= MYΘOΣ Nο 1:H ψευδαίσθηση της αφθονίας IT IS PLENTY MYΘOΣ Nο 2: Το διαθέσιμο νερό φτάνει για να καλύψει τις ανάγκες

=

MYΘOΣ Nο 1:H ψευδαίσθηση της αφθονίαςIT IS PLENTY

MYΘOΣ Nο 2: Το διαθέσιμο νερό φτάνει για να καλύψει τις ανάγκες μαςENOUGH FOR EVERYONE

MYΘOΣ Nο 3: Οι ανησυχίες μας δεν είναι δικαιολογημένεςNO REASON TO BE SCEPTICAL

MYΘOΣ Nο 4 : Tο πρόβλημα συνεπώς δεν αφορά εμάς, αλλά μόνο τον τρίτο κόσμο!

THIS CONCERNS ONLY THE THIRD WORLD

MYΘOΣ Nο 5: H Tεχνολογία για μια ακόμη φορά θα δώσει τη λύση.TECHNOLOGY CAN SOLVE ANY PROBLEM

FRESH WATER:THE MOST PRECIOUS FRESH WATER:THE MOST PRECIOUS AND RARE GOODAND RARE GOOD

In the near future 4 million people will suffer from water lack, even if each we spend only

40l/day/person

Population increaseHigh biotic levelOverconsumptionPollutionClimate change

MODIFICATION OF RIVER BANKS AND BED

A substance which is present at concentrations which cause harm or

exceed an environmental standard is considered to pollute the environment. In

reality any change or disturbances in the environment due to human activity

may affect the mean abundance of populations or may not, at least at some

temporal scale, but are extremely important for the long-term persistence or

conservation (rates of reproduction or mortality) of a species (Underwood 1991)

or the spatial dispersion of the organisms.

DISTURBANCES

Types of disturbances

There are pulse disturbanceswhich are acute, short-term episodes of disturbance

although a short-term change may itself cause long-term consequences.

There is a press disturbance which is a sustained or chronic interference

with a natural population which would provoke long-term

and usually non-recoverable changes in the populations.

Finally, there exist catastrophes Under which organisms can not recover

because their habitat is actually removed.

The time course of the first two disturbances is intimately related to the life cycleand longevity of the potentially affected organisms.

BIOMONITORING

Biomonitoring is the measurement of effects of pollutants on natural aquatic test organisms ranging from bacteria to fish. Effects include mortality, growth inhibition, cancers and tumours, genetic alteration and reproductive failure. Effects can also be measured in the field by measuring species diversity ON A COMMUNITY LEVEL.

Biomonitoring also includes the measurement of pollutants that are accumulated in tissue and other organs of biological organisms. The toxic effect must be monitored for different levels of the biological material organization molecular, cellular, individual and population.

Biomonitoring must lead to an integrated strategy for surveillance, early warning and control of freshwater ecosystem, which will be able to respond to the different impacts in the time and space.

As an element of the global environment monitoring, the biological monitoring is a permanent registration of the biodiversity, the structure and the living system functioning (Socolov & Smirnov, 1978).

Design of sampling and analysis

Despite the enormous and widespread need to be able to identify and,

where possible, predict the effects of human disturbances in natural ecosystems,

there is still insufficient attention paid to the basic requirements of design of

sampling and analysis of quantitative data from field surveys (Calow and Petts,

1995). It is vital that the effort given to monitoring is properly targeted, otherwise

the data collected will have limited value. Collecting data is no substitute for

clear analytical thinking. It is perfectly possible to be "data rich and information poor".

Monitoring and environmental sampling for eventual management and

conservation of habitats and species must operate within a framework of logic

and design around specific anticipated processes and results.

The design of monitoring programmes involves decision-making with regard to four major factors:

1. Sampling sites (there must always exist a sampling site before the

point source of pollution and one after).

2.Sampling frequency (seasonally if possible).

3. Sampling methodology (the same method must be always used for

comparison reasons)

4. Choise of appropriate analytical methodology (including analytical

quality control (AQC) procedures e.t.c.).

Indices- scores or otherIndices- scores or other

• SaprobioticSaprobiotic

• Diversity indicesDiversity indices[I=S(number of species)/ N(total nb. of ind.)]

• Biological indicesBiological indices

• Predictive models leading to Predictive models leading to

an biologic indexan biologic index

Biological monitoring: Animal community changes

The use of changes in community structure to monitor pollution commonly

involve benthic invertebrates and this group is considered the most appropriate

biotic indicators of water quality in EU countries (Metcalfe 1989), including

Greece (Anagnostopoulou et al., 1994).

or other organisms to low oxygen conditions and the effects of organic pollution on community structure.

Nevertheless, as it has been mentioned the application of biotic indices

combined with measurements of physical and chemical parameters provide more

integrated results concerning water pollution.

The biotic indices are based on the tolerance of benthic macroinvertebrates

Benthic macroinvertebrates as biotic indicators

Benthic macroinvertebrates are the most appropriate biotic indicators for

the following reasons: (1) These organisms are relatively sedentary and are

therefore representative of local conditions. (2) Macroinvertebrate communities

are very heterogeneous, consisting of representatives of several phyla. The

probability that at least some of these organisms will react to a particular change

in environmetal condtitions, is therefore high (Hellawell, 1977; De Pauw &

Vanhooren, 1983; Metcalfe, 1989; Mason 1991). Other groups of organisms

(fish, phytoplakton, etc) possess some, but not all, of these important attributes.

(3) Macroinvertebrates are differentially sensitive to pollutants of various types,

and react to them quickly; also, their communities are capable of a gradient

response to a broad spectrum of kinds and degrees of stress. (4) Their life

spans are long enough to provide a record of environmental quality. (5)

Macroinvertebrates are ubiquitous, abundant and relatively easy to collect.

Furthermore, their indentificaton and enumeration is not as tedious and difficult

as that of microorganisms and plankton.

Saprobic Index(Saprobic Index(Q-Q-index, ΒΕΟindex, ΒΕΟLL, Κ 135), Κ 135)(Holland,Germany, E. Europe)(Holland,Germany, E. Europe)

Extended B.IExtended B.I (ΕΒΙ, (ΕΒΙ, ENGLANDENGLAND, 1978), 1978)

Extended B.I.Italy Extended B.I.Italy (ΙΒΕ,(ΙΒΕ,ITALY,ITALY, 1980) 1980)

Chandler indexChandler index((1970, 1970, SCOTLANDSCOTLAND))

G.B.(G.B.(BMWPBMWP))((G.B.G.B., 1978), 1978)

Modified Modified BMWPBMWP(1979, G.B.(1979, G.B.))

Iberian Iberian BMWP’BMWP’(1988, SPAIN(1988, SPAIN))

BBELGIUMELGIUMΒΒΙ, 1983ΒΒΙ, 1983

Trent Trent Biotic Index Biotic Index ((TBI, ENGLANDTBI, ENGLAND, 1964), 1964)FranceFrance

(ΙΒ, 1968)(ΙΒ, 1968)

FranceFrance((IGB, FRANCEIGB, FRANCE, ,

1982)1982)

GlobalGlobal((IGB, FRANCEIGB, FRANCE, ,

19851985))

Lincoln, ENGLAND(ASPT+BMWP)

The Hellenic score’The Hellenic score’

(2000, GREECE(2000, GREECE))

Ametabola

Collembola (Springtails)

CHILICERATA (ARACHNIDA)

BIVALVIA

OLIGOCHAETA

PLATYELMINTHES COELENTERATA NEMATOMORPHA

INSECTS

Hemimetabola Nymphs: Plecoptera (stone flies) Ephemeroptra (may flies) Odonata (dragon flies)

a) the number of tail appendages

b) the presence or absence of gills

c) the presence of an obvious protrusible labium

Heteroptera (Hemiptera)

The REST of

ARTRODODA

MOLLUSCA

ANNELIDA

POLYZOA TURBELARIA (Tricladida)

Holometabolous Larvae: Diptera Trichoptera Coleoptera Neuroptera (alder flies) Lepidoptera Hymenoptera Pupae: Diptera Coleoptera Trichoptera

CRUSTACEA

GASTERODODA

HIRUDINEA

HYDROZOA GORDIOIDEA

Photo:Maria Lazaridou

3 minute kick-sweep method

 Πίνακας II Συνολικός αριθμός taxa (ταξινομικές ομάδες )

0-1 2-5 6-10 11-15 15+Eίδη σύμφωνα με τηνευαισθησία τους ως προς το O2(1)νύμφες από Plecoptera

αριθμός ειδών >1 - 7 8 9 10αριθμός ειδών =1 - 6 7 8 9

(2)νύμφες από Ephemeroptera εκτός από το Baetis rhodani*αριθμός ειδών >1 - 6 7 8 9αριθμός ειδών =1 - 5 6 7 8

(3)προνύμφες από Trichoptera και τα Baetis rhodaniαριθμός ειδών >1 - 6 7 8 9αριθμός ειδών =1 4 4 5 6 7

 (4) Oικογένεια Gammaridae (Kαρκινοειδή, Aμφίποδα του γλυκού νερού).Aπουσία των παραπάνω ειδών.

3 4 5 6 7 

(5) Oικογένεια Asellidae (Kαρκινοειδή, Iσόποδα του γλυκού νερού). Aπουσία των παραπάνω ειδών.

2 3 4 5 6 (6) Προνύμφες της οικογένειας Chironomidae (Diptera). Aπουσία των παραπάνω ειδών.

1 2 3 4 5 

(7) Aπουσία όλων σχεδόν των μορφών. (Παρουσία μερικών ανθεκτικών προνυμφών της τάξης Diptera, π.χ. του γένους Eristalis).

0 1 2 - - 

* Tα Baetis rhodani είναι η κοινότερη νύμφη των εφημεροπτέρων. Aυτή όμως δεν είναι τόσο ευαίσθητη στη ρύπανση όσο οι άλλες νύμφες.

BMWP’ Oικογένειες/families Bαθμός

Score Eφημερόπτερα Siphlonuridae, Heptageniidae, Leptophlebiidae, Potamanthidae,

Ephemeridae 10

Πλεκόπτερα Taeniopterygidae, Leuctridae, Capniidae, Perlodidae, Perlidae, Chloroperlidae

Hμίπτερα Aphelocheiridae Tριχόπτερα Δίπτερα

Phryganeidae, Molannidae, Beraeidae, Odontoceridae, Leptoceridae, Goeridae, Lepidostomatidae, Brachycentridae, Sericostomatidae Athericidae, Blephariceridae

Kαρκινοειδή Astacidae 8 Oδοντόγναθα Lestidae, Agriidae (Calopterygidae), Gomphidae, Cordulegasteridae,

Aeshnidae, Corduliidae, Libellulidae

Tριχόπτερα Psychomyidae, Philopotamidae, Glossosomatidae Eφημερόπτερα Ephemerellidae 7 Πλεκόπτερα Nemouridae Tριχόπτερα Rhyacophilidae, Polycentropodidae, Limnephilidae Mαλάκια Neritidae, Viviparidae, Ancylidae Unionidae 6 Tριχόπτερα Hydroptilidae Aμφίποδα Corophiidae, Gammaridae Oδοντόγναθα Platycnemididae, Coenagriidae Eφημερόπτερα Kολεόπτερα Tριχόπτερα Δίπτερα Πλατυέλμινθες

Oligoneuriidae Dryopidae, Elminthidae, Helophoridae, Hydrochidae, Hydraenidae, Clambidae Hydropsychidae Tipulidae, Simuliidae Planariidae, Dendrocoelidae, Dugesiidae

5

Eφημερόπτερα Kολεόπτερα Δίπτερα

Baetidae, Caenidae Haliplidae, Curculionidae, Chrysomelidae Tabanidae, Stratiomyidae, Empididae, Dolichopodidae, Dixidae, Ceratopogonidae, Anthomyidae, Limoniidae, Psychodidae

4

Mεγαλόπτερα Aκάρεα

Sialidae Hidracarina

Bδέλλες Piscicolidae Mαλάκια Hμίπτερα Kολεόπτερα

Valvatidae, Hydrobiidae, Lymnaeidae, Physidae, Planorbidae Sphaeriidae, Bithyniidae, Bythinellidae Mesoveliidae, Hydrometridae, Gerridae, Nepidae, Naucoridae, Notonectidae, Pleidae, Corixidae Helodidae, Hydrophilidae, Hygrobiidae, Dytiscidae, Gyrinidae

3

Bδέλλες Glossiphonidae, Hirudinidae, Erpobdellidae Iσόποδα Asellidae, Ostracoda Δίπτερα Chironomidae, Culicidae, Muscidae, Thaumaleidae, Ephydridae 2 Oλιγόχαιτοι Oligochaeta (όλη η κλάση) 1

Ευρύτερη περιοχή μελέτης Studied riversΕυρύτερη περιοχή μελέτης Studied rivers

Rivers Aliakmon, Axios , Almopeos, Aggitis and the creeks of Rivers Aliakmon, Axios , Almopeos, Aggitis and the creeks of Skouries and Olympiada (Chalkidiki)Skouries and Olympiada (Chalkidiki)

ΒΒ

Hellenic biotic indexHellenic biotic indexReevaluation of the familiesReevaluation of the families

Substrate: three categories:Substrate: three categories: coarsecoarse (>70%), (>70%), slightly coarseslightly coarse

(>70%) (>70%) and mixedand mixed..

A tsaxonomic group had to be present in 5 samples in order to A tsaxonomic group had to be present in 5 samples in order to

be taken into considerationbe taken into consideration

The Imperial The Imperial BMWP’ and the BMWP’ and the IASPT’ were used as a basisIASPT’ were used as a basis

For the familiesFor the families Neritidae Neritidae και και SphaeriidaeSphaeriidae were were

kept the original evaluationskept the original evaluations

293 samples from different rivers of N. Greece293 samples from different rivers of N. Greece

Ελληνικό Σύστημα Αξιολόγησης

Ταξινομικές ομάδες Παρούσες (0 - 1%)

Κοινές (1.01 - 10%)

Άφθονες (>10%)

α) Capniidae , Chloroperlidae , β) Siplonuridae , γ ) Aphelocheiridae, , δ ) Blephariceridae ε ) Phryganceidae, Molanidae, Odontoceridae, Bareidae,

Lepidosto matidae, Thremmatidae, Brachycentridae, Helicopsychlidae

10

10

10

α ) Leuctridae, Perlodidae, Perlidae, β ) Sericostomatidae, Goeridae, γ ) Neoephemeridae

9

9.5

10

α ) Nemouridae, Taeniopterygidae, β ) Ephemeridae, Heptageniidae, Leptophle biidae, γ ) Leptoceridae, Polycentropodidae, Psychomyidae,

Philopotamidae, Limnephilidae, Rhyacophilidae, Glossosomatidae, Ecnomidae,

δ ) Aeshnidae, Lestidae, Corduliidae, Libeliidae, ε ) Athericidae, Dixidae,

στ ) Helodidae, Gyrinidae, Hydraenidae, ζ ) Si alidae, η ) Brachyura, θ ) Astacidae

8

8.5

8.8

α ) Potamanthidae, β ) Calopterygidae, Cordulegasteridae γ ) Stratiomyidae, δ ) Hydrobiidae

7

7.5

7.8

α ) Platycnemididae, Gomphidae, β ) Tabanidae, Ceratopogonidae, Empididae, γ ) Elminthid ae, δ ) Viviparidae, Neritidae, ε ) Unionidae, στ ) Corophidae

6

6

6

α ) Caenidae, Oligoneuriidae, Polymitarcidae, Isonychiidae, β ) Hydropsychidae, γ ) Ancylidae, δ ) Gammaridae, ε ) Planariidae, Dendrocoelidae, Dugesiidae,

στ ) Dryopidae, Hel ophoridae, Hydrochidae, Clambidae

5

5

5

α ) Ephemerellidae, Baetidae, β ) Hydroptilidae, γ ) Tipulidae, Dolichopodidae, Anthomyidae, Limoniidae, δ ) Haliplidae, Curculionidae, Chrysomelidae, ε ) Hydracarina

4

3.8

3.5

α ) Coenagriidae, β ) Chironomidae ( όχι τα κόκκινα ), γ ) Dytiscidae, Hydrophilidae, Hygrobiidae, δ ) Corixidae, Hebridae, Veliidae, Mesoveliidae, Hydrometridae,

Gerridae, Nepidae, Pleidae, Naucoridae, Notonectidae, Belostomatidae,

ε ) Asellidae, Ostrac oda, στ ) Physidae, Bythiniidae, Bythinellidae, Acroloxidae, Malaniidae, Ellobiidae,

ζ ) Hirudinidae, η ) Sphaeriidae θ ) Oligochaeta ( εκ . Tubificidae)

3

2.5

2

α ) Chironomidae ( τα κόκκινα ), Rhagionidae, Culicidae, Muscidae, Thaumaleidae, Ephydridae , Ephemeridae, Heptageniidae, Leptophlebiidae,

β ) Lymnaeidae, Planorbidae, γ ) Erpobdellidae

2

1.5

1

α ) Tubificidae, β ) Valvatidae, γ ) Syrphidae 1 0.8 0.5

HELLENIC BIOTIC INDEX

δείγματα που συλλέχθηκαν από πολλούς τύπους ενδιαιτημάτων

samples collected from rich habitat

Eλληνικό σύστημα αξιολόγησης

Υ Μέσος Όρος Δείκτη ανά ταξινομική

Ομάδα

Χ

151+ 121 - 150 91 - 120 61 - 90 31 - 60 15 - 30 0 - 1 4

7 6 5 4 3 2 1

6.0+ 5.5 - 5.9 5.1 - 5.4 4.6 - 5.0 3.6 - 4.5 2.6 - 3.5

0 - 2.5

7 6 5 4 3 2 1

Δείγματα που συλλέχθηκαν από λίγους τύπους ενδιαιτημάτων

Samples collected from poor habitat

Eλληνικό σύστημα αξιολόγησης ΕΣΑ

Υ ΜΟΔΤ Χ

121+ 101 - 120 81 - 100 51 - 80 25 - 50 10 - 24 0 - 9

7 6 5 4 3 2 1

5.0+ 4.5 - 4.9 4.1 - 4.4 3.6 - 4.0 3.1 - 3.5 2.1 - 3.0

0 - 2.1

7 6 5 4 3 2 1

Tελική τιμή Σύντομη ερμηνεία Eρμηνεία 6+ A ++ Άριστη ποιότητα 5.5 A + Άριστη ποιότητα 5 A Άριστη ποιότητα 4.5 B Kαλή ποιότητα 4 Γ Kαλή ποιότητα 3.5 Δ Mέτρια ποιότητα 3 E Mέτρια ποιότητα

2.5 Z Kακή ποιότητα 2 H Kακή ποιότητα 1.5 Θ Πολύ κακή ποιότητα 1 I Πολύ κακή ποιότητα

HELLENIC BMWP Hellenic ASPT

Final value Index Interpretationexcellent

good

moderate

poor

Very poor

Τάξη Ποταμού Χημικά κριτήρια Βιολογικά κριτήρια Εν δυνάμει χρήσεις

Τάξη 1 DO > 80% ΒΟD < 2.5

Μη τοξικό για τα ψάρια

Πόσιμο νερό Τάξη 2 DO > 70%

ΒΟD < 4

Μη τοξικό για τα ψάρια, μπορεί να περιλαμβάνει ποταμούς που έχουν υψηλής ποιότητας αποροές

Αλιεία δυνατή

Τάξη 3 DO > 60% ΒΟD < 6 Μη τοξικό για τα ψάρια,

στο νερό δεν υπάρχουν ορατές ενδείξεις ρύπανσης,

Πόσιμο νερό μετά από επεξεργασία,

Τάξη 4 DO > 50% ΒΟD < 8

εκτός από ορισμένoυς Αλιεία προβληματική

Τάξη 5 DO > 20% ΒΟD < 15

Απουσία ή σποραδική εμφάνιση ψαριών

Κατάλληλο για τη βιομηχανία

De Pauw N. & Vanhooren G. (1983). Method for biologicalquality assessment of water courses inBelgium. Hydrobiologia, 100, 153-168.

European Union Commission. (1978). Directive on the quality of fresh water for the protection andimprovement of fish life. Official Journal of the European Communities, No 222/1/ 14.8.78.

European Union Commission. (1980). Directive on the quality of drinking water. Official Journal of theEuropean Communities, No 80/778/15.07.80

European Union Commission (1997). Proposal for a Council Directive establishing a framework forCommunity action in the field of water policy . Official Journal of the European Communities, NoC 184/20, 17.6.97.

Extence C.A., Bates A.J., Forbes W.J. and Barham P.J. (1987). Biologically based water qualitymanagement. Environmental Pollution 45, 221-236.

Farmer, A. (1997). Managing environmental pollution. Routledge Environmental Management Series.Ford J., Yfantis G., Artemiadou V., Lazaridou-Dimitriadou M., White K. N. (1998). Ecological

evaluation of water quality in river Mavrolakkas (Olympiada, Halkidiki), from May to August1997. Proceedings of the International Conference "Protection and Restoration of theEnvironment IV", 1-4 July, Sani Halkidiki, 144-152.

Goldman G.R. & Horne A.J. (1983). Limnology. McGraw - Hill Book Company.

Harper, D.M. and Ferguson, A.J.D. (eds) (1995). The ecological basis for river management. JohnWiley & Sons.

Haslam, S.M. (1995). River Pollution: An Ecological Perspective. John Wiley & Sons.Hellawell J.M. (1986). Biological indicators of freshwater pollution and environmental management .

Elsevier Applied Science Publishers, London.

Hill M.O. (1979). DECORANA - A Fortran program for detrended correspondence alalysis andreciprocal averaging. Ecology and Systematics, Cornell University, Ithaca, New York.

Hynes H.B.N. (1970). The Ecology of Running Waters. Liverpool University Press.

Jeffries M. & Mills D. (1990). Freshwater Ecology, Principles and Applications. Belhaven Press,London and New York.

Karr J.R. & Chu E.N. (1999). Restoring life in running waters. Better Biological Monitoring. IslandPress. U.S.A.

Krenkel P.A. & Novotny V. (1980). Water quality management. Academic Press Inc.

Langrick J.M., Artemiadou V., Yfantis G., Lazaridou-Dimitriadou M., White K. N. (1998). Anintegrated water quality assessmentof the river Axios, Northern Greece. Proceedings of theInternational Conference "Protection and Restoration of the Environment IV" , 1-4 July, SaniHalkidiki, 135-143.

Lazaridou-Dimitriadou M., Artemiadou V., Yfantis G., Mourelatos S. and Mylopoulos J. (1998).Contribution to the ecological quality of running waters in the river Aliakmon (Macedonia,Hellas). A multivariate approach. Submitted.

Mason C.F. (1991). Biology of freshwater pollution. Longman Group U.K. Ltd.

Metcalfe L.J. (1989). Biological water quality assessment of running waters based onmacroinvertebrate communities : History and present status in Europe. Environmental Pollution60, 101-139.

Metcalfe L.J. & Smith M. (1994). Invertebrates ecology and survey methods, in The RiversHandbook, Hydrological and ecological principles. Vol. 2, edited by Calow P. & Petts G.E.,Blackwell Scientific Publications.

Ter Braak C.J.F. (1988). CANOCO - a FORTRAN program for canonical community ordination(version 2.1).Tecnical report: LWA-88-02.

Wright J.F., Hiley P.D., Cameron A.C., Wigham M.E. and Berrie A.D. (1983). A quantitative study ofthe macroinvertebrate fauna of five biotopes in the river Lambourn, Berkshire, England. Arch.Hydrobiol. 96, 271-292.

Modelling

A wide range of techniques, including standard survey procedures and

modelling software for analysis of the results, are now available for the pollution

manager, and these are proving very robust for a wide range of purposes.Many

policy decisions are nationally based, and country-wide monitoring networks are

essential to inform future decisions. Finally, of course, international cooperation

on monitoring is essential, as much pollution crosses national frontiers, e.g.

monitoring acid rain across Europe, the transfer of pollutants in marine waters or

the movement of radionuclides from the Chernobyl accident. International

cooperation in the European Union was enhanced by the recent formation of the

European Environment Agency (EAA) based in Copenhagen. Currently, the work

of the EAA has focused on establishing "topic centres" in each member state to

coordinate the supply of environmental monitoring data to produce a clearer

picture of the state of the environment within the EU and how this might be used

to aid production of future EU legislation.

The use of a predictive model , which take into consideration both the

biotic and physicochemical approach for the detection of water pollution and

monitoring of the water quality, is probably the best tool for the management and

improvement of water resources, and especially of rivers. A predictive model,

applied on data collected with a standard sampling method, can also produce a

classification scheme according to the degree of pollution that rivers receive.

This may allow inter and intra site comparisons, which could lead to an effective

conservation strategy.

For the establishment of these models, one approach is to identify the "best

achievable community" which can occur under a particular set of physical,

chemical, geological and geographical conditions. So the surveyed community

can then be compared with the above one and hence the degree of change

objectively assessed.

During the 70's, multivariate analytical techniques have been introduced as

a new tool for the assessment of water quality. Between 1978 and 1988, in the

UK a biological classification of unpolluted freshwater sites (483 sites on 80

rivers, 700 have been assessed up today) was developed based on

macroinvertebrate fauna (see 5.1.3.). It was attempted to assess whether the

type of macroinvertebrate community at a given site maybe predicted using

physicochemical parameters.

This proved to be feasible and led to the formation of RIVPACS (River

InVertebrate Prediction And Classification System).

Two main techinques are used for RIVPACS: Twinspan and Decorana.

Twinspan (two way indicator species analysis) classifies organisms at each site into an hierarchy on the basis of their taxonomic composition. At the same time, species are classified on the basis of their occurrence in site groups (sites are classified into 10-25 groups). It also identifies indicator species that show the greatest difference between site-groups in the frequency of occurrence (Figure 1). A common problem in community ecology and ecotoxicology is to discover how a multitude of species respond to external factors such as environmental variables, pollutants and management regimes. Forthis, data are collected (species and external variables) at a number of points in space and time. Decorana (detrended correspondence analysis) is an ordination technique which arranges sites into a subjective order, those sites with similar biota being placed close together. It also relates community type to physicochemical parameters. In a survey which took place over the whole of the United Kingdom in the 1970's, Decorana revealed 11 key variables which produced 58% chance of correct first prediction of one of 10-25group-sites. These parameters were: 1) distance from the source (1-10), 2) discharge (1-10), 3) latitude, 4) longitude, 5) altitude, 6) slope, 7) width, 8) depth, 9) substrate (% 5 categories), 10) alkalinity 11) chloride.

From the above information the following predictions can be made about a site:

1) presence/absence of families, 2) presence/absence of species, 3) BMWP score (Biological Monitoring Working Party), 4) ASPT score (Average Score Per Taxon).

If a site has a probability of less than 5%, one does not proceed.

For site classification, three seasons data per year (3 samples per site) are requested, while for fauna prediction one season's data is adequate.

From the original survey, the ASPT was predicted in the U.K. for a site directly using a suite of 5 variables in a multiple regression equation, which explains 68% of the total variation (there have been used 118 families and 578 taxa at the species level). The equation of ASPT prediction was the following:

ASPT=7.331-0.00269A-0.876C-0.133Too-0.05395S-0.051D (where A: alkalinity, C: log10 chloride, Too: log10 total oxidized oxygen, S: mean substratum, D: log10 distance from the source).

Extension of Twinspan and Decorana

Statistical analyses available so far have either assumed linear relationships (but relationships may be unimodal, like a bell shaped Gaussian curve) or were restricted to regression analyses of the response of each species seperately. CANOCO has been mainly developed to overcome the above problem: The CANOCO program is an extension of Decorana. It escapes the assumption of linearity and is able to detect unimodal relationships between species (Figure 2) or/and sites (Figure 3) and external variables. It is particularly good for a forward selection of environmental variables in order to determine which variables best explain the species data. It selects a linear combination of environmental variables, while it maximizes the dispersion of the scores of the species and allows us to see whether species are related to environmental variables (This uses theMonte Carlo permutation test). CANOCO can analyse 1,000 samples, 700 species, 75 environmental variables and 100 covariables (total data size < 80,000).

The other problem was the classification of communities at each site into an hierarchical way on the basis of their taxonomic composition. Species are classified simultaneously on the basis of their occurrence in site groups. FUZZY overcame this problem. FUZZY is an extension of Twinspan. Species are classified as well as samples. Both ordination and classification are done. In the results, there is no clearcut transition from one class to another and many intermediate situations may occur. It does not assume the existence of discrete benthic populations between the various streches of a river system, but identifies the continuum and gradual change in their faunal composition. The maximum Fuzzy membership values are usually low (0.5-0.7) and they rarely exceed the value of 0.9, which agrees with the fact that communities are formed along gradients, without sharp boundaries, except in cases of pulse or chronic disturbances (Figure 3).The number of clusters (groups) are decided according to a parameter which is an integer number between 2-30: the largest the partition coefficient the best except if the number is very high. If convergence fails then we start from the beginning with a different number of clusters.

OBSERVED TAXA

ENVIRONMENTAL DATA

Latitude/Longitude, air temperature mean and range, altitude, distance from source,

channel width & depth, discharge, substratum, alkalinity

ENVIRONMENTAL QUALITY INDEX (EQI)

RIVPACS

Descriminant analysis (based on 483 unpolluted sites)

Predicted taxa (with probabilities of capture)

Comparison (observed/predicted)

From the above information the following predictions can be made about a site:

1) presence/absence of families, 2) presence/absence of species, 3) BMWP score (Biological Monitoring Working Party), 4) ASPT score (Average Score Per Taxon).

If a site has a probability of less than 5%, one does not proceed.

For site classification, three seasons data per year (3 samples per site) are requested, while for fauna prediction one season's data is adequate.

From the original survey, the ASPT was predicted in the U.K. for a site directly using a suite of 5 variables in a multiple regression equation, which explains 68% of the total variation (there have been used 118 families and 578 taxa at the species level). The equation of ASPT prediction was the following:

ASPT=7.331-0.00269A-0.876C-0.133Too-0.05395S-0.051D (where A: alkalinity, C: log10 chloride, Too: log10 total oxidized oxygen, S: mean substratum, D: log10 distance from the source).

REFERENCESAnagnostopoulou, M. (1993). The relationship between the macroinvertebrate community

and water quality, and the applicability of biotic indices in the River Almopeos system(Greece).- M. Sc. thesis, Department of Environmental Biology Manchester, U. K.

Anagnostopoulou M., Lazaridou-Dimitriadou M. & White K. N. (1994). The freshwaterinvertebrate community of the system of the river Almopeos, N. Greece. Proc. 6thZoogeogr. Intern. Congr. (Thessaloniki, 1993), Bios, 2: 79-86.

Armitage P.D., Moss D., Wright J.F, and Furse M.T. (1983). The performance of a newbiological water quality score system based on macroinvertebrates over a wide range ofunpolluted running water sites. Wat. Res. 17, 333-347.

British Ecological Society (1990). River water quality, Ecological studies No. 1, Field StudiesCouncil, 1-43.

Calow, P. and Petts, G.E. (eds) (1992). The Rivers Handbook, Hydrological and ecologicalprinciples. Vol. 1. Blackwell Science.

Calow, P.and Petts, G.E. (eds) (1994). The Rivers Handbook, Hydrological and ecologicalprinciples. Vol. 2. Blackwell Science.

Copeland R.S., Lazaridou-Dimitriadou M., ArtemiadouV., Yfantis G., White K.N. and MourelatosS. (1997). Ecological quality of the water in the catchment of river Aliakmonas(Macedonia, Hellas). Proceedings of the 5th Conference on Environment Science andTechnology, Molyvos, 1-4 September, 27-36.

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