The Effect of Water Temperature Regime on Benthic Macroinvertebrates

The effect of water temperature regime on benthic macroinvertebrates

A contribution to the ecological assessment of rivers

Inaugural-Dissertation

zur Erlangung des Doktorgrades Dr. rer. nat. des Fachbereichs Biologie und Geografie an der Universität Duisburg-Essen, CE

vorgelegt von

Alexandra Haidekker

aus Hamburg

Oktober 2004

Die der vorliegenden Arbeit zugrunde liegenden Experimente wurden in der Abteilung Hydrobiologie des Instituts für Ökologie der Universität Duisburg-Essen durchgeführt.

1. Gutachter: PD Dr. Hering (Universität Duisburg-Essen)

2. Gutachter: Prof. Dr. Greven (Universität Düsseldorf)

Vorsitzender des Prüfungsausschusses: Prof. Dr. Schuhmacher (Universität Duisburg-Essen)

Tag der mündlichen Prüfung: 22.02.2005

πάντα ρει̃

Table of Contents

1. General introduction ...... 1

2. The temperature characteristics in two river-types in the Lower Mountain Range of Western Germany ...... 6

2.1. Introduction...... 6

2.2. Materials and methods ...... 7 2.2.1. Study area and study sites ...... 7 2.2.2. Recording of temperature data, assessment of environmental variables ...... 11 2.2.3. Weather data...... 14 2.2.4. Temperature data processing...... 14 2.2.5. Temperature parameters used for the analysis ...... 15 2.2.6. Data analysis ...... 16

2.3. Results...... 18 2.3.1. Thermal parameters – ecological significance...... 18 2.3.2. Thermal parameters – typological significance ...... 21 2.3.3. Small- and mid-sized rivers – are they thermally homogeneous? ...... 23 2.3.4. Thermal pattern in relation to environmental conditions...... 25 2.3.5. Classification with other river-types ...... 27

2.4. Discussion...... 32 2.4.1. Temperature parameters of typological and ecological importance ...... 32 2.4.2. Thermal pattern of the river-types and environmental influence...... 35 2.4.3. Classification with other river-types ...... 38

3. Influence of a modified temperature regime on the development of two Hydropsyche species...... 40

3.1. Introduction...... 40

3.2. Materials and methods ...... 42 3.2.1. Study area and sampling sites ...... 42 3.2.2. Assessment of environmental data...... 43 3.2.3. Invertebrate data: sampling and determination ...... 46 3.2.4. Statistical tests...... 47

3.3. Results...... 48 3.3.1. Thermal changes downstream from the Bigge confluence and the power station...... 48 3.3.2. Comparison of the thermal changes by hypolimnic and cooling water ...... 50 3.3.3. Development of two Hydropsyche spp. in different thermal regimes ...... 52

3.4. Discussion ...... 58 3.4.1. Environmental situation ...... 58 3.4.2. Longitudinal influence of thermal changes...... 59 3.4.3. Development of Hydropsyche larvae ...... 60

4. The influence of water temperature on the macroinvertebrate community...... 65

4.1. Introduction...... 65

4.2. Materials and methods ...... 66 4.2.1. Study area and sites...... 66 4.2.2. Sampling ...... 67 4.2.3. Data processing ...... 69 4.2.4. Data analysis ...... 70

4.3. Results...... 71 4.3.1. Variation of environmental and thermal characteristics ...... 71 4.3.2. The EPTC-community ...... 73 4.3.3. Community pattern and habitat...... 74 4.3.4. Explanatory power of the environmental variables...... 75

4.4. Discussion...... 81 4.4.1. Temperature and community pattern ...... 81 4.4.2. Temperature parameters in comparison ...... 81 4.4.3. Other environmental variables and their effects on community ...... 82 4.4.4. Temperature-sensitive species: do they reflect strategies for different thermal environments?...... 84

5. Summary and conclusion...... 90

6. Zusammenfassung ...... 93

7. References...... 104

8. Appendices ...... 122

1. General introduction

One early reason for intensifying research in running waters was the rising pollution level in the beginning of the 20th century (Schönborn 2003). Kolkwitz & Marsson (1908, 1909) developed the first saprobic index to assess the organic pollution in rivers. In the following years several other saprobic systems (e.g. Liebmann 1962, Pantle & Buck 1955, Zelinka & Marvan 1961) and biotic indices (e.g. Woodiwiss 1964, Beak 1965) were developed in different regions and countries in Europe in order to assess the water quality. With increasing knowledge and the attention paid to the ecological state of rivers the focus turned also to other stressors apart from organic pollution, since rivers have been modified in many ways: in particular anthropogenic influence caused further changes in stream-water physico-chemistry, i.e. acidification, eutrophication, input of toxic substances, modifications of the thermal regime, as well as changes in river morphology, hydrology, and habitat diversity. An increasing number of assessment systems have been developed to measure the state of degradation for most of the stressors mentioned above, using mainly benthic invertebrates, but also fish, phytobenthos, phytoplankton, or macrophytes as indicators. The majority of methods has been developed to detect the degree of organic pollution and general degradation in rivers (e.g. Kokes et al. 2001, Schöll & Haybach 2001), followed by assessment methods for hydromorphological degradations (e.g. Buhmann & Hutter 1996, LAWA 2001, Lorenz et al. 2004), eutrophication (e.g. Institut für Wassergüte 2002, Bayerisches Landesamt für Wasserwirtschaft 1998), acidification (e.g. Braukmann 1999, Rutt et al. 1990) and toxic substances (e.g.Wogram & Liess 2001) (for a review on assessment methods see Birk & Hering 2002, Knoben et al. 1995). Recently, the EU Water Framework Directive (WFD; Directive 2000/60/EC – Establish- ing a Framework for Community Action in the Field of Water Policy) sets uniform stan- dards in water management practices for Europe with the focus on biotic indicators to survey the ecological status of the water bodies. For most of the stressors mentioned above experienced assessment methods existed, which were reviewed and partly integrated in a new ecological assessment system (Hering et al. 2004). It is remarkable that up to today there are no methods for the evaluation of modifications of the thermal regime and its ecological consequences, although the WFD requires thermal parameters as one of the physico-chemical parameters to be assessed for evaluating the ecological quality of running waters (Annex V 1.1 and 1.2.).

1 1. General introduction

Consequently, this leads to the question whether a survey of the thermal regime in rivers is necessary, - which it is, when thermal modifications lead to ecologically relevant degradations (e.g. effect the benthic biota).

The factor temperature in rivers The ecological condition of flowing waters depends on five major complex factors: hydrology, morphology, physical and chemical factors, and biological components. All factors are interrelated to a complex system. Regarding the abiotic environment, flow characteristics and temperature are generally considered to be key factors for organisms in running waters (e.g. Elliott 1978, Humpesch & Elliott 1980, Vannote & Sweeney 1980, Stanford & Ward 1983, Reyiol et al. 2001). The temperature holds a particular position among the abiotic factors, since its influence on an organism may be both direct and indirect: its direct influence on an organism con- cerns, for example, temperature dependent metabolic reactions or enzyme functions. In- direct influences take place via temperature dependent chemical or physical processes in the environment, namely the solubility and effects of substances, e.g. the oxygen content of the water or the toxicity of pesticides. The natural temperature regime in rivers is mainly determined by the following factors: first, water temperature is dependent on the climate, defined by the geographical location (latitude, longitude) and by the altitude, connected with the respective air temperature, precipitation, wind condition, air pressure, humidity and solar radiation. Air temperature, compared to the other factors, is considered to be of major importance concerning its influence on water temperature (Smith & Lavis 1975, Crisp & Howson 1982). Second, hydrologic factors influence the water temperature regime, i.e. the spring, tributaries, groundwater influx and the flow regime. Third, the isolation of the water body by riparian vegetation is of high importance for the temperature regime, because it is attenuating temperature extremes in smaller rivers. Fourth, the river morphology, i.e. the channel form, is important as it determines the exchange of temperature between air and water, and finally, the topography, i.e. the valley shape and orientation is responsible for the daily exposition to solar radiation (Ward 1985). Apart from these factors, the natural water temperature regime in rivers has been modified considerably through anthropogenic influence: for example, severe temperature changes are caused by the deep-water release of impoundments with biologically relevant effects on thermal amplitudes, minima and maxima for several kilometers downstream (e.g. Ward & Stanford 1982, Webb & Walling 1993). In addition to that, the discharge of heated industrial water or cooling water of power stations effects the natural temperature regime, intensifying the rate of metabolism, decreasing dissolved oxygen and increasing the rate of nitrification. Changes in the benthic community were observed due to the artificial rise of temperature (e.g. Descy & Mouyet 1984, Fey 1976, Caspers 1978).

2 1. General introduction

Furthermore, modifications of the groundwater level caused by land-use changes effect the temperature regime in running waters, either directly by changing the amount of direct groundwater input, or through changes in the relation of surface runoff to groundwater input, causing modifications in the hydrological regime, which indirectly modifies the temperature characteristics of the river (Hynes 1970, LeBlanc 1997). The removal of riparian vegetation results in less isolation of the water body, therefore leading to extreme temperatures. Besides, higher stream bank erosion caused by the lack of riparian vegetation changes the channel morphology which again effects the thermal regime because of a modified water surface-to-volume proportion (Sweeney 1993, LeBlanc 1997). Climate change is another relevant factor to the thermal regime of rivers, its potential effects being currently discussed (e.g. Gleick 2000, Murdoch et al. 2000).

The influence of temperature on benthic invertebrates The importance of the thermal regime in the evolution, distribution and ecology of the fauna in rivers has long been recognized (e.g. Thienemann 1950, Illies 1969, Hynes 1970, Ward & Stanford 1982). Changes in the distribution and in the community pattern of invertebrate assemblages have been connected to temperature in early works (Ide 1935). Several species have been identified to be restricted in their distribution by the temperature range (e.g. Elliott 1978, Humpesch & Elliott 1980, Reyiol et al. 2001), and species richness is found to be temperature dependent as well (e.g. Vannote et al. 1980, Brussock & Brown 1991, Stanford & Ward 1983, Ward 1982). Furthermore, temperature has been recognized as an important gradient in the physical and ecological continuum of rivers: for example in the concept of a longitudinal zonation of rivers (Illies & Botosane- anu 1963), in the River Continuum Concept (Vannote et al. 1980) or in the Thermal Equilibrium Hypothesis (Vannote & Sweeney 1980). Species distributions are known to change with temperature along elevational and altitudinal gradients (Vannote & Sweeney 1980, Ward 1985, Quinn & Hickey 1990). Moreover, river water temperature is one of the key factors determining the life history characteristics of stream invertebrates. Embryonic development, nymphal growth, emergence, metabolism and survivorship of certain taxa are regarded to be temperature dependent (e.g. Hynes 1970, Sweeney 1984, Lillehammer et al. 1989, Robinson & Minshall 1998, Watanabe et al. 1999).

Classifications of rivers including temperature Rivers have been classified according to their temperature ranges into different thermal types. Brehm & Ruttner (1926) distinguish between summer-warm and summer-cold brooks, Schwoerbel (1999) classifies rivers according to their maximum temperatures. In their relation between fish regions and temperature ranges, running waters have been

3 1. General introduction

classified into different longitudinal zones (e.g. Illies 1952, 1961, Schmitz 1954). Among other factors varying in altitude and latitude, the thermal gradient is known to be a key factor for the distribution of macroinvertebrates, as discussed in the River Continuum Concept (Vannote et al. 1980), and in the Thermal Equilibrium Hypothesis (Vannote & Sweeney 1980). The basis for thermal classification concepts reach from single temperature measurements to means based on more detailed studies, but the temperature ranges which have been used to distinguish between the different zones and types of rivers were contradictory (Malicky 1978, Moog & Wimmer 1994). It is remarkable that although the importance of temperature for the ecosystem in running waters is undisputed, there is still a lack of detailed, systematic temperature data especially in Central Europe. In the USA several detailed and long-term studies with continuous recordings of the temperature regime exist, e.g. in the drainage system of the White Clay Creek, Pennsylvania (Sweeney 1993). Detailed recordings of temperature exist also for New Zealand (e.g. Quinn et al. 1990, 1994, 1997). In Europe, most detailed studies with continuous temperature recording were conducted in several catchment areas of the UK (e.g. Edington 1965, Crisp & Le Cren 1970, Smith & Lavis 1975, Crisp et al. 1982, Crisp & Howson 1982, Webb & Walling 1986), whereas in other European countries similar studies with continuous temperature recordings are scarce, and detailed analysis of the thermal regime of rivers such as the study in the Ardèche in France (Dolédec 1987), in the Elz (Hofius 1971) and in the Dreisam (Bau- meister 2001) in Germany are the exception. Recently, glacial streams and alpine rivers have been thermally characterized in detail (Uehlinger et al. 2003). Until today, the majority of data on thermal conditions is sampled in an unsystematical way; most of the data are scattered and have been collected as background information in the course of ecological site-specific studies. It has repeatedly been criticized that detailed studies on the annual water temperature regime with its components including absolute minimum and maximum water temperature, annual and daily variation, number of degree days, and seasonal changes have rarely been evaluated simultaneously (Vinson & Hawkins 1998, Malicky 1978, Ward 1985). Fey (1984) noted that sporadic measurements might lead to false estimations of the temperature regime, as they can only offer a far too rough estimation for ecological studies.

The following conclusions can be drawn from the situation described above: The natural water temperature regime represents a key factor of the abiotic environment in rivers. Today, this factor is often modified by anthropogenic influence. Thermal changes were shown to have an effect on benthic invertebrates in various laboratory studies. In spite of these conditions, until now temperature has rarely been evaluated in detail in surveys on the ecological quality in rivers. The guidelines of the WFD stipulate a river assessment based on biotic indicators (macrozoobenthos, fish, aquatic flora), with the ecological status being defined by com-

4 1. General introduction

paring the biotic community composition at a sampling site with the community that is present under near natural reference conditions. This survey method is based on a river- type-specific classification. Thermal data to the currently established typology of rivers meeting the requirements of the WFD (Pottgiesser & Sommerhäuser 2004) do not yet exist. Furthermore, the question as to whether temperature-sensitive taxa could function as indicators for thermal changes in this assessment system has not yet been studied.

Aims of the present study were • to characterize the thermal regime of two river-types with the objective to test if a thermal typology can be established in accord with the typology developed to meet the requirements of the WFD, and if significant temperature parameters for a thermal assessment can be identified, • to analyse a river with an artificially changed temperature regime in detail, i.e. with emphasis on thermal variability, and observe the effects on the development of two benthic species, • to relate variation in the invertebrate community to temperature, to compare the significance of temperature with other environmental factors, and to identify species related to the thermal environment.

To meet these aims, three main tasks were subject of the underlying study which will be dealt with in three separate chapters as follows:

• the detailed characterization of the temperature regime in two river-types in the Lower Mountain Range of Western Germany, • the influence of a modified temperature regime on the development of two Hy- dropsyche species, • the influence of the temperature regime on the macroinvertebrate community in 20 rivers in the Lower Mountain Range of Western Germany – a field approach.

5 2. Temperature characteristics Introduction

2. The temperature characteristics in two river-types in the Lower Mountain Range of Western Germany

2.1. Introduction

The EU Water Framework Directive (2000) requires thermal parameters as one of the physico-chemical parameters to be assessed for evaluating the ecological quality of running waters (Annex V 1.1. and 1.2.), but still a basis for the evaluation of the thermal regime is lacking. The WFD stipulates a stream-typology with corresponding reference conditions in order to assess the ecological quality of the respective rivers. A typology has been established, based primarily on ecoregions (according to Illies 1978a), size classes, altitude classes and the geology of the catchment. Pottgiesser & Sommerhäuser (2004) established and extended this to a biocoenotic relevant river typology. Based on this typology, two stream types have been chosen for the underlying study in order to characterize them thermally. Temperature data of several other stream types have additionally been used for comparison.

This chapter focuses on 1. reviewing temperature parameters that have shown to be of ecological significance for stream zoobenthos, 2. examining if the stream types can be separated by a classification based on different temperature parameters and 3. defining, which environmental conditions are most closely associated with the characteristics of this thermal pattern.

6 2. Temperature characteristics Materials and methods

2.2. Materials and methods

2.2.1. Study area and study sites The study sites of both river-types are situated in the ecoregion 9, in the Rheinisches Schiefergebirge1 in Germany, with 11 sites located in the Sauerland and nine sites in the Eifel (Table 2-1). The study area is characterized by siliceous, carbonic rocks. Approximately 50% of the area is forested, the remaining parts are characterized by pasture, agriculture and urban settlements. Both stream types are for the most part located in floodplain valleys, which are predominantly in agricultural use. Forest in the floodplain, if present, is mainly restricted to the bank-sides. For a detailed characterization of the thermal regime, 20 representative sites were chosen in two river-types. According to the typology established for the rivers in Germany (Pottgiesser et al. 2004, Pottgiesser & Sommerhäuser 2004) one group of rivers belongs to Type 5, which is formed of small-sized streams (catchment area 10 to100 km²), predominated by coarse-sized gravel. The second group of rivers belongs to Type 9 with mid-sized rivers (catchment area 100 to 1,000 km²), predominated by fine- to coarse- sized gravel. Both groups of rivers are located in the siliceous lower mountains. Study sites for the thermal analysis in these two rivers types were selected with the objective to have similar catchment areas, altitudes, hydrological, morphological and chemical factors within each stream type, whereas vegetation cover and thus shading in the floodplain and catchment area should change from site to site. In addition to the detailed temperature analysis of the two above mentioned stream types, exemplary data of other stream types were used for an extended comparison: external temperature data of larger rivers, of rivers influenced by impoundment as well as temperature recordings in the lowlands were included (Table 2-1 continued). All temperature data were integrated in four groups, depending on the stream typology and on the source of the data. The characterization follows the typology applied by AQEM2, which uses ecoregion (Illies 1978), altitude, size of catchment area and geology as descriptors; this was recently integrated in the typology for Germany by Pottgiesser & Sommerhäuser (2004) with emphasis on biocoenotic relevance.

1 Rhenish Slate Mountains 2 The Development and Testing of an Integrated Assessment System for the Ecological Quality of Streams and Rivers throughout Europe using Benthic Macroinvertebrates – Putting the EU Water Framework Di- rective into Practice. Acronym: AQEM; Contract No: EVK1-1999-00132; Assessment of European streams using benthic macroinvertebrates. For details see AQEM consortium (2002).

Table 2-1. Study sites for temperature recordings. Group 1: small-sized mountainous rivers, group 2: mid-sized mountainous rivers, group 3: mid- 2. Tem sized low-land rivers, group 4: sampling sites with temperature data acquired externally (various river types). River type according to Pottgiesser & Sommerhäuser (2004), for details see text.

Region: SaLd: Sauerland, Webl: Weserbergland, N.Geest: Lower Geest, S.Geest: Stader Geest, HeBl: Hessisches Bergland, Bl: Bergisches Land. p erature characteristics Federal states: NRW: Nordrhein-Westfalen (Northrhine-Westphalia), H: Hessen (Hesse), RhP: Rheinland-Pfalz (Rhineland-Palatinate), NS: Nieder- sachsen (Lower Saxony), SH: Schleswig-Holstein. CA: catchment area. Recording time: dates of the temperature-registration. * Missing data: Kyll: Sep to Oct/00, Nims: Oct to Dec/00, Eder: Jun to Nov/00, Röhr: Jul to Oct/00.

River Region CA Altitude Recording time (com- Site pro- Group River Site Code type (fed state) [km²] [m.a.s.l.] plete months) tocol 1 Weiße Wehe Hürtgen WWE 5 Eifel (NRW) 11.2 300 Jun/00 - Jun/01 x 1 Kall Lammersdorf KAL (Kao) 5 Eifel (NRW) 17.4 450 Jun/00 - Jun/01 x 1 Erkensruhr Hirschrott ERK 5 Eifel (NRW) 22.5 400 Jun/00 - Jun/01 x

1 Volme Kierspe VOL 5 SaLd (NRW) 17.5 345 Jun/00 - Jun/01 x 1 Waldbach Endorf WAL 5 SaLd (NRW) 9.0 370 Jun/00 - Jun/01 x

1 Röhr Endorf ROE 5 SaLd (NRW) 4.7 370 Jun/00 - Jun/01* x 1 Salwey Obersalwey SAL 5 SaLd (NRW) 15.5 340 Jun/00 - Jun/01 x 1 Elbrighäuser B. Battenberg ELB 5 SaLd (H) 12.6 380 Jun/00 - Jun/01 x 1 Laasphe Bad Laasphe LAA 5 SaLd (NRW) 14.8 375 Jun/00 - Jun/01 x 1 Dreisbach Dreis-Tiefenbach DRE 5 SaLd (NRW) 25.9 270 Jun/00 - Jun/01 x

2 Rur Wiselsley RUR 9 Eifel (NRW) 154.0 360 Jun/00 - Sep/03 x Materialsandm 2 Kyll Densborn KYL 9 Eifel (RhP) 471.8 305 Jun/00 - Sep/03* x 2 Prüm W Waxweiler PRW 9 Eifel (RhP) 286.5 315 Jun/00 - Sep/03 x 2 Our Auel OUR 9 Eifel (RhP) 293.6 360 Jun/00 - Sep/03 x 2 Nims Birtlingen NIM 9 Eifel (RhP) 222.1 245 Jun/00 - Sep/03* x 2 Lenne Altenhundem LEN 9 SaLd (NRW) 190.0 280 Jun/00 - Sep/03 x 2 Nuhne Neukirchen NUH 9 SaLd (H) 134.4 310 Jun/00 - Sep/03 x 2 Eder Röddenau EDE 9 SaLd (H) 523.7 280 Jun/00 - Sep/03* x 2 Orke Reckenberg ORK 9 SaLd (H) 275.0 295 Jun/00 - Sep/03 x e 2 Prüm B Beifels PRB 9 Eifel (RhP) 327.1 280 Jun/00 - Sep/03 x thods

Table 2-1. continued 2. Tem

p erature characteristics River Region CA altitude recording time (com- site Group River Site Code type (fed state) [km²] [m.a.s.l.] plete months) protocol 3 Osterau Bad Bramstedt OST 15 N.Geest (SH) 172 13 Jun/02 - Mar/03 3 Schmalfelder Au Bad Bramstedt SFA 15 N.Geest (SH) 300 10 Jun/02 - Mar/03 3 Este Buxtehude EST 15 S.Geest (NS) 300 10 Jun/02 - Mar/03 3 Wümme Scheeßel WUE 15 S.Geest (NS) 288 24 Jun/02 - Mar/03 4 Diemel Helminghausen DIH 9 HeBl (H) 103 337 Jun/00 - Sep/03 4 Diemel Talsperre DIT (9) HeBl (H) 102 340 Jun/00 - Sep/03 4 Eder Schmittlotheim EDS 9 HeBl (H) 1,202 246 Jun/00 - Sep/03 4 Eder Talsperre EDT (9) HeBl (H) 1,443 240 Jun/00 - Sep/03 4 Fulda Guntershausen FUL 10 HeBl (H) 6,366 141 Jun/00 - Sep/03

4 Rhein Köln RHK 10 Bl(NRW) 144,232 34 Jun/00 - Sep/03 4 Rhein Ruhrort RHR 20 Bl(NRW) 152,895 16 Jun/00 - Sep/03 4 Mosel Cochem MOS 10 Eifel (RhP) 27,088 77 Jun/00 - Sep/03

4 Werra Allendorf WER 10 HeBl (H) 5,166 144 Jun/00 - Sep/03

Materialsandm e thods 9

2. Temperature characteristics Materials and methods

The river-types being analysed additionally to the types 5 and 9 mentioned above included the following: Type 10: large rivers (1,000 to 10,000 km²) in the Lower Mountain Range (ecoregion 9) characterized by gravel; Type 15: mid-sized lowland rivers (ecoregion 14) mainly with sand and loam; Type 20: very large-sized lowland rivers (ecoregion 14) characterized by sand. In Table 2-1 the sampling sites, location, typology and the source of data are summarized.

The characteristics of the four groups of study sites can be summarized as follows: Group 1 (Type 5: small-sized mountainous): ten monitoring sites in ten small-sized rivers in the lower mountainous area of Central Europe: altitude: 280 to 450 m.a.s.l., catchment areas: 13 to 26 km².

Group 2 (Type 9: mid-sized mountainous): ten monitoring sites in nine mid-sized rivers of the same ecoregion: altitude: 270 to 400 m.a.s.l., catchment areas: 143 to 524 km².

Group 3 (Type 15: mid-sized lowland): four temperature monitoring stations were installed in mid-sized lowland rivers. They were located in the Stader Geest in Niedersachsen3 (two monitoring sites, catchment areas: 288 and 300 km²), and in the Niedere Geest in Schleswig-Holstein (two monitoring sites, catchment areas: 172 and 300 km², altitude: 13 and 10 m.a.s.l. respectively).

Group 4 (external data of rivers Type 9, 10, 20): external temperature data has been provided by the Bundesanstalt für Gewässerkunde in Koblenz for nine sites in seven rivers, all located in the same ecoregion as group 1 and 2, i.e. in the lower mountainous region of the Eifel and Sauerland. These monitoring sites include three size types with mid-sized (>100 to 1,000 km²), large-sized (>1,000 to 10,000 km²) and very large-sized catchment areas (>10,000 km²).

3 Lower Saxony

10 2. Temperature characteristics Materials and methods

2.2.2. Recording of temperature data, assessment of environmental variables

Recording of temperature data The temperature of the two river-types was recorded for time periods between one and three years, starting in June 2000. The time periods of temperature recordings at all sampling sites are listed in Table 2-1. The temperature was recorded at regular intervals of 30 minutes. For the temperature registration, loggers of the type Gemini Data Loggers Tinytag Plus were used. The loggers have a resolution of ± 0.2°C. They were calibrated during 24 hours before installing them at the study site. They were also checked and calibrated every time when the data was read out from the loggers about every three months. The loggers were installed at the study site in a way securing that they: • stay under water at all water levels, • are located above the ground to avoid sediments covering the loggers, • are located in flowing, not stagnant water, • are always in the shade to avoid solar radiation heating the loggers directly.

To install the loggers in the stream, steal piles were fixed in the river bottom about 10 to 20 cm above the ground and the loggers were tied to them with rustproof wire. The overall-recording time covered one year for group 1 (small-sized mountainous), three years for group 2 (mid-sized mountainous), ten months for group 3 (mid-sized lowland) and three years for group 4 (external data).

Assessment of environmental variables Each study site of the small- and mid-sized rivers in the mountainous region (groups 1 and 2) was characterized by site protocols including chemical, hydrological and geographical data. These cover characteristics of the catchment area, of upstream and downstream sections of the sampling site (5 km upstream and downstream from the sampling sites in small-sized rivers, and 10 km upstream and downstream from the sampling sites in mid-sized rivers), and characteristics of the sampling site (250 m upstream and downstream from the sampling point in small-sized rivers, and 500 m upstream and downstream from the sampling point in mid-sized rivers (for more details see AQEM consortium 2002, Hering et al. 2004). These protocols included more than 130 environmental parameters for each sampling site. For further analysis only those variables were chosen that may possibly effect ther-

11 2. Temperature characteristics Materials and methods

mal characteristics of the rivers (Table 2-2). These were correlated with the temperature data. Catchment size, distance of the study site to the source, altitude and mean slope of the valley floor were taken from topographical maps at scales of 1:25,000 and 1:50,000. River width, depth and current velocity were assessed at the sampling site, the average width was measured from the actual shorelines, mean depth and mean current velocity were calculated from 20 replicates at the sampling site. The amount of forest (in 10 % intervals) in the catchment area and in the floodplain was estimated using the Corine Land Cover System (Statistisches Bundesamt 1997). While in the AQEM site protocol native- and non-native forest is considered separately, the percentages of both forest types were summed up to form one parameter for the analysis in the present study. The parameter “shade” is an average value calculated from the following variables: the left and right shoreline covered with woody riparian vegetation at the sampling site, and the foliage cover shading the channel at the zenith (values in percent). No discharge values were available for the small-sized rivers, but size-related parameters as catchment area and current velocity, width and depth are meant to substitute discharge. In addition to the environmental variables assembled in the AQEM site protocol, the azimuth was measured on topographical maps (1:50,000 and 1:100,000) as the angle (in degrees) at which the overall stream channel differed from due south: i.e. due south = 0°, due west = 90°, due east = -90° . For analysis, the absolute value of azimuth was used, as the amount of radiation a stream receives has to be equal for the orientation of the same magnitude but different sign (Hawkins et al. 1997). As far as environmental variables for river groups 3 and 4 (external data and lowland rivers) are concerned, no site protocols were available for the stations of group 3 and 4; sampling sites with site protocol data are marked in Table 2-1, last column.

2. Tem

Table 2-2. Geomorphological and environmental variables for the study sites of small- (Type 5) and mid-sized rivers (Type 9). CA:Catchment area, Alt

[masl]: altitude [meters above sea level], Currvel: current velocity (mean), Ca_for: forest in the catchment area, Shade: riparian vegetation 250m (Type5) p erature characteristics /500m (Type9) upstream, Fp_forest: 1km length. Azimuth: channel orientation (for details see materials and methods), Conduct: conductivity. * data not used in analysis, given for later interpretation.** parameter assessed additionally to the AQEM site protocol.

CA Alt Slope Width Depth Currvel Ca_for Fp_for * Shade Azimuth** Conduct* Site Type [km²] [masl] [%] [m] [cm] [m/s] [%] [%] [%] [degrees] [µS/cm] WWE 5 11.2 300 1.4 4.1 18.3 0.52 70 100 90 8 173 KAL 5 17.4 450 2.5 5.0 22.9 0.69 10 80 70 18 185 ERK 5 22.5 400 3.8 3.6 31.6 1.05 70 40 50 16 93 VOL 5 17.5 345 1.1 4.5 25.9 0.61 20 0 10 20 673 WAL 5 9.0 370 1.8 4.5 20.3 0.72 100 70 70 9 163

ROE 5 4.7 370 2.6 3.1 18.7 0.64 90 30 57.5 12 236 SAL 5 15.5 340 1.2 4.0 38.5 1.06 70 0 25 57 265 ELB 5 12.6 380 1.7 4.0 22.0 0.80 100 90 82.5 53 174 LAA 5 14.8 375 2.7 4.0 22.4 0.62 100 80 85 15 150 DRE 5 25.9 270 0.9 4.0 6.9 0.13 90 0 0 50 153 RUR 9 154.0 360 1.0 9 30.5 0.83 40 100 60 60 121

KYL 9 471.8 305 0.4 18 51.8 0.95 50 0 30 5 299 PRW 9 286.5 315 0.5 17 50.5 0.41 40 0 20 15 196

OUR 9 293.6 360 0.2 14 50.8 0.70 40 0 72.5 33 136 Materialsandm NIM 9 222.1 245 0.5 20 23.5 0.59 30 0 85 5 440 LEN 9 190.0 280 0.3 20 53.0 0.91 80 0 0 75 279 NUH 9 134.4 310 0.7 7 43.5 0.86 60 0 10 68 364 EDE 9 523.7 280 0.2 20 48.5 0.96 70 0 42.5 60 208 ORK 9 275.0 295 0.5 15 34.0 0.72 70 0 20 70 328 PRB 9 327.1 280 0.3 18 55.5 0.84 40 50 50 15 204 e thods 13

2. Temperature characteristics Materials and methods

2.2.3. Weather data Daily minimum, maximum and mean air temperature and precipitation, covering the same time period as the temperature registration in the rivers from June 2000 to June 2001, were provided by the weather service DWD (Deutscher Wetterdienst, Offenbach). The weather stations are located within a distance of at most 25 km to the study sites. Concerning the air temperature, the years 2000 and 2001 were on the average warmer compared to long-term records (1961-1990): spring 2000 was warmer by 2°C, the summer by 1 to 1.5°C . The winter 2000/2001 was 1 to 2°C above the long-term records. Precipitation in 2000 to 2001 in the regions of the sampling sites was higher compared to long-term records (1961-1990): Nordrhein-Westfalen4 had 8%, Hessen5 4% and Rheinland-Pfalz6 17% more precipitation, respectively. In the catchment area of the Rhein7, 12% more precipitation fell in the year 2000 and 11% more in 2001 compared to long-term records.

2.2.4. Temperature data processing Water temperature of a one-year period from June 2000 to June 2001 was used for the analysis of small- and mid-sized rivers of group 1 and 2. Missing data due to logger- failure (Table 2-1) were extrapolated using the temperature registration of the following two years; on the basis of these data the relative differences between the temperature of the individual rivers (which stayed fairly constant over three years of temperature registration) was calculated and transferred to the thermal conditions of the year 2000/2001. For the comparison with other river-types (of group 3 and 4: external data and lowland rivers), temperature data of different time periods had to be processed for analysis (Table 2-1). Data of group 2 (mid-sized mountainous) and group 4 (external data) were recorded from June 2000 to September 2003. Those of group 1 (small-sized mountainous) was recorded from June 2000 to June 2001 and of group 3 (mid-sized lowland) from June 2002 to March 2003. To maximize data base within the same time period, temperature recordings of summer 2002 and winter 2002/2003 were used (which also had the advantage of avoiding the extrapolation of data). Only the recordings of small-sized rivers (group 1), with temperature data between summer 2000 to summer 2001, did not cover the same time period. Comparison of the monthly means of group 1 (small-sized mountainous) and 2 (mid-sized mountainous) of the years 2001, 2002, 2003 with the monthly means of 2000 showed that 2002 was the year with the smallest differences compared to

4 Northrhine-Westphalia 5 Hesse 6 Rhineland-Palatinate 7 Rhine

14 2. Temperature characteristics Materials and methods

2000 (Student t-Test not significant for Jun, Aug, Jan, Mar, May for 2000/2002, while 2000/2001 and 2000/2003 differences were always significant). In all probability this approach implies the smallest error for the small-sized rivers within this time period. The external temperature data set (group 4) was based on daily means, so daily amplitudes and temperature extremes were not available. For the analysis of this data, minimum and maximum daily means were used instead of absolute minima and maxima, and these values were calculated likewise for the temperature data of groups 1, 2, and 3.

2.2.5. Temperature parameters used for the analysis Temperature parameters were selected to meet two purposes: First, parameters that may be useful for a thermal typology, with the emphasis on the distinction between river- types. This includes testing not only mean parameters as mostly applied in ecological studies in rivers, but also the variability of extreme values or seasonal differences (e.g. winter temperatures or daily amplitudes in spring). Second, parameters that have shown to be of ecological significance for benthic fauna (i.e. benthic invertebrates). The majority of ecological studies relate mean temperatures to the biota, but it has been stressed that maximum and minimum temperatures as well as degree-days are of major importance to the fauna (Malicky 1978, Fey 1984, Vinson & Hawkins 1998).

Temperature parameters taken into account in the present analysis can be summarized in four groups: 1. Mean temperature parameters: • means of different time periods (e.g. month, season, year) • degree-days, which are mainly used in ecological studies. Since for the data set in this study mean values and degree-days were highly correlated (r>0.92), only mean values were analysed. 2. Extreme temperature parameters: • absolute minima and maxima recorded within a time period (e.g. month, season, year) • means of minima and maxima for time periods that are ecologically of interest, e.g. seasons.

15 2. Temperature characteristics Materials and methods

3. Amplitudes: • yearly amplitude (based on temperature extremes or monthly means) • monthly amplitudes • daily amplitudes (maxima: year, month; mean: year, month) 4. Rates of temperature change: • seasonal warming/cooling rates • daily warming/cooling rates

The rate of temperature change within 24 hours was directly proportional to the magnitude of change within the same time span, for that reason warming or cooling rates will not be analysed in this study. Since the external data (Group 4) consisted of daily mean values, daily amplitudes and temperature extremes could not be used in the analysis. Consequently, the classification by thermal parameters consisted of the following variables: summer and winter mean temperatures, maximum daily means and minimum daily means measured within the one-year time period, and the yearly amplitude.

Additionally, temperature parameters were assessed that were demonstrated to be of ecological significance for benthic invertebrates. These were used for a comparison with parameters defined to be relevant for a typological characterization. For the assessment of these parameters, literature was reviewed to collect information on laboratory experiments or field studies that connect development, life-history traits or distribution of invertebrates with temperature.

2.2.6. Data analysis Three statistical approaches were used in this study: First, the temperature data (yearly mean, maximum and mean daily amplitude, summer maximum and mean) was tested on a normal distribution using the Shapiro-Wilk W Test. For all temperature parameters the W statistic was not significant (p>0.2), so the hypothesis that the data is distributed normally is accepted. The Student t-Test was used to define temperature parameters that differed significantly between the small- and mid- sized rivers of the lower mountainous region. Second, multiple linear regression analysis (forward stepwise regression) was used to extract environmental factors (as listed in Table 2-2) that explained the temperature patterns observed in the two data sets.

16 2. Temperature characteristics Materials and methods

Third, a cluster analysis was performed with extreme, mean and amplitude temperature parameters of all data sets to visualize groups of rivers with similar thermal patterns. The method for analysis was minimum variance clustering (Ward’s method) using squared Eucledian Distance. For the Student t-Test and the multiple linear regression analysis Statistica 6.1. StatSoft Inc. (2003) was used. Cluster analysis was performed by PCOrd 4.01, MjM Software (1999).

17 2. Temperature characteristics Results

2.3. Results

2.3.1. Thermal parameters – ecological significance In order to compile temperature parameters that have shown to be of ecological significance for the benthic fauna, literature has been viewed for studies concerning the influence of different temperature conditions on the life-history traits and on the distribution of benthic invertebrates. Though the majority of these studies are laboratory experiments, which are conducted under constant thermal conditions different from field situations, there also exist several studies on seasonality, extreme temperatures and temperature fluctuations. Some few studies are based on field observations concerning geographic distributions or community changes. The influence of different thermal parameters on the macroinvertebrates are listed in Table 2-3. The importance of mean temperature conditions during specific time periods or seasons has been shown to effect the growth rate and size of larvae and adult invertebrates as well as the emergence. For example, winter temperatures were shown to be important for the growth rate of a spring-emerging Plecopteran species, or the summer temperatures were identified to be important for the emergence timing of an Ephemeropteran species. De- gree-days are correlated with the above mentioned traits, with the egg development, hatching success and the diapause induction and termination. Extreme temperatures, i.e. maxima and minima, effect embryonic development, hatching success, larval development, growth rate and survival. Maximum temperatures influence hatching success and larval survival, while minimum temperatures form thresholds that are required for growth. Maximum and minimum temperatures were also found to be limiting to the distribution of species. Only few studies concern daily temperature fluctuations, the results being contradictory. While some studies found a correlation between magnitude of daily fluctuation and egg development and larval growth rate, others did not observe any connection.

Table 2-3. Temperature parameters: typological and ecological values. Typological value: Month and season in which temperature parameters were significantly different for small- and mid-sized rivers with p-values of this study (**Chapter 2.3.2.). Ecological importance: Invertebrate groups, for 2. which a dependence on temperature parameters was found at different life stages. In parenthesis: Numbers of references, see next page. Tem p erature distribution, community Egg: devel, hatching Larvae/nymph: growth Temperature variables typological value** p(Student t-test)** Adult: size, weight phenology - emergence diapause structure success rates, survival Mean temperature values

year yes p<0.05 characteristics Su p<0.001 Spr: (10) Su:(2, 19, 25) Amphi (45), Wi: Ple season spring, summer, autumn Su: Ple (8), Wi: Ple(53) Su: Ephe (58) Spr/Au: p<0.05 Aut:(10, 2) (53), Su: Tri (57) Apr p<0.05, May- Dec, Jan, Feb : Ple (53) Apr: Ple (24) June-Aug: months Apr - Sep Dec, Jan, Feb : Ple (53) Sep p<0.001 Apr: Ple (24) Ephe (58) degree-days /year yes p<0.05 Su: p<0.001 degree-days /season spring, summer, autumn S Ple (46,59), Col/ Ephe (22,40,46,48,55), pr/Au p<0.05 Tri (57) Ephe (58) Ple (59) Apr p<0.05 Dipt:(23) Ple(40) degree-days/month Apr - Sep

May-Sep p<0.001 Extreme temperature values minimum/year no n.s. Ple: (6,13,20,44,49), Mollu (50), Amphip (11) Ephe:(14,26,28) mean min winter no n.s.

maximum/year yes p<0.001 (Jun) comm (12), Tri (30,39), Ple: (6,13,20,49) Ephe,Ple,Tri;Cru,Oli, Dipt (30), Turbell (31) Ephe: (14,26,28) Moll, Crust (47) Tri (56) mean max summer yes p<0.001 Amph (11) Amplitudes* year (based on means) yes p<0.001 year (based on extremes) yes p<0.05 month May, Jun p<0.05 day yearly mean no n.s. Yes: Ple (17,20), Ephe model (Ward & Stanford day max no n.s. (May) (54) Heteropt (52) No: Ephe (54) 1982) Ple (38,59) day monthly mean Nov-May p<0.05 day monthly max Nov, Jan-Apr p<0.05 day season mean autumn, winter, spring p<0.05

day season max winter, spring p<0.05 Results constant temperatures

19 Ephe (27,3,4) Col (1) Ephe (21), Ple Ephe(3), Ple T const Ple(5,7,9,15,16,18,32,33, (5,32,33,34,36) (35)

Table 2-3. continued: list of references 2. Tem

Nr. Reference Nr. Reference Nr. Reference p 1 Aiken (1986) 21 Giberson & Rosenberg (1992) 41 Marten & Zwick (1989) erature characteristics 2 Baumann (1979) 22 Giberson & Rosenberg (1994) 42 Marten (1991) 3 Bohle (1969) 23 Gillooly & Dodson (2000) 43 Milner et al. (2001) 4 Brittain & Campbell (1991) 24 Gregory et al. (2000) 44 Moreira & Peckarsky (1994) 5 Brittain, Lillehammer & Saltveit (1984) 25 Hawkins et al. (1997) 45 Panov & McQueen (1998) 6 Brittain & Lillehammer (1987) 26 Humpesch & Elliott (1980) 46 Pritchard et al. (1996) 7 Brittain (1977) 27 Humpesch (1980) 47 Quinn et al. (1994) 8 Brittain (1983) 28 Humpesch (1982) 48 Rosillon (1988) 9 Brittain (1991) 29 Huryn (1996) 49 Saltveit & Lillehammer (1984) 10 Brittain et al. (2001) 30 Lamberti & Resh (1985) 50 Schöll (2000) 11 Brujis et al. (2001) 31 Lascombe et al. (1975) 51 Snook (2001) 12 Castella et al. (2001) 32 Lillehammer (1985) 52 Sweeney & Schnack (1977)

13 Elliott (1995) 33 Lillehammer (1986) 53 Sweeney & Vannote (1986) 14 Elliott (1978) 34 Lillehammer (1987 a) 54 Sweeney (1978) 15 Elliott (1987b) 35 Lillehammer (1987) 55 Tokeshi (1985) 16 Elliott (1988) 36 Lillehammer (1987b) 56 Wagner (1990) 17 Elliott (1989) 37 Lillehammer et al. (1989) 57 Wagner (2002) 18 Elliott (1992) 38 Lillehammer, Salveit & Brusven (1991) 58 Watanabe et al. (1999)

19 Fairchild & Holomuzki (2002) 39 Lowe & Hauer (1999) 59 Zwick (1996) 20 Frutiger, A. (1996) 40 Markarian (1980)

Results

2. Temperature characteristics Results

Table 2-4. Some temperature parameters for small- (Type 5) and mid-sized (Type 9) rivers for the time period June 2000 to June 2001. Cluster numbers see Figure 2-1. Average temperatures: Msu: mean summer temperatures. Mwi: mean winter temperatures. Extreme temperatures: Max: absolute maximum (recorded Jun/2000). Min: absolute minimum (recorded Jan/2001). Daily amplitudes: Adsumx: summer maximum, Adwinx: winter maximum; Adsum: mean summer; Adwinm: mean winter, Adspm: mean spring. Summer: Jun-Aug/2000, winter: Dec/2000-Feb/ 2001, spring: Mar-May/2001. Temperature values in °C.

River Cluster Msu Mwi Max Min Adsumx Adwinx Adsum Adwinm Adspm type number Wwe 5 1 13.0 4.1 18.8 -0.3 3.7 2.8 2.0 1.4 3.1 Kal 5 1 13.0 4.0 18.5 0.6 4.7 2.1 2.5 1.2 3.0 Erk 5 1 12.7 4.4 18.6 -0.1 3.9 2.3 2.1 1.2 2.5 Vol 5 2 14.3 5.5 19.4 1.3 4.3 3.1 2.0 1.3 3.0 Wal 5 1 11.8 4.6 16.9 0.5 4.0 2.5 2.3 1.1 2.5 Roe 5 1 12.9 4.5 18.2 0.1 5.9 2.8 3.0 1.3 2.9 Sal 5 1 12.0 5.2 16.6 1.9 3.8 1.7 2.1 0.8 1.9 Elb 5 1 12.4 3.5 17.2 0.0 4.0 2.2 2.3 1.1 2.4 Laa 5 1 12.3 3.3 17.2 -0.1 3.9 2.4 2.1 1.1 2.9 Dre 5 2 14.4 5.6 20.3 1.3 5.6 2.1 3.0 1.0 2.3 Rur 9 3 14.2 4.1 22.7 0.4 5.6 1.6 2.8 0.9 2.2 Kyl 9 2 15.2 4.6 20.7 2.0 3.8 1.9 2.2 0.9 1.9 PrW 9 3 15.7 4.4 23.1 -0.2 4.7 2.1 2.4 1.0 2.1 Our 9 3 14.8 4.7 23.0 -0.2 5.8 2.0 3.2 1.0 2.4 Nim 9 2 14.1 5.3 18.3 3.2 2.0 1.8 0.9 0.7 1.2 Len 9 2 14.6 4.4 19.5 -0.1 2.9 2.1 1.5 0.9 1.9 Nuh 9 2 14.2 3.7 20.0 -0.2 4.3 2.0 2.3 1.1 2.7 Ede 9 3 16.7 3.8 21.9 -0.2 4.1 2.1 2.6 0.8 1.7 Ork 9 3 15.4 3.3 21.5 -0.1 5.9 2.2 3.1 1.0 2.8 PrB 9 3 16.3 4.3 25.3 -0.2 6.2 2.1 3.4 1.0 2.1

2.3.2. Thermal parameters – typological significance In the typological approach conducted in this study, differences in thermal conditions between the two river-types (small- and mid-sized mountain streams) were tested. The values for mean and extreme temperatures as well as fluctuation parameters are given for each river in Table 2-4. Summer mean temperatures varied between 11.8 and 14.4°C in small-sized rivers, while in mid-sized rivers they ranged between 14.1. and 16.7°C. Small-sized rivers reached maxima of above 20°C; in mid-sized rivers temperatures of over 25°C occurred in summer. Winter temperatures were close to 0°C in most rivers, but in some small- and mid-sized rivers they did not fall below 1 or 2°C. In summer, daily amplitudes ranged between 2 and 6.2°C, a maximum value of 7.9°C being recorded in June 2000 in the Prüm at Beifels (PrB: see Appendix 8). To explore the temperature parameters that are significantly different between the two river-types, a Student t-Test was performed on the mean and maximum values of monthly, seasonal and yearly time periods, and daily, monthly, seasonal and yearly fluctuations, respectively. The results are listed in Table 2-5: the small-sized rivers were sig-

21 2. Temperature characteristics Results

nificantly colder than the mid-sized rivers in seasonal mean values of spring, summer and autumn, and during the months from April to September.

Table 2-5. Mean of temperature parameters (°C) in small and mid-sized rivers; Abbre- viations: su: summer, au: autumn, wi: winter, sp: spring; p-values for significant differences in thermal characteristics (Student t-Test) are printed in bold letters. Results for degree-days are similar to mean parameters, they are not listed here.

small mid p-value small mid p-value mean values mean mean t-test amplitudes mean mean t-test year 8.6 9.5 0.002 year (mo. means) 9.8 12.6 < 0.001 season su 12.9 15.1 < 0.001 year (extremes) 17.7 21.4 0.001 season au 9.6 10.2 0.028 daily (mean year) 1.8 1.6 0.202 season wi 4.5 4.2 0.484 daily (maximum) 6.2 6.1 0.833 season sp 7.5 8.5 0.002 months daily (mo. mean) Jun 12.9 15.5 < 0.001 Jun 2.8 3.1 0.410 Jul 12.2 14.1 < 0.001 Jul 1.6 1.9 0.323 Aug 13.5 15.8 < 0.001 Aug 2.3 2.3 0.878 Sep 12.2 13.4 0.001 Sep 1.5 1.3 0.482 Oct 9.7 10.1 0.150 Oct 1.2 0.9 0.017 Nov 6.9 7.1 0.420 Nov 1.1 0.7 < 0.001 Dec 5.4 5.3 0.825 Dec 1.0 0.9 0.021 Jan 3.7 3.4 0.307 Jan 1.1 0.8 0.005 Feb 4.3 4.1 0.493 Feb 1.4 1.1 0.004 Mar 5.1 5.2 0.862 Mar 1.7 1.4 0.014 Apr 6.6 7.1 0.018 Apr 2.5 1.8 0.001 May 11.0 13.1 < 0.001 May 3.8 3.1 0.046

daily (mo. max) extreme values Jun 5.2 5.7 0.488 maximum/year 18.1 21.6 < 0.001 Jul 3.5 4.1 0.109 minimum/year 0.5 0.4 0.857 Aug 3.7 3.8 0.919 minimum wi 0.9 0.5 0.380 Sep 2.8 2.7 0.798 maximum su 16.8 19.8 < 0.001 Oct 2.1 1.9 0.328 Nov 2.1 1.8 0.023 Dec 2.2 2.2 0.924 Jan 2.4 1.8 0.039 Feb 2.5 2.0 0.001 Mar 3.4 2.8 0.030 Apr 4.6 3.3 0.001 May 6.0 5.3 0.211

The same applied to the summer extreme values. On the other hand, temperature parameters concerning the colder period of the year, i.e. from October to March, were not significantly different, and winter minimum values were similar in both stream types. Significantly different amplitudes of the two river-types were recorded from November to May, small-sized rivers having higher daily amplitudes in winter than mid-sized rivers. As to the higher amplitudes in spring, the significant difference between the two river-

22 2. Temperature characteristics Results

types was only valid, when amplitudes of the respective months for both stream types were tested in pairs: in mid-sized rivers highest daily fluctuations occurred one month later (in June) than in small-sized rivers (in May). Comparing the mean and maximum daily amplitudes measured during the one-year period independently of the month, the difference was not significant (p=0.833).

2.3.3. Small- and mid-sized rivers – are they thermally homogeneous? In this study, a classification of small- and mid-sized rivers using cluster analysis was performed using the following temperature parameters: summer and winter mean temperatures, summer maxima and winter minima, yearly amplitude, and summer and spring maximum amplitudes (Figure 2-1). The cluster analysis revealed three distinct thermal groups of rivers (Table 2-6): the first group with low summer mean temperatures of up to 13°C, with summer maxima below 19°C (max 18.8°C Group 1 Table 2-6), and daily fluctuations in spring and summer between 3.2 and 5.9°C. The yearly amplitude stayed below 11°C. This group consists only of small-sized rivers. The second group shows intermediate summer mean temperatures between 14.1 and 15.2°C (maxima below 21°C). Some of these rivers have higher winter minima (twice 1.3 and once 3.2°C, see Table 2-4), and low temperature fluctuations of only 2°C (Table 2-4). As Figure 2-1 displays, this group consists of two small- and four mid-sized rivers. The third group only differs from the first two groups by having highest summer maximum temperatures between 21.5 and 25.3°C (Table 2-6), (summer mean temperatures range from 14.2 to 16.7°C and are consequently overlapping with the second group, Table 2-4). The rivers of the third group show a similar range of daily amplitudes and likewise low winter minima. This is a group consisting only of mid-sized rivers. Thus the cluster analysis revealed that most small- and mid-sized rivers clearly divided up into two distinct thermally homogeneous groups. Another cluster formed with rivers of both types revealed greater similarity within this group than in their corresponding thermal cluster. The two small-sized rivers of this second group, Dreisbach and Volme, were warmer than the remaining small-sized rivers of type 5. Table 2-2 shows that the Dreisbach is shallower and has a lower current velocity. Moreover, it is not shaded at the sampling site and has no forest in the floodplain. The Volme differs from the other small- sized rivers only by its low amount of forest in the catchment area and floodplain and by its lack of shade at the sampling site. The morphological parameters and the current velocity are within the range of the other small-sized rivers, but conductivity is high. The four mid-sized rivers of cluster 2 do not differ as clearly as the two small-sized rivers from their corresponding groups concerning environmental variables (Table 2-2).

23 2. Temperature characteristics Results

Cluster analysis for t-parameters Distance (Objective Function) 5.1E-01 7E+01 1.4E+02 2.1E+02 2.8E+02 Information Remaining (%) 100 75 50 25 0 5-W we 5-Kao 5-Erk 1 5-Roe 5-Elb 5-Laa 5-W al 5-Sal 5-Vol 5-Dre 9-Kyl 2 9-Len 9-Nuh 9-Nim 9-Rur 9-Our 3 9-PrB 9-PrW 9-Ede 9-Ork

Figure 2-1. Classification of small- and mid-sized rivers by minimum variance clustering (Ward’s method) using squared Eucledian Distance. For codes and details on sampling sites see Table 2-1. Numbers preceding codes of sampling sites: 5: Mid-sized rivers, 9: Small-sized rivers.

24 2. Temperature characteristics Results

Table 2-6. Mean and range of minimum - maximum of temperature parameters (°C) of the three groups formed by cluster analysis as shown in Figure 2-1.

Summer mean Winter mean Amplitude year Group mean min max mean min max mean min max 1 12.5 11.8 13.0 4.2 3.3 5.2 9.7 7.7 10.9 2 14.5 14.1 15.2 4.8 3.7 5.6 11.2 9.9 12.5 3 15.5 14.2 16.7 4.1 3.3 4.7 13.1 11.7 14.7

Amplitude - max summer Amplitude - max spring Group mean min max mean min max 1 4.2 3.7 5.9 4.5 3.2 5.4 2 3.8 2.0 5.6 4.0 2.2 5.4 3 5.4 4.1 6.2 4.0 3.2 5.3

Maximum Minimum Group mean min max mean min max 1 17.7 16.6 18.8 0.4 -0.3 1.9 2 19.7 18.3 20.7 1.3 -0.2 3.2 3 22.9 21.5 25.3 0.1 -0.2 0.4

2.3.4. Thermal pattern in relation to environmental conditions In order to determine the environmental variables that were most strongly associated with the thermal regime of small- and mid-sized rivers, a multiple stepwise regression was performed. From the site protocol, nine environmental parameters (see Table 2-2) were chosen as independent variables, and the relative influence of these variables on the nine dependent temperature parameters (see Table 2-4) was tested for the data sets of the 20 sampling sites in small- and mid-sized rivers forming one unit, and additional for the small- and mid-sized rivers separately.

The results of the significant models of the multiple stepwise regression analysis are shown in Table 2-7 . Variation in summer mean temperature was related to the size of the catchment area and to current velocity. For small-sized rivers, the amount of forest in the catchment area explained a significant part of the variance in mean summer temperature, additionally to current velocity, whereas only the catchment area was significantly related to summer temperatures in mid-sized rivers.

25 2. Temperature characteristics Results

Table 2-7. Results from forward stepwise multiple regression analysis. Models for winter temperatures, for temperature extremes and for amplitudes were not significant. A significant Beta is marked by bold values and an X, Beta* signifying a coefficient for partial correlation of standardized variables. Values for Beta not typed in bold signify non-significant partial correlation.

Model: all rivers all rivers all rivers Summer mean Winter mean Amplitude spring F=27.68 F=3.11 F=5.78 p<0.001 p=0.041 p=0.005 R²adj=0.88 Beta* R²adj=0.40 Beta* R²adj=0.05 Beta*

Catchment area X 0.83 -0.50 Altitude 0.32 Slope X -0.91 Width X -0.70 Depth -0.54 0.35 Current velocity X -0.28 0.42 -0.35 Forest in CA -0.18 Shade -0.20 X -0.63 Azimuth 0.13 X -0.85

Model: small-sized small-sized small-sized Summer mean Winter min Summer ampl max

F=10.70 F=11.97 F=7.24 p=0.02 p=0.02 p=0.04 R²adj=0.84 Beta* R²adj=0.86 Beta* R²adj=0.90 Beta*

Catchment area 0.22 -0.37 -0.34 Altitude X 0.63 Slope Width -0.40 X 0.40 -0.38 Depth X -0.88 Current velocity X -0.69 Forest in CA X -0.60 Shade -0.19 X -0.90 X -0.89 Azimuth 0.30

Model: mid-sized mid-sized mid-sized Summer mean Winter min Winter ampl max

F=6.72 F=10.48 F=11.26 p=0.02 p=0.02 p=0.02 R²adj=0.56 Beta* R²adj=0.93 Beta* R²adj=0.85 Beta*

Catchment area X 0.67 -0.28 Altitude Slope X -0.98 Width -0.29 Depth 0.25 X -0.85 Current velocity 0.42 -0.35 Forest in CA 0.74 Shade X -0.54 Azimuth X -1.50 0.19

26 2. Temperature characteristics Results

Winter temperatures in small-sized rivers determined shade as the variable explaining most variance in the model, whereas in mid-sized rivers azimuth was the most important factor related to winter minima, furthermore depth explaining a smaller part of the variability. With regard to the model including all rivers, mean winter temperatures proved to be related to shade, azimuth and slope. Amplitudes in winter and spring were most strongly correlated with stream width. Winter amplitudes in mid-sized rivers were additionally related to depth, slope and shade. Re- garding summer amplitudes, only the model for the maximum values in small-sized rivers was significant, extracting depth, shade and altitude as significant parameters. Models of mean temperature values in winter and summer turned out to be often more significant and revealed better correlation coefficients than models of extreme winter- and summer values. As to mean and extreme values of amplitudes, no such difference showed.

2.3.5. Classification with other river-types Another approach to testing similarities of thermal characteristics with more data from other stream types was realized by cluster analysis. It has to be noted that only daily mean temperatures were analysed, so the results of the classification might be different, when daily temperature fluctuations or extreme temperatures are included in the model in the same way as done in the analysis of small- and mid-sized rivers. Sampling sites, and the corresponding thermal parameters are listed in Table 2-8. The result of the classification of 34 sites is shown in Figure 2-2 .

The sampling sites divide into three main groups, their thermal characteristics are summarized in Table 2-9, detailed temperature data to each river in the corresponding clusters can be seen in Table 2-8. • The first consists of sites with the lowest yearly amplitudes (7.7 to 10.9°C) caused by low summer temperatures. It is the largest of the three groups, and within this group three homogeneous clusters of rivers emerged: a first cluster (1a in Figure 2-2)- with thermally very similar rivers of low summer- but also low winter temperatures close to 0°C - consists only of small-sized rivers of the lower mountainous region. A second cluster (1b) consists of three different river-types: two small-sized mountainous rivers, one mid-sized mountainous river and two mid-sized lowland rivers, showing low summer but higher winter temperatures (e.g. Nims 3.1°C in Table 2-8) than the rivers above. The third cluster (1c) has summer temperatures of up to 14.9°C (Table 2-9) and higher winter temperatures of 1.3 to 2.3°C (Table 2-8), thus being the group with the

27 2. Temperature characteristics Results

lowest yearly amplitudes of all sites. Two small-sized rivers and a site in a mid- sized river below an impoundment belong to this cluster.

• The second group shows yearly amplitudes between 11.7 and 15.9°C (Table 2- 9), with minimum temperatures around 0°C and mean summer temperatures between 14.2 and 16.7°C. It consists mainly of mid-sized mountainous rivers; two lowland rivers belong to this group as well as a river influenced by a tributary with an impoundment.

• Main characteristics of the third group are high winter temperatures. Three rivers are very large-sized rivers with the highest summer temperatures compared to all other data sets (23.8 to 27.4°C in Table 2-8). The remaining three rivers are mid-sized rivers having slightly lower summer temperatures between 22.9 and 27.4°C (Table 2-8).

Small-, mid- and very large-sized rivers could be classified in different groups based on the distinct summer temperatures. On the other hand, the mid-sized lowland rivers do not form a separate group: two rivers are found in a cluster together with two of the warmest small-sized and the coldest mid-sized river. The other two lowland rivers form a group including a cold mid-sized and a large river influenced by hypolimnic water.

28 2. Temperature characteristics Results

Table 2-8. Temperature parameters (°C) for all river types that were used in cluster analysis. Cluster numbers see Figure 2-2. Temperature values: Mean su: summer mean, Mean wi: winter mean, Max dmw: maximum daily mean , Min dmw: minimum daily mean, Ayear: yearly amplitude. For codes of rivers see Table 2-1.

River Cluster Catchm Mean su Mean wi Max dmw Min dmw Ayear type number area [km²] Wwe 5 1a 11.2 13.0 4.1 17.1 -0.1 10.5 Kal 5 1a 17.4 13.0 4.0 15.4 1.3 10.5 Erk 5 1a 22.5 12.7 4.4 16.6 0.4 9.6 Vol 5 1b 17.5 14.3 5.5 17.7 2.1 9.9 Wal 5 1c 9.0 11.8 4.6 14.9 1.4 8.2 Roe 5 1a 4.7 12.9 4.5 16.1 0.7 9.6 Sal 5 1c 15.5 12.0 5.2 14.8 2.3 7.7 Elb 5 1a 12.6 12.4 3.5 15.5 0.0 10.7 Laa 5 1a 14.8 12.3 3.3 16.2 -0.1 10.9 Dre 5 1b 25.9 14.4 5.6 18.5 1.8 10.2 Rur 9 2a 154.0 14.2 4.1 19.2 0.9 11.7 Kyl 9 2a 471.8 15.2 4.6 18.7 1.8 12.3 PrW 9 2c 286.5 15.7 4.4 20.8 -0.2 13.0 Our 9 2a 293.6 14.8 4.7 19.7 0.4 11.7 Nim 9 1b 222.1 14.1 5.3 16.8 3.1 10.4 Len 9 2a 190.0 14.6 4.4 19.8 0.0 11.9 Nuh 9 2b 134.4 14.2 3.7 18.1 -0.1 12.5 Ede 9 2c 523.7 16.7 3.8 22.6 0.0 14.7 Ork 9 2c 275.0 15.4 3.3 20.6 -0.1 14.1 PrB 9 2c 327.1 16.3 4.3 21.6 -0.2 13.6 RhKoe 10 3a 144,232.0 23.4 6.8 27.4 3.7 19.0 RhRuh 20 3a 152,895.0 21.3 7.9 25.4 3.0 15.7 Mosel 10 3a 27,088.0 20.2 6.9 23.8 2.9 15.6 Werra 10 2c 5,166.0 16.7 3.4 21.8 -0.8 15.9 DieTs 9 3b 102.0 18.4 3.4 22.9 1.2 17.6 DieHel 9 1c 103.0 9.6 4.2 13.5 1.3 9.6 EdeTs 9 3b 1,443.0 19.7 4.5 23.7 1.3 17.7 EdeSlh 9 3b 1,202.0 17.4 3.3 23.3 2.6 16.1 FuldGun 10 2b 6,366.0 15.4 3.7 17.6 -0.2 13.4 Ost 14 1b 172.0 14.3 4.2 17.2 0.7 10.9 Est 15 1b 300.0 13.7 5.2 17.0 1.3 10.9 Wue 15 2b 288.0 15.8 2.6 18.4 -0.2 14.7 SfAu 14 2b 300.0 15.6 3.5 18.6 0.0 13.0

29 2. Temperature characteristics Results

cluster analysis all rivers Distance (Objective Function) 2E-01 2.4E+02 4.9E+02 7.3E+02 9.7E+02

Information Remaining (%) 100 75 50 25 0

5-Wwe 5-Erk 5-Roe 1a 5-Kao 5-Elb 5-Laa 5-Vol 5-Dre 1b 9-Nim 14-Ost 15-Est 5-Wal 1c 5-Sal 9-DieHel 9-Rur 9-Our 2a 9-Len 9-Kyl 9-Nuh 10-FulGu 2b 14-SfAu 15-Wue 9-PrW 9-PrB 2c 9-Ork 9-Ede 10-Werra 10-RhKoe 3a 20-RhRuh 10-Mosel 9-DieTs 3b 9-EdeTs 9-EdeSlh

Figure 2-2. Classification of all rivers by temperature parameters using extreme, mean and amplitude values. Minimum variance clustering (Ward’s method) using squared Eucledian Distance. Rivers are coded by river-type number and abbreviation of river names as in Table 2-1.

30 2. Temperature characteristics Results

Table 2-9. Mean and range of temperature parameters (°C) of the respective groups shown in Figure 2-2.

Summer mean Winter mean Group mean min max mean min max 1a 12.7 12.3 13.0 4.0 3.3 4.5 1b 14.2 13.7 14.4 5.1 4.2 5.6 1c 11.1 9.6 12.0 4.7 4.2 5.2 2a 14.8 14.2 15.2 4.6 4.1 4.7 2b 15.3 14.2 15.8 3.4 2.6 3.7 2c 16.2 15.4 16.7 3.8 3.3 4.4 3a 21.6 20.2 23.4 7.2 6.8 7.9 3b 18.5 17.4 19.7 3.7 3.3 4.5

Max daily mean Min daily mean Amplitude year Group mean min max mean min max mean min max 1a 16.1 15.4 17.1 0.4 -0.1 1.3 10.3 9.6 10.9 1b 17.4 16.8 18.5 1.8 0.7 3.1 10.4 9.9 10.9 1c 14.4 13.5 14.9 1.7 -0.1 2.3 8.5 7.7 9.6 2a 19.1 18.7 19.8 0.7 0.0 1.8 12.0 11.7 12.3 2b 18.2 17.6 18.6 -0.1 -0.2 0.0 13.4 12.5 14.7 2c 21.5 20.6 22.6 -0.2 -0.8 0.0 14.3 13.0 15.9 3a 25.5 23.8 27.4 3.2 2.9 3.7 16.7 15.6 19.0 3b 23.3 22.9 23.7 1.7 1.2 2.6 17.1 16.1 17.7

31 2. Temperature characteristics Discussion

2.4. Discussion

2.4.1. Temperature parameters of typological and ecological importance Summer temperatures Taking the results of this study into account, the conclusion can be drawn that summer temperatures differ considerably between the small- and mid-sized rivers: from May to September, monthly mean and maximum temperatures and the corresponding degree- days are discriminating on a highly significant basis. Although the smaller-sized rivers have significantly lower water temperatures during summer than the larger-sized rivers, the individual rivers within one group differ substan- tially in their temperature regime, too. Monthly mean values often hide the fact that extreme temperatures may prevail in the water for several hours (see Figure 2-3 for May 2001). To give an example from the present study: two small-sized rivers (Volme and Dreisbach) reached temperatures of 18°C and higher for two to three percent of the time in May 2001, which equals 17 to 22 hours within this time period, whereas on the other hand 16.3°C was the maximum temperature the other rivers of comparable size reached in May. These patterns clearly show that calculating degree-days based on mean values is not sufficient because critical thresholds might be neglected. Similarly, in the works of Crisp & Le Cren (1970) and Crisp et al. (1982) it has been stressed that for many biological applications mean values as sums of degree days may not be adequate, as the temperature may be above a certain baseline for the fauna for several hours a day.

Table 2-10. Comparison of water temperature ranges for the characterization of the biocoenotic zones. Modified after Moog & Wimmer (1994). Macroinvertebrates have been sampled in 2000/2001 in spring and summer at the 20 monitoring sites, and biocoenotic regions have been determined according to the percentage of the community preferring a certain zone. Ay mean: mean yearly amplitudes.

Ay mean Ay mean Ay mean Illies (1961) DVWK (1984) Hebauer (1986) This study Eucrenal 2 °C Hypocrenal 5 °C Epirhithral 9 °C 8.2 - 10.9 °C < 20 °C 5-10 °C Metarhithral 13 °C 7.7 - 14.1 °C Hyporhithral 8-14 °C 18 °C 12.3 - 14.7 °C Epipotamal 12-18 °C 20 °C Metapotamal > 20 °C 16-20 °C 18 °C Hypopotamal 16-20 °C 15 °C

32 2. Temperature characteristics Discussion

Summer temperatures have been traditionally used for the classification of rivers (e.g. Brehm & Ruttner 1926, Schwoerbel 1999). Illies (1961) established a longitudinal zonation of rivers based on the relation of fish regions and temperature zones, a concept, which has been adopted and modified by other authors (e.g. Illies & Botosaneanu 1963, Schindler 1963, Sandrock 1981, DVWK 1984, Hebauer 1986). Most of these concepts are based on sporadic unsystematic measurements, with the consequence that the temperature ranges for the differentiation of the biocoenotical zones are contradictory. Moog & Wimmer (1994) analysed these studies in detail, testing the proposals for temperature ranges in rivers with available corresponding biotic data. Values of the DVWK (1984) for monthly summer means and the annual range of minima and maxima by Hebauer (1986) were found to correspond best with the zones based on the biocoenotic data. The temperature ranges of the biocoenotic zones proposed by Hebauer correspond fairly well with the temperature data of this study and the respective zones (Table 2-10). On the other hand, the estimations of the DVWK temperature ranges are lower than in this

study.

10.3 10.7 10.8 11.0 11.2 11.7 11.8 11.8 12.7 13.1 13.5 13.6 13.7 13.8 14.0 14.0 14.1 15.1 15.5 16.7

100%

90%

80%

70% 22-max

20-<22

60%

18-<20

50% 16-<18

14-<16

40%

12-<14

30% 10-<12

<10

20%

10%

0% l l l l

r e a k k b e e h e o n r a y B o a m l W r r r u a i a r e d u o w r u V K S E E W L D O N O L P K E N P R R W 5- 9- 5- 5- 5- 5- 5- 5- 9- 9- 9- 9- 9- 5- 9- 5- 9- 5- 9- 5-

Figure 2-3. Percentage of time in one month (May 2001) with water temperature given in ranges of two-degree-steps. Values above the columns: monthly mean values. All temperature values in °C. River types (Type 5: small-sized rivers, Type 9: mid-sized rivers) and abbreviations for sampling sites see Table 2.1. and text.

33 2. Temperature characteristics Discussion

For many stream insects, summer is an especially critical season for several reasons: much of the invertebrate production occurs in summer (Benke 1993), maximum temperatures form upper thresholds for taxa (e.g. Chadwick & Feminella 2001, Quinn et al. 1994) and influence the distribution and the invertebrate composition at a site (Sponseller et al. 2001, Jacobsen et al. 1997). Summer temperatures take influence on different life- history traits, as listed in Table 2-3. Hence, summer temperatures are specific for the two stream types and are indicative for the taxa naturally occurring there.

Winter temperatures As shown in Table 2-3, winter temperatures limit growth, they are important for the onset and termination of dormancies and for synchronizing the life cycle (e.g. Frutiger 1996, Zwick 1996). In large rivers with navigation, minimal temperatures also limit the distribution of neozoic taxa if being below a certain threshold in winter. Higher winter temperatures (>2°C) may enable these taxa to establish populations (e.g. Schöll 2000). For that reason the evaluation of winter temperatures is also valuable for the assessment of the ecological quality in small- and mid-sized rivers. In this study, winter temperatures did not show to be discriminating between the two mountainous river-types: minima generally reached values between 0 and 1°C in small- and mid-sized rivers, but groundwater influence rose winter temperatures by several °C in one mid-sized river. Consequently, rivers influenced by groundwater should be evaluated separately in order to develop a thermal typology. In mid-sized rivers winter temperatures are often altered by river regulation, impoundments for electricity-generation or water storage, and by effluents of power stations and industry (see Chapter 3).

Amplitudes Amplitudes (yearly mean and maxima) were not significantly different between the two river-types, but the daily fluctuations differed between the individual rivers. Mid-sized rivers had the tendency to have lower temperature fluctuations, especially in winter, than small-sized rivers. Daily amplitudes tend to increase from the crenal downstream (Schmitz 1954, Baumeis- ter 2001). Vannote & Sweeney (1980) observed an increase of up to 4th/5th order streams, and in higher orders a decrease due to the large water volume and the high specific heat of water. In this study, a decrease in daily fluctuations seems to begin already at 2nd to 3rd order streams (for stream orders see Appendix 3), an observation, that has also been made by Brussock & Bown (1991) and Webb & Walling (1986). This difference might be based on the morphology of the streams: in Central Europe many streams are morpho- logically altered in a way that the stream channel is narrowed and deepened (AQEM consortium 2002), which reduces the air-water temperature exchange by reducing the water surface relative to its volume. On the other hand, broad, shallow mid-sized rivers have

34 2. Temperature characteristics Discussion

naturally high temperature fluctuations, because the water surface is large compared to the volume, and moreover riparian vegetation will shade only part of the channel. So natural temperature fluctuations of mid-sized rivers might be higher than the fluctuations measured in the mid-sized rivers in this study. As to the ecological importance of amplitudes, results of laboratory studies are contradictory: laboratory studies give evidence of accelerated embryonic development in some Plecoptera under fluctuating temperature conditions (e.g. Frutiger 1996, Brittain 1977), other studies substantiated that fluctuating temperatures are of no influence on the embryonic development of other Plecoptera and Ephemeroptera (e.g. Zwick 1996, Humpesch 1982, Lillehammer et al. 1991). The literature review (Table 2-3) revealed that numerous laboratory studies give a broad overview of the temperature dependence of benthic invertebrates concerning the distribution, development and phenology. But then, the vast majority of studies on the influence of temperature on benthic invertebrates has been conducted under constant temperature conditions, accordingly the effect of temperature fluctuations on invertebrates needs further clarification.

2.4.2. Thermal pattern of the river-types and environmental influence The classification of the small- and mid-sized rivers based exclusively on thermal parameters showed that rivers did not cluster only in their corresponding river typology, but formed three distinct groups: the first and the third group consisted of only small- and only mid-sized rivers, respectively, while the second group consisted of rivers of both types. The latter was characterized by mean summer temperatures (with respect to the range of summer temperatures in both river-types), and low amplitudes, whereas winter temperatures varied between the rivers. On the basis of the results of the regression analysis and the observation of environmental conditions of the corresponding rivers, possible reasons for the thermal pattern of the second group of rivers shall be discussed in the following section. Current understanding of the factors that determine the water temperature in flowing waters is based on several groups of environmental factors: on a regional scale, climatic factors, the river-size, and hydrological and morphological conditions of the rivers are indisputably the main factors determining the water temperature of running waters (Ward 1985). Hydrological determinants for water temperature are the contribution of groundwater, the current velocity (function of channel form, water volume, substrate type), and discharge (Smith 1972). On the local scale, channel form, orientation (azimuth), vegetation cover, and substrate type have shown to be of importance for water temperature (Hawkins et al. 1997). In this study, representative parameters of these factors were tested on correlations with the temperature pattern of the small- and mid-sized rivers.

35 2. Temperature characteristics Discussion

Regional scale, geographic environmental variables The regression analysis in this study revealed that the size of the catchment area is a factor which is strongly related to summer temperatures (Table 2-7): The size of the small- and mid-sized rivers in the second group of the cluster analysis (mean summer temperatures and low amplitudes) may be too similar and may thus represent a size class of thermal transition between the two river-types. That conclusion can be drawn from comparing the following examples: the Dreisbach (catchment area: 25.9 km²) is the largest small-sized river, but the Volme (catchment area: 17.5 km²) ranges in the middle of its size class. Regarding the mid-sized rivers, the Nuhne is the smallest river with a catchment area of 134.4 km², followed by the Lenne (catchment area: 190 km²), Nims (catchment area: 222 km²) and Kyll (catchment area: 471.8 km²). Thus the smallest mid-sized river is only five times larger than the largest small-sized river, which is possibly not discriminating enough for a distinct thermal regime according to the typology. Nevertheless, even the Nims, with a catchment area of 222 km² at the sampling site, is slightly colder in summer than the two small-sized rivers, and the Kyll and Volme can certainly be expected to have different temperature patterns, the Kyll catchment being 28 times larger than the Volme catchment (For values of the catchment areas see Ta- ble 2-1). Only in one model, altitude was significant for maximum amplitudes in small-sized rivers, and contributed the least significant part to the model, thus being overall of minor importance for the rivers in this study. This confirms that the altitude classes of the river- types are fairly homogeneous for the thermal pattern. The channel orientation was related to variation in winter temperatures and winter amplitudes. This reflects the exposure of the channel to solar radiation (Arscott et al. 2001). In this study, the connection between channel orientation and water temperatures only developed in winter, most likely because streamside vegetation superimposes this effect in summer by shading the stream channel. Hydrology: the water of the Nims, a mid-sized river of the second group (being even colder than the two small-sized rivers), had the lowest summer temperatures, high winter minima (3.2°C and 2°C, respectively) and low daily amplitudes – three parameters, which might be indicating an inflow of groundwater. Corroborating this observation were the high conductivity values of 440 µS/cm for the Nims (see also Baumeister 2001). Be- cause of the siliceous bedrock in the catchment area of the investigated stream types conductivity usually lies within 100 to 300µS/cm (AQEM consortium 2002).

Local scale, channel morphology Width, depth and slope were the most important factors explaining winter- and spring amplitudes. Amplitudes decreased with increasing depth, width and, to a lesser degree,

36 2. Temperature characteristics Discussion

slope. This reflects the fact that the rivers in this study are typical for the most important degradation of small- and mid-sized mountainous rivers of Central Europe: nowadays river banks are generally fixed, and the channels are straightened and deep-cut (AQEM consortium 2002). The Prüm (Beifels) is an exception to this observation: upstream from the sampling site, long, shallow riffle sections allow a high water-air temperature exchange, which most likely caused the highest maxima (25.3°C in June 2000, see Appen- dix 6) and highest amplitudes in late spring (7.9°C in June 2000, see Appendix 8) measured in this study. Additionally, in a study by Hawkins et al. (1997) width was found to increase the temperature exchange and was positively correlated with summer temperatures, but this was likewise valid for the measured riffle sections.

Vegetation cover Summer temperatures in small-sized rivers were primarily influenced by the amount of forest in the catchment area. Especially small-sized rivers are susceptible to local shading because of the small water volume. This observation has been recorded for small head- water streams in several studies: the lack of riparian vegetation resulted in 2 to 5°C higher water maximum temperatures (Sweeney 1992, 1993, Quinn et al. 1997); even models to predict the effect of logging and clearcutting on water temperatures of streams have been developed (Brown 1970, Rutherford et al. 1997, Mitchell 1999). In this study, the Volme and Dreisbach, which were part of the second group according to the cluster analysis (with small- and mid-sized rivers), are the only small-sized rivers with mainly urban land use and basically no riparian forest in the floodplain. This may have led to the relatively high temperatures found in these small-sized rivers.

It can be concluded that the small- and mid-sized rivers in the Lower Mountain Range possess their own characteristical thermal pattern. It is characterized by low winter minima generally close to 0°C in both river-types, by significantly higher summer temperatures in mid-sized rivers compared to small-sized rivers, and by variable daily amplitudes in both rivers types. The thermal characteristics of the rivers belonging to the first and third group of the cluster analysis, i.e. the groups of only small- and only mid-sized rivers, may approach thermal reference conditions of the small- and mid-sized rivers. In this study, several environmental factors – both natural and anthropogenic - could be identified that caused a deviation of the thermal characteristics in some rivers from their corresponding type.

37 2. Temperature characteristics Discussion

The main factor determining the water temperature was found to be the size typology. Rivers differing from the corresponding types were influenced by the following factors: 1. groundwater, 2. lack of forest in the catchment area and lack of riparian vegetation at the sampling site being of primary importance for small-sized rivers, 3. morphological alterations in mid-sized rivers.

The influence of the first factor (groundwater) should be considered separately in a typological approach, whereas the other two factors could be considered as degradations from the thermal types. An adjusted thermal classification can be summarized as shown in (Table 2-11), with the rivers that deviate thermally from their corresponding type being excluded (for the origi- nal temperature data see Table 2-4.). It has to be considered that the prevailing air temperatures of 2000/2001 were about 1°C above the long-term records, so these values should be regarded as a first approach.

Table 2-11. Adjusted temperatures of small- and mid-sized rivers. Parameters of rivers that were not classifying with their corresponding group in cluster analysis were excluded.

Small-sized Mid-sized Mean summer (June-Aug): <13 °C 14 - 17 °C Mean winter (Dec-Feb): 4 °C 4 °C Maximum: < 20 °C 20 - 26°C Minimum: 0 °C 0 °C Maximum Amplitudes (mean Mar-May): 3 - 5 °C 3 - 5 °C

2.4.3. Classification with other river-types The classification of the rivers analysed above together with the large-sized to very large- sized rivers and lowland rivers (Types 10, 15, 20), using cluster analysis with temperature parameters, revealed river-size as the predominant factor for the temperature regime also for other size classes. Small-, mid-sized, and very large rivers cluster in thermally distinct groups (Figure 2-2). Again, several rivers did not cluster according to their typology. The temperature data of the rivers that were added in this cluster analysis were mostly influenced by impound-

38 2. Temperature characteristics Discussion

ments: sites downstream from the dams grouped together with the corresponding lower- size type of rivers, i.e. one mid-sized river (103 km²) clustered with cold small-sized rivers (9 resp.15 km²), whereas two large-sized rivers (6366, resp. 5166 km²) grouped with mid-sized rivers (134 to 524 km²). The annual thermal range is suppressed by the hypolimnic water, and the thermal characteristics of the rivers resemble those of the smaller-sized ones. This pattern fully agrees with the serial discontinuity concept of Ward & Stanford (1983), who state additionally that the highest thermal impact by impoundments would be reached in the middle to lower reaches of rivers. The mid-sized lowland rivers did not classify together, but belonged to groups of mixed river-types. Since the thermal regime of these rivers is influenced by different climatic factors as well as by the low altitude, they would have to be investigated thoroughly in a separate systematic with more sites.

39 3. Modified temperature regime Introduction

3. Influence of a modified temperature regime on the development of two Hydropsyche species

3.1. Introduction

Marked thermal changes are caused by river regulation with large impoundments that have a hypolimnic water release. Generally, these cause lower summer temperatures, higher winter temperatures and an overall decrease in thermal variability (e.g. Webb & Walling 1993). The extent to which the thermal pattern is changed downstream depends mainly on three factors: 1. the operating system, i.e. the release depth and discharge pattern, 2. the limnological factors, such as water retention times, stratification patterns and thermal gradients, and 3. the position of the dam along the longitudinal profile (Ward & Stanford 1983a, Ward 1985, Bergkamp et al. 2000). It has been proposed that temperature has the most significant influence on the benthic fauna downstream from impoundments (Petts 1984). Another great impact on the natural thermal regime of rivers are warm-water releases of cooling water by electricity-generating power stations. The severity of the thermal load- ing depends on the rate of discharge of the river as a dilution factor and on the operating system of the power station. Generally the natural annual temperature range and the diurnal variation are increased. Rising spring temperatures were observed to be ahead of the normal time by three to four weeks, and the autumnal temperature fall was delayed by one to three weeks (Langford 1970, Mason 1996). The oxygen content is generally high in spite of the high temperature due to of the turbulence and agitation of the water within the cooling towers (Langford 1971). Since the 1970s, numerous studies have been conducted concerning the in-stream biota in rivers with thermal impact by heated effluents (e.g. Fey 1976, Gibbons 1976, Well- born & Robinson 1996). These studies revealed that the thermal impact can lead to deleterious consequences on the macroinvertebrate community (Gallup et al. 1975, Parkin & Stahl 1981), but on the other hand, it may also cause no changes or even be of positive influence on benthic organisms (Langford 1971, Gibbons 1976, Poff & Matthews 1986). As to the heated discharges, the same is true for regulated rivers and impoundments: downstream changes in biotic and abiotic components are diverse and complex (e.g. Ward & Stanford 1979, Fraley 1979, Helešic 1998, Camargo & Voelz 1998, Bergkamp 2000). Experimental laboratory studies revealed several possibilities of how temperature can alter development of stream insects at different life stages. But since most of the studies were conducted under different constant temperatures, the question remains if the

40 3. Modified temperature regime Introduction

thermal influence can also be observed under differently fluctuating thermal conditions in the field. The objective of this study is to characterize the effects of a tributary with hypolimnic water and the effects of cooling water from an electricity-generating power station within one river, and to describe the larval development of two Hydropsyche species under these thermal conditions.

This part of the study will focus on: 1. the thermal pattern of the hypolimnic water and the cooling water effluent with special emphasis on thermal variability, 2. the development of Hydropsyche siltalai and H. incognita under the different thermal conditions.

41 3. Modified temperature regime Materials and methods

3.2. Materials and methods

3.2.1. Study area and sampling sites The Lenne is a mid-sized river in the Low Mountainous Region in the Sauerland, Nordr- hein-Westfalen. The Sauerland is located at the northern boundary of the Low Mountain- ous Region, which causes high precipitations of more than 1,000 mm p.a.. A first maximum is generally caused by snowfall in January/February, a second maximum in summer (July/August) by strong rainfalls and thundery showers (Murl 1989). The source of the Lenne is located at 800 m.a.s.l. on the Kahler Asten. The river has a catchment area of 1,353 km² and drains after 128.2 km into the Ruhr. The largest tributaries to the Lenne are the Bigge river with a catchment area of 369 km², and the Hundem river with 129 km². Other tributaries are small and only contribute a small amount of water to the Lenne. The geology in the catchment area is characterized by siliceous schist which causes ir- regular and distinct high and low flow conditions under natural conditions. Today, flow conditions are regulated by several impoundments in the catchment area (Frenz & Hering 2000). The region of the Lenne is highly industrialized. The narrow floodplain is - up to about 50% - occupied by industrial and residential urban sites and traffic systems. While the chemical characteristics between Lennestadt and Altena are fairly homogeneous (Frenz & Hering 2000), the thermal regime varies strongly in this reach of the Lenne: 65 km from the source, the Bigge flows into the Lenne, contributing cold water from three impoundments with hypolimnic release which were constructed mainly for water storage and river regulation, but which are also used for power generation: the first impoundment (Listertalsperre) retains 21.6 million cubic meters of water and contributes water from the Lister into the Bigge at the height of the Bigge lake. The second and largest impoundment, the Biggetalsperre, is situated 12 km upstream from the confluence into the Lenne, and has a dam of ca. 50 m in height, retaining more than 150 million cubic meters of water. The third impoundment (Ahausen) retains 1.8 million cubic meters of water and is located 7 km downstream from the Bigge impoundment. The effluent of the coal-fired electricity-generating facility Elverlingsen is located 30 km downstream from the Bigge confluence, releasing warmed cooling water into the Lenne.

Nine monitoring stations with loggers recording the water temperature were installed between Lennestadt and Hagen: two stations at reference sites upstream from the Bigge confluence, four stations within the 31.2 km stretch between confluence and power station that is influenced by the hypolimnic water of the Lenne, and three stations in a

42 3. Modified temperature regime Materials and methods

stretch of 23.7 km downstream from the power station Elverlingsen (Figure 3-1). In order to secure, that the water at the monitoring stations directly downstream from thermal sources (LEN and SST) is mixed and not stratified, they were installed 3 km downstream from the Bigge confluence and 1.8 km downstream from the power station effluent.

RUHR 8 LAS N 9 BUS 6 DRE 5 WIN 4 PLE 7 SST Power plant 2 FIN 3 LEN 1 ALT LENNE Impoundment

BIGGE

Figure 3-1. Map of the Lenne, NRW, Germany. Arrows show monitoring sites: 1 and 2= reference sites. 3 to 6= sites downstream from the Bigge confluence, influenced by hypolimnic water. 7 to 9= sites downstream from the electricity-generating power station Elverlingsen, influenced by cooling water

3.2.2. Assessment of environmental data The water temperature was recorded with data loggers from March 2001 to March 2002 at intervals of 30 minutes. Loggers of the type Gemini Data Loggers Tinytag Plus with internal temperature sensors were used for the temperature registration. The loggers have a resolution of ± 0.2°C. They were calibrated during 24 hours before installing them at the study sites, and were also checked and calibrated every time when the data was read out from the loggers: in July and October 2001 and in March 2002. As described in more detail in the preceding section, the loggers were installed by tying them with rustproof wire to steal piles. These were fixed in the river bottom in shaded places with flowing water. The loggers were tied at about 10 to 20 cm above the ground to avoid sediment covering them, and they were secured to stay under water at all water levels. The sampling sites and the corresponding recording dates are listed in Table 3-1.

43 3. Modified temperature regime Materials and methods

Table 3-1. Number, location, names of sampling sites in the Lenne, NRW, Germany. Dates of temperature registration and sites of Hydropsyche sampling and chemical data are indicated.

distance to temperature chem. Hyd. location code Ruhr [km] recording data sampl. stations upstream Bigge tributary: 1 Altenhundem ALT 93 3/1/2001 - 3/30/2002 2 Finnentrop (Bamenohl) FIN 74 3/1/2001 - 3/30/2002 X X stations downstream Bigge tributary: 3 Lenhausen LEN 68 3/1/2001 - 3/30/2002 X X 4 Plettenberg PLE 55.2 3/1/2001 - 3/30/2002 5 Wintersohl WIN 47.2 3/1/2001 - 3/30/2002 6 Dresel DRE 36.8 3/1/2001 - 3/30/2002 X X stations downstream power plant: 7 Schwarzer Stein SST 33.2 3/1/2001 - 3/30/2002 X X 8 Lasbek LAS 20.4 3/1/2001 - 3/30/2002 9 Buschmühle (Hagen) BUS 10.5 3/1/2001 - 3/30/2002

Chemical data were provided by a project which monitored the entire reach of the Lenne (Frenz & Hering 2000), other data were assessed in a parallel study (AQEM). The Lenne is moderately polluted, with the chemical parameters being fairly homogeneous at all sampling sites (Table 3-2). Oxygenation is high at all sites, including the reaches downstream from the Bigge confluence and downstream from the effluent of the power station.

Flow conditions for the entire monitoring period have been provided by Staatliches Um- weltamt Siegen for the gauge Rönkhausen downstream from the Bigge confluence, and by Ruhrverband for the gauge Bamenohl upstream from the Bigge confluence. Flow characteristics are regulated by several impoundments in the catchment area of the Lenne. Between March 2001 and September 2001 flow conditions were low. The highest discharge was recorded between January and March 2002 (Figure 3-2).

3. Modifiedtem Table 3-2. Abiotic characteristics of the sampling section. FIN: Finnentrop, upstream from Bigge confluence; LEN: Lenhausen, downstream from the Bigge confluence; DRE: Dresel, upstream from the power station Elverlingsen; SST: Schwarzer Stein, downstream from the power station. Chemical data from research project of Frenz & Hering (2000) and sampled within the assessment of the EU project AQEM (2002).

FIN LEN DRE SST p erature regim mean s.d. n mean s.d. n mean s.d. n mean s.d. n ammonium [mg/l] 0.23 0.1 7 0.38 0.4 4 0.21 0.3 7 0.13 0.1 3 nitrite [mg/l] 0.072 0.1 7 0.078 0.0 4 0.107 0.0 7 0.072 0.0 5

nitrate [mg/l] 10.4 1.7 7 12.8 1.0 4 13.2 1.8 7 12.4 1.6 5 e

ortho-P [µg/l] 81.2 24.9 7 45.0 23.8 4 83.7 17.1 7 72.0 16.4 5 total P [µg/l] 183.2 41.3 7 137.5 57.4 4 176.2 47.3 7 156.0 40.4 5 total hardness 1.17 0.4 7 0.65 0.1 4 0.90 0.1 7 0.93 0.2 5 chloride [mg/l] 23.43 4.3 7 24.50 5.7 4 28.67 8.4 7 30.53 6.8 5

diss.O2 [mg/l] 10.88 1.7 6 12.53 1.4 3 11.94 0.9 6 11.68 0.4 4

O2 sat. [%] 94.68 7.2 6 102.67 13.7 3 102.58 7.7 6 109.00 17.8 4 conduct. [µS/cm] 280.71 101.4 7 189.50 29.4 4 282.00 70.1 7 249.93 39.1 5

pH-value 7.6 0.5 6 7.5 0.2 3 7.8 0.6 6 8.0 0.7 4 BOD5 [mg/l] 3.2 2.2 7 2.1 1.2 4 3.73 3.9 7 1.91 0.3 5 Materialsandm

e thods

3. Modified temperature regime Materials and methods

200 180 160 140 120 s 100 m²/ 80 60 40 20 0 Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. 01 01 01 01 01 01 01 01 01 01 02 02 02

Bamenohl Rönkhausen

Figure 3-2. Monthly flow data from March 2001 to March 2002 of the Lenne upstream and downstream from the Bigge confluence [m³/sec]. Location of the gauges: Bamenohl: Upstream from Bigge confluence close to sampling site 1 (ALT). Rönkhausen: Downstream from Bigge confluence between sampling site 3 (LEN) and 4 (PLE).

3.2.3. Invertebrate data: sampling and determination At nine sampling dates between March and November 2001, Hydropsyche (Trichoptera) larvae were collected. Sampling sites were located upstream and downstream from the Bigge confluence (monitoring stations 2 and 3), and upstream and downstream from the power station (monitoring stations 6 and 7) (Table 3-1). Sampling sites were selected according to riffle sections having comparable current velocity and stony substrate with cobbles and boulders. The larvae were collected by kick- sampling using a shovel-sampler with a 500µm mesh size and additionally by brushing off stones. For analysis, the 3rd to 5th larval stages were used, younger larvae and pupae were not considered here.

46 3. Modified temperature regime Materials and methods

For the determination of the Hydropsyche larvae to species level the following literature was used: Waringer & Graf (1997), Robert & Wichard (1994), Pitsch (1993), Neu (2004). The width of the head-capsules at their broadest part was measured from dorsal, and the larval stages were concluded from the resulting size-groups.

The determination of the 3rd and 4th stage larvae was based on the following assumptions: It is possible to determine H. siltalai also in 3rd and 4th larval stages by the absence of gills on the 7th abdominal segment. The determination of other Hydropsyche species in these larval stages is more difficult, if not impossible. But under the following assumptions species that were not H. siltalai were defined to be H. incognita: First, in the AQEM sampling 2001 (data unpubl.), at the sampling sites Finnentrop and Lennestadt, only the following Hydropsyche species were found (indiv/m²): H. siltalai (36.8), H. incognita (48.8), H. pellucidula (0.8). The occurrence of H. pellucidula at the sampling sites was very low, this species was found to be more frequent in the lower - potamal - sections of the Lenne. Second, the 5th stage larvae of this study were securely determined to species level. From the species that were not H. siltalai, three larvae were identified as H. pellucidula and 396 larvae were H. incognita. One species of H. angusti- pennis was also found. On condition that the distribution of the 5th stage larvae over a year would be the same in 4th and 3rd stage larvae, non-siltalai species were defined to be H. incognita: in the first place, because the probability for these larvae to be another species was p=0.01. In the second place, because also in other benthic samples (AQEM) H. pellucidula was found in very low numbers in the range of the sampling sites of this study.

3.2.4. Statistical tests The temperature differences between the stations upstream and downstream from the Bigge confluence and of the power station were tested on significance using the Student t-Test. The same test was used for analysis of the significance of differences in widths of head capsules comparing the four sampling sites. The difference in the distribution of larval stages at the four sampling sites could not be tested statistically because the main difference lay in the absence of larval stages at different sampling sites and dates – consequently a chi² test could not be applied. For the Student t-Test Statistica 6.1. StatSoft Inc. (2003) was used.

47 3. Modified temperature regime Results

3.3. Results

3.3.1. Thermal changes downstream from the Bigge confluence and the power station A summary of the thermal characteristics at the monitoring sites upstream and downstream from the Bigge confluence and upstream and downstream from the power station is shown in Table 3-3. A detailed listing of the thermal parameters at the nine monitoring sited are shown in Appendix 16. Downstream from the Bigge confluence (monitoring stations LEN, PLE, WIN, DRE), monthly mean summer temperatures of the Lenne (March to October) were lower compared to those of the station upstream from the confluence (station 2 FIN). The temperature differences were most pronounced between May and August, with monthly mean temperatures being up to 5.2°C lower at the monitoring station LEN downstream from the Bigge confluence (Figure 3-3a FIN-LEN). Here, maximum temperatures never exceeded 15°C, and maxima were 5 to 7°C lower than upstream from the Bigge confluence from June to August. Winter mean temperatures from November to March were slightly higher downstream from the confluence (LEN, PLE, WIN, DRE) than those upstream (FIN), but only reached a difference of 1°C (Figure 3-3a). During these months, temperature differences between the stations upstream and downstream from the Bigge confluence were not significant.

20 20 18 18 16 16

] 14 14 C ° 12 12 e [ r u 10 at 10 r e p 8 8 m e t 6 6 4 4 2 2 0 0 t r r r t g y p v c b Jul Jul Ap Oc Jun Jan Mar Au Feb Mar Se Nov Dec Apr Jun Oc Jan Ma Ma May No Ma Aug Fe Sep De

FIN LEN PLE WIN DRE DRE SST LAS BUS Figure 3-3a. Monthly mean temperatures in Figure 3-3b. Monthly mean temperatures in 2001 to 2002 at the reference station 2 (FIN) and 2001 to 2002 upstream from the power station downstream from the Bigge confluence, stations at station 6 (DRE), and downstream from the 3 (LEN) to 6 (DRE). power station at stations 7 (SST) to 9 (BUS).

48 3. Modified temperature regime Results

Daily amplitudes were not very pronounced at the reference site (compared to rivers of the same typology; see Chapter 2): at the station FIN highest monthly means were monitored in May and August between 2.2 and 2.7°C, and maximum amplitudes of 4.9°C were measured in July. Temperature fluctuations were lower downstream from the Bigge confluence, the maximum amplitude being 1.8°C (mean July), and 3°C (maximum June) (Table 3-3, details Appendix 16). Temperatures downstream from the confluence rose along the downstream section of 31 km by 3.7°C (mean July, difference LEN to DRE), so it was still colder at DRE compared to the reference station (FIN) (Figure 3-3a). The warming rate was between 0.10 and 0.14°C/km in the summer months June to August. The electricity-generating power station uses water from the Lenne as cooling-water, which is discharged again at elevated temperatures. Unlike downstream from the Bigge confluence, the thermal regime downstream from the power station was modified throughout the year (Figure 3-3b). Monthly means were found to be higher by 1 to 2°C.

Table 3-3. Characteristic temperature parameters for monitoring sites up- and downstream from the Bigge confluence and up- and downstream from the power station respectively. Temperature values in °C.

FIN LEN DRE SST mean (year) 10.0 8.6 9.8 10.8 degree-days (year) 3,654 3,163 3,577 3,971 highest monthly mean 17 12.3 16 18.2 maximum 21 14.8 19.1 24.2 minimum 0.7 1.9 0.7 0.5 max daily amplitude (monthly mean) 2.7 1.8 1.9 2.9 daily amplitude (max) 4.9 3 3.4 5.6

Considering the amplitude between minimum and maximum values measured within one month, the temperature downstream from the power station varied by 13.2°C (May 2001 SST, see Appendix 16), and was highest compared to all other sampling sites (Figure 3- 4). Nevertheless, mean daily amplitudes downstream from the power station were only moderately higher than those at the stations ALT and FIN (Table 3-3), which indicates that the high fluctuations were not caused by the daily amplitudes. Warm water of the power station was released into the Lenne at a basic frequency of three days. During the release of heated water, the daily temperature rose by about 4 to 5°C, maximum temperatures rose by 5 to 6°C, and dropped back to normal temperatures comparable to those of the station upstream for the next days (Figure 3-5). The maximum temperatures down-

49 3. Modified temperature regime Results

stream from the power station (SST) reached over 24°C while temperatures at the reference station (FIN) had 21°C as a maximum (Table 3-3).

26 24 22 ] C

° 20 e [ r 18 u at

er 16 p m 14 e t 12 10 8 ALT FIN LEN PLE WIN DRE SST LAS BUS

Figure 3-4. Mean, minimum and maximum temperatures of August 2001 along the downstream course from the nine monitoring stations in the Lenne. For abbreviations of the stations see Table 3-1.

Along the 23.7 km downstream, the temperature still rose in the period from May to Au- gust at a rate of 0.04 to 0.05°C/km. This increase in temperatures downstream was evidently caused by two factors acting together: 1. during thermally normal periods, the exchange of water and air temperatures (in warmer spring-, summer- and autumn months) lead to an increase of temperatures downstream; 2. during discharge, thermal peaks are attenuated downstream, though only slightly (about less than 1°C). The fact that the at- tenuation of the peak temperatures is less than the heat uptake during normal periods, leads to the overall warming of temperatures. The pattern of the effluent from the power station is still pronounced at the last monitoring station 23.7 km downstream (Figure 3- 5).

3.3.2. Comparison of the thermal changes by hypolimnic and cooling water Downstream from the Bigge confluence the cumulated degree-days of one year were 13.4% lower than at the reference station. This difference mainly developed during the months May to September by reaching monthly mean temperature differences of 5°C and more. The percentage of monthly temperature difference is shown in Figure 3-6a.

50 3. Modified temperature regime Results

18 17 16

] 15 C [°

e 14 r u t

a 13 r e 12 mp

te 11 10 9 8 1. Oct. 6. Oct. 11. Oct. 16. Oct. 21. Oct. 26. Oct. 31. Oct.

DRE SST BUS

Figure 3-5. Temperature fluctuations upstream (DRE) and downstream (SST) from the power station, and 23 km downstream from the power station (BUS) in October 2001. Temperature registration at 30 minute-intervals.

Downstream from the power station, the cumulated degree-days over the one-year period were 11% higher than at the reference station, but this difference developed over the entire year: consequently the monthly mean temperature change downstream from the power station was about half the difference compared to the confluence (Figure 3-6b). On the other hand, regarding short-term fluctuations, during times of heated discharge the warming of the water downstream from the power station was also elevated intermit- tently by about 5°C. Summarizing these results, yearly temperature changes showed about the same magnitude downstream from the Bigge confluence and downstream from the power station, whereas the structure of temperature change was different: short-term fluctuations downstream from the power station with higher thermal variability in intervals of several days and, on the other hand, seasonal long-term changes with less thermal variability downstream from the Bigge confluence.

3. Modified temperature regime Results

100 100

90 90

80 80

70 70

60 60

50 50

40 40

30 30

20 20

10 10

0 0 l r t r l r r g t y r r n y g v c b p n v c b p

Ju Ju

Ap Oc Jan Ap Ju Ma Au Fe Ma Oc Jan

Ma Ju Se De Ma Au Fe Ma No Ma Se De No

-2 to 0°C -4 to <-2°C -6 to <-4°C <-6 °C >0 to 2°C >2 to 4°C >4°C

Figure 3-6a. Percentage of temperature differ- Figure 3-6b. Percentage of temperature differences upstream and downstream from the Bigge ences upstream and downstream from the confluence (LEN-FIN). The difference missing power station (SST-DRE). The difference to 100% are higher temperatures downstream missing to 100% in the diagram are lower wa- from the Bigge confluence than upstream. (Per- ter temperatures downstream from the power centages based on half-hour temperature sam- station than upstream. ples).

3.3.3. Development of two Hydropsyche spp. in different thermal regimes

Hydropsyche incognita, upstream and downstream from the Bigge confluence The highest number of individuals of H. incognita, over 60 in March, July and August, were found at the reference station (FIN). Downstream from the confluence (LEN) the number of species did not exceed 30, this being the sampling site with the fewest individuals of Hydropsyche. Comparing the development of H. incognita upstream (Figure 3-7b) and downstream (Figure 3-7d) from the Bigge confluence, larvae developed equally until June, followed by a slower development downstream from the Bigge confluence in summer: within the time period between March and June the 3rd and 4th larval stages declined in numbers, in June nearly all larvae are in the 5th stage. Parallel to this, the number of 5th stage larvae declined, indicating pupation at all sites. At the beginning of July the development downstream from the confluence began to lag behind the development of the reference station: while the next cohort of 3rd to 4th stage larvae appeared at the reference station, these could not be found downstream from the Bigge confluence until August. In November the developmental stages were distributed equally again at both stations.

52 3. Modified temperature regime Results

Hyd sil - FIN Hyd inc - FIN 35 80 30 70 60 25 50 20 40 15 30 10 20 5 10 0 0 Mar May Jun Jul I Jul II Aug Sep Nov Mar May Jun Jul I Jul II Aug Sep Nov

Figure 3-7a. Station 2 (FIN): H. siltalai Figure 3-7b. Station 2 (FIN) : H. incognita

Hyd sil - LEN 25 Hyd inc - LEN 35 20 30 25 15 20 10 15 10 5 5 0 0 Mar May Jun Jul I Jul II Aug Sep Nov Mar May Jun Jul I Jul II Aug Sep Nov

Figure 3-7c. Station 3 (LEN): H. siltalai Figure 3-7d. Station 3 (LEN): H. incognita

Hyd sil - DRE Hyd inc - DRE 80 60 70 50 60 40 50 40 30 30 20 20 10 10 0 0 Mar May Jun Jul I Jul II Aug Sep Nov Mar May Jun Jul I Jul II Aug Sep Nov

Figure 3-7e. Station 6 (DRE): H. siltalai Figure 3-7f. Station 6 (LEN): H. incognita

Hyd sil - SST Hyd inc - SST 45 50 40 35 40 30 30 25 20 20 15 10 10 5 0 0 Mar May Jun Jul I Jul II Aug Sep Nov Mar May Jun Jul I Jul II Aug Sep Nov

Figure 3-7g. Station 7 (SST): H. siltalai Figure 3-7h. Station 7 (SST): H. incognita Figure 3-7 a-h. Distribution of larval stages found during the 1-year period at the stations upstream and downstream from the Bigge confluence and upstream and downstream from the power station. Left column: H. siltalai , right column: H. incognita: Black=3rd larval stage, grey=4th larval stage, white=5th larval stage. Y-axis: number of individuals . 53 3. Modified temperature regime Results

Table 3-4a. Numbers of Hydropsyche incognita larvae found at the sampling sites up- and downstream from the Bigge confluence and up- and downstream from the power station effluent. Mean, minimum and maximum values of the head capsule width (in mm) are given for each sampling date and for the 3rd to 5th larval stage (III to V). Using the Student t-Test, differences in head capsule sizes were tested on significance pairwise for the sampling sites up- and downstream from thermal changes (FIN-LEN and DRE-SST).

FIN LEN DRE SST H.inc III H.inc IV H.inc V H.inc III H.inc IV H.inc V H.inc III H.inc IV H.inc V H.inc III H.inc IV H.inc V 5.3.01 n 8 23 35 3 10 17 0 6 18 0 9 7 MW 0.64 1.04 1.68 0.63 1.05 1.70 1.05 1.70 1.05 1.69 min 0.61 0.97 1.44 0.60 0.92 1.52 0.99 1.53 0.96 1.59 max 0.68 1.12 1.87 0.67 1.16 1.79 1.12 1.89 1.11 1.77 s.d. 0.02 0.05 0.09 0.04 0.06 0.07 0.04 0.09 0.05 0.06 t-Test (p) 0.349 0.433 0.495 0.766 0.783

7.5.01 n 0107034006017 MW 0.99 1.63 1.04 1.66 1.72 1.00 1.61 min 0.92 1.55 0.99 1.62 1.60 1.53 max 1.06 1.69 1.10 1.72 1.77 1.73 s.d. 0.04 0.05 0.05 0.05 0.07 0.07 t-Test (p) 0.111 0.320 0.017

12.6.01 n 008026001001 MW 1.62 1.04 1.61 1.60 1.59 min 1.55 1.00 1.52 max 1.71 1.08 1.69 s.d. 0.05 0.05 0.07 t-Test (p) 0.647

6.7.01 n 13118004213280 MW 0.70 1.13 1.64 1.69 0.59 0.91 1.61 0.69 1.08 min 0.65 1.04 1.55 1.64 0.47 1.53 0.67 1.02 max 0.75 1.20 1.82 1.75 0.71 1.67 0.71 1.11 s.d. 0.03 0.04 0.09 0.05 0.17 0.08 0.03 0.03 t-Test (p) 0.306 0.491

26.7.01 n 2 25 40 0 0 0 0 13 12 2 12 24 MW 0.54 1.13 1.64 1.08 1.62 0.65 1.06 1.60 min 0.40 1.05 1.50 0.97 1.54 0.62 0.98 1.49 max 0.67 1.19 1.76 1.19 1.73 0.67 1.15 1.73 s.d. 0.19 0.04 0.07 0.06 0.07 0.04 0.04 0.07 t-Test (p) 0.373 0.394

24.8.01 n 6 34 29 2 13 4 8 22 20 6 22 16 MW 0.65 1.09 1.75 0.67 1.14 1.68 0.65 1.10 1.75 0.64 1.06 1.65 min 0.63 0.99 1.65 0.67 1.07 1.59 0.63 1.01 1.63 0.60 1.02 1.57 max 0.68 1.18 1.87 0.67 1.18 1.73 0.69 1.20 1.89 0.66 1.12 1.73 s.d. 0.02 0.05 0.06 0.00 0.03 0.07 0.02 0.05 0.08 0.02 0.03 0.05 t-Test (p) 0.163 0.005 0.044 0.325 < 0.001 < 0.001

28.9.01 n 0 7170 8130 3210 421 MW 1.05 1.68 1.05 1.73 1.06 1.69 1.06 1.67 min 1.00 1.59 0.99 1.62 1.04 1.45 1.05 1.53 max 1.10 1.82 1.11 1.82 1.07 1.84 1.08 1.80 s.d. 0.03 0.08 0.04 0.07 0.02 0.08 0.01 0.07 t-Test (p) 0.875 0.089 0.591 0.494

22.11.01 n 1 7220 9160 0 2 1 3 7 MW 0.65 1.00 1.67 1.05 1.71 1.68 0.59 1.02 1.67 min 0.98 1.53 1.01 1.55 1.58 0.95 1.61 max 1.05 1.77 1.11 1.86 1.79 1.07 1.71 s.d. 0.03 0.07 0.03 0.09 0.15 0.06 0.03 t-Test (p) 0.003 0.102 0.811

54 3. Modified temperature regime Results

Table 3-4b. Numbers of Hydropsyche siltalai larvae found at the sampling sites up- and downstream from the Bigge confluence and up- and downstream from the power station effluent. Mean, minimum and maximum values of the head capsule widths (in mm) are given for each sampling date and for the 3rd to 5th larval stage (III to V). Using the Student t-Test, differences in head capsule sizes were tested on significance pairwise for the sampling sites up- and downstream from thermal changes (FIN-LEN and DRE-SST).

FIN LEN DRE SST H.sil III H.sil IV H.sil V H.sil III H.sil IV H.sil V H.sil III H.sil IV H.sil V H.sil III H.sil IV H.sil V 5.3.01 n 723012207445250 MW 0.59 0.94 0.61 0.92 0.59 0.94 1.45 0.56 0.93 min 0.57 0.88 0.86 0.56 0.84 1.40 0.56 0.88 max 0.60 1.01 1.02 0.62 1.02 1.53 0.56 1.00 s.d. 0.01 0.04 0.04 0.02 0.04 0.05 0.00 0.04 t-Test (p) 0.255 0.086 0.476

7.5.01 n 04903706270728 MW 0.92 1.46 0.90 1.46 0.91 1.45 0.91 1.46 min 0.85 1.37 0.89 1.42 0.86 1.37 0.88 1.37 max 0.97 1.51 0.92 1.53 0.96 1.53 0.93 1.56 s.d. 0.05 0.05 0.01 0.05 0.04 0.05 0.02 0.05 t-Test (p) 0.627 0.719 0.979 0.671

12.6.01 n 0 0170 0180 1470 040 MW 1.47 1.47 0.86 1.42 1.44 min 1.38 1.26 1.31 1.29 max 1.57 1.60 1.55 1.53 s.d. 0.05 0.07 0.06 0.07 t-Test (p) 0.955 0.317

6.7.01 n 002001600700029 MW 1.39 1.49 1.42 1.43 min 1.38 1.38 1.33 1.29 max 1.40 1.56 1.55 1.51 s.d. 0.01 0.04 0.06 0.05 t-Test (p) 0.006 0.905

26.7.01 n 0000010024007 MW 1.40 1.37 1.40 min 1.27 1.34 max 1.47 1.47 s.d. 0.05 0.04 t-Test (p) 0.156

24.8.01 n 000002000000 MW 1.33 min 1.26 max 1.40 s.d. 0.10 t-Test (p)

28.9.01 n 100000220000 MW 0.59 0.46 0.92 min 0.36 0.91 max 0.56 0.94 s.d. 0.14 0.02 t-Test (p)

22.11.01 n 0301400431030 MW 0.94 0.57 0.93 0.93 1.43 0.91 min 0.90 0.92 0.86 0.89 max 0.96 0.94 1.00 0.92 s.d. 0.03 0.01 0.03 0.01 t-Test (p) 0.517 0.237

55 3. Modified temperature regime Results

H. incognita, upstream and downstream from the power station The development of H. incognita was found to be similar upstream and downstream from the power station (Figure 3-7 f, h). 5th stage larvae declined in numbers until June, in July the second cohort appeared. The same development was observed at the station FIN upstream from the Bigge confluence. The width of the head capsules of the size-groups of 3rd to 5th stage larvae did not differ significantly among the four stations (Table 3-4).

H. siltalai, upstream and downstream from the Bigge confluence Only few individuals were found at the reference station and downstream from the Bigge confluence, while the greatest abundance was found at the station upstream from the power station, with up to 70 species in July. The development of H. siltalai shows similar tendencies in the development at the different sampling sites as H. incognita. Upstream and downstream from the Bigge confluence the larval stages are equal until June, followed by a slower development downstream from the confluence observed in summer: downstream from the Bigge confluence 5th stage larvae could still be found in August (though in very low numbers of n=2) (Figure 3-7c). At this sampling site, the next generation of 3rd and 4th stage larvae was found in November, also in very low numbers.

H. siltalai, upstream and downstream from the power station Upstream and downstream from the power station the larval development of H. siltalai is similar (Figure 3-7 e, g). In August, no individuals of H. siltalai were found, the next generation appeared in November in very low numbers. The sizes of the head capsules of the corresponding larval stages do not differ significantly among the stations (Table 3-4).

In Table 3-5 changes in temperature factors and the development of the two Hydropsyche species are summarized. The magnitude of the yearly change is similar downstream from the Bigge confluence and downstream from the power station, but the thermal pattern is different. The changes in larval development of both Hydropsyche species are pronounced downstream from the Bigge confluence, whereas downstream from the power station no changes in larval development were observed.

56 3. Modified temperature regime Results

Table 3-5. Summary of the thermal parameters and the development of Hydropsyche siltalai and H. incognita. thermal factors below Bigge confluence below power plant degree-days 14% lower 11% higher diurnal variability lower slightly higher seasonal variability lower unchanged summer temperatures lower short-term elevations winter temperatures slightly higher short-term elevations larval development spring growth unchanged unchanged pupation H. siltalai 1 month later unchanged pupation H. incognita 1 month later / 1 cohort unchanged 2 cohorts size of head capsule unchanged unchanged abundance H. siltalai unchanged unchanged abundance H. incognita low unchanged

57 3. Modified temperature regime Discussion

3.4. Discussion

3.4.1. Environmental situation Generally, several factors such as the thermal regime, flow-fluctuation, sediment flow, oxygen content and others change according to dam types, operating systems, and the environment into which the impoundments are built. These factors may act together, yielding a multitude of conditions that are site-specific and very variable, thus resulting in different degrees of intensity and different types of impacts (Berkamp 2000, Camargo & Voelz 1998, Helešic et al. 1998). The water in deep reservoirs like the Bigge impoundment stratifies, and hypolimnic out- lets cause a characteristic thermal pattern downstream, that was observed in the present study: summer temperature depression, winter temperature elevation and an overall decrease in thermal variability. The temperature changes were even pronounced after the confluence of the Bigge and the Lenne. The thermal modifications were attenuated but still persistent 30 km downstream from the confluence. In other studies, it has also been noted that temperature changes of hypolimnic releases can be very persistent: in eastern North America they were observed farther than 70 km (Sweeney 1993) downstream and even 180 km downstream from a dam in South Africa (Davies 1979 in Bergkamp 2000). In New South Wales cold hypolimnic water (of more than 5°C below normal) affects river stretches of 300 km downstream from each dam (Bergkamp 2000).The thermal modifications along the recovery gradient are known to cause a change in species composition (Sweeney 1993, Voelz & Ward 1991, Carmargo & Voelz 1998). So, for example, the expansion of cold-water species upstream is evidently favoured (Céréghino et al. 1997). Oxygen content and chemical parameters were found to be within a normal range downstream from the Bigge confluence, and not significantly different from those upstream from the confluence. Since the flow of the Bigge and Lenne is turbulent, the water was well oxygenated. Many impoundments with deep-water releases show only minor to in- significant hydrochemical changes in contrast to thermal modifications (e.g. Voelz & Ward 1991, Helešic et al. 1998, Brittain & Saltveit 1989, Saltveit et al. 1994). On the other hand, the flow regime is highly modified downstream from impoundments with intermittent hydropeaking for power generation, which effects most strongly macroinvertebrates, along with the thermal modifications (Helešic et al. 1998, Carmargo & Voelz 1998). In the underlying study, the Bigge impoundment is primarily used for water storage, so there were no sudden short-term rises in discharge, and a major effect on the fauna was not to be expected.

58 3. Modified temperature regime Discussion

Like the temperature changes of impounded rivers, thermal modifications of heated discharges are complex and differ depending on the type and operation of the industrial in- stallation and on the environmental situation, e.g. the river discharge (Wellborn & Rob- inson 1996). In the underlying study the influence of the heated discharges by the power station was measured 1.8 km downstream from the effluent, and short-term elevations of temperature were registered throughout the year. The maxima (24°C) recorded here did not reach extreme temperatures as measured in other studies downstream from power station effluents: even 30 or 40°C have been recorded (e.g. Langford 1983, Parkin & Stahl 1981).

3.4.2. Longitudinal influence of thermal changes The importance of the longitudinal thermal gradient of rivers has been acknowledged in early works (Ule 1925, Brehm & Ruttner 1926). Based on the observation that the faunal community changes downstream, a river zonation has been established in connection with thermal conditions prevailing in the respective biocoenotic region (Illies & Boto- saneanu 1963). The role of temperature for the faunal change along the stream continuum has also been pointed out in other works, that focus on physical and ecological gradients of streams and their disruptions (e.g. Vannote et al. 1980, Ward & Stanford 1983, Ward & Stanford 1979). In the present study of the Lenne, the longitudinal thermal continuum was disrupted twice, as can be seen when applying the thermal classification in biocoenotic regions according to Illies & Botosaneanu (1963), together with the corresponding thermal conditions as suggested by the DVWK (1984) (which are more detailed than the estimations of Illies & Botosaneanu): the annual temperature range at the reference station of 11°C - based on monthly means - could be classified as hyporhithral. The first thermal interruption is the Bigge confluence, where the annual temperature range is lowered to 8°C, a characteristic that is representative for the epi- to metarhithral. The conditions of the cold hypolimnic water - (lower annual and daily amplitude) attenuated downstream through the interchange of air - and water temperature, and after a course of 30 km the thermal conditions were again - though slightly colder than at the reference station - corresponding to the thermal conditions of the hyporhithral (compare Ward & Stanford 1983). The second interruption of the thermal gradient was caused by the thermal effluent of the power station: here, the prevailing temperature range of 13.3°C shifted the thermal zone from the hyporhithral to the epipotamal. As stated above, the thermal changes were very persistent downstream - extreme temperatures attenuated slightly, but through the uptake of more heat downstream, the colder thermal conditions that were left out in the continuum because of the sudden thermal input, did not repeat along the downstream course.

59 3. Modified temperature regime Discussion

Hence, the warmed river did not recover to the thermal characteristics it would have naturally in the given section, and the thermal zone that would normally prevail in the given section - in this case the meta- to hyporhithral - was clearly shortened in the river continuum.

3.4.3. Development of Hydropsyche larvae Changed temperature regimes are known to affect development patterns (Ward & Stan- ford 1979, Prat 1981, Ward & Stanford 1982, Sweeney 1984, Raddum & Fjellheim 1993) and growth rates (in laboratory studies: e.g. Elliott 1978, Humpesh 1982, Sweeney & Vannote 1986, Sweeney et al. 1986, Gibersen & Rosenberg 1992, Frutiger 1996; in field observations: e.g. Boon 1988, Brittain & Saltveit 1989, Sweeney 1993, Pritchard & Zloty 1994, Wagner 2002) of benthic invertebrates. The developmental time is usually negatively related to temperature, when studied experimentally at different constant temperatures (review Sweeney 1984). Unseasonably warm temperatures may result in shorter development periods and premature metamorphosis on winter- and spring- growing species (Fey 1967, Sweeney & Vannote 1981, Vannote & Sweeney 1980). In multivoltine species the developmental time is shorter for the summer cohorts under warmer conditions (review: Sweeney 1984). As far as this study is concerned, within the river mentioned above the temperature was depressed (by the hypolimnic water) and further downstream elevated (by the effluent of the power station) showing nearly the same magnitude: 13% less degree-days accumulated downstream from the confluence with hypolimnic water, and 11% more degree- days accumulated over a year downstream from the power station. Thus, it could be expected that the thermal differences slow the development of temperature-sensitive species in the cooler water downstream from the Bigge confluence and accelerate the development in the warmer water downstream from the power station. In fact, the results of this study revealed a slower development of two Hydropsyche species downstream from the Bigge confluence, but the same two species did not develop faster in the warmer river section downstream from the power station. Apparently, assessing the effects of the impact of thermal changes requires taking into account the different types of modifications: not only the temperature differences them- selves, but also their temporal scale. This seems to be especially important, since species can be of varying sensitivity to temperatures and have different thresholds at different life stages and consequently in the corresponding seasons of the year. Laboratory studies revealed that embryonic and larval development, dormancy, growth rates and maturation, emergence and voltinism can be variably influenced by temperature, depending on the species and its evolutionary adaptation (review: Sweeney 1993, Ward & Stanford 1982, Pritchard et al. 1996). This hypothesis shall be discussed by bringing together the thermal patterns with the species life-history traits:

60 3. Modified temperature regime Discussion

H. siltalai is a univoltine species (Schuhmacher 1970), that emerges in summer from June to September. After hatching it grows slowly during autumn until reaching the 3rd larval stage to overwinter. In spring, the larvae reach their highest growth rate and pupate between May and July (Tobias & Tobias 1981, review species traits: Ahn 2002). Since in this study the main temperature difference with significantly lower temperatures started in May, the time of slower development of H. siltalai downstream from the Bigge confluence matches the time span of cold conditions during a developmental period of high thermal demand, when the larvae reach their highest growth rate before pupation. The life-cycle of H. incognita is more complex and less known. The occurrence of two flight periods, the first in June and the second in August, is currently discussed (Pitsch, Neu, pers. comm.). The study at hand shows that at three sites, i.e. at the reference station and at the sampling sites upstream and downstream from the power station, 5th stage larvae were observed to decline in numbers twice, which may indicate pupation once in June and a second time in August/September. This was not the case downstream from the confluence: here, no second decline in 5th stage larvae from August to September could be observed. Again, this matches the time of the lower temperatures in summer. Downstream from the confluence with hypolimnic water the main temperature change is restricted to the summer season, with monthly mean differences of more than 5°C. This is especially important for the species that have their main growth period and thermally dependent life-stages within the warm season: late spring and summer emergers. It has been observed in other studies that the degree-day accumulation is of special importance during the last larval instars for the timing of emergence of a summer emerging mayfly – in this case the period from late June to late August (Watanabe et al. 1999, Takemon 1990). The importance of seasonal growth patterns in relation to water temperatures prevailing during the main growth periods has also been stressed by Hogue & Hawkins (1991) and Céréghino et al. (1997): the temperature-to-growth relationship determined by laboratory results may not necessarily occur the same way in nature, since some factors are often not considered under laboratory conditions, such as the seasonality of temperatures and the phenology of species. The authors mentioned above showed that various Trichoptera and Plecoptera species lack the temperature-to-growth relationship, when the main growth season occurred mainly from autumn to spring under rather low temperature conditions. More studies on the influences of temperature on benthic invertebrates should be conducted under natural conditions in order to test whether the relationship can also be observed under natural conditions.

Moreover, not only colder mean temperatures might be of influence for the species, but also the low thermal variability of the environment. The advantage of fluctuating thermal conditions for benthic species from the community-perspective has been proposed by Vannote & Sweeney (1980) in the “thermal equilibrium hypothesis”. According to this hypothesis, more species with different thermal optima find their developmental optimum in thermally fluctuating environments, and consequently species diversity is highest

61 3. Modified temperature regime Discussion

in river reaches of highest thermal variability. Considering the low thermal variability in the Lenne downstream from the Bigge confluence, the species do not encounter higher temperatures even for a short period of a day, as might be the case in cold, but thermally variable environments. Consequently, a compensation for the colder conditions during summer was not possible, since the thermal fluctuations – annually and daily - were very low. Céréghino et al. (1997) observed the opposite thermal change downstream from a hydroelectric power station: on account of the hydropeaking hypolimnic releases in the River Oriège the annual temperature range was reduced, but daily fluctuations were increased – with the result that life history patterns, hatching periods and growth patterns were similar to those described under conditions without thermal impact.

The question remains how the species catch up with their development during the rest of the year. Both species are not known to have winter dormancies (Ahn 2002) in order to synchronize their life cycle as is common in other species. One possibility could be that during the early larval stages after hatching in late summer with growth rates being low, the species are relatively independent of temperature and are not influenced by lower temperatures. Another possible explanation is that the slightly warmer winter conditions might sufficiently accelerate growth to synchronize again the life cycle in this period. Accelerated growth rates, emergence time and larger nymphal size have been observed in a winter emerging Plecoptera below hypolimnic influence, when winter temperatures are elevated by 2 to 3°C, while a late spring emerger had different population densities, but no altered growth rate or emergence (Perry et al. 1987).

As already mentioned above, a faster larval development could be expected under warmer conditions downstream from the power station, but no changes in the development of the two Hydropsyche species have been observed in this study under the elevated thermal conditions.

The following differences in the thermal pattern could explain this phenomenon: 1. Summer temperature elevations: if the thermal dependency of the two Hydropsyche species is highest only during their last instars, temperature differences of up to 2°C (monthly mean summer) downstream from the power station might not be strong enough in this period of time to cause a change in the growth rate. (The temperature change downstream from the confluence was only recorded during summer, but showed 5°C).

2. Daily fluctuations: these never exceeded 5.6°C which is within the normal range for this type of rivers (see Chapter 2) and consequently within the normal range for these species. The temperature elevations had a frequency of several days, mean temperatures being elevated but the daily fluctuation being within the normal range during times of

62 3. Modified temperature regime Discussion

discharge. Maximum temperatures did not exceed 24°C. This temperature variability within a normal range might present a situation in which the species find their thermal “activity range” for several hours each day and develop normally as is proposed in the thermal equilibrium hypothesis (Vannote & Sweeney 1980).

3. Winter temperature elevations: larval stages in spring (March, May) did not give evidence of accelerated growth rates in winter. This is in contrast with observations from Fey (1976) who conducted a study downstream from the power station Elverlingsen and observed the development and emergence of H. pellucidula 500 m downstream from the effluent. At this place, the heated water was still separate from the remaining cold water, the mixing being prevented by an island in the middle of the Lenne. Thus temperature fluctuations were more extreme than in the underlying study. Fey found H. pellucidula to hatch in February, three to four months earlier than normal. He concluded that thermal maxima especially in summer did not have deleterious effects, since species were warm-adapted at that time, whereas, according to Fey the high temperature shocks during winter had a strong impact on H. pellucidula. At the sampling site chosen for this study, i.e. more than 1 km downstream from the sampling site of Fey, the temperature change was most likely not strong enough to cause a change in growth rates (monthly mean temperature elevations of 1°C). The developmental threshold for the two species might be higher than the temperature reached during this time period.

It may be concluded that thermal influences of short-term warm-water effluents on the thermal regime are not very persistent downstream. But it has to be taken into consideration that the Hydropsyche species analysed in the present study are not known to be very temperature sensitive. In the part of the study that deals with the distribution of benthic macroinvertebrates at different thermal regimes in small- and mid-sized rivers (Chapter 4) H. incognita occurred more frequently in mid-sized rivers with higher summer temperatures, while H. siltalai was widespread in small- and mid-sized rivers but likewise more frequent at warmer sites. This agrees with these two species being sensitive on the summer temperature depression downstream from the Bigge confluence, whereas the summer temperature elevation downstream from the warm-water effluent did not affect their development. Thermally more sensitive i.e. cold-stenothermic species, especially found among Plecop- tera and some Ephemeroptera, might show developmental changes downstream from the warm-water effluent. Temperature maxima elevated by two to three degrees might have considerable influence on the life-cycle of these species (e.g. Perlodes microcephalus, Brachyptera risi, Habrophlebia lauta: see Chapter 4). The comparison of species, differ-

63 3. Modified temperature regime Discussion

ing with respect to their thermal sensitivity in a longitudinal thermal gradient like the above, would be a task for further research, aiming at finding out whether effects of short term elevations of temperature attenuate quickly downstream and thus do not have a strong longitudinal effect, or whether this is species-dependent.

Concluding, it can be remarked that evaluating thermal impacts on stream ecosystems requires taking into account two perspectives: the thermal demand of species may be different at different life stages, and consequently, if the life cycle is to be correlated with temperature, the corresponding period of time has to be considered. Secondly, the type of thermal change with special focus on fluctuations in different time spans has to be taken into consideration.

64 4. Temperature and benthic community Introduction

4. The influence of water temperature on the macroinvertebrate community

4.1. Introduction

It has been criticized that too few comparative studies have been conducted within similar-sized streams to allow generalizations about the magnitude of thermal variation or about how this variation affects biota (Hawkins et al. 1997). This is, in particular, of current interest in Europe, since the EU-Water Framework Directive stipulates a stream type-specific monitoring and classification of the ecological quality of surface waters. So far, no temperature data exist to allow a type-specific description of the thermal reference conditions in rivers or of thermal modifications and their effects on the specific macroinvertebrate community. Moreover, rarely have several temperature components been tested simultaneously. Wa- ter temperature is characterized by annual and daily fluctuations and can be described by parameters such as the rate of temperature change, maxima and minima and the time of occurrence or by the accumulated degree-days (Vinson & Hawkins 1998). Each of these parameters may separately affect the stream biota. But even in most recent ecological studies temperature data have often been collected only as background information. These sporadic measurements cannot reflect the thermal characteristics that the fauna is exposed to in running waters. Up to today, in Europe only glacier-fed rivers have been thoroughly characterized on a broad geographical level, with environmental conditions having been related to community structure (for synthesis: Milner et al. 2001).

In this chapter, the composition of macroinvertebrate communities in 20 rivers in the lower mountainous area in Germany is related to detailed environmental measurements with special emphasis on the following objectives: 1. Determination if macroinvertebrate distribution patterns are significantly related to temperature parameters. 2. Determination of the relative importance of thermal parameters compared to other environmental variables in respect to the macroinvertebrate distribution pattern. 3. Identification of species correlated with temperature and a review of literature to compare the results of laboratory studies on temperature preferences with those of the field study, in order to suggest species that may indicate thermal conditions.

65 4. Temperature and benthic community Materials and methods

4.2. Materials and methods

4.2.1. Study area and sites The sampling sites studied here are the same sites as used for analysis in Chapter 2 (temperature characteristics). Nevertheless, a short introduction to the study sites, with emphasis on the descriptions that are important for this chapter, will be given. The study includes 20 small- and mid-sized rivers in the Low Mountain Ranges of Western Ger- many, in three German federal states: Nordrhein-Westfalen, Hessen and Rheinland-Pfalz. They are located in the lower mountainous areas of the Sauerland (11 sites), as well as in the Eifel (nine sites). The investigated rivers were grouped into two types according to the typology for rivers of Germany based on biocoenotically relevant regions (Pottgiesser et al. 2004, Pottgi- esser & Sommerhäuser 2004). According to this typology, the first group belongs to Type 5, i.e. small-sized rivers in the siliceous Lower Mountain Range, predominated by coarse-sized gravel, and the second group of rivers belongs to Type 9, with mid-sized rivers in the siliceous Lower Mountain Range, predominated by fine- to coarse-sized gravel. The investigated small-sized rivers have catchment areas varying between 13 km² and 26 km² (ten sites) while the mid-sized rivers have catchment areas ranging from 143 km² to 524 km² (ten sites). Although the smallest mid-sized river is four times smaller than the largest mid-sized river, they were found to belong to the same size-type, which has been defined bottom-up based on the biocoenosis of benthic invertebrates (Lorenz et al. in press). Study sites of the two river-types were chosen to vary to the lowest possible extent in saprobic, morphological and hydrochemical characteristics, while temperature characteristics should change from site to site by choosing different extents of shading in the floodplain and catchment area. The elevation of the study sites ranges from 245 to 450 m.a.s.l. (See Chapter 2 Table 2-1, river groups 1 and 2). The rivers are characterized by floodplain valleys, which are predominantly in agricultural use (mostly pasture). Riparian forest, if present, is mainly restricted to the bank-sides. River hydromorphology has slightly been changed by straightening, bank fixation and removal of woody debris. Substrate diversity is high, consisting of large blocks, cobbles, coarse to fine gravel in various size-classes, sand and mud. Since the study area is characterized by siliceous geology, the water has a moderate conductivity up to 300 µS/cm at most sampling sites, and pH-values between 6.5 and 8. The water quality is comparable in all investigated rivers (saprobic indices according to the methods described by DIN 38410 Saprobienindex (2003): below 2.0 = unpolluted or slightly polluted).

66 4. Temperature and benthic community Materials and methods

4.2.2. Sampling Environmental data At each sampling site 130 environmental parameters were recorded using a standardized site protocol at two occasions in spring and summer 2000, including data on hydrology, geology, hydromorphology, chemistry, and land use (for details compare AQEM consortium 2002). Water temperature that was investigated in this study was monitored for a one-year- period from June 2000 to June 2001. As described in more detail in Chapter 2, the temperature was recorded at intervals of 30 minutes. For temperature registration, loggers of the type Gemini Data Loggers Tinytag Plus were used, which were calibrated for 24 hours before installing them at the study sites, and were also checked and calibrated every time when read out. They were fixed in the river bottom 10 to 20 cm above the ground, securing their permanent location under water at all water levels, being in flowing water, and situated in the shade to avoid direct heating by solar radiation. Missing data due to logger-failure (see Chapter 2 Table 2-1) was extrapolated using the temperature registration of the following two years; with this data the relative difference between the temperatures of the individual rivers (which stayed fairly constant over three years of temperature registration) was calculated and transferred to the thermal conditions of the year 2000/ 2001.

Invertebrate sampling Macroinvertebrates were collected twice, in spring and summer 2000, using a shovel- sampler with a 500 µm mesh size and a sampler frame size of 0.25 x 0.25 m. The Multi Habitat Sampling technique was applied (AQEM consortium 2002, Hering et al. 2004). A sample consisted of 20 sampling units, taken from all microhabitat types with at least 5% coverage at the sampling site. The 20 sampling units were chosen according to the relative share of the microhabitat types. Whenever riffles and pools were distinguishable, the sampling units were distributed among these according to their spatial share. The 20 sampling units were pooled, the macroinvertebrates were sorted out quantitatively, and Ephemeroptera, Plecoptera, Trichoptera and Coleoptera were determined to species level whenever possible.

67 4. Temperature and benthic community Materials and methods

Table 4-1. Environmental variables and codes used in the Monte Carlo permutation test for group- wise selection of variables. Details on the method for selecting the variables used in this test see Chapter 4.2.3. (Data processing). Chemical samples and microhabitat parameters were taken at the spring and summer sampling, general parameters, land use and human impact were surveyed once in the study period.

Group Parameters / index Unit / site protocol parameters Code General catchment area [km²] CA altitude [m] alt average stream width [m] width average stream depth [cm] depth mean current velocity [m/s] currvel Chemical pH pH conductivity [µS/cm] cond total hardness [mmol/l] hardtot chloride [mg/l] cl ammonium [mg/l] amm total phosphate [µg/l] p-tot nitrate [mg/l] na Microhabitats akal [%] gravel >0.2 – 2 cm akal psammal [%] mud and sand psamm habitat diversity-index % mineral substrates, all size classes - SubDiv Simpson´s index organic material-index % and amount of organic substrates, OrgMat FPOM, CPOM Land use forest in floodplain % native and non-native forest in flood- fp_for plain forest in catchment area % native and non-native forest in catch- ca_for ment area Other human shade width of woody vegetation [m], shoreline shade impacts covered with woody vegetation [%], shading at zenith (foliage cover) [%] dams number of dams, impoundments, other dams transverse structures channel form straightening, cut-off meanders, scouring chann below surface [m] removal CWD removal of Coarse Woody Detritus up- CWD stream, downstream, at sampling site bank fixation stones, plastering with/ without intersti- fix ces, concrete [%] Temperature summer temperature cumulative degree-days June to Aug-00 T Sum spring temperature cumulative degree-days Jan to Jun-01 T SP mean temperature Aug-00 Warmest monthly mean in study period MW8_00 maximum temp. June-00 Maximum temperature in study period Max6_00 degree-days 1 year cumulative degree-days Jul-00 to Jun-01 SUMJ amplitude 1 year maximum mean – minimum mean JAMW maximum amplitude max. daily amplitude in 2001 maxAMAX mean amplitude max. monthly mean of daily amplitude in maxAMW ´01

68 4. Temperature and benthic community Materials and methods

4.2.3. Data processing

Selection of environmental parameters From the 130 environmental parameters describing each of the 20 sampling sites, the parameters relevant for this study were selected. They were integrated into six groups: 1. general parameters and hydrology, 2. chemistry, 3. microhabitats, 4. land use, 5. other human impacts, 6. temperature. For parameter selection first a Pearson correlation matrix (STATISTICA 5.5 software package; Statsoft Inc. 2000) of all environmental factors was calculated to reveal strongly correlated and redundant parameters. From groups of correlated factors with r > 0.7 respectively r < -0.7 one representative variable was chosen. Site protocol parameters belonging to the groups microhabitat, land use and human impact were partly combined to indices (Table 4-1). Of the 31 remaining parameters (including eight temperature parameters, listed in Table 4-1) each was tested for its correlation with the change in faunal composition using stepwise forward selection and the Monte Carlo Permutation Test. For each parameter group (general-, chemical-, microhabitat-, land use-, human impact- and temperature- parameters) the best correlating factors were chosen and were tested together in a final model. The selected environmental variables were standardized to norm in the multivariate analysis.

Selection of fauna parameters Of 318 macroinvertebrate taxa collected, only Ephemeroptera, Plecoptera, Trichoptera and Coleoptera (EPTC-taxa) were used. Species only occurring in either the Eifel or the Sauerland were not regarded; Eifel: Rhyacophila nubila, RHG/Sauerland: Rhyacophila dorsalis, Agapetus ochripes, Brachycentrus maculatus, Micrasema longulum, Micrasema minimum. For the multivariate analysis the species data was log10(x+1) transformed.

Three data sets were used for analysis: 1. the complete set, including data on all rivers samples (n=20). 2. small-sized rivers (n=10) and 3. mid-sized rivers (n=10) . For all data sets, the spring and summer sampling was analysed separately (Table 4-2).

69 4. Temperature and benthic community Materials and methods

Table 4-2. Data sets for analysis. n: number of samples; EPTC n: number of EPTC taxa found at the sampling site

Samples n Season EPTC n All rivers 20 Spring 148 Small-sized rivers 10 Spring 85 Mid-sized rivers 10 Spring 115 All rivers 20 Summer 153 Small-sized rivers 10 Summer 99 Mid-sized rivers 10 Summer 105

4.2.4. Data analysis Principal Components Analysis (PCA) was performed to describe the community structure. Measured environmental variables can be projected post hoc into the ordination space, showing the correlation with the hypothetical gradients. To detect possible relationships between species and environmental factors, constrained ordination was performed using RDA (Redundancy Analysis), which corresponds basically to regressions with multivariate explanatory (=environmental variables) and response variables (=species data). In this study the RDA was used in two different ways: first, as a hybrid ordination with only the temperature as environmental variable to explore to which extent the variability of species composition is explained only by this factor. In that way, the first axis is constrained to temperature and the following axes are unconstrained. Second, in order to detect how much of the variability in species composition is explained by the other environmental factors, a partial RDA was performed in which the effect of temperature was subtracted (as a covariable, corresponding to ANCOVA), and a constrained ordination was performed on the residual variability in species data. In order to compare the extent to which each environmental variable explains the variation in the species community separately, the contribution of each variable was calculated with the Monte Carlo Permutation Test using the forward selection: the marginal effects are calculated, i.e. the independent effect of each environmental variable, and the conditional effects indicating how much each variable contributes in addition to already selected variables. All tests were performed separately for the three datasets described above using CANOCO 4.5 (Ter Braak & Šmilauer 2002).

70 4. Temperature and benthic community Results

4.3. Results

4.3.1. Variation of environmental and thermal characteristics The environmental and thermal factors characterizing the two river-types are given in Table 4-3. Physico-chemical parameters like pH, conductivity and oxygen saturation, chloride, hardness and nitrate were fairly constant in both sampling periods when comparing the individual small- and mid-sized rivers. Most variance coefficients ranged between 5 and 50% (Table 4-3). Nevertheless, the highest variability was measured for ammonium and phosphate in spring in small-sized rivers, with variance coefficients of above 100%. Regarding hydromorphological parameters, the rivers showed a gradient with greater variation up to variance coefficients of more than 200%, namely the amount of the substrate types psammal and akal at the sampling site, transverse structures in the rivers, and the extent of fixed river banks. The thermal characteristics of the 20 sampling sites are analysed and discussed in detail in Chapter 2. For better understanding, the thermal conditions of relevance for the underlying study are shortly summarized as follows: in August 2000 the highest monthly mean within the recording time was measured, and temperatures in small-sized rivers were on average 2.5°C colder than in mid-sized rivers. Within each river-type, the warmest river was 3°C warmer than the coldest. This variation causes a temperature overlap between the warmest small-sized and the coldest mid-sized rivers. Maximum temperatures were reached in June 2000. On average, mid-sized rivers had higher maxima by 3°C than small-sized rivers. Maxima were varying by 4°C within small-sized rivers and by 7°C within mid-sized rivers, with an overlap of 2°C between the warmest small- and the coldest mid-sized river. Variation in winter temperatures was low. Almost all rivers reached minimum temperatures of 0°C in January/February 2001, with the exception of one mid-sized river with a minimum temperature of 2.6°C and three small-sized rivers with minimum values between 1 and 2°C. Highest daily temperature fluctuations were recorded in late spring (June 2000 and May, June 2001), with monthly mean amplitudes around 3°C and maximum amplitudes between 3°C and 8°C within each river-type. As can be deduced, amplitudes did not differ significantly between small- and mid-sized rivers, but within the river-types.

Table 4-3. Results for site protocol parameters used in multivariate analysis. For each data-group of small-and mid-sized rivers in spring and summer re- 4. spective n=10. *: calculated indices from site protocol parameters (Appendix 3), for details see material and methods.**: Temperature parameters (in °C): Tem Abbreviations: MW8_00, 5_01: monthly means Aug/00 and May/01; Cumulative degree-days: SUMJ: one year, Tsum+: summer (Jun – Aug), SUM_W: p

Winter (Jan/Feb), SUM_SP: spring (Mar-May); JA_MW mean yearly amplitude; daily amplitudes: maxAMW: maximum monthly mean, maxAMAX: erature monthly maximum, AJMW: yearly mean of amplitudes, AJMAX: yearly mean of maximum amplitudes, Max6_00: maximum Jun/00;. s.d.: Standard deviation. CV: Coefficient of variation. and small-sized spring mid-sized spring small-sized summer mid-sized summer

min max mean s.d. CV min max mean s.d. CV min max mean s.d. CV min max mean s.d. CV benthic catchment area [km²] 4.7 25.9 15.1 6.2 41.2 134.4 523.7 287.8 127.6 44.3 4.7 25.9 15.1 6.2 41.2 134.4 523.7 287.8 127.6 44.3 altitude [m] 270 450 360 50.39 14.0 245 360 303 36.07 11.9 stream width [m] 3.1 5.0 4.1 0.5 12.7 7.0 20.0 15.8 4.6 29.2 1.8 6.0 3.2 1.3 39.7 6.0 40.0 17.8 9.9 55.8 com mean depth [cm] 6.9 38.5 22.7 8.4 36.8 23.5 55.5 44.1 11.0 24.9 3.6 36.2 15.5 9.3 60.3 20.0 47.0 30.2 9.2 30.3 mean current vel. [m/s] 0.1 1.1 0.7 0.3 38.9 0.4 1.0 0.8 0.2 22.4 0.2 1.0 0.4 0.2 63.9 0.3 0.8 0.5 0.2 34.6 m

pH 6.2 7.7 7.3 0.5 7.3 7.0 8.7 7.5 0.7 8.8 6.1 8.3 7.6 0.6 8.3 7.3 8.4 7.9 0.4 4.5 unity conductivity [µS/cm] 89.3 255.0 157.7 55.2 35.0 120.8 427.0 206.8 91.3 44.1 93.0 673.0 226.5 163.8 72.3 120.8 440.0 257.5 102.4 39.8 oxygen saturation [%] 93.6 111.0 102.8 6.1 5.9 97.2 110.0 102.3 3.9 3.8 89.0 122.0 102.4 10.1 9.9 91.1 144.0 105.3 15.1 14.4 total hardness [mmol/l] 0.4 0.8 0.6 0.1 23.1 0.3 2.0 0.9 0.5 54.7 0.4 1.4 0.8 0.3 31.8 0.4 1.8 1.0 0.4 42.9 chloride [mg/l] 10.0 36.0 20.6 7.1 34.3 17.0 32.0 23.2 5.1 22.1 12.0 60.0 23.4 13.7 58.7 20.0 30.0 24.9 4.1 16.6 ammonium [mg/l] 0.0 0.3 0.1 0.1 119.0 0.0 0.2 0.1 0.1 57.9 0.0 0.2 0.1 0.0 59.1 0.0 0.5 0.2 0.1 89.1 Results nitrate [mg/l] 2.8 20.1 10.0 5.1 50.8 6.4 21.5 12.3 6.1 49.4 0.7 19.7 9.9 6.4 64.5 3.2 20.7 12.7 6.2 48.6 total phosphate [µg/l] 29.0 1156 185.4 346.6 187.0 38.0 306.0 152.7 82.3 53.9 45.0 1948 410.1 677.6 165.2 85.6 896 341.6 244.2 71.5 akal [%] 0.0 10.0 4.1 3.8 93.7 0.0 0.0 0.0 0.0 0.0 5.0 3.1 2.5 79.7 0.0 1.0 0.1 0.3 316.2 psammal [%] 0.0 10.0 2.5 3.5 141.4 0.0 30.0 4.6 9.4 204.2 0.0 10.0 2.3 3.3 145.0 0.0 10.0 1.5 3.0 201.8 substrate diversity* 0.2 0.9 0.4 0.2 45.9 0.4 1.0 0.7 0.2 29.0 0.2 0.4 0.3 0.1 19.0 0.4 1.0 0.7 0.2 29.6 organic material* 1.0 20.0 8.1 6.2 76.2 1.0 34.0 9.3 9.8 105.6 1.0 12.0 6.2 3.3 53.7 1.0 38.0 11.2 13.6 121.8 forest in catchm. area* 10100723345 3080521732 10100723345 3080 52 17 32 forest in floodplain* 0 100 49 40 81 0 100 15 34 225 0 100 49 40 81 0 100 15 34 225 channnel form* 2.0 9.0 5.9 2.3 38.7 2.0 9.0 5.1 2.2 42.8 2.0 9.0 5.9 2.3 38.7 2.0 9.0 5.1 2.2 42.8 transverse structures* 0.0 12.0 1.7 3.7 218.4 6.0 18.0 9.7 3.4 35.4 0.0 12.0 1.7 3.7 218.4 6.0 18.0 9.7 3.4 35.4 woody material removed* 0.0 3.0 1.8 1.4 77.7 1.0 3.0 2.3 0.8 35.8 0.0 3.0 1.8 1.4 77.7 1.0 3.0 2.3 0.8 35.8 shade* 0 90 54 32 60 0 85 39 28 71 0 90 54 32 60 0 85 39 28 71 rivers banks fixed* 0.0 85.0 29.5 29.2 99.0 0.0 100.0 16.5 30.6 185.2 0.0 85.0 29.5 29.2 99.0 0.0 100.0 16.5 30.6 185.2 MW8_00** 12.2 15.2 13.4 0.9 7.1 14.6 17.5 15.8 0.9 5.9 12.2 15.2 13.4 0.9 7.1 14.6 17.5 15.8 0.9 5.9 MW5_01** 10.2 12.6 11.0 0.8 7.0 11.8 14.4 13.1 0.8 6.2 10.2 12.6 11.0 0.8 7.0 11.8 14.4 13.1 0.8 6.2 SUMJ ** 3202 4021 3494 282 8 3656 4143 3908 171 4 3202 4021 3494 282 8 3656 4143 3908 171 4 Tsum+ ** 1084 1326 1180 81 7 1298 1539 1392 83 6 1084 1326 1180 81 7 1298 1539 1392 83 6 SUM_W ** 884 1285 1068 131 12 943 1223 1070 77 7 884 1285 1068 131 12 943 1223 1070 77 7 SUM_SP ** 1100 1498 1275 131 10 1266 1515 1422 87 6 1100 1498 1275 131 10 1266 1515 1422 87 6 JA_MW** 7.7 10.9 9.7 1.1 11.2 10.4 14.7 12.6 1.3 10.2 7.7 10.9 9.7 1.1 11.2 10.4 14.7 12.6 1.3 10.2 maxAMW** 2.9 4.9 3.8 0.6 15.6 1.5 4.4 3.3 1.0 29.0 2.9 4.9 3.8 0.6 15.6 1.5 4.4 3.3 1.0 29.0 maxAMAX** 4.2 7.7 6.1 1.0 16.5 2.6 7.9 5.8 1.7 29.6 4.2 7.7 6.1 1.0 16.5 2.6 7.9 5.8 1.7 29.6

72 AJMW** 1.4 2.2 1.9 0.2 10.5 1.0 2.1 1.7 0.4 23.3 1.4 2.2 1.9 0.2 10.5 1.0 2.1 1.7 0.4 23.3

AJMAX** 2.7 4.0 3.6 0.4 11.2 1.8 4.0 3.3 0.7 21.3 2.7 4.0 3.6 0.4 11.2 1.8 4.0 3.3 0.7 21.3 Max6_00** 16.6 20.3 18.1 1.2 6.6 18.3 25.3 21.6 2.1 9.5 16.6 20.3 18.1 1.2 6.6 18.3 25.3 21.6 2.1 9.5

4. Temperature and benthic community Results

4.3.2. The EPTC-community A total of 207 EPTC-taxa was found at the 20 sampling sites, regarding both the spring and summer samples. A total of 160 EPTC-taxa was identified in spring samples and 159 in summer samples. The abundance (Ind/m²) was higher in mid-sized than in small-sized rivers, and more individuals per m² were counted in summer than in spring. The number of taxa occurring at the sampling sites was fairly even for small and mid-sized rivers, and for spring and summer samples (Table 4-4). Ephemeroptera were contributing over one- third to the community, with Baetis rhodani, Serratella ignita and Epeorus sylvicola dominating in both river-size classes, and Torleya major being abundant in mid-sized rivers. Trichoptera, also having a share of one-third of the population in small-sized rivers, were dominated by Glossosoma conformis, Philopotamus ludificatus and Hydropsy- che instabilis. In mid-sized rivers, between 40 and 50% of the community was repre- sented by Trichoptera, mainly Rhyacophila nubila, Brachycentrus maculatus and Hy- dropsyche siltalai. Coleoptera contributed up to 10% to the EPTC-community with Elmis maugetii and Limnius volckmari in mid-sized rivers and Hydraena gracilis and Limnius perrisi in small-sized rivers. Plecoptera had the smallest share of EPTC-taxa in mid-sized rivers, and were dominated by Leuctra geniculata, while in small-sized rivers Perla marginata, Brachyptera risi and B. seticornis were the most frequent representatives of Ple- coptera.

Table 4-4. Characteristics of the EPTC- community of small-sized (n=10) and mid-sized (n=10) rivers in spring (sp) and summer (su)

small sp small su mid-sp mid-su Abundance [Ind./m²] mean 359 705 623 1130 s.d. 207 340 313 900 CV [%] 58 48 50 80

Number of taxa total 92 102 123 111 mean 32 37 38 38 s.d. 8 13 8 10 CV [%] 25 33 22 27

Percentage of community Ephe [%] 48 31 37 36 Ple [%] 16 20 3 7 Cole [%] 10 10 5 10 Trich [%] 27 39 55 46

73 4. Temperature and benthic community Results

4.3.3. Community pattern and habitat In both the small-sized and the mid-sized rivers, the first two gradients in the PCA explain about half of the variability of the species composition. The first ordination axis for all rivers explains about one-third of the variability, for both the spring and the summer sampling. Evaluating the data of the small- and mid-sized rivers separately, the relationship still remains about the same (Table 4-5). Small-sized and mid-sized rivers differ in their community pattern, yet some sampling sites in small-sized and medium-sized rivers have a similar community composition (Figure 4-1). For the spring data, a post-hoc projection of the environmental variables into the ordination space reveals a negative correlation of the parameters catchment area, summer temperatures and conductivity with the first gradient, while higher substrate diversity, share of forest in the catchment area and share of fine- to medium-sized gravel (akal) are positively correlated with the first gradient. The only environmental parameter clearly correlated with the second axis is the riverbank fixation (fix). The corresponding diagram for the summer samples is very similar; with the only difference showing a better correlation of ammonium concentration with the first axis and a weaker correlation of conductivity.

Table 4-5. Results of PCA, RDA – Temperature: hybrid RDA (only environmental variable: Temperature, i.e. first axis constrained to temperature, second axis unconstrained) and RDA – Covariable: partial RDA (temperature as covariable). Data centered by species. % ax 1/2: species variability explained by first/second axis. F: F-ratio statistics. p: probability value obtained by Monte Carlo permutation test, 499 permutations (Type I error probability). * Species variability explained by all canonical axes.

PCA RDA - Temperature RDA - Covariable data set % ax 1 % ax 2 % ax 1 r ax 1 % ax 2 F ax 1 p ax 1 % ax * F ax all p ax all all SP 33.8 14.1 28.3 0.93 15.5 7.089 0.002 19.3 1.847 0.004 sm SP 37 15.7 27.7 0.894 17.2 3.07 0.008 25.2 1.431 n.s. mid SP 26.7 23.4 20.7 0.963 26.6 2.095 0.01 25 1.384 n.s. all SU 33.4 11.4 29.1 0.944 11.7 7.378 0.002 24.6 1.86 0.002 sm SU 38.4 15.5 25.8 0.849 19.4 2.769 0.012 23.4 1.567 n.s. mid SU 23.6 15.1 16.4 0.947 20.4 1.567 0.02 13.1 1.306 n.s.

74 4. Temperature and benthic community Results

spring sampling summer sampling 0 0 . . 1 1 fix CA ca_for TSum amm Sub Div akal T Sum akal cond Sub Div ca_for CA fix 0 0 . . -1 -1 -1.0 1.0 -1.0 1.0

Figure 4-1. Diagram of a PCA for spring and summer EPTC species data with the environmental variables post hoc projected into the ordination space. Solid circles: small-sized rivers; open circles: mid-sized rivers; arrows: environmental variables. Abbreviations: see Table 4-1, codes.

4.3.4. Explanatory power of the environmental variables

Temperature Those variables correlating best with the community pattern were selected for each parameter group. For temperature parameters, only one summer temperature factor was chosen, since the individual parameters were closely correlated: the factor “cumulative degree-days for summer (June, July and August (T SUM+)” was selected, as it had the strongest correlation to species composition in all data sets. Other parameters concerning mean and maximum temperatures, like “monthly means of August and May”, and “maximum temperature” (which occurred in June 2000), were also highly correlated. Cumulative degree-days for the entire year, for spring/early summer (January to July: SUM_FJ) and winter (October to March: SUM_WH) had weaker correlations in some of the data sets. On the other hand, parameters concerning daily amplitudes (mean and maxima) did not show any correlation with the faunal composition.

The temperature alone is explaining up to one-third (26 to 29%) of the differences in species composition in the data sets for all rivers and for the small-sized rivers (Table 4-

75 4. Temperature and benthic community Results

5: see RDA – Temperature). In mid-sized rivers, temperature explains much less, i.e. only 16 to 20% of the species variability. The second (unconstrained) ordination axis in the RDA explains less variance in the species community than the axis constrained to temperature. The probability of the influence of the first ordination axis is significant (p<0.05) in all data sets. If the variability of the species composition explained by temperature is removed from the model (as a covariable, comparable to ANCOVA), the remaining factors together explain between 13 and 25 % of the differences in species composition. In this case, only the data sets for all rivers in spring and summer are significant (Table 4-5: see RDA - Covariable).

Comparison of environmental variables The analysis shown above revealed that temperature is an important factor explaining species variability in the present study. In the following section the factor temperature was compared with other environmental variables. Temperature had the highest explanatory power for the species distribution compared to all other analysed environmental variables. Nevertheless, there were several other variables correlated with the fauna: conductivity, fixed river banks, the amount of akal, the amount of forest in the floodplain and catchment area, and the size of the river (Figure 4- 2: see columns of marginal effects). Part of the explanatory power of these variables can be attributed to the correlation with temperature. When temperature is selected out of the model, the value of explanatory power is calculated, that the remaining variables bring into the model additionally to temperature (i.e. the correlation with temperature is subtracted) (Figure 4-2: see columns of conditional effects). For the data set of all rivers, the explanatory power of these variables declined drastically. Conductivity, amount of akal, the amount of forest in the catchment area, and the river-size added less than 8% to explaining species variability additionally to temperature. Only the river bank fixation was not correlated with other environmental factors. For the data set of small-sized rivers, the conditional effect of conductivity and the amount of forest in the floodplain added 13 to 15% to explaining species variability, indicating that in small-sized rivers these factors are important for species distribution. The data set for mid-sized rivers showed a marked difference to the data sets above: temperature was much less important for the fauna here, explaining only 20% of the species variability in spring, and in the summer sampling the explanatory power of temperature was not even significant. River-size and conductivity were explaining species variability to a greater extent than in the other data sets.

76 4. Temperature and benthic community Results

% % all rivers - spring all rivers - summer 0.35 0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 0 0 T Sum cond fix T Sum CA akal ca_for fix

small-sized rivers mid-sized rivers 0.35 0.35 0.3 0.3

0.25 0.25

0.2 0.2

0.15 0.15

0.1 0.1

0.05 0.05 0 0 T Sum cond (SP) fp_for (SU) T Sum (SP) CA (SU) cond (SP) (SP/SU)

marginal conditional

Figure 4-2. Explanatory power of environmental variables in the species variability (in percent). Grey column: Marginal effect, i.e. the amount, the variable explains when correlated with the faunal data (Lambda1). Black column: Conditional effect, i.e. the amount, the variable explains additionally to the already selected, stronger correlated variable (LambdaA). The lower this value in comparison to the marginal effect, the more is the respective variable correlated with other factors. For small and mid-sized rivers results for summer and spring sampling are presented in the same diagram, but they were tested separately. All shown variables significant to <0.05 (Monte Carlo Permutation Test).

Taxa correlated with the temperature gradient 28 taxa were negatively correlated and 33 taxa were positively correlated with summer mean temperature (August 2000). They correlate better with the temperature gradient than with any other of the measured parameters. Among those species, the greater part of Plecoptera prefer colder water temperatures, while the majority of Ephemeroptera, Trichoptera and Coleoptera occur more frequently at higher temperatures (Figure 4-3).

77 4. Temperature and benthic community Results

Among the species negatively correlated with temperature, some taxa only occur in small rivers and prefer those with a mean temperature below 14°C (warmest month), e.g. Ec- dyonurus venosus, Dinocras cephalotes, Limnius perrisi, Rhyacophila obliterata and Glossosoma conformis. A second group occurs at almost all sampling sites, but the frequency decreases with increasing temperatures: Epeorus sylvicola, Rhitrogena semicolorata, Brachyptera risi, Hydropsyche instabilis and Chaetopteryx villosa. A third group can be found in small rivers and cold mid-sized rivers, but only at sampling sites up to the following temperatures: Brachyptera seticornis (below 15°C), Perlodes microcephalus (below 16°C), Oreodytes sanmarkii (below 15°C), Odontocerum albicorne (up to 16°C). The same patterns can be observed for the species positively correlated with temperature: some species can only be found in the mid-sized rivers, but they are increasing in number with rising temperatures: Baetis lutheri, Elmis maugetii, Athripsodes cinereus. Species occurring in either size class of rivers but changing in frequency with the temperature are: Ecdyonurus dispar, Baetis scambus, Limnius volckmari. Species occurring more frequently at warmer sampling sites, but in both river-types are: Ecdyonurus torrentis ( T > 14°C), Leuctra geniculata ( T > 14°C), Oulimnius tuberculatus ( T > 14°C), Orectochi- lus villosus (T > 12°C), Polycentropus flavomaculatus (> 14°C).

4. Tem p erature andbenthiccom m unity

Results

4. Tem p erature andbenthiccom m unity

Figure 4-3. (two pages, see also previous page) Species distribution at the sampling sites (Ind/m²) in symbols (see legend). Sampling sites are listed according to their summer temperature from left: coldest river, to right: warmest river. (Species abbreviations see appendix). sc-all/sc-S/sc-M: species scores in RDA of data sets for all rivers, and small- and mid-sized rivers separately. Negative species scores: Taxon is negatively correlated to temperatures; positive species scores: Taxon positively correlated to temperature. Sampling site abbreviations: S: small-sized rivers, M: mid-sized rivers. Sampling site numbers: S1: Wwe, S2: Kal, S3: Erk, S4: Vol, S5: Wal, S6: Roe, S7: Sal, S8: Elb, S9: Laa, S10: Dre, M1: Rur, M2: Kyl, M3: PrW, M4:

80 Our, M5: Nim, M6: Len, M7: Nuh, M8: Ede, M9: Ork, M10: PrB. Last numbers: -1: Spring sampling, -2 summer sampling. Results

4. Temperature and benthic community D is cussio n

4.4. Discussion

4.4.1. Temperature and community pattern The results in this study proposed that macroinvertebrate community changed primarily along the temperature gradient between small- and mid-sized rivers. It has long been un- derstood that ectothermic organisms, such as stream insects, are strongly influenced by their thermal environment. The change of distribution patterns along the thermal gradient in the stream continuum has been described for several species and groups of organisms (e.g. Ide 1935, Vannote et al. 1980, Roux et al. 1992, Jacobsen et al. 1997). But also within the small-sized rivers of this study, with temperature differences of up to 3°C, changes in the invertebrate composition were predominantly explained by this factor. This result supports the statement of Sweeney (1993) who emphasized that small changes in temperature (2 to 5°C) are often underestimated in their effect on life history parameters of stream invertebrates. He states that this temperature difference can be important during all stages of the life cycle, and that it will undoubtedly alter key life- history parameters.

Comparing the two river-types in the underlying study, this seems to be especially true for the invertebrate community inhabiting small-sized rivers. Since in small-sized rivers temperature explained a great fraction of the species distribution within a temperature gradient of 3°C, this fact is likely to reflect a high thermic sensitivity of stenothermic taxa. In mid-sized rivers, temperature explained much less of the variability in species distribution in this study, the temperature range being even slightly higher than in small- sized rivers. Since the thermal gradient explained the variability in species only to a limited extent for the mid-sized rivers it may be assumed that this phenomenon may reflect a decreasing sensitivity on temperature of more eurythermic and less specialized invertebrate taxa in larger rivers.

4.4.2. Temperature parameters in comparison Mean temperatures of late spring and summer months and degree-days revealed the best explanatory power for the species distribution in the rivers of the present study. Maxi- mum temperatures were correlated (r²= 0.76) with mean temperatures, but had less explanatory power, and daily amplitudes and winter temperatures were found to be insig- nificant for the faunal distribution. Parameters reflecting the thermal demand of the fauna throughout their development period, such as degree-days and other parameters based on

81 4. Temperature and benthic community Discussion

means, seem to correlate best with the species distribution in this study. Several studies have tested the influence of degree-days on development or distribution of benthic species and found strong correlations or directly proportional growth rates (Markarian 1980, Lowe & Hauer 1999, Watanabe et al. 1999). Other studies measured maxima and found this factor also to be important for species growth and distribution (Jacobsen et al. 1997, Lowe & Hauer 1999, Sponseller et al. 2001). For some species, different temperature parameters were found to be important: winter temperatures from December to February correlated best with differences in larval growth and adult size of the stonefly Soyedina carolinensis (Sweeney & Vannote 1986), and April temperature was critical for the timing of the emergence of the stonefly Pteronarcys californica (Gregory et al. 2000). The effect of daily amplitudes on the benthic fauna is still unclear: in some laboratory studies, development rates of Plecoptera and Ephemeroptera correlated with daily temperature pulse (Sweeney 1978, Humpesch 1982, Wagner 1986, Frutiger 1996), while in other studies daily amplitudes had no influence on development (Brittain 1977, Elliott 1987,1988,1991, Marten 1991, Zwick 1996). Vannote et al. (1980) hypothesized greater species richness in habitats with greater thermal variability, since more species find their specific thermal optima. Brussock & Brown (1991) found a negative relationship between invertebrate richness and daily temperature amplitude, while Kamler (1965) found Ephemeroptera richness being positively and Plecoptera richness being negatively correlated with daily variability. In this study, daily temperature amplitudes varied to a great degree between individual rivers, but the magnitude of daily amplitudes did not explain variation in community patterns significantly. The magnitude of temperature change measured here - up to 8°C - did not seem to limit or promote species abundances.

4.4.3. Other environmental variables and their effects on community The following environmental variables contributed significantly to explaining species variability, though they evidently added less explanation to the model than temperature itself: For the spring sampling community, conductivity was the second important factor. As the rivers are located in siliceous carbonic rocks, conductivity is expected to be fairly low (below 300µS/cm). On the other hand, local changes of the geology may be responsible for changes in the conductivity. The Nims, for example, being the river with the highest conductivity, flows in its lower section through regions of limestone, a fact that may also be the cause for differences in the faunal composition. The habitat composition was another significant factor for the distributional pattern of the benthic community: the amount of fine- to medium-sized gravel (0.2 to 2 cm), and the percentage of river bank fixation with stone plastering were significantly correlated with the faunal distribution. The importance of the substrate type for the fauna has been acknowledged in other studies. The more so, since the substrate particle size is an important

82 4. Temperature and benthic community Discussion

key factor for macroinvertebrate diversity, with particle sizes of about 1 mm and substratum mixtures of gravel and sand exhibiting highest macroinvertebrate density (e.g. Culp et al. 1983, Barton & Lock 1979, Wiberg-Larsen et al. 2000). Substrate size and type are important predictors for site assemblages, for example for Hydropsyche species along an upstream – downstream gradient (Fairchild & Holomuzki 2002), and for chironomid communities in a large river (Franquet 1999). Increments of fine sediment with a particle size smaller than 2 mm to natural substratum mix changed macroinvertebrate density, biomass and species composition in a field experiment (Angradi 1999). Another environmental parameter explaining a smaller but significant part of the variability of species distribution was the amount of forest present. Especially the invertebrate community in small-sized rivers was dependent on the presence or absence of local riparian forest. The effect of shading on water temperature and thus on the community has already been discussed above. Apart from this, other environmental changes caused by the presence or absence of riparian forest influence the benthic community: solar radiation, food input, debris, water chemistry, channel morphology and stream hydrology are modified when the streamside vegetation is removed for different land use practices (Sweeney 1992, Rutherford et al. 1997, Quinn et al. 1997, Johnson & Gage 1997).

Current velocity explained a non-significant 4 to 8% of species variability. Since the hydraulic condition in flowing waters is generally known to be a dominant factor influencing organisms, this factor is expected to be of much more importance. Hydraulic conditions of the rivers sampled in this study may not vary enough to reach limiting conditions for the species present. Another possible explanation is that the hydraulic conditions have an influence on a different spatial scale: Fairchild & Holomuzki (2002) found current velocity to explain a small but significant amount of variation in Hydropsychid micro distribution, where current was measured in microhabitats. The hydraulic conditions at the stream bottom depend to a great extent on the substrate type (Culp et al. 1983), and consequently influence the fauna on a micro scale. The mean current velocity measured in this study represents the mean of all sampled habitats, and the species data does not provide information on the distribution in microhabitats. Under extreme hydraulic conditions, changes in species distribution have also been observed on a large scale: below a hydroelectric power station, the spates during hydropeaking are one of the main parameters changing species distribution (Céréghino et al. 1997).

83 4. Temperature and benthic community Discussion

4.4.4. Temperature-sensitive species: do they reflect strategies for different thermal environments? Benthic invertebrates spend the greater part of their life in the aquatic environment, and temperature represents a selection pressure that organisms have to deal with when adapt- ing to new environments by adopting certain life cycle strategies. This has become especially apparent in Plecoptera and Ephemeroptera: stoneflies are mostly cool-water species, while mayflies are common in tropical waters. Both orders have developed different strategies to adapt to their environment: the developmental zero and the thermal demand for a successful egg development are often lower at colder temperatures in cold-adapted species, since their enzyme function is probably specialized accordingly (Pritchard et al. 1996). Consequently, these species often have a summer diapause to avoid high temperatures. On the other hand, warm adapted species, like many Ephemeroptera, develop faster at higher temperatures and sometimes have a winter quiescence (Brittain 1990, Pritchard et al. 1996). Univoltinism is common in Ephemeroptera and Plecoptera, but while in warm-adapted Ephemeroptera multivoltinism is also common, many Plecoptera are semivoltine (Brit- tain 1990). These examples and other life history strategies not mentioned here explain the stronger adaptation of Ephemeroptera to tropical environments and Plecoptera to alpine and arctic areas. Cold-water adapted Plecoptera tend to emerge in spring, while warm-water adapted Ephemeroptera emerge mostly in summer (Brittain 1990). In this study, several species are either significantly positively or negatively correlated with the temperature gradient within the 6°C difference between the coldest and warmest rivers. The subject has not yet been fully researched if the life history strategies differ according to the above mentioned concept between the positively- and negatively- correlated species also on this small-scale temperature gradient. (With the main focus on egg development, emergence timing and voltinism.)

Plecoptera Within the Plecopteran order, the majority of Euholognathan species and some Systel- lognatha are cold-adapted, and most likely of cold-water origin. Some few species seem to have adapted to warmer environments, but generally remain in cool-water habitats (Illies 1965, Zwick 1980, Pritchard et al. 1996). Unlike the Ephemeroptera and Trichoptera as shown in this study, more Plecoptera species were negatively correlated with temperature than positively correlated. For four out of six Plecoptera species that were negatively correlated with temperature, species traits and laboratory studies corroborate the observation that their distribution in the field reflects their preference for cooler habitats and that they are restricted in their abundance at higher temperatures:

84 4. Temperature and benthic community Discussion

Perlodes microcephalus (Pictet 1833) was present in small- and mid-sized rivers with mean temperatures below 16°C, its abundance peaked at 12 to 13°C. Its negative correlation with temperature corresponds to the following observations: P. microcephalus predominantly occurs in the meta- to hyporhithral with a univoltine life-cycle and it emerges in spring. It has a summer diapause in the egg stage and the main larval growth takes place in autumn; these life-history traits show the developmental efficiency at lower temperatures and the avoidance of higher summer temperatures with an egg diapause. Labo- ratory studies revealed that this species is especially suited for a successful development at lower temperatures, because it has a lower thermal demand for the egg development and a faster embryonic development at lower temperatures (Marten 1991).

Brachyptera risi (Morton 1896) was present in small- and mid-sized rivers up to 16°C, with a maximum presence at 12 to 13°C. In this study it was observed that the species is negatively correlated with temperature, this finding also agrees with the species traits and with laboratory results: B. risi predominantly occurs in the epi- to metarhithral, with a univoltine life-cycle and a summer diapause in the egg stage. The main growth rate is directly before the emergence in spring, i.e. it avoids the higher summer temperatures and has its highest growth rates in the cooler spring-season. Laboratory studies revealed that the optimum temperature for the egg development is 8 to 9°C, and the species has been described as cold-stenothermic (Elliott 1988, 1992). Hatching success and incubation period at a given temperature are stable in British and Norwegian populations in spite of their geographical isolation, no significant intraspecific variation has been observed (Elliott 1988). Thus the thermal demands seem to be genetically conservative.

In the underlying study, Protonemoura auberti (Illies 1954) and Brachyptera seticornis (Klapalek 1902) were present only in small-sized rivers below 14°C. Their maximum presence was observed at a temperature of 12 to 13°C. No literature on laboratory studies exists on the thermal demand of the embryonic development, there is also no information about a summer diapause in their life history. Both species occur parallel in the same habitat and have a similar life-cycle, but B. seticornis emerges earlier in spring than P. auberti (Illies 1955). Their restricted presence in the crenal and epirhithral in mountainous and sub-mountainous regions characterizes these species as adapted to cold summer temperatures.

On the other hand, two species that only occurred in the colder small-sized rivers in this study could not be characterized as cold-adapted species: Dinocras cephalotes (Curtis 1827) was only present in small-sized rivers and up to 14°C. Laboratory studies revealed that the egg development is highly temperature dependent, with an optimum hatching success at high temperatures between 16 and 20°C. D. cepha- loted has a plastic semivoltine life-cycle and emerges in summer. It has a wide tempera-

85 4. Temperature and benthic community Discussion

ture range, it is warm-stenothermic and of Mediterranean origin (Lillehammer 1987, Zwick 1996, Frutiger 1996). D. cephalotes occurs in varying local populations, geographical, and regional adaptations with different genetically determined cue temperatures for the egg development (Frutiger 1996, Imhof 1994, Elliott 1995, Zwick 1996). Because of the plasticity in its life cycle, D. cephalotes has successful populations out- side its thermal optimum (Lillehammer 1987b), it can be found from southern Spain to northern Norway, and is the most widespread Perlidae in Europe (Elliott 1989). But this species is restricted to the crenal and epi- to metarhithral. Consequently, the distribution in this study reflects only the lower part of its distributional range, and so it may be inter- preted as limited by the river-size. If smaller streams had been part of the study, it might have shown a different distributional pattern. Perla marginata (Panzer 1799) likewise was only present in small-sized rivers. This species is known to occur mainly in crenal and epirhithral regions, but is a eurythermic, widespread species with a semivoltine life-cycle (2 to 3 years). The temperature dependency of this species is identical to D. cephalotes, but generally it is not genetically as variable as D. cephalotes (Frutiger 1996). The distributional pattern is also similar. The life-history traits show that the distribution of D. cephalotes and P. marginata reflects the preference for small streams of crenal and epirhithral zones with other factors being important, rather than having a distribution along a temperature gradient.

Two Plecopteran species were positively correlated with water temperatures in this study: Perla burmeisteriana (Claassen 1936) was present only in rivers with summer means above 14°C and seems to be at its lower thermal limit. This observation is supported by a study of Marten (1991) who found in laboratory experiments that the temperature dependency of this species is very high, requiring at least 12°C for a successful hatch, hence it is described as very thermophilous. Perla burmeisteriana emerges in late spring/early summer.

Leuctra geniculata (Stephens 1836) was present above 13°C, with maximum abundances between 14 and 17°C. This corresponds to the fact that L. geniculata is known to be eurythermic, its egg development is temperature dependent. It belongs to the group of warm-adapted Euholognatha. Laboratory studies show that the thermal demand for its embryonic development is lower at higher temperatures (Elliott 1987). The main growth period of the larvae is in summer (May to August), just before the summer emergence. It does not have an egg diapause.

The species that were not correlated with temperature in this study are known to be univoltine, but two have a variable life history, i.e. uni- or semivoltine, depending on the

86 4. Temperature and benthic community Discussion

thermal environment (Leuctra nigra - a crenal species, and Nemoura cinerea). None of the species have an egg diapause in summer, except for Protonemoura intricata (Medi- terranean species, diapause in summer for protection of desiccation!). There is no tendency for similar main growth periods or a similar time of emergence within this group of species. Most have been classified as eury- or warm-stenothermic according to the embryonic development (e.g. Nemoura cinerea, Protonemoura meyeri, Leuctra nigra) (Elliott 1988).

Ephemeroptera For the most part Ephemeropteran species in this study were positively correlated with temperature, which matches assumptions on the evolutionary history of this group: Pritchard et al. (1996) summarize that the evolutionary history of the Ephemeroptera is unclear, but they might share the tropical origin with the sister-group Odonata. Most temperate-region mayfly species are warm-adapted regarding the embryonic development. On the other hand, some mayfly species live in cold environments with a diapause or quiescence in summer. Less information exists on the development of Ephemeroptera under different thermal regimes than for Plecoptera, but for some of the species observed to be correlated with temperature in this study, information on thermal preferences were found: Baetis alpinus (Pictet 1843-1845) was negatively correlated with temperature in this study, it was present at sites up to 16°C and had its maximum presence at 12°C. This concurs with the observations that B. alpinus is described to have an optimal development between 8 and 11°C, it is known to occur in temperature ranges between 5 and 13°C. B. alpinus is a cold stenothermic species (Schmedtje & Kohmann 1992), having a bivoltine life-cycle, but is univoltine at higher altitudes (Ahn 2002). There is no laboratory data on temperature preferences for the species Habrophlebia lauta, Epeorus sylvicola and Ephemerella mucronata which were found to be negatively correlated with temperature in this study. The latter species might have a summer diapause in the egg or larval stage (Ahn 2002), and all three species have a univoltine life- cycle with the emergence starting mainly in May. This could indicate an avoidance of higher summer temperatures.

Rhithrogena semicolorata and Ecdyonurus venosus were also negatively correlated with temperature in this study. But in contrast to this observation other studies revealed that these species develop faster at higher temperatures, and the relationship between hatching time and temperature was described by the power-law between 5.9 and 19.9°C and 2.8 and 18.1°C respectively (Humpesch & Elliott 1980a,b 1982).

87 4. Temperature and benthic community Discussion

Regarding the Ephemeroptera that were observed to be positively correlated with temperature in this study, this finding agrees with results of a study of Humpesch & Elliott (1980 a, b) for Ecdyonurus insignis, E. dispar, and E. torrentis: their embryonic development is characterized by a lower thermal demand at higher temperatures. For that reason they are considered to be a warm-adapted species (Pritchard 1996).

Some general observations concerning the life-histories of this group can be noted: for the most part the positively correlated species tend to emerge in summer (June – August) (Baetis scambus, Ecdyonurus dispar, Torleya major, Caenis luctuosa, Ecdyonurus insignis), and several species are bivoltine or variable in their life cycle (e.g. Baetis lutheri, B. scambus, Caenis luctuosa). The Ephemeroptera that were found to be negatively correlated with temperatures tend to emerge earlier in the year.

Trichoptera Even fewer studies exist on the effect of temperature on the life cycle of European Trichoptera: The size of Chaetopteryx villosa was found to be dependent on temperature in a study by Wagner (2002). It avoids warm temperatures by a summer dormancy as a larva. These observations corroborate the results of this study that the species is negatively correlated with higher temperatures. For the remaining species that were found to be correlated with temperature in this study, no further laboratory experiments were found testing the thermal dependency of the species. The results will be discussed when comparing general observations on the species. Hydropsyche siltalai was present in small- and mid-sized rivers with a higher abundancy in warmer rivers. Limiting temperatures were not reached in this study. Roux et al. (1992) found this species to be adapted to the temperature regime of the rhithron, its metabolism functioning well at temperatures between 5 and 20°C. Hydropsyche pellucidula shows a maximum presence at 16°C, declining in numbers above and below these temperatures. H. pellucidula is characterized as an ubiquitous species with its metabolism adapted to temperatures below 20°C, as observed in rhithron species, but is also able to tolerate higher temperatures as potamon species (Roux et al. 1992). Taking everything into account, it seems to be obvious that temperature has a dominant influence in this order, but further research into this subject would help to corroborate this observation.

88 4. Temperature and benthic community Discussion

For many of the species found to be significantly correlated with temperatures either negatively or positively, these observations in the field are in accord with the results of experimental laboratory studies. For some species, the thermal amplitude of the studied rivers was not wide enough to match those findings. Still, it can be concluded that temperature as a dominant factor on the development of ectothermic organisms can influence community patterns in such a way that also on a limited thermal amplitude the species distribution reflects their thermal preferences. It can be concluded that even moderate temperature changes as a result of different land use practices and changes in riparian shading may result in different community patterns.

89 5. Summary and conclusion

5. Summary and conclusion

Main objectives of this study were: 1. to thermally characterize two representative river-types, in order to test if near-natural reference conditions for temperature could be defined to meet the requirements of the WFD for a typology, 2. to study anthropogenic modifications of the thermal regime with emphasis on temperature variability, and the effect on the development of benthic species, 3. to analyse the significance of water temperature for the EPTC-community in comparison to other environmental factors and to identify species correlated with the thermal environment that might function as indicator organisms for an ecological assessment of thermal conditions. The objectives of this study were chosen to fulfil the requirements of the WFD for a river-type specific assessment of the ecological water quality of rivers using biological indicators, with the focus of this study being on the thermal condition of rivers.

Three basic approaches were chosen: 1. At ten sampling sites in each of the two river-types (small- and mid-sized rivers in the Lower Mountain Range of Western Germany) temperature data was continuously recorded for at least a one-year period up to three years from 2000 to 2003. Extreme and mean temperature parameters and temperature variability were analysed with the focus on a characteristical pattern for each type. Environmental conditions were assessed and correlated with temperature parameters. For comparison, temperature data of other river-types were included (external data). Temperature parameters shown to be of ecological significance for benthic invertebrates were additionally assembled from literature.

2. The anthropogenic modifications of the temperature regime, with the main focus on the differences in thermal variability, were investigated in the Lenne, a river influenced by cold water originating from a tributary with hypolimnic water of an impoundment, and, further downstream, influenced by warm water originating from a power station. Thermal characteristics were monitored continuously over a one-year period upstream and downstream from the two thermal sources. The development of two Trichoptera species in the different thermal conditions was observed by sampling larvae during the same time-period.

90 5. Summary and conclusion

3. The significance of water temperature for the benthic community composition was studied in comparison with other environmental factors at the 20 sampling sites in the two stream types mentioned in approach 1. Temperature was recorded continuously using loggers, several other environmental variables were assessed, benthic invertebrates were sampled both in spring and in summer, and the data was analysed using multivariate techniques. Species correlated with the thermal conditions were identified, and the temperature preferences of the species observed in this field observation were compared with autecological information and results of laboratory studies.

The results showed that the small- and mid-sized rivers (Types 5 and 9) differed significantly in thermal parameters concerning spring- and summer temperatures, whereas winter temperatures were not significantly different. Daily temperature variation differed in winter and early spring comparing small- and mid-sized rivers, whereas the maximum amplitudes had a similar range in both types, being very variable between the individual rivers. Nevertheless, daily amplitudes of the mid-sized rivers may be higher when the channel morphology is broader and shallower, and not artificially narrowed and straightened. Main determinants for temperature characteristics were river-size and channel morphology, and, concerning only small-sized rivers, vegetation cover. Rivers, not grouping into the corresponding river-type (identified by cluster analysis on thermal variables), with temperature characteristics modified by anthropogenic and groundwater influence were excluded and temperature ranges were suggested for a thermal typology for the two river-types.

Main environmental factor changing from site to site in the Lenne was water temperature. Hypolimnic water and the effluent of a power station caused a decrease respectively increase in degree-days measured over a one-year period of more than 10% compared to the uninfluenced temperature condition. The main thermal change by the hypolimnic water was restricted to summer, depressing temperatures and decreasing thermal variability. Temperatures recovered again to almost normal conditions 30 km downstream. On the other hand, the power station increased thermal variability and elevated the temperature throughout the year, but temperature changes were not as large as downstream from the confluence with hypolimnic water. Temperatures increased further downstream, shortening the thermal rhithral section without recovery. Hydropsyche siltalai and Hydropsyche incognita developed slower downstream from the hypolimnic confluence, whereas the development did not change downstream from the effluent of the power station. Three reasons were discussed: 1. different temperature sus- ceptibility at different life stages cause a change in development in the main growth period before pupation in late spring/summer. The more moderate temperature change downstream from the power station during the same time period did not suffice to cause a change, nor did the temperature changes in winter; 2. low thermal variability downstream from the confluence with hypolimnic water reduced the chance to encounter optimum temperature conditions for the development during the day, whereas compensation of unfavourably warm thermal conditions may be possible at the site with higher

91 5. Summary and conclusion

thermal variability downstream from the power station; 3. the two Hydropsyche species were found to prefer warmer temperature conditions in the analysis of the Chapter 4, consequently temperature conditions might still be favourable downstream from the power station, whereas the species were are at their lower thermal limit downstream from the Bigge confluence.

The EPTC-community was changing primarily along the thermal gradient of small- and mid-sized rivers. Up to 29% of the variability in the EPTC-community was explained by summer temperature in the data set for both river-types as well as for small-sized rivers. Other environmental variables explaining a smaller, but significant part of the variability in species distribution were conductivity, substrate type, and amount of local riparian forest. In mid-sized rivers, temperature showed to be less important for the faunal composition, which might be due to less specialized, more tolerant or eurythermic species composition in larger rivers. Taxa found to be correlated with temperature were identified, and a comparison with autecological data revealed that a great part of the taxa (most Plecoptera and some Ephemeroptera) corroborate observations made in laboratory studies concerning thermal adaptations. No laboratory results were available for the majority of Trichoptera and Col- eoptera and Ephemeroptera. But life-history traits that were recognized in literature to show tendencies for adaptations to the specific thermal conditions were compared with the results of the present study. This concerned species traits, such as egg dormancies to avoid warmer temperatures in cold-adapted species, or plasticity in the development to take advantage of warmer temperatures in warm-adapted species. Only two species did not distribute according to the general observation of temperature preference: eurythermic species that were found to be negatively correlated with temperature.

In this study a methodical approach is shown to distinguish different river types by their characteristical thermal parameters. Furthermore, it is possible to identify potential indicator taxa for thermal conditions within river-types by combining the results of multivariate analysis of field studies with data achieved by laboratory experiments. As a consequence, the assessment of the thermal condition in rivers may be possible using macroinvertebrates as biotic indicator organisms.

92 6. Zusammenfassung

6. Zusammenfassung

Die Bedeutung der Wassertemperatur für das Makrozoobenthos – ein Beitrag zur Bewertung von Fließgewässern

1. Einleitung Neben der Strömung wird die Wassertemperatur allgemein als Faktor angesehen, der für die Lebensgemeinschaften von Fließgewässern eine besondere Bedeutung besitzt. Die Temperatur hat zum einen indirekt Einfluß auf die Organismen im Gewässer, indem abiotische Parameter (z.B. Sauerstoffsättigung des Wassers, Stoffumsatz, Nitrifikation, Löslichkeit toxischer Substanzen) beeinflußt werden. Zum anderen wirkt die Temperatur auch direkt auf metabolische und enzymatische Prozesse in den Organismen. So sind auf ökosystemarer Basis Produktivitätsparameter vom Temperaturhaushalt abhängig: Wach- stum, Entwicklungszeiten und Lebenszyklus vieler Arten werden maßgeblich von der Temperatur bestimmt. Dieses wirkt sich schließlich auch auf die regionale Verbreitung der Arten und somit auf die Zusammensetzung der Lebensgemeinschaften von Fließge- wässern aus. Folglich wurde die Wassertemperatur vielfach als Kriterium zur Abgren- zung regionaler Fließgewässertypen und für eine Längszonierung von Fließgewässern verwendet.

Durch anthropogene Eingriffe, wie die Einleitung von Brauch- und Abwasser und was- serbauliche Maßnahmen, sind neben der Wasserqualität und der Gewässerstruktur auch die Temperaturverhältnisse von Fließgewässern nachhaltig modifiziert worden. Gravie- rende Veränderungen des natürlichen Temperaturregimes der Gewässer sind beispiels- weise auf den Tiefenwasserablaß von Talsperren oder das Kühlwasser von Wärmekraft- werken zurückzuführen. Auch die Veränderung der Ufergehölzvegetation und die Modi- fikation des Grundwasserhaushaltes beeinflussen signifikant die Wassertemperatur.

Trotz der großen Bedeutung für die Biozönose in Gewässern hat die Wassertemperatur bisher praktisch keine Verwendung bei der Bewertung des Zustandes von Fließgewäs- sern gefunden. So wird zum Beispiel die Temperatur in einem Entwurf zur chemischen Güteklassifizierung der Fließgewässer (Gewässergütebericht 1996, Landesumweltamt NRW) nicht berücksichtigt. Lediglich in den Allgemeinen Güteanforderungen für Fließ- gewässer (AGA 1991) des Landes Nordrhein-Westfalen findet die Temperatur als Kenngröße Verwendung. Darüber hinaus berücksichtigt die EU-Wasserrahmenrichtlinie

93 6. Zusammenfassung

(EU-WRRL) zur Beurteilung des ökologischen Zustandes der Gewässer neben den biologischen Komponenten, wie etwa der Zusammensetzung und der Abundanz der benthischen Wirbellosenfauna, auch physiko-chemische Parameter, welche die thermischen Bedingungen einschließen (Anhang V 1.1. und 1.2.). Mit welchem Verfahren der Tem- peraturhaushalt in einem zukünftigen Bewertungssystem berücksichtigt werden soll, ist bislang jedoch nicht geklärt. Die Grundlagen hierfür müssen noch erarbeitet werden.

Ziele der vorliegenden Arbeit sind, 1. zwei repräsentative Fließgewässertypen thermisch zu charakterisieren und zu prüfen, ob thermische Referenzbedingungen für die Typologie der EU-WRRL beschrieben werden können, 2. einen Fluß mit anthropogen beeinflußtem Temperaturhaushalt mit Schwerpunkt auf thermischer Variabilität zu analysieren und zu untersuchen, wie sich diese Temperaturveränderungen auf die Larvalentwicklung zweier Trichopterenarten auswirken, 3. die Bedeutung der Wassertemperatur für die Makrozoobenthoszusammensetzung zu untersuchen (auch vergleichend mit anderen Umweltparametern) und Taxa zu identifizieren, deren Vorkommen mit der Temperatur korreliert ist und die als Indikatororganismen für die Beurteilung der thermischen Situation eines Fließ- gewässers in Frage kommen könnten.

Diese Ziele wurden formuliert, um einen Beitrag zur typspezifischen Beurteilung von Fließgewässern anhand biotischer Organismen zu leisten, wobei der Schwerpunkt auf der Untersuchung des Temperaturhaushalts von Fließgewässern liegt.

Um diese Ziele zu erreichen, wurden drei Arbeitsschwerpunkte gesetzt, die in drei ge- trennten Kapiteln behandelt wurden: 1. Untersuchung des Temperaturhaushaltes kleiner und mittelgroßer Fließgewässer im Mittelgebirge Westdeutschlands; die Temperaturen wurden über einen länge- ren Zeitraum kontinuierlich aufgezeichnet. Es wurde untersucht, ob die zwei für die EU-WRRL definierten Gewässertypen sich auch hinsichtlich ihres Tempera- turhaushaltes signifikant unterscheiden, welche Parameter für eine Differenzie- rung in Frage kommen könnten und welche anderen abiotischen Faktoren eng mit den Temperaturverhältnissen im Gewässer assoziiert sind. Um einen Ver- gleich zwischen den typologisch bedeutsamen Temperaturparametern und öko- logisch relevanten Parametern zu ziehen, wurde untersucht, welche Temperatur- faktoren mit Einfluß auf die benthische Fauna bisher in anderen Studien heraus- gestellt wurden.

94 6. Zusammenfassung

2. Analyse eines Fließgewässers mit anthropogen verändertem Temperaturhaushalt im Längsverlauf, am Beispiel der Lenne (Sauerland): Zufluß von hypolimnischem Wasser aus Talsperren, Einleitung von Kühlwasser aus einem Kraftwerk. Bei dieser Untersuchung sollte die Temperaturvariabilität besonders berücksich- tigt und die Larvalentwicklung zweier Hydropsychearten unter den veränderten thermischen Bedingungen beschrieben werden. 3. Analyse des Einflusses des Temperaturhaushaltes auf die Makrozoobenthoszu- sammensetzung an 20 Probestellen in kleinen und mittelgroßen Fließgewässern. Dieser Einfluß soll zudem mit anderen abiotischen Faktoren verglichen werden, die ebenfalls potentiell auf die benthische Lebensgemeinschaft wirken. Darüber hinaus sollen Arten identifiziert werden, deren Vorkommen positiv oder negativ mit der Temperatur korreliert ist. Die gefundenen Temperaturpräferenzen der Ar- ten sollen mit autökologischen Daten verglichen werden. Gegebenenfalls können bei einer Übereinstimmung die Arten als Indikatoren für thermische Bedingun- gen in den Gewässertypen definiert werden.

2. Charakteristische Temperaturverhältnisse zweier Fließgewässertypen Auswahl der Probestellen Es wurden zwei Fließgewässertypen untersucht. Zum einen der Typ Mittelgebirgsbach mit zehn Probestellen in zehn Untersuchungsgewässern, die an den Probestellen Ein- zugsgebietsgrößen zwischen sechs und 26 km² haben. Zum anderen der Typ Mittelge- birgsfluß mit zehn Probestellen an neun Untersuchungsgewässern, die an den Probestel- len Einzugsgebietsgrößen zwischen 150 und 550 km² haben. Alle Probestellen befinden sich im Rheinischen Schiefergebirge zwischen 250 und 450 m.ü.N.N.; pro Gewässertyp wurde jeweils eine Gruppe linksrheinischer Gewässer in der Eifel und rechtsrheinischer Gewässer im Sauerland und im Rothaargebirge ausgewählt. Die Probestellen liegen in Nordrhein-Westfalen, Rheinland-Pfalz und Hessen. An diesen 20 Probestellen wurde die Temperatur kontinuierlich mit Hilfe von Loggern im Zeitraum zwischen 2000 und 2003 aufgezeichnet und ausgewertet. Keine der hier untersuchten Probestellen ist durch Stauungen, Restwasser, massive organische Ver- schmutzung oder Versauerung beeinträchtigt.

95 6. Zusammenfassung

Aufnahme abiotischer Daten Wasserdichte Temperaturlogger wurden an Einschlaghülsen festgeschraubt und so im Gewässer installiert, daß die folgenden Bedingungen erfüllt waren: • die Logger durften auch bei niedrigen Wasserständen nicht trockenfallen, • sie waren so über der Gewässersohle zu installieren, daß sie nicht von Sediment be- deckt werden konnten, • das Wasser sollte an der Probestelle strömend, nicht stagnierend sein, • die Probestelle sollte beschattet sein, um zu vermeiden, daß direkte Sonneneinstrah- lung die Logger erhitzt. Die Logger wurden auf eine halbstündliche Probenahme programmiert. Vor der Installa- tion wurden sie 24 Stunden geeicht, die Eichung wurde bei jedem Auslesen der Daten wiederholt.

Zusätzlich wurden Wassertemperaturdaten zu Vergleichszwecken herangezogen. Die Bundesanstalt für Gewässerkunde in Koblenz stellte Daten von neun Pegeln in Flüssen unterschiedlichen Gewässertyps zur Verfügung, die derselben Ökoregion angehörten wie die oben genannten Untersuchungsgewässer. Ferner wurden Daten weiterer Tempe- raturaufzeichnungen im Tiefland (Schleswig-Holstein, Niedersachsen) zum Vergleich herangezogen; die Temperaturdaten wurden auch dort mit Loggern - wie oben beschrieben - aufgezeichnet.

Charakterisierung durch physiographische und physiko-chemische Daten In Form eines Site Protocol (AQEM Consortium 2002) wurden das Einzugsgebiet, die Aue, die Strecken ober- und unterhalb der Probestelle und die Probestelle selbst durch chemische, hydrologische, morphologische und geographische Daten charakterisiert. Aus den mehr als 130 Parametern wurden jeweils die für die entsprechende Fragestel- lung relevanten Parameter ausgewählt. In einer Literaturstudie wurde zusätzlich untersucht, welche Parameter sich in früheren Studien als relevant für die Fauna erwiesen haben.

Datenverarbeitung Die Temperaturparameter, die für die detaillierte Analyse ausgewählt wurden, setzen sich aus folgenden Gruppen zusammen: • Durchschnittswerte: mittlere Temperaturen unterschiedlicher Zeiträume (Monat, Jahreszeit, Jahr), Temperatursummen

96 6. Zusammenfassung

• extreme Werte: absolute Minima und Maxima unterschiedlicher Zeiträume (Monat, Jahreszeit, Jahr), Mittelwerte der Extrema in ökologisch relevanten Zeiträumen (z.B. Jahreszeit) • Amplituden: Jahresamplitude, Monatsamplitude, Tagesamplitude und ihre Mittel- werte unterschiedlicher Zeiträume.

Diese oben aufgezählten Parameter wurden mit Blick auf unterschiedliche thermische Charakteristika der zwei Fließgewässertypen ausgewertet. Mittels eines Student t-Test wurden die Parameter identifiziert, die sich signifikant zwischen beiden Gewässertypen unterscheiden. Auf welche Weise sich die Fließgewässer anhand thermischer Parameter gruppieren, wurde mittels Klassifikationsanalyse untersucht. Zusätzlich wurde die Be- deutung anderer Umweltvariablen für den Temperaturhaushalt untersucht, und mittels multipler Regression wurden signifikante Faktoren ermittelt, die sich auf die jeweiligen Temperaturparameter auswirken.

Zahlreiche experimentelle Arbeiten geben einen Überblick über die Abhängigkeit der Verbreitung, Entwicklung und Phänologie einzelner benthischer Invertebraten von der Temperatur. In der vorliegenden Arbeit wurde zusammengestellt, welche Temperaturpa- rameter untersucht wurden, die von Bedeutung für die Taxa sind. Der Großteil dieser experimentellen Studien wurde jedoch unter unterschiedlichen konstanten Temperaturen durchgeführt. Die in der Natur gemessenen Temperaturen der verschiedenen Jahreszei- ten, kombiniert mit der Phänologie der Tiere, konnten die im Labor beobachteten Tem- peraturabhängigkeiten nicht immer bestätigen. Während in verschiedenen Arbeiten die Bedeutung von Temperaturextrema, mittleren Temperaturen unterschiedlicher Zeitab- schnitte und von Tagesgraden zu Entwicklungserfolg, Verbreitung und Phänologie der Invertebraten in Beziehung gesetzt worden ist, bedarf die Auswirkung variierender Temperaturverhältnisse, wie z.B. von Tagesamplituden, noch weiterer Klärung.

Ergebnisse Die Temperaturuntersuchungen in den beiden Fließgewässertypen ergaben, daß sich die Mittelgebirgsbäche und Mittelgebirgsflüsse insbesondere in ihren Frühjahrs- und Som- mertemperaturen voneinander unterschieden, während die Wintertemperaturen keinen signifikanten Unterschied aufwiesen. Die Tagesamplituden zwischen den beiden Fließ- gewässertypen unterschieden sich ebenfalls nicht signifikant voneinander. Sie variierten jedoch um mehrere Grad Celsius zwischen den einzelnen Flüssen innerhalb der Gewässertypen. Betrachtet man alle Probestellen an Bächen und Flüssen insgesamt, konnte festgestellt werden, daß die Gewässergröße und die Strömungsverhältnisse signifikanten Einfluß auf die Sommertemperaturen haben. Innerhalb der Gruppe der Mittelgebirgsbäche beein-

97 6. Zusammenfassung

flußten die Strömungsverhältnisse die Sommertemperaturen, auch der Waldanteil im Einzugsgebiet erwies sich als fast in gleichem Maße relevant. In der Gruppe der Mittel- gebirgsflüsse hatte jedoch nur noch die Gewässergröße Einfluß auf die Sommertempera- tur. Im Winter waren an den Bächen der Gehölzanteil und die Gewässerbreite von ent- scheidendem Einfluß, während die Orientierung des Gewässers und die Wassertiefe die Wintertemperaturen in den Flüssen entscheidend bestimmten. Die Tagesamplituden korrelierten hauptsächlich mit morphologischen Parametern wie Gewässerbreite, -tiefe und Gefälle, aber auch mit der geographischen Höhe der Probestelle.

Die Clusteranalyse ergab, daß sich der Großteil der Fließgewässer in ihre jeweiligen Gewässertypen gruppieren. Es formte sich jedoch auch eine dritte Gruppe, die sowohl aus Mittelgebirgsbächen als auch aus Mittelgebirgsflüssen besteht, weil sie in ihren Temperaturcharakteristika intermediär zwischen den beiden Typen liegt. Diese Gruppe besteht aus zwei der wärmsten Bäche und aus den Flüssen, in denen kühlere Sommer- temperaturen und geringere Tagesamplituden gemessen wurden. Anhand weiterer Um- weltparameter konnte ermittelt werden, daß einer der Bäche dieser Gruppe die geringste Gewässertiefe und eine langsame Fließgeschwindigkeit hat und außerdem dieser Bach, ebenso wie auch der zweite Bach dieser Gruppe, nur einen sehr geringen Anteil an Ge- hölz an der Probestelle, in der Aue und im gesamten Einzugsgebiet hat. Die Mittelgebirgsflüsse dieser Gruppe zeigen hingegen hinsichtlich ihrer Umweltparameter keine so eindeutigen Unterschiede zu den übrigen Gewässern ihres Typs, mit der Ausnahme eines Flusses mit offensichtlichem Grundwasserzufluß.

Um gewässertypspezifische Temperaturcharakteristika zu definieren, wurden die Flüsse, die sich anhand ihrer Temperaturparameter nicht mit den Gewässern ihres Typs grup- pierten, da ihre Temperaturen durch Grundwasserzufluß oder durch anthropogene Ein- flüsse verändert waren, nicht in die Bewertung einbezogen.

Die Temperaturverhältnisse der übrigen Gewässer wurden als thermische Charakteristi- ka für Mittelgebirgsbäche und Mittelgebirgsflüsse wie folgt vorgeschlagen: Sommermittelwerte: Bach: < 13°C Fluß: 14-17°C Wintermittelwerte: Bach: 4°C Fluß: 4°C Maximum: Bach: < 20°C Fluß: 20-26°C Minimum: Bach: 0°C Fluß: 0°C Max. Amplitude*: Bach: 3-5°C Fluß: 3-5°C (*Die Tagesamplitude errechnet sich aus dem Mittel der im Frühjahr auftretenden Ma- xima).

98 6. Zusammenfassung

3. Anthropogene Veränderungen des Temperaturhaushaltes und ihre Wir- kung auf die Entwicklung zweier Trichopterenarten

Aufnahme der Daten Die Untersuchung anthropogener Veränderungen im Temperaturhaushalt eines Flusses wurde am Beispiel der Lenne im Sauerland in Nordrhein-Westfalen durchgeführt. Etwa 70 km vor ihrer Mündung in die Ruhr fließt der Lenne kaltes, hypolimnisches Wasser von Talsperren aus der Bigge zu. 30 km weiter unterhalb wird vom Kohlekraftwerk El- verlingsen warmes Kühlwasser eingeleitet. Im Längsverlauf des Flusses wurden neun Logger zur kontinuierlichen Temperaturaufzeichnung eingebracht und über den Zeit- raum von einem Jahr die Temperatur registriert. Im selben Zeitraum wurden die Larven zweier Köcherfliegenarten (Hydropsyche siltalai, H. incognita) an vier Probestellen jeweils unter- und oberhalb des Biggezuflusses und unter- und oberhalb der Kraft- werkseinleitung besammelt, um deren Larvalentwicklung zu untersuchen. Die Larven wurden im Labor bestimmt, die Kopfkapselbreite wurde gemessen und anhand der Grö- ßenverteilung das Larvalstadium ermittelt. Unterschiede an den Probestellen wurden mittels Student t-Test (Temperaturparameter) und Chi²-Test (Kopfkapselbreiten) auf Signifikanz geprüft. Zusätzliche Informationen über physiko-chemische Daten ergaben sich aus einem parallel durchgeführten Projekt; das staatliche Umweltamt Siegen stellte Abflußdaten der Lenne für den entsprechenden Zeitraum zur Verfügung.

Verarbeitung der Daten Die Temperatur stellte den am stärksten variierenden abiotischen Faktor im untersuchten Abschnitt im Längsverlauf der Lenne dar. Der hypolimnische Einfluß der Bigge kühlte das Lennewasser insgesamt deutlich ab. Die sommerlichen Temperaturen überstiegen nie 15°C und waren damit um 6°C niedriger als oberhalb des Biggezuflusses. Die win- terlichen Temperaturen änderten sich dagegen nur wenig, sanken aber nicht unter 2°C. Tagesamplituden erreichten maximal 3°C. Die Summe der Tagesgrade war nach einem Jahr im Vergleich zur oberhalb gelegenen Stelle um 13% geringer. Im Verlauf von knapp 30 km Fließstrecke konnten sich die Temperaturverhältnisse des Wassers fast normalisieren. Unterhalb des Kraftwerks war die Temperatur so erwärmt, daß die Summe der Tages- grade um über 11% höher war im Vergleich zur Untersuchungsstelle oberhalb der Kraftwerkseinleitung. Diese deutlichen Temperaturunterschiede beschränkten sich jedoch nicht auf den Sommer, wie unterhalb des Biggezuflusses, sondern verteilten sich über das gesamte Jahr. Verglichen mit allen Probestellen zeigten sich an dieser Stelle die größten Temperaturschwankungen innerhalb eines Monats, während sich die Tages-

99 6. Zusammenfassung

schwankungen um knapp 2°C erhöhten. Darüber hinaus wurde beobachtet, daß die Temperatur jeweils etwa in einem Drei-Tage-Rhythmus (der aber auch in seiner Länge variierte) erhöht war, die Tagesamplituden innerhalb dieses Zeitraumes jedoch keine extremen Werte aufwiesen. Mit 24,2°C maximaler Temperatur liegt die Lenne an dieser Probestelle nicht außerhalb des normalen Temperaturbereiches von Mittelgebirgsflüssen (siehe Kapitel 2), aber um knapp 5°C höher als oberhalb der Kühlwassereinleitung. Der Drei-Tage-Rhythmus der Wassererwärmung war auch noch 20 km unterhalb meßbar. Auf dieser Fließstrecke wird in der wärmeren Jahreszeit durch die natürlichen Aus- tauschprozesse der Temperaturen zwischen Luft und Wasser auch weiterhin Wärme auf- genommen, so daß sich die Temperaturen nicht zurückbilden, sondern weiter erhöhen. Somit wird ein Temperaturbereich innerhalb des Kontinuums des Flusses übersprungen und nicht, wie unterhalb des Biggezuflusses, wiederholt. Vergleichend kann gesagt werden, daß die Temperaturdifferenzen mit jeweils etwas über 10% der Summe der Tagesgrade unterhalb des Biggezuflusses und unterhalb der Kraftwerkseinleitung ähnlich waren, die Art der Temperaturveränderungen sich jedoch in zwei Punkten unterschied: 1. Die Temperaturvariabilität (Tagesamplitude, Jahresam- plitude) nahm unter hypolimnischem Einfluß ab, während sie unterhalb der Kühlwasser- einleitung zunahm, und 2. die größten Temperaturunterschiede traten unterhalb des Big- gezuflusses im Sommer auf, während sie unterhalb des Kraftwerks im gesamten Jahr auftraten.

Die Beobachtungen zur Larvalentwicklung von H. siltalai und H. incognita ergaben folgende Resultate: Unterhalb der Einmündung von kaltem hypolimnischen Wasser waren beide Trichopterenarten rund einen Monat langsamer in ihrer Entwicklung als oberhalb der Einmündungsstelle. Die Kopfkapselbreite unterschied sich dagegen nicht signifikant. Unterhalb des Kraftwerkszuflusses hingegen waren keine zeitlichen Veränderungen der Larvalentwicklung im Vergleich zum anthropogen nicht beeinflußten Teil der Lenne zu beobachten. Laborergebnissen zufolge hätte man jedoch auch erwarten können, daß sich die Larvalentwicklung unter dem Einfluß des warmen Wassers beschleunigen könnte.

Drei mögliche Erklärungen wurden hierzu diskutiert: 1. Unterschiedliche Temperaturansprüche im Lebenszyklus der Taxa und saisonal unterschiedliche Temperaturänderungen: Sollte der größte Temperaturbedarf im Zeitraum der stärksten Größenzunahme der Larven liegen, wäre dies für die hier untersuchten Arten der Spätfrühling und Sommer. In diesem Zeitraum ist die Temperaturdifferenz im Vergleich zur unbeeinflußten Probestelle unterhalb der Biggemündung deutlich größer (ca. 5°C kälter) als unterhalb des Kraftwerks (ca. 2°C wärmer).

100 6. Zusammenfassung

2. Temperaturvariabilität: Die Temperaturschwankungen sind unterhalb der Big- gemündung deutlich reduziert, so daß die zwei Köcherfliegenarten auch kurzfri- stig keine optimalen Wachstumsbedingungen finden, wenn die Temperatur unter ihrem Optimum liegen. Unterhalb des Kraftwerksausflusses könnten die Hydro- psychelarven durch höhere Temperaturschwankungen – die hier keine extremen Werte erreichen – optimale Wachstumsbedingungen innerhalb eines jeden Tages finden. 3. In den Ergebnissen des folgenden Kapitels wurden H. siltalai und H. incognita als Taxa identifiziert, die in größeren Abundanzen an wärmeren Probestellen vorkommen. Die Bedingungen unterhalb des Kraftwerks könnten also noch positiv für die Entwicklung der Larven sein, während sie unterhalb der Biggemün- dung an ihre thermische Grenze gelangen. Für weitere Erforschung bleibt die Frage offen, wie sich die Larvalentwicklung thermisch stenöker, speziell kaltstenothermer oder warmstenothermer Arten unter diesen unterschiedlichen Bedingungen darstellen würde.

4. Die Bedeutung der Wassertemperatur für die Makrozoobenthoszusam- mensetzung Um die Relation zwischen dem Temperaturhaushalt und der benthischen Gemeinschaft herzustellen, wurden 20 Mittelgebirgsbäche und -flüsse thermisch und faunistisch untersucht. Die Probestellen und die Methoden zur Temperaturaufzeichnung sowie die Erfas- sung weiterer abiotischer Parameter in einem Site Protocol sind in der Untersuchung zum Temperaturregime (Abschnitt 2) dargestellt. Zusätzlich wurde im ersten Untersu- chungsjahr jeweils im Frühjahr und im Sommer eine Makrozoobenthosaufsammlung durchgeführt. Beprobt wurde habitatspezifisch nach dem Multi Habitat Sampling, d.h. die Habitate werden in ihrem Vorkommen anteilsmäßig geschätzt und die Gesamtzahl von 20 Teilproben entsprechend der Habitate verteilt. Für die vorliegende Arbeit wurden im Labor alle Ephemeropteren, Plecopteren, Trichopteren und Coleopteren (EPTC- Gemeinschaft), soweit möglich, auf Artniveau bestimmt. Die Temperaturparameter (siehe Abschnitt 2.) wurden in die Analyse einbezogen. Für die Untersuchung wurden aus dem Site Protocol relevante Parameter ausgesucht und mittels Korrelationsmatrix von stark miteinander korrelierten Faktoren jeweils eine re- präsentative Variable ausgewählt, um Redundanzen zu vermeiden. Die Korrelation der Temperaturdaten und der anderen abiotischen Parameter mit der Makrozoobenthoszusammensetzung erfolgte mittels Principal Components Analysis (PCA) und Redundancy Analysis (RDA).

101 6. Zusammenfassung

In einem ersten Schritt wurde geprüft, welche Temperaturparameter am besten mit der Artvariabilität der EPTC-Gemeinschaft korrelieren. Die stärkste Korrelation hatten sommerliche Mittelwerte, d.h. Temperatursummen der Monate Juni, Juli, August, welche für die folgenden Analysen herangezogen wurden, ebenso wie monatliche Mittel- werte besonders von Mai und August. Ferner korrelierten, mit etwas weniger Erklä- rungsanteil, aber auch signifikant, die sommerlichen Maximalwerte. Deutlich geringere Korrelationen zur Artgemeinschaft ergaben sich mit Frühjahrs-, Herbst- und Wintertem- peraturwerten sowie Jahresdurchschnittswerten. Keinerlei Korrelation mit der Artvaria- bilität zeigten Temperaturparameter der Tagesamplituden.

Die Analyse mittels RDA ergab, daß die Zusammensetzung der EPTC- (Ephemeropte- ren-, Plecopteren-, Trichopteren- und Coleopteren-) Gemeinschaft zu einem großen Teil durch den Temperaturgradienten zwischen den Probestellen erklärt werden kann. Im gemeinsamen Datensatz der Bäche und Flüsse vermochte die Sommertemperatur 28 % der Artvariabilität an den Probestellen zu erklären. Weitere abiotische Parameter, die einen geringeren, aber noch signifikanten Anteil der EPTC-Zusammensetzung erklärten, waren Leitfähigkeit, Substrattyp und Uferbefestigung sowie der Waldanteil im Einzugs- gebiet. Die Ergebnisse für die Mittelgebirgsbäche zeigten, daß auch hier die Temperatur 29 % der Artvariabilität erklärte. Signifikant, jedoch mit geringerem Erklärungsanteil, sind hier die Faktoren Leitfähigkeit und Waldanteil in der Aue. In Mittelgebirgsflüssen ist die Sommertemperatur nur noch für die Artvariabilität der Taxa der Frühjahrsauf- sammlung signifikant, und sie hat einen geringeren Erklärungsanteil (20 %) als im Da- tensatz der Bäche und der beiden Gewässertypen gemeinsam. In den Flüssen haben die Faktoren Gewässergröße und Leitfähigkeit einen fast ebenso großen Erklärungsanteil wie die Temperatur. Im folgenden wurde diskutiert, worin der Unterschied der Erklärungsanteile der Tempe- ratur in Bächen und Flüssen begründet sein kann. Die geringere Korrelation der Tempe- ratur mit der Artvariabilität in Mittelgebirgsflüssen könnte darauf zurückzuführen sein, daß die Artzusammensetzung in Flüssen aus weniger spezialisierten Arten, und so auch eher eurythermen Taxa besteht, während in Bächen die Arten anspruchsvoller in ihren Temperaturansprüchen sind, was sich im hohen Erklärungsanteil der Temperatur wider- spiegelt.

In einem letzten Schritt wurden die Taxa identifiziert, die am stärksten mit dem Tempe- raturgradienten korreliert sind. Zur Analyse dieser Ergebnisse wurden durch Literatur- studien Ergebnisse von Laboruntersuchungen zu Temperaturpräferenzen zum Vergleich herangezogen. Leider wurden für den überwiegenden Teil der Trichoptera und Coleopte- ra, aber auch für einen großen Teil der Ephemeroptera, keine autökologischen Daten zur Temperatur gefunden. Für die Plecoptera und Ephemeroptera, über die Untersuchungen vorlagen, bestätigte ein Großteil dieser Ergebnisse aus der Literatur die hier gefundenen

102 6. Zusammenfassung

Temperaturpräferenzen. Nur zwei Taxa entsprachen in ihrer Temperaturpräferenz nicht den autökologischen Daten. Beispielhaft seien hier einige Arten genannt, die in dieser Studie Korrelationen mit der Temperatur zeigten: Negativ korreliert waren zum Beispiel unter den Plecoptera Perlodes microcephalus, Brachyptera risi und Protonemoura auberti, unter den Ephemeroptera Baetis alpinus, unter den Trichoptera Chaetopteryx villosa und unter den Coleoptera Esolus angustatus und Oreodytes sanmarkii. Positive Korrelationen zur Temperatur ergaben sich für Plecoptera mit Perla burmeisteriana und Leuctra geniculata, für Ephemeroptera mit Ecdyonuus insignis, E. dispar und E. torrentis, für Trichoptera mit Hydropsyche siltalai und H. pellucidula, und für Cole- optera mit Elmis maugetii und Orectochilus villosus.

Die vorliegende Arbeit zeigt Möglichkeiten, Fließgewässertypen durch einen für sie typischen Temperaturhaushalt zu charakterisieren. Durch das Zusammenspiel von mul- tivariater Analyse bei Feldstudien mit experimentellen Laborstudien können Taxa identifiziert werden, die sich als Indikatoren für thermische Bedingungen innerhalb eines Ge- wässertyps erweisen können und somit den Anforderungen der EU-WRRL entsprechen. Das bedeutet, daß sich die Beurteilung auch der thermischen Gegebenheiten im Gewäs- ser anhand biotischer Indikatoren als möglich erweist.

103 7. References

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121 8. Appendices

8. Appendices

Appendix 1. Abbreviations for the sampling sites of small- and mid-sized rivers. Appendix 2. Abbreviations for taxa in Chapter 4. Appendix 3. Site Protocol parameters. Appendix 4. Monthly mean temperatures for small- and mid-sized rivers. Appendix 5. Monthly minimum temperatures for small- and mid-sized rivers. Appendix 6. Monthly maximum temperatures for small- and mid-sized rivers. Appendix 7. Mean daily amplitudes for small- and mid-sized rivers. Appendix 8. Maximum daily amplitudes for small- and mid-sized rivers. Appendix 9. Summary of the EPTC taxa. Spring sampling. Ephemeroptera. Appendix 10. Summary of the EPTC taxa. Spring sampling. Plecoptera. Appendix 11. Summary of the EPTC taxa. Spring sampling. Coleoptera. Appendix 12. Summary of the EPTC taxa. Spring sampling. Trichoptera. Appendix 13. Summary of the EPTC taxa. Summer sampling. Ephemeroptera. Appendix 14. Summary of the EPTC taxa. Summer sampling. Plecoptera. Appendix 15. Summary of the EPTC taxa. Summer sampling. Coleoptera, Trichoptera. Appendix 16. Temperature parameters for the Lenne in Chapter 3.

122 8. Appendices

Appendix 1. Abbreviations for the sampling sites of small- and mid-sized rivers. Region: SaLd: Sauerland, Webl: Weserbergland, N.Geest: lower Geest, S.Geest: Stader Geest, HeBl: Hessisches Bergland, Bl: Bergisches Land. Federal states: NRW: Nordrhein-Westfalen, H: Hessen, RhP: Rheinland-Pfalz, NS: Niedersachsen, SH: Schleswig-Holstein. Stream types: Type 5: small-sized streams in the siliceous lower mountains, predominated by coarse-sized gravel, Type 9: mid-sized rivers in the siliceous lower mountains, predominated by fine- to coarse-sized gravel. Stream order after Strahler (1957).

Region River Stream Abbrev. River Site (fed.state) Type order WWE Weiße Wehe Hürtgen Eifel (NRW) 5 2 KAL (Kao) Kall Lammersdorf Eifel (NRW) 5 3 ERK Erkensruhr Hirschrott Eifel (NRW) 5 3 VOL Volme Kierspe SaLd (NRW) 5 3 WAL Waldbach Endorf SaLd (NRW) 5 2 ROE Röhr Endorf SaLd (NRW) 5 2 SAL Salwey Obersalwey SaLd (NRW) 5 2 ELB Elbrighäuser Bach Battenberg SaLd (H) 5 2 LAA Laasphe Bad Laasphe SaLd (NRW) 5 3 DRE Dreisbach Dreis-Tiefenbach SaLd (NRW) 5 3 RUR Rur Wiselsley Eifel (NRW) 9 3 KYL Kyll Densborn Eifel (RhP) 9 3 PRW Prüm Waxweiler Eifel (RhP) 9 3 OUR Our Auel Eifel (RhP) 9 3 NIM Nims Birtlingen Eifel (RhP) 9 3 LEN Lenne Altenhundem SaLd (NRW) 9 3 NUH Nuhne Neukirchen SaLd (H) 9 3 EDE Eder Röddenau SaLd (H) 9 4 ORK Orke Reckenberg SaLd (H) 9 3 PRB Püm Beifels Eifel (RhP) 9 3

123 8. Appendices

Appendix 2. Abbreviations for taxa in Chapter 4. Ephemeroptera Bae alp Baetis alpinus (PICTET, 1843-1845) Ecd ven Ecdyonurus venosus (FABRICIUS, 1775) Hab lau Habrophlebia lauta (EATON, 1884) Eph muc Ephemerella mucronata (BENGTSSON, 1909) Epe syl Epeorus sylvicola (PICTET, 1865) Rhi sem Rhithrogena semicolorata (CURTIS, 1834) Par sub Paraleptophlebia submarginata (STEPHENS, 1835) Ecd ins Ecdyonurus insignis (EATON, 1870) Ecd mac Ecdyonurus macani (THOMAS & SOWA, 1970) Bae lut Baetis lutheri (MÜLLER-LIEBENAU, 1967) Cae luc Caenis luctuosa (BURMEISTER, 1839) Tor maj Torleya major (KLAPÁLEK, 1905) Ecd dis Ecdyonurus dispar (CURTIS, 1834) Ecd tor Ecdyonurus torrentis (KIMMINS, 1942) Bae sca Baetis scambus (EATON, 1870) Plecoptera Bra set Brachyptera seticornis (KLAPALEK, 1902) Din cep Dinocras cephalotes (CURTIS, 1827) Peo mic Perlodes microcephalus (PICTET, 1833) Per mar Perla marginata (PANZER, 1799) Pro sp Protonemoura sp. Pro aub Protonemoura auberti (ILLIES, 1954) Bra ris Brachyptera risi (MORTON, 1896) Per bur Perla burmeisteriana (CLAASSEN, 1936) Leu gen Leuctra geniculata (STEPHENS, 1836) Coleoptera Lim peA Limnius perrisi (DUFOUR, 1843) Eso ang Esolus angustatus (MÜLLER, 1821) Ore san Oreodytes sanmarkii (SAHLBERG, 1834) Hya gra Hydraena gracilis (GERMAR, 1824) Eso par Esolus parallelepipedus (MÜLLER, 1806) Hya den Hydraena dentipes Ad. (GERMAR, 1844) Elm mau Elmis maugetii (LATREILLE, 1798) Oul tuA Oulimnius tuberculatus (MÜLLER, 1806) Lim opA Limnius opacus (MÜLLER, 1806) Lim voA Limnius volckmari (PANZER, 1793) Ore ViL Orectochilus villosus (MÜLLER, 1776) Trichoptera Rhy obl Rhyacophila obliterata (McLACHLAN, 1863) Glo con Glossosoma conformis (NEBOISS, 1963) Phi lud Philopotamus ludificatus (McLACHLAN, 1878) Odo alb Odontocerum albicorne (SCOPOLI, 1763) Phi mon Philopotamus montanus (DONOVAN, 1813) Hyd ins Hydropsyche instabilis (CURTIS, 1834) Cha vil Chaetopteryx villosa (FABRICIUS, 1789) Ano cha Anomalopterygella chauviniana ( STEIN, 1874) Ecc dal Ecclisopteryx dalecarlica (KOLENATI, 1848) Hyd pel Hydropsyche pellucidula (CURTIS, 1834) Bra sub Brachycentrus subnubilus (CURTIS, 1834) Mic set Micrasema setiferum (PICTET, 1834) Psy pus Psychomyia pusilla (FABRICIUS, 1781) Che lep Cheumatopsyche lepida (PICTET, 1834) Lep hir Lepidostoma hirtum (FABRICIUS, 1775) Ath alb Athripsodes albifrons (LINNAEUS, 1758) Ath cin Athripsodes cinereus (CURTIS, 1834) Cer ann Ceraclea annulicornis (STEPHENS, 1836) All aur Allogamus auricollis (PICTET, 1834) Las bas Lasiocephala basalis (KOLENATI, 1848) Hyd inc Hydropsyche incognita (PITSCH, 1993) Pol fla Polycentropus flavomaculatus (PICTET, 1834) Goe pil Goera pilosa (FABRICIUS, 1775) Mys azu Mystacides azurea (LINNAEUS, 1761) Hyd sil Hydropsyche siltalai (DÖHLER, 1963)

124

Appendix 3. Site Protocol parameters. 8. Appendices______

Site related information Nr. parameter Wwe1 Kao1 Erk1 Vol1 Wal1 Roe1 Sal1 Elb1 Laa1 Dre1 Rur1 Kyl1 PrW1 Our1 Nim1 Len1 Nuh1 Ede1 Ork1 PrB1 a7 stream order 23332222233333333433 a8 distance to source[km] 5.13 7.5 8.2 6.58 3.6 4.49 7.44 5.8 3.9 13.3 24 60 39 33 44 41 25 77.6 28 48 a11 altitude [m] 300 450 400 345 370 370 340 380 375 270 360 305 315 360 245 280 310 280 295 280 a15 catchment area[km²] 11.16 17.4 22.5 17.5 9 4.72 15.5 12.6 14.8 25.9 154 471.8 286.5 293.6 222.1 190 134.4 523.7 275 327.1 a17 stream density[km/km²] 0.96 0.96 0.96 1.44 1.44 1.44 1.44 1.40 1.40 1.40 1.40 1.40 1.40 1.40 1.40 1.56 1.16 1.16 1.16 1.40 a18_1 acid silicate rocks 100 70 100 100 100 100 100 100 100 100 90 80 100 100 40 100 100 100 100 100 a18_3 carbonate rocks 0 0 0 0 0 0 0 0 0 0 0 20 0 0 60 0 0 0 0 0 a18_4 alluvial deposits 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a18_7 moraines 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a18_8 sander 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a18_9 marine deposits 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a18_10 organic formations 0 30 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 a18_11 loess 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a19_1 deciduous native forest 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a19_3 mixed native forest 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a19_4 wetland (mire) 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 a19_5 open grass-/bushland 0 20 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 a19_9 standing waters 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a19_10 non-native forest 70 10 70 20 100 90 70 100 100 90 40 50 40 40 30 80 60 70 70 40 a19_12 crop land 10 10 20 10 0 10 10 0 0 0 0 10 10 0 20 10 10 10 10 10 a19_13 pasture 20 50 10 20 0 0 10 0 0 0 30 30 40 50 40 0 20 10 10 40 a19_14 clear-cutting 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a19_15 urban sites (resid.) 0 10 0 50 0 0 10 0 0 10 10 10 10 10 10 10 10 10 10 10 a19_16 urban sites (industr.) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Stream morphology and hydrology a20 MQ [m³/s] 0 0 0 0 0 0 0 0 0 0 3.52 6.76 3.75 3.84 2.82 3.95 2.47 11.34 5.89 4.53 a21 MNQ [m³/s] 0 0 0 0 0 0 0 0 0 0 0.076 1.48 0.56 0.58 0.49 1.876 0.19 0.959 0.487 0.192 a22 MHQ [m³/s] 0 0 0 0 0 0 0 0 0 0 55.02 93.4 58.7 59.74 48.95 14.08 40.43 149.3 79.95 67.72 a23_1 jan. 0 0 0 0 0 0 0 0 0 0 2.09 1.78 0 0 1.95 2.02 0 1.77 0 1.89 a23_2 feb. 0 0 0 0 0 0 0 0 0 0 1.43 1.6 0 0 0.42 1.37 0 1.4 0 0.26 a23_3 march 0 0 0 0 0 0 0 0 0 0 1.62 1.6 0 0 0.78 1.84 0 1.56 0 1.02 a23_4 april 0 0 0 0 0 0 0 0 0 0 0.91 1.08 0 0 1.45 1.09 0 1.25 0 1.56 a23_5 may. 0 0 0 0 0 0 0 0 0 0 0.39 0.67 0 0 0.47 0.39 0 0.59 0 0.29 a23_6 june 0 0 0 0 0 0 0 0 0 0 0.285 0.519 0 0 0.4 0.287 0 0.455 0 0.197 a23_7 july 0 0 0 0 0 0 0 0 0 0 0.31 0.5 0 0 0.29 0.28 0 0.47 0 0.09 a23_8 aug. 0 0 0 0 0 0 0 0 0 0 0.134 0.357 0 0 0.223 0.196 0 0.349 0 0.053 a23_9 sep. 0 0 0 0 0 0 0 0 0 0 0.62 0.36 0 0 0.9 0.5 0 0.37 0 1.46 a23_10 oct. 0 0 0 0 0 0 0 0 0 0 0.75 0.69 0 0 1.66 0.79 0 0.66 0 1.81 a23_11 nov. 0 0 0 0 0 0 0 0 0 0 1.29 1.07 0 0 1.69 1.32 0 1.2 0 1.56 a23_12 dec. 0 0 0 0 0 0 0 0 0 0 2.34 1.83 0 0 1.71 1.91 0 1.94 0 1.73 125 a24_0 hydrologic stream type 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a24_1 permanent 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 a24_2 periodic 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

a25 lakes in the stream continuum upstream 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0

Appendix 3. continued. 8. Appendices______

Stream morphology and hydrology - continued Nr. parameter Wwe1 Kao1 Erk1 Vol1 Wal1 Roe1 Sal1 Elb1 Laa1 Dre1 Rur1 Kyl1 PrW1 Our1 Nim1 Len1 Nuh1 Ede1 Ork1 PrB1 a26 width of the floodplain [m] 80 30 60 35 30 40 140 35 40 30 30 250 60 150 50 30 100 500 100 30 a27 mean slope of the valley floor [%] 1.38 2.5 3.75 1.14 1.76 2.63 1.2 1.72 2.72 0.85 1.04 0.42 0.54 0.23 0.5 0.28 0.71 0.17 0.51 0.28 a28 mean slope of the thalweg [%] 1.18 2.14 3.3 1.1 1.76 2.38 1.1 1.67 2.6 0.85 0.97 0.41 0.5 0.22 0.48 0.27 0.65 0.16 0.45 0.26 a30_1 deciduous native forest 100 30 40 0 50 0 0 90 80 0 10 0 0 0 0 0 0 0 0 50 a30_2 coniferous native forest 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a30_3 mixed native forest 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a30_4wetland (mire) 00000000000000000000 a30_5 open grass-/bushland 0 0 0 20 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 a30_6reeds 00000000000000000000 a30_7naturally unvegetated 00000000000000000000 a30_10non-native forest 050002030000090000000000 a30_12crop land 0002000000000000002000 a30_13 pasture 0 20 60 50 30 70 100 0 20 0 0 0 100 100 100 50 100 80 100 50 a30_14clear-cutting 0000000100100000000000 a30_15urban sites (resid.) 000000000300000000000 a30_16 urban sites (industr.) 0 0 0 10 0 0 0 0 0 60 0 0 0 0 0 50 0 0 0 0 Human impacts on stream morphology upstream and downstream a31 number of other transverse structures 0 0 0 1 0 0 4 0 0 0 4 2 6 3 2 4 3 4 3 2 a32 torrent modification 00000000000000000000 a34 straightening 01110110011000010100 a35 removal of CWD 01110010010111111111 a36 cut-off meanders 01000000000000000110 a37_1 m below surface 0.2 1 0.5 1.2 0.4 0.3 0.8 0.5 0.8 2.1 0 1 1 1 1 1 1 1.5 1 0 a38 culverting 00000000000000000000 a39 number of other transverse structures 0 0 0 0 0 0 2 0 0 0 3 4 2 4 4 4 2 2 2 4 a40 torrent modification 00000000000000000000 a41 channelling for navigation 00000000000000000000 a42 straightening 00111110111100010100 a43 removal of CWD 01110111010111111101 a44 cut-off meanders 00000000000000000000 a45_1 below surface 0.2 0.6 0.8 1.6 0.7 0.8 0.8 0.6 1.2 2.1 0 1 1 1 1 1 1 2 0.5 0 a46 culverting 00000000000000000000 a47 no. of dams retaining sediment 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 4 0 3 0 0 a48 cumul.height of dams retaining sediment 00000000000000000000 a49 no. of dams obstructing migration 01000000000001040200 a50 cumul. height of dams obstructing migration 050000000000000000000 Human impacts on hydrology upstream and downstream a51 length of stretches with residual flow [m] 00000000000000000000 a53 pulse releases 01000000010000000000 a54 length of stretches with residual flow [m] 00000000000000000000

126

Appendix 3. continued. 8. Appendices______

Human impacts on floodplain upstream and downstream Nr. parameter Wwe1 Kao1 Erk1 Vol1 Wal1 Roe1 Sal1 Elb1 Laa1 Dre1 Rur1 Kyl1 PrW1 Our1 Nim1 Len1 Nuh1 Ede1 Ork1 PrB1 a56 impoundments or dams (% of length) 0 10 0 0 0 0 0 0 0 0 90 60 40 10 20 20 40 30 10 40 a56alack of natural woody vegetation 01111110011010110100 a56bnon-native woody vegetation 00000100001010000000 a57 lack of natural woody vegetation 00111111111010110100 a58 non-native woody vegetation 00000100001000000000 a59 impoundments or dams (% of length) 0 0 0 0 0 0 0 0 0 0 50 60 50 10 10 20 10 30 10 40 Pollution upstream of sampling site a60 source pollution 00010000011111111111 a61 non-source pollution 11110110011111101110 a62 sewage overflows 01000000011111111111 a63 eutrophication 11110110010011111111 a64 acidification 00000000000000000000 a65 liming 00000000000000000000 a66 mining 00000000000000000000 a67 toxic substances 00000000010000000000 Stream morphology and hydrology at sampling site a68 mean depth at bankfull discharge [cm] 63 61 48 160 65 35 72 58 92 210 50 144.6 115 133 50 156.5 109 201 74.5 120 a69 shading at zenith (foliage cover) 80 80 60 0 60 60 40 80 80 0 20 20 0 60 80 0 0 20 20 20 a70_l mean width of woody riparian veget.[m] left 603235003030500022012005 a70_r mean width of woody riparian veget.[m] right 10032430335305020022012025 a71_0channel form 00000000000000000000 a71_1meandering 00000000000000000000 a71_2braided 00000000000000000000 a71_3anabranching 11000000001000100000 a71_4sinuate 00001101100101001111 a71_5constrained (natural) 00000000000000000000 a71_6constrained (artificial) 00110010010010010000 a73 standing water bodies in floodplain 00111110110110011000 a74 no. of debris dams 30002011300000200123 a75 no. of logs 700040226032156008101618 a76_l shoreline with woody riparian veget.left 100 80 60 20 60 20 0 70 90 0 100 0 50 100 90 0 10 50 10 80 a76_r shoreline with woody riparian veget.right 100 40 20 20 100 90 20 100 90 0 100 80 30 70 90 0 30 80 30 80 Human impacts on stream morphology at sampling site a77_1dams 00000000000000110000 a77_2cumulative height 0000000000000011.50000 a78_1other transverse structures 00000060000000000000 a78_2cumulative height 0000001.20000000000000 a79l1concrete without seams 000000000010000000000 a79l2concrete with seams 00000000000000000000 a79l3 stones 0 0 0 0 0 10 10 10 0 0 0 0 0 0 0 100 30 20 0 0 a79l4wood 00000000000000000000

127 a79l5trees 00000000000000000000 a79l6stone plast. with interst. 040407000000100000000000 a79l7stone plast. without interst. 000000000600000000000

Appendix 3. continued. 8. Appendices______

Human impacts on stream morphology at sampling site - continued Nr. parameter Wwe1 Kao1 Erk1 Vol1 Wal1 Roe1 Sal1 Elb1 Laa1 Dre1 Rur1 Kyl1 PrW1 Our1 Nim1 Len1 Nuh1 Ede1 Ork1 PrB1 a79l8other materials 000000100000000000000 a79l9 no bank fixation 100 60 60 30 100 90 80 90 100 30 90 100 100 100 100 0 70 80 100 100 a79r1 concrete without seams 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a79r2concrete with seams 00000000000000000000 a79r3 stones 0 0 0 0 0 60 20 0 0 0 0 0 30 0 10 100 20 10 0 0 a79r4wood 000000001000000000000 a79r5trees 00000000000000000000 a79r6 stone plast. with interst. 0 10 80 40 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 a79r7 stone plast. without interst. 0 0 0 0 0 0 0 0 0 80 0 0 0 0 0 0 0 0 0 0 a79r8other materials 000000100000000000000 a79r9 no bank fixation 100 90 20 60 100 40 70 100 90 0 100 100 70 100 90 0 80 90 100 100 a80_3 stones 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a80_4wood 00000000000000000000 a80_6 stone plast. with interst. 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 a80_9 no bank fixation 100 100 100 100 100 100 100 100 100 90 100 100 100 100 100 100 100 100 100 100 a81 stagnation 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 a83 channelling for navigation 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a84 straightening 00110110010001011110 a85 removal of CWD 01110110011110011100 a86 cut-off meanders 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a87_1 m below surface 0.2 0.8 0.7 1.6 0.4 0.4 0.8 0.5 1.2 2.1 0 1.5 1 1 1.5 1.5 1.5 2 0.5 0 a88 culverting 00010000000000000000 Human impact on hydrology at sampling site a89 pulse releases 01010000000000000000 a90 residual flow in compar. to unimpaired sites 00000000000000000000 Human impacts on floodplain at sampling site a92 impoundments at sampling site 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 a93 removal/lack of natural floodplain veget. 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 a94 non-native woody riparian vegetation 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pollution at sampling site a95 source pollution 00010000010010101100 a96 non-source pollution 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 a97 sewage overflows 01000000000000010000 a98 eutrophication 11110110000000100100 a99 acidification 00000000000000000000 a100liming 00000000000000000000 a101mining 00000000000000000000 a102 toxic substances 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

128

Appendix 3. continued. 8. Appendices______

Sample related information. Summer sampling. Nr. parameter Wwe2 Kao2 Erk2 Vol2 Wal2 Roe2 Sal2 Elb2 Laa2 Dre2 Rur2 Kyl2 PrW2 Our2 Nim2 Len2 Nuh2 Ede2 Ork2 PrB2 Microhabitat composition a103_1hygropetric sites 00000001001001100111 a103_2megalithal >40cm 03050501015101011112013001 a103_3 macrolithal >20cm to 40cm 5 25 20 10 30 10 15 35 25 0 10 1 0 20 1 80 0 20 20 60 a103_4 mesolithal >6cm to 20cm 45 25 45 60 20 55 50 40 45 55 80 100 100 80 85 0 100 50 80 40 a103_5 microlithal >2cm to 6cm 40 10 25 30 40 30 15 20 25 25 0 0 0 0 0 0 0 0 0 0 a103_6akal >0.2cm to 2cm 55515505000000010000 a103_7psammal/psammopelal 5501101001001011010101 a103_8argyllal <6µm 000000100000000500000 a104_1phytal 00000010001100020102011 a104_2algae 00100010001000001111 a104_3submerged macrophy. 050010111001001001005 a104_4emergent macrophytes 00001100000000000000 a104_5living parts of ter. plants 15511015111110101111 a104_6xylal 51015111501101501111 a104_7CPOM 01100111100000100101 a104_8FPOM 100000000010111000000 a104_9sewage bacteria 00100000000000000000 a104_10organic mud 11011011000000001000 a104_11debris 00000000000000000000 a105 average stream width[m] 2.5 3.7 2.9 4.5 3 2.3 3.5 2 1.8 6 40 18 17 14 25 6 6.5 20 11 20 a108 odours 00010000010100010000 a109 foam 01010000000000001100 a110 pH-value 6.11 7.44 7.17 8 7.72 8.13 8.25 7.78 7.7 8.12 7.72 8 7.9 7.32 8.24 7.96 7.33 8.03 8.44 7.95 a111 conductivity [µS/cm] 173 185 93 673 163 236 265 174 150 153 120.8 299 196 136 440 279 364 208 328 204 a112 reduction phenomena 00000000000000000000 a113 waste 00010010010000010000 a114 dissolved oxygen [mg/l] 11.6 10.35 9.98 10.1 10.35 9.9 11.1 9.5 10.35 11.5 11.3 9.7 9.4 10.4 9.5 8.9 9.73 10.3 11.36 13.1 a115 oxygen saturation [%] 122 110 93.6 96 110 89 99 98 110 96.8 105 95.9 92.5 111 104 97.5 91.1 103.4 109 144 a117 mean depth[cm] 12.4 12.05 9.1 11.25 15.4 3.55 17.3 21.95 --- 36.2 27 40.25 47 38.75 24.25 22.25 20 25.75 23 34.2 a118 maximum depth[cm] 27 32 27 19 82 5 28 51 --- 50 50 65 65 60 45 30 40 50 40 55 a119 mean current velocity[m/s] 0.169 0.415 0.437 0.325 0.321 0.327 0.269 0.227 --- 1.012 0.374 0.846 0.485 0.733 0.55 0.528 0.359 0.449 0.274 0.721 a120 max. current velocity[m/s] 0.57 1.06 0.83 0.68 0.96 0.58 0.75 0.7 --- 1.5 0.97 1.18 0.94 1.31 1.34 0.98 0.7 1.26 0.64 1.31 chemistry a121 alkalinity [mmol/l] 0.5 0.8 0.45 1.9 1.1 1.5 2 0.72 0.45 0.5 0.27 1.15 0.42 1.67 0.43 0.75 1.4 1 1 1.47 a122 total hardness [mmol/l] 0.72 0.74 0.42 1.4 0.8 0.9 1 0.84 0.7 0.63 0.42 1.24 0.78 0.5 1.76 1 1.55 1 1.25 0.7 a123 chloride [mg/l] 22 22 16 60 20 18 18 12 16 30 22 24 30 30 22 21 30 22 28 20 a124 BOD5 [mg/l] 2.441 0.39 0.78 8.33 1.49 1.49 1.57 1.2 9.27 1.16 1.34 1.83 1.45 1.49 1.52 5.57 2.61 1.35 1.97 1.71 a125 ammonium [mg/l] 0.129 0.092 0.08 0.065 0.034 0.026 0.068 0.049 0.068 0.184 0.016 0.115 0.144 0.126 0.154 0.227 0.55 0.068 0.133 0.107

129 a126 nitrite [mg/l] 0.029 0.02 0.01 0.113 0.02 0.05 0.269 0.005 0.007 0.037 0.015 0.036 0.043 0.033 0.035 0.077 0.26 0.023 0.104 0.054 a127 nitrate [mg/l] 16.78 11.92 4.244 19.71 7.456 80287 16.44 0.71 3.054 10.22 10.31 9.121 20.69 17.41 18.54 5.574 8.846 3.201 13.77 19.63 a128 ortho-phosphate [µg/l] 34 30 55 1262 49 169 789.3 43 269 111 52.16 179 151 72 304 174.1 552 136.3 1042 161 a129 total phosphate [µg/l] 138 64 48 1948 136 190 78 45 66 1388 85.61 302 245 122 433 367.8 568 157.7 896 239

Appendix 3 continued. 8. Appendices______Sample related information. Summer sampling. Nr. parameter Wwe2 Kao2 Erk2 Vol2 Wal2 Roe2 Sal2 Elb2 Laa2 Dre2 Rur2 Kyl2 PrW2 Our2 Nim2 Len2 Nuh2 Ede2 Ork2 PrB2 Microhabitat composition a103_1hygropetric sites 00000001001001100111 a103_2 megalithal >40cm 0 30 5 0 5 0 10 1 5 10 10 1 1 1 1 20 1 30 0 1 a103_3 macrolithal >20cm to 40cm 5 25 20 10 30 10 15 35 25 0 10 1 0 20 1 80 0 20 20 60 a103_4 mesolithal >6cm to 20cm 45 25 45 60 20 55 50 40 45 55 80 100 100 80 85 0 100 50 80 40 a103_5microlithal >2cm to 6cm 401025304030152025250000000000 a103_6akal >0.2cm to 2cm 55515505000000010000 a103_7psammal/psammopelal 5501101001001011010101 a103_8argyllal <6µm 000000100000000500000 a104_1phytal 00000010001100020102011 a104_2algae 00100010001000001111 a104_3submerged macrophy. 050010111001001001005 a104_4emergent macrophytes 00001100000000000000 a104_5living parts of ter. plants 15511015111110101111 a104_6xylal 51015111501101501111 a104_7CPOM 01100111100000100101 a104_8FPOM 100000000010111000000 a104_9sewage bacteria 00100000000000000000 a104_10organic mud 11011011000000001000 a104_11debris 00000000000000000000 a105 average stream width[m] 2.5 3.7 2.9 4.5 3 2.3 3.5 2 1.8 6 40 18 17 14 25 6 6.5 20 11 20 a108 odours 00010000010100010000 a109 foam 01010000000000001100 a110 pH-value 6.11 7.44 7.17 8 7.72 8.13 8.25 7.78 7.7 8.12 7.72 8 7.9 7.32 8.24 7.96 7.33 8.03 8.44 7.95 a111 conductivity [µS/cm] 173 185 93 673 163 236 265 174 150 153 120.8 299 196 136 440 279 364 208 328 204 a112 reduction phenomena 00000000000000000000 a113 waste 00010010010000010000 a114 dissolved oxygen [mg/l] 11.6 10.35 9.98 10.1 10.35 9.9 11.1 9.5 10.35 11.5 11.3 9.7 9.4 10.4 9.5 8.9 9.73 10.3 11.36 13.1 a115 oxygen saturation [%] 122 110 93.6 96 110 89 99 98 110 96.8 105 95.9 92.5 111 104 97.5 91.1 103.4 109 144 a117 mean depth[cm] 12.4 12.05 9.1 11.25 15.4 3.55 17.3 21.95 --- 36.2 27 40.25 47 38.75 24.25 22.25 20 25.75 23 34.2 a118 maximum depth[cm] 27 32 27 19 82 5 28 51 --- 50 50 65 65 60 45 30 40 50 40 55 a119 mean current velocity[m/s] 0.169 0.415 0.437 0.325 0.321 0.327 0.269 0.227 --- 1.012 0.374 0.846 0.485 0.733 0.55 0.528 0.359 0.449 0.274 0.721 a120 max. current velocity[m/s] 0.57 1.06 0.83 0.68 0.96 0.58 0.75 0.7 --- 1.5 0.97 1.18 0.94 1.31 1.34 0.98 0.7 1.26 0.64 1.31 chemistry a121 alkalinity [mmol/l] 0.5 0.8 0.45 1.9 1.1 1.5 2 0.72 0.45 0.5 0.27 1.15 0.42 1.67 0.43 0.75 1.4 1 1 1.47 a122 total hardness [mmol/l] 0.72 0.74 0.42 1.4 0.8 0.9 1 0.84 0.7 0.63 0.42 1.24 0.78 0.5 1.76 1 1.55 1 1.25 0.7 a123 chloride [mg/l] 22 22 16 60 20 18 18 12 16 30 22 24 30 30 22 21 30 22 28 20 a124 BOD5 [mg/l] 2.441 0.39 0.78 8.33 1.49 1.49 1.57 1.2 9.27 1.16 1.34 1.83 1.45 1.49 1.52 5.57 2.61 1.35 1.97 1.71 a125 ammonium [mg/l] 0.129 0.092 0.08 0.065 0.034 0.026 0.068 0.049 0.068 0.184 0.016 0.115 0.144 0.126 0.154 0.227 0.55 0.068 0.133 0.107 a126 nitrite [mg/l] 0.029 0.02 0.01 0.113 0.02 0.05 0.269 0.005 0.007 0.037 0.015 0.036 0.043 0.033 0.035 0.077 0.26 0.023 0.104 0.054 a127 nitrate [mg/l] 16.78 11.92 4.244 19.71 7.456 80287 16.44 0.71 3.054 10.22 10.31 9.121 20.69 17.41 18.54 5.574 8.846 3.201 13.77 19.63 130 a128 ortho-phosphate [µg/l] 34 30 55 1262 49 169 789.3 43 269 111 52.16 179 151 72 304 174.1 552 136.3 1042 161 a129 total phosphate [µg/l] 138 64 48 1948 136 190 78 45 66 1388 85.61 302 245 122 433 367.8 568 157.7 896 239

8. Appendices

Appendix 4. Monthly mean temperatures for small- and mid-sized rivers. Values in italics have been extrapolated (for explanations see Chapter 2.2.4.)

Mean temperatures Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Wwe 13.0 12.2 13.6 12.7 9.8 6.4 5.1 3.1 4.2 5.4 6.8 11.2 11.3 Kao 12.6 12.6 13.6 12.5 10.1 6.5 5.1 3.1 3.7 5.0 6.6 11.1 11.5 Erk 12.8 12.0 13.3 12.3 9.6 6.6 5.5 3.7 3.9 5.0 6.3 10.5 10.9 Vol 14.6 13.5 14.7 13.4 11.3 8.5 6.6 4.8 5.1 5.9 7.4 12.6 13.7 Wal 12.1 11.1 12.2 11.2 8.9 6.6 5.3 4.0 4.6 5.1 6.3 10.2 10.4 Roe 13.0 12.2 13.5 12.5 9.6 6.4 5.2 3.9 4.5 5.1 6.5 10.7 10.9 Sal 12.1 11.5 12.3 11.1 9.1 7.1 5.9 4.6 5.2 5.7 6.9 10.3 10.7 Elb 12.0 11.8 13.4 11.8 9.0 6.3 4.5 2.7 3.2 3.9 5.9 10.5 11.2 Laa 12.1 11.7 13.3 11.7 9.1 6.4 4.5 2.4 2.9 3.7 5.6 10.6 11.1 Dre 14.2 13.9 15.2 13.2 10.6 7.9 6.2 5.0 5.5 6.2 7.2 11.9 13.4 Rur 15.0 13.1 14.6 12.7 9.5 6.6 5.3 3.3 3.7 4.8 6.5 12.2 13.2 Kyl 15.4 14.2 16.0 13.3 10.4 6.2 5.5 3.7 4.5 5.7 7.5 13.0 14.1 PrW 16.5 14.3 16.3 13.6 10.0 7.4 5.6 3.5 4.0 5.5 7.3 13.7 15.2 Our 15.6 13.6 15.1 12.9 9.5 7.1 5.7 3.9 4.4 5.7 7.3 12.7 14.1 Nim 14.0 13.4 14.9 13.7 10.5 7.4 6.1 4.5 5.3 6.4 7.9 12.7 13.3 Len 14.9 13.5 15.5 13.2 10.2 7.1 5.3 3.6 4.2 5.1 6.9 12.9 13.6 Nuh 14.0 13.5 15.2 13.0 10.5 7.3 4.7 2.7 3.6 4.3 6.5 11.8 12.9 Ede 17.0 15.7 17.5 14.2 10.3 7.6 5.1 2.8 3.5 4.4 7.2 14.4 15.7 Ork 15.5 14.5 16.3 13.3 10.1 6.7 4.4 2.2 3.4 4.2 6.9 13.1 14.2 PrB 17.0 14.8 16.9 13.9 10.2 7.6 5.6 3.4 3.9 5.5 7.3 14.1 15.7

Appendix 5. Monthly minimum temperatures for small- and mid-sized rivers. Minimum temperatures Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Wwe 8.3 9.9 10.5 10.4 7.2 4.4 0.8 -0.3 0.0 1.4 2.0 5.7 8.0 Kao 8.5 10.2 10.2 9.9 7.5 4.8 1.4 0.6 0.3 1.4 2.4 6.0 7.9 Erk 8.5 9.6 10.7 9.6 6.9 4.7 1.9 -0.1 -0.2 1.0 2.0 5.7 7.8 Vol 11.0 11.3 12.1 11.8 9.7 6.2 2.3 1.3 1.4 1.9 3.6 7.5 9.3 Wal 8.2 9.3 9.8 9.3 6.4 4.7 2.2 0.5 1.0 1.5 3.0 6.4 7.7 Roe 8.7 9.7 10.6 9.7 7.1 4.7 1.3 0.1 0.6 0.9 2.6 6.4 7.9 Sal 9.0 9.6 10.1 9.3 7.4 5.1 2.7 1.9 2.9 3.2 4.4 7.3 8.3 Elb 7.8 8.6 10.6 8.3 5.7 4.0 0.0 0.0 0.0 0.0 2.1 6.9 7.9 Laa 7.7 8.5 10.6 8.8 5.4 4.1 -0.1 -0.1 -0.1 -0.1 0.9 6.8 7.3 Dre 10.0 10.9 12.0 10.6 7.4 5.4 1.9 1.3 1.9 2.9 4.4 8.5 9.7 Rur 9.6 10.4 11.4 10.2 7.6 5.2 1.9 0.4 0.1 1.1 3.5 7.1 9.3 Kyl 11.2 11.9 13.8 10.7 8.2 5.7 0.5 2.0 1.0 2.9 4.7 8.2 10.1 PrW 10.9 11.3 13.4 10.5 7.7 5.8 0.6 -0.2 0.0 0.9 3.8 8.0 10.9 Our 9.6 10.7 11.3 10.2 7.4 5.7 1.3 -0.2 0.1 2.2 3.7 7.5 9.6 Nim 10.5 11.5 13.6 12.1 8.4 6.2 0.7 3.2 2.6 3.7 5.4 8.8 10.5 Len 9.6 11.8 13.0 10.9 8.3 6.5 0.6 -0.1 0.1 1.9 3.3 7.6 9.4 Nuh 8.4 11.2 12.4 10.2 8.7 5.0 -0.2 -0.2 0.1 0.4 2.1 7.2 8.8 Ede 11.6 13.9 15.1 11.1 8.9 5.8 0.8 -0.2 -0.2 1.4 4.2 8.4 11.4 Ork 9.9 11.9 12.4 9.6 7.1 4.5 -0.1 -0.1 -0.1 -0.1 2.6 8.0 9.7 PrB 10.9 11.7 13.3 10.7 8.0 5.9 0.2 -0.2 -0.2 1.5 3.8 8.1 10.7

131 8. Appendices

Appendix 6. Monthly maximum temperatures for small- and mid-sized rivers. Maximum temperatures Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Wwe 18.8 15.3 16.6 16.2 13.0 8.7 9.8 6.6 7.9 8.8 12.9 16.1 16.7 Kao 18.5 17.2 16.5 15.6 12.8 8.9 9.0 6.0 7.1 8.2 11.7 16.3 17.6 Erk 18.6 16.1 16.2 15.8 12.1 8.5 9.0 6.9 7.2 8.0 10.7 15.8 16.3 Vol 19.4 17.9 18.2 14.8 13.8 10.1 9.9 7.4 8.1 8.3 12.1 18.8 20.2 Wal 16.9 13.9 15.3 14.4 11.5 8.1 8.4 6.3 7.3 7.6 11.2 14.2 14.3 Roe 18.2 15.3 16.4 14.7 12.2 8.5 8.9 6.8 7.5 8.0 11.8 15.7 15.3 Sal 16.6 14.3 14.9 13.1 11.2 8.2 8.5 6.3 7.2 7.7 10.9 13.9 13.7 Elb 17.2 14.2 16.4 14.7 12.3 8.0 8.5 5.7 6.4 6.8 10.8 14.7 15.0 Laa 17.2 14.2 16.0 14.5 12.4 8.2 8.3 5.8 6.5 7.5 12.4 15.7 16.6 Dre 20.3 18.2 20.1 15.8 13.5 9.7 9.3 8.3 8.4 9.2 11.8 18.0 19.8 Rur 22.7 19.1 18.2 16.2 12.2 8.3 8.5 5.6 6.2 7.5 10.7 19.1 20.4 Kyl 20.7 18.7 18.3 15.8 14.0 8.9 8.8 4.8 7.0 8.0 10.7 19.6 20.5 PrW 23.1 19.8 19.6 16.9 13.1 9.2 9.2 6.9 6.9 8.6 11.2 20.2 22.4 Our 23.0 20.3 19.0 16.7 12.7 8.5 9.1 6.5 7.4 8.8 11.2 19.8 21.6 Nim 18.3 16.7 16.4 15.9 13.9 9.0 9.7 5.6 7.4 8.6 10.8 16.3 17.5 Len 19.5 15.7 18.2 15.6 13.2 8.7 9.1 6.8 7.5 7.8 12.1 17.5 18.4 Nuh 20.0 16.4 18.4 16.0 13.5 9.2 8.8 5.7 6.5 7.6 12.2 18.0 19.0 Ede 21.9 19.9 20.0 18.2 12.6 10.1 8.7 6.8 6.3 7.1 12.4 18.9 20.8 Ork 21.5 19.7 20.4 17.3 14.0 8.5 8.8 5.4 6.2 7.5 13.0 19.7 20.4 PrB 25.3 21.8 21.8 17.8 13.4 9.3 9.3 7.1 6.8 8.5 11.2 22.2 23.7

Appendix 7. Mean daily amplitudes for small- and mid-sized rivers.

Mean daily amplitudes Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Wwe 2.6 1.4 2.0 1.5 1.4 1.3 1.3 1.4 1.6 2.2 3.0 4.0 2.5 Kao 2.8 2.0 2.8 1.9 1.2 1.1 1.1 1.1 1.5 2.0 2.7 4.4 3.5 Erk 2.8 1.4 2.0 1.7 1.3 1.1 1.0 1.2 1.3 1.8 2.3 3.4 2.4 Vol 2.6 1.5 2.0 0.9 0.7 0.9 1.2 1.2 1.6 1.7 2.4 4.9 3.9 Wal 2.9 1.5 2.5 1.7 1.6 1.0 0.9 1.1 1.3 1.6 2.2 3.8 2.4 Roe 3.0 1.7 2.4 1.6 1.4 1.2 1.1 1.2 1.6 1.8 2.7 4.2 2.5 Sal 2.6 1.6 2.1 1.1 0.8 0.7 0.7 0.7 0.9 1.1 1.7 2.9 1.9 Elb 3.0 1.6 2.4 1.7 1.5 1.3 1.0 0.9 1.4 1.6 2.4 3.3 2.2 Laa 2.8 1.4 2.1 1.5 1.5 1.2 1.0 0.9 1.5 1.8 3.0 4.0 2.8 Dre 3.9 2.1 3.0 1.8 1.5 1.0 0.8 1.0 1.1 1.2 2.3 3.3 3.3 Rur 4.1 1.6 2.7 1.7 1.0 0.8 0.8 0.8 1.0 1.3 1.6 3.8 3.5 Kyl 3.0 1.5 1.9 1.7 1.0 0.7 0.9 0.7 1.0 1.1 1.4 3.3 3.5 PrW 3.1 1.8 2.3 1.4 0.9 0.7 0.9 1.0 1.2 1.5 1.8 3.0 2.6 Our 4.5 2.0 3.1 2.0 1.0 0.7 0.9 1.0 1.2 1.5 2.0 3.7 3.9 Nim 1.3 0.8 0.8 0.7 0.5 0.6 0.8 0.6 0.8 1.0 1.0 1.5 1.3 Len 2.3 1.1 1.4 1.0 0.7 0.7 0.9 0.8 1.1 1.3 1.8 2.6 2.1 Nuh 3.2 1.6 2.1 1.2 0.8 0.7 1.0 0.9 1.3 1.6 2.7 3.8 3.2 Ede 2.2 0.6 1.2 1.3 0.5 0.8 0.7 0.7 1.0 1.2 1.7 2.1 2.0 Ork 3.9 2.6 3.2 2.1 1.4 0.8 0.9 0.8 1.3 1.7 2.3 4.4 3.7 PrB 4.7 2.1 3.4 1.9 0.9 0.7 0.9 1.0 1.2 1.4 1.6 4.0 4.1

132 8. Appendices

Appendix 8. Maximum daily amplitudes for small- and mid-sized rivers.

Maximum daily amplitudes Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Wwe 4.8 3.2 3.2 2.6 2.2 2.7 2.9 2.7 2.7 3.8 5.3 7.0 4.6 Kao 5.5 4.1 4.4 3.6 2.3 2.1 2.2 1.6 2.5 3.2 4.9 6.7 6.4 Erk 5.2 2.8 3.7 3.4 2.1 2.2 2.2 2.6 2.2 3.4 4.4 5.7 4.7 Vol 5.3 3.9 3.7 1.9 1.3 1.7 2.7 4.0 2.6 3.7 4.7 7.7 7.4 Wal 5.4 2.9 3.7 3.3 2.7 1.8 1.7 3.2 2.7 3.6 3.8 5.7 4.3 Roe 5.9 4.0 4.3 3.3 2.6 2.0 2.2 2.9 3.2 4.1 4.7 6.3 5.0 Sal 4.7 3.0 3.8 2.3 1.4 1.6 1.6 1.8 1.8 2.1 3.2 4.2 3.6 Elb 5.2 3.3 3.5 3.2 2.6 2.4 2.1 2.0 2.7 2.7 3.9 5.0 4.1 Laa 5.2 3.0 3.5 2.7 2.6 2.1 2.2 2.3 2.7 3.8 5.5 6.1 5.8 Dre 7.0 4.9 4.9 3.2 2.6 2.3 1.9 2.1 2.3 3.8 5.3 5.2 6.6 Rur 7.7 4.6 4.6 3.4 1.9 1.5 1.5 1.5 1.9 2.3 3.3 6.4 7.2 Kyl 4.8 3.6 3.0 3.9 2.1 1.5 1.7 1.3 1.6 2.1 2.5 5.3 5.7 PrW 5.2 5.2 3.6 2.6 1.7 1.7 1.9 2.3 2.0 2.7 3.2 4.7 6.1 Our 7.5 5.0 5.0 4.2 1.8 1.8 2.1 2.1 1.9 3.0 4.0 5.9 6.9 Nim 2.4 2.3 1.3 1.1 1.3 1.6 1.8 1.1 1.3 2.0 1.9 2.6 2.2 Len 4.0 2.4 2.3 1.9 1.5 1.7 2.1 2.2 2.1 3.0 3.6 4.2 4.0 Nuh 6.0 3.4 3.5 2.2 1.6 1.9 2.0 1.7 2.3 3.7 4.4 6.0 6.1 Ede 3.8 1.9 2.0 2.5 1.4 1.9 2.3 1.6 2.3 2.8 3.3 3.6 3.8 Ork 7.5 4.9 5.4 4.2 2.4 1.5 2.0 2.2 2.3 3.7 4.2 7.9 7.5 PrB 7.9 5.2 5.6 4.1 1.7 1.6 1.9 2.5 1.8 2.3 2.7 6.5 7.3

133 8. Appendices

Appendix 9. Summary of the EPTC taxa. Spring sampling. Ephemeroptera. Numbers represent Indiviuals/m². For abbreviations of sampling sites in the small- and mid-sized rivers in the Lower Mountains of Western Germany see Appendix 1.

Ephemeroptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork Alainites muticus 0000000000000000000 Baetis alpinus 0000124.81.600003.20000000 Baetis buceratus 0000000000000000000 Baetis fuscatus 0000000000000000000 Baetis fuscatus- Gr. 0000000000000000000 Baetis lutheri 0000000000011.2000.80011.23.2 Baetis melanonyx 0000000000000000000 Baetis rhodani 12.8 0 7.2 52 1.6 30.4 38.4 1.6 9.6 0.8 66.4 87.2 5.6 28 72 31.2 91.2 76 97.6 Baetis scambus 0000000000.8000000000 Baetis sp. 4 0.8 9.6 12.8 22.4 8 14.4 1.6 8.8 0.8 87.2 87.2 1.6 20 26.4 4 35.2 39.2 40 Baetis vernus 000000000000000000.80.8 Centroptilum luteolum 0000000000000000000 Nigrobaetis niger 0.8000000000000000000 Ecdyonurus dispar 0000000000.8000000000 Ecdyonurus insignis 000000000000000000.80 Ecdyonurus macani 000000000000000000.80.8 Ecdyonurus torrentis 0000000000003.22.47.201.600 Ecdyonurus venosus 1.60001.640000000000000 Ecdyonurus venosus- Gr. 0.8 0 0.8 0.8 10.4 23.2 3.2 0.8 1.6 4 12.8 0.8 0 2.4 8 0 2.4 0 0 Electrogena lateralis 0000000000000000000 Electrogena sp. 0000000000000000000 Epeorus sylvicola 21.60.82.407611825.662.446.4087.26.404002.41217.6 Heptageniasp. 0000000000002.4000000 Heptagenia sulphurea 0000000000002.4000000 Heptageniidae Gen. sp. 10.40.84.8010.410.45.601.606.41.6000009.64.8 Rhithrogena hercynia 00800004.815.2000000008.80.8 Rhithrogena semicolorata- Gr. 48.8 5.6 26.4 0.8 66.4 134 144 38.4 130 0 130 8 0.8 0 0.8 0 4 188 31.2 Habroleptoides confusa 8.8 0 22.1 10.4 35.2 32 0 24.8 85.6 0 5.6 2.4 8 0 0 0.8 0.8 4 26.4 Habrophlebia lauta 001.90000000000000000 Habrophlebia sp. 0000000000001000000 Leptophlebiidae Gen. sp. 00.800000000000000000 Paraleptophlebia submarginata 0000.80040000011.200.8004.80 Ephemera danica 4.82.4000.800002.400.811.25.612.80.8000 Ephemera sp. 0000000000000000000 Ephemerella mucronata 000021.616.866.40.88.80000001.64800 Serratela ignita 00000000057.6000000000 Torleya major 0000000000015035.230.494.40.806.431.2 Caenis beskidensis 0000000000000000000 Caenis luctuosa 0000000000005.6000.8068.54.8 Caenis acrura 0000000000000000000 Caenis pseudorivulorum 0000000000000000000 Caenis rivulorum 00000000000.80000001.90.8 Caenis sp. 0000000000000000000

Appendix 10. Summary of the EPTC taxa. Spring sampling. Plecoptera.

Plecoptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Isoperla sp. 002.40.840.824.82014.401.6000000764.80 Perlodes microcephalus 1.6000.82.44.83.20.81.600.8000000000 Perlodidae Gen. sp. 00000000000000000000 Dinocras cephalotes 00003.20.802.44.800000000000 Perla burmeisteriana 00000000000000.80001.600 Perla marginata 0040127.2010.4400.8000.8000000 Perlidae Gen. sp. 0000000.80000000000000 Siphonoperla sp. 12.80.84.80.84.884812010.4000000.86.42.40 Brachyptera risi 2.414.417.600000002.42.401.6000000 Brachyptera seticornis 4.8020.800000001.6000000000 Amphinemura sp. 2.4036.803.22.41.64.823.2022.40000.800000 Nemoura avicularis 0.800000000.800000000000 Nemoura cinerea 00000000000000000.803.20 Nemoura marginata-Gr. 11.20000000120000000001.60 Nemoura sciurus 00000000000000000.8000 Nemoura sp. 1.62.400.83.24.80.84.811.202.400000.82.401.60.8 Protonemura auberti 00000000000000000000 Protonemura intricata 00000000000000000000 Protonemura meyeri 002.400000008000000000 Protonemura nimborum 01.60.800000000000000000 Protonemura nitida-Gr. 00000000000000000000 Protonemura praecox 000.8000000.800000000000 Protonemura sp. 3.21.64.801.6042.424.807.2000000000 Leuctra braueri 00003.2000000000000000 Leuctra geniculata 00000000010.40000000000 Leuctra nigra 000000003.200.8000000000 Leuctra sp. 243.21.61.62.4825.6834.44.813.600003.20000 134 8. Appendices

Appendix 11. Summary of the EPTC taxa. Spring sampling. Coleoptera.

Coleoptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Hydroporinae Gen. sp. Lv. 00000000000000000000 Nebrioporus elegans Ad. 00000000000000000000 Oreodytes sanmarkii Ad. 0000000.800000000.800000 Platambus maculatus Lv. 000000000.800000000000 Stictotarsus duodecimpustulatus 00000000000000000000 Elmis aenea Ad. 000000000000003.90002.60 Elmis aenea/maugetii Ad. 0.888.80.85.61.69.6000000000001.20 Elmis maugetii Ad. 000000000004.87.2028.900.815.290 Elmis rioloides Ad. 00000000002.4000000000 Elmis sp. Ad. 000000000.800000000000.8 Elmis sp. Lv. 5.6 1.6 2.4 0.8 0 3.2 6.4 0 2.4 0.8 3.2 8 14.4 4.8 26.4 2.4 0 19.2 9.6 10.4 Esolus angustatus Ad. 00.87.20400.80.811.200000000000 Esolus parallelepipedus Ad. 0000000.8000000000012.800 Esolus sp. Lv. 0426.404.810.41.63.212.8000000005.600 Limnius opacus Ad. 000000000000000001.600 Limnius opacus Lv. 0000000000000000019.200 Limnius perrisi Ad. 82.4002.402.41.60.800000000000 Limnius perrisi Lv. 00000000000.81.60000.80000 Limnius sp. Lv. 365.64.80.817.65216.83.280.80000000000 Limnius volckmari Ad. 000000000000005.60001.60 Limnius volckmari Lv. 0000000000042.405.6006.42.40 Oulimnius tuberculatus Ad. 0000000000003.2040088.80 Oulimnius tuberculatus Lv. 00000000000.80.82.42.40.800.840.80 Stenelmis canaliculata Ad. 00000000000000000000 Stenelmis canaliculata Lv. 000000000000000003.200 Orectochilus villosus Lv. 0.8000.800000008.814.401.6005.62.46.4 Brychius elevatus Ad. 00000000000000000000 Brychius elevatus Lv. 000000000000.800000000 Helophorus arvernicus Ad. 00000000000.8000000000 Helophorus brevipalpis Ad. 00000000000000000000 Hydraena dentipes Ad. 001.2000004.8000000003.200 Hydraena gracilis Ad. 0.81.63.602.405.61.6002.40.800005.63.240 Hydraena minutissima Ad. 00000000000000000000 Hydraena reyi Ad. 000000000000000.800000 Hydraena sp. Ad. 00000000000000000000 Hydraena sp. Lv. 00000000000000.8000000 Limnebius truncatellus Ad. 0.80000000000000000000 Anacaena globulus Ad. 0.80000000000000000000 Hydrophilidae Gen. sp. Lv. 0.80000000000000000000 Laccobius sp. Lv. 00000000000000000000 Dryops sp. Lv. 000000000000000.800000 Cyphon sp. Ad. 00000000000000000000 Elodes marginata Lv. 0.80000000.8000000000000 Hydrocyphon deflexicollis Lv. 120000000000000000000

135 8. Appendices

Appendix 12. Summary of the EPTC taxa. Spring sampling. Trichoptera.

Trichoptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork Rhyacophila dorsalis 0.80000000001.618.50014.40000 Rhyacophila fasciata 00000000001.67.90000000 Rhyacophila nubila 0004.811.2418.43.23.26.40000011.221.637.647.2 Rhyacophila obliterata 0000000000000000000 Rhyacophila praemorsa 00000000.800000000000 Rhyacophila sp. 0000000000000.8000000 Rhyacophila tristis 00000.80000.80000000000 Agapetus delicatulus 0000000000000000000 Agapetus fuscipes 000008.80000000000000 Agapetus ochripes 000000000001170000000 Agapetus sp. 0000000000000000000 Glossosoma conformis 4818.496.8089.66.406.41370000000000 Glossosoma sp. 0000000000000000000 Hydroptila sp. 000000000000.80000000 Chimarra marginata 000000000000000000.80 Philopotamus ludificatus 000000005.60000000000 Philopotamus montanus 1.640064.8802.400000000000 Philopotamus sp. 000.80000000000000000 Philopotamus variegatus 000000.80000000000000 Wormaldia occipitalis 0000000000000000000 Cheumatopsyche lepida 0000000000021.68.80000606.4 Hydropsyche dinarica 9.60003.202.44.81.6022.400000000 Hydropsyche incognita 00000000009.645.61.64.801.64.8012.8 Hydropsyche instabilis 0000000.80000001.60.8000.80 Hydropsyche pellucidula 000000.800000000.82.40.89.600 Hydropsyche saxonica 00000.800000000000000 Hydropsyche siltalai 01.608000000.84.853.617.62066.428.864.855.255.2 Hydropsyche sp. 14.4 0 14.4 0 31.2 11.2 29.6 8 14.4 0.8 34.4 19.2 1.6 4.8 32 11.2 22.4 25.6 28.8 Cyrnus trimaculatus 0000000000001.6000000 Plectrocnemia conspersa 0000000000000000000 Plectrocnemia geniculata 0000000000000000000 Plectrocnemia sp. 00000000000.800000000 Polycentropodidae Gen. sp. 00.800000000000000000 Polycentropus flavomaculatus 0000.800000002.48.800005.68.8 Polycentropus sp. 0000000000000.8000000 Psychomyia pusilla 000000000000.80.82.409.601.60 Tinodes rostocki 00.800000000000000000 Brachycentrus maculatus 0000000000015573.61698.40000 Brachycentrus subnubilus 0000000000000000000 Micrasema longulum 024000000000.800000000 Micrasema minimum 00000000004400000000 Micrasema setiferum 0000.80000000020300002550 Allogamus auricollis 000000000001.612.8424.804.8023.2 Anabolia nervosa 000000000000000000.81.6 Annitella obscurata 0000000000000000000 Anomalopterygella chauviniana 0001.6005.60012.813.600000000.8 Chaetopteryx villosa 000000000000003.20000 Drusinae Gen. sp. 000.80000000000000000 Drusus annulatus 0.8000000000000000000 Ecclisopteryx dalecarlica 00000000003.2000000.800 Ecclisopteryx guttulata 00000.800000000000000 Glyphotaelius pellucidus 0000000000000000000 Halesus digitatus 0000000000000.83.200.83.200 Halesus radiatus 7.2000000000000000000 Halesus rubricollis 0000000000000000000 Halesus sp. 0000000.800000000.80000 Halesus tesselatus 00000000000001.600000 Limnephilidae Gen. sp. 02.40.80000.81.6000.83.201.6120000 Melampophylax mucoreus 0000000000000000000 Micropterna/Stenophylax Gen. sp. 0000000000000000000 Potamophylax cingulatus 0000000080000000000 Potamophylax latipennis 00000000000000004.800 Potamophylax luctuosus 0000000000000000002.4 Potamophylax rotundipennis 0000000000000000000 Potamophylax sp. 26.402.400.800000000040000 Goera pilosa 0000000000000001.600.80 Goeridae Gen. sp. 0000000000000000000 Silo nigricornis 0000000000007.2000000 Silo pallipes 11.24.802.41.61.600000.800000000 Silo piceus 000000000010.48.800.80.805.60.84.8

136 8. Appendices

Appendix 12. continued.

Trichoptera continued Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Crunoecia irrorata 0000000000000001.60000 Lasiocephala basalis 000000000005.645.65.618.40000.8161 Lepidostoma hirtum 00000000006011.2100360.810.4032426.4 Adicella reducta 00000000000000000000 Athripsodes albifrons 00000000000000000600 Athripsodes bilineatus 00000000000.800004.8000.80 Athripsodes cinereus 000000000001.6201.600011.60.81.6 Athripsodes sp. 00000000001.6000000000 Ceraclea annulicornis 000000000000.859.20.80003.2031.2 Ceraclea dissimilis 00000000000000000000 Ceraclea nigronervosa 000000000000000000.800 Leptoceridae Gen. sp. 000000000000.80000.801.600 Mystacides azurea 0000000000.8000006.40000 Mystacides longicornis 00000000000000000000 Mystacides nigra 00000000000000000000 Mystacides sp. 0000000000000.800000.800 Oecetis notata 00000000000000000000 Oecetis testacea 00000000002.402.40.800.80000 Oecismus monedula 00000000000000000000 Sericostoma flavicorne 00000000000000000000 Sericostoma personatum 000000009.600000000000 Sericostoma sp. 6.4 1.6 7.2 0 8 6.4 0 16 0.8 0 17.6 34.4 32 17.6 50.4 0 16 0 12 84.8 Odontocerum albicorne 406.404.84.80.8169.6000009.600000

Appendix 13. Summary of the EPTC taxa. Summer sampling. Ephemeroptera.

Ephemeroptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Alainites muticus 000001.600000000000000 Baetis alpinus 00000000000000000000 Baetis buceratus 000000000000000.800000 Baetis fuscatus 00000000001.617.12.400006.400 Baetis fuscatus- Gr. 1.60000000000000000000 Baetis lutheri 000000000006401.6800128.80 Baetis melanonyx 00.8000000000000000000 Baetis rhodani 4 20.8 45.6 4 4 2.4 40.8 2.4 6.4 46.4 5.6 21.6 3.2 16.8 27.2 66.4 78.4 96.8 78.4 0 Baetis scambus 0000003.20000.814.906.45.6400.8025.60 Baetis sp. 4 12.8 152 29.6 41.6 45.6 157 1.6 16.8 26.4 24 393 4 32 160 109 308 59.2 326 0 Baetis vernus 00000000000.80001.600000 Centroptilum luteolum 0000000000000000000.80 Nigrobaetis niger 00000000000000000000 Ecdyonurus dispar 00000.800000011.243.20.802.412.800 Ecdyonurus insignis 00000000000000000000 Ecdyonurus macani 00000000000000000000 Ecdyonurus torrentis 0000000000.81.601.60000000 Ecdyonurus venosus 0.80000.843.203.200000000000 Ecdyonurus venosus -Gr. 0 0 2.4 2.4 18.4 8.8 8.8 12 27.2 0 11.2 12.8 29.6 5.6 0 0 59.2 46.4 11.2 0.8 Electrogena lateralis 00001.60.800000000000000 Electrogena sp. 00000004000000000000 Epeorus sylvicola 5.61.612038.4204.825.627.2035.2000002.41.61.60 Heptageniasp. 000003.2000000000000.800 Heptagenia sulphurea 00000000000000000000 Heptageniidae Gen. sp. 0 0 1.6 0 10.4 0 1.6 3.2 3.2 3.2 1.6 00000000.80 Rhithrogena hercynia 00000000000000000000 Rhithrogena semicolorata- Gr. 002.401.6016.84.81.620.80.8000001.6000 Habroleptoides confusa 11.609.1034.60002.60.8000000002.20 Habrophlebia lauta 11.606.105511.2427.227.804000000010 Habrophlebia sp. 00000000000000000000 Leptophlebiidae Gen. sp. 00000000000000000.8000 Paraleptophlebia submarginata 00000000000000000000 Ephemera danica 22.413.600400.80003.22.410.42.412.80001.60 Ephemera sp. 00000003.2000000000000 Ephemerella mucronata 00000000000000000000 Serratela ignita 7.2 48 130 124 43.2 76 283 31.2 121 0 78.4 609 84 17.6 36.8 51.2 333 466 103 0 Torleya major 00000000000000000000 Caenis beskidensis 000000000001.606.4001.6000 Caenis luctuosa 00000.8000000012.800.80054.47.50 Caenis acrura 0000000000000000003.70 Caenis pseudorivulorum 0000000000000000000.80 Caenis rivulorum 00000000003.200.80003.2000 Caenis sp. 00000000000000000000.8

137 8. Appendices

Appendix 14. Summary of the EPTC taxa. Summer sampling. Plecoptera.

Plecoptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Isoperla sp. 001.60001.6003.20000000000 Perlodes microcephalus 0000000000.80000000000 Perlodidae Gen. sp. 000014.4000000000000000 Dinocras cephalotes 0.806.3020.48.31.65.137.100000000000 Perla burmeisteriana 000000000000000003.200 Perla marginata 0011.3023.614.9022.117.300000000000 Perlidae Gen. sp. 00000000000.8000000000 Siphonoperla sp. 5.6000000.80015.20000000000 Brachyptera risi 00006.4422.346.456.8011.300.802.404000 Brachyptera seticornis 000010.40.80.912.858.407.8000000000 Amphinemura sp. 003.200000000000000000 Nemoura avicularis 00000000000000000000 Nemoura cinerea 00000000000000000000 Nemoura marginata-Gr. 00000000000000000000 Nemoura sciurus 00000000000000000000 Nemoura sp. 2.4000000004.80000000000 Protonemura auberti 0.80.8000.80.800.8000000000000 Protonemura intricata 00400000000000000000 Protonemura meyeri 00000000000000000000 Protonemura nimborum 00000000000000000000 Protonemura nitida-Gr. 56.8846.4053.60.8031.227.202.40000.800000 Protonemura praecox 00000000000000000000 Protonemura sp. 12.800.800000000000000.8000 Leuctra braueri 00008.80000.800000000000 Leuctra geniculata 00000000006.440.820419.2011.24447.28.8 Leuctra nigra 00000000000000000000 Leuctra sp. 40 14.4 138 139 72 51.2 165 20 94.4 5.6 18.4 71.2 8 29.6 12.8 10.4 214 39.2 144 21.6

Appendix 15. Summary of the EPTC taxa. Summer sampling. Coleoptera.

Coleoptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Hydroporinae Gen. sp. Lv. 000000000.800000000400 Nebrioporus elegans Ad. 00000.800000000000000.80 Oreodytes sanmarkii Ad. 002.400.803.22.41200000000000 Platambus maculatus Lv. 00000000000000000000 Stictotarsus duodecimpustulatus Ad. 00000.8000000000000000 Elmis aenea Ad. 00000000002.600010.400001.7 Elmis aenea/maugetii Ad. 18.4 53.6 20.8 0.8 33.6 16 3.2 72 1.6 0 0 0 0 0 0 0 3.5 0 0 0 Elmis maugetii Ad. 0 0 0 0 0 0 0 0 0 0 1.4 0 0 14.4 41.6 6.4 30.1 78.8 46.8 124 Elmis rioloides Ad. 000000000020.800000021.23.60 Elmis sp. Ad. 0000000000003.20000000 Elmis sp. Lv. 4 9.6 3.2 12 2.4 3.2 6.4 0.8 0.8 0 20 23.2 3.2 6.4 28.8 2.4 23.2 70.4 24 30.4 Esolus angustatus Ad. 4.8 1.6 4.8 0 4.8 4 0.8 2.4 0.8 0 0 0 0 0 0 0 0 0 0 0 Esolus parallelepipedus Ad. 0000000000000000080.80 Esolus sp. Lv. 0 0 12 0 4 0 0.8 0 0 0 0.8 0.8 0 0 0.8 0 0 0 4 0.8 Limnius opacus Ad. 00000000.80000000000.801.6 Limnius opacus Lv. 0000000000000000038.402.4 Limnius perrisi Ad. 9.6 3.2 1.6 0 13.6 12.8 8 4.8 5.6 0 0 0 0 0 0 0 0 0 0 0 Limnius perrisi Lv. 00000000000.80.80.80.8000000 Limnius sp. Lv. 32.8 4 11.2 4.8 17.6 20 8.8 10.4 2.4 6.4 0 0 0 0 0 0 0 0 0 0 Limnius volckmari Ad. 0.8 0 0 0 0 0.8 0 6.4 0.8 0 1.6 0.8 0.8 0 4.8 0 0.8 7.2 4 5.6 Limnius volckmari Lv. 0 0 0 0 0 0 0 0 0 0 0.8 16 0 0 42.4 0 4.8 3.2 2.4 16 Oulimnius tuberculatus Ad. 0000000000001.60401.64.83.24 Oulimnius tuberculatus Lv. 0 0 0 0 0 0 0 0 0 1.6 0 2.4 3.2 2.4 2.4 0.8 1.6 6.4 9.6 4 Stenelmis canaliculata Ad. 000000000000000004.800 Stenelmis canaliculata Lv. 0000000000000000010.400 Orectochilus villosus Lv. 0000000000.80000000000 Brychius elevatus Ad. 00000.8000000000000000 Brychius elevatus Lv. 00000000000000000000 Helophorus arvernicus Ad. 00000000000000000000 Helophorus brevipalpis Ad. 00000000000000000.8000 Hydraena dentipes Ad. 00003.6004.1000000003.58.83.20 Hydraena gracilis Ad. 5.6 1.2 12 0.8 12.4 4 8.8 15.1 0 1.6 1.6 3.2 1.6 4.8 2.4 0 31.4 0 4.8 3.7 Hydraena minutissima Ad. 01.2000000000000000000 Hydraena reyi Ad. 00000000000000003.5001.9 Hydraena sp. Ad. 000000000.80000000.80000 Hydraena sp. Lv. 00000000000000000000 Limnebius truncatellus Ad. 00000000000000000000 Anacaena globulus Ad. 00000000000000000000 Hydrophilidae Gen. sp. Lv. 00000000000000000000 Laccobius sp. Lv. 0000000000000.80000000 Dryops sp. Lv. 00000000000000000000 Cyphon sp. Ad. 0.80000000000000000000 Elodes marginata Lv. 21.600012000.8000000000000 Hydrocyphon deflexicollis Lv. 360000001.6000000000000

138 8. Appendices

Appendix 15. Summary of the EPTC taxa. Summer sampling. Trichoptera.

Trichoptera Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Rhyacophila dorsalis 13.711.22.5000000012.428.8035.234000078.7 Rhyacophila fasciata 3.94.814.4000000012.432001.201.40019.7 Rhyacophila nubila 00045.615.23246.484.815.20000013811898.41460 Rhyacophila obliterata 03.215.205.6004.81.600000000000 Rhyacophila praemorsa 00000000000000000000 Rhyacophila sp. 00000000000000000000 Rhyacophila tristis 06.4003.2000.8000000000000 Agapetus delicatulus 0060.400000000000000000 Agapetus fuscipes 007.60000000019.50.800000023.3 Agapetus ochripes 00000000000101000000010.3 Agapetus sp. 00002.4000000000000000 Glossosoma conformis 12.8029.601.60012.89.600000000000 Glossosoma sp. 00000000000000000000.8 Hydroptila sp. 0000000000040.803.200000 Chimarra marginata 0000000000000000017.600 Philopotamus ludificatus 037.164.701280011916.200000000000 Philopotamus montanus 02.60020111.2025.4700000000000.8 Philopotamus sp. 1.600000000000000000.800 Philopotamus variegatus 006.500000000000000000 Wormaldia occipitalis 0000000017.600000000000 Cheumatopsyche lepida 000000000001200000.812804.8 Hydropsyche dinarica 10.40402.40.804.8000000000000.8 Hydropsyche incognita 0000000000870.43.234.4047.202.4023.2 Hydropsyche instabilis 2.4032.80138442419818.40000000005.60 Hydropsyche pellucidula 00000000002.4080000058.45.6 Hydropsyche saxonica 00000000000000000000 Hydropsyche siltalai 12.8 4.8 56 18.4 0 0.8 8.8 26.4 0.8 2.4 331 13.6 3.2 0 15.2 8 78.4 210 0 3.2 Hydropsyche sp. 6.4 2.4 30.4 7.2 20 34.4 1.6 28 0.8 0.8 8 186 5.6 120 12.8 33.6 38.4 15.2 240 83.2 Cyrnus trimaculatus 00000000000000000000 Plectrocnemia conspersa 0.80000000000000000000 Plectrocnemia geniculata 000000000.800000000000 Plectrocnemia sp. 00000000000000000000 Polycentropodidae Gen. sp. 00000000000000000000 Polycentropus flavomaculatus 01.601.600000033.65.615.205.600801629.6 Polycentropus sp. 00000000000000000000 Psychomyia pusilla 00000000000724.84014.408.81.64.8 Tinodes rostocki 00000000000000000000 Brachycentrus maculatus 000000000001168000000028.8 Brachycentrus subnubilus 000000000001.60000054.40.80 Micrasema longulum 00000000001.6000000000 Micrasema minimum 0.800000000092.80000000032 Micrasema setiferum 000000000000000005.600 Allogamus auricollis 000000000000.82.40.812083.201633.6 Anabolia nervosa 00000000000.800000409.60 Annitella obscurata 00000013.6000000000005.60.8 Anomalopterygella chauviniana 0 0.8 0.8 1.6 0 0.8 27.2 0 0 42.4 1.6 0 0 0 1.6 0 2.4 0.8 0 0 Chaetopteryx villosa 14.4 163 2.4 14.4 18.4 0 12 80 36 0 0 1.6 0 4.8 21.6 0 25.6 0.8 0 0 Drusinae Gen. sp. 00000000000000000000 Drusus annulatus 00000000000000000000 Ecclisopteryx dalecarlica 00000000000000000000 Ecclisopteryx guttulata 00000000000000000000 Glyphotaelius pellucidus 00000.8000000000000000 Halesus digitatus 2.400001.62.403.200000000.8000.8 Halesus radiatus 3.20001.6001.6000000000000 Halesus rubricollis 00000000000000000000.8 Halesus sp. 00000000000000000000 Halesus tesselatus 0000000000001.60.8000000.8 Limnephilidae Gen. sp. 39.20.82.4064.81.6025.613.60000.80000000 Melampophylax mucoreus 0000000.80000000000000 Micropterna/Stenophylax Gen.sp. 00000000001.6000000000 Potamophylax cingulatus 401.6000.8000.800000000000 Potamophylax latipennis 00000000000000000000 Potamophylax luctuosus 000000000.800000001.6000 Potamophylax rotundipennis 02.4000.8000000000000000 Potamophylax sp. 00000000000000000000 Goera pilosa 00000000020001.60000000 Goeridae Gen. sp. 00.80000.8000000000.80.8001.60.8 Silo nigricornis 00000000000000000000 Silo pallipes 6.40000000000000000000 Silo piceus 000000000000.81.60000.8000

139 8. Appendices

Appendix 15. continued.

Trichoptera continued Wwe Kao Erk Vol Wal Roe Sal Elb Laa Dre Rur Kyl PrW Our Nim Len Nuh Ede Ork PrB Crunoecia irrorata 00000000000000000000 Lasiocephala basalis 00000000000000000000 Lepidostoma hirtum 00000000009.65.63.24.800035.219.25.6 Adicella reducta 000.800000000000000000 Athripsodes albifrons 000000000001011.60000126.43.6 Athripsodes bilineatus 00000000000000009.60.84.81.4 Athripsodes cinereus 000000000000000004.801.4 Athripsodes sp. 00000000000000000000 Ceraclea annulicornis 00000000000000000000 Ceraclea dissimilis 000000000000000003.200 Ceraclea nigronervosa 00000000000000000000 Leptoceridae Gen. sp. 000000000000000.8000.82.40 Mystacides azurea 00000000001.604.85.60001.600 Mystacides longicornis 000000000000000001.600 Mystacides nigra 000000000000000.800.8000 Mystacides sp. 00000000000000000000 Oecetis notata 000000000000000006.400 Oecetis testacea 000000000014.4000000000 Oecismus monedula 004.800.8004.8000000000000 Sericostoma flavicorne 000.8000000000.80000000.80 Sericostoma personatum 0.801.600000000000000000 Sericostoma sp. 54.4 8.8 41.6 1.6 59.2 5.6 1.6 90.4 25.6 0 80 79.2 15.2 12.8 31.2 0 69.6 0 12 19.2 Odontocerum albicorne 15.204015.216.812.834.449.6000.80037.600000

140 8. Appendices

Appendix 16. Temperature parameters of the Lenne, Chapter 3.

Mean temperatures ALT FIN LEN PLE WIN DRE SST LAS BUS Mar-01 5.5 6.0 5.6 6.0 6.0 6.0 6.7 7.4 7.2 Apr-01 6.9 7.5 7.2 7.2 7.5 7.8 8.4 9.1 9.0 May-01 12.9 13.2 10.7 11.6 12.1 13.0 14.3 14.6 15.1 Jun-01 13.6 14.0 11.4 12.7 13.2 14.1 14.5 15.1 15.5 Jul-01 17.0 16.9 12.3 14.3 14.8 16.0 16.8 17.5 18.1 Aug-01 16.9 17.0 11.8 13.7 14.1 15.2 18.2 18.8 19.0 Sep-01 11.5 12.0 11.1 11.7 11.6 11.9 13.3 13.9 13.9 Oct-01 10.9 11.4 10.5 11.2 10.9 11.1 13.0 13.4 13.4 Nov-01 6.5 7.1 8.0 7.9 7.7 7.6 8.6 8.9 8.8 Dec-01 3.8 4.7 5.4 5.2 5.1 5.0 5.3 5.7 5.4 Jan-02 3.0 3.9 4.2 4.1 4.0 3.9 4.9 5.4 5.1 Feb-02 5.6 6.0 5.6 5.8 5.7 5.7 6.2 6.6 6.5 Mar-02 5.3 5.9 5.7 5.7 5.7 5.8 6.5 7.0 6.9

Minimum temperatures ALT FIN LEN PLE WIN DRE SST LAS BUS Mar-01 1.9 3.0 3.3 3.8 4.2 3.5 5.1 6.2 5.4 Apr-01 3.3 4.6 5.2 4.6 5.3 5.6 5.6 6.5 5.8 May-01 7.6 8.3 7.2 7.4 7.7 8.3 8.4 9.3 8.8 Jun-01 9.4 10.6 8.3 9.0 9.6 10.0 9.9 10.9 10.9 Jul-01 13.6 14.1 10.4 11.9 12.3 13.1 13.6 14.2 14.3 Aug-01 12.7 12.9 9.9 11.4 12.1 12.9 13.2 14.7 14.3 Sep-01 8.5 9.1 9.3 9.6 9.9 10.1 10.6 11.2 10.9 Oct-01 9.3 10.0 9.7 10.0 10.0 10.2 10.4 10.5 10.7 Nov-01 3.9 4.6 6.3 6.1 6.2 6.0 6.3 6.5 6.4 Dec-01 -0.1 1.2 2.3 2.0 1.4 1.4 1.3 1.9 1.2 Jan-02 -0.2 0.7 1.9 0.9 0.9 0.7 0.5 1.1 0.5 Feb-02 3.7 4.0 4.4 4.0 4.2 4.2 4.4 4.8 4.5 Mar-02 3.7 4.3 4.5 4.4 4.5 4.5 4.6 5.0 4.8

Maximum temperatures ALT FIN LEN PLE WIN DRE SST LAS BUS Mar-01 7.8 8.0 7.4 7.6 7.6 7.8 8.5 9.1 9.1 Apr-01 12.1 12.1 10.9 12.1 12.0 12.1 12.0 11.8 12.8 May-01 17.5 17.4 14.0 15.4 15.4 16.5 21.5 19.1 19.4 Jun-01 18.4 19.2 14.2 16.6 17.0 18.3 20.0 19.6 21.0 Jul-01 21.8 21.0 14.8 17.0 17.6 19.1 24.2 22.1 22.7 Aug-01 21.0 20.8 13.7 16.3 18.2 18.1 23.7 22.4 23.4 Sep-01 16.0 16.0 12.6 13.7 13.2 14.2 18.1 18.1 18.0 Oct-01 13.5 13.9 12.2 13.4 13.6 12.6 16.9 16.1 16.0 Nov-01 9.7 10.0 10.0 10.2 10.1 10.3 12.6 13.6 13.1 Dec-01 8.3 8.5 8.8 8.8 8.8 8.8 9.4 9.6 9.6 Jan-02 7.5 7.7 6.8 7.0 6.9 7.0 7.7 7.6 7.5 Feb-02 7.9 8.1 7.4 7.6 7.5 7.4 8.2 8.9 8.8 Mar-02 6.8 7.3 6.9 7.1 6.9 7.0 8.1 9.1 9.0

141 8. Appendices

Appendix 16. continued.

Monthly amplitudes ALT FIN LEN PLE WIN DRE SST LAS BUS Mar-01 5.9 5.0 4.0 3.8 3.4 4.3 3.4 2.9 3.7 Apr-01 8.7 7.6 5.7 7.4 6.6 6.4 6.3 5.3 7.0 May-01 9.9 9.1 6.8 8.0 7.6 8.3 13.2 9.8 10.6 Jun-01 9.0 8.6 5.9 7.6 7.4 8.3 10.0 8.6 10.1 Jul-01 8.2 7.0 4.4 5.1 5.4 6.0 10.6 7.8 8.4 Aug-01 8.3 7.9 3.8 4.9 6.2 5.2 10.5 7.8 9.1 Sep-01 7.5 6.9 3.3 4.1 3.4 4.1 7.5 6.9 7.1 Oct-01 4.2 3.9 2.5 3.3 3.6 2.4 6.5 5.6 5.3 Nov-01 5.8 5.4 3.7 4.0 3.9 4.4 6.3 7.1 6.8 Dec-01 8.4 7.2 6.5 6.8 7.4 7.4 8.1 7.7 8.5 Jan-02 7.6 7.0 4.9 6.1 6.0 6.3 7.2 6.4 7.0 Feb-02 4.3 4.1 2.9 3.5 3.3 3.2 3.8 4.1 4.3 Mar-02 3.1 3.0 2.4 2.7 2.4 2.5 3.5 4.0 4.2

Mean daily amplitudes ALT FIN LEN PLE WIN DRE SST LAS BUS Mar-01 1.4 1.4 0.9 0.9 0.6 0.8 1.0 0.6 0.8 Apr-01 1.8 1.7 1.1 1.6 1.1 0.9 1.2 0.9 1.5 May-01 2.6 2.2 1.8 2.6 1.6 1.9 3.0 1.8 2.2 Jun-01 2.1 2.2 1.7 1.9 1.2 1.4 2.1 1.5 2.1 Jul-01 2.6 2.4 1.8 1.9 1.2 1.7 2.4 1.7 2.1 Aug-01 2.4 2.7 1.5 1.6 1.3 1.3 2.9 1.7 2.0 Sep-01 0.8 0.9 0.7 0.8 0.6 0.6 1.4 1.2 1.0 Oct-01 0.8 0.9 0.7 0.8 0.6 0.6 1.4 1.2 1.0 Nov-01 0.8 0.7 0.5 0.7 0.5 0.5 1.2 1.1 1.1 Dec-01 0.9 0.8 0.6 0.8 0.7 0.6 0.8 0.8 0.9 Jan-02 0.9 0.7 0.5 0.8 0.6 0.6 1.0 0.9 0.9 Feb-02 1.1 0.9 0.7 0.8 0.6 0.6 0.7 0.7 0.8 Mar-02 1.2 1.1 0.7 0.9 0.7 0.6 0.8 1.0 1.0

Maximum daily amplitudes ALT FIN LEN PLE WIN DRE SST LAS BUS Mar-01 3.0 2.6 2.0 2.1 1.8 1.5 2.9 1.7 1.6 Apr-01 3.6 3.1 2.3 2.9 2.1 1.9 2.5 2.3 3.4 May-01 4.2 3.6 2.7 4.3 2.8 3.1 5.4 3.8 4.0 Jun-01 4.0 3.9 3.0 4.7 2.1 2.5 4.0 3.0 4.4 Jul-01 4.4 4.9 2.7 2.9 2.5 3.4 4.8 2.9 3.6 Aug-01 4.0 4.5 2.9 2.8 4.7 2.2 5.6 3.4 3.4 Sep-01 1.8 2.2 1.6 1.8 1.1 1.0 4.7 3.0 2.0 Oct-01 1.7 1.9 1.3 1.6 1.7 0.9 5.1 3.9 2.5 Nov-01 1.5 1.4 1.2 1.5 0.9 1.0 3.9 3.3 2.6 Dec-01 2.5 2.7 1.8 2.0 2.0 2.1 2.4 2.6 2.5 Jan-02 2.7 1.8 1.3 1.6 1.4 1.5 2.3 3.0 3.0 Feb-02 1.8 1.7 1.3 1.3 1.2 1.2 1.6 1.7 1.7 Mar-02 1.8 1.6 1.1 1.5 1.1 0.8 1.4 1.7 1.9

142

Danksagung

An dieser Stelle möchte ich mich bei all jenen bedanken, die mich während der Fertigstellung der Dissertation in vielfältiger Weise unterstützt haben.

Mein besonderer Dank gilt Herrn PD Dr. D. Hering, der trotz gedrängten Terminplans immer Zeit fand, um mir in anregenden Diskussionen fachlich weiterzuhelfen. Stets hatte er ein offenes Ohr für inhaltliche, methodische oder auch organisatorische Fragen, - vielen Dank für die Geduld, die vielen Ratschläge und Ermutigungen! Herrn Prof. Dr. H. Schuhmacher danke ich für die Überlassung des Themas, sowie für die Un- terstützung und Förderung über den gesamten Zeitraum. Der Bundesstiftung Umwelt sei gedankt für die finanzielle Unterstützung durch das dreijährige Promotionsstipendium und für die anregenden interdisziplinären Seminare und die Betreuung. Herrn Prof. Dr. J. Schwoerbel† möchte ich für eine wunderbare Einführung in die Limnologie in Konstanz danken, die meine Begeisterung für dieses Fachgebiet weckte und meinen weiteren Weg bestimmte. Herrn T. Ehlert sei gedankt für die Ermutigung zum Thema dieser Arbeit, und für die Unter- stützung in ihrer Entstehungsphase. Mein Dank gilt auch Herrn Dr. M. Sommerhäuser und Frau T. Pottgiesser für Tips und Gespräche vor allem in der Anfangsphase meiner Arbeit.

Der gesamten Abteilung möchte ich herzlich danken, denn durch die nette Zusammenarbeit und Hilfsbereitschaft ist die Arbeit in dieser Abteilung ein Vergnügen! Sei es auf den vielen Ausfahrten zu Probenahmen oder in der Uni, bei der Bestimmungsarbei- ten, Computerarbeiten, bei organisatorischen Problemen sowie Korrekturlesen – für die vielen Ratschläge und Hilfen möchte ich mich besonders bei Carolin Meier, sowie bei Armin Lorenz und Peter Rolauffs bedanken. Dafür, daß ich Ausfahrten nie allein bestreiten musste, sondern mich immer großartige Unter- stützung begleitete, danke ich: Lutz Janzen, Thomas Korte, Tim Kröffges, Silke Rödiger, Han- no Seebens, Ulf Unterberg und Marta Wenikajtys. Sandra Kramm sei gedankt für die unermüdliche Hilfe und ihre Ratschläge nicht nur, aber ge- rade auch bei kniffeligen Verwaltungs- und organisatorischen Angelegenheiten sowie fürs Kor- rekturlesen. Jörg Strackbein sei gedankt für digitale Rettung in höchster Not. Hilfestellung bei der Bestimmungsarbeit und faunistische Tips erhielt ich von Christian Feld, Peter Neu, Thomas Pitsch, und Berthold Robert, – vielen Dank! Für die Durchführung der chemischen Analysen möchte ich Jörg Kaminski, Gudrun Mert- schenk und Birgit Mückenheim danken. Den Staatlichen Umweltämtern, insbesondere Herrn C. Brügger vom StuA Siegen, sowie den Damen und Herren der StUA Hagen, StUA Lippstadt, StUA Itzehoe, dem Landesamt für Natur und Umwelt Kiel, dem Niedersächischen Landesamt für Ökologie und der BfG sei für die Aus- kunft über die aktuellen Wasserstände zur Planung der Ausfahrten sowie für Pegeldaten und Temperaturdaten und für Informationen über die Gewässer gedankt.

Ferntherapeutische statistische Unterstützung und Tips von Dr. Mark Haidekker konnte ich dank digitaler Technik und Satellitenkommunikation auch über den großen Teich hinweg be- kommen - vielen Dank!

Für die Unterstützung, die ich auf privater Ebene erhielt, möchte ich mich ganz besonders bei meiner Familie bedanken: Kai sei ganz herzlich gedankt für die Einsatzbereitschaft, mit der er all diese Jahre zu dem Drahtseilakt des Zeitmanagements gestanden hat: „...denn meine Frau hat das Auto und ich hab die Kinder...“, und mit welch unerschütterlicher Geduld er mich immer unterstützt und motiviert hat! Meinen Kindern Frederic und Vincent möchte ich für die fröhliche Anteilname an meiner Ar- beit sowie auch für ihre Geduld in der Endphase der Arbeit danken. Ein ganz besonderes Dankeschön geht an meine Eltern, die mich in meinem Studienwunsch und den Auslandsaufenthalten immer unterstützt, motiviert und gestärkt haben, mir das Studi- um ermöglichten, immer ein offenes Ohr und viele Hilfen bereithielten, unermüdlich Korrektur lasen, Ausfahrtenserien und lange Seminare ermöglichten durch liebevolle Enkelkinderbetreu- ung...– und zu alledem einfach wunderbare Eltern sind!

Lebenslauf

Name: Alexandra Haidekker

Anschrift: Krekelingheide 28 45259 Essen

Geburtsdatum: 05.08.1968

Geburtsort: Hamburg

Staatsangehörigkeit: Deutsch

Familienstand: Verheiratet, zwei Kinder

Schulbildung: 1974–1978 Grundschule 1978–1985 Wolfgang-Borchert-Gymnasium Halstenbek 1985–1986 Schüler-Auslandsaufenthalt in den USA 1986–1989 Theodor-Heuss-Gymnasium Pinneberg

Schulabschluß: 1989 Abitur

Studium: 1989 – 1991 Universität Hannover Diplomstudiengang Biologie 1991 – 1992 Universität Konstanz 1992 – 1993 Universidad Nacional, Costa Rica (IAS-Stipendium DAAD) 1993 – 1995 Universität Konstanz

Studienabschluß: Dipl.-Biologin

Zusätzliche 1995 Graduate Record Exam Prüfung: Berufstätigkeit: 1995 – 1999 Telearbeit in Hannover und Detmold für InfOrg GmbH. Aufgabenfelder: u.a. Entwicklung von Konzepten für Umweltschutz und Lebensmittelhygiene für gewerbliche Unternehmen; Mitwirkung bei Markt- und Meinungs- forschungsprojekten (Betriebsklimaanalysen).

Juli 1999 – Dez 1999: WHK an der Universität GH Essen, Abteilung Hydrobiologie. Projekt: „Typologieentwicklung und Leitbildfindung für mittelgroße und große Fließgewässer in NRW“. Auftraggeber: Landesumweltamt NRW.

Mai 2000 – April 2003: Promotionsstipendium der Deutschen Bundesstiftung Umwelt.

Juni 2004 – Dez. 2004: Wissenschaftliche Mitarbeiterin der Universität Duisburg-Essen. EU-Projekt Eurolimpacs: „Integrated Project to Evaluate the Impacts of Global Climate Change on European Freshwater Ecosystems“. Aufgabenfeld: „Indicators of Ecosystem Health“

Erklärung:

Hiermit erkläre ich, gem. § 6 Abs. 2, Nr. 7 der Promotionsordnung der Fachbereiche 6 bis 9 zur Erlangung des Dr. rer. nat., dass ich das Arbeitsgebiet, dem das Thema “The effect of water temperature on benthic macroinvertebrates – a contribution to the ecological assessment of rivers.” zuzuordnen ist, in Forschung und Lehre vertrete und den Antrag von Frau Alexandra Haidekker befürworte. Essen, d. ______PD Dr. Daniel Hering

Erklärung:

Hiermit erkläre ich, gem. § 6 Abs. 2, Nr. 6 der Promotionsordnung der Fachbereiche 6 bis 9 zur Erlangung des Dr. rer. nat., dass ich die vorliegende Dissertation selbständig verfasst und mich keiner anderen als der angegebenen Hilfsmittel bedient habe. Essen, d. ______Alexandra Haidekker

Erklärung:

Hiermit erkläre ich, gem. § 6 Abs. 2, Nr. 8 der Promotionsordnung der Fachbereiche 6 bis 9 zur Erlangung des Dr. rer. nat., dass ich keine anderen Promotionen bzw. Promotionsversuche in der Vergangenheit durchgeführt habe und dass diese Arbeit von keiner anderen Fakultät abge- lehnt worden ist. Essen, d. ______Alexandra Haidekker