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Identification and Measure of Hydromorphological Degradation in Central European Lowland Streams

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The objective of the current study was to identify hydromorphological variables that are suitable to define and describe hydromorphological degradation. Stream type-specific and spatial scale-dependent multivariate analysis (Non-metric Multidimensional Scaling, NMS) of 106 hydromorphological variables derived from 275 samples at 147 sites and indicator value analysis (IndVal) resulted in the identification of key factors describing hydromorphological differences in Central European lowland streams. Sample sites represented six European stream types from Sweden (1 stream type), The Netherlands (2 stream types), and Germany (3 stream types). The four large-scale hydro(geo)morphological variables: catchment size, geology (`% moraines', `% alluvial deposits'), and natural land use (`% natural forest') explained inter-stream type differences best. On the smaller site scale, riparian vegetation described inter-stream type differences best. On catchment scale, `% natural forest', and `agricultural land use' illustrated inter-stream type hydromorphological degradation of all six stream types very well. Four site related variables (`% wooded riparian vegetation', `% shading', `average stream width', and `% macrolithal (cobbles, 20 to 40 cm long) account for hydromorphological degradation on the smaller reach-scale. An analysis of indicator variables restricted to German stream types only resulted in four factors, namely `% xylal' (tree trunks, branches, roots, etc.), `no of debris dams >0.3 m3', `no of logs >10 cm ∅', and `% fixed banks' as important descriptors of hydromorphological degradation. Intra-stream type hydromorphological degradation is illustrated for `mid-sized sand bottom streams in the German lowlands'. For this stream type, a clear gradient of degradation was revealed, and 25 variables were identified to entirely characterize reference conditions and degradation. The variables that described the degradation gradient best were combined to the new German Structure Index (GSI), which can be implemented to continuously measure hydromorphological degradation.
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Hydrobiologia 516: 69–90, 2004.
D. Hering, P.F.M. Verdonschot, O. Moog & L. Sandin (eds), Integrated Assessment of Running Waters in Europe.
© 2004 Kluwer Academic Publishers. Printed in the Netherlands.
69
Identification and measure of hydromorphological degradation in Central
European lowland streams
Christian K. Feld
Institute of Ecology, Department of Hydrobiology, University of Essen, Universitätsstraße 5, D-45117 Essen,
Germany
Tel.: +49(0)201 183-4390. Fax: +49(0)201 183-4442. E-mail: christian.feld@uni-essen.de
www.uni-essen.de/hydrobiology
Key words: assessment, hydromorphology, reference condition, degradation, German Structure Index, lowland
rivers
Abstract
The objective of the current study was to identify hydromorphological variables that are suitable to define and
describe hydromorphological degradation. Stream type-specific and spatial scale-dependent multivariate analysis
(Non-metric Multidimensional Scaling, NMS) of 106 hydromorphological variables derived from 275 samples
at 147 sites and indicator value analysis (IndVal) resulted in the identification of key factors describing hydro-
morphological differences in Central European lowland streams. Sample sites represented six European stream
types from Sweden (1 stream type), The Netherlands (2 stream types), and Germany (3 stream types). The four
large-scale hydro(geo)morphological variables: catchment size, geology (‘% moraines’, ‘% alluvial deposits’), and
natural land use (‘% natural forest’) explained inter-stream type differences best. On the smaller site scale, riparian
vegetation described inter-stream type differences best.
On catchment scale, ‘% natural forest’, and ‘agricultural land use’ illustrated inter-stream type hydromorpho-
logical degradation of all six stream types very well. Four site related variables (‘% wooded riparian vegetation’,
‘% shading’, ‘average stream width’, and ‘% macrolithal (cobbles, 20 to 40 cm long) account for hydromorpholo-
gical degradation on the smaller reach-scale. An analysis of indicator variables restricted to German stream types
only resulted in four factors, namely ‘% xylal’ (tree trunks, branches, roots, etc.), ‘no of debris dams >0.3 m3’,
‘no of logs >10 cm ’, and ‘% fixed banks’ as important descriptors of hydromorphological degradation.
Intra-stream type hydromorphological degradation is illustrated for ‘mid-sized sand bottom streams in the German
lowlands’. For this stream type, a clear gradient of degradation was revealed, and 25 variables were identified to
entirely characterize reference conditions and degradation. The variables that described the degradation gradient
best were combined to the new German Structure Index (GSI), which can be implemented to continuously measure
hydromorphological degradation.
Introduction
Since the introduction of the European Water Frame-
work Directive (WFD) in 2000, physical habitat evalu-
ation has a major focus in Europe (Raven et al., 2002).
In particular, hydromorphological degradation has be-
come an important stressor affecting the instream biota
in many Central European stream types (Feld et al.,
2002; Lorenz et al., 2004; Ofenböck et al., 2004;
Raven et al., 2002). In this context, saprobic indices
have a restricted applicability in stream assessment,
since they aim to detect the single stress factor i.e.,
organic pollution only. They are not capable of assess-
ing other sources of impairment. The WFD, therefore,
necessitate the development of new tools to assess the
ecological quality of streams and rivers (Hering et al.,
2004), including hydromorphology. In order to fulfil
the demands of the WFD, stream and river assess-
ment must be changed fundamentally from a single
index system, such as e.g., Saprobic systems (Czech
70
Saprobic Index: CSN, 1998; German ‘Saprobienin-
dex’: DEV, 1992; see also Rolauffs et al., 2004) to a
more ‘holistic’ approach. The latter refers to multiple
indices, capable of assessing the impact of differ-
ent habitat pressures on both, the instream biota and
the physical habitat. Running water ecosystems are
controlled mainly by geological, hydrological, mor-
phological, and water chemistry attributes (Franquet
et al., 1995; Hildrew, 1996; Richards et al., 1996).
The physical habitat controls riverine biota on both
temporal and spatial scale (Allan et al., 1997; Beisel
et al., 1998a, b; Davies et al., 2000 ; Sponseller
et al., 2001). In particular, the scale-dependent relation
between hydromorphology and the macroinvertebrate
community in streams and rivers has been widely dis-
cussed (e.g., Rabeni, 2000; Sponseller et al., 2001;
Statzner et al., 2001). Some authors emphasize the
role of large-scale variables, such as catchment geo-
logy, while others state sub-catchment, such as land
use, and reach-scale habitat attributes, such as riparian
buffer width, to mainly influence instream communit-
ies. Moreover, on a finer spatial scale, the influence
of single hydromorphological features, for example
woody debris or riparian vegetation, on instream biota
is well-known and widely discussed (Dudley & An-
derson, 1982; Benke et al., 1985; Hoffmann & Hering,
2000; Richards et al., 1996).
Several methods to measure habitat quality and
habitat degradation exist (e.g., Agence de l’Eau Rhin-
Meuse, 1996 for France; Barbour et al., 1999 for the
USA; LAWA, 2000 for Germany; Raven et al., 1998,
2002 for the U.K.). But Raven et al. (2002) have also
shown that the cited methods lead to different results
due to the different definition of ‘near-natural land use’
in the French and German protocol. Moreover, the lack
of stream type specifity, as is, for example, the case for
the German ‘Strukturgütekartierung’, requires a revi-
sion of existing methods to fulfil the demands of the
WFD. Due to the complex relationship between hy-
dromorphological attributes and instream biota, it still
remains controversial how to define habitat degrada-
tion and on what spatial scale(s). Hydromorphological
assessment within the EU-funded research project
‘The development and testing of an integrated assess-
ment system for the ecological quality of streams and
rivers throughout Europe using benthic macroinver-
tebrates’ (AQEM) generally followed the approach to
compare test site characteristics with specific refer-
ence characteristics per stream type (Barbour et al.,
1999; Raven et al., 2002). Therefore, stream type-
specific hydromorphological reference conditions had
to be defined prior to assessment. This step demands
knowledge on the hydromorphological conditions oc-
curring under undisturbed conditions (high status)
as a basis for the definition of four hydromorpho-
logical degradation classes (good, moderate, poor,
bad status) as demanded by the five-class classific-
ation of the WFD. Three major questions arise: (i)
what is physical habitat (hydromorphological)degrad-
ation? (ii) which spatial scale is appropriate to describe
hydromorphological quality? (iii) which groups of
hydromorphological variables (e.g., land use, hydro-
graph, mesohabitat, riparian vegetation) are suited
and minimally necessary to measure the impact of
hydromorphological degradation?
In this study, I present results from stream type-
specific, as well as scale-dependent, statistical analysis
of hydromorphological characteristics of six stream
types in ecoregions 13 and 14 of Europe (according
to Illies, 1978). The aim was, (i) to analyse spatial
scale-dependent hydromorphological differences, and
(ii) to identify hydromorphological variables suited to
describe reference conditions and different states of
degradation within a single stream type.
Study sites, material and methods
Data collection
In total, 275 samples collected at 147 sites be-
longing to six different stream types and distrib-
uted over three different countries (Sweden, The
Netherlands and Germany) were analysed (Table 1,
Fig. 1). German and Swedish sites were sampled
twice in March/April/May 2000 and June/July 2000,
with the exception of sites of stream type D03,
which were sampled three times in June and Septem-
ber 2000, and March 2001. Dutch sites were
sampled once or twice in April/May/June and/or Au-
gust/September/October 2000). All sites belong to the
Central European Lowlands (ecoregion 14), except
Dutch sites south of River Rhine, which belong to the
Western European Lowlands (ecoregion 13).
The hydromorphological status of each site was
derived from a set of variables compiled using
the AQEM site protocol. A detailed description
and a downloadable site protocol is available at
www.aqem.de (see also AQEM consortium, 2002;
Hering et al., 2003). In total, 130 hydromorpholo-
gical and geological variables were recorded on three
different spatial scales:
71
Table 1. General characteristics of investigated stream types (stream type codes according to Hering et al., 2003).
Stream type Code River system(s) Ecoregion Catchment Altitude pH Conductivity No. No. Total no. Total no.
(acc. to Illies, size (m a.s.l.) (µScm
1)reference reference of sites of samples
1978) (km2)sites samples
Small sand bottom streams D01 River Rhine, 14 9–151 33–136 6.7–8.3 295–1750 1 2 12 23
in the German lowlands Ijssel, Ems
Small organic type brooks D02 River Rhine 14 0.1–11.3 30–50 4.2–7.4 200–640 4 4 13 13
in the German lowlands
Mid-sized sand bottom streams D03 Ijssel, Ems, 14 120–760 25–60 7.2–8.5 330–815 515 18 54
in the German lowlands Elbe, Odra (6400)a
Mid-sized streams in South S05 Norrström, 14 32–1005 15–200 5.2–8.2 60–1550 510 15 30
Swedish lowlands Motala ström,
Virån, Helge å,
Kävlingeån,
Saxån, Rönne å,
Lagan
Small Dutch slow running streams N01 River Rhine, 13, 14 0.5–190 1–180 4.4–8.6 100–895 32 58 78 141
Meuse (Maas),
Drentse A
Small Dutch fast running streams N02 River Rhine, 13, 14 0.5–137 5–180 6.5–8.4 120–950 6 8 11 14
Meuse (Maas),
Drentse A
53 97 147 275
aSingle site at River Spree (Brandenburg, Germany).
72
(1) Catchment-related variables consider the whole
catchment from the stream source to the sample
site, for example distance to source, stream or-
der, catchment geology, and catchment land use.
They were derived from topographical and geolo-
gical maps (scale: 1:50 000 to 1:300 000). When
available, land use was measured using ArcView
GIS and data from Corine Landcover (e.g., Stat-
istisches Bundesamt, 1997 for Germany). Since
catchment variables are generally constant over a
long period of time, they were recorded only once
for each sample site.
(2) The longitudinal extent of reach-related (up-
/downstream) variables depends on the size class
of a stream type. For small streams (10–100 km2
catchment area), a stretch of 5 km up- and down-
stream of the sample site was taken into con-
sideration (=10 km), whereas in case of mid-
sized streams (100–1000 km2catchment area) a
stretch of 10 km up- and downstream was analysed
(=20 km). Percent (%) length of impoundments,
lack of natural vegetation, or water abstraction
represent typical up-/downstream variables, which
were usually derived from topographical maps
(scale: 1 : 50 000) and recorded once per sampling
site.
(3) Site-related variables were recorded for each
sampling occasion separately. They refer to a
stretch of 250 m up- and downstream (=500 m)
of the sample site for small streams and 500 m
up- and downstream (=1000 m) in case of mid-
sized streams. Habitat composition and physical-
chemical variables are typical site related vari-
ables.
Stream characteristics
Sites of ‘mid-sized lowland streams in south Sweden’
(type S05) are usually slow-flowing permanent
streams without a distinct valley. The natural low-
gradient stream course is usually meandering. Benthic
diatoms represent dominating primary producers in
lotic reaches, whereas deep and slow flowing reaches
are dominated by macrophytes and epiphytic algae as
primary producers. The prevailing degradation factor
is a mixture of organic and nutrient pollution (eutroph-
ication), and locally acidification is very important.
Degraded sites of this stream type are also hydromor-
phologically impaired (e.g., through straightening)
and situated in agricultural areas (see also Dahl et al.,
2004).
The Dutch streams belong to two stream types:
‘Small Dutch slow running streams’ (type N01) and
‘small Dutch fast running streams’ (type N02). The
latter are characterized by higher gradients (mean
slope of the thalweg), situated in U-shaped valleys
with higher proportions of gravel on the stream bot-
tom. ‘Small Dutch fast running streams’ show a
permanent and relatively constant discharge pattern.
Stream morphology is always altered by channel reg-
ulation and agricultural land use. Thus, high quality
reference sites are almost completely lacking.
‘Small Dutch slow running streams’ (type N01)
naturally have a plain floodplain with a meandering
channel, and instream habitat comprises a higher pro-
portion of sand and particulate organic material, when
compared to hill streams. Due to extensive alteration
of the stream morphology (straightening, scouring,
and removal of floodplain vegetation) and eutrophic-
ation, this stream type is almost entirely affected by
severe degradation (see also Vlek et al., 2004).
Pristine (reference) sites of ‘small sand bottom
streams in the German lowlands’ (type D01) are char-
acterized by sand of fine to medium grain size and a
meandering channel flowing in varying valley forms
(trough valley, meander valley, plain floodplain). Or-
ganic substrates range from 10 to 50% with a consid-
erable amount of CWD (coarse woody debris: logs,
debris dams).
‘Small organic type brooks in the German low-
lands’ (type D02) are naturally characterized by a
U-shaped valley and a braided channel. Organic mi-
crohabitats cover most of the stream bottom, for
example phytal [floating stands of Potamogeton poly-
gonifolius Pourr. and water mosses such as Sphag-
num spp. and Scapania undulate (L.)], xylal (woody
debris, root mats) and CPOM (coarse particulate or-
ganic matter: fallen leaves, twigs). The brownish water
is often acidic. Both small stream types have been
nearly completely degraded by scouring, straighten-
ing, impoundments, stagnation, removal of CWD, and
devastation of floodplain vegetation in the past.
References of ‘mid-sized sand bottom streams in
the German lowlands’ (type D03) are characterized
by sand of fine to coarse grain size, and a sinuate
to meandering channel flowing in a meander val-
ley or a plain floodplain. Organic substrates cover
between 10 and 50% of the bottom, of which CWD
(logs, debris dams) causes high substrate and current
diversity. The wide floodplain is dominated by de-
ciduous wooded vegetation, and standing water bodies
(side arms, backwaters) occur regularly except dur-
73
ing summer when they dry out. Almost all streams
of this stream type have been extensively degraded
by scouring, straightening, impoundments, stagnation,
removal of CWD, and devastation of floodplain veget-
ation due to agricultural land use. Small near-natural
fragments occur in northeastern Germany and Poland
(Pauls et al., 2002).
Selection of sampling sites
Due to an extensive sampling programme, the number
of samples taken for a single stream type was re-
stricted. Therefore, sample sites were pre-selected ac-
cording to a subjective estimation of their degradation
status. The aim of the pre-selection was a set of sites
that covered a degradation gradient from reference
(high status) to heavily degraded sites (bad status).
Degradation was related to the (main) stressor affect-
ing a single stream type, which was organic/nutrient
pollution (type S05), hydromorphological degradation
(types D01, D02 and D03), or general degradation
(types N01 and N02). The pre-selection was supported
by information derived from maps, for example, chan-
nel form, stream size, stream order or accessibility.
Additional information on stream status and stream
reaches was compiled using data from earlier stud-
ies, monitoring reports, and data on habitat quality,
such as the German river habitat survey ‘Struktur-
gütekartierung’ (LAWA, 2000). The pre-selection was
then evaluated during field trips yielding the final set
of sample sites.
As a general frame, a set of sites for a single
stream type comprised at least three sites each of a
supposed high (reference conditions), good and mod-
erate quality, respectively. Poor and bad states were
each represented by at least one site, so that a min-
imum number of eleven sites were sampled per stream
type (see also Hering et al., 2003; Hering et al.,
2004). Definition of reference sites followed the ba-
sic statements of Hughes (1995) and Wiederholm &
Johnson (1996) and aspects defined by Nijboer at al.
(2004). When reference sites were not available due to
degradation of an entire stream type, the best avail-
able sites served as ‘assessment references’, which
was the case for the Dutch stream types N01 and N02.
The ‘assessment references’ represented a ‘good eco-
logical quality’ instead of a ‘high ecological quality’
according to the WFD.
Evaluation of stream type assignment and
hydromorphological degradation
Stream type definition and assignment followed Sys-
tem B of the WFD (for detailed description see Hering
et al., 2003). When available, stream type tables were
used to support proper stream type assignment (e.g.,
LUA NRW, 2001 for German stream types). In addi-
tion, hydromorphological variables were analysed to
look for further typologically relevant factors import-
ant for proper stream type allocation. The analysis of
typologically relevant hydromorphological variables
was exclusively related to 97 samples of a supposed
good or high quality, since any kind of degradation
may affect or superimpose the results. Six samples
were excluded from the analysis due to gaps in the
respective datasets.
In order to visualize the general structure of
the environmental dataset, the whole set comprising
275 sampling occasions including 106 out of 130 re-
corded hydromorphological and geological variables.
Twenty-four site protocol variables were excluded
from the analysis due to the casewise deletion of
missing data. For the analysis of inter-stream type
hydromorphological degradation, a two class classi-
fication was introduced, since a reduced classification
was supposed to facilitate the recognition of a gen-
eral hydromorphological gradient. Therefore, samples
pre-classified as of high or good hydromorphological
quality were summarized to the category ‘unstressed’,
whereas lower quality sites (moderate, poor or bad)
were defined as ‘stressed’.
The hydromorphological degradation of the Ger-
man stream types D01, D02, and D03 was analysed
using 90 samples with 104 site protocol variables. Ger-
man samples only represented stream types, for which
hydromorphological degradation was the presumed
main stressor.
Development of a Structure Index for mid-sized sand
bottom streams in the German lowlands
The German Structure Index (GSI) combines several
stream type-specific hydromorphological features on
different spatial scales, such as land use, channel mor-
phology, or riparian vegetation, to a single index value.
Because the GSI is based on objective variables re-
corded from either field surveys or maps, it provides
a more objective measure of hydromorphological de-
gradation compared to the rather subjective judgment
of the pre-selection. However, the objectivity was in-
fluenced in three cases, when weighing factors were
74
Table 2. Hydromorphological variables used to calculate group indices for mid-sized sand bottom streams in the
German lowlands (D03), with respective spatial scale and calculation formula.
Group Hydromorphological Spatial Calculation
index variable scale formula
‘Positive’ Debris # Debris dams (>0.3 m3), Site 3*#Debrisdams+#Logs
Index # Logs (>10 cm diameter)
Organic % Xylal (e.g., dead wood, Site % Xylal/% Organic substrates
substrate branches, roots),
Index # Organic substrates
Shading % Shading at zenith Site % Shading * Average stream
Index (foliage cover), width
Average stream width
Shoreline % Shoreline covered with Reach/ % Shoreline covered with
Index wooded vegetation, site wooded riparian vegetation *
Average width of wooded Average width of wooded
riparian vegetation vegetation
‘Negative’ ‘Positive’/ Presence/absence: Reach/ Backwaters (0/1) – Stagnation
‘Negative’ Index – Backwaters site (0/1) – Straightening (0/1) –
– Stagnation Impoundments (0/1) – Removal
– Straightening of CWD (0/1)
– Impoundments
– Removal of CWD
Land Use % Pasture/grassland Catchment/ % Urban sites * 5 + % Crop land
Index % Crop land reach * 3 + % Pasture/grassland
% Urban sites
Scouring Scouring below floodplain Reach/ Original measure from site
Index level site protocol (cm)
Bank Fixation % Concrete Site % Concrete * 5 + % Stones * 3 +
Index % Stones % Wood/trees
% Wood/trees
used (see below). NMS and subsequently ‘IndVal’
analysis (see paragraph ‘Indicator variable analysis
using IndVal’) were used to identify hydromorpholo-
gical variables ‘best’-suited to describe hydromorpho-
logical degradation. The variables were divided into
‘positive’ or ‘negative’, representing either high/good
or moderate/poor/bad hydromorphological conditions.
Selected variables were tested for significant differ-
ences between the two groups (Mann–Whitney U-
test). Redundant variables were identified using cor-
relation analysis. However, similar variables may give
different information when recorded on different spa-
tial scales, and, hence, the information on the hydro-
morphological status of a site is also different, even if
strong inter-correlation between those variables occur.
For example a high proportion of native forest in the
catchment indicates the morphological integrity of a
site, whereas ‘% shading at zenith (foliage cover)’ of a
site provides informationabout the riparian vegetation
and instream habitat quality itself, without being ne-
cessarily linked to a high proportion of native forests
in the catchment. Hence, variables were not automat-
ically rejected, if interdependence was high (having
a Pearson’s Correlation Coefficient >0.700). A group
index was calculated for each variable group, repres-
enting a certain habitat quality feature (Table 2). Three
group indices (‘Debris Index’, ‘Land Use Index’,
‘Bank Fixation Index’) were calculated using weigh-
ing factors in order to consider the different quality of
categories present for a single variable. For example,
in case of the ‘Bank Fixation Index’, concrete-fixed
banks are weighed higher than stones (rip rap) and
stones more than wood-fixed banks (Table 2). ‘Posit-
ive’ and ‘negative’ group indices were finally summed
up to form the GSI. A list of site protocol variables
used for this study with information on the spatial
75
Figure 1. Location of the 147 investigated sites in Sweden, Germany and The Netherlands. Ecoregion delineation according to Illies (1978),
ecoregion numbers in italics.
scale is given in Appendix 1. The GSI was used to cor-
relate biota (represented by biocoenotic metrics) with
hydromorphological quality of a site (see also Feld
et al., 2002; Lorenz et al., 2004; Pauls et al., 2002).
Statistical analysis
Correlation analysis and Mann–Whitney U-tests were
performed with the XLStat 5.2 statistical soft-
ware package (Addinsoft SARL, 2002). The Mann-
Whitney-U-Test for non-parametric data was chosen,
since frequency plots revealed a lack of normal dis-
tribution for all variables. As variables differed in
numerical scaling and units of measurement (nom-
inal (binary), ordinal, and interval scales), non-metric
Multidimensional Scaling (NMS) was used for mul-
tivariate analysis, as it provides an appropriate tool
for non-parametric data of different numerical scales
(McCune & Mefford, 1999).
To provide comparability between hydromorpho-
logical variables of different measurement units, all
variables were standardized by dividing each value by
the square root of the respective variables sum of all
squared values (Formula 1). Thus, the sum of squares
will become 1 for each variable, which equalizes
the contribution of variables to the analysis (Podani,
2000).
b=xij
2
n
j=1
x2
ij
,(1)
b=standardized value xij =raw value of the ith
variable in the jth sample.
All NMS analysis was performed using PC-Ord’s
(McCune & Mefford, 1999) ‘autopilot’ settings: a
four-dimensional solution as a starting point based
on Bray-Curtis distance measures with medium speed
and thoroughness; 15 runs with real data and 30
runs with randomized data, and a stability criterion
of 0.0001. The variance explained by each multivari-
ate axis and Pearson’s Correlation Coefficient for
the correlation of hydromorphological variables with
each multivariate axis were calculated using PC-Ord.
Presented two-dimensional ordination plots always
show axes pairs, which explain the maximum vari-
ance of the hydromorphological variables used for the
respective analysis. The ‘final stress’, a measure that
explains the discrepancy between the multidimension-
ality of the data and the final (low-dimensional) ordin-
ation is given. According to Clarke (1993) and Podani
76
(2000), stress values between 0.1 and 0.2 represent
acceptable results.
Joint plots show the relationship between sample
units and hydromorphological variables, the latter
drawn as lines radiating from the centroid of the ordin-
ation scores. The angle and length of the line tell the
direction and strength of the relationship (McCune &
Mefford, 1999). For a given variable, the line forms
the hypotenuse of a right triangle with the two other
sides being correlation coefficients (rvalues) between
the variable and the two axes. Only variables (lines)
are shown, which rvalue exceeds 0.500.
‘IndVal’ provides a tool to analyse species as-
semblages and uncover indicator species (Dufrêne &
Legendre, 1997). In this study, ‘IndVal’ was used in
a different way to identify hydromorphological vari-
ables that are suited to indicate high or low quality
sites. Therefore, similar to Discriminant Analysis, a
site-grouping variable had to be defined prior to ana-
lysis. Consequently, results are strongly affected by
subjective judgment on group membership of sites,
which was performed during pre-selection of sampling
sites. In order to minimize the influence of a subjective
judgment on statistical analysis and to make group al-
location as transparent as possible, NMS analysis was
used a posteriori to determine the number of groups
and the sites belonging to a single group (Fig. 2). Ac-
cordingly, the samples were divided into two groups:
reference (high status) and heavily degraded (poor or
bad status) (Table 3). The two groups represent ex-
tremes of the hydromorphological gradient without
any overlap to adjacent quality classes (Fig. 2) and
comprise 15 samples each. Samples of a pre-classified
‘good’ or ‘moderate’ status were omitted.
The better a (hydromorphological) variable ex-
plains a group, the higher is the resulting ‘IndVal’
index. The highest explanation is reached (i.e., the
index reaches its maximum value of 100 %), if all
records of a single variable are found in a single group
of samples and if the variable occurs in all samples
of that group. The statistical significance of the ‘Ind-
Val’ Index values is evaluated using a randomization
procedure (Dufrêne & Legendre, 1997).
Results
Stream type assignment
The first two axes of the NMS of the hydromorpho-
logical variables account for 83% of its total variance
(Fig. 3). The first axis is correlated mainly with large-
scale catchment characteristics, such as catchment
size, geology, and natural land use practices, whereas
the second axis is correlated with agricultural land use
on the catchment scale and the natural shoreline veget-
ation and the degree of shading on the reach and site
scale (Table 4). Reach or site-related variables are also
typologically important, if the substrate composition
at a site is taken into consideration.
Out of the stream types pre-defined using the
WFD, five stream types can be identified from Fig. 3:
Small organic type brooks in the German lowlands
(type D02), small and mid-sized sand bottom streams
in the German lowlands (D01 and D03), and mid-
sized streams in the South Swedish lowlands (S05).
However, sites of type D01 comprise only two samples
and, thus, lack a sufficient sample size for a valid sep-
aration. Taking this into consideration, Fig. 3 reveals
only four stream types. Dutch samples form a dis-
tinct cluster separated from other stream types but with
considerable overlap of Dutch slow running streams
(N01) and Dutch fast running streams (N02).
Evaluation of hydromorphological degradation: All
stream types
A gradient of hydromorphological degradation is evid-
ent along axis 1 (Fig. 4). Both axes of the NMS plot
account for nearly 85% of the total variance of the
environmental dataset. The first axis (60% variance)
represents the degradation and is, for example, neg-
atively correlated with ‘% land use: native forest’,
‘% shoreline covered with wooded vegetation’, and
‘% shading at zenith (foliage cover)’ (Table 5). These
variables indicate high hydromorphological quality
(‘unstressed’) and are represented by sites located on
the left hand side of the NMS plot (empty symbols in
Fig. 4). In contrast, ‘stressed’ sites are best explained
by, for example, ‘% land use: agriculture’, which is
positively correlated with the first axis of the NMS
plot.
The second axis of the NMS ordination plot
(Fig. 4) is strongly correlated with catchment geo-
logy. Sites dominated by alluvial deposits are situ-
ated in the upper part of the NMS plot, whereas
moraine-dominated sites can be found at the bottom.
‘(%) land use: native forest’ is negatively correlated
with NMS axis 2 (Table 5). Sites with a high propor-
tion of native forest in their catchment, a rather strong
descriptor of hydromorphological reference condi-
tions, are clustered in the lower left corner of the NMS
77
Table 3. Median value and range of hydromorphological variables of stream type D03, signific-
antly differing between reference and heavily degraded sites (poor or bad hydromorphological status)
(p<0.001, Mann–Whitney U-test).
Hydromorphological variable Reference Heavily degraded
Median (range) Median (range)
Catchment: Land use: % Native forest 20 (0–40) 0 (0)
Site: Land use: % Native forest 90 (80–100) 0 (0)
Site: Land use: % Total agriculture 0 (0–10) 85 (10–100)
Reach: % Impoundments/dams up-/downstream 0 (0) 85 (40–100)
Site: % Shading at zenith (foliage cover) 80 (60–80) 0 (0)
Site: Average width of wooded riparian vegetation (m) 150 (110–200) 6 (0–16)
Site: # Debris dams (>0.3 m3)4 (3–22) 0 (0)
Site: # Logs (>10 cm diameter) 63 (35–100) 0 (0)
Site: % Shoreline covered with wooded riparian vegetation 100 (90–100) 20 (0–75)
Site: % Bank fixation stones (rip rap) 0 (0) 100 (20–100)
Site: # Organic substrates 3 (2–5) 1 (0–2)
Site: Max. current velocity (cm s1) 43 (31–63) 26 (7–53)
Table 4. Pearson’s Correlation Coefficient of hydromorphological variables with the first two NMS axes of the ordination of
typological aspects (Fig. 3). Only correlations >0.500 listed.
Axis 1 rAxis 2 r
Catchment: Geology: % Moraines 0.884 Site: % CPOM 0.608
Catchment: Land use: % Native forest 0.851 Catchment: Land use: % Pasture 0.585
Catchment: Geology: % Alluvial deposits 0.823 Catchment: Land use: % Agriculture 0.559
Catchment: Land use: % Wetland 0.678 Site: % Shading at zenith (foliage cover) 0.524
Catchment: Land use: % Non-native forest 0.608 Site: % Shoreline covered with wooded vegetation 0.507
Site: % Psammal/psammopelal (sand/sand and mud) 0.596
Site: Average stream width 0.596
Site: % Macrolithal (cobbles, 20–40 cm long) 0.585
Catchment: Distance to source 0.567
Catchment: Catchment area 0.566
Site: % Megalithal (cobbles and blocks >40 cm) 0.555
Site: % Shoreline covered with wooded vegetation 0.531
Catchment: Geology: % Acid silicate rocks 0.526
Reach: Altitude 0.523
Catchment: Geology: % Organic formations 0.519
Table 5. Pearson’s Correlation Coefficient of hydromorphological variables with the two NMS axes of the ordination of habitat
degradation (Fig. 4). Only correlations >0.500 listed
Axis 1 rAxis 2 r
Catchment: Land use: % Native forest 0.713 Catchment: Geology: % Moraines 0.763
Site: % Shading at zenith (foliage cover) 0.630 Catchment: Land use: % Native forest 0.727
Site: % Shoreline covered with wooded vegetation 0.595 Catchment: Geology: % Alluvial deposits 0.662
Catchment: Land use: % Wetland 0.506 Catchment: Land use: % Non-native forest 0.573
Catchment: Land use: % Agriculture 0.509 Site: Average stream width 0.559
Site: % Macrolithal (cobbles, 20–40 cm long) 0.525
78
Figure 2. NMS joint plot of 95 hydromorphological variables of 54 samples of ‘mid-sized sand bottom streams in the German lowlands’.
Lines indicate variables suited to describe the hydromorphological status best (cut-off level: 0.500), and arrow shows the gradient of
hydromorphological degradation. Final stress: 0.114.
Table 6. Pearson’s Correlation Coefficient of hydromorphological variables with NMS axes of the ordination of habitat degradation
in German stream types (Fig. 5). Only correlations >0.500 listed.
Axis 1 rAxis 2 r
Site: % Xylal (e.g., dead wood, branches, roots) 0.761 Catchment: Geology: % Alluvial deposits 0.651
Site: % Shading at zenith (foliage cover) 0.750 Catchment: Land use: % Open grassland/bush land 0.637
Site: % Unfixed banks 0.725 Site: # Logs (>10 cm diameter) 0.594
Site: # Logs (>10 cm diameter) 0.700 Catchment: Geology: % Sander 0.505
Site: % Bank fixation stones (rip rap) 0.666 Catchment: Geology: % Moraines 0.502
Site: % Shoreline covered with wooded vegetation 0.657
Catchment: Land use: % Urban sites 0.612
Reach: % Impoundments 0.600
Site: # Organic substrates 0.576
Site: % CPOM 0.569
Site: # Debris dams (>0.3 m3)0.537
Catchment: Land use: % Native forest 0.536
79
Figure 3. NMS ordination plot of 97 reference samples of six European stream types (see Table 1). Final stress: 0.155.
Figure 4. NMS ordination plot of 275 samples of six investigated stream types (explanation of stream types in Table 1). Symbols indicate stream
type and status of degradation pre-classified as ‘U’ =unstressed (empty symbols, pre-classified ‘high’ or ‘good status’) and ‘S’ =stressed (filled
symbols, pre-classified moderate, poor, or bad status). Final stress: 0.172.
80
Figure 5. NMS joint plot of hydromorphological degradation of 90 samples of three German stream types (D01, D02, and D03). Lines indicate
variables that describe high and low quality sites best (cut-off level: 0.500). Arrows indicate gradients of hydromorphological degradation.
Final stress: 0.108.
plot (in particular stream type S05). Fig. 4 reveals
a clear gradient of hydromorphological degradation
for the German stream types D01, D02, and D03
(see also Fig. 5), coinciding with the presumed main
stressor ‘hydromorphological degradation’ for these
stream types. In contrast, stream types S05, N01, and
N02 show a considerable overlap of ‘unstressed’ and
‘stressed’ sites.
Evaluation of hydromorphological degradation:
German stream types
A gradient of hydromorphological degradation is evid-
ent along axis 1 (Fig. 5) for both, small and mid-sized
streams. This gradient is best explained by site-scaled
variables (Table 6). In particular, the proportion and
number of organic substrates on the stream bed, the
proportions of wooded shoreline and bank fixation ex-
plain the gradient due to their correlations with axis 1.
On a catchment scale, it is the proportion of urban
areas that indicates hydromorphological degradation
for the three German stream types. The separation
of small and mid-sized samples along axis 2 is pre-
dominantly based on catchment geology (‘% alluvial
deposits’ vs. ‘% moraines’), ‘% land use: grassland’,
and‘#logs>10 cm ’ on the stream bed (Table 6),
the latter being more frequent in mid-sized streams.
However, the pre-classified hydromorphological refer-
ence site of ‘small sand bottom streams in the German
lowlands’ (D01) is clustered with the reference sites
of mid-sized sand bottom streams (D03). High shares
of organic substrates characterize the respective site
(D01 0001 in Fig. 5). In particular, ‘# logs >10 cm
on the streambed and stream width resembled those
recorded for D03 reference sites.
Hydromorphological degradation of type D03 can
be derived almost entirely from the site protocol vari-
ables, as reflected by a clear gradient for this stream
type. The overlap at the transition from good to
moderate and from moderate to poor status (Fig. 5)
disappeared, when stream type D03 was analysed sep-
arately (Fig. 2). Here, the pre-classification is well
reflected by the NMS ordination, which accounts for
almost 88% of the total variance in the environmental
dataset. A similar result is evident for ‘small sand
bottom streams in the German lowlands’ (D01) and
‘organic type brooks in the German lowlands’ (D02),
when analysed separately (not shown here). Hence,
the three German stream types, as well as their hy-
dromorphological status, can be identified solely by
environmental parameters recorded in the site pro-
tocol.
81
Table 7. ‘IndVal’ results of suitable core variables to describe the hydromorphological status of a sample of stream type D03 (significance level:
<0.05, based on random samples and 499 iterations). ‘Positive’ variables indicate reference conditions (high quality), ‘Negative’ variables heavily
degraded conditions (poor or bad quality). (IV =‘IndVal’ index).
‘Positive’ variable IV ‘Negative’ variable IV
Site: Max. current velocity (cm s1) 95.54 Reach: Land use: % Urban sites 100.00
Site: # Logs (>10 cm diameter) 75.63 Reach: Culverting up-/downstream 100.00
Reach: Land use: % Native forest 63.52 Reach: # Dams obstructing migration up-/downstream 100.00
Site: Average width of wooded riparian vegetation 61.62 Site: % Bank fixation stones (rip rap) 56.23
Catchment: Land use: % Native forest 60.31 Site: % Bed fixation stones 50.00
Site: % Xylal (e.g., dead wood, branches, roots) 55.56 Reach: % Impoundments/dams 45.07
Site: # Debris dams (>0.3 m3)50.65 Reach: # Transverse structures (e.g., weirs, dams, bridges) 44.69
Site: % Unfixed banks 43.10 Reach: Stagnation 43.80
Site: % CPOM 35.46 Reach: Straightening 38.46
Site: % Shoreline covered with wooded riparian vegetation 33.01 Site: Removal of coarse woody debris (CWD) 30.30
Site: CV depth 27.50 Reach: Channel form 27.95
Site: % Shading at zenith (foliage cover) 43.48 Site: Scouring 25.00
Site: # Organic substrates 29.90
Figure 6. Correlation of ‘% native forests in the floodplain’ and
instream ‘number of logs’ for 12 sites of stream type D03.
Development of a Structure Index for ’mid-sized sand
bottom streams in the German lowlands’
In total ‘IndVal’ analysis revealed 25 variables, which
significantly describe the end points of the hydromor-
phological gradient (Table 7). The variables can be
separated into those, which predominantly indicate
reference conditions (‘positive’) and those which are
connected with a heavily degraded hydromorphology
(‘negative’). Some variables revealed a considerable
correlation, as it was for example evident for the pro-
portion of native forests on catchment and reach scale
and the number of logs in the stream channel (Fig. 6).
Measures of several hydromorphological variables
were significantly different between reference and
heavily degraded sites (Table 3). Consequently, heav-
ily degraded sites are mainly characterized by extens-
ive agricultural land use in the floodplain, extensive
bank modification, lack of dense riparian wooded ve-
getation, and thus lack of shading of the channel and
woody debris on the stream bottom. In addition, only
a small amount of organic substrate occurs at sites of a
poor or bad hydromorphologicalstatus, and hydrology
is strongly affected by stagnation due to weirs, which
reduce maximum current velocities significantly.
In a next step, variables representing a certain
habitat quality feature (e.g., woody debris, channel
modification, or land use), are combined to group in-
dices. Group indices are related to different spatial
scales. Altogether, eight group indices were defined
and calculated (Table 2).
The ‘Debris Index’ weighs debris dams more
(factor 3) than logs, for debris dams provide a higher
habitat complexity and diversity. The relative ‘% xy-
lal’ in relation to the ‘total % organic substrates’ is
amalgamated to the ‘Organic Substrate Index’. As the
maximum degree of shading usually decreases with
increasing stream channel width, the ‘Shading Index’
considers both by the relation to the width-dependent
maximum value. However, if a sample site is nearly
complete shaded, 100% is taken as the resulting shad-
ing index independent of the respective stream width.
The ’Shoreline Index’ refers to the two dimensional
extension of the wooded riparian vegetation (along the
stream course as well as in the floodplain), and thus
assesses the buffer strip functionality. Certain ‘posit-
82
Figure 7. German Structure Indices (GSI) for 54 samples of ‘mid-sized sand bottom streams in the German lowlands’ (D03) in decreasing
order.
ive’ and ‘negative’ hydromorphological features are
on a presence/absence level – combined to the ‘Pos-
itive/Negative Index’. The extent of land use in the
floodplain is summarized with the ‘Land Use Index’,
and a weighing factor allows for the severity (urban
areas >crop land >pasture, meadow or open grass-
land). The ‘Scouring Index’ directly represents the
measured incision depth of the stream channel. The
‘Bank Fixation Index’ is related to the total share of
fixed banks, and different qualities of fixation are al-
lowed for by weighing (concrete >stones >wood or
trees). For each sample, group indices are calculated
and related to the respective stream type-specific max-
imum value of a single index plus 10%. Thus, each
index value is related to a 110% basis, which con-
siders that the samples do not necessarily reflect the
best (or worst, respectively) conditions present for a
certain stream type. An addition of 10% was supposed
to be sufficient, since reference sites of stream type
D03 already represent a relatively high hydromorpho-
logical quality. Afterwards, re-scaled percent values
of ‘negative’ group indices are simply added up and
subtracted from the sum of ‘positive’ group indices.
The resulting value represents the German Structure
Index (GSI). Results for 54 samples of mid-sized sand
bottom streams in the German lowlands are presented
(Fig. 7).
Discussion
The objective of this study was to identify suitable
variables to describe hydromorphological degradation
of stream types in ecoregions 13 and 14 of Central
Europe. If data analysis was changed from several
stream types to single stream typesonly, the respective
scale of hydromorphological variables also changed
from catchment scale to reach or site scale. Thus,
the set of hydromorphological variables to identify
hydromorphological degradation strongly depends on
the spatial scale. Earlier studies have also stressed
the role of spatial scale in physical habitat assessment
(Richards et al., 1996; Allan et al., 1997; Davies et al.,
2000; Sponseller et al., 2001), and some have argued
a distinct spatial hierarchy exists that influence envir-
onmental variables in riverine habitats (Frissell et al.,
1986; Rabeni, 2000). The results of this study support
this hierarchical organisation of hydromorphological
variables.
83
Stream type assignment
According to ‘System A’ of the WFD, ‘surface water
body types’ can be characterized by four factors: eco-
region (according to Illies, 1978), altitude, catchment
size class, and geology. Those factors usually refer
to a relatively large area and reflect the common use
of spatially large scaled variables for the analysis of
typological aspects (e.g., Omernik, 1987 and Whittier
et al., 1988 for the U.S.A.; EU commission, 2000
for Europe; LUA NRW, 2002 for the Federal State
of North Rhine-Westphalia, Germany). In contrast to
‘System A’, ‘System B’ considers several obligatory
(e.g., altitude, latitude, longitude, geology) and addi-
tional variables (e.g., distance to source, mean depth,
valley shape, substratum composition). The results of
my study support the typological relevance of these
hydrological and geological variables. Catchment geo-
logy, altitude, substrate composition, and stream and
catchment size are clearly suitable to discriminate
between investigated stream types of ecoregions 13
and 14 in Central Europe (Table 4). In addition, the
current study revealed land use characteristics as im-
portant typological variables on catchment scale. For
example, the ‘% native forest’ correlates very well
with axis 1 of the typological NMS (Table 4). How-
ever, catchment land use characteristics reflect the
degree of human activities in the catchment, and,
thus already reveal hydromorphological degradation.
In case of type S05, both outlier samples (Fig. 3) were
influenced by high shares of agricultural land use and
therefore likely do not represent real hydromorpholo-
gical reference sites. Allan et al. (1997) and Richards
et al. (1996) found catchment geology and land use
attributes, in particular the proportion of row-crop ag-
riculture, to be strong descriptors of stream habitat
conditions and macroinvertebrate communities. The
land use-controlled discrimination between lowland
stream types of Central and Western Europe in this
study does not correspond very well with the potential
natural vegetation expected for this region. The natural
vegetation of both ecoregions is deciduous forest (El-
lenberg, 1996). Land use appears to reflect degrada-
tion rather than typological aspects. The consideration
of additional site scale hydromorphological features,
such as ‘% shoreline coveredwith wooded vegetation’
and ‘% shading at zenith’ supports this assumption.
Both variables are closely related to degradation, and
dense riparian vegetation, usually dominated by Alnus
glutinosa (Black Alder) and Salix spp. (Willow), can
be expected along streams and rivers in ecoregions 13
and 14 (Ellenberg, 1996). In regard to catchment land
use properties, the reference dataset considered for
this study does not appear to fulfil the essential re-
quirements on reference conditions (Hughes, 1995;
Wiederholm & Johnson, 1996; Hering et al., 2004).
The Dutch stream types N01 and N02 were not
separated when using hydromorphological variables
on a large spatial scale (Fig. 3). It seems that they
are similar from a hydromorphological point of view,
which is also the case for the small German stream
types D01 and D02. In case of N01 and N02, this
makes sense, since the pre-selection of the Dutch
sites was not focussed on the detection of hydromor-
phological degradation. Moreover, this is a matter of
spatial scale chosen in the study, and stream type dis-
crimination presumably becomes clearer, when ana-
lysed on smaller spatial scales, for example, on a
sub-catchment or reach scale.
Evaluation of hydromorphological degradation
The analysis of the hydromorphological degradation
reveals two groups. The first group comprises Dutch
types N01 and N02 but also the Swedish type S05. The
second group consists of the German types D01, D02
and D03. Hydromorphological degradation was de-
tectable for German types and samples, whereas Dutch
and Swedish samples of various pre-classified quality
clustered together (Fig. 4). This reflects the fact that
hydromorphological degradation was the presumed
main stressor only for German stream types. Thus,
it is not surprising that German sites were ordered
along a hydromorphologicalgradient and Swedish and
Dutch sites were not. The presumed main stressor
for the Swedish stream type was nutrient pollution,
whereas general degradation was presumed to mainly
affect Dutch stream types. Swedish samples cluster on
the opposite site of the ordination space compared to
Dutch samples (Fig. 4). Consequently, Swedish sites
are only weakly affected by hydromorphological de-
gradation, whereas Dutch sites are predominantly in
moderate to bad hydromorphological condition. This
is evident by comparing, for example, the land use
category ‘% natural forest’, which is zero in case of all
Dutch samples and ranges from 20–90% (mean: 63%)
for Swedish samples. Consequently, hydromorpholo-
gical degradation strongly affects the Dutch stream
types.
The analysis of hydromorphological variables on
stream type scale was mainly governed by catchment
properties, of which only land use characteristics re-
84
flect the degree of human impact. However, on reach-
and site-scale, several variables, such as ‘% shoreline
covered with wooded vegetation’ and ‘% shading
at zenith’, were shown to be suitable descriptors
of hydromorphological impact. Therefore, environ-
mental variables, compiled to evaluate the physical
habitat quality, should include small-scale variables
measured for stretches of 10 up to 1000 m. The
AQEM site protocol considers different spatial scales,
of which only catchment properties and some up-
/downstream (stretch of 500–1000 m) variables are
available through topographical and geological maps.
Thus, physical habitat assessment necessitates field
work to obtain several important small-scale vari-
ables. The role of small-scale hydromorphological
variables becomes evident by restricting the analysis
to German stream types. Here, small-scale variables
are major descriptors of hydromorphological degrad-
ation, in particular the amount and quality of organic
substrates (woody debris, CPOM) and variables de-
scribing riparian vegetation and channel modification.
Urbanization and ‘% native forest’ are subordinate
hydromorphological features on catchment scale as
indicated by lower r-values in Table 6. However,
Jones & Clark (1987) and Benke et al. (1981) stressed
the role of urbanization as a major impact on the
benthic invertebrate communities.
There is a clear hydromorphological gradient at
small and mid-sized sand bottom streams, as well as
for small organic type brooks in the German low-
lands (Fig. 5). The different stream types can be
described by similar habitat attributes. In particular,
woody debris appears to be an important factor influ-
encing the hydromorphological status of these stream
types (Harmon et al., 1986; Gurnell et al., 1995; Her-
ing & Reich, 1997; Mutz, 2000). Riparian buffer strips
are important to control the influence of sediment in-
put from row-crop agricultural areas on the riverine
benthic community (Newbold et al., 1980; Allan et al.,
1997; Tabacchi et al., 1998). Newbold et al. (1980)
defined a minimum width of 30 m for riparian buffer
strips as sufficient to provide optimal habitat con-
ditions for macroinvertebrates. Allan et al. (1997)
stressed the role of riparian buffer strips as a barrier for
nutrient supply and sediment delivery. The importance
of both, a dense and wide riparian buffer is also made
evident in the current study. The ‘IndVal’ analysis of
hydromorphological variables for type D03 (Table 7)
revealed the ‘% shoreline covered with wooded veget-
ation’ and the ‘average width of wooded riparian ve-
getation’ to significantly differ between reference sites
and sites of a poor to bad hydromorphological status.
Reference sites of ‘mid-sized sand bottom streams in
the German lowlands’ were characterized by riparian
trees, which covered 90–100% of the shoreline and
extended between 110 and 200 m into the floodplain.
It appears that the extent of riparian vegetation in the
floodplain plays a major role, which is accounted for
in the calculation of the ‘Shoreline Index’ (Table 2).
The separation of German lowland stream types
was, amongst other variables, controlled by catch-
ment geology. Geology differed between catchments
of mid-sized and small streams, however, this is crit-
ical when applied to the entire Central lowlands of
Germany.The Central lowlands of Germany can be di-
vided by the borderline of the last (‘Weichsel’) glacial
period. The majority of mid-sized sites of the current
study were located in East Germany, which is domin-
ated by moraine and sander deposits of the ‘Weichsel’
glaciers. In contrast, all small sites were located in
the part of West Germany that was unaffected by the
‘Weichsel’ glaciers. This area of the West German
lowlands is generally dominated by alluvial (fluvi-
atile) deposits (Bundesanstalt für Geowissenschaften
und Rohstoffe, 1993).
‘Mid-sized sand bottom streams in the German
lowlands’ (type D03) clearly clustered apart from
small streams (types D01 and D02) (Fig. 5), even if
sites are in a poor to bad hydromorphological status.
This underlines the classification as an own stream
type. The subjective pre-classification of sites of this
stream type was reflected by the more objective field-
recorded and map-derived variables. Thus, stream
type D03 allowed the definition of distinct hydro-
morphological degradation classes using AQEM site
protocol variables (Fig. 2). Even if a separation of the
two small types D01 and D02 was not possible, when
analysed together with stream type D03 (Fig. 5) a sep-
arate analysis of the small types (not included in this
paper) showed that both types can be separated solely
from the hydromorphological variables recorded in the
AQEM site protocol.
On a regional scale (level of one stream type),
hydromorphological degradation appears to be bet-
ter described by site scale variables (Table 3). Thus,
site related physical habitat evaluation is especially
important, when habitat evaluation is applied on a
smaller spatial scale. Several methods integrate this
site related evaluation in Europe, such as the British
River Habitat Survey (RHS, Raven et al., 1997, 1998,
2002), the German ‘Strukturgütekartierung’ (LAWA,
2000) or the French SEQ-MP (Agence de l’Eau Rhin-
85
Meuse, 1996). However, these methods do not cover
all variables listed in Tables 3 and 7. The methods
could be improved by adding additional field records
of site scale variables.
Physical habitat evaluation applying the AQEM
site protocol provides the specific information for nu-
merous hydromorphological variables, such as the
number of organic substrates, the amount of woody
debris (debris dams, logs), maximum current velocit-
ies and the coefficient of variation (CV) of channel
depth, that directly or indirectly influence the in-
stream biocoenosis. These mesohabitat characteristics
have previously been reported as important descriptors
of the macroinvertebrate community structure (Beisel
et al., 1998).
Development of a Structure Index for mid-sized sand
bottom streams in the German lowlands
The results presented in this study stress the im-
portance of environmental variables for the develop-
ment and implication of tools to assess river health
in Europe. However, future assessment systems for
European streams and rivers should predominantly be
based on riverine biota (EU commission, 2000). The
WFD has designated several Biological Quality Ele-
ments (BQE; e.g., fish, benthic macroinvertebrates)
instead of abiotic factors, such as physical habitat
characteristics, to be predominantly used for assess-
ment. The results of this study on the potential of
hydromorphological variables to detect and describe
hydromorphological degradation, therefore, have to
be integrated with a system that is based on bio-
coenotic measures of the riverine community. This
was achieved by combining eight groups of hydro-
morphological variables (woody debris, organic sub-
strates, shading, shoreline, positive and negative struc-
ture elements, land use, scouring, and bank fixation)
to a newly developed measure, the German Struc-
ture Index (GSI). Finally, single community measures
(metrics, e.g., feeding types, current preferences, sub-
strate preferences) and single indicator taxa can be
identified to provide candidate metrics of a multi-
metric index to assess the ecological quality of a site
(Hering et al., 2004). Lorenz et al. (2004) documented
the interdependence between the hydromorphological
quality of a site and numerous metrics derived from
the macroinvertebrate community sampled at that site.
Feld et al. (2002) found, for example, the number of
Simuliid taxa to be significantly higher at hydromor-
phologically ‘unstressed’ sites.
In comparison to the existing methods of phys-
ical habitat evaluation (e.g., the German ‘Struktur-
gütekartierung’; LAWA, 2000), the GSI provides two
advantages: First, on a numerical scale, the GSI is
a continuous measure of hydromorphological qual-
ity, allowing of simple correlation with biocoenotic
metrics. Second, the development of the GSI refers
to hydromorphological reference conditions, which
represent one end of the hydromorphological gradi-
ent. Thus, even if the pre-classified reference sites
are already influenced by slight hydromorphological
degradation, the variables identified to describe the re-
spective end of the gradient are likely to be the same
variables suited to describe the reference conditions.
A potential deficit of the AQEM approach was the
subjective pre-selection of candidate sites according to
the researcher’s subjective judgment on the stressor-
specific ecological status of a site. This approach was
chosen to cover the whole gradient of the presumed
main stressor as good as possible. This is arguably
a prerequisite for the detection of a gradual impact
of this stressor. For German stream types, the main
stressor appears to be hydromorphological degrada-
tion; organic pollution and acidification play a minor
role (e.g., HMULF, 1999; MUNLV/LUA NRW, 2000;
NLÖ, 2000). Acidification can be objectively meas-
ured, however, hydromorphological quality is rather
difficult to scale. The GSI represents a method to
measure hydromorphological degradation based on
objectively recorded hydromorphological attributes.
Numerous site protocol variables clearly described
high quality and poor or bad quality sites (Fig. 5).
They revealed an obvious gradient, which even al-
lowed the establishment of a five-class classification
system (Fig. 2). Some subjectivity remained in the
definition of quality groups necessary for ‘IndVal’
analysis. However, this was ‘objectified’ by using only
the extremes of the hydromorphological gradient re-
vealed by NMS ordination (i.e., reference and heavily
degraded).
Lorenz et al. (2004), Feld et al. (2002) and Pauls
et al. (2002) reported the GSI a suitable measure for
the identification of biocoenotic metrics to assess the
impact of hydromorphological degradation on benthic
macroinvertebrates.
Acknowledgements
I would like to thank Dr V. W. Framenau, Western
Australian Museum, Perth, Australia, for numerous
86
valuable comments and linguistic revision of the ma-
nuscript. Dr Daniel Hering, University of Essen, Ger-
many, provided valuable comments that contributed
to this paper. Many thanks to Hanneke Vlek, Alterra,
Wageningen, The Netherlands, and an anonymous re-
viewer, who helped to improve the manuscript by
numerous important remarks and critical comments.
I am also grateful to Melissa L. Thomas, University of
California, San Diego, U.S.A., for valuable comments
on the manuscript.
AQEM was funded by the European Commission,
5th Framework Program, Energy, Environment and
Sustainable Development, Key Action Water, Contract
no. EVK1-CT1999-00027.
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Appendix 1. List of site protocol variables with notes on numerical and spatial scale. Variable usage for different multivariate
analysis is indicated by a ‘+’, exclusion from analysis by a ‘’. Numerical scale assigned according to Podani (2000). Areal and
longitudinal extent of spatial scale is explained in Chapter ‘Evaluation of hydromorphological degradation’.
Variable Variable name Numerical Spatial Typo- Degradation
code scale scale logy All German Stream
stream stream type
types types D03
7 Stream order (Strahler system) Ordinal Catchment ++ + +
8 Distance to source (km) Interval Catchment ++ + +
11 Altitude (m a.s.l.) Interval Catchment ++ + +
12 Ecoregion (according to Illies, 1978) Nominal Catchment ++ +
15 Catchment area (km2) Interval Catchment ++ + +
16 Size typology according to the WFD (EU commission, 2000) Ordinal Catchment ++ +
17 Stream density (km km2) Interval Catchment ++ + +
18–1 Geology: Acid silicate rocks (%) Ratio Catchment ++ +
18–3 Geology: Carbonate rocks (%) Ratio Catchment ++ + +
18–4 Geology: Alluvial deposits (%) Ratio Catchment ++ +
18–7 Geology: Moraines (%) Ratio Catchment ++ + +
18–8 Geology: Sander (%) Ratio Catchment ++ + +
18–9 Geology: Marine deposits (%) Ratio Catchment ++ +
18–10 Geology: Organic formations (%) Ratio Catchment ++ + +
18–11 Geology: Loess (%) Ratio Catchment ++ + +
18a Geological typology (silicate, carbonate, organic) Ratio Catchment ++ + +
19–91 Land use: Native forest (%) Ratio Catchment ++ + +
19–4 Land use: Wetland (mire) (%) Ratio Catchment ++ −
19–5 Land use: Open grass-/bush land (%) Ratio Catchment ++ + +
19–9 Land use: Artificial standing water bodies (ponds, etc.) (%) Ratio Catchment ++ + +
19–10 Land use: Non-native forest (%) Ratio Catchment ++ + +
19–12 Land use: Crop land (%) Ratio Catchment ++ + +
19–13 Land use: Pasture (%) Ratio Catchment ++ + +
19–92 Land use: Total agriculture (%) Ratio Catchment ++ +
19–15 Land use: Urban sites (residential) (%) Ratio Catchment ++ + +
24 Hydrologic stream type (permanent, Nominal Catchment ++ +
periodic/intermittent, episodic)
25 Presence of lakes in the whole upstream Binary Catchment ++ + +
continuum
26 Width of the floodplain (m) Interval Site −− − +
29 Valley shape (V-shaped, U-shaped, Nominal Site ++ +
trough, meander valley, etc.)
30–91 Land use: Native forest (%) Ratio Site −− − +
30–92 Land use: Open grass-/bush land, reeds (%) Ratio Site −− − +
30–10 Land use: Non-native forest (%) Ratio Site −− − +
30–12 Land use: Crop land (%) Ratio Site −− − +
30–13 Land use: Pasture (%) Ratio Site −− − +
30–93 Land use: Total agriculture (%) Ratio Site −− − +
30–15 Land use: Urban sites (residential) (%) Ratio Site −− − +
31 Number of other transverse structures Interval Upstream ++ + +
34 Straightening Binary Upstream ++ + +
Continued on p. 89
89
Appendix 1. Continued.
Variable Variable name Numerical Spatial Typo- Degradation
code scale scale logy All German Stream
stream stream type
types types D03
35 Removal of coarse woody debris (CWD) Binary Upstream ++ + +
36 Cut-off meanders Binary Upstream ++ + +
37 Scouring below bank top (m) Interval Upstream ++ + +
38 Culverting Binary Upstream ++ + +
39 Number of other transverse structures Interval Downstream ++ + +
42 Straightening Binary Downstream ++ + +
43 Removal of coarse woody debris (CWD) Binary Downstream ++ + +
44 Cut-off meanders Binary Downstream ++ + +
45 Scouring below bank top (m) Interval Downstream ++ + +
46 Culverting Binary Downstream ++ + +
47 Number of dams retaining sediment Interval Upstream ++ + +
49 Number of dams obstructing migration Interval Downstream ++ + +
56 Impoundments or dams (% of length) Ratio Upstream ++ + +
56a Lack of natural wooded vegetation Binary Upstream ++ + +
56b Non-native wooded vegetation Binary Upstream −− − +
57 Lack of natural wooded vegetation Binary Downstream ++ + +
58 Non-native wooded vegetation Binary Downstream −− − +
59 Impoundments or dams (% of length) Ratio Downstream ++ + +
61 Non-source pollution Binary Upstream ++ +
63 Eutrophication Binary Upstream ++ +
68 Mean depth at bankfull discharge (m) Interval Site ++ + +
69 Shading at zenith (foliage cover) (%) Ratio Site ++ + +
70-91 Average width of wooded riparian Interval Site ++ + +
vegetation right +left (m)
71 Channel form (braided, meandering, sinuate, etc.) Nominal Site ++ + +
73 Presence of natural standing water bodies Binary Site ++ + +
in the floodplain (e.g. backwaters)
74 Number of debris dams >0.3 m3Interval Site ++ + +
75 Number of logs >10 cm diameter Interval Site ++ + +
76–91 Shoreline covered with wooded riparian vegetation right +left (%) Ratio Site ++ + +
77 Number of dams Interval Site ++ + +
78 Number of other transverse structures Interval Site ++ + +
79–91 Bank fixation stones (rip rap) (%) Ratio Site ++ + +
79–92 Bank fixation wood/trees (%) Ratio Site ++ + +
79–93 No bank fixation (%) Ratio Site ++ + +
80–3 Bed fixation stones (%) Ratio Site ++ + +
80–9 No bed fixation (%) Ratio Site ++ + +
81 Stagnation Binary Site ++ + +
84 Straightening Binary Site ++ + +
85 Removal of coarse woody debris (CWD) Binary Site ++ + +
86 Cut-off meanders Binary Site ++ + +
87 Scouring below bank top (m) Interval Site ++ + +
88 Culverting Binary Site ++ + +
92 Impoundments at sampling site Binary Site ++ + +
Continued on p. 90
90
Appendix 1. Continued.
Variable Variable name Numerical Spatial Typo- Degradation
code scale scale logy All German Stream
stream stream type
types types D03
93 Removal/lack of natural floodplain vegetation Binary Site ++ + +
94 Non-native wooded riparian vegetation Binary Site −− − +
95 Source pollution Binary Site ++ +
96 Non-source pollution Binary Site ++ +
97 Sewage overflows Binary Site ++ +
98 Eutrophication Binary Site ++ +
103–2 Megalithal (>40 cm) (%) Ratio Site ++ −
103–3 Macrolithal (>20 cm to 40 cm) (%) Ratio Site ++ + +
103–4 Mesolithal (>6 cm to 20 cm) (%) Ratio Site ++ + +
103–5 Microlithal (>2 cm to 6 cm) (%) Ratio Site ++ + +
103–6 Akal (>0.2 cm to 2 cm) (%) Ratio Site ++ + +
103–7 Psammal/psammopelal (%) Ratio Site ++ + +
103–8 Argyllal (<6µm) (%) Ratio Site ++ +
104–2 Algae (%) Ratio Site ++ + +
104–3 Submerged macrophytes (%) Ratio Site ++ + +
104–4 Emergent macrophytes (%) Ratio Site ++ + +
104–5 Living parts of terrestrial plants (%) Ratio Site ++ + +
104–6 Xylal (wood) (%) Ratio Site ++ + +
104–7 CPOM (%) Ratio Site ++ + +
104–8 FPOM (%) Ratio Site ++ + +
104–10 Organic mud, sludge (%) Ratio Site ++ + +
104–11 Debris (e.g. empty mollusc shells at the shore zone) (%) Ratio Site −− − +
104–91 Number of organic substrates Interval Site ++ + +
105 Average stream width (m) Interval Site ++ + +
110 pH value Interval Site ++ +
111 Conductivity (µScm
1) Interval Site ++ +
112 Reduction phenomena Binary Site ++ +
113 Waste Binary Site ++ +
114 Dissolved oxygen content (mg l1) Interval Site ++ +
118 Max. depth (cm) Interval Site −− − +
120 Max. current velocity (m s1) Interval Site −− − +
121 Mean depth (cm) Interval Site ++ + +
122 CV depth Ratio Site ++ + +
123 Mean current velocity (m s1) Interval Site ++ + +
124 CV current velocity Ratio Site ++ + +
125 Ammonium (mg l1) Interval Site ++ +
127 Nitrate (mg l1) Interval Site ++ +
128 Ortho phosphate (µgl
1) Interval Site ++ +
129 Total phosphate (µgl
1) Interval Site ++ +
... Recent advances in machine learning algorithms, together with the increased availability of dense time series data, have further enhanced the precision and the accuracy of land use mapping (Potapov et al., 2015;Holloway and Mengersen, 2018;Venter and Sydenham, 2021). Since streams are particularly sensitive to land use at local and catchment scales, land cover classes and land use indices (LUI) have been frequently used to assess, indirectly, the quality of stream water (Feld, 2004;Tran et al., 2010). However, the most adequate spatial scale to assess human influence on stream quality is still uncertain, because different scales (catchment scale, reach scale, local buffers, riparian corridors) can provide contrasting results (Fernandes et al., 2011;Monteagudo et al., 2012;Wahl et al., 2013;Erba et al., 2015). ...
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Hydromorphological alterations influence a wide range of environmental conditions as well as riparian vegetation and the structure of the macroinvertebrate community. We studied relationships between the structure and diversity of the macroinvertebrate community and hydromorphological and other environmental conditions in the river Gradaščica (central Slovenia). The Gradaščica river is a pre-Alpine torrential river that has been morphologically altered by humans. A selection of abiotic factors was measured, the ecomorphological status of the river was assessed, vegetation in the riparian zone was surveyed and benthic macroinvertebrates were sampled. Correlations between diversity and the structure of the macroinvertebrate community, environmental parameters and occurrence of invasive alien plant species in the riparian zone were identified. The significance of the influence of environmental parameters on the structure of the macroinvertebrate community was examined. We found that hydromorphological alterations in the river have had a significant influence on the diversity and composition of the macroinvertebrate community because of changes of flow velocity and the spread of invasive alien plant species that has followed those changes. Factors that also significantly influence the composition of macroinvertebrate community are distance from the source and conductivity. Our findings suggest minimization of further human hydromorphological changes of watercourses could prevent the loss of biodiversity of riverine ecosystems.
... An index of land use modification in the catchment (Land Use Index catchment; LUIc) was calculated for all sites. The index follows the scoring system described in [30] and [31] and assigns a score to non-natural land uses. LUIc values range from 0 (100% natural) to 5 (100% urban). ...
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Background Despite the efforts made in the last century to counteract the nutrient enrichment from diffuse and point-sources, the excess of nitrogen and phosphorous is among the main causes of degradation of European rivers. In this context, determining natural background concentrations of nutrients in rivers is crucial for a correct definition of their ecological status. In the most anthropized regions, this is a difficult task. This study provides a nation-wide assessment of the nutrient concentration variability between Italian river reference sites. Results We applied the Affinity Propagation technique to identify groups of river sites classified as reference based on measured nutrients and oxygen water saturation. The role of natural and anthropogenic factors determining differences in nutrients concentration between groups of sites was explored. Nitrate concentrations varied from 0.01 mg N l ⁻¹ to more than 5 mg N l ⁻¹ . Ammonia and total phosphorous varied between 0.001 and 0.12 mg l ⁻¹ . Observed nutrient levels, although in line with those identified for reference sites in other countries, largely exceed the ranges reported for natural basins. Atmospheric deposition of inorganic N and artificial and/or high-impact agricultural land use are the major factors determining differences in nutrient concentration. Factors like, e.g. catchment size, precipitation amount and altitude do not play a relevant role in explaining nutrient differences between groups of reference sites. Conclusions We especially focused on (i) major causes of failure in the selection of appropriate reference sites in Italy; (ii) the potential of setting higher NO 3 -N thresholds for the classification of ecological status in specific areas, and (iii) the prospective of a regionalization approach, in which human effects are accepted to a low degree for reference site selection or when setting thresholds for peculiar geographical areas.
... Capture of springs is often connected with hydromorphological degradation (HD), which was the most common type of human impact observed in half of the springs studied. The HD of aquatic habitats has been the subject of many hydrobiological studies that have repeatedly confirmed its negative effects on benthic macroinvertebrates (Feld, 2004;Friberg, Sandin & Pedersen, 2009;Urbanič, 2014 (2015) found that restoring springs had a positive effect on the stability of surface soil aggregates, vegetation cover and channel width. Shortly after spring habitat adjustments, reflected in a change of hydrological conditions and an increase in habitat quality, weak positive responses were also observed in benthic macroinvertebrate diversity (Ilmonen et al., 2012;Lehosmaa et al., 2017). ...
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1. Springs are perceived by human society as essential sources of drinking water, but on the other hand they represent peculiar and vulnerable ecosystems. They differ from other watercourses in the relatively high stability of their physicochemical conditions. As a result, springs represent ecosystems with an insular character, usually inhabited by specific aquatic communities. 2. Although springs are generally considered species-rich habitats across the world, they have been outside scientific and conservation interest in the karst mountains of the Western Carpathians. This study, therefore, examined the diversity of spring benthic macroinvertebrates and compared it with that of other watercourses of the Western Carpathian riverine landscape. 3. The results of the study showed that, in contrast to rhithral streams, individual karst springs often had low species richness, and therefore they cannot be considered biodiversity hotspots. However, their metacommunity diversity is characterized by high taxonomic turnover, resulting in high gamma diversity. This means that the individual springs often harbour unique macroinvertebrate communities, so they are worthy of protection. 4. However, the present level of spring habitat protection is insufficient as even springs located in protected areas are often captured as sources of drinking water and hydromorphologically or otherwise disturbed. A simple method to evaluate the spring conservation priority (CP) was developed to find a better trade-off between their use and protection. It uses the number of Red List, endemic and crenal taxa, as well as the total species richness at each site. Based on this classification method, 16% of the springs studied reached very high CP, 39% high CP, 33% moderate CP and 12% low CP. 5. The proposed management recommendations based on findings of conservation priority of the Western Carpathian karst springs can significantly contribute to their more effective protection and the creation of a legislative framework relating to spring habitat protection in general.
... Today, a majority of the small stream channels in lowland areas have been degraded (Lorenz et al., 2009;Zwick, 1992). Relatively few lowland streams have retained their natural characteristics, particularly in forested areas and along short reaches in their upper courses (Feld, 2004). It has also been reported that beavers can facilitate the renaturalisation of degraded streams through hydrological transformations (Gorczyca et al., 2018). ...
Article
Beavers are an exception among animals in terms of the scale of environmental transformations they achieve. This study investigated primary environmental factors influencing the occurrence of aquatic invertebrates in lowland streams inhabited by the Eurasian beaver. The study was conducted in two forest streams inhabited by beavers, and in an uninhabited stream. In streams inhabited by beavers, the study covered seven ponds. Sections with flowing water were also analysed downstream and upstream of the ponds. Benthos and water samples were collected at each site. Dissolved oxygen (DO) concentration and saturation were the only physicochemical parameters that indicated decreases in water quality in beaver ponds. The benthic communities of different beaver ponds were similar. The taxa that exerted the greatest influence on the similarity of the invertebrate fauna in the ponds were Oligochaeta and Chironomidae. Ostracods were also abundant in the ponds, whereas they were few in the flowing sections. Mayflies (Cloeon) and caddisflies belonging to the family Phryganeidae were also closely associated with the ponds. Caddisflies (Plectrocnemia and Sericostoma), mayflies (Baetis) and stoneflies (Nemourella and Leuctra) exhibited the highest correlation with DO concentrations, which is typical of flowing sections, and avoided stream fragments dammed by beavers. Bivalvia (Pisidium) were also abundant in each of the streams along the flowing sections. The highest number of taxa and greatest taxonomic diversity was observed in sections flowing below the beaver ponds. The engineering activity of beavers transformed the studied lowland streams, resulting in the development of rheophilic and stagnophilic communities of aquatic invertebrates, in freeflowing and dammed sections, respectively.
... Some analysed bank variables, individually (e.g., reinforced bank, resectioned bank, natural bank), showed important but somewhat intercorrelated effects. The importance of riverbank conditions for aquatic communities has been highlighted in previous studies (e.g., Feld, 2004;Erba et al., 2006;Szoszkiewicz et al., 2006;Petkovska & Urbanič, 2014). This is expected, as riverbanks are closely interlinked with the aquatic environment and have significant direct and indirect influences on, among others, channel morphology, habitat diversity and water quality (Pusey & Atrhington, 2003). ...
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Large river management is in increasing demand to establish the ecological quality set by sustainability-oriented legislation. However, there is still a lack of research regarding most relevant scales at which pressures should be addressed. In this study, we compared the relative effects of riparian land-cover at 16 different buffer sizes and hydromorphology at four different river lengths on benthic invertebrate (BI) and fish assemblages in large rivers. Environmental data were obtained digitally (applying GIS) on a broad range of abiotic conditions and were related to biological data using direct gradient analyses. Compared to land-cover, hydromorphology showed greater effect on BI and fish, with in-channel habitat quality characteristics explaining most biotic variability. Both assemblages were best explained by hydromorphol-ogy inventoried at the largest 5,000 m scale. We found, however, consideration of longer river segments to be more important for explaining fish variability. Riparian land-cover explained similar percentages of biotic variability across most analysed riparian buffers. Nevertheless, a significant effect of riparian length rather than width was identified. Our findings indicate that, in large rivers, hydromorpho-logical and riparian land-cover conditions affect BI and especially fish at generally longer river segments, implying potential ecological benefits of management measures implemented at larger spatial scales.
... Some analysed bank variables, individually (e.g., reinforced bank, resectioned bank, natural bank), showed important but somewhat intercorrelated effects. The importance of riverbank conditions for aquatic communities has been highlighted in previous studies (e.g., Feld, 2004;Erba et al., 2006;Szoszkiewicz et al., 2006;Petkovska & Urbanič, 2014). This is expected, as riverbanks are closely interlinked with the aquatic environment and have significant direct and indirect influences on, among others, channel morphology, habitat diversity and water quality (Pusey & Atrhington, 2003). ...
Article
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Large river management is in increasing demand to establish the ecological quality set by sustainability-oriented legislation. However, there is still a lack of research regarding most relevant scales at which pressures should be addressed. In this study, we compared the relative effects of riparian land-cover at 16 different buffer sizes and hydromorphology at four different river lengths on benthic invertebrate (BI) and fish assemblages in large rivers. Environmental data were obtained digitally (applying GIS) on a broad range of abiotic conditions and were related to biological data using direct gradient analyses. Compared to land-cover, hydromorphology showed greater effect on BI and fish, with in-channel habitat quality characteristics explaining most biotic variability. Both assemblages were best explained by hydromorphology inventoried at the largest 5,000 m scale. We found, however, consideration of longer river segments to be more important for explaining fish variability. Riparian land-cover explained similar percentages of biotic variability across most analysed riparian buffers. Nevertheless, a significant effect of riparian length rather than width was identified. Our findings indicate that, in large rivers, hydromorphological and riparian land-cover conditions affect BI and especially fish at generally longer river segments, implying potential ecological benefits of management measures implemented at larger spatial scales.
... This may come along with fine sediment pollution (% Psammal) and a lack of large wood (# Logs), in particular if riparian wooded vegetation is scarce (Density of riparian vegetation; Table 2). The site-scale BN confirms the strong relevance of large wood (% Xylal), which constitutes a key habitat for benthic invertebrates in mid-sized sand-bottom lowland rivers (Feld, 2004). Contrastingly, large stones (% Macrolithal) constitute a clear sign of habitat degradation in the targeted river type, which is often linked to bank enforcement with riprap. ...
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River assessments are predominantly based on biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality. Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various symptoms (macroinvertebrate metrics) and probabilistically link them to various potential causes of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. Water quality‐related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53%–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. Synthesis and applications. Bayesian inference can greatly assist the diagnosis of potential causes of ecosystem deterioration based on a selection of diagnostic biological metrics. If integrated into a Bayesian network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve biological and ecological status. Diagnostic Bayesian networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. Bayesian inference can greatly assist the diagnosis of potential causes of ecosystem deterioration based on a selection of diagnostic biological metrics. If integrated into a Bayesian network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve biological and ecological status. Diagnostic Bayesian networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures.
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We investigated the mechanisms involved in the relationship between land-use changes and aquatic biodiversity, using stream fish assemblages of the Brazilian Savanna (i.e., Cerrado) as a study model. We tested the prediction that landscape degradation would decrease environmental heterogeneity and change predominant physical-habitat types, which in turn would decrease the functional diversity and alter the functional identity of fish assemblages. We sampled fish from 40 streams in the Upper Paraná River basin, and assessed catchment and instream conditions. We then conducted an ecomorphological analysis to functionally characterize all species (36) and quantify different facets of the functional structure of assemblages. We detected multiple pathways of the impacts from landscape changes on the fish assemblages. Catchment degradation reduced the stream-bed complexity and the heterogeneity of canopy shading, decreasing assemblage functional specialization and divergence. Landscape changes also reduced the water volume and the amount of large rocks in streams, resulting in decreased abundances of species with large bodies and with morphological traits that favor swimming in the water column. We conclude that land-use intensification caused significant changes in aquatic biodiversity in the Cerrado, reinforcing the need to pay special attention to this global hotspot.
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Benthic macroinvertebrate communities are used widely to assess change in biological quality of streams, with taxa generally pooled across a variable number of different mesohabitats sampled at each station. Computer simulations showed the potential variation in the record of a station community according to the combination of mesohabitats sampled. For each station, all the faunal lists obtained by combining six, eight or ten sampled mesohabitats were compared on the basis of their structure (biocenotic indices) and composition (Jaccard's similarity index). Relative abundances of taxa varied depending on the combination of mesohabitats sampled. Total abundance and the dominance index in a station community were the most variable parameters, whereas taxonomic richness depended to a lesser extent on mesohabitats sampled. Differences in community composition were readily explained by taxa which were only present in one, two, or three mesohabitats. These taxa accounted for a minimum of 46 % taxonomic richness in each station and were mainly present with low abundances. Because most mesohabitats contained these kinds of taxa, invertebrate assemblages contrasted in composition although they displayed similar taxonomic richness. Variability in the faunal assessment of a station assemblage owing to the mesohabitats sampled can have a strong impact on the biological assessment of this station. Recommendations are suggested to alleviate this problem.
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This paper presents a new and simple method to find indicator species and species assemblages characterizing groups of sites. The novelty of our approach lies in the way we combine a species relative abundance with its relative frequency of occurrence in the various groups of sites. This index is maximum when all individuals of a species are found in a single group of sites and when the species occurs in all sites of that group; it is a symmetric indicator. The statistical significance of the species indicator values is evaluated using a randomization procedure. Contrary to TWINSPAN, our indicator index for a given species is independent of the other species relative abundances, and there is no need to use pseudospecies. The new method identifies indicator species for typologies of species releves obtained by any hierarchical or nonhierarchical classification procedure; its use is independent of the classification method. Because indicator species give ecological meaning to groups of sites, this method provides criteria to compare typologies, to identify where to stop dividing clusters into subsets, and to point out the main levels in a hierarchical classification of sites. Species can be grouped on the basis of their indicator values for each clustering level, the heterogeneous nature of species assemblages observed in any one site being well preserved. Such assemblages are usually a mixture of eurytopic (higher level) and stenotopic species (characteristic of lower level clusters). The species assemblage approach demonstrates the importance of the 'sampled patch size,' i.e., the diversity of sampled ecological combinations, when we compare the frequencies of core and Satellite species. A new way to present species-site tables, accounting for the hierarchical relationships among species, is proposed. A large data set of carabid beetle distributions in open habitats of Belgium is used as a case study to illustrate the new method.
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The main objective of the European Union (EU) funded project AQEM1was to develop a framework of an assessment system for streams in Europe based on benthic macroinvertebrates that fulfils the requirements of the EU Water Framework Directive. Initial assessment methods for 28 European stream types and more generally applicable tools for stream biomonitoring in Europe were generated. The development of the system was based on a newly collected data set covering stream types in Austria, the Czech Republic, Germany, Greece, Italy, The Netherlands, Portugal and Sweden. Altogether, 901 benthic invertebrate samples were taken using a standardised multi-habitat sampling procedure and a large number of parameters describing the streams and their catchments was recorded for all sampling sites. From the stream and catchment characteristics measures of stress were derived. A large number of metrics was tested independently for each of the stream types, to identify the response of each metric to degradation of a site. This process resulted in up to 18 core metrics for the individual stream types, which were combined into a different multimetric index in each country. The multimetric AQEM assessment system is used to classify a stream stretch into an Ecological Quality Class ranging from 5 (high quality) to 1 (bad quality) and often provides information on the possible causes of degradation. AQEM provides a taxa list of 9557 European macroinvertebrate taxa with associated autecological information, a software package for performing all the calculations necessary for applying the multimetric AQEM assessment system and a manual describing all aspects of the application of the system from site selection to data interpretation.
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Since ratification of the EU Water Framework Directive (EU-WFD), river assessment in Europe has taken a new turn, with biological quality indicators becoming the main focus and primary tool for describing ecological river quality. Implementing the EU-WFD requires new assessment and monitoring methods, which meet both legal demands and those of water management application. Within the project AQEM (The development and testing of an integrated assessment system for the ecological quality of streams and rivers throughout Europe using benthic macroinvertebrates) a method meeting these demands based on multi-habitat sampling and modular assessment was developed. It considers organic and structural degradation as the two main impact factors currently affecting stream biota. We describe the development of this system for lowland river types in northern Germany. The module for assessing the impact of morphological stream degradation on the aquatic biota is described in further detail based on data from about 100 samples. A multimetric index for evaluating ecological stream quality using macroinvertebrates is introduced. The main component is a "Faunaindex" based on indicator species. The suggested assessment method provides a tool for correlating macroinvertebrate biocoenoses with structural degradation and for discernment of five classes of structural quality, regardless of organic pollution. Possibilities and advantages of implementing the method in water management are discussed.