Biotic interactions in the infralittoral of Lake Constance
Dissertation zur Erlangung des Doktorgrades der Mathematisch − Naturwissenschaftlichen Sektion,
Fachbereich Biologie der Universität Konstanz
vorgelegt von
Martin Mörtl
aus Hof/Saale Konstanz 2003
Tag der mündlichen Prüfung: 19.05.2004 1. Referent: Prof. Dr. Karl-Otto Rothhaupt 2. Referent: PD Dr. Dietmar Straile
1 Introduction 1 2 A comparison of new sampling methods for macroinvertebrates in the
stony littoral zone of lakes 7
2.1 Introduction 8
2.2 Methods 9
2.3 Results 14
2.4 Discussion 20
3 Community structure of macroinvertebrates in the littoral zone of Lake
Constance: vertical distribution dynamics 29
3.1 Introduction 30
3.2 Methods 32
3.3 Results 41
3.4 Discussion 62
3.5 Conclusion 73
Annex 1 76
Annex 2 78
4 Effects of adult Dreissena polymorpha on settling juveniles, and associated
macroinvertebrates 81
4.1 Introduction 82
4.2 Methods 83
4.3 Results 85
4.4 Discussion 87
5 Predation by wintering waterfowl on zebra mussels and associated
macroinvertebrates 93
5.1 Introduction 94
5.2 Methods 95
5.3 Results 99
5.4 Discussion 108
6 In situ optical assessment of zebra mussel abundance and biomass 113
6.1 Introduction 114
6.2 Materials and procedures 115
6.3 Assessment 117
6.4 Discussion 124
6.5 Comments and recommendations 125 7 Conclusions and perspectives 127
8 Summary 133
9 Zusammenfassung 137
10 References 141
Danke 153
Curriculum vitae 155
List of publications 157
1 General Introduction
Benthic macroinvertebrates are the linking trophic levels between primary production, allochthonous input and top predators in the littoral zone. They are important in the food web at littoral and profundal areas in most freshwater and marine systems. Benthic macroinvertebrates are extremely diverse, are represented by many phyla and have differing requirements for feeding, growth, and reproduction. This often results in an extremely heterogeneous distribution of the macroinvertebrate community (Wetzel, 2001).
Even though the impact of benthic macroinvertebrates is better understood in lotic systems, research has recently focused on benthic macroinvertebrates in the littoral zone of lakes (e.g. Diehl, 1995). Only little is known about depth and time patterns of benthic macroinvertebrates in this habitat. While particularly the number of different microhabitats in streams makes it difficult to obtain representative samples (Peckarsky, 1984), the lack of unidirectional flow in the stony littoral zone adds to the problems caused by habitat heterogeneity. Hence, hard bottom substrates, which make up a major part of the upper littoral zone of large lakes, are probably the most difficult habitats to examine quantitatively (Downing, 1984). No standardised or commonly used method for quantitative sampling of lentic systems has been developed. Therefore, the
community, productivity, and trophic interrelationships of the benthic fauna are poorly understood in lakes (Wetzel, 2001).
The presence or absence of organisms in a lake might depend on large-scale factors, such as climate, geology, or colonisation history (Johnson and Goedkoop, 2002). Comparisons of the fauna of littoral zones in different lakes revealed the importance of various environmental variables. Lake morphometry, productivity, and water chemistry (Jackson and Harvey, 1993; Bailey et al., 1995), as well as biotic factors, e.g. the presence of fish and the extent of predation exerted on the macroinvertebrate community (Jackson and Harvey, 1993), have proven to be good predictors for comparisons of invertebrate communities among lakes (Wong et al., 1998).
Within a lake, biotic factors, such as predator-prey interactions, competition and life-history traits, play a major role for the community structure (e.g. Gilinsky, 1984;
Johnson et al., 1996; Harrison and Hildrew, 1998b; Harrison and Hildrew, 2001).
However, the importance of single factors in the interplay of biological interactions and physical characteristics for the benthic community is not yet clear. Furthermore, wave action, substrate type, habitat stability, temperature, and the availability of shelter also correlate with invertebrate assemblages (Dall et al., 1984; Winnell and Jude, 1987;
Death, 1995; Röck, 1999; Tolonen et al., 2001).
While studying the impact of single factors and mechanisms in situ, it is necessary to consider habitat gradients and seasonal changes in the community. These features add to the “noise” of the data structure by overlapping with the biological signal if their impacts are not known or not considered (Reid et al., 1995; Johnson, 1998). Horizontal gradients, e.g. provided by macrophyte stands, lake inflows, or wind exposure, can potentially influence the community structure (Röck, 1999; Tolonen et al., 2001). Vertical gradients are often strongly intercorrelated. Hydraulic stress on organisms caused by wave action is lower in deeper water. Radiation attenuates, and the light spectrum becomes narrower with depth. Temperature and daily temperature fluctuations vary depending on water depth and the degree of internal seiches. Substrate particle size and the epilithic algal community — a food resource for grazers — change.
Furthermore, benthic communities are often influenced by nonindigenous and nuisance species, like zebra mussels (Dreissena polymorpha), Asian clams (Corbicula
sp.) or amphipods (Corophium curvispinum, Echinogammarus ischnus, Dikerogammarus villosus) and many more. Their impact on the benthic community as well as on the whole ecosystem can be severe (Uhlmann and Horn, 2001).
The settlement of zebra mussels strongly influences benthic communities. Basic studies have been carried out in Eastern Europe (Karatayev et al., 1997) and in North America after the Laurentian Lakes were invaded by mussels in the mid-1980s (e.g.
D´Itri, 1997). There, the major impacts of the immigration of the zebra mussel were a change in the structure of the substrate and increasing accumulation of organic matter by biodeposition (Botts et al., 1996; Stewart et al., 1998; Ricciardi et al., 1997). Many benthic taxa increased in abundance, e.g. annelids, gastropods, amphipods, decapods, and insect larvae (Stewart and Hynes, 1994), and invading zebra mussels negatively affected unionids (Ricciardi et al., 1996). Subsequently, these changes in the benthic community also affect other trophic levels, such as plankton (e.g. Holland, 1993) and fish (e.g. Mayer et al., 2001).
Even though the zebra mussel is also abundant in many Central European lakes and rivers (Glöer and Meier-Brook, 1994), there is a lack of studies on impacts on the benthic community in Central European lakes. Synecological research has mainly focused on interactions between zebra mussels and waterfowl (De Leeuw et al., 1999) and native mussels (Bauer et al., 2002). However, knowing how zebra mussels affect associated macroinvertebrates would be helpful to explain temporal and spatial distributions of macroinvertebrates.
Besides the direct impact zebra mussels have on benthic macroinvertebrates, winter numbers of waterfowl increased 3-4 fold since zebra mussels were introduced into Lake Constance in 1965 (Siessegger, 1969). Primarily three species known as mussel feeders [tufted ducks (Aythya fuligula), pochards (Aythya ferina) and coots (Fulica atra)] are responsible for this increase. They actually represent about 80% of all wintering waterfowl (Stark et al., 1999).
Suter (1982a) and Cleven and Frenzel (1993) observed a reduction of abundance and biomass of more than 90% of the zebra mussel standing crop due to predation in the lake outlets of Lake Constance. The results of those studies can only partially be transferred to Upper Lake Constance, because the outlets of the two basins of Lake Constance are characterised by a shallow morphology, no stratification and a
continuous supply of planktonic food from the main basins of the lake. Lake outlets are known as privileged habitats for filter-feeding mussels (Pusch et al., 2001).
Moreover, as annotated by Mitchell el al. (2000), studies in European lakes have been observational. Either foraging by waterfowl has been observed and concurrent declines in mussel abundance have been then attributed to predation, or the impact of predation has been estimated from counts of predators present and their experimentally presumed intake. Hence, for Lake Constance there has not been any experimental proof of waterfowl predation on zebra mussels.
Due to the high numbers of waterfowl reported feeding on zebra mussels (Stark et al., 1999), consequences at ecosystem level have to be presumed. To assess those consequences, information on temporal and spatial distribution of zebra mussels is indispensable. The spatial extension of areas covered by mussels can be registered using side-scan sonar in large dimension (Berkman et al., 1998), but abundance and biomass are almost impossible to estimate with this method. To date, zebra mussel abundance is mainly estimated by ship-supported invasive sampling methods using, e.g., ponar grabs (Schloesser and Nalepa, 1994), or by in situ counting or sampling by SCUBA divers (Mellina and Rasmussen, 1994; Effler and Siegfried, 1998). Even though these methods allow abundance and biomass to be estimated accurately, they are time consuming and therefore often lay constraint upon the total number of feasible samples. Furthermore, processing of the samples, i.e., counting and measuring the zebra mussels, trends to be extremely time consuming.
This thesis investigated the topics mentioned above. I studied biotic interactions in the infralittoral of Lake Constance between 1998 and 2003 by observatory fieldwork as well as by laboratory and field experiments.
To gain background knowledge on the benthic macroinvertebrate community of the littoral zone, sampling methods had to be developed and evaluated (Chapter 2).
Together with Daniel Baumgärtner, I carried out a routine sampling programme to retrieve data on spatial and temporal patterns of macroinvertebrates (Chapter 3). Based on those data, the role of zebra mussels as ecological engineers became a focal point of this study, and I estimated the effects of zebra mussel colonization on associated macroinvertebrates (Chapter 4). During the routine monitoring program, we also recognized a dramatic predation of waterfowl on zebra mussels and conducted a field
exclosure experiment to survey predation effects on zebra mussels and associated macroinvertebrates at different depth (Chapter 5). As waterfowl were able to reduce zebra mussel biomass extensively, their feeding activity may possibly have a severe impact on the lake. Therefore, a cost effective method for the assessment of zebra mussel density and biomass was established to quantify abundances patterns on a lake wide scale (Chapter 6).
2 A comparison of new sampling methods for macroinvertebrates in the stony littoral zone of lakes
Two new devices were developed for sampling the benthic macroinvertebrate community in the stony littoral zone of lakes quantitatively.
Technical descriptions of the samplers used in the upper and deeper littoral zones are presented. The eulittoral suction sampler (ESS) and the infralittoral suction sampler (ISS) minimise loss of animals during sampling in the current generated by the suction, which drags suspended material into a filter inset. Two people, wading in shallow water or SCUBA diving in deeper water, are required to operate the samplers.
The accuracy and precision of the samplers were evaluated by mesocosm and field sampling and compared to the exposure of artificial substrate (AS).
Under standardised conditions using plastic pellets, both devices were precise, but the accuracy was higher for the ISS. Under field conditions, differences between the two samplers occurred only when contiguously distributed taxa were sampled.
The larger sampling area of the ESS yielded a better accuracy and precision.
The efficiency of AS reached a maximum 50%. In field sampling, in-benthic taxa were overestimated.
Both suction samplers were shown to be adequate tools for the assessment of macroinvertebrate communities in the littoral zone of lakes. The current generated by the samplers is the determining factor for their good performance, which exceeds that of common grabs. The suction samplers even have advantages over air-lift samplers.
2.1 Introduction
Studies on predator–prey interactions and invasion of species have directed a focus of research to the littoral zone (e.g. Nalepa and Schloesser, 1993; Diehl, 1995;
D´Itri, 1997; Harrison and Hildrew, 1998; Magoulick and Lewis, 2002). However, investigations on the macroinvertebrate community in the stony littoral of lakes have long played a minor role compared e.g. to studies investigating all aspects of stream benthos. While particularly the number of different microhabitats in streams makes it difficult to sample representatively (Peckarsky, 1984), the lack of unidirectional flow in the stony littoral zone adds to the problems caused by habitat heterogeneity. Thus, hard substrates, which often occur in the upper littoral zone of large lakes, are probably the most difficult habitats to examine quantitatively (Downing, 1984). No standardised or commonly used method for quantitative sampling of lentic systems has been developed.
For lotic systems, fixed-quadrat samplers (Surber sampler, Hess sampler) or artificial substrates are commonly used (Carter and Resh, 2001; Peckarsky, 1984).
The sampling methods applied in studies of littoral invertebrates cover a broad methodological spectrum. Kick-sampling with hand nets (Johnson and Goedkoop, 2002) allows a rapid bioassessment approach (Carter and Resh, 2001). For quantitative data, grabs (e.g. Dall, 1981), air-lift sampler operated from boats (Mackey, 1972;
Pearson, Litterick and Jones, 1973), SCUBA divers using frames, hand nets, or specific samplers, or the counting of individuals in situ are used (Mellina and Rasmussen, 1994;
Gale and Thompson, 1975; Hunter and Bailey, 1992). Several researchers have developed a multitude of samplers designed to be convenient for their demands (Wetzel and Likens, 1990; Elliott et al., 1993), but studies addressing accuracy and precision of sampling devices in both lotic and lentic systems are rare (however, see: Elliott and Drake, 1981; Drake and Elliott, 1982; Drake and Elliott, 1983; Bretschko and Schonbauer, 1998; Muzaffar and Colbo, 2002; Pehofer, 1998; Schloesser and Nalepa, 2002).
We developed two new samplers with a goal of broad applicability for investigations of abundance, distribution patterns and dynamics of benthic invertebrates in the stony littoral zone. The samplers were to fulfil the following requirements:
convenient to use, inexpensive in acquisition and maintenance, and accurate and precise sampling of organisms in a defined area. In this paper, we present the technical descriptions of these samplers and a test of their accuracy and precision in comparison
to artificial substrates, both under field conditions and in a standardised mesocosm sampling.
2.2 Methods
Principles of the samplers
We developed two versions of fixed-quadrat samplers; one can be used in the eulittoral (eulittoral suction sampler, ESS) up to a depth of 1 m and the other in the infralittoral (infralittoral suction sampler, ISS) up to depth of 10 m or more. A current, which minimises loss of individuals during sampling, was generated by a motorised suction pump. This current drags suspended particles into the sampler, where they are restrained in a filter inset. The pump is placed on the shore or on a boat and is connected to the sampler by tubing. By increasing the length and number of tubings used, sampling can be performed at greater depths and further distances from the pump, respectively.
The sampler used in the shallow littoral zone in some respects resembles a Surber sampler (Surber, 1937); the device for sublittoral samples was designed specifically for convenient use by SCUBA divers.
All hand nets and filter insets used in the study were made with 200-µm gauze.
Fig. 2.1: ESS. A: Top view. B: Side view. C: Sectional drawing with aperture for filter inset. D:
Schematic sampler operation.
Specification of sampling devices
The ESS can be used in shallow water from > 30 cm up to about 1 m by operators equipped with waders (Fig. 2.1). The body of the sampler consists of an aluminium tin that encloses the sampled area (30 cm × 40 cm). The filter inset is inserted into an aperture on one side of the sampler (Fig. 2.2). This end of the sampler is connected with the tubing of the suction pump by a bayonet mount. Before sampling, a clean filter inset is placed into the aperture. When the sampler is situated on the sediment, the pump is engaged immediately, and the current is directed onto the filter inset. Stones and other substrates inside the sampling area are placed in a hand net held close to or in the sampling square. After all substrate is collected, the filter inset is covered with a lid, and the pump is switched off. The filter inset is removed, and the sample is processed. Two people are needed for handling the sampler, and one person is needed for operating the pump and processing the samples.
Fig. 2.2: Filter inset. A: Filter inset, from top and side view. B: Lid, from top and side view.
The ISS, a small device, was designed for convenient handling at water depth >
1 m by SCUBA divers (Fig. 2.3). The cylindrical body of the sampler made of perspex is connected to tubing by a valve, which is essential to regulate the suction power during the sampling and to reduce the pressure for the exchange of the filter inset.
Therefore, the pump can remain activated during the entire diving period. A filter inset is placed into the perspex cylinder. At the front, a short intake nozzle is attached by a
bayonet socket. The far end of this tube is connected to a stainless-steel frame, which encloses the sampling area (25 cm × 25 cm). For sampling, the boat is positioned exactly using range-finder, sonic depth finder, or GPS. The sampler is then lowered to the sampling area, and the SCUBA divers descend with the intake nozzle, the frame, hand nets and closed filter insets. One diver handles the sampler, closes the valve, inserts the filter inset into the sampler, and connects the intake nozzle. The second diver places the frame onto the substrate. After connecting the intake nozzle to the frame, the valve is opened and the current increases. The second diver collects the substrate in the hand net, which is held close to the frame; fine particles and escaping organisms are sucked into the sampler. The valve is closed, and the intake nozzle is carefully replaced by a lid. Divers experienced in handling this sampler are able to operate it alone.
Fig. 2.3: ISS: A: Body of the sampler with valve. B: Filter inset and lid. C: Intake nozzle connected to sampling frame. D: Schematic sampler operation.
Accuracy and precision under standard conditions
The accuracy and precision of the samplers under standard conditions were comparatively tested in a mesocosm experiment. The term “accuracy” refers to the mean catch and indicates the representativeness of the sampling procedure. In the standardised mesocosm experiment, it can be related to the known number of particles actually present and is then called “efficiency” (%). Precision qualifies variation of the samples in the term of standard error and, therefore, reproducibility (Elliott and Drake, 1981; Downing, 1984).
For mesocosm sampling, two basins (2 × 2 m) were filled with an approximately 15-cm layer of either gravel (∅ 1–2 cm) or stones (∅ 7–12 cm). For standard condition sampling, low-density (1.04 g cm–3) and high-density (1.46 g cm–3) plastic pellets were used. Considering the relative density these particles have in water at 10 °C and their weight (low-density: 22.6 mg pellet–1; high-density: 24.6 mg pellet–1), the high-density pellets are about 8-fold heavier in water than the low-density pellets. The pellets were 3–4 mm in length and therefore represent macroinvertebrates well (Drake and Elliott, 1983). The two types of pellets were placed into the basin at the same abundance (5000 m–2) and were distributed as evenly as possible. After placement of the pellets, the substrate in the tanks was stirred well by an intense water-jet or a pitchfork. Sampling under these standardised conditions allowed the determination of whether the devices accurately sample a known number of pellets and whether there are differences on the two substrate types between samplers for each pellet density and between pellet types for each sampling device.
Each suction device sampled each sediment type three times; this sampling regime was repeated five times. The number of pellets was estimated by a number/dry weight regression after drying the pellets for 4 h at 60 °C. Additionally, the two samplers were compared with another often-used method, the exposure of artificial substrates (AS). Three wire baskets (13 cm × 13 cm × 13 cm) filled with gravel or stones were placed into the mesocosm prior to the insertion of the pellets. The baskets were sampled by lifting them carefully into a hand net held just beneath. This method was repeated five times.
Additionally, the effect of the generated current was observed by separately counting the pellets caught in the filter inset and in the hand net for the experiment
carried out on gravel. The number of replicates was 12 for the ESS and 15 for the ISS sampler.
Accuracy and precision under field conditions
As a second test of functionality, the results of both samplers and AS in the field were compared. The stony littoral zone of Lake Constance was sampled with both samplers on March 20, 2001 and wire baskets (36 cm × 19.5 cm × 12.5 cm) filled with stones with an average diameter of 8 cm, which were exposed for seven weeks, were removed. This time span is sufficient for a complete colonisation of the AS (Rosenberg and Resh, 1982). The AS were located at the average low-water level of Lake Constance. When samples were taken, the water depth was 0.85 m.
Five replicates each were analysed. In the laboratory, all material was gently brushed off the stones, and organic material (animals and detritus) was separated by suspending and decanting through a 200-µm mesh. Heavier organisms (molluscs and case-bearing caddisfly larvae) were sorted out of the residue. Organisms were taxonomically determined using a dissecting microscope (10–15× magnification) to the genus or species level, with the exception of nematodes, oligochaetes and chironomids.
The volume of the sampled hard substrate (stones and gravel) was determined by measuring the displaced water volume. This made it possible to relate the abundances to a given amount of hard substrate (2 L) and to compensate for the greater sediment depth, which was sampled by the AS in comparison to the compact natural substrate, which could be sampled only on the surface by ESS and ISS.
Statistical analyses
For the mesocosm sampling, the obtained abundances of pellets for each sampling device and substrate combination were compared to the expected value of 5000 individuals m–2 by t-tests. Differences between the methods for each pellet type and also between the pellet types for each sampler were tested by ANOVA.
Homogeneity of variances was checked by Bartlett’s test. To achieve homogeneity of variances, the number of low-density pellets in the stone treatment were log (x+1) transformed. In this treatment, homogeneity of variances could not be achieved for high-density pellets. The number of samples (n) needed to obtain a specified degree of precision (D, SE = ± 20% of the mean) was calculated with the equation of Elliott (1977):
2 2 2
x D n s
= × , (1)
where x is the mean and s2 the variance of the sampled pellets.
For the field sampling, the invertebrate abundances were specified as ind. m–2 and additionally related to 2 L of hard substrate. Abundances of the taxa were sqrt (x) transformed to achieve homogeneity of variance. Multiple tests were subject to a sequential Bonferroni correction (Rice, 1989). To minimise the number of ANOVA, only species with a mean abundance of at least 280 ind. m–2 across all samples were considered. This number arises from Downing’s (1984) equation of sampling size efficiency and indicates the number of individuals that can theoretically be sampled accurately by the size of the smallest sampled area (0.0625 m–2, ISS) and the number of replicates taken. The same taxa were included in an ANOVA of abundances with regard to sediment volume. To verify the assumption of sampling size efficiency, mean abundances of single taxa (ind. m–2) obtained by different samplers were inserted in Downing’s (1984) equation. Additionally, equation 1 above was used to determine the needed number of replicates, considering obtained variance (s2) and mean of the actually sampled taxa for both ind. m–2 and ind. 2 L–1. All analyses were carried out using STATISTICA 6.1 (StatSoft, Tulsa, Okla., USA).
2.3 Results
Accuracy and precision under standard conditions
The accuracy and precision of the sampling devices were determined with low- density and with high-density pellets on gravel and on stones and varied with substrate type and specific particle density. The accuracy and precision of exposed AS were determined with low-density and high-density pellets on gravel only since pellets were rinsed out of larger interstices between stones during extraction. The results are summarised in Table 2.1 and Fig. 2.4.
On gravel, low-density particles were sampled similarly by the two suction samplers (Tukey-test, p = 0.99), whereas exposure of AS yielded only half of the initial introduced pellets, and therefore differed significantly from both suction samplers (Tukey-test, p < 0.05, Table 2.2). For the sampling of high-density pellets a similar pattern was observed. Lowest accuracy was obtained with AS and differed from both suction samplers (Tukey-test, p < 0.05), whereas ISS and ESS yielded comparable
numbers of high density pellets (Tukey-test, p = 0.17). Highest accuracy reached the ISS (4100 ± 453 mean pellets m–2) and this value did not differ from the expected value and was not different from the values obtained with the two samplers with low-density pellets (Fig. 2.4A).
Table 2.1: Comparison between samplers under standard conditions. The mean numbers of pellets yielded with each sampler (± SE), the efficiency as percentage between the mean and the initial density of pellets (5000 m−2), the precision as percentage between SE and the mean, the number of samples needed to obtain a given precision (SE = ± 20% of the mean, Elliott 1977) and results of the comparison with the expected value (t-test) are given for each sampling method and each pellet type on gravel and stony substrate. Artificial substrates as sampling method failed on stones.
Sampler ESS ISS AS
Pellet Density High Low High Low High Low
Gravel
Mean ± SE 3098 ± 362 4117 ± 363 4100 ± 453 4043 ± 350 1493 ± 322 2462 ± 409
Efficiency [%] 61.9 82.3 82.0 80.9 29.9 49.2
Precision [%] 11.7 8.8 11.0 8.7 21.6 16.6
N samples 5 3 5 3 17 10
Comparison with
expected value p < 0.001 p = 0.03 p = 0.07 p = 0.02 p < 0.001 p < 0.001 Stones
Mean ± SE 221 ± 91 4768 ± 1238 6764 ± 1567 4196 ± 636 -- --
Efficiency [%] 4.4 95.4 135.3 83.9 -- --
Precision [%] 41.2 26.0 23.2 15.2 -- --
N samples 64 25 20 9 -- --
Comparison with
expected value p < 0.001 p = 0.85 p = 0.27 p = 0.23 -- --
Statistically, low- and high-density pellets were sampled similarly with each of the three methods (Table 2.3), although the mean rate of yield of high-density pellets was lower in the ESS and AS. The ESS had a sampling efficiency of 82% for low- density pellets, but only 62% for high-density pellets (Table 2.1). The ISS had almost identical sampling efficiency for low-density (81%) and high-density (82%) pellets (Table 2.1). The difference in sampling efficiency of low-density and high-density pellets by exposed AS was not significant, even though an efficiency of 49% and 30%, respectively (Table 2.1, Table 2.2). The precision of the methods, i.e. the SE, was less than 20% of the mean, with exception of the AS sampling of high-density pellets (SE = 22% of the mean). In this case, the number of samples needed to obtain a tolerable precision would be 17 replicates (Table 2.1, Table 2.2).
Table 2.2: Comparison of samplers ESS, ISS and AS (ANOVA). On stones, only ESS and ISS are compared.
Substrate type Pellet type df eff,err F p
Gravel H 2,42 11.8 <0.001
Gravel L 2,42 6.2 0.004
Stones H 1,28 17.4 <0.001
Stones L 1,28 0.06 0.81
On stones, sampling by the ESS and ISS was compared. With low-density pellets, the ESS showed a better accuracy, but a lower precision (4769 ± 1238 m–2) than the ISS (4196 ± 636 m–2). High-density pellets were overestimated by the ISS and revealed a high variation (6764 ± 1567 m–2). With the ESS, significantly fewer high- density pellets could be sampled (221 ± 91 m–2; Fig. 2.4B; Table 2.2). With exception of the latter case, none of the sampling conditions differed significantly from the expected value (Table 2.1). A significant difference between the numbers of high- and low-density pellets was observed for the ESS, but not for the ISS (Table 2.3). Precision was lower than 20% of the mean only for the ISS sampling the low-density pellets. All other treatments revealed SE higher than 20% of the mean, which indicates that more replicates would be needed, i.e. 64 and 25 for the ESS with high- and low-density pellets, respectively, and 20 for the ISS with high-density pellets (Table 2.1).
Fig. 2.4: Mesocosm experiment under standard conditions. A: Sampling with ESS, ISS and exposed AS on gravel. B: Sampling with ESS and ISS on stones. Capital letters indicate differences between samplers (one-way ANOVA). * indicate significant differences from the expected value (t-test).
Table 2.3: Differences in sampling of high density and low density pellet types, p-values (t-test).
ESS ISS AS
Gravel 0.06 0.92 0.07 Stones <0.001 0.14 −
The impact of the generated current can be illustrated by comparing the results from the filter inset and hand net, each analysed separately. With the ISS, 55% of both pellet types were caught in the filter inset, while 45% were sampled in the hand net.
With the ESS, only 18% of the high-density pellets were sampled with the filter inset, whereas 60% of the low-density pellets were sampled (Fig. 2.5A, B).
Fig. 2.5: Amounts of high- and low-density pellets counted in the filter inset or the hand net in the mesocosm experiment on gravel. A: ESS. B: ISS. Capital letters indicate significant differences between filter inset and hand net.
A
B
Accuracy and precision under field conditions
Ten of 39 determined taxa reached mean abundances of ≥ 280 ind. m–2 across all samples: oligochaetes, adult and juvenile Dreissena polymorpha (molluscs), Caenis sp., Centroptilum luteolum (mayfly nymphs), Tinodes waeneri (caddisfly larvae), Gammarus roeseli (Amphipoda), Chironominae, Corynoneura sp., Orthocladiinae excluding Corynoneura sp., and Tanypodinae (Diptera). Therewith 80% of all determined individuals were represented. Together with the sum of all macroinvertebrates, 12 groups were considered for statistical analyses. The mean abundances of these groups are summarised in Table 2.4. The abundance in relation to 1 m2 or 2 L sediment is shown in Figs. 6 and 7, respectively.
Table 2.4: Mean densities of single taxa according to surface area (ind. m–2) or sediment volume (ind.
2 L–1) obtained with different sampling devices ± SE. In the second line of each value the number of replicates appear needed to meet assumptions of Downing (in brackets, regarding density and sampled area; 1984) or Elliott (regarding SE = ± 20% of the mean; 1977).
Based on calculations to 1 m2, mayflies were sampled at the same magnitude by all three methods (Fig. 2.6). The abundance of oligochaetes and Chironominae did not significantly differ between sampling methods, whereas the mean abundance of the
Mean density
(ind. m–2) Mean density
(ind. 2 L–1)
Taxa ESS ISS AS ESS ISS AS
Oligochaetes 485 (2) ± 66
2 1098 (2) ± 105
1 1457
(2)± 545
18 62 ± 6
1 177 ± 28
3 94 ± 36 18 D. polymorpha adult 2612
(1) ± 738
10 259 (5) ± 243
110 140 (7)± 81
42 323 ± 70
6 30 ± 28
106 9 ± 5 45 D. polymorpha
juvenile 1347 (1) ± 235
4 252
(6) ± 73
10 546
(3)± 51
1 172 ± 20
2 40 ± 13
13 35 ± 3 1 Caenis sp. 3597
(1) ± 471
2 4346 (1) ± 292
1 5406
(1)± 1076
5 461 ± 42
1 765 ± 181
7 347 ± 73 6 Centroptilum
luteolum 348
(2) ± 55
3 266
(5) ± 56
5 494
(3)± 75
3 48 ± 11
7 47 ± 14
11 32 ± 5 3 Tinodes waeneri 993
(1) ± 117
2 166
(7) ± 44
9 111
(8)± 21
4 128 ± 11
1 28 ± 7
9 7 ± 1
4 Gammarus roeseli 213
(3) ± 76
16 323 (5) ± 83
8 1254
(2)± 383
12 25 ± 7
10 50 ± 13
8 79 ± 24 11 Chironominae 6276
(1) ± 965
3 5126 (1) ± 619
2 16366 (1)± 7236
24 799 ± 72
1 792 ± 75
1 1064 ± 486 26 Corynoneura sp. 100
(5) ± 35
15 106 (9) ± 41
19 1226 (2)± 395
13 12 ± 4
14 19 ± 7
20 77 ± 24 12 Orthocladiinae other
than Corynoneura 2280 (1) ± 320
2 1680 (2) ± 415
8 24755 (1)± 3634
3 304 ± 58
5 298 ± 88
11 1566 ± 208 2 Tanypodinae 1243
(1) ± 159
2 1405 (2) ± 185
2 2697
(1)± 414
3 162 ± 23
2 238 ± 50
6 172 ± 27 3 All taxa 24319
(1) ± 3496
3 18893 (1) ± 1506
1 65598 (1)± 9466
3 3111 ± 298
1 3130 ± 542
4 4196 ± 647 3
organisms in the wire baskets was approximately 2–3-fold higher than the mean abundance sampled by the suction samplers. However, the precision was too low to recognise differences.
Fig. 2.6: Field sampling with abundances of evaluated taxa related to 1 m–2. Results of ANOVA are shown as p-values. Significant differences between three different sampling techniques after sequential Bonferroni adjustment are given by capital letters.
Gammarus roeseli, Corynoneura sp., other Orthocladiinae, Tanypodinae and the sum of all taxa were equally represented in the ISS and ESS samplers and reached high values on the exposed AS. The sessile zebra mussels and the caddisfly larva T. waeneri were sampled in high abundances with the ESS, but there were no differences between the ISS and the AS (Fig. 2.6).
Based on calculations to 2 L substrate, three different patterns of abundance of macroinvertebrates were obtained (Fig. 2.7): 1) similar quantities were obtained with all three methods for C. luteolum, Chironominae, Tanypodinae and all taxa, and differences between sampling methods for oligochaetes, Caenis sp., G. roeseli and Corynoneura sp.
were barely significant; 2) Orthocladiinae dominated on the exposed AS, and their numbers sampled with the other devices were similar; and 3) sessile taxa (D.
polymorpha and T. waeneri) were sampled in highest abundances with the ESS;
numbers of T. waeneri were lower with exposed AS than with the ISS, whereas there were no differences in numbers of zebra mussels using these two methods.
Using the sample data, the number of replicates needed to meet the preconditions of Downing (1984) and Elliott (1977) was verified. With respect to the abundance of the taxa in relation to the sampling area (ind. m–2), the ESS yielded all groups precisely, i.e. the calculated number of replicates is ≤ five, whereas the ISS and the AS would have needed a higher replicate number for three and two taxa, respectively (Table 2.4). If the mean abundance and its variance are considered, three, six and five taxa were not precisely sampled by the ESS, the ISS and the AS, respectively (Table 2.4). Abundances calculated according to the sampled substrate volume led to the estimation of only the relation between mean abundance (ind. 2 L–1), its variance and needed replicate number according Elliott (1977). Three out of 12 groups sampled by the ESS would have needed more than five replicates. Three and six groups were sampled precisely by the ISS and the AS, respectively (Table 2.4).
2.4 Discussion
Accuracy and precision under standard conditions
Depending on the substrates used for this experiment (gravel or stones), the devices reached different efficiencies in sampling plastic pellets of two different densities. Two different groups of macroinvertebrates were represented by these pellets.
Fig. 2.7: Field sampling with abundances of evaluated taxa related to 2 L–1 hard substrate. Results of ANOVA shown as p-values. Significant differences between three different sampling techniques after sequential Bonferroni adjustment are given by capital letters.
Organisms lacking heavy structures such as shells or cases (e.g. mayflies, chironomids) corresponded well to the low-density pellets (Drake and Elliott, 1983), whereas molluscs or case-bearing caddisfly larvae were represented by the high-density pellets (M. Mörtl, unpublished data).
On gravel, an efficiency of greater than 80% of the particles actually present was found for both suction samplers sampling low-density pellets. Lower accuracy was observed for the ESS sampling high-density pellets, but the accuracy was still higher than that of the AS.
On stones, both samplers reached efficiencies of 84–135%, except for the ESS sampling high-density pellets. Although studies of the accuracy of samplers under standard conditions are scarce, some comparable mesocosm experiments were done to develop sampling devices for rivers with a gravel bed (Elliott and Drake, 1981; Drake and Elliott, 1982; Drake and Elliott, 1983). Trials to evaluate the applicability of grabs for sampling benthic macroinvertebrates showed low accuracy in terms of mean catch, expressed as a percentage of the number actually present. In comparable substrates (16–
32 mm), the most accurate samplers, the weighted ponar and the Birge-Ekman grab, achieved about 20% accuracy. On larger substrates, catches for all grabs were zero or almost zero (Elliott and Drake, 1981). If 50% is a minimum acceptable accuracy (Elliott and Drake, 1981), the results obtained here with the new suction samplers contribute better to an accurate estimation of littoral macroinvertebrates with only one exception (ESS, high-density pellets, stones). Air-lift samplers have been developed for use in deep rivers on a stony substrate (Drake and Elliott, 1982; Drake and Elliott, 1983). A new quantitative air-lift accurately samples plastic pellets in tank experiments eminently on substrates ranging from fine gravel (2–4 mm) to fine stones (16–20 mm). However, on stones of 32–36 mm modal length the efficiency of the air-lift decreases markedly (150% in the surface layer, <75% in layers deeper than 6 cm) because of a change in the shape of the excavated holes. On fine substrates, the holes are cylindrical and correlate to the dimension of the sampler. On coarse substrates, however, the excavated holes become more conical in shape, resulting in an overestimation of pellets near the surface and an underestimation in deeper substrate layers (Drake and Elliott, 1983).
The lower accuracy of the ESS with high-density pellets is caused by the characteristics of the pellets. Although the specific densities of the materials used correspond to densities of several macroinvertebrates with or without heavy body structures, e.g. shells or cases, it should be noted that the particles are different from macroinvertebrates in that they do not adhere to surfaces as snails or case-bearing caddisflies do. Therefore, particularly the heavy pellets in the experiments with stones invaded the interstices or even sank to the bottom of the tank, where the current of the ESS was not strong enough to pull them into the filter inset because of its larger
aperture. The ISS on the other hand even caught particles outside of the sampling frame, which led to an overestimation of the number of particles. However, in general, the use of a current contributes considerably to the good performance of the samplers. This is shown by the large proportion of pellets sampled in the filter inset with both samplers.
With the ISS, more than half of each particle types were caught in the filter inset. With the ESS, 20% of high-density pellets and 60% of low-density pellets were caught by the current.
The AS samplings under standard conditions were also biased. The interstices between the substrates were not filled by fine sediment or organic matter, unlike what happens in the lake. Therefore, the particles, particularly the high-density particles, were rinsed out during sampling.
With regard to precision on gravel, i.e. the tolerance of a standard error equal to 20% of the mean, fewer samples than actually taken were sufficient with the new samplers. On stones, generally more replicates would have been needed to obtain precise sampling. This higher variance in the samples on stones possibly reflects a more contagious distribution of the pellets. Despite these differences to the natural environment, sampling under standard conditions is a worthwhile complementation to field samplings because it allows a comparison at a known density and an estimation of efficiency. Many comparative studies under field conditions lack a reliable estimation of efficiency since the actual densities of animals are not known (Schloesser and Nalepa, 2002).
Accuracy and precision in field sampling
Calculations of abundances of macroinvertebrates in relation to 1 m2 revealed highest abundances of all taxa except sessile zebra mussels and T. waeneri when sampled with AS. Mean abundances of G. roeseli, Tanypodinae, Corynoneura sp., Orthocladiinae and the sum of all macroinvertebrates were significantly higher than in the samples from the suction samplers. This results from the thicker substrate layer sampled by the AS; only the surface layer of the natural substrate was sampled by the suction samplers. The relation of abundance to surface area, unweighted with regard to sediment depth or sediment volume, causes an overestimation of taxa that live on stone surfaces and in the interstices between stones. Therefore, a calculation relating stone volume facilitates a weighted comparison of all three methods.
With regard to sediment volume (2 L), similar abundances of Centroptilum sp., Chironominae, Tanypodinae and the sum of all taxa were sampled using all three methods. Oligochaetes and Caenis sp. were slightly more frequent in the samples obtained by the ISS. The higher suction power of this sampler might have led to a higher intake of fine sediments deposited between stones from outside the sampling frame, a habitat preferred by these taxa (Malzacher, 1986). To some extent this can also explain the differences between abundances of Tanypodinae and G. roeseli, which were more frequently sampled by the ISS than by the ESS. Amphipods and especially Corynoneura sp. and other Orthocladiinae reached higher abundances in the AS.
Orthocladiinae are the dominating chironomids in the littoral zone of exposed shores of lakes. They prefer coarse, loosely packed substrata of stones or gravel (Pinder, 1995).
G. roeseli has shown highest preference for coarse substrates in laboratory experiments (Baumgärtner, Koch and Rothhaupt, 2003). The consistency of the AS used may therefore represent an optimal habitat for these taxa, whereas the natural sediment sampled with the ESS and the ISS consisted of a stony layer, whose interstitial spaces were filled up with consolidated fine material. Therefore, these results point to a disadvantage of AS — their selectivity for settlement by different species (Rosenberg and Resh, 1982).
Highest abundances of zebra mussels (juvenile and adult) were sampled with the ESS. This device had the largest sampling area, and therefore enables the description of contagious distributions more accurately than smaller devices (Downing, 1984).
Because zebra mussels are massively preyed upon by wintering waterfowl (Cleven and Frenzel, 1993; chapter 5), their abundances are comparatively low in March, and surviving mussels are contagiously distributed, particularly on side surfaces of boulders (M. Mörtl, pers. observations), which sometimes are too large to be sampled by the ISS.
The caddisfly larva T. waeneri is strongly associated with aggregates of larger zebra mussels (M. Mörtl, pers. observation). Thus, it is understandable that they would reach lowest abundances on introduced substrates, which were hardly settled by adult zebra mussels, and on substrates sampled with the ISS.
Downing’s equation for mean abundance, sampler size and numbers of replicates (Downing, 1984) was used for the taxa included in the analysis. This assumption was verified by calculating the sample numbers with the actual data. The ESS sampling method had the highest precision, i.e. the lowest number of replicates needed to obtain a SE of ± 20% of the mean, followed by the ISS and AS sampling
methods. Using Elliott’s equation (1977), the real variance in relation to the mean is considered for the estimation of sample number. Again the ESS had the lowest number of samples, followed by the AS and the ISS. Particularly the low numbers of adult and juvenile zebra mussels and associated T. waeneri larvae contributed to these results, which differ from those obtained with the ISS under standard conditions. Hence, the different sizes of the sampling area crucially affect the performance, particularly if taxa are contagiously distributed.
Comparison to other samplers
For sampling benthic macroinvertebrates in lentic systems, grabs have been used in most cases. However, on hard substrates, the grabs have low accuracy and precision (Elliott and Drake, 1981). Dall (1981) has developed a grab for use in the upper stony littoral zone. One major disadvantage of this method is that organisms might escape owing to perturbation during sampling. Furthermore, it might not be easy for SCUBA divers to use the grabs in deeper water.
Another quantitative method for the use in the littoral zone is the air-lift sampler (Drake et al., 1983; Drake and Elliott, 1983). The air-lift has some basic differences to the samplers presented in this study. One difference is the definition of sampled area, as mentioned above. For the air-lift sampler, the sampled area or the volume of sampled material depends on three factors: duration of sampling, strength of sampling, i.e. air- flow, and the condition of the sampled sediment. As grain sizes from the shore to deeper zones differ, an extensive calibration of the sampling procedure would be necessary to evaluate expansion of holes excavated by the air-lift. Additionally, boulders, which sometimes occur in the upper littoral zone, are very difficult to sample with this sampler and may disturb sampling (Drake and Elliott, 1983). Two other samplers described in the literature, diver-operated suction samplers, use the air-lift principle (The Finnish IBP-PM Group, 1969; Christie and Allen, 1972). Both samplers consist of a closed funnel-like or cylindrical sampling device connected to hoses and an air supply. Inside the sampling devices, large or heavy organisms might not be vacuumed up and therefore remain unsampled (The Finnish IBP-PM Group, 1969). Additionally, sometimes technical problems are reported, e.g. insufficient influx of water to compensate outflow (The Finnish IBP-PM Group, 1969). Changing the sampling bags at the surface requires that divers ascend or personnel are present in a boat equipped with communication media (line, talk-back circuit), whereas our samplers allow the
operators to control the sampling procedure completely. Especially during work in deeper water (> 5 m), it is of great advantage for the diver to be independent from communication with personnel on a boat.
A hand-operated suction sampler (Boulton, 1985) can only be used in at lower depths, and stones have to be cleaned from sessile animals inside of the sampler. An electrically driven suction sampler has been developed by Gale and Thompson (1975) for use in deep rivers. The sampled area has to be totally screened from the current of the river. This causes a major disadvantage — the underestimation of organisms too large (Decapods) or too mobile (e.g. Baetidae) to be vacuumed (Gale and Thompson, 1975).
Hence, the samplers presented in this study are an improvement as they combine many advantages and avoid disadvantages of the other samplers designed to date. The new samplers allow a controlled and observable transferring of stones in a hand net by the wading or diving operator. The current generated by the new samplers minimises loss of individuals. The two samplers described here provide accurate and precise results in most cases. Exceptions probably include mobile invertebrates with heavy structures (snails), which can be underrepresented by ESS samplings due to the lower suction power of the sampler. The ISS had a lower precision than the ESS in the field experiments, which might be a result of the smaller sampled area. The processing time for the determination of the samples is dependent on sample size, and nearly twice the amount of time is needed to process one sample from the ESS than from the ISS. To take and to count more replicates with the ISS is therefore a better solution than increasing the sample area unless the organisms are contagiously distributed (Downing, 1984).
In conclusion, the newly developed samplers generally show high accuracy and precision. In the field test, the ESS performed better with regard to precision, owing to its larger sampling area. Even contagiously distributed taxa were sampled accurately.
Under standard conditions, the ISS showed a better accuracy and precision than the ESS and less effort is needed for processing the samples. With the AS, more in-benthic taxa were estimated, which results in a different composition of the benthos. In comparison with other quantitative sampling methods suitable for hard substrates (air-lift, other suction samplers), the new samplers have as a crucial advantage in that the entire sampling procedure is visible to the operator. Indeed, in depth > 1 m, the assignment of scientific divers is inevitable, which can raise costs, but is also advantageous because
the use of diver-operated samplers is probably the most accurate method of sampling macroinvertebrates in deeper waters (Drake and Elliott, 1983).
3 Community structure of macroinvertebrates in the littoral zone of Lake Constance:
vertical distribution dynamics
Abundance and biomass of benthic macroinvertebrates and their seasonal dynamics were assessed along the depth gradient in the littoral zone of Lake Constance, a large pre-alpine lake in Central Europe. The community patterns in the depth zones differed significantly, partly because of species turnover, but mostly the result of different dominance structures, which suggested that biotic interactions play an important role for the community structure. The diversity pattern indicated that the community is also influenced by physical disturbances, such as water level fluctuations and the impact of wave action, following the intermediate disturbance hypothesis.
Settlement area was a limiting resource for the epilithic community, and the dynamically flooded eulittoral zone was an important habitat for pioneer species, which have implications for nature conservation measures since Lake Constance is the only large pre-alpine lake whose water level is not extensively regulated.
Seasonal dynamics caused large variations in the community. Because of the high natural variability of benthic macroinvertebrate samples, variations owing to season and to small differences in depth must be considered when designing surveys of the macroinvertebrate fauna in order to minimise the sampling bias.
3.1 Introduction
The organisation of the macroinvertebrate community in the littoral zone of lakes has been subject to a number of studies since the early days of limnology. They range from mainly commented species lists and descriptions of faunal assemblages (Wesenberg-Lund 1908; Muckle 1942; Ehrenberg 1957) to quantitative studies involving productivity (Hayne and Ball 1956) and effects of habitat variables and biotic interactions on the invertebrate community (Macan 1966; Gilinsky 1984; Diehl 1992;
Cobb and Watzin 1998). The availability of personal computers and sophisticated multivariate methods has facilitated the search for patterns in communities and correlations to environmental variables (Bailey et al. 1995; Brodersen et al. 1998;
Tolonen et al. 2001). Modern methods such as stable isotope analyses can be used to examine benthic food webs (Gu et al. 1994; France 1995).
However, the relative importance of biotic and abiotic processes in the structuring of macroinvertebrate communities of littoral habitats is largely unknown (Johnson and Goedkoop 2002). Compared to the large research effort in studying the water column of lakes or the benthos of streams, relatively little is known about the stony littoral zone of lakes (Harrison and Hildrew 1998a). Part of the difficulty arises in the large variation in replicate samples. Extensive efforts are required to obtain sufficient statistical data to detect changes in the fauna. Spatial heterogeneity in the littoral zone, the patchy distribution of benthic organisms, and sampling bias can overlap with potential impacts of changes in the environment or experimental perturbations. It is therefore crucial to optimise sampling precision and to take into account other sources of variation.
The presence or absence of organisms in a lake might depend on large-scale factors, such as climate, geology, or colonisation history (Johnson and Goedkoop 2002).
Comparisons of fauna of the littoral zones of lakes have pointed to the importance of environmental variables. Lake morphometry, productivity, and water chemistry (Jackson and Harvey 1993; Bailey et al. 1995), and biotic factors, e.g. the presence of fish and the extent of predation exerted on the macroinvertebrate community have proven to be good predictors in comparisons of invertebrate communities among lakes (Jackson and Harvey 1993; Wong et al. 1998).
Within a lake, biotic factors, such as predator-prey interactions, competition, and life-history traits, play a major role for the community structure (e.g. Gilinsky 1984;
Johnson et al. 1996; Harrison and Hildrew 1998b; Harrison and Hildrew 2001).
However, the importance of single factors in the interplay of biological interactions and physical characteristics for the benthic community is not yet clear. Wave action, substrate type, habitat stability, temperature, and the availability of shelter also correlate with the invertebrate assemblages (Winnell and Jude 1987; Dall et al., 1984; Death 1995; Röck 1999; Tolonen et al. 2001).
In field studies of the impact of single factors and mechanisms, it is necessary to consider habitat gradients and seasonal changes in the community since they add to the
“noise” of the data structure by overlapping with the biological signal if their impacts are not known or not considered (Reid et al. 1995; Johnson 1998). Horizontal gradients, e.g. provided by macrophyte stands, lake inflows, or wind exposure, can potentially influence the community structure (Röck 1999; Tolonen et al. 2001). Vertical gradients are often strongly intercorrelated. Hydraulic stress on organisms caused by wave action is lower in deeper water. Radiation attenuates and the light spectrum becomes narrower with depth. Temperature and daily temperature fluctuations vary depending on water depth and the degree of internal seiches. Substrate particle size and the epilithic algal community — a food resource for grazers — change.
The aim of this study was to asses the changes in the macroinvertebrate community along a vertical habitat gradient and its seasonal dynamics. The influence of water depth and correlated factors on the community structure and the abundance and biomass of the invertebrates in the stony littoral zone of Lake Constance were investigated on a small spatial scale. We examined multivariate patterns using ordination and tested for differences in univariate community indices and multivariate community structure.
Although the chosen site can be regarded as representative for the stony littoral zone of Lake Constance (Fischer and Eckmann 1997b), the obtained results will not be considered valid for the littoral zone of the entire lake (see Hurlbert 1984). This will be the task of a subsequent study that includes sampling sites from different parts of Upper Lake Constance.
The ecological patterns described in this study provide a basis for the proposal of new hypotheses towards a better understanding of the abiotic factors and biotic processes that drive spatial and temporal patterns in the littoral community. In addition, the data are important for applied purposes, such as the success control of lakeshore
restorations and water-quality monitoring as demanded by the EU water framework directive.
3.2 Methods
Study area
Upper Lake Constance (47°39’N, 9°18’E) is a large pre-alpine lake (500 km2) in Central Europe with a maximum depth of 254 m. At mean water level, about 12% (57 km2) of the area is shallow water less than 10 m deep and is therefore classified as the littoral zone (IGKB 1994). The water level normally fluctuates by about 2 m every year, with a minimum water level in February and a maximum in the summer, triggered by melting water runoff in the Alps (Fig. 3.1).
S O N D J F M A M J J A S O N D J F M A M J J A S O N D J 250
300 350 400 450 500
1999 2001
Gauging Level (cm)
2000
Fig. 3.1: Course of the gauging level of Lake Constance, station Constance Harbour. Long-term average low-water level: 262 cm. Gap: no data available.
The sampling was conducted on the south-western shore of the Überlingen Basin, a part of Upper Lake Constance (Fig. 3.2), on a leeward erosion bank with a wide boulder shore. The littoral sediment there consists mainly of silty sands with a more-or-less packed stony overlay, a habitat that occurs frequently in the littoral zone of Lake Constance (Fischer and Eckmann 1997b).
Fig. 3.2: Location of the study site in Lake Constance.
Sampling design
Starting in September 1999, benthic invertebrates were sampled every 3 months over a period of 2 years from up to 7 depths; the number of depth zones depended on the water level (Fig. 3.3). Sampling dates were September 23, 1999, December 14, 1999, March 14, 2000, June 20, 2000, September 29, 2000, December 18, 2000, March 20, 2001, and June 26, 2001. To observe temporal changes on a monthly base, an additional sampling was carried out on April 12, 2000.
Drift line 0.4 m
Long-term average low-water level 1 m
LWL –1 m (1–3 m)
LWL –3 m (3–5 m)
LWL –7 m (7–9 m)
eulittoralinfralittoral
Fig. 3.3: Vertical zonation of sampling stations.
Upper Lake Überlingen
Basin
Lower Lake
Study site
0 5 10 Km
Five replicates were taken randomly in a shoreline of 30 to 50 m, three of which were counted. Four fixed stations were sampled: the zone of the average low-water level (LWL), which is equivalent to a gauging level of 2.62 m measured at Constance harbour; and three infralittoral stations, 1, 3 and 7 m below the LWL (–1, –3, and –7 m).
At these stations, water depth changed according to the water level. In the eulittoral zone, up to three shifting stations were sampled; the water depth was invariable, but the location depended on the water level: the drift line (surf zone), 0.4 m and 1 m water depth.
Shifting sampling stations in the eulittoral zone were chosen since the actual water depth should have a considerable influence on the macroinvertebrate community and should therefore be kept fixed. Water depth is also correlated with other abiotic factors that affect benthic invertebrates. The eulittoral sampling station in 1 m water depth was only present in the June samples when the water level was high.
Sampling devices
For benthos sampling, two different suction samplers were used. The eulittoral zone down to the average LWL was sampled using the eulittoral suction sampler (ESS) with a sampling area of 30×40 cm2; the infralittoral zone was sampled using the infralittoral suction sampler (ISS, sampling area 25×25 cm2) and SCUBA diving. A detailed description of these samplers and a comparison of their sampling precision and accuracy are given in chapter 2. Drift-line invertebrates were sampled with a frame (25×25 cm2) and a hand net. Mesh size was always 200 µm.
Interstitial sampling
Whether recolonisation from the interstitial occurred in the eulittoral zone was examined on February 5, 2001, by digging out holes on shore 0.3 m deep in 1.5 and 2.2 m distance from the drift line in the dry littoral zone and sieving the water through a 200 µm gauze following the protocol in Schwoerbel (1994). An additional quantitative sampling was carried out on March 18, 2001, by sampling a defined area (0.25×0.25 m2) at a distance of 3, 5 and 7.5 m from the drift line. The vertical distribution of invertebrates in the upper 6 cm of the fine sediment below the layer with stones and gravel on the dry littoral zone was determined in 2 cm layers. Deeper below, the sediment was too consolidated for this kind of sampling. The sampling locations had been dry for at least the previous 24 weeks.
Sorting and sieving
The benthos samples were brought to a climate chamber (4°C) on the same day and processed in the laboratory on the same or next day. Stones were rinsed and brushed gently to remove all organisms. Fine sediments were stirred up repeatedly, and the floating organisms and debris were collected on a 200 µm sieve. The remaining sediments were sorted by hand and searched for dense animals such as molluscs and caddisflies. Each sample was then preserved in 70% ethanol.
Abiotic factors: temperature and gauging level
Temperature was measured at 6 or 10 min intervals with ONS-WTA08-05+37 sensors (Synotech) and data loggers at every sampling depth in the infralittoral zone and at the LWL beginning in March 2000. Another sensor was exposed in the eulittoral zone at a depth around 0.4 m. Daily temperature span and average temperature was calculated. The gauging level was obtained by the measurements at the Constance harbour (Fig. 3.1).
Biomass estimation
Invertebrate biomass was estimated using length–dry mass regressions (Meyer 1989; Burgherr and Meyer 1997; Benke et al. 1999, Baumgärtner and Rothhaupt 2003).
Each taxon was grouped into three size classes. For each taxon, the sampling date was recorded and size class and body length parameters of 10–20 individuals were determined. From the median of each size class, the average dry mass was determined, and the population biomass was calculated by extrapolating the weighted means of these size classes. Average dry mass values were used for several taxa for which body size measurements are not useful, such as hydrozoa, oligochaeta, and hirudinea. For molluscs and caddisfly larvae, dry mass was calculated for the soft body without shells or cases.
Statistical analyses
Univariate community measures
The invertebrate assemblages of the different depth zones sampled in each month were described by univariate community indices based on abundances. Diversity was expressed using the Shannon (or Shannon-Wiener) diversity index (Pielou 1975):
