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MYΘOΣ Nο 1:H ψευδαίσθηση της αφθονίας
IT IS PLENTY
MYΘOΣ Nο 2: Το διαθέσιμο νερό φτάνει για να καλύψει τις ανάγκες μας
ENOUGH FOR EVERYONE
=
MYΘOΣ Nο 3: Οι ανησυχίες μας δεν είναι δικαιολογημένες
NO 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
AND RARE GOOD
In the near future 4 million people will suffer
from water lack, even if each we spend only
40l/day/person
Population increase
High biotic level
Overconsumption
Pollution
Climate change
MODIFICATION OF RIVER
BANKS AND BED
DISTURBANCES
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.
Types of disturbances
There are pulse disturbances
which 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 cycle
and 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 other
•
Saprobiotic
• Diversity indices
[I=S(number of species)/ N(total nb. of ind.)]
• Biological indices
• Predictive models leading to
an 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).
The biotic indices are based on the tolerance of benthic macroinvertebrates
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.
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(Q-index, ΒΕΟL, Κ 135)
(Holland,Germany, E. Europe)
France
(ΙΒ, 1968)
Trent Biotic Index
(TBI, ENGLAND, 1964)
Chandler index
(1970, SCOTLAND)
France
(IGB, FRANCE,
1982)
G.B.(BMWP)
(G.B., 1978)
Global
(IGB, FRANCE,
1985)
Modified BMWP
(1979, G.B.)
BELGIUM
ΒΒΙ, 1983
Iberian BMWP’
(1988, SPAIN)
Extended B.I
(ΕΒΙ, ENGLAND, 1978)
Extended B.I.Italy
(ΙΒΕ,ITALY, 1980)
Lincoln, ENGLAND
(ASPT+BMWP)
The Hellenic score’
(2000, GREECE)
INSECTS
Ametabola
Collembola
(Springtails)
Hemimetabola
Holometabolous
Nymphs:
Larvae:
Plecoptera
Diptera
(stone flies)
Trichoptera
Ephemeroptra
Coleoptera
(may flies)
Neuroptera
(alder flies)
Odonata
(dragon flies)
Lepidoptera
a) the number of tail
Hymenoptera
appendages
b) the presence or
Pupae:
absence of gills
Diptera
c) the presence of an
Coleoptera
obvious protrusible
Trichoptera
labium
Heteroptera
(Hemiptera)
The REST of
ARTRODODA
CHILICERATA
(ARACHNIDA)
CRUSTACEA
MOLLUSCA
BIVALVIA
GASTERODODA
ANNELIDA
OLIGOCHAETA
PLATYELMINTHES
COELENTERATA
NEMATOMORPHA
HIRUDINEA
POLYZOA
HYDROZOA
TURBELARIA GORDIOIDEA
(Tricladida)
3 minute kick-sweep method
Photo:Maria Lazaridou
Πίνακας II
Συνολικός αριθμός taxa (ταξινομικές ομάδες )
0-1
2-5
6-10
11-15
15+
Eίδη σύμφωνα με την
ευαισθησία τους ως προς το O2
(1)νύμφες από Plecoptera
αριθμός ειδών
>1
7
8
9
10
αριθμός ειδών
=1
6
7
8
9
(2)νύμφες από Ephemeroptera εκτός από το Baetis rhodani*
αριθμός ειδών
>1
6
7
8
9
αριθμός ειδών
=1
5
6
7
8
(3)προνύμφες από Trichoptera και τα Baetis rhodani
αριθμός ειδών
>1
6
7
8
9
αριθμός 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.
(6)
2
3
4
5
Προνύμφες της οικογένειας Chironomidae (Diptera). Aπουσία των παραπάνω ειδών.
1
2
3
4
6
5
(7)
Aπουσία όλων σχεδόν των μορφών. (Παρουσία μερικών ανθεκτικών προνυμφών της τάξης
Diptera, π.χ. του γένους Eristalis).
0
1
2
-
BMWP’
Eφημερόπτερα
Πλεκόπτερα
Hμίπτερα
Tριχόπτερα
Δίπτερα
Kαρκινοειδή
Oδοντόγναθα
Tριχόπτερα
Eφημερόπτερα
Πλεκόπτερα
Tριχόπτερα
Mαλάκια
Tριχόπτερα
Aμφίποδα
Oδοντόγναθα
Eφημερόπτερα
Kολεόπτερα
Tριχόπτερα
Δίπτερα
Πλατυέλμινθες
Eφημερόπτερα
Kολεόπτερα
Δίπτερα
Mεγαλόπτερα
Aκάρεα
Bδέλλες
Mαλάκια
Hμίπτερα
Kολεόπτερα
Bδέλλες
Iσόποδα
Δίπτερα
Oλιγόχαιτοι
Oικογένειες/families
Siphlonuridae, Heptageniidae, Leptophlebiidae, Potamanthidae,
Ephemeridae
Taeniopterygidae, Leuctridae, Capniidae, Perlodidae, Perlidae,
Chloroperlidae
Aphelocheiridae
Phryganeidae, Molannidae, Beraeidae, Odontoceridae, Leptoceridae,
Goeridae, Lepidostomatidae, Brachycentridae, Sericostomatidae
Athericidae, Blephariceridae
Astacidae
Lestidae, Agriidae (Calopterygidae), Gomphidae, Cordulegasteridae,
Aeshnidae, Corduliidae, Libellulidae
Psychomyidae, Philopotamidae, Glossosomatidae
Ephemerellidae
Nemouridae
Rhyacophilidae, Polycentropodidae, Limnephilidae
Neritidae, Viviparidae, Ancylidae Unionidae
Hydroptilidae
Corophiidae, Gammaridae
Platycnemididae, Coenagriidae
Oligoneuriidae
Dryopidae, Elminthidae, Helophoridae, Hydrochidae, Hydraenidae,
Clambidae
Hydropsychidae
Tipulidae, Simuliidae
Planariidae, Dendrocoelidae, Dugesiidae
Baetidae, Caenidae
Haliplidae, Curculionidae, Chrysomelidae
Tabanidae, Stratiomyidae, Empididae, Dolichopodidae, Dixidae,
Ceratopogonidae, Anthomyidae, Limoniidae, Psychodidae
Sialidae
Hidracarina
Piscicolidae
Valvatidae, Hydrobiidae, Lymnaeidae, Physidae, Planorbidae
Sphaeriidae, Bithyniidae, Bythinellidae
Mesoveliidae, Hydrometridae, Gerridae, Nepidae, Naucoridae,
Notonectidae, Pleidae, Corixidae
Helodidae, Hydrophilidae, Hygrobiidae, Dytiscidae, Gyrinidae
Glossiphonidae, Hirudinidae, Erpobdellidae
Asellidae, Ostracoda
Chironomidae, Culicidae, Muscidae, Thaumaleidae, Ephydridae
Oligochaeta (όλη η κλάση)
Bαθμός
Score
10
8
7
6
5
4
3
2
1
Ευρύτερη περιοχή μελέτης Studied rivers
Β
Rivers Aliakmon, Axios , Almopeos, Aggitis and the creeks of
Skouries and Olympiada (Chalkidiki)
Hellenic biotic index
Reevaluation of the families
293 samples from different rivers of N. Greece
Substrate: three categories: coarse (>70%), slightly coarse
(>70%) and mixed.
A tsaxonomic group had to be present in 5 samples in order to
be taken into consideration
The Imperial BMWP’ and the IASPT’ were used as a basis
For the families Neritidae και Sphaeriidae were
kept the original evaluations
Ελληνικό Σύστημα Αξιολόγησης
HELLENIC BIOTIC INDEX
Ταξινομικές ομάδες
α) Capniidae
, Chloroperlidae
, β) Siplonuridae
,
γ ) Aphelocheiridae, ,
δ ) Blephariceridae
ε ) Phryganceidae, Molanidae, Odontoceridae, Bareidae,
Lepidosto
matidae, Thremmatidae, Brachycentridae,
Helicopsychlidae
α ) Leuctridae, Perlodidae, Perlidae,
β ) Sericostomatidae, Goeridae,
γ ) Neoephemeridae
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
α ) Potamanthidae,
β ) Calopterygidae, Cordulegasteridae
γ ) Stratiomyidae,
δ ) Hydrobiidae
α ) Platycnemididae, Gomphidae,
β ) Tabanidae, Ceratopogonidae, Empididae,
γ ) Elminthid
δ ) Viviparidae, Neritidae,
ae,
ε ) Unionidae,
στ ) Corophidae
α ) Caenidae, Oligoneuriidae, Polymitarcidae, Isonychiidae,
β ) Hydropsychidae,
γ ) Ancylidae,
δ ) Gammaridae,
ε ) Planariidae, Dendrocoelidae, Dugesiidae,
στ ) Dryopidae, Hel
ophoridae, Hydrochidae, Clambidae
α ) Ephemerellidae, Baetidae,
β ) Hydroptilidae,
γ ) Tipulidae, Dolichopodidae, Anthomyidae, Limoniidae,
δ ) Haliplidae, Curculionidae, Chrysomelidae,
ε ) Hydracarina
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)
α ) Chironomidae (
τα κόκκινα
), Rhagionidae, Culicidae, Muscidae,
Thaumaleidae, Ephydridae
, Ephemeridae, Heptageniidae,
Leptophlebiidae,
β ) Lymnaeidae, Planorbidae,
γ ) Erpobdellidae
α ) Tubificidae,
β ) Valvatidae,
γ ) Syrphidae
Παρούσες
(0 - 1%)
Κοινές
(1.01
- 10%)
Άφθονες
(>10%)
10
10
10
9
9.5
10
8
8.5
8.8
7
7.5
7.8
6
6
6
5
5
5
4
3.8
3.5
3
2.5
2
2
1.5
1
1
0.8
0.5
),
δείγματα που συλλέχθηκαν από πολλούς τύπους ενδιαιτημάτων
samples collected from rich habitat
E λληνικό σύστημα
αξιολόγησης
Υ
HELLENIC BMWP
151+
121 - 150
91 - 120
61 - 90
31 - 60
15 - 30
0 -1 4
7
6
5
4
3
2
1
Μέσος Όρος Δείκτη
ανά ταξινομική
Hellenic ASPT
Ομάδα
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
Χ
7
6
5
4
3
2
1
Δείγματα που συλλέχθηκαν από λίγους τύπους ενδιαιτημάτων
Samples collected from poor habitat
E λληνικό σύστημα
αξιολόγησης ΕΣΑ
121+
101 - 120
81 - 100
51 - 80
25 - 50
10 - 24
0 -9
T ελική τιμή
6+
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
Final value
Υ
ΜΟΔΤ
Χ
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
Σύντομη ερμηνεία
A ++
A+
A
B
Γ
Δ
E
Z
H
Θ
I
Index
E ρμηνεία Interpretation
Άριστη ποιότητα excellent
Άριστη ποιότητα
Άριστη ποιότητα
K αλή ποιότητα good
K αλή ποιότητα
M έτρια ποιότητα moderate
M έτρια ποιότητα
K ακή ποιότητα poor
K ακή ποιότητα
Πολύ κακή ποιότητα Very poor
Πολύ κακή ποιότητα
Τάξη Ποταμού Χημικά κριτήρια
Βιολογικά κριτήρια Εν δυνάμει χρήσεις
Τάξη 1
Μη τοξικό για τα ψάρια
DO > 80%
ΒΟD < 2.5
Τάξη 2
DO > 70%
ΒΟD < 4
Τάξη 3
DO > 60%
ΒΟD < 6
Τάξη 4
DO > 50%
ΒΟD < 8
Τάξη 5
DO > 20%
ΒΟD < 15
Μη τοξικό για τα ψάρια,
μπορεί να περιλαμβάνει
ποταμούς που έχουν υψηλής
ποιότητας αποροές
Μη τοξικό για τα ψάρια,
στο νερό δεν υπάρχουν
ορατές ενδείξεις ρύπανσης,
εκτός από ορισμένoυς
Απουσία ή σποραδική
εμφάνιση ψαριών
Πόσιμο νερό
Αλιεία δυνατή
Πόσιμο νερό μετά
από επεξεργασία,
Αλιεία
προβληματική
Κατάλληλο για τη
βιομηχανία
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 Decoran
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. For
this, 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 the
Monte 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.
RIVPACS
ENVIRONMENTAL DATA
Latitude/Longitude, air temperature mean
and range, altitude, distance from source,
channel width & depth, discharge,
substratum, alkalinity
OBSERVED TAXA
Descriminant analysis
(based on 483 unpolluted sites)
Predicted taxa
(with probabilities of capture)
Comparison
(observed/predicted)
ENVIRONMENTAL QUALITY INDEX
(EQI)
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).
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.
ALL LECTURES OF THIS IP ARE FOUND IN
THE FOLLWING WEB ADDRESS:
http://river.bio.auth.gr/lueneburg/index.htm