Physicochemical stream bed characteristics and recruitment of the

Transcription

Freshwater Biology (2007) 52, 2299–2316
doi:10.1111/j.1365-2427.2007.01812.x
Physicochemical stream bed characteristics and
recruitment of the freshwater pearl mussel
(Margaritifera margaritifera)
JUERGEN GEIST*,† AND KARL AUERSWALD‡
*Fish Biology Unit, Department of Animal Science, Technische Universität München, Freising, Germany
†
Aquatic Toxicology Laboratory, Department of Anatomy, Physiology and Cell Biology, University of California, Davis, CA,
U.S.A.
‡
Lehrstuhl für Grünlandlehre, Technische Universität München, Freising, Germany
SUMMARY
1. The freshwater pearl mussel (Margaritifera margaritifera) is endangered and of
conservation importance. We used its survival/mortality during the critical post-parasitic
phase as a biological indicator for the habitat quality of the stream substratum.
2. We established and tested biological, physical and chemical methods of assessing the
stream bed in 26 streams from seven European countries. We analysed penetration
resistance, texture, the concentrations and ratios of C, N, S, P, Fe, Mn in fine material
<100 lm, and redox, pH and electric conductivity at the surface and at 5 and 10 cm into
the substratum.
3. Sites with high stream bed quality (promoting pearl mussel populations with good
juvenile recruitment) had coarser and better sorted substrata with significantly lower
quantities of fines, and a higher Mn concentration in the fines, than poor quality sites.
Redox potential (Eh) at sites without recruitment differed markedly between the freeflowing water at the surface and at 5 and 10 cm in the bed, whereas no differences were
detectable at good quality sites. This was also true of electric conductivity and, to a lesser
extent, pH. The stream bed at sites lacking pearl mussel recruitment had a more variable
and higher penetration resistance, indicating clogging of the interstitial macropore system
by the deposition of mud and compaction of the stream bed.
4. Our results show that habitat quality for pearl mussels depends strongly on the
exchange between the surface and the interstices, which is governed by physicochemical
characteristics of the stream substratum. Combined measurements of penetration
resistance, depth gradients of Eh and texture were most suitable for assessing stream bed
quality, while water chemistry was insufficient because of the decoupling of interstitial
and free-flowing water at poor quality sites.
Keywords: ecosystem health, redox, sediment, stream substrate, unionid conservation
Introduction
Increasing evidence suggests that freshwater organisms are among the most threatened biota (Stein &
Correspondence: Juergen Geist, Fish Biology Unit, Department
of Animal Science, Technische Universität München,
D-85350 Freising, Germany. E-mail: [email protected]
Flack, 1997; Ricciardi & Rasmussen, 1999) and that
degradation of running waters is globally widespread
(Gleick, 2003). Generally, overexploitation, water pollution, flow modification, destruction or degradation
of habitat, and invasion by exotic species are the five
major threats to global freshwater biodiversity (Dudgeon et al., 2006). Although water quality of European
rivers has steadily improved over recent decades
2007 The Authors, Journal compilation 2007 Blackwell Publishing Ltd
2299
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J. Geist and K. Auerswald
(Aarts, Van den Brink & Nienhuis, 2004), the recovery
of many endangered aquatic species, mostly habitat
specialists, is not proceeding accordingly. Most of
these species depend on a high-quality stream substratum as a permanent or temporary key habitat for
completion of their life-cycles.
Aquatic ecologists have only recently become
increasingly aware that biodiversity, ecosystem processes and ecosystem health in streams often depend
on interactions between surface and groundwater
(Palmer et al., 1997; Boulton et al., 1998; Altmüller &
Dettmer, 2000; Battin et al., 2003), in the hyporheic
zone (Orghidan, 1959). Stream bed conditions and
their dynamics are governed by the flow regime that
determines erosion and sediment deposition and
remobilization. Changes in climate and land-use, as
well as flow regulation and in-stream habitat alteration, can easily change these sensitive processes,
have severe impacts on stream bed and, in turn, on
overall ecosystem functioning. Major shifts in stream
bed composition and processes can alter species
distributions, productivity and even change the production of greenhouse gases (Palmer et al., 1997).
Particular interest in the substratum quality of
streams has mostly been focused on the impacts of
the hyporheic environment to the survival of salmonid eggs in gravel bed streams (e.g. Acornley & Sear,
1999; Soulsby et al., 2001a,b; Malcolm, Youngson &
Soulsby, 2003; Greig, Sear & Carling, 2005). For a
much greater fraction of their long lives, freshwater
mussels depend on the stream substratum quality and
are perhaps the most endangered group of all freshwater animals (e.g. Bogan, 1993, 1998; Williams et al.,
1993; Neves et al., 1997; Strayer et al., 2004). Given
their high original numbers and biomass, and the role
of bivalve molluscs in particle processing, nutrient
release and sediment mixing (reviewed by Vaughn &
Hakenkamp, 2001), the decline of freshwater mussels
could affect profoundly ecosystem processes in
streams.
The characteristics of freshwater pearl mussels
(Margaritifera margaritifera Linnaeus) match those of
’indicator’, ’flagship’, ’umbrella’ and ’keystone’ species, making them the focus of conservation efforts
(Geist, 2005). Margaritifera margaritifera was formerly a
widespread and abundant species, distributed from
the Arctic and temperate regions of Western Russia
through Europe to the north-eastern seaboard of
northern America (Jungbluth, Coomans & Groh,
1985). Several studies have revealed dramatic declines
throughout its range (e.g. Bauer, 1988), and the species
is at present under a serious threat of extinction in
Europe with only a small number of successfully
recruiting populations remaining (Ziuganov et al.,
1994; Young, Cosgrove & Hastie, 2001; Geist, 2005).
As with all unionid mussels, freshwater pearl mussels
have a complex life-cycle, comprising a parasitic
phase on a host fish and a post-parasitic phase, in
which the juvenile mussels live buried within the
stream substratum. Single females can produce several million larvae (glochidia) per year (Young &
Williams, 1984). As the proportion of adults producing glochidia is relatively high, even in sparse
populations (Young & Williams, 1983; Schmidt &
Wenz, 2000, 2001; Hastie & Young, 2003a), reduced
fecundity does not seem to be the limiting factor
preventing juvenile recruitment in most pearl mussel
populations. Similarly, a recent study into the availability of host fishes in European freshwater pearl
mussel streams revealed that a sparse host fish
population seems to limit the recruitment in only a
fraction of populations (Geist, Porkka & Kuehn, 2006).
As juvenile pearl mussels, in their post-parasitic
phase, depend on a continuously well-aerated and
partly stable substratum for a period of at least
5 years, this phase in their development in which they
live buried in the bed is considered to be the most
vulnerable and limiting for juvenile recruitment (e.g.
Bauer, 1988; Buddensiek et al., 1993; Geist, 1999a,b).
River bed substratum characteristics appear to be the
best physicochemical parameters for describing
M. margaritifera habitat and for explaining their highly
aggregated, non-random spatial dispersion (Hastie,
Boon & Young, 2000). Survival of the juvenile, postparasitic stage is also therefore a potentially useful
measure of stream-bed quality.
Integrative methods are needed to assess and
monitor the substratum quality of streams and rivers.
Such methods are not only necessary with respect to
the conservation of freshwater invertebrates (Strayer,
2006), but are applicable in a much broader context.
Here, we suggest and test an integrative assessment of
stream substratum quality, based partly on a transfer
of methods from soil science to stream ecology. The
recruitment of M. margaritifera was used as a biological indicator for habitat quality to assess the discrimination of sites based on the various physicochemical
habitat parameters.
2007 The Authors, Journal compilation 2007 Blackwell Publishing Ltd, Freshwater Biology, 52, 2299–2316
Stream bed characteristics and mussel recruitment
Methods
Study area and biological categorization
In 2002–05, 26 European streams with extant freshwater pearl mussel populations from the drainage
systems of the rivers Elbe, Danube, Weser, Rhine,
Aulne, Lorient, Kemijoki, Tuloma and Owenriff, in
Germany, the Czech Republic, Belgium, Finland,
France, Luxembourg and Ireland were investigated
(Table 1; Fig. 1). Criteria for the selection of the
streams were the occurrence of significant numbers
Fig. 1 Map of sampling sites.
2301
or densities of pearl mussels (at present or in the past),
the requirement for a wide and representative geographical range of sites, and the availability of actual
information about the distribution and recruitment
status of pearl mussels and their host fishes (for
details see Geist et al., 2006). Additionally, we aimed
to include as many populations with juvenile recruitment as possible, and matched such ’functional’ (F)
streams with ’non-functional’ (NF) streams (without
recent recruitment) of similar geomorphological and
physicochemical properties.
Prior to investigation of the stream bed, the
substratum was checked for the presence of mussels
with glass bottom viewing baskets or by snorkelling.
Within each stream, the present range of mussels was
subdivided into stretches of equal length. Depending
on the total length of river with mussels, their
distribution pattern, and the structural variability of
the stream, five to 17 stretches along each stream were
defined (mostly 5–50 m long). Within each stretch, a
typical transect across the stream was analysed,
including at least the middle of the channel and two
spots on either side near the bank. In ’F’ populations,
additional spots were analysed directly in areas where
high densities of living juvenile post-parasitic pearl
mussels were found, to include a sufficient number of
’F’ areas. These additional areas and the transects are
referred to as ’sites’ throughout the text. In each spot,
all measurements were made directly adjacent to each
other, in order to be able to link the different
parameters. Individual ‘spots’ were 1 m2 in size, as
defined by a metal frame placed on the stream
bottom. First, substratum penetration resistance was
measured, followed by redox measurements and
subsequent water sampling at various depths in the
substratum. Finally, substratum samples for texture
analysis were collected (see below).
In 15 of the streams investigated, there has been no
recruitment of juvenile pearl mussels for at least
30 years. These streams are referred to as ’NF’. Recent
recruitment (more than two juvenile mussels younger
than 5 years per m2 at sampling sites) was observed
in seven ’F’ streams. A third category ’potentially
functional’ (PF) was established for streams and sites
where there was either (i) only very sparse juvenile
recruitment at the sites investigated (i.e. only one or
two mussels younger than 5 years per m2) or (ii) no or
sparse juvenile recruitment in the stream for reasons
clearly other than substratum quality, e.g. lack/local
Sächsische Saale
Sächsische Saale
Sächsische Saale
Sächsische Saale
Sächsische Saale
Weiße Elster
Weiße Elster
Weiße Elster
Moldau
Naab
Naab
Regen
Gaißa
Fränkische
Saale ﬁ Main
Sauer ﬁ Mosel
Aller
Elbe
Danube
Rhine
Lutto
Tuloma
Owenriff
OU
LU
EL
LF
RE
Our
Lutter
Elez
Le Fao
Ruisseaux de l’
Etang du Loc’h
Pikku-Luiro
Jojojoki
Kolmiloukkonen
Ruohojärvenoja
Owenriff
PI
JO
KK
FR
OW
ZI
SR
WB
HB
MB
TB
RB
HaB
BL
CC
WN
BI
WR
KO
RA
SC
Code
Zinnbach
Südliche Regnitz
Wolfsbach
Höllbach
Mähringsbach
Triebelbach
Raunerbach
Haarbach
Blanice
Hruška channel
Waldnaab
Biberbach
Wolfertsrieder Bach
Kleine Ohe
Ranna
Schondra
Population
FIN
FIN
FIN
FIN
IRE
L, D, B
D
F
F
F
D
D
D
D
D
D
D
D
CZ
CZ
D
D
D
D
D
D
Country
F/NF
F/NF
F/NF
F/NF
F
NF/PF
F/NF
PF
PF
PF
NF
NF
NF
NF
NF
NF
NF
NF
F
PF
PF
NF
NF
PF/NF
NF
NF
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Redox
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Conductivity
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Texture
analyses
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C, N,
P, Fe,
S, Mn
F, functional, i.e. recruitment takes place; NF, non-functional, i.e. without juvenile pearl mussels; PF, potentially functional, i.e. either indication but no sufficient proof for sufficient
juvenile recruitment, or sites with lack of juvenile mussels because of other reasons, such as lack of host fish or recent construction of culturing channel; Country codes: B, Belgium;
CZ, Czech Republic; D, Germany; F, France; FIN, Finland; IRE, Ireland; L, Luxembourg.
Luiro
Kemijoki
Lorient
Weser
Aulne
Subdrainage
Drainage
Functionality
categories
included
(F/NF/PF)
Table 1 Functionality classification for individual streams and applied analyses for bed quality assessment; ’functionality’ indicates if the stream supports recruitment of postparasitic freshwater pearl mussels (Margaritifera margaritifera)
2302
extinction of host fish. An artificial channel for the
rearing of pearl mussels (the so-called ‘Hruška-channel’), with an artificial high-quality substratum, which
will probably allow successful recruitment of pearl
mussels, was also included in this category. Generally,
all the pearl mussel streams included were oligotrophic, low in calcium, and were located in areas of
similar geology with granite being the most common
primary rock.
In addition to the initial screening of the stream bed
for mussels, grab samples for textural analyses were
also screened for juvenile mussels in the field, and
again when sieves were checked during textural
analysis in the laboratory. Finally, after taking texture
samples, the substratum at each spot was gradually
removed by hand to a depth of 10–15 cm, to search for
buried juvenile mussels. According to these screenings, each site was assigned to F, PF or NF. Areas
without juveniles within F streams were also classified
as NF. A total of 275 sites (about 1000 individual
spots) comprising 46 F sites, 39 PF sites and 190 NF
sites were analysed with a differing number of
replicates depending on the parameter analysed (see
below). In some cases, however, not all parameters
could be measured because of technical (e.g. restricted
substratum depth) or conservation limitations (e.g.
restricted texture sampling at sites with many juvenile
mussels).
An unpublished pilot study showed that bed
quality was poorest at the time of lowest discharge
and highest water temperature during late summer,
when depositions of fine sediments were greatest and
oxygen concentration within the stream substratum
was lowest. Thus, this period of worst-case conditions
was selected for sampling, as it is limiting for the
performance and survival of sediment-dwelling
organisms.
Texture analyses
Stream substratum was sampled from 104 sites (each
about 2–3 kg dry weight, each combined from all
’spots’ at one site). Sampling followed a slight modification of the cylinder method described previously
(Geist, 1999a,b). Briefly, a plastic tube (total diameter
of 7.4 cm, inner diameter 7.2 cm, total length 60 cm)
was pushed into the substratum to a depth of 10 cm
and closed with a trowel from below. The cylinder
was then lifted and the material transferred into
2303
buckets. In accordance with the other analyses
applied in this study, only the upper 10 cm layer
was considered, as biologically the most important
zone. Several studies state that species richness and
abundance of the fauna in the substratum is highest in
the upper layers (e.g. Palmer et al., 1997) and that this
zone is also most relevant for spawning salmonids
(e.g. Grost, Hubert & Wesche, 1991) and juvenile pearl
mussels. Although the use of a corer is prone to the
loss of fines, with greatest losses at sites with most
fines and/or where large stones prevent easy sealing
of the corer, the close correlation between undisturbed
penetration resistance and texture shows that such
losses are small compared with differences in textures
within and between streams. Additionally, other
sampling methods (e.g. freeze-core) would not have
complied with regulations for sample collection in
pearl mussel streams. Samples were cooled immediately to reduce degradation of organic matter, transported to the laboratory and stored for a maximum of
3 days at 4 C before analysis. Grain sizes were
fractioned with a wet-sieving tower (Fritsch, IdarOberstein, Germany) of decreasing mesh sizes (20, 6.3,
3.15, 2.0, 1.0, 0.63, 0.2 and 0.1 mm). The fractions
retained on each sieve were dried at 70 C and
weighed (to nearest g). The proportions of the different grain fractions, the geometric mean particle
diameter (dg) and the geometric standard deviation
(SDg) were calculated according to Sinowski &
Auerswald (1999). Considering the restricted grab
sample volumes, the largest fraction >20 mm was
generally excluded from further analyses.
Chemical analyses
The biologically most active fraction (<100 lm)
retained from the texture analyses was homogenized
with a mortar and pestle and 3 g were analysed for
total C, N, S, P, Fe and Mn. Concentrations of C, N
and S were measured with a Vario Max CNS analyser
(Elementar, Hanau, Germany) according to German
standard norm (DIN standard) protocols. The P, Fe
and Mn were analysed by ICP-OES with an Optima
3000 (Perkin-Elmer, Norwalk, CT, U.S.A.) after acidic
sample preparations according to DIN 38414 and DIN
EN ISO/IEC 17025 (DIN, Deutsches Institut für
Normung, 2001a,b). Pearl mussel streams are extremely low in carbonate and measurements of C thus
reflect organic C.
2304
Gradients in redox potential
In order to measure redox potential (Eh) in stream
water and at various substratum depths, a Platinum
electrode was constructed, consisting of a Pt tip
(2 · 5 mm free Pt) embedded and fixed into a
1 cm · 0.7 m plastic tube filled with epoxid resin.
This resembles a basic design used previously with
multiple and fixed probes for measuring Eh in
microsites of the soil (Fischer, Flessa & Schaller,
1989). A standard copper electrode cable was linked
to the Pt tip and also embedded into the epoxid resin
inside the plastic tube. The electric potential between
the Pt tip and an Ag/AgCl2 reference electrode
(SCHOTT, Mainz, Germany) was measured with a
handheld voltmeter (315i pH-meter; WTW, Weilheim,
Germany) and Eh values referring to the standard
hydrogen potential were obtained by correcting for
Ag/AgCl2 and temperature. The low electric conductivity in most of the pearl mussel streams (often below
150 lS cm)1) was compensated by inserting the Ag/
AgCl2 reference electrode into a 3 M KCl agarose-gel
filled salt bridge, a 2-cm wide and 0.7-m long PVC
tube with a permeable ceramic membrane on the
bottom end. The Eh and reference electrodes were
calibrated with a standardized redox buffer solution
of Eh ¼ 220 mV and pH 7.0 (Mettler Toledo Process
Analytics, Greifensee, Switzerland).
Redox potential was measured at 5–17 transects
(45–153 spots) per stream. At each single spot, Eh was
first measured in the free-flowing water, and then at
depths into the substratum of 5 and 10 cm. Values
below Eh ¼ 300 mV indicate anoxia and values above
300 mV oxic conditions (Schlesinger, 1991).
Gradients in conductivity and pH
Differences in electrical conductivity and pH between
interstitial and free-flowing water indicate low hydrological exchange between the water column and
interstices. Conductivity (corrected to 20 C) and pH
were measured using handheld 315i conductivity and
315i pH-meters (WTW, Weilheim, Germany) in freeflowing water and in interstitial water from 5 and
10 cm depth. Samples of interstitial water were taken
using a 30-cm long fixed PVC tube (outside diameter
5 mm, inner diameter 3.5 mm), attached to a 1-m long
flexible plastic hose and a 50-mL syringe (Braun,
Melsungen, Germany), which was used to create a
vacuum. The tube was marked with coloured lines for
sampling pore water from 5 and 10-cm depths. For
each sampling spot, 15 mL of water were extracted
from each depth and from the free-flowing water.
Samples were transferred to 50-mL Falcon tubes
(ROTH, Karlsruhe, Germany) for immediate measurement.
Although tube sampling in coarse substrata may be
prone to water being drawn down the sides of the
tube from the surface, our experiments in various
pearl mussel streams revealed significant physicochemical differences between interstitial and surface
water. Moreover, the properties of water from different depths did not suggest mixing. Both observations
indicate that the method is reliable. No measurements
of dissolved gases were carried out in tube water
samples, as we were cautious about changes in
dissolved gases because of vacuum sampling.
Penetration resistance of the substratum
Resistance of the substratum surface was measured
with a hand-held pocket penetrometer (Eijkelkamp
Agrisearch Equipment, Giesbeek, the Netherlands) to
assess the interface between free-flowing water and
the interstitial zone. Low resistance indicates unconsolidated fine sediment, whereas high values can
either indicate consolidation, e.g. by colmation, or an
extremely coarse substratum. Although penetrometer
analyses are frequently used in soil science (e.g.
Nearing & West, 1988; Bradford, Truman & Huang,
1992; Becher, 1994), only a few authors (e.g. Johnson &
Brown, 2000) have used them for assessment of
stream beds. Normally, penetration resistance was
measured nine times at each site (three in each spot on
either side near the bank and in the middle of the
stream). In F areas, measurements were carried out
directly where juvenile pearl mussels occurred. At NF
and PF areas, measurements were made where the
stream bed seemed to be a potential habitat for
sediment-dwelling organisms. This included gaps
between larger stones and boulders, but excluded
outcrops of pure bedrock, boulders or stones. The
blunt ended tip of the penetrometer was pushed into
the stream bed to a depth of 6 mm and the resulting
resistance was read in kg cm)2. In order to ensure
penetrometer readings over a wide variety of stream
bed types, four metal adapter discs with diameters of
15, 18, 20 and 25 mm were used and the resulting
readings corrected according to the adapter area. A
minimum value of 0.001 kg cm)2 was assigned to
areas with extremely soft mud, where even the use of
the largest adapter disc did not produce any detectable reading.
2305
(a)
Statistical analyses
The F, NF and PF sites were mainly compared by oneway A N O V A , ordinary linear regressions and t-tests
implemented in S A S (version 9.1, SAS Institute Inc.,
Cary, NC, U.S.A.). In some cases, in which the averages
between groups were similar but the variances were
different, Cochran’s test (Cochran, 1941) was performed to test for significant (P < 0.05) differences in
variances. To account for the different sample sizes
between functionality groups, the harmonic mean of
the sample size was used. To characterize the distribution of values within the different functionality
groups, Box–Whisker plots were drawn, and additionally, kernel density distributions were estimated.
Kernel density distribution is advantageous where the
distribution is multi-modal or where differences
between distributions are small. For the theory of
kernel density estimation see Silverman (1986).
Stepwise logistic regression was used to identify the
most relevant parameters for discriminating between
F and NF sites. Their interdependence was then tested
further by multiple regression analyses containing a
dummy variable, where the dummy variable was 1
for NF and 0 for F (Fox, 1997). The regression
coefficient of the dummy variable then denotes the
trued difference between F and NF.
Results
Single factor analysis
Water chemistry Although F sites had significantly
lower conductivity and pH, and higher Eh than NF
sites, the quantitative differences were modest, the
variation was wide and the groups overlapped
(Fig. 2a–c). Generally, all streams included in this
study had continuously high oxygen saturation in the
water column and no fish or mussel mortality because
of oxygen depletions had been observed in any of
these streams. The free-flowing water conditions per se
seem not likely to be crucial for the survival of
juvenile freshwater pearl mussels.
(b)
(c)
Fig. 2 Box–Whisker plots (crosses: minimum, maximum;
Whisker: 0.05 and 0.95 percentiles; Box: 0.25 quartile, median
and 0.75 quartile) of depth profiles for redox potentials (a)
electrical conductivity (b) and pH (c) at functional (n ¼ 109, 46,
44, respectively), potentially functional (n ¼ 57, 42, 42, respectively) and non-functional sites (n ¼ 254, 244, 210, respectively),
in the free-flowing water and at 5 and 10-cm depth. The right
axis indicates experimentally determined Eh values at pH 7
where Mn2+ and Fe2+ formation start and below which O2 and
NO
3 are no longer detectable (after Brümmer, 2002).
Penetration resistance Log-transformed penetrometer
readings differed highly significantly (P < 0.001) in
SD between F, NF and PF according to Cochran’s test,
but there was no significant difference between
means. Geometric mean penetration resistance at F
sites averaged 0.16 kg cm)2 and varied over one order
of magnitude ranging from 0.04 to 0.39 kg cm)2
(Fig. 3). In contrast, NF sites had similar resistance
(geometric mean, 0.18 kg cm)2), but the variability
ranged over more than three orders of magnitude
from <0.001 kg cm)2 at sites with accumulation of soft
–
2306
Fig. 3 Box–Whisker plots (crosses: minimum, maximum;
Whisker: 0.05 and 0.95 percentiles; Box: 0.25 quartile, median
and 0.75 quartile) for penetration resistance at functional (n ¼
182), potentially functional (n ¼ 160) and non-functional sites
(n ¼ 659).
mud up to 4.00 kg cm)2. PF sites were intermediate
with resistances ranging from 0.03 to 0.80 kg cm)2.
For a number of NF sites, where penetration resistance was monitored over several months, resistances
were found to be smallest in October and highest in
April, apparently caused by the accumulation of soft
fine material over the summer and its remobilization
during spring floods.
Substratum texture The F and NF streams differed
markedly in texture, with a higher percentage of fines
at NF sites (Fig. 4). Grain size distribution generally
followed a log-distribution and can thus be described
by the mean geometric particle diameter dg and its
SDg. On average, dg was 7 mm at F sites and 3 mm at
NF sites, while average SDg was significantly larger
(P < 0.001, Cochran’s test) for the NF sites.
On average, F sites contained <3% (max. 7%) of
particles <200 lm and <2% (max. 5%) of particles
<100 lm. In contrast, NF sites had on average 13%
(max. 78%) of material <200 lm and 9% (max. 56%)
particles <100 lm. A similar relation was also evident
for fines <1 mm, with NF sites having on average 35%
(max. 96%), and F sites only containing 18% (max.
38%). Opposite patterns were observed for the larger
grain sizes, with F sites generally having a coarser
substratum.
Chemical composition of fines The concentrations and
ratios of N, C, S, P and Fe varied largely and
overlapped widely between the different functionality
categories (Table 2). Although the mean concentrations of S and P were significantly greater at F than at
NF sites, concentrations overlapped between the two.
Mn discriminated strongly between the two groups of
streams, with an average 10-fold higher concentration
at F than at NF sites. This difference is mainly
attributable to one-third of the F samples, which had
Mn concentrations up to about 150 g kg)1 in the
material <100 lm. The highest concentrations of Mn
were found at well-sorted sites with a low percentage
of fines. The precipitation of the sensitive redoxindicator Mn (reduced and mobile when oxygen is
just depleted, and reoxidized and immobile at Eh ¼
0.35; Brümmer, 2002) was evident only at sites with
SDg <10 lm, i.e. presumably where reduced Mn from
deeper, anaerobic layers was oxidized and immobilized in the top layer because of a good exchange
between free-flowing water and interstitial water. At
NF sites with a high percentage of fines and anoxic
conditions in the surface layer, Mn was also mobilized
from these zones, resulting in Mn values below the
minimum values for F sites (Table 2). For Fe, which
only becomes mobile at Eh 0.15 V, no difference
between F and NF sites was evident, presumably
because such low Eh values did not occur.
Spatial interactions
Fig. 4 Grain size distribution of the stream bed at functional
(n ¼ 14), potentially functional (n ¼ 21) and non-functional
(n ¼ 69) sites; shaded areas indicate the range between minimum and maximum values.
Depth profiles in redox potential Redox potential depth
profiles differed markedly between F and NF sites
(Fig. 2a). At F sites, Eh in the free-flowing water
averaged 0.53 V with only marginal differences in Eh
Table 2 Comparison of the chemical composition of the biologically most active fraction <100 lm between non-functional
(n ¼ 76), potentially functional (n ¼ 17) and functional streams
(n ¼ 13) where the fraction <100 lm on average contributes
7.8%, 1.4% and 1.5%, respectively
NonPotentially
functional functional Functional
N (%)
Minimum
0.14
0.22
5 percentile
0.18
0.26
Arithmetic
0.32 A
0.42 B
mean
95 percentile
0.53
0.59
Maximum
0.61
0.82
C (%)
Minimum
1.30
1.80
5 percentile
1.70
2.90
Arithmetic
3.69 A
4.65 A
mean
95 percentile
6.50
7.75
Maximum
9.60
9.85
C/N
Minimum
8.08
8.18
5 percentile
8.76
9.64
Arithmetic
11.24 A
11.02 A
mean
95 percentile
14.00
12.32
Maximum
15.74
13.08
S (%)
Minimum
0.03
0.05
5 percentile
0.04
0.07
Arithmetic
0.12 A
0.16 B
mean
95 percentile
0.35
0.35
Maximum
0.54
0.38
P (mg kg)1)
Minimum
323
215
5 percentile
383
256
Arithmetic
765 A
1801 B
mean
95 percentile 1306
3336
Maximum
2406
3556
Fe (g kg)1)
Minimum
14.5
18.6
5 percentile
20.8
23.8
Arithmetic
40.0 A
37.4 A
mean
95 percentile
53.9
60.0
Maximum
63.2
67.1
Mn (g kg)1)
Minimum
0.3
0.4
5 percentile
0.6
0.6
Arithmetic
2.8 A
3.1 A
mean
95 percentile
7.0
7.8
Maximum
10.3
9.2
All
0.14
0.18
0.38 AB
0.14
0.19
0.35
0.60
0.72
0.58
0.82
1.41
2.22
4.60 A
1.30
1.73
4.01
7.89
12.99
7.25
12.99
9.97
9.99
11.82 A
8.08
8.79
11.28
15.90
17.99
14.06
17.99
0.07
0.07
0.25 C
0.03
0.04
0.14
0.53
0.64
0.43
0.64
280
197
1451 B
215
338
1064
3034
3042
3039
3556
17.1
25.3
51.7 A
109.2
116.8
1.4
1.7
28.6 B
115.1
148.3
14.5
20.1
41.1
61.6
116.8
0.3
0.6
6.5
10.0
148.3
Means within a row followed by different letters differ significantly at P < 0.05.
2307
at 5 cm (on average 0.51 V) and 10-cm depth (0.47 V),
indicating either an intense exchange between the
water column and interstitial water or low levels of
respiration. At all F sites, Eh at 5 and 10-cm depth
(with a few exceptions at 10 cm) indicated oxic
conditions even during summer (min. Eh at 5 and
10-cm depth at 0.33 V and 0.24 V, respectively). In
contrast, average Eh at NF sites decreased from 0.47 V
in the free-flowing water to 0.33 V at 5 cm and 0.27 V
at 10 cm depth. PF sites were intermediate. Long-term
monitoring series of Eh at specific sites (data not
shown) showed that Eh was generally lowest during
summer, coinciding with high water temperature, low
water flow, accumulation of fines, low oxygen concentration and rapid decomposition. During winter, at
low water temperatures and high flow rates, interstitial Eh was much higher and oxygen was often
available at sites which were clearly identified as NF
and oxygen-depleted during the summer. For instance, the average difference in Eh at 10 cm between
F and NF sites for one continuously monitored stream
was 0.24 V during ‘worst-case’ conditions in summer,
but only 0.18 V at other times of the year.
Depth profiles in conductivity and pH Conductivity in
the free-flowing water ranged from 11 lS cm)1 in
northern Lapland to 154 lS cm)1 in Germany. The
mean electric conductivity differed significantly
among stream groups. This was evident in the freeflowing water and at 5 and 10-cm depth (Fig. 2b).
Mean electric conductivity was lowest at F sites.
The mean electric conductivity of the free-flowing
water was similar to that at 5 and 10-cm depth in all
three functionality groups. This was explained by the
fact that a separation between the water column and
interstitial water resulted in either higher or lower
conductivity in the interstitial, but without changing
the average substantially. The absolute difference
(positive or negative) between the free-flowing and
interstitial water at 5 and 10-cm depth was about
20 lS cm)1 greater at NF sites than at F or PF sites,
where only minor deviations of a few lS cm)1 were
found (Fig. 5). The mean absolute difference from the
free-flowing water was only slightly larger for 10-cm
depth than for 5-cm depth (for NF sites on average 21
versus 18 lS cm)1), indicating a barrier between freeflowing and interstitial water within the first 5 cm. In
particular in NF sites, apparently restricted exchange
between free-flowing and interstitial water occasion-
2308
Table 3 Final coefficients of a stepwise logistic regression with
SE and error P of the Wald chi-square test for the odds of
functional versus non-functional streams depending on geometric mean diameter dg and penetration resistance (n ¼ 78)
Intercept
dg (mm)
Penetration resistance (kg cm)2)
ally caused differences in conductivity of 100 lS cm)1
or more, with a maximum deviation of 300 lS cm)1,
whereas F sites only had a maximum difference of
18 lS cm)1. Only four out of 138 F sites (<3%) had a
deviation >10 lS cm)1 between surface and interstitial water.
The pH differed less clearly between F and NF
sites than did the other parameters. While on average
the pH in the free flowing water was higher at PF
and NF sites than at F sites (Table 2), this difference
decreased with increasing depth into the substratum
(Fig. 2c). The higher mean in the free-flowing water
at NF and PF sites was mainly caused by some very
high values (up to pH 8.8) indicating intense CO2
removal during the day. Because of a restricted
hydrological exchange with interstitial water, especially at NF sites, these peaks were either only partly
transferred to the interstitial water or compensated
for by respiration. Depth profiles in conductivity and
pH identified a number of NF sites with limited
hydrological exchange between the surface and the
interstitial zone.
SE
P-value
)1.5
1.3
)31.1
1.0
0.4
11.9
0.131
0.001
0.009
classified 95% of the sites (whereas the best combination of all water chemistry parameters correctly
classified only 77%). The mechanistic interdependence between both parameters, however, was better
reflected in a multiple regression, using dg as independent and penetration resistance as dependent
variable, and a dummy variable to separate sites
according to the presence or absence of juvenile
mussels (’F’ versus ’NF’) (Fig. 6):
lgðrÞ ¼ 0:9ð0:3Þ þ 0:71ð0:09ÞlgðdÞ 0:5ð0:1Þf
r2 ¼ 0:463; P < 0:001; n ¼ 78 ðSE’s in parenthesesÞ
With r ¼ resistance in kg cm)2, d ¼ median grain
diameter in mm and f ¼ functionality (1 ¼ F, 0 ¼ NF).
The regression indicates that penetration resistance
increases highly significantly with increasing d.
Coarse substrata have a higher penetration resistance
than fine textured substrata. Variation, however,
is high and can be more than one order of magnitude, especially in coarse stream beds. The highly
significant (P < 0.001) coefficient of parameter f
–
Fig. 5 Frequency of the absolute difference in electrical conductivity between the free-flowing water and the interstitial
water in either 5 or 10-cm depth for functional (n ¼ 92), potentially functional (n ¼ 84) and non-functional sites (n ¼ 480).
Coefficient
Interactions between parameters
Multivariate analyses were found to discriminate
most effectively between F and NF sites. Stepwise
logistic regression retained only substratum parameters, but no physicochemical parameters of the freeflowing water. In particular, Eh (which is, in turn,
influenced most strongly by dg and penetration
resistance), explained most of the differentiation
between F and NF sites. The best logistic regression
used only dg and penetration resistance to distinguish
between F and NF sites (Table 3) and correctly
Fig. 6 Dummy regression of penetration resistance depending
on geometric mean diameter and functionality for functional (d)
and non-functional (+) sites (r2 ¼ 0.463, P < 0.001, for equation
see text). Potentially functional sites (s) are shown but not
included in the regression. For comparison, the logistic regression function is displayed as shades of grey.
indicates that NF sites had a threefold higher penetration resistance than F sites at an identical d.
The interaction of penetration resistance and grain
size determines stream bed quality for pearl mussels,
indicating that juveniles need a coarse but loose
substratum where neither grain size nor compaction
restrict the exchange of free-flowing water with the
interstitial. In addition, there was a weaker though
significant correlation between penetration resistance
and SDg (r2 ¼ 0.046, P < 0.05). Heterogeneous substrata, where pores created by large particles are more
prone to be filled or clogged by fine particles, have
more inter-particle bonds than better sorted sediments. Penetration resistance was also found to
decrease with increasing carbon content (r2 ¼ 0.117,
P < 0.001) because of the lesser compaction caused by
the approximately eight times lower density of
organic particles in water (c. 1.2–1.0 ¼ 0.2 g cm)3)
when compared with inorganics (c. 2.6–1.0 ¼
1.6 g cm)3). However, as dg and organic matter
content were linked (r2 ¼ 0.086, P < 0.01), both regressions were partly biased by this collinearity.
Redox potential within the stream bed decreased
strongly with decreasing dg (Fig. 7). Eh at 10-cm depth
was about 100 mV lower than at 5-cm depth, although
texture had identical effects at both depths. This
relation was not caused by the organic matter content,
which increased with decreasing dg, but presumably
by the reduced exchange of water between the
interstitial and the stream in finer sediments. Eh was
independent of the organic matter content (r2 ¼ 0.007,
NS at 5-cm depth and r2 ¼ 0.001, NS below), indicating that oxygen concentration and redox in the
hyporheic zone were primarily governed by the
2309
supply of oxygen from the free-flowing water into
the interstitial zone, and not primarily by the amount
of labile organic material in the stream bed. The effect
of dg on Eh in the free-flowing water was less
pronounced (r2 ¼0.092, P < 0.01) than the effect within the stream bed (r2 ¼ 0.284, P < 0.001 at 5-cm depth
and r2 ¼ 0.246, P < 0.001 below). Eh also decreased
with SDg of the texture (r2 ¼ 0.171, P < 0.001, r2 ¼
0.249, P < 0.01 and r2 ¼ 0.164, P < 0.001 in free-flowing water and at 5 and 10-cm depth, respectively),
indicating that poorly sorted stream beds were more
prone to oxygen depletion.
An inverse correlation between the C/N ratio in fine
material and electric conductivity was observed (a
high C/N is expected when organic matter is derived
from forested catchments and a low C/N indicates
eutrophication or agricultural-derived organic matter)
(r2 ¼ 0.042, P < 0.5, r2 ¼ 0.040, P < 0.01 and r2 ¼ 0.024
NS, in free-flowing water, and at 5 and 10-cm depth,
respectively). Highest electric conductivity was found
at sites with a C/N ratio of 8–10. Similarly, the C/N
ratio decreased slightly with pH, presumably because
of the stronger acidification of forested catchments
as compared with agricultural, limed catchments
(r2 ¼ 0.023 NS, r2 ¼ 0.091, P < 0.01 and r2 ¼ 0.067,
P < 0.01 in free-flowing water, and at 5 and 10-cm
depth, respectively). The pH decreased slightly with
decreasing dg, indicating a higher probability for
acidification in catchments with coarse textured
stream beds, but the degree of acidification was low
and thus not limiting (r2 ¼ 0.097, P < 0.01, r2 ¼ 0.065,
P < 0.01 and r2 ¼ 0.061, P < 0.05 in free-flowing
water, and at 5 and 10-cm depth, respectively).
Discussion
The discrepancy between free-flowing and interstitial
water
Fig. 7 Influence of geometric mean substratum particle diameter on redox potentials for the free-flowing stream water (x),
and for 5 cm (s) and 10 cm (d) stream-bed depths (n = 90).
The characteristics of the stream substratum, in
particular the Eh depth profile and a combination of
penetration and textural analyses, were powerful
indicators of pearl mussel recruitment and discriminated well between ’F’ and ’NF’ sites. Physicochemical
parameters of the free-flowing water overlapped
between F and NF streams and were less useful for
assessing the suitability of individual sites for pearl
mussels. This concurs with observations that the
survival of juvenile pearl mussels raised in cages in
2310
the water column can strongly differ from that within
the natural stream substratum in the same streams
(Strecker, Bauer & Wächtler, 1990). Our findings do
not necessarily contradict previous studies, which
showed that long-term data sets for several water
parameters can be useful in defining the water quality
requirements of pearl mussels (e.g. Bauer, 1988;
Sachteleben et al., 2004). The higher predictive power
of the substratum parameters can be explained by the
fact that: (i) the habitat requirements of adult and
juvenile pearl mussels differ; (ii) only substratum
parameters can accurately describe the habitat of
juveniles and (iii) substratum parameters are less
prone to short-term fluctuations (which limit the use
of free-flowing water parameters if long-term data
sets are not available).
We observed that adult and juvenile pearl mussels
used different microhabitats, although both life stages
occurred simultaneously in the same habitat patches.
During summer, adult mussels were usually only
partly buried and filtered free-flowing stream water,
whereas juveniles (younger than 5 years) were completely buried and thus exposed to interstitial water.
Our findings that physicochemical conditions between the surface and interstices were very similar
in F streams but could differ strongly in NF streams,
shows that conditions in the free-flowing water are
weak predictors of interstitial water quality and thus
of habitat quality for juvenile pearl mussels and other
interstitial organisms.
Based on our results, habitat quality seems to be
governed by the physical connectivity of free-flowing
water and the interstitial zone. The hyporheic zone
can account for a high proportion of the total
respiration of the stream system (e.g. Fellows, Valett
& Dahm, 2001; Battin et al., 2003) and, at sites with
insufficient exchange between both compartments,
anoxic conditions within the stream substratum can
occur. Although pearl mussels can withstand shortterm oxygen deficits, the bivalve body plan imposes
limitations to oxygen regulation (Wilson & Moorkens,
1992). A low concentration of dissolved oxygen can
slow growth in clams and mussels (e.g. Belanger,
1991), and may result in reduced fitness.
The pronounced Eh depth gradient, the discrepancy
in electrical conductivity between free-flowing and
interstitial water, and the threefold higher penetration
resistance at NF sites, all suggest a separation of
interstitial and free-flowing water.
Factors controlling stream bed quality
Previous studies suggested a link between the species
composition and abundance of unionids and substratum particle size distribution (e.g. Neves & Widlak,
1987; Leff, Burch & McArthur, 1990; Hastie et al.,
2000), although single parameters often failed to
explain fully mussel distributions and recruitment.
Brim Box & Mossa (1999) suggested that the lack of
correlation between particle size and mussel distribution in some studies may result from inadequate
sampling and insufficient substratum analyses, but it
seems more likely that this was caused by the
complexity of stream bed processes, which are determined by several factors at the same time.
In our study, multivariate models discriminated
best between F and NF pearl mussel habitats. Two
different multivariate analyses were most successful;
(i) the measurement of Eh at different depths and (ii)
the combination of textural analysis and penetration
resistance. The measurement of Eh depth gradients
quantifies the effects of the hydrological barrier on
juvenile mussel survival, while the combined measurement of texture and penetration resistance assesses
the causes of hydrological segregation of surface and
subsurface water, namely the consolidation of the
stream bed surface. NF stream beds often suffered
from a lack of sorting and a deposition of fines, which
were most probably caused by anthropogenic habitat
modifications, including changes of the natural flow
regime, constructions of dams and weirs, the removal
of wood and boulders from streams, abstraction and
increased soil erosion. Both the smaller grain sizes
and the larger variation in grain size at NF sites
decrease the inter-particle voids, because smaller
grains can occupy voids between larger grains
(Hwang & Powers, 2003). Hence, smaller interstitial
pore volumes and, particularly, lower interstitial
water flow can be expected at NF sites. Furthermore,
only a relatively small range of penetration resistance
values seems to allow for successful mussel recruitment and a number of NF sites may be either too soft
or too hard. Because of the large overlap of penetration resistance or texture between NF and F sites,
however, only the combination of both is sufficient to
characterize stream bed quality.
Physicochemical gradients in the interstices result
from several processes, including (i) the hyporheic
flow pattern and the different properties of surface
and groundwater; (ii) retention, caused by the filtering
effect of pore size and lithologic sorption as well as
the transient storage of solutes caused by reduced
water velocity and (iii) biogeochemical transformations in conjunction with local residence time (Brunke
& Gonser, 1997). In our data set, these gradients seem
to be mainly influenced by the physical state of the
stream bed. F sites had a non-compacted, well-sorted,
coarse substratum with a significantly lower fraction
of fines than NF sites. Interstitial Eh, pH and electrical
conductivity at F sites showed no or only weak
deviations from those in the free-flowing water.
Substratum quality and pearl mussel recruitment thus
seem to be closely linked to the exchange rate between
free-flowing and interstitial water. The intensity of
this exchange largely depends on the stream-bed
permeability, which is influenced by the available
pore volume, pore size and the boundary layer at the
substratum surface. Our results for depth profiles in
Eh and the water chemical parameters suggest that
the streambed surface is most crucial for the rate of
exchange between free-flowing and interstitial water.
In poorly sorted or fine-grained stream beds, the
macropore system can clog and restrict these exchange rates (e.g. Schälchli, 1992; Richards & Bacon,
1994).
Fine sediments may be fatal where they limit
oxygen supply of the interstitial zone and where
eutrophication effects foster high biological oxygen
demands in both the free-flowing water and the
interstitial zone. Hence, the lowest Eh values were
observed during periods of high water temperature
and at sites with a high percentage of fines <100 lm.
Fine sediment loads thus can substantially impact the
hyporheic exchange and associated ecological processes depending on the stream flow conditions, the rate
and frequency of bed remobilization, and the extent of
interaction of the introduced fines with bed sediments
(Regh, Packman & Ren, 2005). Previous studies have
reported negative effects of increased fine sediment
deposition on pearl mussel habitats (Bauer, 1988;
Buddensiek et al., 1993; Geist, 1999a,b), and for other
invertebrate taxa (Strayer et al., 1997). The effect of
fines on unionid growth under artificial culturing
conditions remains controversial (Jones, Mair &
Neves, 2005; Barnhart, 2006), and it seems they can
promote microbial growth and have nutritional effects
when oxygen is not limiting. Under natural conditions, only a few authors observed successful pearl
2311
mussel reproduction in fine sediment or peat-dominated areas (Cosgrove & Harvey, 2003; Altmüller,
unpubl. data).
Salmonid egg development is also negatively affected by fine sediment deposition (e.g. Magee, McMahon & Thurow, 1996; Malcolm et al., 2003; Curry &
McNeill, 2004). However, poor quality (NF) streams in
terms of Eh and texture (e.g. ZI, SR, BI, KO, WR, WB,
SC) were nevertheless found to have high densities
and biomass of sediment spawning fish, such as
brown trout (Salmo trutta) and bullhead (Cottus gobio)
(Geist et al., 2006). Gravel-spawning fish seem to be
more tolerant of unfavourable substratum conditions
than pearl mussels, as (i) spawning usually takes
place in winter when flow and oxygen supply are
high and respiration low; (ii) reproductive success
depends on a much shorter time interval in the stream
bed; (iii) fish can actively search for the most favourable sites and (iv) fish are able to actively engineer the
substratum in redds and spawning pockets, i.e. by
cleaning spawning sites from fines (e.g. Grost et al.,
1991). Indeed, great differences in interstitial physicochemical conditions between summer and winter
were evident for most NF sites (data not shown) and
have also been described in other studies (e.g. Olsen &
Townsend, 2003).
Surprisingly, C, N, the C/N ratio, S and Fe and P in
fines did not separate F and NF sites, although a link
between eutrophication indicators and stream habitat
quality for pearl mussels was expected from previous
studies (Bauer, 1988; Sachteleben et al., 2004). Even
though small significant differences between F and
NF streams could be detected for these parameters,
they resulted from collinearity with the causal factors,
but could neither clearly indicate nor explain functionality. Slight eutrophication in itself does not seem
to be harmful for pearl mussels at sites where the
stream substratum still has a good hydrological
exchange between surface and interstitial water.
An ideal substratum for pearl mussels combines at
the same time attributes of substratum quality and, to
a certain extent, structural stream bed stability, preventing the destruction of the pearl mussel microhabitats and the scouring and drift of juvenile pearl
mussels to less favourable sites. Howard & Cuffey
(2006a) found highest recruitment and lowest mortality of a North American pearl mussel, Margaritifera
falcata (Gould), at low shear stress in low-discharge
years, while large floods can even produce severe
2312
short-term population depletions of the adults (Hastie
et al., 2001). Further, Strayer (1999), Johnson & Brown
(2000) and Gangloff & Feminella (2007) report that
mussel aggregations occurred primarily within hydraulic refugia, i.e. areas of the stream bed that remain
stable at high flow. Our observations indicate that the
best pearl mussel microhabitats in larger rivers are
mostly at sites where coarse, well-sorted sediment is
stabilized by boulders or stones and where both
requirements of substratum quality and stability are
fulfilled. Unstable substratum conditions could cause
the lack of juvenile mussels in some of the PF sites. For
instance, the transport of sand was associated with a
lack of pearl mussel reproduction in northern Germany (Altmüller & Dettmer, 1996). On the other hand,
substratum disturbance, mobility and rearrangement
during floods may remove the fines and thus be
important in preventing compaction of sediment. The
requirements for structural substratum stability over
the 5-year period of larval development and for clean,
oxic substrata may be fulfilled under two principal
conditions: (i) overall stable stream beds, with a low
rate of fine sediment deposition and eutrophication,
plus very scarce (every 5–10 years) flood events that
clean the stream bed and (ii) dynamic co-occurrence
and succession of unstable and stable habitat patches
in one stream, resulting in different recruitment
patterns.
Implications for conservation and stream habitat
restoration
Stream bed quality appears to be the most important
habitat factor limiting the recruitment of the endangered freshwater pearl mussel in many European
rivers. Unionid mussels, in particular juvenile freshwater pearl mussels with their stringent habitat
requirements, are useful indicators of stream substratum quality and processes, themselves playing an
important role in aquatic ecosystems with impacts on
other benthic organisms (Howard & Cuffey, 2006b;
Spooner & Vaughn, 2006). Our results underscore the
importance of considering the interstitial habitat for
the conservation and restoration of functional stream
ecosystems.
The strong substratum-dependence of pearl mussel
recruitment, and the small number of remnant pearl
mussel populations indicate that the balance of
erosion, deposition, sorting and flow in lotic ecosys-
tems is a subtle and fragile system which can easily be
altered. The role of natural disturbances (e.g. flood
events) and human impacts (e.g. catchment land-use,
flow regulation) governs the function of the ‘hyporheic zone’ (Boulton et al., 1998), and should be an
important focus of long-term conservation and restoration strategies. The ecological integrity of river
ecosystems depends on their dynamic character (Poff
et al., 1997) and requires the restoration of natural
flow dynamics in whole river catchments, rather than
artificial and short-term measures, such as the local
addition of gravel.
Efforts to bridge substratum-dependent stages in
the life cycles of endangered organisms, such as
breeding and culturing of freshwater pearl mussels in
cages (Buddensiek, 1995; Hastie & Young, 2003b) or
the breeding of other endangered unionids in captivity (e.g. Jones et al., 2005; Barnhart, 2006 and references therein), can only be seen as emergency
measures to retain the evolutionary and genetic
potential of such species (Geist & Kuehn, 2005; Geist,
2005). They cannot replace measures to restore the
natural habitat.
Both bioindicators, such as the recruitment of
unionid mussels, and technical approaches can be
used to assess habitat quality, e.g. to evaluate
habitat restoration measures. Biological indicators
have high ecological relevance but usually involve
long lag-times (about 5 years for pearl mussels).
Technical measures, such as the measurement of Eh
depth profiles suggested here, are a suitable and
convenient indicator for stream-bed quality, and can
be derived from in situ measurements instantaneously. The combination of penetration measurements and textural analyses (or their replacement
by in situ field ratings, e.g. McDonald et al., 1990;
Oberthür, Goovaerts & Dobermann, 1999) provides
a powerful indicator of a decoupling between
interstitial and free-flowing water and may also be
useful in assessing the effects of changes in the
flow-regimes, which seem to govern stream-bed
processes.
The methodology applied here may provide a tool
for assessing and improving stream substratum quality for freshwater pearl mussels and other substratum-dependent species. The successful restoration of
stream substrata is costly and time intense but is
probably the most essential aspect of ecosystem health
in rivers.
Acknowledgments
We are grateful to A. Wurzinger and W. Sitte from
‘Bayerische Landesanstalt für Landwirtschaft’ for their
support with chemical analyses, to F. Krüger for
technical assistance with Pt electrode construction
and to I. Kögel-Knabner and H. H. Becher for providing laboratory space for texture analyses. J. Geist
acknowledges the financial support provided by ‘Bayerischer Naturschutzfonds’, ‘Landesfischereiverband
Bayern’ and ‘Bayerische Forschungsstiftung’. The
authors would like to thank Markku Porkka (Finland),
S. Terren (Belgium), Dr E. Moorkens and I. Killeen
(Ireland), E. Holder (France), J. Hruška (the Czech
Republic), Dr R. Altmüller, R. Dettmer, M. Lange,
F. Elender, W. Silkenat, K. Dietl (Germany) for their
hospitality, their support and assistance during sample
collections and field investigations. The assistance of
A. Beck, C. Bottlender, M. Formánková, F. and Chr.
Geist and B. Reindl during field sampling was highly
appreciated. We also acknowledge the help of all
government and park authorities involved for providing permits for investigating protected pearl mussel
streams and habitats in various regions of Europe.
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(Manuscript accepted 3 May 2007)

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