DISTURBANCES

Types of disturbancesThere are pulse disturbances (pollutant: pathogens, heavy metals, organicpollution, suspended solids, nitrate pollution, eutrophication, acidification,thermal pollution, pesticides, environmental oestrogens) (fig.1)which are acute,short-term episodes of disturbance which may create a temporary pulse ofchange in a population, although a short-term change may itself cause long-termconsequences. There is a press disturbance (Table I) which is a sustained orchronic interference with a natural population which would provoke long-termand usually non-recoverable changes in the populations. Finally, there existcatastrophes which are major, often planned, destructions of habitat, underwhich organisms can not recover because their habitat is actually removed. Thetime course of the first two disturbances is intimately related to the life cycle andlongevity of the potentially affected organisms.

BIOMONITORINGBiomonitoring 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. Biomonitoring also includes the measurement of pollutants that are accumulated in tissue and other organs of biological organisms. 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. The conception monitoring is a specific information system for an assessment of the anthropogenic character of changes and the environmental fluctuations on a background of natural ones. 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). The toxic effect must be monitored for different levels of the biological material organization molecular, cellular, individual and population.

Pollution and seasonMediterranean running water ecosystems exhibit seasonal peculiarities,concerning their hydrological conditions. During summer period temperature isgreatly increased, while humidity decreases in a varying degree. The result is adecrease of water discharge, causing the rivers to get sometimes completelydry. The above process ultimately affects the structure of benthicmacroinvertebrate communities (fig.2). On the other hand, rivers are largelyused for the irrigation of agricultural activities. In order to meet the increasedwater demands for the irrigation of fields, large ammounts of water are removedfrom the rivers via dams , thus aggravating the reduced water discharge. Thedecreased discharge provokes the concentration of nutrients or/and some heavymetals (fig.3) and ultimately affects the structure and synthesis of benthicmacroinvertebrate communities.Recolonization.In the mediterranean region streams are also dried during summer. Recolonization happens in these streams or creaks in the beginning of the autumn or winter depending on the climatic conditions of a country. The first stages of recolonization usually include tolerant taxa and this may result to inaccurate assessment of water quality (fig.4).

Chronobiological implicationsA major problem concerning any biological sampling is that temporal and spacial patterns are not simple to separate. Understanding the natural temporal changes in populations is, however, essential for any interpretation of their ecology and, particularly, for any programme of management and conservation.It is impossible to make realistic predictions about future abundunces if temporal changes are not understood. For example, the assessment of water quality based on biotic indices can be proved inaccurate if the chronobiology and life cycles of benthic organisms is not taken into consideration. The presence or absence of several taxa from a site may be related to their life cycles (e.g.Plecoptera fig.6) and thus can modify the values of the biotic indices providing misleading results to whether these changes are related to a source of pollution or not. On the other hand, pollution events may influence variousdevelopmental or behavioural stages affecting the abundance of the organisms resulting to controversial conclusions. So much more thought is required about the processes by which detection, interpretation and prediction of environmental threats can be made. Additionally good environmental programmes of sampling (sampling sites, sampling frequency, sampling methodology) (see 5.1.1) and a good knowledge of the biology of organisms is needed for monitoring any type of the environment.

Physical or Biological implications

Standards and Toxicity testingA key tool in monitoring is the use of environmental standards. Manystandards are set to protect human health, but there are also a range of otherstandards for the natural environment (as a particular type of fishery).For many substances there is no threshold for effect. In this case a riskassessment is undertaken and a value is derived which is considered"reasonable". For example, the air quality standard for particulates for humanhealth is not set at a level below which it is thought that there is no effect, but ata level below which the effects are thought to be minimal.The simplest type of development of standards is to examine data forthresholds for significant effects and then use this number as the standard ( e.g.critical levels of air pollution, phosphate standards for freshwater ecosystemse.t.c.). In toxicology, such as threshold is commonly referred to as the no effectlevel (NOEL), either measured directly or estimated by a statistical analysis ofresponse data to pollutant exposure.

DirectivesThere exist directives which demand from the Member States of the E.U. toestablish monitoring networks to follow the quality of the air (92/72/EEC for ozonpollution) or of fresh water (C184/20/17.6.97). Many countries of the E.U. havespecialized organisations or institutions to undertake the regular monitoring.One of the biggest obstacles to good information from monitoring is that a widerange of organisations undertake monitoring, although not always in relation toeach other. It is sometimes difficult, therefore, to compare information and thusform a more holistic view of the environment. The Environment Agency in theU.K. is currently attempting to rectify this. By building a common framework andovercome problems of data sharing etc. land-use management decisions can beaided, and better informed policy and legislation can be produced. :.

Environmental monitoring and surveillance of water bodiesWater quality monitoringHowever, all water quality monitoring techniques and samplingmethodologies introduce some bias. The advantages of chemical approach(discrete or automatic samplers) are the smaller variability between replicatesand the highly specific nature of the data obtained in terms of which pollutants/toxins are present and at which concentrations. But they must be continuous inorder to be more representative and this is possible only for some determinandssuch as pH, ammonium,temperature, dissolved oxygen, turbidity and chlorophyll-a (currently it is developed for others too). Whereas biological monitoringencompases the ecological quality and uses the responses of biota to changesin water quality as a method of assessing such changes. Water quality impactmay be monitored at a number of different levels, from community status (e.g.fish/invertebrate indices), through population effects (which take into accountfecundity and age-specific survivorship), to the presence/absence of indicatorspecies (individual species of mayfly or caddis-fly larvae have differenttolerances to water acidification) and effects on the individual which range fromwhole organism mortality to sub-lethal tests at the cellular or tissue level. Thislast group includes behavioural techniques, pathology, developmentaltests(e.g. the oyster embryo development bioassay), biochemical assays (e.g.the induction of cholinesterase enzyme activity), physiological tests(respiratory activity, ventilation rate, e.t.c.) and bioaccumulation studies(Calow and Petts 1995). The best and the most integraded method of monitoringthough is the one that takes into consideration both biological aspects andphysicochemical parameters of the environment.

In nature many factors determine whether a species are present or not. When human activity introduce a change in a water quality or in habitat structure, different species react differently. Some will increase in numbers, new species may appear, other species may be reduced or disappear. The exercise of analysing the situation is then the important part. Sometimes the reasons for differences and changes is clear, but often there might be various causes. To struggle with complex problems, as environmental problems generally creates discussions and hopefully a better understanding of conflicts and problems. Water is a resource in itself and its quality is reflected by the organisms that live in it. The best pictures appears when you know the complete species composition, the conditions of the individuals, the structure of populations and have additive information about the chemical and physical environment. However, even with limited information about the species and only a few simple observations about the waterbody, the water chemistry and the surroundings, one can get a very informative picture. Monitoring over time, from year to year, will give an information about a stable situation or a situation under change.Whether a species is present or not in a waterbody dependes on eventually geographical barriers. Geographical barriers appear in all aquatic ecosystems, but particularly in freshwater habitats. Freshwaters are often aquatic "islands" in a terrestrial "ocean", and many species are not able to colonize them. If, however, an organism has been able to reach an area, a further success depends on the selective forces acting there. Those will decide whether a certain species or form will survive and reproduce or go extinct. These forces can be biotic or abiotic.

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 ofsampling and analysis of quantitative data from field surveys (Calow anf Petts,1995). It is vital that the effort given to monitoring is properly targeted, otherwisethe data collected will have limited value. Collecting data is no substitute forclear analytical thinking. It is perfectly possible to be "data tich and informationpoor".Monitoring and environmental sampling for eventual management andconservation of habitats and species must operate within a framework of logicand design around specific anticipated processes and results. If not sampling isclearly confounded and any effect of the disturbance cannot be distinguishedfrom natural variation between locations or time. The design of monitoringprogrammes involves decision-making with regard to four major factors:1. Sampling sites (there must always exist a sampling site before thesource point pollution and one after).2.Sampling frequency (see 1.5.1, 1.5.1.2).3. Sampling methodology (the same method must be always used forcomparison reasons)(see 1.4.2).4. Choise of appropriate analytical methodology (including analyticalquality control (AQC) procedures e.t.c.).

Indices- scores or other SaprobioticDiversity indicesBiological indicesPredictive models leading to an biologic index

Saprobic Index(Q-index, L, 135)(Holland,Germany, E. Europe)Iberian BMWP(1988, )Lincoln (ASPT+BMWP)

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

BMWP

O/families

B

Score

E

Siphlonuridae, Heptageniidae, Leptophlebiidae, Potamanthidae, Ephemeridae

10

Taeniopterygidae, Leuctridae, Capniidae, Perlodidae, Perlidae, Chloroperlidae

H

Aphelocheiridae

T

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

Athericidae, Blephariceridae

K

Astacidae

8

O

Lestidae, Agriidae (Calopterygidae), Gomphidae, Cordulegasteridae, Aeshnidae, Corduliidae, Libellulidae

T

Psychomyidae, Philopotamidae, Glossosomatidae

E

Ephemerellidae

7

Nemouridae

T

Rhyacophilidae, Polycentropodidae, Limnephilidae

M

Neritidae, Viviparidae, Ancylidae Unionidae

6

T

Hydroptilidae

A

Corophiidae, Gammaridae

O

Platycnemididae, Coenagriidae

E

K

T

Oligoneuriidae

Dryopidae, Elminthidae, Helophoridae, Hydrochidae, Hydraenidae, Clambidae

Hydropsychidae

Tipulidae, Simuliidae

Planariidae, Dendrocoelidae, Dugesiidae

5

E

K

Baetidae, Caenidae

Haliplidae, Curculionidae, Chrysomelidae

Tabanidae, Stratiomyidae, Empididae, Dolichopodidae, Dixidae, Ceratopogonidae, Anthomyidae, Limoniidae, Psychodidae

4

M

A

Sialidae

Hidracarina

B

Piscicolidae

M

H

K

Valvatidae, Hydrobiidae, Lymnaeidae, Physidae, Planorbidae Sphaeriidae, Bithyniidae, Bythinellidae

Mesoveliidae, Hydrometridae, Gerridae, Nepidae, Naucoridae, Notonectidae, Pleidae, Corixidae

Helodidae, Hydrophilidae, Hygrobiidae, Dytiscidae, Gyrinidae

3

B

Glossiphonidae, Hirudinidae, Erpobdellidae

I

Asellidae, Ostracoda

Chironomidae, Culicidae, Muscidae, Thaumaleidae, Ephydridae

2

O

Oligochaeta ( )

1

) Chironomidae ( ), Rhagionidae, Culicidae, Muscidae, Thaumaleidae, Ephydridae, Ephemeridae, Heptageniidae, Leptophlebiidae, ) Lymnaeidae, Planorbidae, ) Erpobdellidae 2 1.5 1 ) Tubificidae, ) Valvatidae, ) Syrphidae 1 0.8 0.5 HELLENIC BIOTIC INDEX

2.5 Z K 2 H K 1.5 1 I HELLENIC BMWPHellenic ASPTFinal valueIndexInterpretationexcellentgoodmoderatepoorVery poor

Number of taxaIBMWP scoreHellenic BMWP

Chart2

0

3

9

27

13

16

10

5

25

6

13

N

.

Slide 17

BMWP' ""N E N

0000

1113

2529

326327

417413

513516

69610

7575

811825

9096

10231013

&F

Page &P

Slide 17

&F

Page &P

N

BMWP' ""

.

&F

Page &P

N

.

Chart5

0

1

5

26

17

13

9

5

11

0

23

N

BMWP' ""

.

Slide 17

BMWP' ""N E N

0000

1113

2529

326327

417413

513516

69610

7575

811825

9096

10231013

&F

Page &P

Slide 17

&F

Page &P

N

BMWP' ""

.

&F

Page &P

N

.

Number of taxaUnits of decrease in relation to the first gradingUnits of increase in relation to the first grading

Chart1

113211

31

11

111

2

.

Sheet1

1113211

231

311

4111

52

Sheet1

00000000000

00000000000

00000000000

00000000000

00000000000

.

Sheet2

Sheet3

Chart2

3221211

132111

2

.

Sheet1

3221211

132111

2

Sheet1

00000000

00000000

00000000

00000000

00000000

.

Sheet2

Sheet3

Added taxonomicv groupsNumber of taxaScoresEphem.DipteraGastropoda TrichopteraHemiptera

Chart4

1

1

33

2

1

1

2

.

Sheet1

11

21

333

4

52

6

7

81

91

102

Sheet1

.

Sheet2

Sheet3

Biological monitoring: Animal community changesThe use of changes in community structure to monitor pollution commonlyinvolve benthic invertebrates and this group is considered the most appropriatebiotic indicators of water quality in EU countries (Metcalfe 1989), includingGreece (Anagnostopoulou et al., 1994). The biotic indices are based on thetolerance of benthic macroinvertebrates or other organisms to low oxygenconditions and the effects of organic pollution on community structure.Nevertheless, as it has been mentioned the application of biotic indicescombined with measurements of physical and chemical parameters provide moreintegrated results concerning water pollution.

In the last decades, industrial, agricultural and urban development hascaused an extended degradation of the natural environment both on a local andon a global scale. Pollution of water bodies is one of the major issues that haveto be seriously taken under consideration since they are the main final receptorsof all kinds of pollution.Water quality assessment based only on chemical methods has beenproved less accurate than the combination of biotic and abiotic approaches,since chemical measurements represent the instantaneous situation of the riverbody, at the time of sampling. On the other hand, organisms integrateenvironmental conditions over long periods of time and thus they are moreinformative concerning the water quality before and during sampling. Concerningthe biotic indices, their performance in slow flowing water courses displayssome problems, since sensitive to lack of oxygen taxa naturally occur only in fastflowing courses rather than in slow ones, despite the presence or absence ofpollution sources.

Benthic macroinvertebrates as biotic indicators Benthic macroinvertebrates are the most appropriate biotic indicators forthe following reasons: (1) These organisms are relatively sedentary and aretherefore representative of local conditions. (2) Macroinvertebrate communitiesare very heterogeneous, consisting of representatives of several phyla. Theprobability that at least some of these organisms will react to a particular changein 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 gradientresponse to a broad spectrum of kinds and degrees of stress. (4) Their lifespans 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 difficultas that of microorganisms and plankton.

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

REFERENCES

Anagnostopoulou, 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 freshwater

invertebrate community of the system of the river Almopeos, N. Greece.

Proc. 6th

Zoogeogr. 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 new

biological water quality score system based on macroinvertebrates over a wide range of

unpolluted running water sites.

Wat. Res.

17, 333-347.

British Ecological Society (1990). River water quality, Ecological studies No. 1, Field Studies

Council, 1-43.

Calow, P. and Petts, G.E. (eds) (1992). The Rivers Handbook, Hydrological and ecological

principles. Vol. 1. Blackwell Science.

Calow, P.and Petts, G.E. (eds) (1994). The Rivers Handbook, Hydrological and ecological

principles. Vol. 2. Blackwell Science.

Copeland R.S., Lazaridou-Dimitriadou M., ArtemiadouV., Yfantis G., White K.N. and Mourelatos

S. (1997). Ecological quality of the water in the catchment of river Aliakmonas

(Macedonia, Hellas).

Proceedings of the 5th Conference on Environment Science and

Technology

, Molyvos, 1-4 September, 27-36.

De Pauw N. & Vanhooren G. (1983). Method for biologicalquality assessment of water courses in

Belgium. Hydrobiologia, 100, 153-168.

European Union Commission. (1978). Directive on the quality of fresh water for the protection and

improvement 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 the

European Communities

, No 80/778/15.07.80

European Union Commission (1997). Proposal for a Council Directive establishing a framework for

Community action in the field of water policy

.

Official Journal of the European Communities

, No

C 184/20, 17.6.97.

Extence C.A., Bates A.J., Forbes W.J. and Barham P.J. (1987). Biologically based water quality

management.

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 August

1997.

Proceedings of the International Conference "Protection and Restoration of the

Environment 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. John

Wiley & 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 and

reciprocal 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. Island

Press. 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). An

integrated water quality assessmentof the river Axios, Northern Greece.

Proceedings of the

International Conference "Protection and Restoration of the Environment IV"

, 1-4 July, Sani

Halkidiki, 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 on

macroinvertebrate communities : History and present status in Europe.

Environmental Pollution

60, 101-139.

Metcalfe L.J. & Smith M. (1994). Invertebrates ecology and survey methods, in The Rivers

Handbook, 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 of

the macroinvertebrate fauna of five biotopes in the river Lambourn, Berkshire, England.

Arch.

Hydrobiol.

96, 271-292.

ModellingA wide range of techniques, including standard survey procedures andmodelling software for analysis of the results, are now available for the pollutionmanager, and these are proving very robust for a wide range of purposes.Manypolicy decisions are nationally based, and country-wide monitoring networks areessential to inform future decisions. Finally, of course, international cooperationon monitoring is essential, as much pollution crosses national frontiers, e.g.monitoring acid rain across Europe, the transfer of pollutants in marine waters orthe movement of radionuclides from the Chernobyl accident. Internationalcooperation in the European Union was enhanced by the recent formation of theEuropean Environment Agency (EAA) based in Copenhagen. Currently, the workof the EAA has focused on establishing "topic centres" in each member state tocoordinate the supply of environmental monitoring data to produce a clearerpicture of the state of the environment within the EU and how this might be usedto aid production of future EU legislation.

The use of a predictive model, which take into consideration both thebiotic and physicochemical approach for the detection of water pollution andmonitoring of the water quality, is probably the best tool for the management andimprovement of water resources, and especially of rivers. A predictive model,applied on data collected with a standard sampling method, can also produce aclassification scheme according to the degree of pollution that rivers receive.This may allow inter and intra site comparisons, which could lead to an effectiveconservation strategy.For the establishment of these models, one approach is to identify the "bestachievable community" which can occur under a particular set of physical,chemical, geological and geographical conditions. So the surveyed communitycan then be compared with the above one and hence the degree of changeobjectively assessed.

During the 70's, multivariate analytical techniques have been introduced asa new tool for the assessment of water quality. Between 1978 and 1988, in theUK a biological classification of unpolluted freshwater sites (483 sites on 80rivers, 700 have been assessed up today) was developed based onmacroinvertebrate fauna (see 5.1.3.). It was attempted to assess whether thetype of macroinvertebrate community at a given site maybe predicted usingphysicochemical parameters.This proved to be feasible and led to the formation of RIVPACS (RiverInVertebrate 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.

Biodiversity in the Water programmeThe water-programme is centered around biodiversity. That is first of all because biodiversity is an adequate tool to characterise the environment. Biodiversity means in general all aspects of variety in the living world, but more specifically it can be expressed as: 1) the collection of species present in an area, 2) the amount of genetic variation or 3) the number of community types. In this context we are talking about biodiversity as the assembly of species.Both the collection of species that are present and those that are absent, will say something about the water itself and the water habitat. Comparisons of biodiversity between localities, and variation over time, will give information about the environmental status and indicate eventual qualitative changes. Other reasons for using the biodiversity in this porgramme are the educational aspects. Many species can be identified with small resources without expensive apparatus. To some extent it can be done by the students and their teachers. In addition are environmental problems normally complex problems.

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

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 80/778/15.07.80European 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). Ecologicalevaluation 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.