.600 0.949a 0.600 0.400 0.949a 0.400
0.200 0.400 1.000a <0.001 1.000a <0.001 0.800 1.000a 0.800
0.800 0.400 0.400 <0.001 0.400 0.200 <0.001 0.400 <0.001
0.200 0.200 1.000a <0.001 1.000a <0.001 0.800 1.000a 0.800
Significant at the 0.05 level.
To remove the influence of sampling effort on c and b, the constant cmax that was obtained by fitting the SACs to the Michaelis–Menten equation (Eq. (1)), and that the true values of b-diversity (bmax) were calculated according to the Eq. (3). The outputs showed that the values of b-diversity obtained showed an decreasing trend from station A to D, while high values of c-diversity occurred at stations B and C compared to low value at station D (Table 2). It should be noted that the K constant, i.e., the number of samples when accumulative species number reach 50% of cmax exhibited a clear decreasing trend from station A to D (Table 2).
3.4. Linkage between SAC-based species diversity and abiota The relationships between SAC-based species diversity, the K constant and environmental variables were summarized in Table 3. Among the four ecological parameters, the a-/b-diversities and the K constant were significantly correlated with DO, transparency and the nutrient NH4–N (P < 0.05) (Table 3; Fig. 4). However, no significant correlation was found between the c-diversity and abiotic factor (Table 3).
4. Discussion Based on the presence/absence resolution of a community, Whittaker (1972) defined the species richness of a region as cdiversity, the one of sampling stations within the region as adiversity, and the difference in species composition between these stations as b-diversity. However, the total species richness is difficult and impractical to measure due to the influence of sampling effort (Clarke et al., 2011; Xu et al., 2012). The SAC have proved to be a powerful tool to estimate the a-, b- and c-diversity among habitats based on equal-size sampling strategies (Dove and Cribb, 2006; Flather, 1996; Gotelli and Colwell, 2001; Thompson et al., 2003; Xu et al., 2014d). The a- and b-diversity measures commonly describe the species richness of average single-location (or single-community) and the relative change in species composition between locations (or communities), respectively (Whittaker, 1972; Jost, 2007; Veech and Crist, 2010). In this study, we proposed the temporal variability in species composition might be considered as a b-diversity measure senso latu. Based on our data, although the SACs were build after rarefaction and ignored the seasonal shift that was driven by the ‘‘nature’’ environmental variables at each station, the SAC curves were well fitted to the Michaelis–Menten equation Furthermore, the b-diversity measure might clearly reflect the water quality status among the four stations. Species diversity indices are commonly used to assess water quality status (Ismael and Dorgham, 2003; Xu et al., 2012, 2014a). In general, the higher the values are, the better the water quality is (Connell, 1978; Ismael and Dorgham, 2003). In this study, correlation analyses indicated that among three SAC-based biodiversity measures, a- and b-diversity were significantly correlated with the nutrient NH4–N. Furthermore, the K constant based on the Michaelis–Menten model equation showed a significant relationship with the environmental variables. Thus, we suggest that these three parameters may be used as a potential bioindicator of water quality status in marine ecosystem. It should be addressed that the relationships between SAC-based species diversity and water quality were specific to microperiphyton fauna survey methods for the monitoring program. However, the method we describe is transferable and might provide a theoretically sound framework for deriving a comparable indicator of a-, b- and c-diversity among study stations or areas at different sampling effort. Otherwise, the total species richness (i.e., c-diversity) is commonly negatively correlated with concentrations of organic pollutants and toxic levels. However, in this study, no significant correlation was found between it and the nutrients. This may imply that the environmental stress at the stations B and C remained at intermediate level compared to the more polluted station A and less stressed station D. In summary, the SAC curves well fitted the Michaelis–Menten equation. Otherwise, the b- and c-diversity, as well as the number
Fig. 4. Comparison between spatial variation in a- (a), b-diversity (b) and K constant (c) and change of average values of NH4–N concentration among four sampling stations (A–D) in coastal waters of the Yellow Sea, near Qingdao, northern China during the study period.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
of samples to 50% of the maximum species number (Michaelis– Menten constant K), can be statistically estimated based on the formulation. Furthermore, the rarefied a-diversity represented a significant negative correlation with the changes in the nutrient NH4–N, while the estimated b-diversity and the K constant were significantly positively related to the concentration of NH4–N. Based on our data, it is suggested that the SAC-based a-, b-diversity measures and K constant might be used as a potential bioindicator of water quality in marine ecosystems. Further studies, however, on a range of marine waters and over extended time periods are warranted in order to verify this conclusion. Acknowledgements This work was supported by ‘‘The Natural Science Foundation of China’’ (Project No.: 41076089), and Scholarship Award for Excellent Doctoral Student granted by Chinese Ministry of Education.
Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.marpolbul.2014. 11.041. References APHA, 1992. Standard Methods for Examination of Water and Waste Water, 18th ed. American Public Health Association, Washington, DC. Clarke, K., Lewis, M., Ostendorf, B., 2011. Additive partitioning of rarefaction curves: removing the influence of sampling on species-diversity in vegetation surveys. Ecol. Ind. 11, 132–139. Colwell, R.K., Mao, C.X., Chang, J., 2004. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85, 2717–2727. Connell, J.H., 1978. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310. Crist, T.O., Veech, J.A., 2006. Additive partitioning of rarefaction curves and speciesarea relationships: unifying alpha-, beta- and gamma-diversity with sample size and habitat area. Ecol. Lett. 9, 923–932. Dove, A.D.M., Cribb, T.H., 2006. Species accumulation curves and their applications in parasite ecology. Trend Parasit. 22, 568–573. Fischer, H., Sachse, A., Steinberg, C.E.W., Pusch, M., 2002. Differential retention and utilization of dissolved organic carbon by bacteria in river sediments. Limnol. Oceanogr. 47, 1702–1711. Flather, C.H., 1996. Fitting species-accumulation functions and assessing regional land use impacts on avian diversity. J. Biogeogr. 23, 155–168. Gold, C., Reurtet-Mazel, A., Coste, M., Boudou, A., 2002. Field transfer of periphytic diatom communities to assess short-term structural effects of metals (Cd, Zn). Water Res. 36, 3654–3664. Gotelli, N.J., Colwell, R.K., 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391.
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Ismael, A.A., Dorgham, M.M., 2003. Ecological indices as a tool for assessing pollution in El-Dekhaila Harbour (Alexandria, Egypt). Oceanologia Acta 45, 121– 131. Jost, L., 2007. Partitioning diversity into independent alpha and beta components. Ecology 88, 2427–2439. Kathol, M., Norf, H., Arndt, H., Weitere, M., 2009. Effects of temperature increase on the grazing of planktonic bacteria by biofilm-dwelling consumers. Aquat. Microb. Ecol. 55, 65–79. Khatoon, H., Yusoff, F.M., Banerjee, S., Shariff, M., Mohamed, S., 2007. Use of periphytic cyanobacterium and mixed diatoms coated substrate for improving water qualily, survival and growth of Penaeus monodon Fabricius postlarvae. Aquaculture 27, 196–205. Mao, C.X., Colwell, R.K., Chang, J., 2005. Estimating the species accumulation curve using mixtures. Biometrics 61, 433–441. Morin, S., Pesce, S., Tlili, A., Coste, M., Montuelle, B., 2010. Recovery potential of periphytic communities in a river impacted by a vineyard watershed. Ecol. Ind. 10, 419–426. Norf, H., Arndt, H., Weitere, M., 2009. Effects of resource supplements on mature ciliate biofilms: an empirical test using a new type of flow cell. Biofouling 25, 769–778. Ricotta, C., 2008. Computing additive [beta]-diversity from presence and absence scores: a critique and alternative parameters. Theor. Popul. Biol. 73, 244–249. Risse-Buhl, U., Küsel, K., 2009. Colonization dynamics of biofilm-associated ciliate morphotypes at different flow velocities. Eur. J. Protistol. 45, 64–76. Song, W., Warren, A., Hu, X., 2009. Free-living Ciliates in the Bohai and Yellow Seas. Science Press, Beijing, China (in both Chinese and English). Thompson, G.G., Withers, P.C., Pianka, E.R., Thompson, S.A., 2003. Assessing biodiversity with species accumulation curves; inventories of small reptiles by pit-trapping in Western Australia. Austral Ecol. 28, 361–383. Veech, J.A., Crist, T.O., 2010. Diversity partitioning without statistical independence of alpha and beta. Ecology 91, 1964–1969. Whittaker, R.H., 1972. Evolution and measurement of species diversity. Taxon 21, 213–251. Xu, H., Min, G.S., Choi, J.K., Jung, J.H., Park, M.H., 2009. Approach to analyses of periphytic ciliate colonization for monitoring water quality using a modified artificial substrate in Korean coastal waters. Mar. Pollut. Bull. 58, 1278–1285. Xu, H., Zhang, W., Jiang, Y., Zhu, M., Al-Rasheid, K.A.S., Warren, A., Song, W., 2011. An approach to determining sampling effort for analyzing biofilm-dwelling ciliate colonization using an artificial substratum in coastal waters. Biofouling 27, 357–366. Xu, H., Zhang, W., Jiang, Y., Zhu, M., Al-Rasheid, K.A.S., 2012. Influence of sampling sufficiency on biodiversity analysis of microperiphyton communities for marine bioassessment. Environ. Sci. Pollut. Res. 19, 540–549. Xu, H., Zhang, W., Jiang, Y., Yang, E.J., 2014a. Use of biofilm-dwelling ciliate communities to determine environmental quality status of coastal waters. Sci. Total Environ. 470–471, 511–518. Xu, H., Zhang, W., Jiang, Y., 2014b. Do early colonization patterns of periphytic ciliate fauna reveal environmental quality status in coastal water? Environ. Sci. Pollut. Res. 21, 7097–7112. Xu, Y., Zhang, W., Liu, Y., Xu, H., 2014c. Identification of potential surrogates to determine functional parameters of periphytic ciliate colonization for bioassessment in coastal waters. Ecol. Ind. 46, 438–446. Xu, G., Zhong, X., Wang, Y., Xu, H., 2014d. A multivariate approach to the determination of an indicator species pool for community-based bioassessment of marine water quality. Mar. Pollut. Bull. 87, 147–151. Zhang, W., Xu, H., Jiang, Y., Zhu, M., Al-Reshaid, K.A.S., 2012. Colonization dynamics in trophic-functional structure of periphytic protist communities in coastal waters. Mar. Biol. 159, 735–748. Zhong, X., Xu, G., Wang, Y., Xu, H., 2014. An approach to determination of functional species pool for community research. Ecol. Ind. 46, 78–83.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
Contents lists available at ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
An approach to bioassessment of water quality using diversity measures based on species accumulative curves Guangjian Xu a, Wei Zhang b, Henglong Xu a,⇑ a b
College of Marine Life Science, Ocean University of China, Qingdao 266003, China College of Fisheries, Ocean University of China, Qingdao 266003, China
a r t i c l e
i n f o
Article history: Available online xxxx Keywords: Species diversity Bioassessment Microperiphyton Species-accumulative curves Marine ecosystems
a b s t r a c t Traditional community-based bioassessment is time-consuming because they rely on full species-abundance data of a community. To improve bioassessment efficiency, the feasibility of the diversity measures based on species accumulative curves for bioassessment of water quality status was studied based on a dataset of microperiphyton fauna. The results showed that: (1) the species accumulative curves well fitted the Michaelis–Menten equation; (2) the b- and c-diversity, as well as the number of samples to 50% of the maximum species number (Michaelis–Menten constant K), can be statistically estimated based on the formulation; (3) the rarefied a-diversity represented a significant negative correlation with the changes in the nutrient NH4–N; and (4) the estimated b-diversity and the K constant were significantly positively related to the concentration of NH4–N. The results suggest that the diversity measures based on species accumulative curves might be used as a potential bioindicator of water quality in marine ecosystems. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Microperiphyton is a primary component of periphytic microfauna, and plays an important role in mediating the flux of both matter and energy from plankton to benthos in most aquatic ecosystems (Fischer et al., 2002; Kathol et al., 2009; Norf et al., 2009; Xu et al., 2011, 2014a, b, c; Zhong et al., 2014). Because they respond rapidly to environmental changes, are easily sampled and allow standardization for spatial and/or temporal comparisons, the microperiphyton has successfully be used as a useful bioindicator of water quality status in aquatic environments (Gold et al., 2002; Khatoon et al., 2007; Risse-Buhl and Küsel, 2009; Morin et al., 2010; Zhang et al., 2012; Xu et al., 2014a, b). However, traditional community-based monitoring programs are time-consuming because they rely on the both identification and enumeration of all species within the community. In order to improve bioassessment efficiency, the potential of species diversity measures based on the species accumulative curve (SAC) was explored to confirm the feasibility as a useful bioindicator of water quality status. In this study, the feasibility of the SAC-based diversity measures for evaluating water quality status was studied, based a dataset of microperiphyton communities. Our aim was to estab-
⇑ Corresponding author. Tel./fax: +86 532 8203 2082. E-mail address: [email protected] (H. Xu).
lish a methodology based on the SACs. The main points for this objective were: (1) to extract the a-, b- and c-diversity measures and the relevant parameter based on the SACs; (2) to reveal their relationships with the environmental conditions; and (3) to develop a method allows removing the influence of sampling effort on species diversity information in marine ecosystems. 2. Materials and methods 2.1. Data collection Microperiphyton communities were collected from four sampling stations in coastal waters, near Qingdao, northern China, each with different levels of water quality (Fig. 1: Xu et al., 2014a). A total of 40 samples were collected monthly at the four stations from August 2011 to July 2012 (Fig. 1). Microscopy glass slides, each with an area of 2.5 7.5 cm, were used as artificial substrates for collecting the microperiphyton. For each sampling at each station, 20 glass slides, as two replicates of the sample with 10 slides each, were immersed at a depth of 1 m below the water surface and exposed for 14 days in order to allow colonization by microperiphyton species. Thus, a total of 960 glass slides were examined during the study period. The slides were then retrieved, transferred into Petri dishes with the in-situ sea water from the station, placed in a cooling box, and transported to the laboratory for identification (Xu et al., 2009).
http://dx.doi.org/10.1016/j.marpolbul.2014.11.041 0025-326X/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
2
G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
Species identification was conducted following the methods described by Xu et al. (2014a). Protargol staining was carried out for species identification where necessary (Song et al., 2009). Identifications were performed using keys and guides references such as Song et al. (2009). Although the glass slide substrates used in this study were colonized by a range of microperiphyton organisms, including bacteria, fungi, algae, and micrometazoa, we have restricted our taxonomic analyses to ciliated protozoa. The environmental factors, nitrate nitrogen (NO3–N), nitrite nitrogen (NO2–N), ammonium nitrogen (NH3–N), soluble reactive phosphate (SRP) and COD were measured according to the ‘‘Standard Methods for the Examination of Water and Wastewater’’ (APHA, 1992). The measurements of water temperature (T), pH, salinity (Sal) and dissolved oxygen (DO) were recorded using WTW Multi 3500i sensor. Transparency (Tra) was measured in situ using a transparent scale.
where a is a-diversity, b is b-diversity and c is c-diversity (Ricotta, 2008). Thus, the b-diversity and the sampling effort influence can be predicted through the equation that was derived from the Eqs. (1) and (2):
2.2. Data analyses
The average values of each environmental factor at the four sampling stations are summarized in Table S1. Salinity ranged from 28.7 to 29.3 psu, with the lowest at station A and the highest value at station B. The pH ranged from 8.13 to 8.28 among the four stations. The values of transparency ranged from 2.0 to 3.6 m, with the highest at station D and the lowest at station A. The values of DO were generally >7.5 mg L1 at all stations, with the highest at station D and the lowest value at station A. Nutrients NO3–N and SPR were usually lowest at station C and highest at station A, and the NH4–N decreased from station A to D in concentration (Table S1). The lowest were measured at station C, and the highest values of COD and NO2–N at stations B and D. Note that the transparency and the values of DO had an increasing trend, while NH4– N had a generally decreasing trend, from the more polluted areas (A and B) to the less polluted ones (C and D).
The SACs were modeled by the asymptotic Michaelis–Menten equation:
cn ¼ cmax =ð1 þ K=nÞ
ð1Þ
where cn is the accumulative species number at nth sample; cmax is the predicted maximum accumulative species number; n is the number of samples; K is a constant, i.e., the number of samples to 50% of the predicted maximum accumulative species number (Flather, 1996; Xu et al., 2014b). The fitness of the equation was tested using the SIGMAPLOT (Xu et al., 2012). Before analysis, the SACs were rarefied using the software package EstimateS v8.2 (Colwell et al., 2004; Mao et al., 2005; Clarke et al., 2011). The a-, b- and c-diversities were extracted from SACs: the adiversity equates to the average number of species, which is mathematically identical to the first point on the SAC, and the final point on the SAC is the total number of species recorded, or the c-diversity (Crist and Veech, 2006). The b-diversity, as the difference in species richness between the first and last points, was calculated according to the equation developed by Ricotta (2008):
b ¼ 1 a=c
ð2Þ
bn ¼ 1 a=cmax ð1 þ K=nÞ
ð3Þ
where bn is b-diversity and at nth sample. The correlation analyses between species diversity measures and environmental variables were carried out using the statistical program SPSS v16 at the 0.05 level (Xu et al., 2011). Data were logtransformed before analysis. 3. Results 3.1. Environmental variables
3.2. Observed species richness and SACs The average and accumulative species number recorded in the microperiphyton communities at each sampling station are summarized in Table 1. A total of 144 species were recorded, which occurred in low average number at station A and in high richness at the other three stations (Table 1). However, in terms of accumulative species number, the high values occurred at stations B and C and low at the other two stations (Table 1). The SACs for all communities at each station were shown in Fig. 2. The curves indicated that the number of accumulative species number (i.e., c-diversity) in the communities at each station increased with the increase of sampling effort (number of samples) from each initial value (i.e. a-diversity) (Fig. 2). Linear regression revealed that all four SACs were well fitted to the Michaelis–Menten equation (R2 > 0.99; P < 0.05) (Fig. 3). 3.3. Diversity based on SACs The rarefied a-diversities in microperiphyton communities at each station were summarized in Table 2. The values of both adiversity showed an increasing trend from station A to D (Table 2).
Fig. 1. Sampling stations in coastal waters of the Yellow Sea, near Qingdao, northern China. A: station A, heavily stressed area in Jiaozhou Bay, the pollution being mainly in the form of organic pollutants and nutrients from domestic sewage and industrial discharges from several rivers; B: station B, moderately polluted area Jiaozhou Bay by minor discharges from a small river entering the bay; C: station C, slightly polluted area near the mouth of Jiaozhou Bay and relatively distant from the rivers entering the bay; D: station D, relatively clean area which was outside the bay and distant from the river discharges.
Table 1 Observed average species number and accumulative species number of microperiphyton fauna at four stations in coastal waters of the Yellow Sea during the study period. S
St. A
St. B
St. C
St. D
AvS AcS
31 103
36 115
35 111
35 101
AcS: accumulative species number; AvS: average species number. St. A–D: stations A–D.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
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Fig. 2. Sample-based rarefaction curves of the communities at the sampling stations A (a), B (b), C (c) and d (d) in coastal waters of Yellow Sea, near Qingdao, northern China, during the study period. Dotted lines show the values of a-diversity.
Fig. 3. Estimated c-diversity and the K constant of the communities at the sampling stations A (a), B (b), C (c) and d (d) in coastal waters of Yellow Sea, near Qingdao, northern China, during the study period. K constant, the number of samples to 50% maximum accumulative species number.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
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G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
Table 2 Species biodiversity (a-, b- and c-diversity) and K constant of microperiphyton fauna at four stations in coastal waters of the Yellow Sea during the study period. Parameters
St. A
St. B
St. C
St. D
a-diversity
31 0.78 139 3.6
35 0.77 152 3.4
35 0.76 146 3.3
36 0.72 128 2.6
b-diversity c-diversity K constant
Table 3 Spearman correlations between species biodiversity (a-, b- and c-diversity) and K constant and average of environmental variables with spatial variations at four sampling stations in coastal waters of the Yellow Sea, near Qingdao, northern China during the study period.
a
Parameters
a-diversity
b-diversity
c-diversity
K constant
Sal pH DO COD Tra NO2–N NO3–N NH4–N SRP
0.400 0.800 0.949a 0.600 0.949a 0.600 0.400 0.949a 0.400
0.200 0.400 1.000a <0.001 1.000a <0.001 0.800 1.000a 0.800
0.800 0.400 0.400 <0.001 0.400 0.200 <0.001 0.400 <0.001
0.200 0.200 1.000a <0.001 1.000a <0.001 0.800 1.000a 0.800
Significant at the 0.05 level.
To remove the influence of sampling effort on c and b, the constant cmax that was obtained by fitting the SACs to the Michaelis–Menten equation (Eq. (1)), and that the true values of b-diversity (bmax) were calculated according to the Eq. (3). The outputs showed that the values of b-diversity obtained showed an decreasing trend from station A to D, while high values of c-diversity occurred at stations B and C compared to low value at station D (Table 2). It should be noted that the K constant, i.e., the number of samples when accumulative species number reach 50% of cmax exhibited a clear decreasing trend from station A to D (Table 2).
3.4. Linkage between SAC-based species diversity and abiota The relationships between SAC-based species diversity, the K constant and environmental variables were summarized in Table 3. Among the four ecological parameters, the a-/b-diversities and the K constant were significantly correlated with DO, transparency and the nutrient NH4–N (P < 0.05) (Table 3; Fig. 4). However, no significant correlation was found between the c-diversity and abiotic factor (Table 3).
4. Discussion Based on the presence/absence resolution of a community, Whittaker (1972) defined the species richness of a region as cdiversity, the one of sampling stations within the region as adiversity, and the difference in species composition between these stations as b-diversity. However, the total species richness is difficult and impractical to measure due to the influence of sampling effort (Clarke et al., 2011; Xu et al., 2012). The SAC have proved to be a powerful tool to estimate the a-, b- and c-diversity among habitats based on equal-size sampling strategies (Dove and Cribb, 2006; Flather, 1996; Gotelli and Colwell, 2001; Thompson et al., 2003; Xu et al., 2014d). The a- and b-diversity measures commonly describe the species richness of average single-location (or single-community) and the relative change in species composition between locations (or communities), respectively (Whittaker, 1972; Jost, 2007; Veech and Crist, 2010). In this study, we proposed the temporal variability in species composition might be considered as a b-diversity measure senso latu. Based on our data, although the SACs were build after rarefaction and ignored the seasonal shift that was driven by the ‘‘nature’’ environmental variables at each station, the SAC curves were well fitted to the Michaelis–Menten equation Furthermore, the b-diversity measure might clearly reflect the water quality status among the four stations. Species diversity indices are commonly used to assess water quality status (Ismael and Dorgham, 2003; Xu et al., 2012, 2014a). In general, the higher the values are, the better the water quality is (Connell, 1978; Ismael and Dorgham, 2003). In this study, correlation analyses indicated that among three SAC-based biodiversity measures, a- and b-diversity were significantly correlated with the nutrient NH4–N. Furthermore, the K constant based on the Michaelis–Menten model equation showed a significant relationship with the environmental variables. Thus, we suggest that these three parameters may be used as a potential bioindicator of water quality status in marine ecosystem. It should be addressed that the relationships between SAC-based species diversity and water quality were specific to microperiphyton fauna survey methods for the monitoring program. However, the method we describe is transferable and might provide a theoretically sound framework for deriving a comparable indicator of a-, b- and c-diversity among study stations or areas at different sampling effort. Otherwise, the total species richness (i.e., c-diversity) is commonly negatively correlated with concentrations of organic pollutants and toxic levels. However, in this study, no significant correlation was found between it and the nutrients. This may imply that the environmental stress at the stations B and C remained at intermediate level compared to the more polluted station A and less stressed station D. In summary, the SAC curves well fitted the Michaelis–Menten equation. Otherwise, the b- and c-diversity, as well as the number
Fig. 4. Comparison between spatial variation in a- (a), b-diversity (b) and K constant (c) and change of average values of NH4–N concentration among four sampling stations (A–D) in coastal waters of the Yellow Sea, near Qingdao, northern China during the study period.
Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041
G. Xu et al. / Marine Pollution Bulletin xxx (2014) xxx–xxx
of samples to 50% of the maximum species number (Michaelis– Menten constant K), can be statistically estimated based on the formulation. Furthermore, the rarefied a-diversity represented a significant negative correlation with the changes in the nutrient NH4–N, while the estimated b-diversity and the K constant were significantly positively related to the concentration of NH4–N. Based on our data, it is suggested that the SAC-based a-, b-diversity measures and K constant might be used as a potential bioindicator of water quality in marine ecosystems. Further studies, however, on a range of marine waters and over extended time periods are warranted in order to verify this conclusion. Acknowledgements This work was supported by ‘‘The Natural Science Foundation of China’’ (Project No.: 41076089), and Scholarship Award for Excellent Doctoral Student granted by Chinese Ministry of Education.
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Please cite this article in press as: Xu, G., et al. An approach to bioassessment of water quality using diversity measures based on species accumulative curves. Mar. Pollut. Bull. (2014), http://dx.doi.org/10.1016/j.marpolbul.2014.11.041