연구동향 분석
박사

Structure, persistence, and dynamics of benthic macroinvertebrate communities responding to natural and anthropogenic variability in streams = 저서성 대형무척추동물 군집의 구조성, 지속성 및 시간 동태를 통한 환경 변이 반응 연구

김동환 2015년

논문상세정보
인용/피인용
' Structure, persistence, and dynamics of benthic macroinvertebrate communities responding to natural and anthropogenic variability in streams = 저서성 대형무척추동물 군집의 구조성, 지속성 및 시간 동태를 통한 환경 변이 반응 연구' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 구조적 변이
  • 군집 지속성
  • 생태학적 온전성
  • 시계열 동태
  • 저서성 대형무척추동물
  • 하천 생태계
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
51 0

0.0%

' Structure, persistence, and dynamics of benthic macroinvertebrate communities responding to natural and anthropogenic variability in streams = 저서성 대형무척추동물 군집의 구조성, 지속성 및 시간 동태를 통한 환경 변이 반응 연구' 의 참고문헌

  • Zar JH. 1984. Biostatistical analysis. Prentice-Hall, New Jersey.
  • Yoon IB. 1995. Aquatic insects of Korea. Jeonghaengsa, Seoul, Korea.
  • Yoon IB, Park JH, Chun SP. 1998. Community structure of aquatic insectsin a mountain stream of Piagol valley in Mt. Chiri. Korean Journal ofBiota 3:401-410.
  • Yoon BJ, Chon TS. 1996. Effects of Environmental Disturbances onCommunity Dynamics of Chironomids in the Soktae Stream, atuributary of the Suyong River. Korean Society of Applied Entomology44-44.
  • Yemshanov D, Perera A. 2002. A spatially explicit stochastic model tosimulate boreal forest cover transitions: general structure and properties.Ecological Modeling 150: 189-209.
  • Wright JF, Sutcliffe DW, Furse MT. 2000. Assessing the biological qualityof fresh waters: RIVPACS and other techniques. Freshwater BiologicalAssociation, Ambleside, England.
  • Wootton JT. 2001b. Local interactions predict large-scale pattern inempirically derived cellular automata. Nature 413: 841?844.
  • Winston WL. 1994. Operations Research: Applications and Algorithms,third ed. Duxbury Press, Belmont, California.
  • Wilson SD, Tilman D. 1991. Component of plant competition along anexperimental gradient of nitrogen availability. Ecology 72: 1050-1065.
  • Williamson M. 1987. Are communities ever stable? Symposia of the BritishEcological Society 26: 353-371.
  • Williams ED. 1978. Botanical composition of the Park Grass plots atRothamsted 1856-1976. Rothamsted Experimental Station Harpenden,United Kingdom
  • Williams CB. 1964. Patterns in the Balance of Nature. Academic Press,London.
  • Wiggins GB. 1996. Larvae of the North American caddisfly genera(trichoptera). University of Toronto Press, Toronto.
  • Wiederholm T. (ed.) 1986. Chironomidae of the Holarctic region. Keys andDiagnoses. Part II. Pupae. Entomologica Scandinavica Supplement 28:1-482.
  • Wiederholm T. (ed.) 1983. Chironomidae of the Holarctic region. Keys andDiagnoses. Part 1. Larvae. Entomologica Scandinavica Supplement 19:1-457.
  • Weatherley N, Ormerod S. 1990. The constancy of invertebrate assemblagesin soft-water streams: implications for the prediction and detection ofenvironmental change. Journal of Applied Ecology 952-964.
  • Warwick R. 1986. A new method for detecting pollution effects on marinemacrobenthic communities. Marine biology 92:557-562.
  • Ward JH. 1963. Hierarchical grouping to optimized an objective function,Journal of the American Statistical Association 58: 236?244.
  • Voegtlin T, Dominey P. 2001. Learning high-degree sequences in a linearnetwork. Proceedings of the IJCNN’ 2001: 940-944.
  • Visser I, Raijmakers M, Molenaar P. 2002. Fitting hidden Markov models tophycological data. Scientific Programming 10: 185?199.
  • Viovy N, Saint G. 1994. Hidden Markov models applied to vegetationdynamics using satellite remote sensing. Geoscience and RemoteSensing, IEEE Transactions on 32: 906?917.
  • Victor DLP, Adrian HDV, Ricardo R. 2007. Multiple hypotheses testing oftransition matrices. journal of risk model validation 1: 69?76.
  • Vesanto J, Himberg J, Alhoniemi E, Parhankangas J. 2000. SOM Toolboxfor Matlab 5. Helsinki University of Technology.
  • Usseglio-Polatera P, Bournaud M, Richoux P, Tachet H. 2000. Biologicaland ecological traits of benthic freshwater macroinvertebrates:relationships and definition of groups with similar traits. Freshwaterbiology 43: 175-205.
  • Usher M. 1981. Modelling ecological succession, with particular referenceto Markovian models. Vegetation 46: 11-18.
  • Ulrich W, Ollik M. 2005. Limits to the estimation of species richness: theuse of relative abundance distributions. Diversity and Distributions 11:265-273.
  • Tucker BC, Anand M. 2004. On the use of stationary versus hidden Markovmodels to detect simple versus complex ecological dynamics.Ecological Modeling 185: 177 ?193.
  • Tucker BC, Anand M. 2003. The use of matrix models to detect natural andpollution-induced forest gradients. Community Ecology 4: 89-110.
  • Townsend C, Scarsbrook M, Doledec S. 1997. Quantifying disturbance instreams: alternative measures of disturbance in relation tomacroinvertebrate species traits and species richness. Journal of theNorth American Benthological Society 16:531-544.
  • Townsend C, Hildrew A, Schofield K, 1987. Persistence of streaminvertebrate communities in relation to environmental variability. TheJournal of Animal Ecology 56: 597-613.
  • Townsend C, Doledec S, Scarsbrook M. 1997. Species traits in relation totemporal and spatial heterogeneity in streams: a test of habitat templettheory. Freshwater Biology 37: 367-387.
  • Tokeshi M. 1999. Species coexistence: ecological and evolutionaryperspectives. Wiley-Blackwell, Oxford, United Kingdom.
  • Tokeshi M. 1993. Species abundance patterns and community structure.Advances in ecological research 24:111-186.
  • Tokeshi M. 1990a. Niche apportionment or random assortment: speciesabundance patterns revisited. The Journal of Animal Ecology 59:1129-1146.
  • Thomson JR, Lake PS, Downes BJ. 2002. The effect of hydrologicaldisturbance on the impact of a benthic invertebrate predator. Ecology83: 628-642.
  • Tang H, Song MY, Cho WS, Park YS, Chon TS. 2010. Species abundancedistribution of benthic chironomids and other macroinvertebrates acrossdifferent levels of pollution in streams. International Journal ofLimnology 44: 53-66.
  • Tan SS, Smeins FE. 1996. Predicting grassland community changes with anartificial neural network model. Ecological Modelling 84: 91-97.
  • Taheri A, Tarihi MR, Abad HBB, Bababeyk H. 2005. Fuzzy HiddenMarkov Models for Speech Recognition on based FEM Algorithm, in:Cemal, A. (Ed.), The Second World Enformatika Conference, WEC’05,February 25-27, 2005. Enformatika, Canakkale, Turkey, pp. 59?61.
  • Svendsen KM, Renshaw CE, Magilligan FJ, Nislow KH, Kaste JM. 2009.Flow and sediment regimes at tributary junctions on a regulated river:impact on sediment residence time and benthic macroinvertebratecommunities. Hydrological Processes 23: 284-296.
  • Stevenson RJ. 1997. Scale-dependent determinants and consequences ofbenthic algal heterogeneity. Journal of the North AmericanBenthological Society 16: 248-262.
  • Statzner B, Bady P, Doledec S, Scholl F. 2005. Invertebrate traits for thebiomonitoring of large European rivers: an initial assessment of traitpatterns in least impacted river reaches. Freshwater Biology 50: 2136-2161.
  • Song MY, Hwang HJ, Kwak IS, Ji CW, Oh YN, Youn BJ, Chon TS. 2007.Self-organizing mapping of benthic macroinvertebrate communitiesimplemented to community assessment and water quality evaluation.Ecological Modelling 203:18-25.
  • Sokal RR, Rohlf FJ. 1995. Biometry: the principles and practice of statisticsin biological research. WH Freeman, New York.
  • Simon G, Lee J, Cottrell M, Verleysen M. 2007. Forecasting the CATSbenchmark with the double vector quantization method.Neurocomputing 70: 2400-2409.
  • Shannon CE, Weaver W. 1949. The mathematical theory ofinformation.University of Illinois Press, Urbana.
  • Schoener TW. 1976. The species-area relation within archipelagos: modelsand evidence from island land birds. Proceedings of the XVIInternational Ornithological Congress, Australian Academy of Science:Canberra, ACT.
  • Scarsbrook M. 2002. Persistence and stability of lotic invertebratecommunities in New Zealand. Freshwater biology 47: 417-431.
  • S renson T. 1948. A method of establishing groups of equal amplitude inplant sociology based on similarity of species content and itsapplication to analyses of the vegetation on Danish commons.Biologislce Skrifler 5: 1-34.
  • Rosenzweig ML. 1995. Species diversity in space and time, CambridgeUniversity Press.
  • Rosenberg a R, Blomqvist M, Nilsson HC, Cederwall H, Dimming A. 2004.Marine quality assessment by use of benthicspecies-abundancedistributions: a proposed new protocolwithin the European Union WaterFramework Directive. Marine Pollution Bulletin 49: 728?739
  • Rosenberg DM, Resh VH. 1993. Freshwater biomonitoring and benthicmacroinvertebrates. Chapman &Hall, New York.
  • Robinson C, Minshall GW, Royer TV. 2000. Inter-annual patterns inmacroinvertebrate communities of wilderness streams in Idaho, USA.Hydrobiologia 421: 187-198.
  • Richards C, Minshall GW. 1992. Spatial and temporal trends in streammacroinvertebrate communities: the influence of catchment disturbance.Hydrobiologia 241: 173-184.
  • Resh VH, Rosenberg DM. 1984. The ecology of aquatic insects Minshall(eds.). Praeger Publishers., New York.
  • Recknagel F. 2003. Ecological informatics: understanding ecology bybiologically-inspired computation. Springer Verlag.
  • Raunki r C. 1909. Formationsundersogelse og formationsstatistik. Bot.Tidskr 30:20-132.
  • Rabiner L. 1989. A tutorial on hidden Markov models and selectedapplications in speech recognition. Proceedings of the IEEE 77: 257?285.
  • Qu X, Song MY, Park YS, Oh YN, Chon TS. 2008. Species abundancepatterns of benthic macroinvertebrate communities in polluted streams.International Journal of Limnology 44:119-133.
  • Pueyo S. 2006b. Self similarity in species-area relationship and in speciesabundance distribution. Oikos 112: 156-162.
  • Preston F. 1962. The canonical distribution of commonness and rarity: Part I.Ecology 43:185-215.
  • Preston F. 1948. The commonness, and rarity, of species. Ecology 29: 254-283.
  • Poff NL. 1997. Landscape filters and species traits: towards mechanisticunderstanding and prediction in stream ecology. Journal of the NorthAmerican Benthological Society: 391-409.
  • Pennak RW. 1978. Freshwater Invertebrates of the United States. JohnWieley & Sons, New York.
  • Patrick R. 1963. The structure of diatom communities under varyingecological conditions. Annals of the New York Academy of Sciences108: 359-365.
  • Park YS, Song MY, Park YC, Oh KH, Choe EC, Chon TS. 2007.Community patterns of benthic macroinvertebrates collected on thenational scale in Korea. Ecological modeling 203: 26-33.
  • Park YS, Cereghino R, Compin A, Lek S. 2003. Applications of artificialneural networks for patterning and predicting aquatic insect speciesrichness in running waters. Ecological Modeling 160: 265-280.
  • Nummelin M. 1998. Log-normal distribution of species abundance is not auniversal indicator of rainforest disturbance. Journal of AppliedEcology. 35: 454-457
  • Motomura I. 1932. On the statistical treatment of communities. Zool. Mag.44: 379-383.
  • Ministry of Environment, 1997. The National Natural EnvironmentMonitoring Protocol. Ministry of Environment, Seoul.
  • Merritt RW, Cummins KW. 1996. An introduction to the aquatic insects ofNorth America. Kendall Hunt, Iowa.
  • Menezes S, Baird DJ, Soares AMVM. 2010. Beyond taxonomy: a review ofmacroinvertebrate trait-based community descriptors as tools forfreshwater biomonitoring. Journal of Applied Ecology 47: 711-719.
  • McGill BJ. 2003b. Does Mother Nature really prefer rare species or are logleft-skewed SADs a sampling artefact? Ecological Letters 6: 766-773.
  • McGill BJ. 2003a. A test of the unified neutral theory of biodiversity.Nature 422: 881-885.
  • McGill BJ, Etienne RS, Gray JS, Alonso D, Anderson MJ, Benecha HK,Dornelas M, Enquist BJ, Green JL, He F, Hurlbert AH, Magurran AE,Marquet PA, Maurer BA, Ostling A, Soykan CU, Ugland KI, White EP,2007. Species abundance distributions: moving beyond singleprediction theories to integration within an ecological framework.Ecological Letters. 10: 995-1015.
  • McCabe DJ, Gotelli NJ. 2000. Effects of disturbance frequency, intensity,and area on assemblages of stream macroinvertebrates. Oecologia 124:270-279.
  • McAuliffe JR. 1984. Competition for space, disturbance and the structure ofa benthic stream community. Ecology 65: 894-908.
  • May RM. 1981. Patterns in multi-species communities. Theoretical ecology:principles and applications. Blackwell Scientific, Oxford, UnitedKingdom.
  • May RM. 1975. Patterns of species abundance and diversity. Ecology andevolution of communities: 81-120.
  • Magurran AE. 2005. Species abundance distributions: pattern or process?Functional Ecology 19: 177-181.
  • Magurran AE. 2004. Measuring biological diversity. Oxford, BlackwellPublishing, United Kingdom.
  • Magurran AE. 1988. Ecological diversity and its measurement. Princeton,Princeton University Press, New Jersey.
  • Magurran AE, Henderson PA. 2012. How selection structures speciesabundance distributions. Proceedings of the Royal Society B:Biological Sciences 279:3722-3726The Mathworks Inc., Matlab version 6.1, 200. Massachusetts.
  • Magurran AE, Henderson PA. 2003. Explaining the excess of rare species innatural species abundance distributions. Nature 422: 714-716.
  • MacDonald I, Raubenheimer D. 1995. Hidden Markov models and animalbehaviour. Biometrical Journal 37: 701?712.
  • MacArthur R. 1960. On the relative abundance of species. AmericanNaturalist: 25-36.
  • Ludwig JA, Reynolds JF. 1988. statistical ecology: a primer of methods andcomputing. Wiley, New York.
  • Liu Y, Lee S-H, Chon T-S. 2011. Analysis of behavioral changes ofzebrafish (Danio rerio) in response to formaldehyde using Selforganizingmap and a hidden Markov model. Ecological Modeling 222:2191-220
  • Lek S, Guegan JF. 2000. Artificial neuronal networks: application toecology and evolution. Springer, Berlin.
  • Lek S, Guegan JF. 1999. Artificial neural networks as a tool in ecologicalmodelling, an introduction. Ecological Modelling 120: 65-73.
  • Legendre P, Legendre L (1998) Numerical Ecology. Elsevier, Amsterdam.
  • Lammert M, Allan JD. 1999. Assessing biotic integrity of streams: effectsof scale in measuring the influence of land use/cover and habitatstructure on fish and macroinvertebrates. Environmental Management23: 257-270.
  • Lamberti GA, Gregory SV, Ashkenas LR, Steinman AD, McIntire CD.1989. Productive capacity of periphyton as a determinant of plantherbivoreinteractions in streams. Ecology: 1840-1856.
  • Lake P. 2003. Ecological effects of perturbation by drought in flowingwaters. Freshwater biology 48: 1161-1172.
  • Lake P. 2000. Disturbance, patchiness, and diversity in streams. Journal ofthe North American Benthological Society 19: 573-592.
  • Kwon TS, Chon TS. 1991. Ecological studies on benthic macroinvertebratesin theSuyong River II. Investigations on distribution and abundance in123its main streamand four tributaries. Korean Journal of Limnology 24:179-198.
  • Krogh A, Brown M, Mian I, Haussler D. 1994. Hidden Markov models incomputational biology: applications to protein modeling. Journal ofMolecular Biology 235: 1501?1531.
  • Kohonen T. 1989. Self-organization and Associative Memory. Springer-Verlag, Berlin.
  • Kim DH, Cho WS, Chon TS. 2013. Self-organizing map and speciesabundance distribution of stream benthic macroinvertebrates inrevealing community patterns in different seasons. EcologicalInformatics 17:14-29.
  • Kevan PG, Greco CF, Belaoussoff S. 1997. Log-normality of biodiversityand abundance in diagnosis and measuring of ecosystematic health:pesticide stress on pollinators on blueberry heaths. Journal of ApplliedEcology 34: 1122-1136.
  • Karr JR. 1993. Defining and assessing ecological integrity: beyond waterquality. Environmental toxicology and chemistry 12: 1521-1531.
  • Karplus K, Sjolander K, Barrett C, Cline M, Haussler D, Hughey R, Holm L,Sander C. 1997. c. Proteins: Structure, Function, and Genetics(Suppliment 1) 134-139.
  • Juang B, Rabiner L. 1991. Hidden Markov models for speech recognition.Technometrics 33, 251?272.
  • Jongman RHG, ter Braak CJF, van Tongerenm OFR (Eds.). 1995. Dataanalysis in community and landscape ecology. Cambridge UniversityPress, Cambridge.
  • Hyun K, Song MY, Kim S, Chon TS. 2005. Using an artificial neuralnetwork to patternize long-term fisheries data from South Korea.Aquatic Sciences 67: 382-389.
  • Hynes H, Coleman MJ. 1968. A simple method of assessing the annualproduction of stream benthos. Limnology and Oceanography 13:569-573.
  • Huntingford C, Cox PM. 1996. Use of statistical and neural networktechniques to detect how stomatal conductance responds to changes inthe local environment. Ecological Modelling 97: 217-246.
  • Hughes JM, Schmidt DJ, Finn DS. 2009. Genes in streams: using DNA tounderstand the movement of freshwater fauna and their riverine habitat.BioScience 59: 573-583.
  • Hubbell SP. 2001. The unified neutral theory of biodiversity andbiogeography. Princeton University Press, Princeton, New Jersey, USA.
  • Horn H. 1975. Markovian properties of forest succession. In: Ecology andEvolution of Communities. Harvard University Press, Massachusetts,USA.
  • Holling, C.S. 1973 “Resilience and stability of ecological systems,” Annual Review of Ecology and Systematics 4:1-23.
  • Hoef JV, Cressie N. 1997. Using hidden Markov chains and empiricalBayes change-point estimation for transect data. Environmental andEcological Statistics 4: 247?264.
  • Hill MF, Witman JD, Caswell H. 2004. Markov chain analysis of successionin a rocky subtidal community. American Naturalist 164: 46?61.
  • Hill JK, Hamer KC, Lace LA, Banham WMT. 1995. Effect of selectivelogging on trophical forest butterflies on Buru, Indonesia. Journal ofApplied Ecology 32:754-760121
  • Hawkes H. 1998. Origin and development of the biological monitoringworking party score system. Water Research 32: 964-968.
  • Gray LJ, Ward JV. 1982. Effects of sediment releases from a reservoir onstream macroinvertebrates. Hydrobiologia 96: 177-184.
  • Gray JS, Pearson TH. 1982. Objective selection of sensitive speciesindicative of pollution-induced change in benthic communities. I.Comparative methodology. Marine Ecology Progress Series 9: 111-119.
  • Gray JS, Mirza FB. 1979. A possible method for the detection of pollutioninduceddisturbance on marine benthic communities. Marine PollutionBulletin 10: 142-146.
  • Ghahramani Z. 1997. Factorial hidden Markov models. Machine Learning29: 245?275.
  • Gauch HG. 1982. Multivariate Analysis in Community Ecology. CambridgeUniversity Press, Cambridge.
  • Frissell CA, Liss WJ, Warren CE, Hurley MD. 1986. A hierarchicalframework for stream habitat classification: viewing streams in awatershed context. Environmental Management 10: 199-214.
  • Forster MA, Warton DI. 2007. A metacommunity scale comparison ofspecies abundance distribution models for plant communities of easternAustralia. Ecography 30: 449-458.
  • Ford NB, Lancaster DL. 2007. The species-abundance distribution of snakesin a bottomland hardwood forest of the southern United States. Journalof Herpetology 41: 385-393.
  • Fisher RA, Corbet AS, Williams C. 1943. The relation between the numberof species and the number of individuals in a random sample of ananimal population. The Journal of Animal Ecology 12: 42-58.
  • Ferreira V, Graca MAS, Feio MJ, Mieiro C. 2004. Water quality in theMondego river basin: pollution and habitat heterogeneity 23:295-306.
  • Enquist BJ, Green JL, He F. 2007. Species abundance distributions: movingbeyond single prediction theories to integration within an ecologicalframework. Ecology letters 10:995-1015.
  • Durbin R, Eddy S, Krogh A, Mitchison G. 2002. Biological SequenceAnalysis. Cambridge University Press, Cambridge, UK.
  • Duncan, D. B. 1955. Multiple range and multiple F tests. Biometrics. 11,1-42.
  • Dole-Olivier MJ, Marmonier P, Beffy JL. 1997. Response of invertebratesto lotic disturbance: is the hyporheic zone a patchy refugium?Freshwater biology 37: 257-276.
  • Diaz RJ, Solan M, Valente RM. 2004. A review of approaches forclassifying benthic habitats and evaluating habitat quality. Journal ofenvironmental management 73: 165-181.
  • Dale M, Dale P, Li C, Biswas G. 2002. Assessing impacts of smallperturbations using a model-based approach. Ecological Modeling 156:185?199.
  • Connell JH, Sousa WP. 1983. On the evidence needed to judge ecologicalstability or persistence. American Naturalist: 789-824.
  • Collier KJ. 2008. Temporal patterns in the stability, persistence andcondition of stream macroinvertebrate communities: relationships withcatchment land use and regional climate. Freshwater Biology 53: 603-616.
  • Chon TS. 2011. Self-organizing maps applied to ecological sciences.Ecological Informatics 6: 50-61.
  • Chon TS, Park YS, Park JH. 2000. Determining temporal pattern ofcommunity dynamics by using unsupervised learning algorithms.Ecological Modelling 132: 151-166.
  • Chon TS, Park YS, Moon KH, Cha EY. 1996. Patternizing communities byusing an artificial neural network. Ecological Modelling 90: 69-78.
  • Chon TS, Kwak IS, Song MY, Park YS, Cho HD, Kim MJ, Cha EY, Lek S.2002. characterizing the effects of water quality on benthic streammacroinvertebrates in South Korea using a self-organizing mappingmodel. Bumwoo Publishing Company, Seoul, Korea.
  • Chon TS, Kwak IS, Park YS, Kim TH, Kim Y. 2001. Patterning and shorttermpredictions of benthic macroinvertebrate community dynamics byusing a recurrent artificial neural network. Ecological Modelling 146:181-193.
  • Chave J. 2004. Neutral theory and community ecology. Ecology letters 7:241-253.
  • Charvet S, Statzner B, Usseglio-Polatera P, Dumont B. 2000. Traits ofbenthic macroinvertebrates in semi-natural French streams: an initialapplication to biomonitoring in Europe. Freshwater biology 43: 277-296.
  • Cereghino R, Park YS. 2008. Review of Self-Organizing Map (SOM)approach in water resources: analysis, modeling andapplication. Environmental Modelling & Software 23: 835?845.
  • Cao Y, Bark AW, Williams WP. 1996. Measuring the responses ofmacroinvertebrate communities to water pollution: a comparison ofmultivariate approaches, biotic and diversity indices. Hydrobiologia341:1-19.
  • Bunn SE, Hughes JM. 1997. Dispersal and recruitment in streams: evidencefrom genetic studies. Journal of the North American BenthologicalSociety: 338-346.
  • Bunn SE, Edward DH, Loneragan NR. 1986. Spatial and temporal variationin the macroinvertebrate fauna of streams of the northern jarrah forest,Western Australia: community structure. Freshwater Biology 16: 67-91.
  • Bunn SE, Davies PM. 2000. Biological processes in running waters andtheir implications for the assessment of ecological integrity.Hydrobiologia 422/423:61 -70.
  • Brinkhurst R. 1986. Guide to the freshwater aquatic microdrile oligochaetesof North America. Dept. of Fisheries and Oceans, Ottawa.
  • Brigham AR, Brigham WU, Gnilka A. 1982. Aquatic insects andoligochaetes of North and South Carolina. Midwest Aquatic Enterprises,IL, USA.
  • Brewin PA, Buckton ST, Ormerod SJ. 2000. The seasonal dynamics andpersistence of stream macroinvertebrates in Nepal: do monsoon floodsrepresent disturbance? Freshwater biology 44:581-694
  • Bond NR, Lake PS, Arthington AH. 2008. The impacts of drought onfreshwater ecosystems: an Australian perspective. Hydrobiologia 600:3-16.
  • Bell G. 2000. The distribution of abundance in neutral communities. TheAmerican Naturalist 155:606-617.
  • Beche LA, Resh VH. 2007. Short-term climatic trends affect the temporalvariability of macroinvertebrates in California ‘Mediterranean’ streams.Freshwater biology 52: 2317-2339.
  • Baum L. 1972. An inequality and associated maximization technique instatistical estimation for probabilistic functions of Markov processes.Inequalities 3: 1?8.
  • Baum L, Petrie T. 1966. Statistical inference for probabilistic functions offinite state Markov chains. Annals of Mathematical Statistics 37: 1554?1563.
  • Baird DJ, Rubach MN, Van den Brinkt PJ. 2008. Trait-based ecological riskassessment (TERA): The new frontier? Integrated EnvironmentalAssessment and Management 4: 2-3.
  • Armitage PD, Moss D, Wright JF, Furse MT. 1983. The performance of anew biological water quality score system based on macroinvertebratesover a wide range of unpolluted running-water sites. Water research17:333-347
  • Allan JD, Castillo MM. 2007. Stream ecology: structure and function ofrunning waters. Chapman & Hall, New York.
  • Adriaenssens V, Verdonschot PFM, Goethals PLM, De Pauw N. 2007.Application of clustering techniques for the characterization ofmacroinvertebrate communities to support river restorationmanagement. Aquatic Ecology 41: 387-398.
  • Adler PB. 2004. Neutral models fail to reproduce observed species-area andspecies-time relationships in Kansas grasslands. Ecology 85: 1265-1272.