Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front Earth Sci    2014, Vol. 8 Issue (1) : 44-57    https://doi.org/10.1007/s11707-013-0420-9
RESEARCH ARTICLE
Inferring community properties of benthic macroinvertebrates in streams using Shannon index and exergy
Tuyen Van NGUYEN1,2, Woon-Seok CHO2, Hungsoo KIM1,3, Il Hyo JUNG1, YongKuk KIM4, Tae-Soo CHON2()
1. Department of Mathematics, Pusan National University, Busan 609-735, Korea; 2. Department of Biological Sciences, Pusan National University, Busan 609-735, Korea; 3. DSPENALO National Robotics Research Center, Pusan National University, Busan 609-735, Korea; 4. Department of Mathematics, Kyungpook National University, Daegu 702-701, Korea
 Download: PDF(763 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Definition of ecological integrity based on community analysis has long been a critical issue in risk assessment for sustainable ecosystem management. In this work, two indices (i.e., Shannon index and exergy) were selected for the analysis of community properties of benthic macroinvertebrate community in streams in Korea. For this purpose, the means and variances of both indices were analyzed. The results found an extra scope of structural and functional properties in communities in response to environmental variabilities and anthropogenic disturbances. The combination of these two parameters (four indices) was feasible in identification of disturbance agents (e.g., industrial pollution or organic pollution) and specifying states of communities. The four-aforementioned parameters (means and variances of Shannon index and exergy) were further used as input data in a self-organizing map for the characterization of water quality. Our results suggested that Shannon index and exergy in combination could be utilized as a suitable reference system and would be an efficient tool for assessment of the health of aquatic ecosystems exposed to environmental disturbances.
Keywords benthic macroinvertebrates      ecological integrity      anthropogenic pollution      self-organizing map     
Corresponding Author(s): CHON Tae-Soo,Email:tschon@pusan.ac.kr   
Issue Date: 05 March 2014
 Cite this article:   
Tuyen Van NGUYEN,Woon-Seok CHO,Hungsoo KIM, et al. Inferring community properties of benthic macroinvertebrates in streams using Shannon index and exergy[J]. Front Earth Sci, 2014, 8(1): 44-57.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-013-0420-9
https://academic.hep.com.cn/fesci/EN/Y2014/V8/I1/44
Fig.1  Average values of BOD and Shannon index. Different alphabets listed near the bars in the figure indicate statistical difference according to Tukey’s test.
Fig.2  Shannon index, exergy and their variances across different level of pollution based on seasonal samplings of benthic macroinvertebrates in streams in Korea from February 2005 to April 2007. Different lines indicating sample sites. (a) Shannon index, (b) Variance of Shannon index, (c) Exergy, and (d) Variance of exergy. The numbers on the -axis represent the timing of sampling (e.g., 05 indicating 2005). The results were calculated based on Eqs. (1), (2), (4), and (5).
Fig.3  Shannon index, exergy and their variances across different level of pollution based on monthly sampling of benthic macroinvertebrates in streams in Korea from February 1992 to March 1995. (a) Shannon index, (b–e) Variance of Shannon index, (f) Exergy, (g–j) Variance of exergy. The results were calculated based on Eqs. (1), (2), (4), and (5). The numbers on the -axis indicate the year of data collection (e.g., 92 equivalents to 1992).
Fig.4  Correlation matrices between the Shannon index, exergy and their variances for seasonal (84 points, left panel) and monthly (144 points, right panel) data for benthic macroinvertebrates. The circles in the subfigure in the right panel presenting the correlation between exergy and Shannon index show both maximum and minimum values of exergy corresponding to low values of the Shannon index.
Fig.5  Clustering by SOM based on the four parameters (means and variances of the Shannon index and exergy) as input data. Seven clusters were observed in relation to different level of pollution (see Results for more explanation). The right subfigure presents dendrogram according to Ward’s method (). The numbers in parentheses following the name of sample sites within the node of SOM indicate the number of samples belonging to the cluster.
Fig.6  Profiles of mean and variances of the Shannon index and exergy, BODand biological indices when the variables were visualized on the SOM (Fig. 5).
Fig.7  Relative abundance (%) of benthic macroinvertebrate in each cluster presented on the SOM (Fig. 5). The top 20 dominant species were listed for clusters Ⅱ, Ⅲ, Ⅳ, and Ⅴ, and the top five dominant species listed in clusters Ⅰ, Ⅵ and Ⅶ (See Appendix for the number of total species and names of dominant species) .
1 Armitage P D, Moss D, Wright J F, Furse M T (1983). The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. Water Res , 17(3): 333-347
doi: 10.1016/0043-1354(83)90188-4
2 Bae M J, Li F, Verdonschot P F M, Park Y S (2013). Characterization of ecological exergy based on benthic macroinvertebrates in lotic ecosystems. Entropy , 15(6): 2319-2339
doi: 10.3390/e15062319
3 Barbour M T, Gerritsen J, Griffith G E, Frydenborg R, McCarron E, White J S, Bastian M L (1996). A framework for biological criteria for Florida streams using benthic macroinvertebrates. J N Am Benthol Soc , 15(2): 185-211
doi: 10.2307/1467948
4 Bastianoni S, Facchini A, Susani L, Tiezzi E (2007). Emergy as a function of exergy. Energy , 32(7): 1158-1162
doi: 10.1016/j.energy.2006.08.009
5 Bendoricchio G, J?rgensen S E (1997). Exergy as goal function of ecosystems dynamic. Ecol Modell , 102(1): 5-15
doi: 10.1016/S0304-3800(97)00091-4
6 Benedetti-Cecchi L (2003). The importance of the variance around the mean effect size of ecological processes. Ecology , 84(9): 2335-2346
doi: 10.1890/02-8011
7 Blocksom K A, Kurtenbach J P, Klemm D J, Fulk F A, Cormier S M (2002). Development and evaluation of the lake macroinvertebrate integrity index (LMII) for New Jersey lakes and reservoirs. Environ Monit Assess , 77(3): 311-333
doi: 10.1023/A:1016096925401 pmid:12194418
8 Chao A, Shen T J (2003). Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample. Environ Ecol Stat , 10(4): 429-443
doi: 10.1023/A:1026096204727
9 Chon T S (2011). Self-organizing maps applied to ecological sciences. Ecol Inform , 6(1): 50-61
doi: 10.1016/j.ecoinf.2010.11.002
10 Chon T S, Qu X, Cho W S, Hwang H J, Tang H, Liu Y, Choi J H, Jung M, Chung B S, Lee H Y, Chung Y R, Koh S C (2013). Evaluation of stream ecosystem health and species association based on multi-taxa (benthic macroinvertebrates, algae, and microorganisms) patterning with different levels of pollution. Ecol Inform , 17: 58-72
doi: 10.1016/j.ecoinf.2013.06.004
11 Dai J, Fath B, Chen B (2012). Constructing a network of the social-economic consumption system of China using extended exergy analysis. Renew Sustain Energy Rev , 16(7): 4796-4808
doi: 10.1016/j.rser.2012.04.027
12 Greene W H (2003). Econometric analysis (5th ed). New Jersey: Pearson Education, Inc., 958pp
13 Hellawell J M (1986). Biological Indicators of Freshwater Pollution and Environmental Management. London and New York: Elsevier Applied Science Publishers, 546 pp
14 Herendeen R (1989). Energy intensity, residence time, exergy, and ascendency in dynamic ecosystems. Ecol Modell , 48(1-2): 19-44
doi: 10.1016/0304-3800(89)90058-6
15 Hering D, Feld C, Moog O, Ofenb?ck T (2006). Cook book for the development of a multimetric index for biological condition of aquatic ecosystems: experiences from the European AQEM and STAR projects and related initiatives. Hydrobiologia , 566(1): 311-324
doi: 10.1007/s10750-006-0087-2
16 Hilsenhoff W L (1987). An improved biotic index of organic stream pollution. Great Lakes Entomol , 20: 31-39
17 Inouye B D (2005). The importance of the variance around the mean effect size of ecological processes. Ecology , 86(1): 262-265 (comment)
doi: 10.1890/03-3180
18 J?rgensen S E (1992). The shifts in species composition and ecological modelling in hydrobiology. Hydrobiologia , 239(2): 115-129
doi: 10.1007/BF00012577
19 J?rgensen S E, Fath B D (2004). Application of thermodynamic principles in ecology. Ecol Complex , 1(4): 267-280
doi: 10.1016/j.ecocom.2004.07.001
20 J?rgensen S E, Ladegaard N, Debeljak M, Marques J C (2005a). Calculations of exergy for organisms. Ecol Modell , 185(2-4): 165-175
doi: 10.1016/j.ecolmodel.2004.11.020
21 J?rgensen S E, Nielsen S N, Mejer H (1995). Emergy, environ, exergy and ecological modelling. Ecol Modell , 77(2-3): 99-109
doi:10.1016/0304-3800(93)E0080-M
22 J?rgensen S E, Nors Nielsen S (2007). Application of exergy as thermodynamic indicator in ecology. Energy , 32(5): 673-685
doi: 10.1016/j.energy.2006.06.011
23 J?rgensen S E, Odum H T, Brown M T (2004). Emergy and exergy stored in genetic information. Ecol Modell , 178(1-2): 11-16
doi: 10.1016/j.ecolmodel.2003.12.036
24 J?rgensen S E, Xu F L, Salas F, Marques J (2005b). Application of indicators for the assessment of ecosystem health. In: J?rgensen S E, Costanza R, Xu F L, eds. Handbook of Ecological Indicators for Assessment of Ecosystem Health. CRC Press, Florida, USA , 464pp
25 Kohonen T (1988). Self-organization and Associative Memory. New York: Springer-Verlag Berlin Heidelberg New York, Inc., 332pp
26 Lenat D R (1988). Water quality assessment of streams using a qualitative collection method for benthic macroinvertebrates. J N Am Benthol Soc , 7(3): 222-233
doi: 10.2307/1467422
27 Li F, Bae M J, Kwon Y S, Chung N, Hwang S J, Park S J, Park H K, Kong D S, Park Y S (2013). Ecological exergy as an indicator of land-use impacts on functional guilds in river ecosystems. Ecol Modell , 252: 53-62
doi: 10.1016/j.ecolmodel.2012.09.006
28 Libralato S, Torricelli P, Pranovi F (2006). Exergy as ecosystem indicator: an application to the recovery process of marine benthic communities. Ecol Modell , 192(3-4): 571-585
doi: 10.1016/j.ecolmodel.2005.07.022
29 Link W A, Nichols J D (1994). On the importance of sampling variance to investigations of temporal variation in animal population size. Oikos , 69(3): 539-544
doi: 10.2307/3545869
30 Magurran A E (2004). Measuring Biological Diversity. Oxford: Blackwell Publishing, 264pp
31 Marchi M, J?rgensen S E, Bécares E, Fernández-Aláez C, Rodríguez C, Fernández-Aláez M, Pulselli F M, Marchettini N, Bastianoni S (2012). Effects of eutrophication and exotic crayfish on health status of two Spanish lakes: a joint application of ecological indicators. Ecol Indic , 20: 92-100
doi: 10.1016/j.ecolind.2012.02.005
32 Mejer H, J?rgensen S E (1979). Energy and ecological buffer capacity. In: State-of-the-Art of Ecological Modelling. Proceeding of the conference on ecological modeling, Copenhagen, Denmark , 829-846
33 Nayak T K (1985). On diversity measures based on entropy functions. Communication in Statistics—Theory and Methods , 141(1): 203-215
34 Niemi G J, McDonald M E (2004). Application of ecological indicators. Annu Rev Ecol Evol Syst , 35(1): 89-111
doi: 10.1146/annurev.ecolsys.35.112202.130132
35 Odum H T (1988). Self-organization, transformity, and information. Science , 242(4882): 1132-1139
doi: 10.1126/science.242.4882.1132 pmid:17799729
36 Osborne L L, Davies R W, Linton K J (1980). Use of hierarchical diversity indices in lotic community analysis. J Appl Ecol , 17(3): 567-580
doi: 10.2307/2402637
37 Park Y S, Céréghino R, Compin A, Lek S (2003). Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Modell , 160(3): 265-280
doi: 10.1016/S0304-3800(02)00258-2
38 Park Y S, Kwak I S, Chon T S, Kim J K, J?rgensen S E (2001). Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams. Ecol Modell , 146(1-3): 143-157
doi: 10.1016/S0304-3800(01)00302-7
39 Park Y S, Lek S, Scardi M, Verdonschot P F M, J?rgensen S E (2006a). Patterning exergy of benthic macroinvertebrate communities using self-organizing maps. Ecol Modell , 195(1-2): 105-113
doi: 10.1016/j.ecolmodel.2005.11.027
40 Park Y S, Song M Y, Park Y C, Oh K H, Cho E, Chon T S (2007). Community patterns of benthic macroinvertebrates collected on the national scale in Korea. Ecol Modell , 203(1-2): 26-33
doi: 10.1016/j.ecolmodel.2006.04.032
41 Park Y S, Tison J, Lek S, Giraudel J L, Coste M, Delmas F (2006b). Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecol Inform , 1(3): 247-257
doi: 10.1016/j.ecoinf.2006.03.005
42 Pielou E C (1977). Mathematical Ecology. New York-London-Sydney-Toronto: John Wiley and Sons, 385pp
43 Pusceddu A, Danovaro R (2009). Exergy, ecosystem functioning and efficiency in a coastal lagoon: the role of auxiliary energy. Estuar Coast Shelf Sci , 84(2): 227-236
doi: 10.1016/j.ecss.2009.06.019
44 Qu X D, Song M Y, Park Y S, Oh Y N, Chon T S (2008). Species abundance patterns of benthic macroinvertebrate communities in polluted streams. Ann Limnol-Int J Lim , 44(2): 119-133
doi: 10.1051/limn:2008013
45 Ramezani H, Holm S, Allard A, St?hl G (2010). Monitoring landscape metrics by point sampling: accuracy in estimating Shannon’s diversity and edge density. Environ Monit Assess , 164(1-4): 403-421
doi: 10.1007/s10661-009-0902-0 pmid: PMID:19415517
46 Reynoldson T B, Norris R H, Resh V H, Day K E, Rosenberg D M (1997). The reference condition: a comparison of multimetric and multivariate approaches to assess water-quality impairment using benthic macroinvertebrates. J N Am Benthol Soc , 16(4): 833-852
doi: 10.2307/1468175
47 Shannon C E (1948). A Mathematical theory of communication. Bell Syst Tech J , 27(3): 379-423
doi: 10.1002/j.1538-7305.1948.tb01338.x
48 Silow E A, Mokry A V (2010). Exergy as a tool for ecosystem health assessment. Entropy , 12(4): 902-925
doi: 10.3390/e12040902
49 Silow E A, In-Hye O (2004). Aquatic ecosystem assessment using exergy. Ecol Indic , 4(3): 189-198
doi: 10.1016/j.ecolind.2004.03.003
50 Song M Y, Hwang H J, Kwak I S, Ji C W, Oh Y N, Youn B J, Chon T S (2007). Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. Ecol Modell , 203(1-2): 18-25
doi: 10.1016/j.ecolmodel.2006.04.027
51 Stra?kraba M, J?rgensen S E, Patten B C (1999). Ecosystems emerging: 2. Dissipation. Ecol Modell , 117(1): 3-39
doi: 10.1016/S0304-3800(98)00194-X
52 Suzuki M, Sagehashi M, Sakoda A (2000). Modelling the structural dynamics of a shallow and eutrophic water ecosystem based on mesocosm observations. Ecol Modell , 128(2-3): 221-243
doi: 10.1016/S0304-3800(99)00231-8
53 Svirezhev Y M (2000). Thermodynamics and ecology. Ecol Modell , 132(1-2): 11-22
doi: 10.1016/S0304-3800(00)00301-X
54 Svirezhev Y M, Steinborn W H, Pomaz V L (2003). Exergy of solar radiation: global scale. Ecol Modell , 169(2-3): 339-346
doi: 10.1016/S0304-3800(03)00280-1
55 Tang H, Song M Y, Cho W S, Park Y S, Chon T S (2010). Species abundance distribution of benthic chironomids and other macroinvertebrates across different levels of pollution in streams. Ann Limnol-Int J Lim , 46(1): 53-66
doi: 10.1051/limn/2009031
56 Ward J H Jr (1963). Hierarchical grouping to optimize an objective function. J Am Stat Assoc , 58(301): 236-244
doi: 10.1080/01621459.1963.10500845
57 Xu F L, J?rgensen S E, Tao S (1999). Ecological indicators for assessing freshwater ecosystem health. Ecol Modell , 116(1): 77-106
doi: 10.1016/S0304-3800(98)00160-4
58 Xu F L, Wang J J, Chen B, Qin N, Wu W J, He W, Wang Y (2011). The variations of exergies and structural exergies along eutrophication gradients in Chinese and Italian lakes. Wetlands in China , 222(2): 337-350
[1] Meilin WU,Youshao WANG,Junde DONG,Fulin SUN,Yutu WANG,Yiguo HONG. Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China[J]. Front. Earth Sci., 2017, 11(1): 114-126.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed