Biocontrol of tomato late blight with the combination of epiphytic antagonists and rhizobacteria
Epiphytic, littoral diatoms as bioindicators of shallow lake trophic status: Trophic Diatom Index...
14
PRIMARY RESEARCH PAPER Epiphytic, littoral diatoms as bioindicators of shallow lake trophic status: Trophic Diatom Index for Lakes (TDIL) developed in Hungary Csilla Stenger-Kova ´cs Krisztina Buczko ´ E ´ va Hajnal Judit Padisa ´k Received: 25 October 2006 / Revised: 27 March 2007 / Accepted: 30 March 2007 / Published online: 21 June 2007 Ó Springer Science+Business Media B.V. 2007 Abstract Littoral diatoms are important con- tributors of the primary production in shallow aquatic ecosystems and they can be used as indicators of the trophic status. The aim of the study was to develop an index to assess trophic status of Hungarian lakes as suggested by the Water Framework Directive. In spring of 2005 and 2006, epiphytic diatom samples were col- lected from 83 shallow lakes. Weighted average method was used to develop and test the TP model. In the developed TP model correlation between the observed and diatom inferred TP was high (r 2 = 0.96, n = 67). The optimum and tolerance TP parametrics of 127 species were determined and trophic indicator and sensibility values were defined for the Trophic Diatom Index for Lakes (TDIL). The TDIL was appli- cable to assess the ecological status of Hungarian shallow lakes. According to the TDIL the ecological status of 4 lakes were in excellent, 25 in good, 21 in medium, 21 in tolerable and 12 in bad status. Keywords Lakes Littoral diatoms Optimum Tolerance TP Trophic state Water Framework Directive Introduction Eutrophication can be defined as an intensified accumulation of plant biomass generally due to an increase in nutrients (primarily phosphorus and nitrogen). The decomposition of this biomass may reduce the oxygen level leading to secondary impacts on aquatic ecosystems (Vollenweider, 1989). These events interfere with different kinds of human water use (e.g. recreation, fishery/ angling, transport, drinking water supply, etc.). During the eutrophication, the original flora and the fauna change (Padisa ´k, 2005; Istva ´ novics et al., 2007) and the ecosystem behaviour might become unpredictable. Handling editor: K. Martens Electronic supplementary material The online version of this article (doi: 10.1007/s10750-007-0729-z) contains supplementary material, which is available to authorized users. C. Stenger-Kova ´cs (&) E ´ . Hajnal J. Padisa ´k Department of Limnology, University of Pannonia, Egyetem u. 10, 158, Veszpre ´m 8200, Hungary e-mail: [email protected] E ´ . Hajnal e-mail: [email protected] J. Padisa ´k e-mail: [email protected] K. Buczko ´ Botanical Department of Hungarian Natural History Museum, 222, Budapest 1476, Hungary e-mail: [email protected] 123 Hydrobiologia (2007) 589:141–154 DOI 10.1007/s10750-007-0729-z
Transcript of Epiphytic, littoral diatoms as bioindicators of shallow lake trophic status: Trophic Diatom Index...
PRIMARY RESEARCH PAPER
Epiphytic, littoral diatoms as bioindicators of shallow laketrophic status: Trophic Diatom Index for Lakes (TDIL)developed in Hungary
Csilla Stenger-Kovacs Æ Krisztina Buczko ÆEva Hajnal Æ Judit Padisak
Received: 25 October 2006 / Revised: 27 March 2007 / Accepted: 30 March 2007 / Published online: 21 June 2007� Springer Science+Business Media B.V. 2007
Abstract Littoral diatoms are important con-
tributors of the primary production in shallow
aquatic ecosystems and they can be used as
indicators of the trophic status. The aim of the
study was to develop an index to assess trophic
status of Hungarian lakes as suggested by the
Water Framework Directive. In spring of 2005
and 2006, epiphytic diatom samples were col-
lected from 83 shallow lakes. Weighted average
method was used to develop and test the TP
model. In the developed TP model correlation
between the observed and diatom inferred TP
was high (r2 = 0.96, n = 67). The optimum and
tolerance TP parametrics of 127 species were
determined and trophic indicator and sensibility
values were defined for the Trophic Diatom
Index for Lakes (TDIL). The TDIL was appli-
cable to assess the ecological status of Hungarian
shallow lakes. According to the TDIL the
ecological status of 4 lakes were in excellent,
25 in good, 21 in medium, 21 in tolerable and 12
in bad status.
Keywords Lakes � Littoral diatoms � Optimum �Tolerance � TP � Trophic state � Water
Framework Directive
Introduction
Eutrophication can be defined as an intensified
accumulation of plant biomass generally due to
an increase in nutrients (primarily phosphorus
and nitrogen). The decomposition of this biomass
may reduce the oxygen level leading to secondary
impacts on aquatic ecosystems (Vollenweider,
1989). These events interfere with different kinds
of human water use (e.g. recreation, fishery/
angling, transport, drinking water supply, etc.).
During the eutrophication, the original flora and
the fauna change (Padisak, 2005; Istvanovics
et al., 2007) and the ecosystem behaviour might
become unpredictable.
Handling editor: K. Martens
Electronic supplementary material The online version ofthis article (doi: 10.1007/s10750-007-0729-z) containssupplementary material, which is available to authorizedusers.
C. Stenger-Kovacs (&) � E. Hajnal � J. PadisakDepartment of Limnology, University of Pannonia,Egyetem u. 10, 158, Veszprem 8200, Hungarye-mail: [email protected]
E. Hajnale-mail: [email protected]
J. Padisake-mail: [email protected]
K. BuczkoBotanical Department of Hungarian Natural HistoryMuseum, 222, Budapest 1476, Hungarye-mail: [email protected]
123
Hydrobiologia (2007) 589:141–154
DOI 10.1007/s10750-007-0729-z
Littoral diatoms are important contributors of
the primary production in shallow aquatic eco-
systems (Wetzel, 1990). Therefore, they can serve
as good indicators of the ecological status of
lakes. A number of methods (Coste in Cemagref,
1982; Rumeau & Coste, 1988; Descy & Coste,
1991; Dell’Uomo, 1996, Lenoir & Coste, 1996;
Kelly, 1998; Prygiel & Coste, 2000) were devel-
oped for use of diatoms as bioindicators of
changing environment, especially in rivers. The
applicability of these indices has been spatially
limited, even for rivers, since distribution of
species may differ markedly (Wu, 1999). As most
described diatom indices were developed and
applied for running waters (Brabecz & Szoszkie-
wicz, 2006), applications for lakes are sporadic
and in many cases doubtful. Although eutrophi-
cation is a common water quality problem in lake
ecosystems all over the world (e.g. Reckhow &
Chapra, 1983), ecological monitoring programs
that include diatoms are rare (Acs et al., 2005).
A number of paleolimnological studies aimed
to reconstruct past eutrophication of lakes based
on paleolimnological evidences (Taylor et al.,
2006) using diatom valves preserved in lake
sediments (Anderson et al., 1993; Alefs et al.,
1996; Reavie et al., 2002) with the weighted
averaging method (ter Braak & van Dam, 1989).
Later this method was used in modern lake and
river monitoring (Schonfelder & Gelbrecht, 2002;
Kovacs et al., 2006; Soininen & Niemela, 2002)
because species-specific environmental optima
and tolerances of modern diatom species (Birks
et al., 1990) are also different.
The EC Water Framework Directive (WFD;
EC Parliament and Council, 2000) initiated a
number of research focusing on basic concepts
(Furse et al., 2006), typology of surface waters
(Sandin & Verdonschot, 2006) and various kinds
of methodical approaches (Comte et al., 2005;
Besse-Lototskaya et al., 2006; Persson et at.,
2006; Springe et al., 2006).
The aim of this study was to develop a trophic
diatom index on the basis of diatom species’
optimum and tolerance characteristics along the
total phosphorus (TP) gradient in a diverse set of
Hungarian lakes. As littoral diatoms (phytoben-
thos) are one of the biotic indicator group
included in the WFD it was an urgent need to
develop diatom-based method that is suitable for
monitoring trophic state of lakes.
Materials and methods
Sampling
During April–June 2005 and April–July 2006 alto-
gether 83 epiphytic diatom samples were collected
in Hungarian shallow (average depth < 4 m) lakes
with surface areas ranging 4 m2 and 594 km2
(important information on the sampled lakes are
given in the Electronic supplementary material).
According to the Hungarian lake typology 10 water
types were investigated (Table 1).
Diatom samples were taken in the littoral
region of the lakes from Phragmites australis
stems or, if it was absent, from other emergent
macrophytes. Sampling of epilithon, epipsammon
or epipelon was preferably avoided in accordance
with earlier studies by Poulıckova et al. (2004)
demonstrating that diatom assemblages on reed
indicate different trophic state than those on
stone or mud. Restriction of sampling to a single
preferred substratum helps to avoid differences
originating from different substrate preference of
the indicator species (Hofmann, 1994). Another
advantage of standardization of sampling to
P. australis lies in its cosmopolitan distribution
and common occurence in the littoral region.
Seasonal and vertical distribution of the epiphytic
diatom assemblages can be markedly different
(Hoagland & Peterson, 1990; Muller, 1994).
Therefore, diatom samples were taken preferably
from stem sections 5–20 cm below the water
surface, in spring (Buczko & Acs, 1996/1997; Acs
et al., 2005) from April to the beginning of July.
Exceptions included some saline lakes and
oxbows in 2006, where sampling time extended
to the summer due to uncommon wheather
conditions (unusually high precipitation and/or
surface shrinking of the small/shallow saline
lakes).
The diatom valves were cleaned by hot hydro-
gen peroxide method and were embedded in
synthetic resin (Zrax�). At least 400 valves were
counted in each sample. The comparability of
identifications was ensured with intercalibration.
142 Hydrobiologia (2007) 589:141–154
123
Diatoms were identified at least to species level
using light microscopy according to Krammer &
Lange-Bertalot (1991–2000), Lange Bertalot
(1995–2002) and Krammer (2002). The counts
were converted to relative abundance.
The physical and chemical parameters of the
water were measured on the field and in parallel
samples taken for water chemistry. TP was
determined by spectrophotometry (Pote & Dan-
iel, 2000) according to international standards.
Model development and statistical analyses
Weigthed average method (WA) was used to
derive the autecological data of the diatoms. The
theorethical base of this method is, that the species
occur in the highest abundance near their ecolog-
ical optima (Birks et al., 1990). The optimum can
be calculated according to the following equation:
uk ¼Xn
i¼1
yikxi=Xn
i¼1
yik regression step ð1Þ
where, uk is the average optimum of the given
environmental parameter for a k taxon, xi is the
value of the environmental variable at site i and
yik is the abundance of species k at site i.
Subsequently, applying the previously-known
optima, the inferred environmental parameter
(xi) can be calculted (Stoermer & Smol, 2001) as:
xi ¼Xm
k¼1
yikuk=Xm
k¼1
yik calibration step ð2Þ
The regression step was used for development of
a TP model with using data from 67 sampling sites
and calculating the TP optima and tolerance levels
(defined as 1 standard deviation) of the individual
species. The calibration step served for testing of
this model by the calibration against with 16
sampling sites. The calculations were performed
with the computer program C2 version 3.1 (http://
www.campus.ncl.ac.uk/staff/Stephen.Juggins). The
root mean squared error of prediction (RMSE)
was calculated directly from the calibration set.
According to the calculated TP optima and
tolerances, trophic indicator values were deter-
mined to only those species which occured in at
least three samples. TP optima were categorized
into 6 classes running from 0 (hypertrophic) to 5
(oligotrophic). The resulting categories had TP
values (0) > 1.500 (1) 0.401–1.500 (2) 0.300–0.400
(3) 0.190–0.299 (4) 0.100–0.189 (5) < 0.090 mg l–1.
According to requirements of the Water Frame-
work Directive (WFD), trophic categories (inter-
vals of quality classes) were equipped with
common qualification (bad, tolerable, medium,
good, excellent). Tolerance values of the species
were sorted into three categories: 1-sensitive
(tolerance: 0.01–0.09 mg l–1), 2-slightly sensitive
(tolerance: 0.1–0.3 mg l–1) and 3-tolerant (toler-
ance: 0.3–3 mg l–1). Trophic Diatom Index for
Lakes (TDIL) was calculated applying the equa-
tion by Zelinka & Marvan (1961):
TDIL ¼P
akskvkPaksk
where a is the relative abundance, s is the
sensibility and v is the trophic indicator value of
the species k. The value of the index varies
between 0 and 5. According to the trophic status
assessed by TDIL 5 water quality classes were
defined similarly to other indices (Padisak et al.,
2006) developed for the purposes of the WFD
Table 1 The investigated 10 lake types according to the Hungarian lake typology (Szilagyi et al., 2004)
1. Lowland, calcareous, 3–15 m depth, large, permanent lakes.2. Lowland, saline, 1–3 m depth, large, permanent lakes.3. Lowland, saline, 1–3 m depth, medium, permanent lakes.4. Lowland, calcareous-saline, < 1 m depth, small, permanent lakes.5. Lowland, calcareous-saline, < 1 m depth, small, temporary lakes.6. Lowland, calcareous-organic, < 4 m depth, small, permanent lakes.7. Lowland, saline, < 3 m depth, small, permanent lakes.8. Lowland, calcareous-saline, < 1.5 m depth, small, temporary lakes.9. Lowland, calcareous-organic, < 3 m depth, small, permanent lakes.10. Lowland, calcareous-organic, < 1.5 m depth, small, temporary lakes
Hydrobiologia (2007) 589:141–154 143
123
(Table 2). In the data analyses Shannon diversity
was also calculated (Shannon & Wiener, 1949).
Results
In the 83 lakes altogether 361 species of diatoms
were identifed but only 247 species (with relative
abundance > 0.5%) were used for the further
analyses. The most common taxa (Achnanthidium
minutissimum, Amphora pediculus, Cocconeis
placentula, Gomphonema parvulum, Navicula
cryptotenella, Navicula veneta and Nitzschia pale-
acea) were cosmopolitan and widely distributed
in inland Hungarian waters. The average species
richness and the standard deviation was 26 ± 8.
The lowest species richness (8) was found in the
Fulop-szek in 2005 (FUL397), the highest (53) in
Lake Balaton in 2006 (BalBD1). The average
diversity was 2.7 ± 0.8 (eveness was 0.6 ± 0.1).
The TP concentrations per sample ranged be-
tween 0.01 and 5.72 mg l–1 in the sample set
(average: 0.50 ± 0.51 mg l–1). The TP model
(Fig. 1) was developed by weighted averaging
without tolerance downweighting regression
(WAtol) with inverse deshrinking to infer TP
concentrations of the lakes. This method pro-
duced the best statistical data (Table 3). Strong
correlation (r2 = 0.96, n = 67) was found between
the measured and the inferred TP concentrations
in the developed TP model. The RMSE was
0.17 mg l–1. The correlation in the test set was
lower (r2 = 0.59, n = 16).
The TP optimum, tolerance and indicator
value of 127 species were determined (Table 4).
Cymbella helvetica, Gomphonema angustum and
Diatoma moniliformis were sensitive and charac-
teristic species for low trophic state. On the other
edge of the trophic spectrum there were several
tolerant, eutrophic taxa e.g. Nitzschia communis,
Amphora veneta, Craticula cuspidata. The taxon
list in Table 4 compared to the list of TDI Austria
(Rott et al., 1999) provided new, applicable indi-
cator values for 31 taxa and, additionally, there
Table 2 Class boundaries and trophic status according tothe TDIL
Class boundary Ecological status
4–5 Excellent3 < 4 Good2 < 3 Medium1 < 2 Tolerable0 < 1 Bad
0.01
0.1
1
10
0.01 0.1 10
log diatom inferred TP
log
mea
sure
d T
P
r2 = 0.96
RMSEP = 0.17
r2 (test set) = 0.59
1
Fig. 1 Relationship between log observed TP and diatominferred TP using weighted averaging without tolerancedownweighting regression (WAtol) with inverse deshrin-king. (empty circles: training set; full triangle: test set)
Table 3 Correlation and root mean squared error (RMSE) of prediction for the weighted averaging and linear regression inthe training and test set
Code of the method Method and deshrinking RMSE(traning set)
r2
(training set)r2
(test set)
WA_INV Weighted averaging model(inverse deshrinking) for TP
0.46 0.71 –
WA_CLAS Weighted averaging model(classical deshrinking) for TP
0.55 0.71 –
WATOL_INV Weighted averaging model(tolerance downweighted,inverse deshrinking) for TP
0.17 0.96 0.59
WATOL_CLA Weighted averaging model(tolerance downweighted,classical deshrinking) for TP
0.17 0.96 –
144 Hydrobiologia (2007) 589:141–154
123
Ta
ble
4L
ist
of
TP
(mg
l–1)
op
tim
a,
tole
ran
ce–
ran
ge
s+
op
tim
um
,n
um
be
ro
fo
ccu
ren
ces
(n),
ind
ica
tor
an
dse
nsi
ble
va
lue
so
fth
esp
eci
es
inT
DIL
,v
an
Da
mtr
op
hic
ind
ex
(va
nD
am
,1
99
4),
TD
IA
ust
ria
(Ro
tt,
19
99
)a
nd
TI
(Ho
fma
nn
,1
99
9)
Fu
llN
am
eA
uth
or
nT
PT
PT
PT
Pv
an
Da
mT
DI
Au
stri
aT
IO
pti
mu
mT
ole
ran
cein
d_
va
lue
sen
s_v
alu
ein
d_
va
lue
ind
_v
alu
ein
d_
va
lue
Ach
na
nth
idiu
mm
inu
tiss
imu
m(K
utz
ing
)C
zarn
ecz
ki
48
0.1
59
0.2
36
42
71
.2A
dla
fia
min
usc
ula
(Gru
no
w)
La
ng
e-B
ert
alo
t3
0.3
45
0.4
04
23
1–
Ad
lafi
am
inu
scu
lav
ar.
mu
rali
s(G
run
ow
)L
an
ge
-Be
rta
lot
81
.10
21
.05
31
35
2.9
5A
mp
hip
leu
rap
ellu
cid
aK
utz
ing
50
.10
00
.02
94
12
2.1
Am
ph
ora
lib
yca
Eh
ren
be
rg1
30
.24
40
.15
73
25
3.5
Am
ph
ora
ov
ali
s(K
utz
ing
)K
utz
ing
40
.19
50
.11
53
25
3.3
Am
ph
ora
ped
icu
lus
(Ku
tzin
g)
Gru
no
w3
70
.25
60
.30
43
35
2.8
Am
ph
ora
ven
eta
Ku
tzin
g2
91
.47
00
.87
31
35
3.8
An
om
oeo
nei
ssp
ha
ero
ph
ora
(Eh
ren
be
rg)
Pfi
tze
r7
2.1
38
0.6
84
03
53
.45
Au
laco
seir
aa
mb
igu
a(G
run
ow
)S
imo
nse
n3
0.1
79
0.1
16
42
5–
Au
laco
seir
ag
ran
ula
ta(G
run
ow
)S
imo
nse
n7
0.3
56
0.1
59
22
5–
Au
laco
seir
am
uz
za
nen
sis
(Me
iste
r)K
ram
me
r3
0.2
09
0.1
07
32
––
Ca
lon
eis
ba
cill
um
(Gru
no
w)
Cle
ve
90
.22
60
.12
23
24
2.5
4C
occ
on
eis
ped
icu
lus
Eh
ren
be
rg6
0.1
10
0.0
79
41
52
.64
.4C
occ
on
eis
pla
cen
tula
Eh
ren
be
rg3
70
.39
60
.23
12
25
2.6
Cra
ticu
lacu
spid
ata
(Ku
tzin
g)
Ma
nn
31
.83
20
.88
20
35
3.8
5C
rati
cula
ha
lop
hil
a(G
run
ow
ex
va
nH
eu
rck
)M
an
n2
92
.03
82
.32
90
35
3.4
5C
ten
op
ho
rap
ulc
hel
la(R
alf
se
xK
utz
ing
)L
an
ge
-Be
rta
lot
14
0.1
49
0.7
76
43
53
.55
Cy
clo
step
ha
no
sd
ub
ius
(Fri
cke
)R
ou
nd
70
.24
00
.15
43
25
2.9
Cy
clo
tell
aa
tom
us
Hu
ste
dt
14
0.2
20
0.1
50
32
5–
Cy
clo
tell
aco
mta
(Eh
ren
be
rg)
Ku
tzin
g4
0.0
72
0.1
31
52
5–
Cy
clo
tell
am
eneg
hin
ian
aK
utz
ing
23
0.2
36
0.2
36
32
52
.8C
ycl
ote
lla
oce
lla
taP
an
tocs
ek
10
0.1
35
0.1
28
42
41
.5C
ycl
ote
lla
pse
ud
ost
elli
ger
aH
ust
ed
t6
0.1
53
0.1
39
42
5–
Cy
mb
ella
affi
nis
Ku
tzin
g1
90
.11
50
.08
14
15
0.7
2.4
Cy
mb
ella
cist
ula
Eh
ren
be
rg(K
irch
ne
r)1
60
.17
30
.11
74
25
–C
ym
bel
lah
elv
etic
aK
utz
ing
10
0.0
90
0.0
83
51
31
.41
.7C
ym
bel
lap
usi
lla
Gru
no
w3
1.4
32
1.4
78
13
–1
.2D
iato
ma
mo
nil
ifo
rmis
Ku
tzin
g1
00
.06
90
.08
15
1–
25
Dia
tom
ate
nu
isA
ga
rdh
17
0.1
52
0.1
01
42
5–
En
cyo
nem
aca
esp
ito
sum
Ku
tzin
g6
0.1
15
0.0
47
41
7–
En
cyo
nem
am
inu
ta(H
ilse
inR
ab
en
ho
rst)
D.G
.M
an
n1
60
.21
40
.09
03
1–
22
En
cyo
nem
asi
lesi
acu
m(B
leis
chin
Ra
be
nh
ors
t)D
.G.
Ma
nn
40
.13
10
.03
54
17
–E
ncy
on
op
sis
mic
roce
ph
ala
(Gru
no
w)
Kra
mm
er
14
0.1
36
0.0
73
41
41
.2E
nto
mo
nei
sa
lata
Eh
ren
be
rg3
0.1
83
0.0
50
41
––
Eo
lim
na
min
ima
(Gru
no
w)
La
ng
e-B
ert
alo
t1
40
.26
20
.13
93
25
2.9
Eo
lim
na
sub
min
usc
ula
(Ma
ng
uin
)L
an
ge
-Be
rta
lot
&M
etz
elt
in5
2.2
19
0.2
78
02
53
.5E
pit
hem
iaa
dn
ata
(Ku
tzin
g)
Bre
bis
son
11
0.2
76
0.1
19
32
42
.2
Hydrobiologia (2007) 589:141–154 145
123
Ta
ble
4co
nti
nu
ed
Fu
llN
am
eA
uth
or
nT
PT
PT
PT
Pv
an
Da
mT
DI
Au
stri
aT
IO
pti
mu
mT
ole
ran
cein
d_
va
lue
sen
s_v
alu
ein
d_
va
lue
ind
_v
alu
ein
d_
va
lue
Ep
ith
emia
sore
xK
utz
ing
90
.19
40
.08
23
15
2.7
Eu
no
tia
bil
un
ari
s(E
hre
nb
erg
)M
ills
13
0.2
44
0.1
63
32
7–
Eu
no
tia
bil
un
ari
sv
ar.
mu
cop
hil
a(E
hre
nb
erg
)C
lev
e3
0.1
56
0.0
31
41
––
Fa
lla
cia
py
gm
aea
(Ku
tzin
g)
Sti
ckle
&M
an
n3
1.9
93
0.8
12
03
53
.74
.5F
istu
life
rasa
pro
ph
ila
(La
ng
e-B
ert
alo
t&
Lo
nik
)L
an
ge
-Be
rta
lot
30
.24
90
.58
03
35
2.6
Fra
gil
ari
aa
cus
(Eh
ren
be
rg)
Cle
ve
50
.20
70
.04
73
12
–F
rag
ila
ria
cap
uci
na
De
sma
ziere
s1
50
.15
60
.07
94
13
1.8
4.5
Fra
gil
ari
aca
pu
cin
av
ar.
dis
tan
s(G
run
ow
)L
an
ge
-Be
rta
lot
40
.17
60
.12
74
2–
–F
rag
ila
ria
cap
uci
na
va
r.g
raci
lis
(Oe
stru
p)
Hu
ste
dt
13
0.3
02
0.5
25
23
21
.1F
rag
ila
ria
cap
uci
na
va
r.ru
mp
ens
(Ku
tzin
g)
La
ng
e-B
ert
alo
t1
20
.27
50
.11
73
22
1F
rag
ila
ria
cap
uci
na
va
r.v
au
cher
iae
(Ku
tzin
g)
La
ng
e-B
ert
alo
t1
30
.13
90
.08
94
15
1.8
5F
rag
ila
ria
dil
ata
ta(B
reb
isso
n)
La
ng
e-B
ert
alo
t4
0.2
49
0.1
50
32
––
Fra
gil
ari
afa
scic
ula
ta(C
.A.
Ag
ard
h)
La
ng
e-B
ert
alo
t1
40
.16
00
.21
54
2–
3.5
5F
rag
ila
ria
na
na
na
La
ng
e-B
ert
alo
t1
70
.16
80
.08
34
12
1.2
2.1
Fra
gil
ari
ap
inn
ata
Eh
ren
be
rg7
0.1
04
0.0
41
41
–2
.2F
rag
ila
ria
ten
era
(W.S
mit
h)
La
ng
e-B
ert
alo
t3
0.1
82
0.0
61
41
21
2.5
Fra
gil
ari
au
lna
(Nit
zsch
)L
an
ge
-Be
rta
lot
30
0.1
89
0.0
88
41
73
.5F
rag
ila
ria
uln
av
ar.
acu
s(K
utz
ing
)L
an
ge
-Be
rta
lot
22
0.2
32
0.1
79
32
–1
.8G
om
ph
on
ema
acu
min
atu
mE
hre
nb
erg
70
.15
10
.11
24
25
2.5
Go
mp
ho
nem
aa
ng
ust
atu
m(K
utz
ing
)R
ab
en
ho
rst
70
.12
10
.04
44
1–
–G
om
ph
on
ema
an
gu
stu
mA
ga
rdh
90
.08
10
.13
95
21
12
Go
mp
ho
nem
aa
uri
tum
A.B
rau
ne
xK
utz
ing
30
.95
70
.37
51
3–
0.6
2.5
Go
mp
ho
nem
acl
av
atu
mE
hre
nb
erg
17
0.2
88
0.1
09
32
4–
Go
mp
ho
nem
ag
raci
leE
hre
nb
erg
60
.36
60
.69
52
33
–G
om
ph
on
ema
min
utu
mA
ga
rdh
60
.11
80
.04
84
15
2.2
4.5
Go
mp
ho
nem
ao
liv
ace
um
(Ho
rne
ma
nn
)B
reb
isso
n1
70
.15
00
.11
14
25
2.9
4.1
Go
mp
ho
nem
ap
arv
ulu
m(K
utz
ing
)K
utz
ing
38
0.3
78
0.7
14
23
53
.61
.5G
om
ph
on
ema
pse
ud
oa
ug
ur
La
ng
e-B
ert
alo
t4
2.1
22
2.8
43
03
63
.75
Go
mp
ho
nem
ate
rges
tin
um
Fri
cke
40
.18
90
.14
64
22
1.4
4G
om
ph
on
ema
tru
nca
tum
Eh
ren
be
rg1
00
.20
80
.12
83
24
1.9
Ha
ntz
sch
iaa
mp
hio
xy
s(E
hre
nb
erg
)G
run
ow
10
0.2
09
0.1
60
32
73
.6H
ipp
od
on
taca
pit
ata
Me
tze
ltin
&W
itk
ow
si1
10
.37
90
.67
72
34
3.4
5L
emn
ico
lah
un
ga
rica
(Gru
no
w)
Ro
un
d&
Ba
sso
n1
00
.31
80
.23
02
26
3.4
5M
ay
am
aea
ato
mu
s(K
utz
ing
)L
an
ge
-Be
rta
lot
80
.20
50
.19
53
26
2.8
Ma
ya
ma
eaa
tom
us
va
r.p
erm
itis
(Hu
ste
dt)
La
ng
e-B
ert
alo
t1
50
.83
00
.56
91
3–
2.3
Mel
osi
rav
ari
an
sA
ga
rdh
12
0.2
39
0.1
16
32
52
.9N
av
icu
laca
pit
ato
rad
iata
Ge
rma
in6
0.1
73
0.0
83
41
53
.34
.8N
av
icu
laci
nct
a(E
hre
nb
erg
)R
alf
s1
91
.30
40
.92
81
35
3.4
146 Hydrobiologia (2007) 589:141–154
123
Ta
ble
4co
nti
nu
ed
Fu
llN
am
eA
uth
or
nT
PT
PT
PT
Pv
an
Da
mT
DI
Au
stri
aT
IO
pti
mu
mT
ole
ran
cein
d_
va
lue
sen
s_v
alu
ein
d_
va
lue
ind
_v
alu
ein
d_
va
lue
Na
vic
ula
cry
pto
cep
ha
laK
utz
ing
70
.68
70
.73
81
37
3.5
4.9
Na
vic
ula
cry
pto
ten
ella
La
ng
e-B
ert
alo
t3
40
.40
00
.64
92
37
2.3
Na
vic
ula
gre
ga
ria
Do
nk
in4
0.2
14
0.2
20
32
53
.55
Na
vic
ula
lan
ceo
lata
(Ag
ard
h)
Eh
ren
be
rg4
0.2
55
0.3
93
33
53
.55
Na
vic
ula
men
iscu
lus
Sch
um
an
n4
0.2
55
0.0
81
31
50
.6N
av
icu
lao
blo
ng
aK
utz
ing
70
.16
50
.12
64
25
2.7
Na
vic
ula
rad
iosa
Ku
tzin
g1
80
.20
10
.09
83
14
0.6
Na
vic
ula
reic
ha
rdti
an
aL
an
ge
-Be
rta
lot
50
.21
00
.09
53
1–
2.3
4.3
Na
vic
ula
trip
un
cta
ta(O
.F.M
ull
er)
Bo
ry8
0.4
07
0.2
03
12
53
.15
Na
vic
ula
ven
eta
Ku
tzin
g3
70
.19
60
.13
33
25
3.5
Na
vic
ula
dic
tase
min
ulu
m(G
run
ow
)L
an
ge
-Be
rta
lot
40
.14
10
.09
44
15
3.2
Nit
zsc
hia
aci
cula
ris
(Ku
tzin
g)
W.M
.S
mit
h1
40
.19
00
.06
03
15
3.6
5N
itz
sch
iaa
mp
hib
iaG
run
ow
26
0.2
28
0.1
87
32
53
.65
Nit
zsc
hia
cla
usi
iH
an
tzsc
h7
1.4
24
0.7
09
13
52
.35
Nit
zsc
hia
com
mu
nis
Ra
be
nh
ors
t1
23
.67
73
.01
80
35
3.9
Nit
zsc
hia
com
mu
tata
Gru
no
w5
0.4
82
0.3
05
13
–3
.9N
itz
sch
iaco
nst
rict
a(K
utz
ing
)R
alf
s1
21
.26
01
.09
51
35
3.5
5N
itz
sch
iad
ebil
is(A
rno
tt)
Gru
no
w3
0.1
18
0.0
98
41
–3
.95
Nit
zsc
hia
dis
sip
ata
(Ku
tzin
g)
Gru
no
w1
90
.16
20
.14
34
24
2.9
4.7
Nit
zsc
hia
fon
tico
laG
run
ow
90
.18
70
.04
24
14
–4
.5N
itz
sch
iafr
ust
ulu
m(K
utz
ing
)G
run
ow
25
0.4
86
0.5
45
13
53
.35
Nit
zsc
hia
gra
cili
sH
an
tzsc
h1
20
.19
60
.16
13
23
2.5
Nit
zsc
hia
inco
nsp
icu
aG
run
ow
15
0.4
29
0.4
35
13
53
.15
Nit
zsc
hia
lin
eari
s(A
ga
rdh
)W
.M.
Sm
ith
90
.28
70
.18
63
24
3.4
5N
itz
sch
iap
ale
a(K
utz
ing
)W
.S
mit
h3
10
.33
90
.60
92
36
3.3
Nit
zsc
hia
pa
lea
cea
(Gru
no
w)
Gru
no
w3
70
.19
20
.19
93
25
2.3
5N
itz
sch
iap
usi
lla
(Ku
tzin
g)
Gru
no
w4
0.2
16
0.0
51
31
72
.75
Nit
zsc
hia
rect
aH
an
tzsc
h4
0.2
10
0.1
08
32
73
Nit
zsc
hia
sup
rali
tore
aL
an
ge
-Be
rta
lot
18
1.0
44
1.3
33
13
52
.95
Nit
zsc
hia
ver
mic
ula
ris
(Ku
tzin
g)
Ha
ntz
sch
30
.14
30
.02
44
17
–N
itz
sch
iav
itre
aN
orm
an
50
.33
80
.29
62
25
–P
inn
ula
ria
bre
bis
son
ii(K
utz
ing
)R
ab
en
ho
rst
40
.31
70
.25
54
25
–P
inn
ula
ria
div
erg
enti
ssim
a(G
run
ow
)C
lev
e6
0.1
58
0.1
09
41
10
.6P
inn
ula
ria
mic
rost
au
ron
(Eh
ren
be
rg)
Cle
ve
30
.15
80
.08
92
27
–2
Pin
nu
lari
av
irid
is(N
itzs
ch)
Eh
ren
be
rg4
0.2
11
0.0
62
31
71
.3
Hydrobiologia (2007) 589:141–154 147
123
Ta
ble
4co
nti
nu
ed
Fu
llN
am
eA
uth
or
nT
PT
PT
PT
Pv
an
Da
mT
DI
Au
stri
aT
IO
pti
mu
mT
ole
ran
cein
d_
va
lue
sen
s_v
alu
ein
d_
va
lue
ind
_v
alu
ein
d_
va
lue
Pla
no
thid
ium
freq
uen
tiss
imu
m(L
an
ge
-Be
rta
lot)
La
ng
e-B
ert
alo
t8
0.2
38
0.1
28
31
72
.8P
lan
oth
idiu
mla
nce
ola
tum
(Bre
bis
son
ex
Ku
tzin
g)
La
ng
e-B
ert
alo
t9
0.2
94
0.0
94
32
5–
Pse
ud
ost
au
rosi
rab
rev
istr
iata
(Gru
no
w)
Wil
lia
ms
&R
ou
nd
40
.07
50
.01
15
17
3R
ho
ico
sph
enia
ab
bre
via
ta(C
.Ag
ard
h)
La
ng
e-B
ert
alo
t2
40
.15
60
.08
54
15
2.9
4.5
Rh
op
alo
dia
gib
ba
(Eh
ren
be
rg)
O.
Mu
lle
r1
30
.15
90
.07
24
15
2.7
Sel
lap
ho
rala
evis
sim
a(K
utz
ing
)D
.G.
Ma
nn
30
.11
10
.06
94
13
1.1
2.5
Sta
uro
nei
sk
rieg
eri
Pa
tric
k4
0.1
89
0.0
92
41
43
.34
Sta
uro
sira
con
stru
ens
Eh
ren
be
rg1
10
.19
10
.11
03
24
2.3
Ste
ph
an
od
iscu
sh
an
tzsc
hii
Gru
no
w1
00
.17
80
.14
54
26
–S
tep
ha
no
dis
cus
inv
isit
atu
sH
on
he
tH
ell
erm
an
n1
00
.22
60
.13
43
2–
–S
tep
ha
no
dis
cus
pa
rvu
sS
toe
rme
re
tH
ak
an
sso
n5
0.2
52
0.1
65
32
6–
Su
rire
lla
bre
bis
son
iiK
ram
me
r&
La
ng
e-B
ert
alo
t7
1.1
05
0.6
13
13
–3
.65
Su
rire
lla
ov
ali
sB
reb
isso
n4
1.7
67
0.4
68
03
5–
Su
rire
lla
pei
son
isP
an
tocs
ek
31
.06
81
.35
51
3–
–T
ryb
lio
nel
lah
un
ga
rica
(Gru
no
w)
D.G
.Ma
nn
41
.43
61
.19
01
35
3.9
148 Hydrobiologia (2007) 589:141–154
123
were 20 new records in comparison to the list
of trophic index by van Dam et al. (1994).
Furthermore, 73% of the species indicate eutro-
phic or hypereutrophic conditions according van
Dam indicator values (van Dam, 1994). Hofmann
(1999) in her TI provided indicator values for
only 50 species in our data set.
Comparison of the TDIL index values
(2.5 ± 0.9) to two other well-known indices
(IPS and IBD; Coste in Cemagref, 1982; Lenoir
& Coste, 1996) showed that the IPS values varied
in the widest range (10.4 ± 4.4); the variability
range of the IBD was smaller (9.7 ± 3.5).
According to the TDIL, the ecological status of
4 lakes were in excellent, 25 in good, 21 in
medium, 21 in tolerable and 12 in bad. Each
tolerable or bad sampling site was shallow, saline
lake with naturally high TP content. The assessed
trophic status by the three indices are shown in
each sampling site on Fig. 2.
0.0
1.0
2.0
3.0
4.0
5.0
Ágas06
Akas06
Alcs06
Atka06
Bába06
BA
L148B
AL149
BA
L150B
AL151
BA
L152B
AL153
BA
L154B
AL155
BalB
D1
BalB
D5
BalB
É3
Bibi06
BO
D187
BO
R180
Bödd06
Büdö06
BüdP
06C
iba06C
SA
188C
sár06C
SE
156C
SI157
Cson06
EG
Y158
EG
Y183
FA
D159
Fegy06
Fehé06
FE
R160
FU
L397F
ülö06
HA
L161H
AM
189H
att06
sampling sites
ind
exva
lue
of
IPS
/4,I
BD
/4an
dT
DIL
IPS/4
IBD/4
TDIL
(a)
0.0
1.0
2.0
3.0
4.0
5.0
KA
R162
Kard06
KE
L163K
ele06K
EN
164K
isr06K
OL165
Kolo06
Kond06
KU
N166
Kurj06
LAZ
167Lázá06LIP
168M
áma06
NA
G169
Nyék06
OR
G398
Orgo06
OS
Z170
Pere06
PE
T171
PIR
172P
irt06S
árk06S
zap06S
zar06S
zarv06S
zelA06
SzelB
06S
zív06S
ZO
184T
IS173
TO
L174
Újla06
VE
L176V
EL177
VE
L178V
örö06Z
AB
179Z
abs06
sampling sites
inde
xva
lue
ofIP
S/4
,IB
D/4
and
TDIL
IPS/4
IBD/4
TDIL
BAD
TOLERABLE
MEDIUM
GOOD
EXCELLENT
BAD
TOLERABLE
MEDIUM
GOOD
EXCELLENT
(b)
Fig. 2 The values of IPS, TDI and TDIL in the different water classes
Hydrobiologia (2007) 589:141–154 149
123
Discussion
On a global scale, the eutrophication is the most
recognized kind of human impact in lakes
(Harper, 1992). Although human activities have
been threatening the lakes from point and diffuse
sources since a long time, consequences became
notorious only the last 100 years (Fritz, 1989).
Phosphorus accumulation typically occurs after
human pollution and only few cases report about
natural reasons (Hickman et al., 1990).
According to OECD (1982) system, all but one
lake included in this study belonged to the
eu-hypertrophic category although far not each
received pollutants of human origin. It is a good
indication that the OECD boundary levels are
not suitable for ecological status assessment of
the Hungarian lakes what agrees with observa-
tions (Kitner & Poulıckova, 2003) in lakes in the
Czech Republic.
The standardization of sampling method is
essential in biomonitoring. The most important
question is the type of the substratum and the
sampling time. In productive water bodies, espe-
cially in foodplain lakes (Kitner & Poulıckova,
2003) where stones – which offer abiotic surfaces
for algae – are absent. Regardless of lack of
stones, epiphytic samples should be collected
from the similar substrata to ensure the compa-
rability between water bodies (King et al., 2006).
The recommended type specific natural substra-
tum (Schaumburg et al., 2004), young reed
(Phragmites australis), the most suitable substra-
tum for the assessment of the lakes’ trophic status
(Poulıckova et al.; 2004), can be commonly found
in the littoral region of shallow lakes (Blanco
et al., 2004) and appropriate for monitoring
purposes (Acs et al., 2005). The need for using
young reed temporarily limits sampling to spring
(from April to the beginning of July) and also
excludes further complications coming from sea-
sonal and successional changes in the composition
epiphytic assemblages (Castenholz, 1960; Hof-
mann, 1994; Muller, 1994; King et al., 2006)
during the year. Sampling from young reed
represents the early successional phase respond-
ing the actual chemical parameters (Poulıckova
et al., 2004) and frequency of dead diatoms from
the previous seasons (Round, 1991) or diatom
cells from the phytoplankton can also be mini-
mized. For the above reasons, and also to average
variations of vertical distribution (Hoagland &
Peterson, 1990), 5–7 stem sections, 5–20 cm below
the water surface are preferred for sampling
(Buczko & Acs, 1996/1997; Acs et al., 2005) in
shallow lakes. In the case of sampling series the
samples shoud be taken within three weeks.
The methods of bioindication cannot replace
physical or chemical analyses, but they comple-
ment them (Kitner & Poulıckova, 2003). If so, real
trophic state can be better assessed by development
of an adaquate bioindicator method (suitable
index) than by the chemical analyses (Blanco et al.,
2004; Poulıckova et al., 2004) and it also harmo-
nizes with the recommendations of the EU WFD.
Instead of the non-unimodal and asymmetrical
response of the species to nutrients, symmetrical
response can be applied using WA method with a
single indicator value, when the aim is to develop
a regional index (Potapova et al., 2004). Using the
WA technique, a robust and accurate TP transfer
function was developed spanning from 0.01 to
5.72 mg l–1 TP. In our Hungarian sample set,
diatom assemblages occurred along wider TP
gradient than the range of other diatom-based
models (Anderson et al., 1993: 25–800 lg l–1; Hall
& Smoll, 1992: 5–28 lg l–1). In the TP model the
correlation was strong (r2 = 0.96, RMSE = 0.17)
using weighted averaging without tolerance
downweighting regression (WAtol) with inverse
deshrinking. Bennion (1994) found a weaker
correlation and similar error in her TP transfer
function (r2 = 0.79, RMSE = 0.16) for shallow,
eutrophic ponds in southeast England. The error
of the model cannot be considerably reduced as a
consequence of the complexity of eutrophic lakes,
particulary if they are shallow and highly produc-
tive (Bennion, 1994).
The TP model was suitable for calculating the
optima and tolerances of the species and estab-
lishing the indicator values of the species at
different TP levels. Indicator values of 127 species
provide the basis of the developed TDIL index.
These species are the most frequent ones in
Hungarian lakes.
Comparing the species list of the TDIL to that
of TDI Austria (Rott et al., 1999), 31 species were
absent in our material, mostly centric diatoms.
150 Hydrobiologia (2007) 589:141–154
123
The consequence of the absence of centric
diatoms indicating nutrient-rich conditions is that
the TDI Austria index can underestimate (opti-
mistic) the trophic status of lakes (Kitner &
Poulıckova, 2003). The TDI Austria developed
for rivers and was used all over Europe (Rott
et al., 2003). It proved to be applicable for
samples from reed stems with actual and average
TP for perialpine lakes, since character of these
lakes was similar to that of the alpine stream
ecosystems (Poulıckova et al.; 2004). Further-
more, 73% of the species in our material were
eutrophic or hypertrophic according to the van
Dam trophic index (van Dam et al., 1994) which
would predict unacceptably high trophic level
similarly to the OECD system. Although in Czech
fishponds the van Dam’s index was appropriate to
recognise extremes (clear and dirty) of lake
environments (Kitner & Poulıckova, 2003), for
the Hungarian lakes it was not suggested (van
Dam et al., 2005). The TI (Hofmann, 1999) was
well applicable in Germany (Schaumburg et al.,
2004) but it includes indicator value for only 50
species of the 127 most frequent diatom species in
the Hungarian shallow lakes. Therefore, the TI
would carry a considerable uncertainity if applied
to assess lake trophic status in Hungary.
Blanco et al. (2004) and Acs et al. (2005)
tested some existing diatom indices. Although
these studies gave satisfactory results, they were
limited by some factors: the analyses of the
Spanish study (Blanco et al., 2004) involved only
6 lake samples while the Hungarian one (Acs
et al., 2005) was restricted to only one single lake
with minor variations of the TP content.
The index values provided by different indices
may differ significantly because they apply differ-
ent indicator values for some, sometimes for
many, species. This is a fundamental problem,
which originates from several different sources:
(a) the published indicator values derive from
other kinds of ecosystem (for example lentic/
lotic) (b) extension of databases (number of the
samples) is different; (c) different taxonomic
approach (together with identification problems)
and (d) different phycogeographical regions.
These examples support the recommendation by
Poulıckova et al. (2004), that each region needs
own ecological calibration.
Water quality management and lake restora-
tion emphasized the necessity of development of
an adaquate diatom index to assess the ecological
status of lakes and there is strong suggestion by
the WFD to a 5 level ecological classification.
Accordingly, the absolute value (optima) of the
TP for the individual species were expressed on a
convenient scale, ranging from 0 to 5. According
to the TDIL, the ecological status of 4 lakes were
in excellent, 25 in good, 21 in medium, 21 in
tolerable and 12 in bad status, which appears
representative for the status of the Hungarian
lakes. Most of the sampling sites in bad or poor
status by the TDIL were shallow, saline lakes with
naturally high TP content and high conductivity.
In these cases further modifactions will be neces-
sary to assess the real ecological status (Stenger-
Kovacs et al., in prep.).
According to the recommendation on basis of
an extended study in Hungary (ECOSURV, van
Dam et al., 2005), a suitable index was necessary
to develop because the existing indices did not
perform satisfactorily in Hungarian freshwater
lakes. The TDIL developed in Hungary is appli-
cable for lake types, which are lowland, shallow
(z < 4 m), permanent or temporary with calcar-
eous or calcareous-saline hydrogeochemistry
(conductivity < 3000 lS cm–1), where the con-
centration and variation of nutrients are higher
than in other standing water types, therefore the
traditional classification cannot be applied. This
problem was recognized already in the Mediter-
ranean region (Blanco et al., 2004), which pro-
vides a good opportunity for testing further the
TDIL in other countries.
Acknowledgements We thank Ms. Ildiko Kiralykuti forher help careful technical assistance in chemical analysesand Ms. Kata Kovacs for her help in field samplings. Thisproject was supported by the Ministry of Environment andWater (ECOSURV) and by the National Foaaundationfor Research and Technology (NKFP 3B/022/2004).
References
Alefs, J., J. Muller & S. Wunsam, 1996. Die Rekonstruk-tion der epilimnischen Phosphorkonzentrationen imAmmersee seit 1958. GWF Wasser Abwasser 137/8:443–447.
Hydrobiologia (2007) 589:141–154 151
123
Acs, E., N. M. Reskone, K. Szabo, Gy. Taba & K. T. Kiss,2005. Application of benthic diatoms in water qualitymonitoring of lake Velence – Recommendations andassignments. Acta Botanica Hungarica 47: 211–223.
Anderson, N. J., B. Rippey & C. E. Gibson, 1993. Acomparison of sedimentary and diatom-inferredphosphorous profiles: implications for defining pre-disturbance nutrient conditions. Hydrobiologia 253:357–366.
Bennion, H., 1994. A diatom-phosphorous transfer func-tion for shallow, eutrophic pond sin southeast Eng-land. Hydrobiologia 275/276: 391–410.
Besse-Lototskaya, A., P. F. M. Verdonschot & J. A. Sin-keldam, 2006. Uncertainty in diatom assessment:Sampling, identification and counting variation. Hyd-robiologia 566: 244–278.
Birks, H. J. B., J. M. Line, S. Juggins, A. C. Stevenson & C.J. F. ter Braak, 1990. Diatoms and pH reconstruction.Philosophical Transactions for the Royal SocietyLondon 327: 263–278.
Blanco, S., L. Ector & E. Becares, 2004. Epiphytic diatomsas water quality indicators in Spanish shallow lakes.Vie Milieu 54(2–3): 71–79.
Brabecz, K. & K. Szoszkiewicz, 2006. Macrophytes anddiatoms – major results and conclusions from theSTAR project. Hydrobiologia 566: 175–178.
Buczko, K. & E. Acs, 1996/1997. Zonation of periphyticalgae in two Hungarian shallow Lakes (Lake Velenceand Fert}o). Acta Botanica Hungarica 40: 21–34.
Castenholz, R. W., 1960. Seasonal changes in the attachedalgae of freshwater and saline lakes int he lower GrandCoulee, Washington. Limnology and Oceanography 5:1–28.
Cemagref, 1982. Etude des Methodes Biologiques Quan-titative D’appreciation de la Qualite des Eaux. Rap-port Q.E. Lyon-A.F. Bassin Rhone-Mediterranee-Corse, Lyon, France.
Comte, K., S. Fayolle & M. Roux, 2005. Quantitative andqualitative variability of epiphytic algae on one Api-aceae (Apium nodiflorum L.) in a karstic river(Southeast France). Hydrobiologa 543: 37–53.
Dell’Uomo, A. 1996. L’indice diatomico di eutrofizzazi-one/polluzione (EPI-D) nel monitoraggio delle acquecorrenti. Linee guida.
Descy, J.-P. & M. Coste, 1991. A test of methods forassessing water quality based on diatoms. Verhandl-ungen der Internationale Vereinigung fur theoreti-sche und Angewandte Limnologie 24: 2112–2116.
EC Parliament and Council, 2000. Directive of the Euro-pean Parliament and of the Council 2000/60/ECEstablishing a Framework for community action inthe field of water policy. European Commission PE-CONS 3639/1/100 Rev 1, Luxembourg.
Fritz, S. C., 1989. Lake development and limnological re-sponse to prehistoric and historic land-use in Diss,Norfolk. Journal of Ecology 77: 182–202.
Furse, M., D. Hering, O. Moog, P. Verdonschot, R. K.Johnson, K. Brabec, K. Gritzalis, A. Buffagni, P.Pinto, N. Friberg, J. Murray-Bligh, J. Kokes, R. Alber,
P. Usseglio-Polatera, P. Haase, R. Sweeting, B. Bis, K.Szoszkiewicz, H. Soszka, G. Springe, F. Sporka & F.Krno, 2006. The STAR project: context, oblectivesand approaches. Hydrobiologia 566: 3–29.
Hall, R. I. & J. P. Smol, 1992. A weighted-averaging regres-sion and calibration model for inferring total phospho-rous concentration from diatoms in British Columbia(Canada) lakes. Freshwater Biology 27: 417–434.
Harper, D., 1992. Eutrophication of Freshwaters. Chap-mann Hall, London, UK, pp. 327.
Hickman, M., C. E. Schweger & D. M. Klarer, 1990. Bap-tise Lake, Alberta-a late Holocene history of changesin a lake and its catchments in the southern borealforest. Journal of Paleolimnology 4: 253–267.
Hoagland, D. & Ch. G. Peterson, 1990. Effect of light andwave disturbance on vertical zonation of attachedmicroalgae in large reservoir. Journal of Phycology26: 450–457.
Hofmann, G., 1994. Aufwuchs-Diatomeen in Seen undihre Eignung als Indikatoren der Trophie. BibliothecaDiatomologica 30. pp. 241.
Hofmann, G., 1999. Trophiebewertung von Seen anhandvon Aufwuchsdiatomeen. In Tumpling, W. & G.Friedrich (eds), Biologische Gewasseruntersuchung 2.pp. 319–333.
Istvanovics, V., A. Clement, L. Somlyody, A. Specziar,L. G.-Toth & J. Padisak, 2007. Updating water qualitytargets for shallow Lake Balaton (Hungary), recov-ering from eutrophication. Hydrobiologia 581: 305–318.
Kelly, M. G., 1998. Use of trophic diatom index to monitoreutrophication in rivers. Water Research 36: 236–242.
King, L., G. Clarke, H. Bennion, M. Kelly & M. Yallop,2006. Recommendations for sampling littoral diatomsin lakes for ecological status assessments. Journal ofApplied Phycology 18: 15–25.
Kitner, M. & A. Poulıckova, 2003. Littoral diatoms asindicators for eutrophication of shallow lakes. Hyd-robiologia 506–509: 519–524.
Kovacs, C., M. Kahlert & J. Padisak, 2006. Benthic diatomcommunities along pH and TP gradients in Hungarianand Swedish streams. Journal of Applied Phycology18: 105–117.
Krammer, K., 2002. Diatoms of Europe. Diatoms of theEuropean Inland Waters and Comparable HabitatsVol. 1–4. A.R.G. Gantner Verlag K.G., Ruggel.
Krammer, K. & H. Lange-Bertalot, 1991–2000. Bacillari-ophyceae 1.-4. Teil: Naviculaceae. In: Pascher, A.(ed.), Susswasserflora von Mitteleuropa. Band 2/1–4.Gustav Fischer Verlag, Heidelberg, Berlin.
Lange-Bertalot, H., 1995–2002. Iconographia Diatomo-logica. Annotated Diatom Micrographs Vol. 1.-9.Koeltz Scientific Books, Konigstein.
Lenoir, A. & M. Coste, 1996. Development of a practicaldiatomic index of overall water quality applicable tothe French National Water Board Network. In Rott,E. (ed.), 2nd Workshop on Algae for MonitoringRivers, Innsbruck 18–19 Sept. 95, Studia Student.G.m.b.H., Innsbruck.
152 Hydrobiologia (2007) 589:141–154
123
Muller, U., 1994. Seasonal development of epiphyticalgae on Phragmites australis in an eutrophic lake.Archiv fur Hydrobiologie 129: 273–292.
OECD, 1982. Eutrophicationof waters. Monitoring,assessment and control. Paris, 155 pp.
Padisak, J., 2005. Altalanos Limnologia [General Lim-nology]. ELTE Eotvos Kiado, Budapest, pp. 310. [inHungarian].
Padisak, J., G. Borics, I. Grigorszky & E. Soroczki-Pinter,2006. Use of phytoplankton assemblages for moni-toring ecological status of lakes within the WaterFramework Directive: the assemblage index. Hydro-biologia 553: 1–14.
Persson, J., H. Hansson & I. Renberg, 2006. The StoneBrusher, a new sampler for submerged epilithicmaterial in shallow streams and lakes. Hydrobiologia560: 385–392.
Potapova, M. G., D. F. Charles, K. C. Ponader & D. M.Winter, 2004. Quantifying species indicator values fortrophic diatom indices: a comparison of approaches.Hydrobiologia 517: 25–41.
Pote, D. H. & T. C. Daniel, 2000. Analyzing for TP andTDP in water samples. In Pierzynski, G. M. (ed.),Methods of phosphorous analysis for soils, sediments,residuals and waters. Kansas State University, Man-hattan, 94–97.
Poulıckova, A., M. Duchoslav & M. Dokulil, 2004. Littoraldiatom assemblages as indicators of lake trophicstatus: A casestudy from perialpine lakes in Austria.European Journal of Phycology 39: 143–152.
Prygiel, J. & M. Coste, 2000. Guide Methodologique pourla mise en oeuvre de l’Indice Biologique Diatomees.NF T 90–354. Etude Agences de l’Eau-CemagrefBordeaux, March 2000, Agences de l’Eau: 134
Reavie, E. D., J. P. Smol & P. J. Dillon, 2002. Inferringlong-term nutrient changes in southeastern Ontariolakes: comparing paleolimnological and mass-balancemodels. Hydrobiologia 481: 61–74.
Reckhow, K. H. & S. C. Chapra, 1983. Engineering Ap-proaches for Lake Managment. Volume 1: DataAnalysis and Empirical Modeling. Butterworth, Bos-ton, pp. 340.
Rott, E., E. Pipp, P. Pfister, H. van Dam, K. Ortler, N.Binder & K. Pall, 1999. Indikationslisten fur Au-fwuchsalgen in osterreichischen Fliessgewassern. Teil2: Trophieindikation (sowie geochemische Praferen-zen; taxonomische und toxikologische Anmerkun-gen). Wasserwirtschaftskataster herasgegeben vomBundesministerium f. Land- u.Forstwirtschaft, Wien.ISBN 3-85 174-25-4. pp. 248
Rott, E., E. Pipp & P. Pfister, 2003. Diatom methods devel-oped for river quality assessment in Austria and a cross-check against numerical trophic indication methodsused in Europe. Algological Studies 110: 91–115.
Round, F. E., 1991. Use of diatoms for monitoring rivers. InWhitton, B. A., E. Rott, & G. Friedrich (eds), Use ofAlgae for Monitoring Rivers. , Dusseldorf, pp 25–32.
Rumeau, A. & M. Coste, 1988. Initiation a la systematiquedes Diatomees d’eau douce pour l’utilisation pratique
d’un indice diatomique generique. Bulletin Francaisde la Peche et de la Pisciculture 309: 1–69.
Sandin, L. & P. F. M. Verdonschot, 2006. Stream and rivertypologies – major results and conclusions from theSTAR project. Hydrobiologia 566: 33–37.
Schaumburg, J., C. Schranz, G. Hofmann, D. Stelzer, S.Schneider & U. Schmedtje, 2004. Macrophytes andphytobenthos as indicators of ecological status inGerman lakes – a contribution to the implementationof the Water Framework Directive. Limnologica 34:302–314.
Schonfelder, I. & J. Gelbrecht, 2002. Relationship betweenlittoral diatoms and their chemical environment innortheastern German lakes and rivers. Journal ofPhycology 36: 66–82.
Shannon, C. E. & W. Wiener, 1949. The Mathematicaltheory of Communication. University of Illinois Press,Urbana, 125.
Soininen, J. & P. Niemela, 2002. Inferring phosphoruslevels of rivers from benthic diatoms using weightedaveraging. Archiv fur Hydrobiologie 154: 1–18.
Springe, G., L. Sandin, A. Briede & A. Skuja, 2006. Bio-logical quality metrics: their variability and appropriatescale for assessing streams. Hydrobiologia 566: 153–172.
Stenger-Kovacs, C., J. Padisak, E. Hajnal & K. Buczko,in prep. Assessment of ecological status of naturalinland saline (NaCO3, NaHCO3) lakes on basis ofperiphytic diatoms with modified index values ofthe Trophic Diatom Index for Lakes (TDIL).Hydrobiologia.
Stoermer, E. F. & J. P. Smol, 2001. The Diatoms: Appli-cation for the Environmental and Earth Sciences.Cambridge Universiy Press, UK, pp. 469.
Szilagyi, F., J. Padisak, E. Szalma & C. Kovacs, 2004.Termeszetes tavak tipologiajanak es specifikus refer-encia viszonyainak jellemzese. Zarojelentes. pp. 74.Characterization of the typology and specific refer-ence status of natural lakes. Final report. [in Hun-garian].
Taylor, D., C. Dalton, M. Leira, P. Jordan, G. Chen, L.Leon-Vintro, K. Irvine, H. Bennion & T. Nolan, 2006.Recent histories of six productive lakes in the IrishEcoregion based on multiproxy paleolimnologicalevidence. Hydrobiologia 571: 237–259.
ter Braak, C. J. F. & H. van Dam, 1989. Inferring pH fromdiatoms: a comparison of old and new calibrationmethods. Hydrobiologia 178: 209–223.
van Dam, H., A. Mertens & J. Sinkeldam, 1994. A codedchecklist and ecological indicator values of freshwaterdiatoms and ecological indicator values of freshwaterdiatoms from Netherland. Netherland Journal ofAquatic Ecology 28: 117–133.
van Dam, H., J. Padisak & C. Kovacs, 2005. ECOSURVBQE Report Phytobenthos. Ministry of Environmentand Water, Hungary.
Vollenweider, R. A., 1989. Global problems of eutrophi-cation and its control. In Salanki, J. & S. Herodek(eds.), Conservation and Managment of Lakes.Akademiai Kiado, Budapest, 19–41.
Hydrobiologia (2007) 589:141–154 153
123
Wetzel, R. G., 1990. Land-water interfaces: metabolic andlimnological regulators. Verhandlungen der interna-tionale Vereinigung fur theoretische und angewandte.Limnologie 24: 6–24.
Wu, J. -T., 1999. A generic index of diatom assemblages asbioindicator of pollution in the Keelung River ofTaiwan. Hydrobiologia 397: 79–87.
Zelinka, M. & P. Marvan, 1961. Zur Prazisirung der bio-logischen Klassifikation der Reinheit fliessenderGewasser. Archiv fur Hydrobiologie 57: 389–407http://www.campus.ncl.ac.uk/staff/Stephen.Juggins.
154 Hydrobiologia (2007) 589:141–154
123
