Diptera, Syrphidae) in the Elbe Floodplain

Internat. Rev. Hydrobiol. 91 2006 4 341–363

DOI: 10.1002/iroh.200510889

FRANK DZIOCK

Technische Universität Berlin, Biodiversity Dynamics of Terrestrial Ecosystems, Rothenburgstr. 12, D-12165 Berlin; e-mail: [email protected]

Life-History Data in Bioindication Procedures, Using the Example of Hoverflies (Diptera, Syrphidae) in the Elbe Floodplain

key words: life history traits, multivariate statistics

Abstract

This is the first study to relate syrphid life history traits to environmental variables with a multi-trait approach. We aimed to answer two questions: 1. Do syrphid species respond to small scale changes in environmental variables in seasonally flooded grasslands in a Central European floodplain (Elbe)? 2. Can species response to environmental variables be explained by the biological characteristics of the species expressed by their life history traits? Despite their large mobility, syrphids did respond significantly to small scale changes in environmental variables (groundwater (GW) depth, cation exchange capacity, amplitude of variation of the GW-depth). On the other hand, the biological traits of the syrphids did not sufficiently explain syrphid occurrence at the sites. Possible explanations are discussed and an outlook for further studies is given.

1. Introduction

The first use of organisms as indicators for environmental conditions dates back to the days of Aristotle, who placed freshwater fish in salt water to observe their reactions (CAIRNS and PRATT, 1993). Farmers have used plants as bioindicators for thousands of years (DIEK- MANN, 2003). The medieval King’s wine tasters or the canaries used to indicate air quality in coal mines are other historical examples for bioindicators (BURRELL and SIEBERT, 1916; CAIRNS and PRATT, 1993). Bioindicators can thus be defined as living organisms indicating environmental conditions through their presence or abundance (DZIOCK et al., 2006). The past 40 years have seen a rapid development of ideas, concepts, and application of bioindicators (for reviews see METCALFE, 1989; CAIRNS and PRATT, 1993; MCGEOCH, 1998; NIEMI and MCDONALD, 2004). Currently, there is a strong need for reliable environmental assessment procedures because of environmental policies (e.g. the EU Habitat Directive) concen- trating on cost-efficiency and applicability of bioindication systems on a large scale (at least pan-European). One potential way of achieving this would be to use general biological traits of organisms that indicate ecological functions (STATZNER et al., 2001a). These traits are used to reveal functional relationships of the species to habitat selective forces. These forces can be viewed as filters occuring at different spatial scales. To join a local community, species must pos- sess appropriate functional attributes (species traits) to pass through the habitat filter (SOUTH- WOOD, 1977, 1996). Such traits and their relationships to the filter (environmental conditions) are considered to hold on a geographical scale and thus have potentially broad generality (POFF, 1997; STATZNER et al., 2001b). By contrast, applied ecologists often describe

© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1434-2944/06/408-341 342 F. DZIOCK or predict patterns of distribution and abundance without reference to biological mechanisms. These correlative approaches are of uncertain generality. Including biological information may make their predictions more robust and generalizable (POFF, 1997). This is because in different biogeographical regions or in the same region but in a different ecosystem, different species might be involved occupying the same functional niche. This transferability in space is augmented by a potential transferability in time. The problems caused by limited trap exposition time and seasonal differences in species occurrence could also be overcome by using species biological traits instead of species occurrence or abundance alone. Using species traits to characterise community composition in terrestrial invertebrates has been largely univariate in approach, i.e., restricted to analysis of single or a few traits (e.g. HODGSON, 1993; PURTAUF et al., 2005; but compare CASTELLA and SPEIGHT, 1996). However, using many traits simultaneously enhances the understanding of how species composition will change as environmental constraints vary across the landscape (POFF, 1997; STATZNER et al., 2001b). Although this multi-trait approach to understanding the relationship between life history and environment has been followed in numerous studies in stream ecology (e.g. POFF, 1997; STATZNER et al., 1997, 2004; USSEGLIO-POLATERA et al., 2000; GAYRAUD et al., 2003), its potential in terrestrial ecosystems, especially for terrestrial invertebrates remains largely unexplored. The family Syrphidae is among the most diverse Diptera groups as regards larval biology and habitat preferences (THOMPSON and ROTHERAY, 1998). Hoverflies can be found in almost every terrestrial and many aquatic habitats. The adults are important pollinators and use only pollen, nectar, and occasionally honey dew as food resources. In contrast, the larvae show an amazing variety of life styles. They live on decaying wood, sap from sapruns on trees, fungi, living or rotting plants, dung, muddy water, aphids, ant eggs, larvae and pupae, or other insects (THOMPSON and ROTHERAY, 1998). One species in Central Europe (Volucella inanis) is even a true parasitoid of wasp larvae (RUPP, 1989). Their spectrum of life history strategies in floodplains also shows a high diversity. Most species are dependent on more than one habitat type, because larval and adult habitats differ from one another. Larvae are restricted to their larval substrate, whereas the emerging adults visit flowers and move around between different biotope types (SSYMANK, 2001). Because larvae are much more specialized than the adults in their feeding preferences, they often play a key role in syrphid species distribution. Syrphid flies have not often been used in bioindication processes, despite their large potential in this respect. This is mainly due to some difficulties concerning the determination of the species arising from the fact that there is no determination book available that covers the whole range of species occurring in Europe (ca. 800 species) or even Central Europe (ca. 550 species). However, very recently a determination book has been published (VAN VEEN, 2004) that covers the whole of Northern Europe with large parts of Western and Central Europe, but excludes the mediterranean and mountainous areas (e.g. the Alps). Another problem was the availability of life history traits data, which is scattered in numerous not-easy-to-obtain publications, often in difficult-to-translate languages. A lot of these data have been collated in the publication by BARKEMEYER (1994). A database with a large amount of data on habitat preferences and some data on life history traits has been compiled by SPEIGHT et al. (2004). This study endeavours to take advantage of these data and aims at testing the use of hoverflies for bioindication in floodplain grasslands while incorporating life history traits data in the analysis process. We aim to answer two questions:

– Do syrphid species respond to small scale changes in environmental variables (e.g. groundwater depth, cation exchange capacity etc.) in seasonally flooded grasslands in the Elbe floodplain?

– Can species response to environmental variables be explained by the biological characteristics of the species expressed by their multiple life history traits as coded in the database Syrph the Net (SPEIGHT et al., 1998)? This is the first study to analyse syrphid species response to environmental variables and simultaneously incorporate life history data on the syrphid species into the statistical approach.

2. Methods

The 15 study sites are situated in Central Germany in the Elbe floodplain (Land Saxony-Anhalt). The study area is part of the UNESCO biosphere reserve “River Elbe Landscape” (SCHOLZ et al., 2005). All study sites can be characterised as open grassland areas which are seasonally flooded. They had been chosen in the course of the “RIVA”-project (HENLE et al., 2006). We carried out a stratified systematic random sampling design (SNEDECOR and COCHRAN, 1980; WILDI, 1986). For more details on the study area see HENLE et al. (2006). A phyto-sociological characterisation of the study sites is given in Table 1.

Table 1. Study sites in the Elbe floodplain, where syrphids were surveyed.

Study area Biotope characteristics Site no.

Steckby Eleocharietum palustris, Ranunculo repentis-Alopecuretum 4 geniculati, Phalaridetum arundinaceae Steckby Potentillion anserinae, Bidenti-Polygonetum hydropiperis 9 Steckby Agropyretum repentis, Phalaridetum arundinaceae, 10 Rumici crispi-Agrostietum stoloniferae Steckby Galio molluginis-Alopecuretum pratensis 20 Steckby Galio molluginis-Alopecuretum pratensis 21 Steckby Dauco carotae-Arrhenateretum elatioris 26 Steckby Sanguisorbo officinalis-Silaetum silai 29 Steckby Sanguisorbo officinalis-Silaetum silai 30 Steckby Sanguisorbo officinalis-Silaetum silai 34 Wörlitz Glycerietum maximae, Bidention tripartitae 39 Wörlitz Glycerietum maximae, Caricetum gracilis 40 Wörlitz Galio molluginis-Alopecuretum pratensis 42 Sandau Rumici crispi-Agrostietum stoloniferae, Rorippo-Oenanthetum 51 aquaticae, Bidenti-Polygonetum hydropiperis Sandau Phalaridetum arundinaceae, Caricetum ripariae 53 Sandau Phalaridetum arundinaceae 57

2.1. Field Sampling

Malaise traps were used in order to sample syrphids simultaneously and with comparable effort at all sites (Fig. 1). Our focus of interest was the local fauna; therefore we used non-attracting malaise traps with black roofs (Marris House Nets, Bournemouth, England). Two malaise traps were placed on each of the 15 sites facing each other (DZIOCK, in press; HENLE et al., 2006). One trap was placed along a north-south axis with the collecting bottle facing south, the other one rectangular to the first (east- west axis). Maximising the trap catch is not necessarily synonymous with maximising the catch of species which have developed locally, because, for instance, in July many migrant species (in our sampling area mainly Episyrphus balteatus, Eupeodes corollae, other Syrphini and some Eristalini, e.g. Helophilus trivittatus) occur in large numbers in the traps, but are not indigenous (SPEIGHT et al., 2000). In order to maximize the number of locally developing species and minimize the number of individuals to deter-

Figure 1. Malaise trap, used for catching syrphids. Biosphere reserve “Mittlere Elbe“, Schöneberger Wiesen NW Dessau, Saxony-Anhalt, central Germany, May 1998.

mine, we chose the following three sampling periods (see DUELLI et al., 1990; PRECHT and CÖLLN, 1996; SPEIGHT and CASTELLA, 1995): • spring: April 27th–May 28th, 1998 • early summer: June 30th–July 17th, 1998 • summer: August 3rd–August 25th, 1998

The preservative used was 70% alcohol and collection bottles were checked and replaced fortnight- ly in the sampling periods given above. The results of the two malaise traps at one site were pooled because a preliminary correspondence analysis of all 30 traps showed that differences between sites were much higher than within a site (DZIOCK unpublished).

2.2. Life History Traits of Syrphidae

Table 2 shows the life history traits of Syrphidae that have been used in the analysis. The degree of association between each species and each category of the traits was expressed with a number between 0 (no association) and 3 (maximal association) (BOURNAUD et al., 1992; CHEVENET et al., 1994; SPEIGHT et al., 1998). The traits data table can be found in the appendix (Table A2). Basically, hoverflies can be classified according to their larval food type (trait variable 1) as micro-, phyto- and zoophagous. Contrary to SPEIGHT et al. (1998) the Melanostoma-species are coded as zoophagous (see GILBERT et al., 1994), and the Eumerus-species as phytophagous. The number of generations per year (trait variable 3) differs considerably between regions. Several species coded as uni- or bi-voltine in SPEIGHT et al. (1998), were coded as bi- or pluri-voltine in our study area: Cheilosia variabilis, Eumerus sp., Helophilus trivittatus, Platycheirus europaeus, P. fulviventris, P. occultus, P. peltatus und Pyrophaena granditarsa. The distribution of a species and local abundance are often highly correlated (BROWN, 1995; OWEN and GILBERT, 1989). To provide an estimation for this, a distribution index was calculated: the number

Table 2. Life history traits data for the analysis. Details and a glossary of the categories can be found in SPEIGHT et al. (1998).

Variable Categories (no.) Source

1 Larval food microphagous, phytophagous, zoophagous (3) SPEIGHT et al. (1998) 2 Larval microhabitat On herbs, in herbs, among/under surface debris, SPEIGHT et al. (1998) nests of social insects, root zone, on/in water plants, submerged sediment/debris, water-saturated ground, on trees (9) 3 No. of generations Less than one, one generation, two generations, SPEIGHT et al. (1998), per year more than two (4) DZIOCK (unpublished) 4 Inundation tolerance No inundation tolerance, tolerant & short SPEIGHT et al. (1998) (It) breathing tube, tolerant & medium sized breathing tube, tolerant & long breathing tube (4) 5 Hibernation stage Larva, pupa, adult (3) SPEIGHT et al. (1998) 6 Adult food Nectar-bearing trees & shrubs, nectar-bearing SPEIGHT et al. (1998) herbs, anemophilous trees & shrubs, anemophilous herbs (4) 7 Migratory status Non-migrating, recorded migrant, SPEIGHT et al. (1998), strongly migratory (3) BARKEMEYER (1997) 8 Body size (mm) Less than 5, 5–5.9, 6–6.9, 7–7.9, 8–8.9, 9–9.9, VAN DER GOOT (1981), 10–11.9, 12–14.9, more than 15 mm (9) original species descriptions (ca. 60 references) 9 Distribution in Europe Ubiquitous, very common, common, local, See text scarce, rare (6) 10 Flight period March 1st half, March 2nd half, Apr 1, SPEIGHT et al. (1998) (northern Germany) Apr 2, May 1, May 2, Jun 1, Jun 2, Jul 1, Jul 2, Aug 1, Aug 2, Sep 1, Sep 2, Oct 1, Oct 2, Nov 1, Nov 2 (18)

of squares where a species has been recorded is taken as a measure for this index. The ratio of squares occupied by the species to the number of squares investigated is calculated. Data come from Schleswig (Germany), Denmark, Belgium, Niedersachsen (Lower Saxony, Germany), and Norway (CLAUSSEN, 1980; TORP, 1994; VERLINDEN, 1991; BARKEMEYER, 1994; STUKE, 1996, 1998; STUKE et al., 1998; NIELSEN, 1999). The distribution index is calculated as follows: (a) Calculation of the average distribution ratio for each species (number of squares occupied divided by number of squares with at least one syrphid record). (b) Calculation of the distribution index class following the approach in the Swiss checklist (Table 3; MAIBACH et al., 1992). (c) The Swiss checklist classifies the species in abundance classes, but does not give the number of localities of a certain species. For this reason, we compare the index calculated here with that given for Switzerland (MAIBACH et al., 1992; GOELDLIN and SPEIGHT, 1997). If the indices differ in more than two classes, the distribution index is increased or decreased by one class. (d) The distribution index gives one value per species and is subsequently coded as a dummy variable (LEPsˇ and Sˇ MILAUER, 2003) with six categories (see Table 3). No species coded as “very rare” were found in the field during our study. Thus we only use six categories for the trait variable “distribution index” in our analysis (Table 2).

Table 3. Calculation of the distribution index class. The average distribution ratio for each species is the number of squares occupied by that species divided by the number of squares with at least one syrphid record.

Distribution index class Average distribution ratio

Ubiquitous >50% very common 25–49.9% common 15–24.9% local 5–14.9% scarce 2–4.99% rare 0.5–1.99% very rare <0.5%

2.3. Environmental Variables

For the purpose of explaining species occurrence/abundance by their environment, the number of environmental variables should not exceed the sample size (TER BRAAK and VERDONSCHOT, 1995; DRAY et al., 2003). Therefore, in most cases, a careful reduction of the number of environmental variables is necessary (KING and JACKSON, 1999; VAUGHAN and ORMEROD, 2005). Over 200 environmental variables (mainly hydrological and soil) have been recorded in the course of the RIVA-project (RINK et al., 2000). From these, the pairwise correlations and principal component analyses (PCA) have been calculated to identify correlations and reduce the set of variables (HETTRICH and ROSENZWEIG, 2003; RINK, 2003; HENLE et al., in press). Six variables were finally chosen to be included in the analysis based on mini- mizing intercorrelations between variables and maximizing potential biological importance for the syrphid fauna (Table 4).

Table 4. Environmental variables, chosen after reduction of the original data set of over 200 environmental variables. These remaining six variables are only weakly correlated.

Environmental variable Abbreviation

Soil effective cation exchange capacity eff. CEC phosophorous content (plant-available) plant-avail. phosph. sand content sand content

Water maximum groundwater depth in June to September 1998 max. GW-depth amplitude of variation of the groundwater depth in the months ampl. GW-depth June to September 1998 distance to temporary pools distance temp pools

2.4. Statistical Analysis

The malaise traps recorded numerous species which could not be indigenous to the test sites because their larvae are dependent on microfeatures not available in grasslands. These are, for example, sapro- xylic species, whose larvae live in sap runs, rot holes, or dead wood (eg. genera Brachyopa, Brachy- palpus, Criorhina, Xylota, and Temnostoma). Their occurence in the traps is an effect of the occurence of old alluvial forest in the immediate vicinity of the test sites, where these species reproduce. In order to reduce distortion of the analysis results by these species, they were excluded from the analysis. This was achieved by only including species in the analysis that have an affinity to CORINE-habitats “open ground, 2” or “freshwater, 6 and 7” as coded by SPEIGHT et al. (1998). Only species coded with the fuzzy code “2” (preferred) or code “3” (maximally preferred) were included in our analysis.This led to

© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com Hoverflies as Bioindicators 347 a decrease in species number from 83 recorded species to 52 potentially indigeneous species (Appen- dix, Table A1). The 31 omitted species had predominantly been recorded in very low numbers. Species abundances were not transformed prior to the analysis. We conducted a five-step-analysis following MURPHY et al. (1994), CASTELLA and SPEIGHT (1996), and DUFRÊNE and LEGENDRE (1997): 1. A between-site Correspondence Analysis (CA) was applied to the species-by-sites abundance data (52 species × 15 sites) in order to graphically present an ordination of the sites based on syrphid occurence. 2. Standardised Principal component analysis (PCA) of the env var-by-sites matrix (6 env var, 15 sites) was performed to graphically present an ordination of the sites based on their environmental characteristics. 3. Co-inertia analysis looks for a common structure of two data matrices (DOLÉDEC and CHESSEL, 1994). We performed a co-inertia based on a PCA of the species-environment matrix and a CA of the species-site matrix. This allows an evaluation of the explanatory strength of the environmental variables for the occurrence of the syrphids. 4. The species-traits matrix was processed following CHEVENET et al. (1994): calculation of a fuzzy coded multiple correspondence analysis (FMCA) of the species-traits matrix (52 species, 63 trait categories) (see also CASTELLA and SPEIGHT, 1996). 5. Co-inertia analysis based on a CA of the species-sites matrix and a FMCA of the species-traits matrix using the row weights from the CA of the species-sites matrix. This allows an investigation of the explanatory strength of specific life history traits for the occurrence of the syrphid species on-site. Monte-Carlo-tests (10 000 permutations) were used to evaluate the statistical significance of the co-inertia analyses. All calculations were carried out using the ADE 4.0 program (THIOULOUSE et al., 1997).

3. Results

3.1. Syrphid Occurence and Environmental Characteristics

The first two axes (F1 & F2) of a correspondence analysis (CA) of the species-sites matrix explained 56% of the total variance. Figure 2 shows the ordination of the test sites on the first plane of this CA. On the first axis, the study area “Sandau” is clearly separated from the others. The species lists of the “Sandau” test sites 51, 53, and 57 are clearly different due to the occurence of Pyrophaena granditarsis, Eristalinus aeneus, Cheilosia barbata and the spring mass occurence of Platycheirus fulviventris. The temporary pool sites (4, 9, 10, 39, 40) are clearly separated from the grassland sites (20, 21, 26, 29, 30, 34) along the second axis, suggesting a gradient of moisture here. Only the drier site 42 is an exception. The temporary pools in the study area “Steckby” are very similar in species composition to site 40 in study area “Wörlitz”, which is reflected by their proximity in the ordination diagram. The two other sites in “Wörlitz” (39, 42) seem to differ siginificantly from those in “Steckby”. The drier sites (20, 21, 26) are quite similar to each other, but they are not very distinctive from the wetter grassland sites. Figure 3 summarises the results of the PCA of the env var-sites matrix. The first two axes together explain 67% of the total variance, 43% and 24% for the first and second axis, respectively. The effective cation exchange capacity and the maximum groundwater depth both indicate a very strong contribution to the first PCA axis. On the second axis, the amplitude of variation of the groundwater depth and the sand content are the main contributing variables. The ordination plot of the sites (Fig. 4) is very similar to that based on the species distribution at the sites (Fig. 2). Again, study area “Sandau” is clearly separated from the others. The first axis can be interpreted as a gradient of nutrients. With an increasing amount of nutrients, the groundwater depth decreases on axis 1. Sites 51, 53, and 57 are quite nutrient rich and are low in comparison with the Elbe level, whereas sites 20, 21, and 26 are nutrient poorer and are situated at a higher elevation. The second axis is interpreted as a gra-

0.8 -1 1.2 Steckby -0.8 34

wet grassland mesophilous 26 30 grassland 20 (a) 21 29

temp. 10 57 pools 4 40 53 9 51 Sandau 39 Wörlitz (b) 42

Figure 2. Ordination of the test sites on the basis of a CA of the species-sites matrix (15 sites, 52 × species, non-transformed abundances). (a) Histogram of the eigenvalues. (b) F1 F2-plot of the test sites, the three study areas are indicated by ellipses, biotope types are indicated by broken ellipses. dient of hydrodynamics expressed by the amplitude of variation of the groundwater depth, which is low for the temporary pool sites 9 and 10, whereas it is high for the drier grassland sites (e.g. 42), which are seasonally flooded and dry out alternately. A simultaneous ordination of the env var-sites matrix and the species-sites matrix can reveal whether the field distribution of the syrphid fauna can be explained by the environ-

sand content

plant-avail. Phosph.

max. GW-depth eff. CEC distance temp pools (a)

(b) ampl. GW-depth

Figure 3. Results of a standardised principal component analysis (PCA, six env var, 15 test sites). (a) Histogram of the eigenvalues. (b) Correlation circle. Abbreviations see Table 4.

depth of groundwater 2.9 -3 3.6 10 -3 9

water dynamics Sandau

26 57 51 53 mesophilous 4 wet grassland grassland 21 29 20 30 34 40 39 Steckby Wörlitz

42 nutrients

Figure 4. Results of a standardised principal component analysis (PCA, six env var, 15 test sites). × F1 F2-plane of the test sites. Figures relate to site numbers. The three arrows correspond to the env var with the highest factor load. mental variables measured on the test sites. This has been done by a co-inertia analysis, the results being summarized in Figure 5 and Table 5. Axis 1 has a high relevance (amount of variance explained) of 77.2% of the total variance. The correlation coefficient between the coordinate-sets from the co-inertia is 0.903 for F1 and 0.856 for F2 (Table 5). The permutation test (10 000 permutations) showed high significance (P = 0.012), indicating that, indeed, the two data sets (species on sites, env var on sites) have a very similar structure. This is illustrated graphically in Figure 5, which shows the ordination of the test sites from the “view” of the environmental variables (circles with site numbers) and from the “view” of the on-site syrphid fauna (tip of the arrows). Generally, the arrows are comparatively short, indicating a high degree of similarity.

3.2. Syrphid Occurence and Their Life History Traits

We carried out a multiple fuzzy coded correspondence analysis (FMCA) of the traits- species matrix to investigate the explanatory power of specific life history traits for the

Table 5. Comparison of inertia resulting from the co-inertia analysis with inertia resulting from the separate analyses of the env var-sites and the species-sites data set. Var env: inertia of the env var table projected on to co-inertia axis. Var fauna: inertia of the species table projected on to co-inertia axis. Inert: maximal projected inertia of the env var table/species table respectively. Covar: covariance of the two sets of coordinates projected on to co-inertia axes. r: correlation between the two new sets of coordinates resulting from the co-inertia.

Axis Var env Var fauna Inert env Inert fauna Covar r

F1 2.556 0.192 2.706 0.202 0.633 0.903 F2 1.479 0.054 1.505 0.139 0.243 0.856

3 10 -1.7 2.4 9 -2

4 53 57 51 20 21 29 30 34 40 39

Figure 5. Comparison of the ordinations of the test sites resulting from co-inertia. Figures in circles × are the test site numbers. Circles indicate the position of the test sites on the F1 F2 plane using standardised coordinates resulting from the environmental ordination. For each site, the tip of the arrow gives the position resulting from the faunistic ordination. The shorter the arrow, the greater is the similarity between the structure of the env var-sites and the species-sites table. occurence of the syrphid species on-site. Six ordination axes have been kept, which all- together explain 55% of the total variance. The correlation ratios of the investigated trait variables with the first six ordination axes are given in Table 6. For each trait, these are ratios of the variance of the factor scores of the categories to the total variance. They help in identifying the traits contributing most to the ordination along each axis by quantifying the way in which the categories of each trait variable are discriminated by the species along the axes (CASTELLA and SPEIGHT, 1996). The trait variables larval food, larval microhabitat, inundation tolerance, and body size have the largest influence on the separation of the species in the ordination. Adult food type and flight period seem to have only a neglegible effect on the ordination (Table 6). The influence of the categories on the separation of the species in the ordination space is × given in Figure 6 as a F1 F2 ordination plot of the FMCA. Each trait variable is plotted separately. The ordination plot of the species can be projected on the ordination plots for × each category, so that the species position in the F1 F2 ordination plane (Fig. 7) can be interpreted from their trait classification. Species on the right side of the plot – mainly Eristalini with rat tail maggots – posess microphagous larvae living in aquatic or semiaquatic situations, are relatively big, and inundation tolerant. On the very left we can find phytophagous species; their larvae live on roots or in herbs (Eumerus and Cheilosia). The two Neoascia-species are plotted at the bottom because of their minute size and rareness in our study. The huge guild of zoophagous species is not differentiated very well (center and top of the plot). In general, species are separated best by their larval life style, only few other life history traits contribute further information. A subsequent co-inertia analysis was calculated to test a common structure of the traits- species matrix and the species-sites matrix. The first two axes explained 82% of the total variance, 57% is explained by the first axis alone. Although the correlation between the two

Table 6. Correlation ratios (CR, variance of the category scores to the total variance) for the trait variables along the first six axes (F1–F6) of the FMCA of the traits-species matrix. CRs larger than the average CR for an axis are given in bold.

Trait variable F1 F2 F3 F4 F5 F6 Larval food 0.721 0.401 0.298 0.059 0.121 0.036 Larval microhabitat 0.627 0.347 0.345 0.150 0.461 0.138 No. of generations per year 0.281 0.194 0.103 0.205 0.356 0.165 Inundation tolerance 0.685 0.449 0.513 0.461 0.061 0.094 Hibernation stage 0.305 0.084 0.297 0.027 0.070 0.268 Adult food 0.031 0.061 0.096 0.005 0.001 0.040 Migratory status 0.223 0.391 0.158 0.107 0.079 0.088 Body size 0.475 0.404 0.325 0.549 0.305 0.643 Distribution in Europe 0.260 0.520 0.191 0.473 0.377 0.201 Flight period 0.050 0.047 0.013 0.012 0.013 0.008 Average CR 0.366 0.290 0.234 0.205 0.184 0.168

zoophag on herbs on trees >2gen none roots under surface 2gen sediment short in herbs long phytophag water-saturated 1gen microphag ground <1gen water plants medium

larval food larval microhabitat no. of generations per year inundation tolerance

strongly adult anemo herbs 6-6.9 10-11.9 pupa anemo trees migratory nectar 8-8.9 12-14.9 herbs recorded 7-7.9 9-9.9 larva migrant nectar trees non- migrating >15 5-5.9 <5

hibernation stage adult food migratory status body size (mm)

ubiquitous scarce 1.3 very common -1.8 2.9 local common -4

rare

distribution in Europe flight period × Figure 6. Ordination of life history traits (10 variables with 63 categories) on the F1 F2 plane by fuzzy correspondence analysis (FMCA). The distribution of the categories is given by the tip of the arrows. These are positioned at the weighted average of species representing that category. Anemo trees: anemophilous trees and shrubs, nectar herbs: nectar-bearing herbs. new coordinate sets from the co-inertia is 0.903 for F1, a permutation test (10 000 permutations) showed no significant common structure between the two data sets (P = 0.998). This means, the life history traits used in the analysis could not sufficiently explain syrphid distribution at our test sites.

Epsy.balt 1.1 Melst.mell -1.1 1.8 Scae.pyra -2.2 Spha.scri

Plat.albi Syrp.vitr Eup.lunig Syrp.ribe Eup.coro Spha.ruep Melst.scal Plat.pelt Plat.clyp Meyna.umbe Eris.arbu Eris.tena Volu.bomb Plat.euro Eup.latfa Meyna.lasi Helo.pend Spha.bata Chry.caut Eris.pert Chei.paga Chei.abit Plat.angu Chry.bici Chei.vulp Plat.occu Spha.taen Plat.fulv Pyro.gran Myat.flor Chei.vari Xant.pedi Chei.carb Eris.aene Chei.barb Pargu.haem Eris.sepu Helo.triv Eume.stri Rhin.camp Temn.vesp Helo.hybr Eume.tube Trop.scit Parh.vers

Chal.nemo

Neoa.tenu

(a)

Neoa.inte (b)

Figure 7. Ordination of syrphid species according to their biology (10 variables with 63 categories) × × on the F1 F2 plane by fuzzy correspondence analysis (FMCA). (a) eigenvalue histogram (b) F1 F2- plot of the species. Species names abbreviations see Table A1.

4. Discussion

In this study, we aimed to answer two questions: 1. Do syrphid species respond to small scale changes in environmental variables in seasonally flooded grasslands in the Elbe floodplain? 2. Can species response to environmental variables be explained by the biological characteristics of the species expressed by their life history traits?

4.1. Environmental Variables

We found a strong and significant relationship between syrphid occurence at the test sites and the environmental variables chosen to characterise the sites. Important factors proved to

© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com Hoverflies as Bioindicators 353 be the depth of the groundwater (which is closely correlated with the gauge of the Elbe river), the effective cation exchange capacity (concluded from the soil-pH, humus content and grain size), and the amplitude of variation of the depth of the groundwater throughout the year (which is a measure of the water-level fluctuation in the test area). The high relevance of water variables for the fauna of a floodplain is not surprising. On the sites with low groundwater depth (sites 51, 53, 57) we find predatory species with preferences for aphids on reeds (Platycheirus fulviventris, P. angustatus, Pyrophaena granditarsa on Typha and Phragmites). Species occuring on sites with a low degree of water fluctuation have low mobility and depend on constant humidity. During the summer drought period, they can only survive in the grasslands by moving to adjacent wetter habitats. One of these species is the wetland species Tropidia scita occurring in numbers on test sites 29, 30, and 34 and with occasional occurrence on test sites 9, 10, and 20. T. scita can only survive the summer periods – when flood channels inhabited by the species dry up – by using the whole-year water-bearing drainage ditch at the edge of the test site Steckby as a summer resort. The drier and nutrient-poorer sites 20, 21, and 26 are characterized by the phytophagous Cheilosia- and Eumerus-species. Platycheirus with a higher need for humidity (P. fulviventris, P. angustatus, P. occultus) are found in lower abundances there. DZIOCK (in press) presented an attempt to use the syrphid data of this study to set up a bioindication system for water and soil conditions on the sites. This attempt was only part- ly successful, as was indicated by a non-significant permutation test following a discrimi- nant analysis of the cluster groups resulting from the factor loads of the CA of the species- sites-matrix (see DZIOCK, in press). Additionally, there are no exclusive species for certain groups of test sites. Therefore, the strong statistical relationship between syrphid occurence at the sites and environmental variables is rather surprising (see Fig. 5). That is, it is possible to determine the main relevant environmental factors for the syrphids, although most of the grassland syrphid species are highly mobile, despite the existence of comparatively few indigenous species in intensively managed grasslands.

4.2. Life History Data

There have been numerous attempts to group species according to their function in the ecosystem instead of using single species. Such delineation has a long history in ecology (SCHIMPER, 1898; ROOT, 1967; GRIME, 1979; WILSON, 1999; HOOPER et al., 2002). These so called functional groups are polyphyletic suites of species that share ecological characteristics. Functional properties may correspond to different ecological characteristics depending on the questions asked and on the traits data available (STENECK, 2001; HOOPER et al., 2002). The main focus is on their function in the ecosystem, e.g. their trophic position or nutrient flux. This is advantageous over a single species approach, because a large part of the information in the species list can be used (BOURNAUD et al., 1992). Indicator species represent only a small fraction of the total species pool, and for this reason a significant part of the available information remains unconsidered (SIMBERLOFF, 1997). Nonetheless, the use of functional groups still represents a simplification and leads to loss of information. We have gone one step beyond the concept of functional groups and attempted to use the sum of the biological traits of all species in the bioindication process, thus using all the information that is in the biology of the species without any loss. Multiple correspondence analyses (FMCA, Fig. 7) showed a clear separation of the species according to larval food along axis 1. Additional trait variables responsible for the separation are larval microhabitat and inundation tolerance. The high relevance of larval food and microhabitat for the separation could be explained by the high diversification of larval lifestyles in general. Syrphids are one of the most biologically diverse groups of Diptera, expressed by their wide range of inhabited ecosystems and adaptations (THOMPSON

© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com 354 F. DZIOCK and ROTHERAY, 1998). For example, they include phytophages, mycophages, saprophages in diverse media, zoophages and even parasitoids. They have a 400-fold range in weight, from 0.5 mg (Neoascia) to 200 mg (Volucella) (ROJO et al., 2003). Inundation tolerance of the larvae could be assumed to be one of the key factors determining occurrence of syrphid species in the floodplain. The importance of this trait variable together with larval food and microhabitat is in accordance with the studies by CASTELLA et al. (1994), MURPHY et al. (1994), CASTELLA and SPEIGHT (1996), and SCHWEIGER et al. (submitted). A very small proportion of variance was explained by adult food, number of generations, and phenology. Virtually all adults of syrphids feed on nectar and pollen from flowers, and are important plant pollinators. However, there are only few examples of feeding specialization on certain plants or plant groups (for example the association of Platycheirus and Melanostoma species with wind-pollinated plant families; see DZIOCK 2002). This is in strong contrast with the wide-spread flower specialization of wild bees (WESTRICH, 1995). Phenology and the number of generations per year also had low contribution to the ordination of the species in the studies by CASTELLA and SPEIGHT (1996) and SCHWEIGER et al. (submitted). The study sites were of rather small size (<50 ×15 m). We know that at least some syrphid species are highly mobile or even migratory (GATTER and SCHMID, 1990; SALVETER and NENTWIG, 1993). In the capture-recapture studies by AUBERT et al. (1969), CONN (1976) and WRATTEN et al. (2003), non-migrant syrphids travelled distances of up to 400 m, while migrants such as Episyrphus balteatus managed up to 111 km a day. Despite their high migratory power, we discovered a high correlation of environment and species composition, even on the small sites used in this study. This indicates that species composition at the sites is not only a random sample of the regional species pool, but is connected to site conditions. Possibly, even migrant syrphids are more confined to their breeding sites during the non- migration season than is indicated by their wide distribution. Migratory status had low explanatory strength in this study and SCHWEIGER et al. (submitted), but contributed significantly in the study by CASTELLA and SPEIGHT (1996). A common structure between the traits and the species data standing up to significant testing could not be found (result of the co-inertia). Therefore indicatory functional groupings of species in the grasslands were not defined. A subsequent RLQ-analysis (DOLÉDEC et al., 1996) which directly links environment and traits has not been used, because the co-inertia of traits and sites failed to be significant and it was therefore unlikely to obtain significant results with an RLQ. However, it would be possible to analyze the trait-environment relationship of subsets of the species based upon their habitat or microhabitat relationships or feeding strategies. This aspect could be expected to provide an improvement in the inter- pretation of the results. There are some points for critical examination of the traits database. Quantification of inundation tolerance using the length of the posterior breathing tube has not proved very successful. Information on migratory behaviour differs significantly depending on the source used (compare GATTER and SCHMID, 1990; BARKEMEYER, 1997; SPEIGHT et al., 1998). Preda- tory species are not well separated using the database (compare with Fig. 7). This is not because their biology is less well known, but the opposite is true! Coding and summary of their biology is insufficient, although much information is apparently there, but it occurs scattered in the literature. The recently published book by ROJO et al. (2003) is an exception, but has not been incorporated in the database yet. On the other hand, there is consid- erable intraspecific variation in trait variables (e.g. oviposition preference; SADEGHI and GILBERT, 1999), so there is the risk of being very typological with respect to species. Grasslands in general are ephemeral habitats. Disturbances are quite frequent (e.g. mow- ing, grazing, flooding) and often they are rich in nutrients. Following habitat templet theory (SOUTHWOOD, 1977, 1996), such habitats which are characterized by frequent disturbances and high productivity, harbour species with a distinctive bionomic profile: wide niche

© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com Hoverflies as Bioindicators 355 breadths, many and small offspring, weak defence mechanisms against predators and often high migratory potential. These are organisms with an r-strategy and high tolerance for disturbances. This is also apparent in the observed syrphid fauna that consists of many predatory generalists with high migratory ability. But precisely these species are not suitable as bioindicators, because ideally indicator organisms should have: (a) high specialization, (b) low migratory power, (c) high lifespan, and (d) good detectablility. These characteristics are generally rare in grassland-inhabiting hoverflies. In those few studies that have to date studied the explanatory power of the biology of species for their distribution in the field, significant relationships were indeed found (CASTELLA and SPEIGHT, 1996; MURPHY et al., 1994). But in those studies the range of habitats surveyed was much broader (sand banks, riparian habitats, alluvial forests); therefore, a relationship is much easier to detect than in our project, where exclusively seasonally flooded grasslands were studied.

5. Conclusions

SOMMAGGIO (1999) gives some examples for the use of hoverflies in environmental assessment. The major shortcoming in the study of hoverflies for bioindication is the lack of data on environmental variables which determine hoverfly occurrence. Additionally, significance tests either on the relationship between environmental variables and hoverfly occurence (HUMPHREY et al., 1999) or on syrphid life history traits and hoverfly occurrence (CASTELLA and SPEIGHT, 1996) are rare. Up to now, I do not know of a single study that relates environmental variables to life history traits of Syrphidae in a given environment. In this work, this relationship was studied on 15 study sites in the river Elbe floodplain. In general, the use of hoverflies in bioindication is promising (see the criteria in MCGEOCH, 1998; HENLE et al., 2006), because there is a very fine biological database (SPEIGHT et al., 1998). Our knowledge regarding species distribution is quite good, we have standardised collecting methods (e.g. malaise traps and pan traps), the taxonomy is clear and determination keys are available at least for Northern and Western Europe (VAN VEEN, 2004). Nonetheless, there are a few points where knowledge gaps have to be filled and further research might be fruitful to carry out: – We did not include data on land use in our analysis. Especially in central Europe, where land use is intense, hoverfly communities are strongly influenced by management (SAL- VETER, 1998; SPEIGHT, 2001; SCHWEIGER et al., 2005). – An improvement of the biological database is needed. We need to include more life history trait data from the literature into the database. There has been no attempt to account for regional differences in species traits. How this could be integrated into the database is an unsolved problem at the moment. – The comparative phylogenetic method (sensu HARVEY and PAGEL, 1991; SANFORD et al., 2002) has been developed to account for phylogenetic relatedness while comparing species characteristics. Until recently, there have been no statistical methods to relate species life-history traits to environmental characteristics (the “fourth-corner” problem sensu LEGENDRE et al., 1997) and simultaneously accounting for the relatedness of the species involved. To our knowledge, there are no published studies for multivariate data sets. DESDEVIDES et al. (2003) have proposed a variance partitioning procedure that accounts for these relationships. Future analyses should use this procedure. In the RIVA-project, we have sampled plants, carabid beetles, and molluscs on the same sites. A comparison of the traits of all species groups suggests itself. It should be very inter- esting to see whether the same patterns as in syrphids will emerge. This investigation requires the careful choice of life history traits, which can be compared among species groups.

6. Acknowledgements

For sorting out a major part of the malaise trap material I would like to thank MARTIN MUSCHE (Halle) and HENNING STEINICKE (Halle). KLAUS FOLLNER (Leipzig), KLAUS HENLE (Leipzig), MARTIN C. D. SPEIGHT (Dublin) and SABINE STAB (Bad Schandau) gave very helpful advice during the whole RIVA project. The comments by FRANCIS FOECKLER (Regensburg), KLAUS HENLE (Leipzig), JENS-HERMANN STUKE (Leer) and two anonymous reviewers greatly improved former versions of the manuscript. Sta- tistical advice came from EMMANUEL CASTELLA (Genève). CLAUS CLAUSSEN (Flensburg), DIETER DOCZKAL (Malsch), and MARTIN C. D. SPEIGHT (Dublin) confirmed determinations of critical specimens. The good cooperation within the RIVA-project and within the Department of Conservation Biology at the Centre for Environmental Research in Leipzig is greatly acknowledged.

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Manuscript received August 30th, 2005; revised April 28th, 2006; accepted May 11th, 2006

Appendix

Table A1: Species list. Nomenclature follows SSYMANK et al. (1999) and DOCZKAL et al. (2002). species name abbreviation

Baccha elongata (FABRICIUS, 1775) Bacc.elon Brachymyia berberina (FABRICIUS, 1805) Bramy.berb Brachyopa insensilis COLLIN, 1939 Brac.inse Brachyopa pilosa COLLIN, 1939 Brac.pilo Brachypalpoides lentus (MEIGEN, 1822) Brap.lent Brachypalpus laphriformis (FALLÉN, 1816) Brapa.laph Brachypalpus valgus (PANZER, 1798) Brapa.valg Chalcosyrphus nemorum (FABRICIUS, 1805) Chal.nemo Cheilosia albitarsis (MEIGEN, 1822) Chei.abit Cheilosia barbata LOEW, 1857 Chei.barb Cheilosia carbonaria EGGER, 1860 Chei.carb Cheilosia cynocephala LOEW, 1840 Chei.cyno Cheilosia gigantea (ZETTERSTEDT, 1838) Chei.giga Cheilosia pagana (MEIGEN, 1822) Chei.paga Cheilosia variabilis (PANZER, 1798) Chei.vari Cheilosia vulpina (MEIGEN, 1822) Chei.vulp Chrysotoxum bicinctum (L., 1758) Chry.bici Chrysotoxum cautum (HARRIS, 1776) Chry.caut Chrysotoxum festivum (L., 1758) Chry.fest Chrysotoxum vernale LOEW, 1841 Chry.vern Chrysotoxum verralli COLLIN, 1940 Chry.verr Criorhina pachymera EGGER, 1858 Crio.pach Dasysyrphus albostriatus (FALLÉN, 1817) Dasy.albo Dasysyrphus hilaris (ZETTERSTEDT, 1843) Dasy.hila Dasysyrphus venustus (MEIGEN, 1822) Dasy.venu Didea intermedia LOEW, 1854 Dide.inte Doros profuges (HARRIS, 1780) Doro.prof Epistrophe eligans (HARRIS, 1780) Epis.elig Epistrophe melanostoma (ZETTERSTEDT, 1843) Epis.mela Epistrophe nitidicollis (MEIGEN, 1822) Epis.niti Episyrphus balteatus (DE GEER, 1776) Epsy.balt Eristalinus aeneus (SCOPOLI, 1763) Eris.aene Eristalinus sepulchralis (L., 1758) Eris.sepu Eristalis arbustorum (L., 1758) Eris.arbu Eristalis intricaria (L., 1758) Eris.intr Eristalis pertinax (SCOPOLI, 1763) Eris.pert Eristalis similis FALLÉN, 1817 Eris.simi Eristalis tenax (L., 1758) Eris.tena Eumerus ornatus MEIGEN, 1822 Eume.orna Eumerus sogdianus STACKELBERG, 1952 Eume.sogd Eumerus strigatus (FALLÉN, 1817) Eume.stri Eumerus tuberculatus RONDANI, 1857 Eume.tube Eupeodes bucculatus (RONDANI, 1857) Eup.buccu Eupeodes corollae (FABRICIUS, 1794) Eup.coro Eupeodes latifasciatus (MACQUART, 1829) Eup.latfa Eupeodes luniger (MEIGEN, 1822) Eup.lunig Fagisyrphus cinctus (FALLÉN, 1817) Fagi.cinc Ferdinandea cuprea (SCOPOLI, 1763) Ferd.cupr Helophilus hybridus LOEW, 1846 Helo.hybr Helophilus pendulus (L., 1758) Helo.pend

Appendix continued 1 species name abbreviation

Helophilus trivittatus (FABRICIUS, 1805) Helo.triv Heringia latitarsis (EGGER, 1865) Heri.lati Heringia senilis SACK, 1938 Heri.seni Heringia vitripennis (MEIGEN, 1822) Heri.vitr Leucozona lucorum (L., 1758) Leuc.luco Melangyna compositarum (VERRALL, 1873) Meyna.comp Melangyna lasiophthalma (ZETTERSTEDT, 1843) Meyna.lasi Melangyna umbellatarum (FABRICIUS, 1794) Meyna.umbe Melanostoma mellinum (L., 1758) Melst.mell Melanostoma scalare (FABRICIUS, 1794) Melst.scal Meligramma trianguliferum (ZETTERSTEDT, 1843) Mgra.tria Meliscaeva auricollis (MEIGEN, 1822) Msca.auri Merodon avidus (ROSSI, 1790) Mero.avid Microdon analis (MACQUART, 1842) Micr.egge Myathropa florea (L., 1758) Myat.flor Neoascia interrupta (MEIGEN, 1822) Neoa.inte Neoascia tenur (HARRIS, 1780) Neoa.tenu Paragus haemorrhous MEIGEN, 1822 Pargu.haem Parasyrphus annulatus (ZETTERSTEDT, 1838) Para.annu Parasyrphus punctulatus (VERRALL, 1873) Para.punc Parhelophilus versicolor (FABRICIUS, 1794) Parh.vers Pipiza luteitarsis ZETTERSTEDT, 1843 Piza.lute Pipizella virens (FABRICIUS, 1805) Plla.vire Platycheirus albimanus (FABRICIUS, 1781) Plat.albi Platycheirus angustatus (ZETTERSTEDT, 1843) Plat.angu Platycheirus clypeatus (MEIGEN, 1822) Plat.clyp Platycheirus europaeus GOELDLIN DE TIEFENAU, MAIBACH & SPEIGHT, 1990 Plat.euro Platycheirus fulviventris (MACQUART, 1829) Plat.fulv Platycheirus occultus GOELDLIN DE TIEFENAU, MAIBACH & SPEIGHT, 1990 Plat.occu Platycheirus peltatus (MEIGEN, 1822) Plat.pelt Platycheirus scutatus (MEIGEN, 1822) Plat.scut Pyrophaena granditarsa (FORSTER, 1771) Pyro.gran Rhingia campestris MEIGEN, 1822 Rhin.camp Scaeva pyrastri (L., 1758) Scae.pyra Scaeva selenitica (MEIGEN, 1822) Scae.sele Sphaerophoria batava GOELDLIN DE TIEFENAU, 1974 Spha.bata Sphaerophoria rueppellii (WIEDEMANN, 1830) Spha.ruep Sphaerophoria scripta (L., 1758) Spha.scri Sphaerophoria taeniata (MEIGEN, 1822) Spha.taen Syritta pipiens (L., 1758) Syri.pipi Syrphus ribesii (L., 1758) Syrp.ribe Syrphus torvus OSTEN-SACKEN, 1875 Syrp.torv Syrphus vitripennis MEIGEN, 1822 Syrp.vitr Temnostoma bombylans (FABRICIUS, 1805) Temn.bomb Temnostoma vespiforme (L., 1758) Temn.vesp Tropidia scita (HARRIS, 1780) Trop.scit Volucella bombylans (L., 1758) Volu.bomb Xanthandrus comtus (HARRIS, 1780) Xant.comt Xanthogramma laetum (FABRICIUS, 1794) Xant.laet Xanthogramma pedissequum (HARRIS, 1776) Xant.pedi Xylota segnis (L., 1758) Xylo.segn Xylota sylvarum (L., 1758) Xylo.sylv

Table A2. Traits data matrix used for the analysis. species Chal.nemo Chei.abit Chei.barb Chei.carb Chei.paga Chei.vari Chei.vulp Chry.bici Chry.caut Epsy.balt Eris.aene Eris.arbu Eris.pert Eris.sepu Eris.tena Eume.stri Eume.tube Eup.coro Eup.latfa Eup.lunig Helo.hybr Helo.pend Helo.triv Melst.mell Melst.scal Meyna.lasi microphag 3 33333 333 phytophag 333333 33 zoophag 333 333 333 on herbs 3322 in herbs 22 under surface 3333 3 2 33 nest social insects roots 233333333 33 3 33 water plants sediment 22222 223 water-saturated ground 2 22222 2 on trees 2 3 2 1 3 <1 gen 1 gen 333 23 3 1 3 2 1 3 2 gen 1 32133 1 1 211332 3 2 >2 gen 333333 3 1 3 32 none It 33333 33 233 3 3 3 short It 33 2 3 3 medium It 3 long It 33333 333 larva Ow 3 3 3 33 333 33 3 333333 pupa Ow 3333 33 adult Ow 33 3 nectar trees 222 222233233332 223333222 nectar herbs 22232222222222223222222222 anemo trees 2 2 2 2 2 anemo herbs 2 2 22 1 12 222 non-migrating 333333333 3 13 33 3 3 recorded migr 32333 strongly migr 33332333 L<5 L5-5,9 3 L6-6,9 3 3 L7-7,9 3 L8-8,9 33 3 L9-9,9 333 3 333 3 L 10-11,9 3 3 333 L 12-14,9 33 3 L>15 333 ubiquitous 33333333 very common 33 33 3 333 3 common 33 local 3 3 3 3 3 3 scarce 3 rare Mar 1 1 Mar 2 11 1 1 Apr 01 1 1 1211 1 113 Apr 02 11 2 1222 1 1112 133 Mai 01 231 222 21222312222113 332 Mai 02 33132321312223122222232331 Jun 01 3222232132222312221122232 Jun 02 3222222332222322221122223 Jul 01 3132222323232322232123233 Jul 02 3132222313233332232233233 Aug 01 3132222313233333232333333 Aug 02 2 31222213233332233333332 Sep 01 1 1 31322311231 3232 Sep 02 1 21312311121 3222 Okt 01 2 2 1 3 1 1 1 1 1 Okt 02 2212 11 Nov 01 1 Nov 02 1

© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com Hoverflies as Bioindicators 363 species Meyna. umbe Myat.flor Neoa.inte Neoa.tenu Pargu.haem Parh.vers Plat.albi Plat.angu Plat.clyp Plat.euro Plat.fulv Plat.occu Plat.pelt Pyro.gran Rhin.camp Scae.pyra Spha.bata Spha.ruep Spha.scri Spha.taen Syrp.ribe Syrp.vitr Temn.vesp Trop.scit Volu.bomb Xant.pedi microphag 333 3 3 33 phytophag zoophag 3 3 33333333 3333333 33 on herbs 3 2323333 2333321 in herbs under surface 22223 nest social insects 33 roots 2 2 2 3 3 water plants 332222 2 sediment 22 3 3 water-saturated ground 2 2 3 2 on trees 2 2 2 2 3 2 3 <1 gen 3 1 gen 2 13 2221 2 333 2 gen 23333 2332323232322222 1 >2 gen 11121 22222 none It 3 3 3 3333 33 33 short It 3333333 3 33 medium It 33 3 long It 33 larva Ow 333333333333333 3333333333 pupa Ow adult Ow 3 nectar trees 232222322 22 322232333233 nectar herbs 222222222 2222222222222222 anemo trees 22 22222 anemo herbs 2223322 22 2 22 2 non-migrating 333333 3 333 33 32 31 3333 recorded migr 33 3 1 3 strongly migr 333 L<5 3 L5-5,9 33 L6-6,9 3 L7-7,9 33 3 L8-8,9 333 L9-9,9 333 3 3 3 3 L 10-11,9 33 333 L 12-14,9 33 3 L>15 3 ubiquitous 33333333 very common 33 33 3 common 33 local 3333 333 scarce 33 3 rare 3 Mar 1 Mar 2 Apr 01 11 11121 Apr 02 11 2 111122 Mai 01 133113211121121222 321111 Mai 02 12231132332331312221331211 Jun 01 12231122223233222221231222 Jun 02 13131322223233222221333332 Jul 01 13122332322213232221332332 Jul 02 13212232332213233232331233 Aug 01 23122132322123333232331112 Aug 02 33122132312133333232231112 Sep 01 12 222 3332213123 1 1 Sep 02 2 221 2221112 23 1 Okt 01 1 111 1 11 1 12 Okt 02 1 111 1 1 11 Nov 01 Nov 02