Effects of multiple stressors on the distribution of fish communities in 203 headwater streams of Rhine, Elbe and Danube

Journal Pre-proofs Effects of multiple stressors on the distribution of fish communities in 203 headwater streams of Rhine, Elbe and Danube Melanie Mueller, Antje M. Bierschenk, Beate M. Bierschenk, Joachim Pander, Juergen Geist PII: DOI: Reference:

S0048-9697(19)34514-0 https://doi.org/10.1016/j.scitotenv.2019.134523 STOTEN 134523

To appear in:

Science of the Total Environment

Received Date: Revised Date: Accepted Date:

1 August 2019 16 September 2019 16 September 2019

Please cite this article as: M. Mueller, A.M. Bierschenk, B.M. Bierschenk, J. Pander, J. Geist, Effects of multiple stressors on the distribution of fish communities in 203 headwater streams of Rhine, Elbe and Danube, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134523

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Effects of multiple stressors on the distribution of fish communities in 203 headwater streams of Rhine, Elbe and Danube

Melanie Mueller*, Antje M. Bierschenk*, Beate M. Bierschenk*, Joachim Pander, Juergen Geist * equally contributing authors

Aquatic Systems Biology Unit, Department of Ecology and Ecosystem Management, Technical University of Munich, Mühlenweg 22, D-85350 Freising, Germany

Corresponding Author Juergen Geist Aquatic Systems Biology Unit, Department of Ecology and Ecosystem Management, Technical University of Munich, Mühlenweg 22, D-85354 Freising, Germany Email: [email protected] Phone: +49-8161-713767 Fax: +49-8161-713477

Running head: Effects of multiple stressors on fish

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Effects of multiple stressors on the distribution of fish communities in 203 headwater streams of Rhine, Elbe and Danube Abstract Fishes in European rivers are threatened by manifold stressors such as structural degradation, water pollution, overexploitation, land-use changes in the catchment, invasive species and global processes including climate change. Identifying main stressors in a stream / river system is of utterly importance for efficiently utilizing the scarce funds for conservation measures in order to achieve the best possible outcome. Within 203 headwater streams of Rhine, Elbe and Danube, we quantified the relative influence of different environmental stressors (water chemistry, food availability (macroinvertebrates), terrestrial predators) and anthropogenic stressors (land use, structural modification of streams) on fish assemblages at different spatial scales based on multivariate biota-environment models. In our analyses, the predictor variables percentage of impoundments, crop farming (especially erosion-prone crops such as maize) and ground sealing in the catchments, the number of wastewater treatment plants and biogas plants in the catchments as well as structural modifications of riverbanks were most often identified as stressors influencing fish community composition. However, the effects of the stressors varied between the investigated survey-area scales (two different catchments sizes and riparian stripes) and regionally (entire study area, major drainage systems, river catchments, stream sizes, geographical subregions). In most cases, fish community composition was simultaneously affected by multiple stressors, underpinning the need for a more holistic and ecosystembased approach in freshwater conservation and restoration.

Keywords: freshwater biodiversity conservation; catchment land use; piscivorous birds; wastewater;

structural

degradation;

2

hydropower

1. Introduction Freshwater fishes are one of the most threatened groups of vertebrates (Reid et al., 2013) with up to 42% of the European species facing extinction and 3% being already extinct (Darwall and Freyhof, 2016; Mueller et al., 2018). Almost all European rivers have been structurally modified and became disconnected from their former floodplains (Auerswald et al., 2019) making them a priority in the context of biodiversity conservation (Geist, 2011; Geist, 2015) and restoration (Pander and Geist, 2013; Geist and Hawkins, 2016). European rivers are impacted by manifold stressors such as structural degradation (e.g. colmation (Hancock, 2002), impoundments (Baxter, 1977), hydro-electrical use (Mueller et al., 2017)), water pollution (e.g. wastewater treatment plants (WWTP; Carey and Migliaccio, 2009), biogas plants (Studer et al., 2017)), overexploitation (Lewis et al., 2006), land-use changes in the catchment (Bierschenk et al., 2019a), invasive species (Strayer et al., 2006; Brandner et al., 2018) and global processes such as climate change (Xenopoulos et al., 2005; Tedesco et al., 2013). The example of the spread of neozoa such as round goby (Neogobius melanostomus P.), which is facilitated by impoundments and man-made rip-rap structures on river banks, illustrates the potential synergies of anthropogenic impacts on changes in fish community composition and the establishment of novel fish communities (Gehrke et al., 2002; Kruk and Penczak, 2003; Brandner et al., 2013). Impoundments upstream of transversal structures result in shifts from rheophilic to stagnophilic fish species due to altered riverine hydrology (from fast running to stagnant water; called ‘potamalization’) (Schmutz and Moog, 2018) as well as disturbance of the continuity of the rivers due to the transversal structures built in the river (Jungwirth et al., 2000). These transversal structures prevent the long- and mediumdistance migrating fish such as Danube salmon (Hucho hucho L.), European eel (Anuguilla anguilla L.), or common nase (Chondrostoma nasus L.) from completing their life cycle (Doenni et al., 2001; Ovidio and Philippart, 2002; Dumont, 2005; Dumont, 2006; Ihut et al., 2014). It can also limit gene flow between upstream and downstream populations resulting in

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a lower genetic diversity and increased vulnerability of these populations to extreme events (Knaepkens et al., 2004; McClure et al., 2008; Raeymaekers et al., 2009; Link and Habit, 2015). Further risks for migrating fish pose passages through hydropower plants (Mueller et al., 2017) or pumping stations (Bierschenk et al., 2019b) due to their potential to cause serious or even fatal injuries depending on fish species and size. Changes and intensifications of catchment land-use have been demonstrated to result in in-stream habitat changes and community shifts (e.g. Knott et al., 2019; Bierschenk et al., 2019a). It is also reflected in rising land consumption (especially an increased ground sealing), resulting in increased hydrologic extremes and amounts of contaminants, nutrients and fine sediment in the storm water runoff (Lozán and Kausch, 1996; Quinn and Stroud, 2002; Owens et al., 2005; Walsh et al., 2005; Schindler, 2006). Thus, habitat and water quality in the affected stream/river stretches degrade and consequently distress fish (Allan, 2004). Nutrient inputs of point-sources such as WWTP were greatly reduced over the past decades in most industrialized countries (Geist, 2014), but potential harmful substances for fish such as xenobiotics, herbicides, insecticides, manure, or digestate are still flushed into the streams and rivers (Sancho et al., 1997; Schwarzenbach et al., 2006; Solomon et al., 2013; Studer et al., 2017). Fish population, distribution, abundances and growth rates (Diehl and Kronijow, 1998; Nislow et al., 1998; Giannico, 2000; Akbaripasand et al., 2014) are also influenced by food availability (e.g. macroinvertebrates; Diehl and Kornijow, 1998; Brandner et al., 2013) and predation by piscivorous birds (Suter, 1995; Beckmann et al., 2006; Harris et al., 2008; Steinmetz et al., 2003; Jepsen et al., 2018). Food availability (e.g. macroinvertebrate density) fluctuates similar to fish populations due to anthropogenic stressor such as water pollution, habitat degradation, and climate change adversely affecting the macroinvertebrate densities (Collier et al., 2016). The age/size structure of fish populations can also vary due to predation of piscivorous birds specialized on a certain fish size (Beckmann et al., 2006) or fish species (e.g. goosander (Mergus merganser L.); Harris et al., 2008).

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To utilize the scarce funds available for conservation measures, identifying the roles of main stressors in a stream / river system is of utterly importance for achieving the best possible outcome.

However,

field

data

on

fish

assemblages

and

stressors

(land

use,

hydromorphological data, predation, food availability, physicochemical data) from the same river catchments are rare or often incomplete. Multiple stressor studies often focus on one stressor group (e.g. Feld et al., 2018) or use modelling without original field data (e.g. Matthaei and Lange, 2016). Herein we took a different approach and analyzed the influence of stressors on fish assemblages using field data from 203 streams within one climatic region with potentially similar fish communities and the same species inventory (southeastern Germany) to identify main stressors. As different stressors (e.g. shading by riverbank vegetation, de-icing salt, impoundments) act on different spatial scales, we investigated the effects of the different stressors on fish community composition within the five different data sets (entire study area, major drainage systems, river catchments, stream sizes, geographical subregions) and four different survey-area scales (see methods)). We hypothesized that i) the distribution of stream fishes is significantly influenced by multiple stressors, ii) the impact of stressors is specific for different drainage systems, river catchments, geological regions and stream sizes and varies with investigation scale (e.g. riparian stripe, catchment) and iii) the impact of stressors is specific for different species or species groups (e.g. gravel-spawning species).

2. Methods 2.1. Study area The study area comprised the Federal state of Bavaria in southeastern Germany with three major drainage systems (Danube, Rhine, Elbe), 14 river catchments, and four geographical subregions (Alps, foothills of the Alps, southwestern and eastern low mountain ranges; Figure 1, part I). A more detailed description of the study area is given in Bierschenk et al. (2019a) and Mueller et al. (2018).

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2.2. Data sources To assess the influence of anthropogenic stressors on fishes, fish community compositions at 296 sample sites in 203 streams and rivers were investigated. The fish community composition data were provided by the Bavarian Institute for Fisheries and originated from the governmental fish monitoring in context of the EU Water Framework Directive (WFD) and Habitats Directive (FFH) monitoring by state authorities in Bavaria from 2004-2013. These data consisted of at least three surveys per sampling site, which were averaged for the further analyses. Following the standardized procedures of CEN (2003) and Dußling (2009) for WFD fish monitoring in Germany, electrofishing was conducted in summer and early autumn (July - October), when the increased size of the young-of-the-years enabled correct species identification, and additionally in spring (March - May) targeting migrating fish species. For the different stream and river reaches, a bank length of 100 m to 5600 m was sampled (Dußling 2009). These bank lengths fulfilled the minimum requirements and corresponded with the 40-manifold (wadable stream) to 100-manifold (sampled by boat) of the stream and river width. All caught fish were identified by specifically trained personnel with in-depth training on correct species identification to species level, classified into species-specific size classes (<5 cm, <10 cm, <20 cm, <30 cm, <40 cm, >40 cm) and numbers of fishes per size class were recorded. The size class division follows the national WFD fish monitoring standards and provides information of the sampled fish stock including recruitment success (Dußling, 2009). The data for the predictor variables, which were included in our analyses (Table 1), as well as information about borders of surface water bodies and catchments, geography, and typification of water courses were provided by the Bavarian Environmental Agency, the Bavarian State Research Centre for agriculture, the Bavarian Institute for Fisheries and the Bavarian state Ministry for Finances, Land development and Homeland. The majority of the environmental data originated from the governmental monitoring of the ecological, chemical,

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and ecomorphological status of the water bodies in context of the WFD monitoring by state authorities in Bavaria from 2004-2015. For the WFD monitoring, the macroinvertebrate community composition, water chemistry, and ecomorphological status of the water bodies were assessed following standardized procedures of the German Working Group on water issues of the Federal States and the Federal Government (Wasser, 2003) and Meier et al. (2006). The assessment of the ecomorphological status of the water bodies (further called hydromorphological quality) was conducted according the protocols of LAWA (2000, 2002) and LUA NRW (2001). The data of the terrestrial predators originated from Bavaria wide wildlife monitoring, the data of agricultural land use from governmental monitoring in context of the integrated administration and control system (IACS) of the European Union by state authorities (Table 1). The numbers of biogas and hydropower plants are registered in the energy – atlas of the Bavarian state in preparation of the exit from nuclear and fossil-fuel energy and the WWTPs in the Bavarian atlas as part of the Federal Water Act (https://geoportal.bayern.de/bayernatlas/?lang=de&topic=ba&bgLayer=atkis&catalogNodes= 11,122). Table 1 comprised all predictor variables, which were accessible and available in the required high resolution, which were necessary for analyses, at the start of the analyses steps (see below).

2.3. Predictor variable groups The investigated predictor variables were grouped for the analyses as follows (see also Table 1): (1) catchment land use (e.g. industrial area, forest, stagnant water, maize, grain, forage crop, etc.), (2) anthropogenic stressors and hydromorphological quality (e.g. WWTP, biogas plants, transversal structures, riverbed dynamics, riverside vegetation, presence of fish passes, etc.), (3) water chemistry (nitrate, phosphor, TOC, etc.), (4) food availability (abundance data and traits of macroinvertebrates (MI)), and (5) terrestrial predators (presence of piscivorous birds and European otter Lutra lutra L.). The predictor variable group food availability only comprised macroinvertebrates, because the information on other

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food sources such as periphyton were too scarce to allow valid conclusions. Moreover, most of the species native to Bavaria mainly feed on macroinvertebrates.

2.4. Data sets and survey-area scales To investigate effects of multiple stressors on fish communities on different spatial scales, five data sets (entire study area, major drainage systems, river catchments, stream sizes and geographical subregions; Figure 1 part I) were used, analogously to the procedures in Mueller et al. (2018) and Bierschenk et al. (2019). Using these five data sets allows to account for different climatic differences of the geographical subregions in Bavaria (Alps, foothills of the Alps, southwestern and eastern low mountain ranges; Figure 1b) and different hydrology (small stream, medium-sized and large rivers; Figure 1b). The classification of the different stream sizes in the study area (stream, medium-sized river, large river) was according

to

catchment

sizes

following

the

national

river

type

classifications

(Umweltbundesamt 2014; stream with a catchment area ≤100 km2, medium-sized rivers with a catchment area >100 ≤ 1000 km2 and large rivers with a catchment area >1000 km2). For each of the five data sets, analyses were conducted utilizing survey-area scales (Figure 1 part II): 1) catchment upstream of the sample sites (SCAT) including the entire catchment area upstream of the sample sites in the analyses (Figure 1c), 2) surface water bodies in each river catchment (SWB, as defined in the EU-WFD, European Communities 2003) with each SWB representing a separate data point in the analyses (Figure 1d), 3) a 100 m wide riparian strip of each stream/river side along the streams and rivers within the surface water body (200 m strip; Figure 1e), and 4) riparian strip with river-specific width along the streams and rivers within the surface water body (river-specific strip; Figure 1f). For the latter riparian strip, its width increased with the width of the streams and rivers (< 10 m river width: 10 m riparian strip, 10-80 m river width: 20 m riparian strip, > 80 m river width: 200 m riparian strip, which equals the 200 m strip for these river stretches). All the riparian strips were calculated from the middle to both sides of each watercourse.

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2.5. Data preparation Prior to the data analyses, the fish abundance data of each individual sampling site were standardized to individuals per 100 m river length (CPUE). MI data were standardized to individuals per m2 and summarized according to class or order. Mueller et al. (2011, 2014) demonstrated that grouping macroinvertebrates on class or order level can be sufficient to detect the effects of anthropogenic impacts. Only for Diptera, the family level was kept for analyses due the complexity of this order. To calculate the numbers of MI per trait, the individuals belonging to the same trait at each sampling site were summarized. In a next step, the standardized data were averaged (arithmetic mean) across the investigated period (10 years) for each sample site. The presence/absence, amount and frequency of the terrestrial predators were first computed for each sampling site separately and then averaged (arithmetic mean) across the investigated period (10 years) for each sample site in the survey-area scales SCAT and SWB within all five data sets. The percentage of area of the different land-use types within each survey-area scale was calculated for all four data sets using ArcGIS 10.2 (for further details see Bierschenk et al., 2019). For the hydromorphological quality of streams and rivers, a weighted average of each predictor variable within each survey-area scale was calculated for all four data sets. The average was weighted by the frequency, how often each quality class, which are assessed for every 100 m of the water courses, occur within the data sets. The amount of structural variables such as WWTP, fish pass, transversal structures, etc. within each survey-area scale were summed for all five data sets. The number of sampling sites varied between the analyzed predictor variable groups 1) catchment land use (397 sites), 2) anthropogenic stressors and hydromorphological quality (297 sites), 3) food availability (macroinvertebrates) (296 sites), 4) water chemistry (215 sites), and 5) terrestrial predators (377 sites) depending on data availability in the different governmental monitoring programs. Also, the locations of the sites varied between water

9

chemistry, macroinvertebrates and fish sampling sites. Therefore, a near-analysis in ArcGIS 10.2, similar to the one described in Mueller et al. (2018), was used to identify geographically matching sampling sites of fish, macroinvertebrates, and water chemistry and afterwards the matching was manually checked for plausibility on the ArcGIS map. The geographical matching resulted in 147 sample sites in 119 streams and rivers including water chemistry and in 296 sample sites in 203 streams and rivers excluding water chemistry. After data exploration using shade plots and histograms in PRIMER v7, the fish community composition data were fourth root transformed, while most of the predictor variables were log-transformed (log(x+1)) to achieve a more normal distribution. Only the predictor variables floodplain dynamics, dissolved oxygen, and BOD5 were not transformed (Table 1), since untransformed data already fitted normal distribution. Before applying multivariate models, all variables were normalized to avoid the impact of different scaled parameters on the analyses.

2.6. Data analyses To model relations between environmental and fish community data, distance based linear modelling (DistLM) was chosen, since this approach allows partitioning of explained variance and therefore a quantification of the relative influence of different stressors. However, the number of investigated predictor variables must not exceed the number of samples minus 1 in DistLM, which created the necessity to pre-select the predictor variables to be included in the final models. Therefore, we used biota-environmental stepwise matching (BEST) analyses at first to achieve an objective pre-selection of predictor variables. BEST offers the advantage that, due to the Mantel-type testing approach of two resemblance matrices (abiotic and biotic), it is not limited to a certain number of predictor variables. However, BEST in contrast to DistLM does not provide partitioning of variance among variables. All analyses were conducted in a stepwise approach (Figure 2):

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1) In a first step, all the available predictor variables (Table 1) were grouped into the five predictor variable groups defined beforehand, because in a trial run including all predictor variables simultaneously it became evident that the required computation capacity exceeded the capacity of the available computers by far.

2) Within each predictor variable group, multivariate 2-way BEST analyses (Bio-Env + Stepwise; Clarke and Gorley, 2015) were conducted using the Biota Environmental matching (BIOENV) method to identify the significant relationships between fish community composition and the different predictor variables within each survey-area scale and data set. BEST sought for rank-based correlations between the Bray-Curtis similarity matrix of the fish community data (response variables) and a matrix of best-matching environmental data (predictor variables).

3) To determine the most important drivers of fish community composition across all predictor variable groups for all data sets and survey area scales, a second BEST analyses was performed. This BEST analyses included all predictor variables from each group which were identified during step 2.

4) In the final step, distance-based linear modelling analyses were conducted for each significant combination of predictor variables identified by BEST to determine the amount of variability explained by each single variable. Prior to DistLM, the variance inflation factors (VIF) was calculated to test for potential multi-collinearity among predictor variables (Fox and Weisberg, 2011) from each significant BEST model from step three. A VIF-value > 10 were used to identify non-independence (Belsley et al., 2005; O’Brien, 2007), which none of the significant models exceeded. Furthermore, the variables, which were included in the different models, were tested for auto-correlations (Table S2). In case of strong correlations (correlation coefficients >0.7; Mela and Kopalle, 2002; Vatcheva et al., 2016), the models were re-calculated excluding these factors. The significant results of the DistLM analyses

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were exemplary visualized as distanced-based redundancy analysis (dbRDA) plots with at least two graphs of each data sets. In cases of similar patterns (e.g. Inn and Altmühl), the DistLM of the data sets with more sampling sites were chosen. When the data sets contained only a few sampling points (n < 10), or when no pattern could be identified in the dbRDA plots, these plots are not presented. To illustrate the relation between those fish species with considerable abundance changes over the past 30 years in the study area (see Mueller et al., 2018) and environmental stressors (predictor variables) in the dbRDA plot, pie charts of abundance of fish species were used for the sampling sites (one pie for each sampling site).

Steps 3) and 4) were each conducted including and excluding water chemistry, since including water chemistry pronouncedly reduced the number of sample sites (147 vs. 296). This was mainly due to the fact that governmental monitoring programs do not provide water chemistry data for each biological monitoring site and/or due to geographical mismatch between biological and chemical monitoring sites following previously set criteria (Mueller et al., 2018). The results of the models including water chemistry data are supplied in the supplementary material (Table S3).

All analyses were conducted using the statistic software PRIMER v7 (PRIMER-e, Massey University, Albany, New Zealand). For all statistical analyses, P ≤ 0.05 was chosen as the convention to determine if findings were consistent with accepting the hypotheses.

3. Results 3.1. Influence of multiple stressors on the distribution of fish assemblages In the final BEST models, stagnant waters and root crop were frequently selected across all BEST models (Figure 3) as variables significantly affecting fish community composition, followed by WWTPs. The predictor variables, which were included in the significant DistLM,

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varied between the different data sets (Table 2) as well as between the different survey-area scales. The predictor variables forage crop, road network, and forest only correlated with the fish community composition on the lower survey-area scales (200 m and river-specific strip), while the predictor variable grove affected the fish composition only on the SWB scale. The other predictor variables were significantly detected by DistLM on mainly the SCAT scale, but some of the predictor variables (e.g. stagnant waters, grain, maize, root crop) were also significantly correlated with the fish community composition at more than one survey-areascale. The percentage of variability, which is explained by the DistLM, ranged from 7.7% (Data set stream size, streams) to 65.7% (Data set river catchments, Saale). For the Elbe drainage system and five of the fourteen river catchments (Wörnitz, Iller, Eger, Ilz, Regen), no significant model was obtained and those are thus not included in Table 2.

3.2 Identification of stressors for the entire study area In the overall distance-based redundancy analysis (dbRDA) (Figure 4a), the fish community composition changed the increasing numbers of WWTPs and structural modifications (stagnant waters, riverbed dynamics) from rheophilic species such as European bullhead (Cottus gobio L.), stone loach (Barbatula barbatula L.), and brown trout (Salmo trutta fario L.) to generalist fish species (European eel and common bleak (Alburnus alburnus L.)). Furthermore, the species number and their abundance per sampling site decreased with increasing root crop farming.

3.3 Identification of regional stressors Data set major drainage system The predictor variables influencing the fish community composition differed between the overall study area and each of the three major drainage systems. In the Danube drainage system (Figure 4b), the sampling sites were separated by a gradient of field crops (x-axis) and industrial area (y-axis). Along the field crop gradient, the fish community composition changed from European bullhead and brown trout in areas with less field crop farming to

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stone loach and common gudgeon (Gobio gobio L.) in areas with more intense crop farming. Furthermore, the spatial increase of industrial areas resulted in a general decrease in species numbers and abundance. In the Main/Rhine drainage system (Figure 4c), the presence/absence of fish passes segregated the fish communities with high abundances of European bullhead and Eurasian minnow (Phoxinus phoxinus L.) and higher numbers of fish passes from those with low abundances of these species and low numbers of fish passes. Along the gradient of MI feeding type grazer, the numbers of European bullhead increased, while the European chub (Squalius cephalus L.) decreased. Furthermore, the numbers of species and their abundance decreased with increasing stagnant waters (SWB) and flood protection structure.

Data set river catchments Fish community composition in the river Inn followed a gradient of maize farming in the riverspecific riparian strip. European bullhead and brown trout were dominant in areas with less maize farming, whereas mainly cyprinid species such as common roach (Rutilus rutilus L.), common nase (Chondrostoma nasus L.) and stone loach were more common in areas with more intense maize farming (Figure 5a). In the river Isar, fish community composition changed along a structural gradient, which is determined by variables such as fish pass (p/a) and hydromorphological quality class on the one side and a gradient in land use and feeding types on the other side. Along the structural gradient (y-axis), Eurasian minnow and common nase dominated areas with fish passes and higher hydromorphological quality classes, while brown trout and common gudgeon were more often detected in areas of lower hydromorphological quality classes. With increasing grain farming in the catchment, the numbers of Eurasian minnow and brown trout decreased, while the abundance of common gudgeon, spirlin (Alburnoides bipunctatus B.), common barbel (Barbus barbus L.) increased (Figure 5b). In the river Lech, brown trout, European grayling (Thymallus thymallus L.), and European bullhead dominated in areas with low structural changes. In areas displaying more structural

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changes due to flood protection structures, common barbel, common gudgeon, and European chub were the dominant species (Figure 5c). In the river Main, the abundance of the different fish species decreased with both increasing WWTPs and biogas plants in the catchment. Furthermore, species composition changed with increasing numbers of WWTPs from brown trout, European grayling, and Eurasian minnow as dominant species to European chub, common roach, and common dace (Leuciscus leuciscus L.) (Figure 5d).

Data set geographical subregions In the foothills of the Alps (Figure 6a), the fish community composition was influenced by maize and root crop farming. Along the gradient of increasing root crop farming, a shift of dominant species from common barbel to silver bream (Blicca bjoerkna L.), common gudgeon, and three-spined stickleback (Gasterosteus aculeatus L.) occurred. The number of species and abundances declined with increasing maize farming in the catchment. In the Eastern low mountain ranges (Figure 6b), the number of species and abundances decreased with rising root crop farming in the catchment and stagnant water areas. The fish community composition also changed along the gradient of biogas plants in the catchments, from brown trout, European bullhead, and common gudgeon being more present at sites with less biogas plants to Danube bleak (Alburnus chalcoides G.) as dominant species in areas of high biogas-plant density.

Data set stream size In streams, the number of species and abundances decreased with rising stagnant water areas in the catchment. Also with increasing root crop farming and spatial increase industrial area, the abundances of brown trout, European grayling, and European bullhead decreased (Figure 6c).

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In large rivers, the species composition segregated along an opposing gradient of grassland and numbers of WWTPs in the catchment (Figure 6d). In areas with high percentage of grassland and low numbers of WWTPs, spirlin, European bullhead, European grayling, and common barbel dominated the species composition, while in areas with a low percentage of grassland and a greater amount of WWTPs, European eel, common bleak, and ide (Leuciscus idus L.) were the dominant species.

4. Discussion 4.1. Influence of multiple stressors on fish community composition Previous studies identified that the decline of fish populations is caused by the simultaneous influence of multiple stressors (e.g. Burkhardt-Holm, 2005; Moyle and Williams, 1990; Radinger et al., 2017). However, little is known about the relative contribution of those stressors and therefore setting priorities for conservation measures is still a challenge. This study quantified the effects of different stressors on fish populations in a multifactorial system and identified impoundment of rivers, agricultural land use and discharge of wastewater as the most crucial factors determining fish community composition across the entire study area. This stresses the importance of a more holistic approach in aquatic conservation and restoration, which simultaneously considers point source impacts (inlets from sewage treatment plants) as well as structural restoration measures, catchment management and the integration of spatial and temporal scales. Moreover, the variable contribution of stressors on different spatial scales indicates a strong need to define regionspecific conservation priorities.

Anthropogenic stressors The most frequent water-chemical variables of all data sets (major drainage system, river catchment, stream size, geographical subregions) were amount of total organic carbon (TOC) and chloride concentration. Chloride concentration can derive from input of road salt,

16

fertilizer or industrial effluents (Beggel and Geist, 2015). In the streams and rivers within the study area, the mean chloride concentration (28 ± 16 mg/L) were distinctly below the European mean for running waters (100 mg/L; Wolfram et al., 2014), most likely having no direct toxic effects on most fishes. However, long-term impacts might be possible due to delayed transport in the running waters, especially of de-icing salt, influencing the chloride concentration in the water all-year-round (Wolfram et al., 2014). Additionally, slightly elevated chloride concentration can interplay with other water chemicals. For example, it can affect the toxicity of other substances, e.g. nitrite, on fishes (Williams and Eddy, 1986). TOC is an indicator for organic pollution in streams and rivers, which can indirectly affect fish community composition by increased oxygen demand mirroring the decomposition of organic compounds. The fish community composition in the major drainage systems Danube and Rhine/Main shifts from sensitive fishes with a high oxygen demand such as brown trout to tolerant fishes such as European gudgeon, common dace, and common roach. Organic pollution within the drainage systems can originate from e.g. agricultural areas via erosion (Caverly et al., 2013) or input of WWTP. In the Isar catchment, the high correlation between TOC and chloride concentration (0.87) as well as between TOC, chloride and grain farming (both 0.84) point towards an input of eroded soil inclusive fertilizer from agricultural land (see Beggel and Geist, 2015). This input of fertilizer, pesticides and fine sediment, which originate from farming land, can have adverse effects on fishes in the receiving stream (Dunier and Siwicki, 1993; Wood and Armitage, 1997; Sutherland et al., 2002; Blann et al., 2009; Pander et al., 2015a; Bierschenk et al., 2019a; Knott et al., 2019). In the Isar catchment, for example, the fish community composition changed along the increasing gradient of grain farming and common gudgeon, which is known to be tolerant against fine sediment and nutrient pollution, being more present in areas of more intense grain farming. Further indication for the influence of agricultural land use on fish community composition was the inclusion of the variable root crop, grain, and oilseed in many of the BEST models. Especially root crop cultures, but also maize cultures, which were the dominant agriculture land use in the foothills of the Alps, rank among the most erosion-prone farming, particularly

17

during heavy rain events. The introduced fine sediment clogs the pores of the interstitial zone, preventing oxygen and nutrients exchange between free flowing and interstitial waters (Hancock, 2002; Geist and Auerswald, 2007; Denic and Geist, 2015). Colmation and the resulting lack of exchange influence notably the recruitment success of rheophilic species such as common nase due to the loss of spawning grounds and reduced hatching success of their eggs (Duerregger et al., 2018; Nagel et al., 2019). In the Danube drainage system, for example, a separation could be found between fish species, which need a well oxygenated interstitial for reproduction, and generalist fish species attaching their sticky eggs to the substrate surface along gradients of percentage of erosion-prone land use in the catchment. Similarly, the fish community composition within the Inn catchment shifted from sensitive species such as spirlin and common barbel to generalist species such as common bleak. One reason for the shift could be the high erosion rates (> 8 t/ha*a; Bavarian State Ministry of the Environment and Health, 2009) in the lower reach of this river catchment, which was dominated by agricultural land use. Here, the amount of eroded material also significantly correlated with the fish community composition on two of the four investigated survey-area scales (SWB, 200 m). Introduced fine sediment also enhances particle and nutrient accumulation (Mueller et al., 2013), which can cause further oxygen depletion due to aerobic decomposition of the latter (Schlosser and Kallemeyn, 2000). Various fish species can tolerate a certain range of oxygen, temperature and nutrient content, but in the long run these tolerance ranges should not be exceeded or undercut to avoid a shift within the fish community composition towards euryecious species. A constant source of nutrients are WWTPs (Carey and Migliaccio, 2009), although over the past decades a lot of effort was taken to reduce nutrient inputs of point-sources such as WWTPs (Carey and Migliaccio, 2009; Geist, 2014). Even efficiently treated wastewater from tertiary WWTPs can adversely affect stream ecosystem function (Gücker et al., 2006) and nutrient retention efficiency (Carey and Migliaccio, 2009). Furthermore, Stalter et al. (2013) and Kinouchi et al. (2007) detected a slight decrease in oxygen concentration and an increase in salinity and temperature accompanying great

18

wastewater fraction. These may be a reason for the decline of sensitive species such as European grayling, common barbel, European bullhead, and spirlin in large rivers along the gradient of numbers of WWTPs in the catchment. A similar trend was observed in the densely populated river catchment of the Main. These correlations between numbers of WWTPs in the catchment (SWB and SCAT) and fish community composition was even detected with a conservative approach based on pure numbers of WWTPs without differentiating between WWTPs on base of discharge (size of the WWTPs), type of wastewater (e.g. municipal versus industrial wastewater), effectivity or age of the treatment plants. This result underlines the need for action in order to reduce the impact of WWTPs on fish communities. A differentiation of WWTPs in our study was not practicable, because it would have decreased the number of plants in the respective analyses to such an extent, that reliable results would not have been feasible. Therefore, the effects of nutrients and other toxicants from the WWTPs effluents on fish community composition could not be determined in our study and need to be assessed in more detail in further studies. Similar to WWTP, the numbers of biogas plants in the catchments can influence the fish community composition, but based on our data set it is not possible to pinpoint the exact source of impact. The significant correlation between biogas plants and fish community composition in the Eastern lower mountain ranges and in the river Main catchment could result from the change in catchment land use after the implementation of the Renewable Energy Directive (European Commission, 2009) in Germany (Britz and Delzeit, 2013). Guaranteed prices for electricity production in biogas plants provided incentives for farmers to build in biogas plants and cultivate green maize (Appel et al., 2016), but also to re-activate set-aside lands and intensify the use of prior extensive managed fields (Drechsler et al., 2009).

Structural changes Structural modifications (e.g. bank reinforcement, channelization, straightening of rivers) and the consequential loss of habitat adversely affect fish community composition (Hortle and

19

Lake, 1983; Knudsen and Dilley, 1987; Pander and Geist, 2016, 2018). In our study, impoundments upstream of transverse structures such as dams or weirs (represented by the predictor variable stagnant water) were often identified as factors influencing the fish community composition. For example, fish community compositions can shift from rheophilic and salmonid-dominated to stagnophilic and cyprinid-dominated fish communities corresponding to the flow characteristics (Copp, 1990; Schmutz et al., 2000; Kruk and Penczak, 2003; Schmutz and Moog, 2018). In stagnant waters, low current velocities and sedimentation processes dominate (potamalization; Schmutz et al., 2000), possibly leading to colmation of the interstitial pores (Brunke, 1999). Colmation, in turn, disrupts the water exchange between hyporheic zone and free flow (Brunke, 1999). Thus, spawning and foraging habitats for gravel spawning fishes can degrade or completely lose their functionality (Kemp et al., 2011; Duerregger et al., 2018). Consequently, the population structure of lithophilic fishes can alter and face a complete population collapse (Rieman and Dunham, 2000; Sutherland et al., 2002). Impoundment structures such as dams and hydropower plants pose barriers for middle or long distance migrating species such as Danube salmon or European eel disconnecting their spawning, juvenile, and forage habitats and rendering their migration as almost impossible (Kruk and Penczak, 2003; Geist et al., 2009). Furthermore, downstream migrating fish can be harmed at hydropower plants when passing through the turbines (Nettles and Gloss, 1987; Killgore et al., 2001; Mueller et al., 2017). Collision with machinery parts, pressure changes, turbulence, shear stress and cavitation forces during the turbine passage can cause severe injuries (e.g. bruises, dermal lesions, amputation of body parts) or even death (Čada et al., 2007; Dedual, 2007; Brown et al., 2012; Čada and Schweizer, 2012; Ebel, 2013; Mueller et al., 2017; Boys et al., 2018). The extent of injuries and mortality rates depend on fish species, body size and migration behavior (Mueller et al., 2017). However, the predictor variable number of hydropower plants in the catchment was not included in the final BEST models after combining all predictor variable groups (structural, chemical, land use, terrestrial predators, and macroinvertebrates). The result must not be interpreted in a

20

way that hydropower plant-caused injuries and mortalities had no effect on fish community composition at the sampling sites. However, this result implies that the effect strength of predictor variables such as impoundments outweighs the effect strength of fish mortality and injury caused by hydropower plants in the statistical models. In our study, the presence or absence of fish passes were identified as another factor influencing the fish community composition in the entire study area, the Main/Rhine drainage system and the river Isar. Nature-like fish passes can offer a high habitat variability with higher current speed, lower water depth and smaller channel width and hence provide valuable habitats for juvenile, small and rheophilic fishes, which often have been lost in the main channels (Pander et al., 2013). Previous studies (e.g. Pander et al., 2013, 2015b, 2018; Nagel et al., 2018) have already demonstrated that fish passes offer high quality habitats for European bullhead and Eurasian minnow as observed in the Rhine/Main catchment in this study. Because of the good habitat quality, fish passes can serve as source for dispersal and re-colonization for up- and downstream river sections and may explain the correlation found in our study. Structural modifications of river banks for bank stabilization and flood protection can also negatively influence fish communities. As consequence, valuable habitat structure of juvenile and forage habitats could get lost, for example habitats of brown trout in the Lech river catchment. Also, in the Rhine/Main drainage system, the inclusion of the predictor variable flood

protection

structures

in

the

significant

BEST

model

indicates

that

the

hydromorphological modifications accompanying the flood protection structures have a negative impact on the fish community composition. Lozán et al. (1996) noted that due to dikes and the resulting loss of lateral connectivity spawning and juvenile habitats got lost and habitats of limnophilic fishes disappeared along the shore line of diked rivers. Only neozoa such as round gobies benefitted from the riverbank modifications (Lozán et al., 1996; Brandner et al., 2013).

Variables with low frequency

21

Some predictor variables were included in BEST models only once or a few times, but the low frequency of occurrence in the BEST models must not be interpreted in a way that these variables are negligible. The predictor variables with low frequency such as the availability of macroinvertebrates can have a significant impact on the fish community composition at the survey-area scales and geographical subregions. For example, benthivorous European bullhead (Cottus gobio) in the Rhine/Main drainage system may benefit from rise of MI grazers. Other food sources which have not been investigated herein (e.g. periphyton) may also have influenced the fish community compositions directly (e.g. herbivorous species) or indirectly through the food web. Furthermore, fish community composition can be influenced by several other variables, e.g. recreational and commercial fishing or shipping traffic, which could not be included in the multivariate analyses, since data for these variables were not available. Analogously, recreational kayaking has the potential to disturb spawning and juvenile habitats of many gravel spawners. Commercial and recreational fishing can lead to overexploitation or selection of certain age classes or size ranges, which may change the structure of the fish community (Lewin et al., 2006).

4.2. Differences between the data sets overall, major drainage systems, river catchments, geographical subregions, and stream sizes. The effects of different stressors on fish community composition varied regionally, geographically and between stream sizes. In the Danube drainage system, the fish community composition was affected by ground sealing (industrial area) and root crop farming reflecting the two of the main land-use types in this area. Four of the five major cities of Bavaria (> 100.000 inhabitants), Munich, Augsburg, Regensburg, and Ingolstadt, are located in this area as well as huge industrial complexes, especially factories for the automobile industry. In the morainal plain within the Danube drainage system, agricultural land use (including maize and root crop farming) dominate the landscape and correspond with the high amount of erosion in this area (Bavarian State Ministry of the Environment and Health, 2009).

22

In contrast, structural modifications (impoundment, habitat loss due to flood protection structures, fish passes) influenced the fish community composition and abundances in the Main/Rhine drainage. The Lower Main River and one of its major tributaries the river Regnitz are part of an important, intensively used shipway from the North Sea to the Black Sea and hence regulated to maintain its navigability. Due to the intensive ship traffic, its shorelines are heavily modified and the rivers themselves are dammed to ensure a navigable water depth. The dams are also used for hydropower production and flood protection inversely affecting habitat quality. In the Main river catchment itself (without tributaries), the fish composition was affected by anthropogenic stressors such as WWTPs and biogas plants. The Main is flowing through a densely populated and intensively agriculturally used area, where five major cities with > 50.000 inhabitants are located. The regional differences were also pronounced in the river catchments of the Danube drainage system. In the Lech river catchment, the numerous barrages (impoundments) for hydropower plants and the associated flood protection structures along the shorelines influence the fish community composition, whereas in the Isar catchment the fish community composition was influenced by the presence or absence of fish passes impairing or enabling fish migration. Similar to the Lech, the water of the Isar is impounded or diverted into channels to generate hydropower in 31 plants on its way. In contrast to the river Main or the Danube, the fish community composition in the river catchment of the Inn segregated along a maize farming gradient, equaling also a gradient from the salmonid to the cyprinid region. In this catchment, the agricultural land use increases in its intensification from the upper to the lower reaches. These regional differences between the assessed catchments also account for the results between the geographical subregions in Bavaria. The foothill of the Alps include the catchments with intense agricultural land use (especially the morainal plain) influencing the fish composition, while the eastern low mountain ranges comprise mountainous catchments,

23

in which the change in crop farming due to the biogas plants (see above) affected the fish community composition. The fish community compositions in streams and large rivers were affected by the different stressors of urbanization and ground sealing. In large rivers, tolerant fish species dominated the fish community composition in dense populated areas with high numbers of WWTPs, which could be an indication of organic or chemical pollutions in these areas. In contrast, in grassland-dominated areas (rural areas), the fish community composition comprised more sensitive fishes such as European grayling and common barbel. In small streams, fish community compositions are affected by a variety of stressors depending on the local or regional situation. In agricultural dominated areas, streams are often affected by input of fine sediment and organic contaminants, while in residential areas, flash floods due to ground sealing and chemical pollutants such as de-icing salt or herbicides are more important. Furthermore, impoundments und associated changes in hydrology directly affect the fish community in streams. This variety of possible stressors also reflect in the small R-value of the DistLM, which only explains 8% of the variability of the data. Therefore, assessments of stressors affecting the fish community in streams should not only be conducted on large spatial scales such as river catchments, the assessments also need to consider locally impairing stressors, which solely can be investigated on smaller spatial scales e.g. river stretches as demonstrated in Pander and Geist (2010).

5. Conclusions Our study provides evidence that the fish community composition is influenced by a multitude of anthropogenic stressors acting in concert, but at different intensities depending on the scale considered. Similar to previous studies (e.g. Baxter, 1977; Box and Mossa, 1999; Walsh et al., 2005; Lange et al., 2014), land use types (impoundments, crop farming, ground sealing) and structural modification of streams and rivers were often identified as stressors influencing the fish species compositions. In addition, WWTPs and biogas plants

24

were also found to affect the fish community composition and therefore these stressors need to be included into future conservation and restoration efforts. The effects of the identified stressors differ between the survey-area scales (two different catchments sizes and riparian stripes) and data sets (entire study area, major drainage systems, river catchments, stream sizes, geographical subregions). Therefore, it is of utterly importance to identify the specific anthropogenic stressors influencing the fish community composition in the respective regions in order to optimize scarce founding in the conservation of endangered stream fishes and freshwater biodiversity. The effects of some identified stressors (e.g. biogas plants) need to be further investigated, because these stressors may rather indirectly than directly influence the fish community composition by e.g. adversely affecting the food availability for fish.

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6. Acknowledgements: We are grateful to the Bavarian State Ministry of Environmental and Consumer Protection, particularly to Dr. A. Steiner and Dr. B. Wolf, and the Bavarian Environmental Agency for initiating and financially supporting this study [grant number 54-0270-71442/2014]. The Bavarian state Ministry for Nutrition, Agriculture and Forestry, the Bavarian regional office for agriculture (LfL), in particular M. Treisch, and the Institute of Fisheries, in particular Dr. M. Schubert, and the “Fischereifachberatungen” are acknowledged for data provision and discussion. We further thank all fisheries and conservation experts participating in the project group for the valuable discussions of the data and the results. C. Truffel, J. Hildebrand and M. Riese are acknowledged for their help with data processing and Prof. M. Anderson (PRIMER-e, Massey University, NZ) for her support with statistical analyses.

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Figures Figure 1: Illustration of different data sets (Part I) and survey-area scales (Part II) used for the analyses: a) major drainage systems (black-rimmed) and river catchments within Bavaria, represented by different colours (blue coloured river catchments = Danube drainage system, green coloured river catchments = Rhine drainage system, orange coloured river catchments = Elbe drainage system); b) geographical subregions and distribution of different stream sizes in Bavaria. Different colours indicate different geographical subregions and stream size. Grey = streams, light blue = medium-sized rivers, dark blue = large rivers; c) Catchments upstream of the respective sample site (SCAT). Location of sample sites (red dots) arbitrarily chosen to illustrate the concept. Light grey and dark grey areas = respective catchment upstream of the sample site (red dots), grey area = remaining area of this river catchment (not included in the calculation of the catchment area of these two sample sites); d) surface water bodies (SWB) in a river catchment in accordance to the classification of the EU WFD. Different colours indicate different SWBs; In e) the 200 m wide riparian strip and f) the river-specific riparian strip of the same stretch of a river are pictured. The river width at this stretch is between 10-80 m wide, hence the width of the river-specific riparian strip is 20m. Different colours indicate different land use within the strip. Information for the different colour-coded land-use types is provided in the legend beneath the figures e) and f) (adapted from Bierschenk et al. 2019).

Figure 2: Flow chart of the stepwise analysis to identify the variable affecting the fish community composition. The survey-area-scales were only used for three of the five predictor variable groups, because water chemistry and macroinvertebrates are parameters, which are measured in the water courses and do not change with the different catchment sizes of the survey-area scales. The calculation for the riparian strip with river-specific width was omitted for the predictor variable groups structural quality of water body and predation due to the similarity of the two riparian strips. BEST = Biota-environmental stepwise matching, DistLM = Distance based linear modelling.

Figure 3: Frequency of the different predictor variables in the BEST analyses (excluding the water chemistry) of all data sets. Frequency diagram of the BEST analyses including water chemistry is given in Figure S1. Total = results from significant and non-significant BEST analyses, significant =

46

results from significant BEST analyses. p/a = presence/absence; (n) = number of; MI = Macroinvertebrates, WWTP = wastewater treatment plants.

Figure 4: Distance-based redundancy analysis (dbRDA) plot of the significant DistLMs illustrating the correlation between the fish community compositions at the sampling sites in a) the entire study area and in the major drainage systems Danube b) and Main/Rhine c). Environmental stressors are given as vectors. The segments of the pie charts relate to the abundance of the corresponding fish species, the length of pie segment represents with the numbers of individuals per 100 m river length (CPUE) at the different sampling sites. The width of pie segment depends only on the number of fish species in the dbRDA plot. The distance between sampling sites (pie charts) represents the dissimilarity of the fish community composition of the sites. Vector length indicate strength of the relationship between the predictor variables and the dbRDA axes. The radius of the circle in the plots equals 100% correlation of the overlaid environmental vectors with the dbRDA diagram. The survey-area scale for most of the predictor variables was SCAT (catchment above sampling site), exceptions are indicated in brackets behind the predictor variable label of the vectors. Minima and maxima of each species are given in the legend (individuals per 100 m river length). SWB = Surface water body, p/a = presence/absence, n = number of sampling sites in the respective data set.

Figure 5: Distance-based redundancy analysis (dbRDA) plots of the significant DistLMs exemplarily illustrating the correlation between the fish community compositions at the sampling sites in river catchments of a) Inn, b) Isar, c) Lech and d) Main. Environmental stressors are given as vectors. The segments of the pie charts relate to the abundance of the corresponding fish species, the length of pie segment represents with the numbers of individuals per 100 m river length (CPUE) at the different sampling sites. The width of pie segment depends only on the number of fish species in the dbRDA plot. The distance between sampling sites (pie charts) represents the dissimilarity of the fish community composition of the sites. Vector length indicate strength of the relationship between the predictor variables and the dbRDA axes. The radius of the circle in the plots equals 100% correlation of the overlaid environmental vectors with the dbRDA diagram. The survey-area scale for most of the predictor variables was SCAT (catchment above sampling site), exceptions are indicated in brackets

47

behind the predictor variable label of the vectors. Minima and maxima of each species are given in the legend (individuals per 100 m river length). Minima and maxima of each species are given in the legend. VAR = river-specific width of the riparian strip, p/a = presence/absence, n = number of sampling sites in the respective data set.

Figure 6: Distance-based redundancy analysis (dbRDA) plots of the significant DistLMs illustrating the correlation between the fish community compositions at the sampling sites of the geographical subregions a) the foothills of the Alps and b) eastern low mountain ranges and of the stream sizes c) streams and d) large rivers. Environmental stressors are given as vectors. The segments of the pie charts relate to the abundance of the corresponding fish species, the length of pie segment represents with the numbers of individuals per 100 m river length (CPUE) at the different sampling sites. The width of pie segment depends only on the number of fish species in the dbRDA plot. The distance between sampling sites (pie charts) represents the dissimilarity of the fish community composition of the sites. Vector length indicate strength of the relationship between the predictor variables and the dbRDA axes. The radius of the circle in the plots equals 100% correlation of the overlaid environmental vectors with the dbRDA diagram. The survey-area scale for most of the predictor variables was SCAT (catchment above sampling site). Minima and maxima of each species are given in the legend (individuals per 100 m river length). p/a = presence/absence, n = number of sampling sites in the respective data set.

48

49

50

51

52

53

54

55

Table 1: Overview of the predictor variable groups and the response variable group, the data preparation for the analyses, transformation to improve their normal distribution, and their origin. log = log10, norm. = normalized. Predictor variable groups Catchment land use

Predictor variable Residential area

Grove

Settlements (high percentage of sealing) Commerce, industry (high percentage of sealing) All roads (path, streets, highways) Not cultivable land (e.g. rocks) Bush, scrub

Forest

Forested areas

Stagnant waters

Slow flowing and not-flowing stretches of running waters including tiny fishing ponds next to the streams Wheat, rye, barley Potatoes, sugar beets Maize

Industrial area

Road network

Infertile land

Grain Root crop Maize Forage crop

Grassland

Cabbage, rutabaga, ensilage, fodder beet, green fodder Agricultural land out of use Meadows

Clover

Clover, lucerne

Oilseed

Canola, soy, sunflower Overall evaluation of the hydromorphologi cal quality classes Natural-like dynamics of water courses (free-flowing, unregulated, unmodified) Natural-like dynamics of floodplains (connection to the water courses, natural flooding, typical vegetation) Presence of natural flooding areas Vegetated river banks Groundsill, rock ramp, dormant

Set-aside

Anthropogenic stressors and hydromorphologi cal quality

Description

Hydromorphologi cal quality class

Riverbed dynamics

Floodplain dynamics

Overflowing ability Riverside vegetation Transversal structures

Data preparation

Transformation

Origin

Proportion [in %] of area within each survey-area scale was separately calculated for all five data sets

log, norm.

Governmental monitoring in context of the EU Water Framework Directive (WFD) by state authorities in Bavaria from 2015

Proportion [in %] of area within each survey-area scale was separately calculated for all five data sets

log, norm.

Governmental monitoring in context of the integrated administration and control system (IACS) of the European Union by state authorities in Bavaria from 2007

Weighted average of each predictor variable within each survey-area scale was separately calculated for all five data sets. The average was weighted by the frequency, how often each quality class occur within the data sets. Quality classes are assessed for every 100 m of the water

log, norm.

Governmental monitoring in context of the EU Water Framework Directive (WFD) by state authorities in Bavaria from 2002

56

- , norm.

log, norm.

Flood protection structures

Dyke, flood masonry wall

Discharge regulation

Natural-like flow dynamics or regulated flow regime Ability to form near-natural water courses Compatible usage of the flood plains and dynamical development of the water course Water retention due to the presence of flood plains Riprap or grass paver along the shore, groin, culverts, pipework Presenceabsence of fish passes at transversal structures Number of wastewater treatment plants in the catchment

Structure forming ability Development potential

Retention

Bank fixation

Fish pass (p/a)

WWTP (n)

Biogas plants (n)

Number of biogas plants in the catchment

Hydropower plants (n)

Number of hydropower plants in the catchment Freshwater crustaceans Freshwater beetles and beetle larvae Aquatic larvae of flies and mosquitos with the families Athericidae, Ceratopogonidae , Chironomidae, Limoniidae, Pediciidae, Ptychopteridae, Simuliidae, Tabanidae, Tipulidae

Food availability Amphipoda (macroinvertebrat es (MI)) Coleoptera

Diptera

Ephemeroptera Heteroptera Hirudinea Isopoda

Mayflies True aquatic and semi-aquatic bugs Freshwater leeches Aquatic woodlice

courses. The amount of structural variables such as fish pass and transversal structures within each survey-area scales were summed for each sampling site for all five data sets.

Numbers of the different plants within each surveyarea scales were separately calculated for all five data sets

log, norm.

The abundance (individuals/ 2 m ) of the species of each invertebrate group were summarized according to class or order. Only for Diptera, the family level was kept for analyses due the complexity of this order The same procedure was used for the biological traits (filter feeder,

log, norm. Traits: filter feeder and predator were logtransforme d; grazer, shredder, and gatherer were not transforme d; all were normalized

57

Registered in the Bavarian atlas energy –as part of the Federal Water Act (https://geoportal.bayern.de/geoportalb ayern; status 2012) Registered in the energy –Atlas of the Bavarian state in preparation of the exit from nuclear and fossil-fuel energy (http://geoportal.bayern.de/energieatlas -karten; status 2015)

Governmental monitoring in context of the EU Water Framework Directive (WFD) by state authorities in Bavaria from 2004-2013

Megaloptera

Mollusca Odonata

Water chemistry

Oligochaeta

Aquatic worms

Ostracoda

Seed shrimps

Plecoptera

Stoneflies

Spongillidae

Freshwater sponges

Trichoptera

Caddiesflies

Turbellaria

Free-living flatworms

Concentration of ammonium-N (mg/L) Concentration of Nitrate-N nitrate-N (mg/L) Concentration of Nitrite-N nitrite-N (mg/L) Concentration of Biochemical biochemical oxygen demand oxygen demand (BOD5) (mg/L) Concentration of Dissolved oxygen dissolved oxygen (mg/L) Concentration of Chloride chloride (mg/L) Concentration of Phosphate-P phosphate-P (mg/L) Concentration of Total phosphor total phosphor (mg/L) Concentration of Total organic total organic carbon (TOC) carbon (mg/L) Ability to conduct Electric or transmit conductivity electricity [µS/cm] pH is equal to −log, norm.10 of pH the hydrogen ion concentration [moles/liter] Number of cormorant Cormorant roosts (Phalacrocorax (n) carbo L.) in the roosts Breeding pairs of cormorant Cormorant (n) (Phalacrocorax carbo L.) Breeding pairs of grey heron Grey heron (n) (Ardea cinerea L.) Breeding pairs of Common common merganser (n) merganser (Mergus Ammonium-N

Terrestrial predators

Aquatic larvae of adlerflies, dobsonflies and fishflies Freshwater mollusks Aquatic larvae of dragonflies and damselflies

grazer, shredder, gatherer, predator). The MI data were included only in the BEST analyses of the entire study area for five data sets, because the MI abundance in the water course does not change with the catchment size of the survey-area scales. Each chemical variable was averaged across the entire investigated for all five data sets

log, norm.

Governmental monitoring in context of the EU Water Framework Directive (WFD) by state authorities in Bavaria from 2004-2013

- , norm.

log, norm.

- , norm.

log, norm.

Numbers of cormorant and frequencies of breeding pairs within SCAT, SWB and 200m riparian strip were separately calculated for all five data sets. Due to the similarity of the two riparian

58

log, norm.

Bavarian wide mapping of breeding birds in context of the EU Conservation of wild birds directive from 2004-2009

merganser L.) Breeding pairs of common kingfisher (Alcedo atthis L.) Breeding pairs of great crested Crested grebe (n) grebe (Podiceps cristatus L.) Breeding pairs of night heron Night heron (n) (Nycticorax nycticorax L.) Breeding pairs of purple heron Purple heron (n) (Ardea purpurea L.) Breeding pairs of white-tailed White-tailed eagle eagle (n) (Haliaeetus albicilla L.) Breeding pairs of Osprey (n) osprey (Pandion haliaetus L.) Common kingfisher (n)

Response variable group Fish community composition

European otter (n)

Number of the European otter (Lutra lutra L.)

Response variable

Description

Arctic char

Salvelinus alpinus L.

Asp

Aspius aspius L.

Brook lamprey Brook trout

Lampetra planeri B. Salvelinus fontinalis M.

Brown trout

Salmo trutta L.

Burbot

Lota lota L.

Cactus roach

Rutilus virgo L.

Common barbel

Barbus barbus L.

Common bleak Common bream Common carp Common chub Common dace

strips, the calculation for the riparian strip with riverspecific width was omitted.

Numbers of otters within SCAT, SWB and 200 m riparian strip were separately calculated for all five data sets. Due to the similarity of the two riparian strips, the calculation for the riparian strip with riverspecific width was omitted. Data preparation For each species and size range, individuals per 100 m river length (CPUE) were calculated.

Alburnus alburnus L. Abramis brama L. Cyprinus carpio L: Squalius cephalus L. Leuciscus leuciscus L.

59

Governmental wildlife conservation monitoring in context of the EU Habitats Directive (FFH) by state authorities in Bavaria from 2004-2012

Transformation th

4 root

Origin Governmental fish monitoring in context of the EU Water Framework Directive (WFD) and Habitats Directive (FFH) monitoring by state authorities in Bavaria from 2004-2013

Common gudgeon

Gobio gobio L.

Common nase

Chondrostoma nasus L.

Common roach

Rutilus rutilus L.

Common rudd

Scardinius erythrophthalmu s L.

Common zingel

Zingel zingel L.

Crucian carp Danube bleak

Carassius carassius L. Alburnus chalcoides G.

Danube longbarbel gudgeon

Romanogobio uranoscopus A.

Danube ruffe

Gymnocephalus baloni H.&H.

Danube salmon

Hucho hucho H.

Danube streber

Zingel streber S.

Danube whitefin gudgeon

Romanogobio vladykovi F. Phoxinus phoxinus L. Gymnocephalus cernua L. Rhodeus amarus B.

Eurasian minnow Eurasian ruffe European bitterling European bullhead

Cottus gobio L.

European river lamprey European weatherfish

Anguilla anguilla L. Thymallus thymallus L. Perca fluviatilis L. Lampetra fluviatilis L. Misgurnus fossilis L.

Ide

Leuciscus idus L.

Kessler's goby

Ponticola kessleri G.

North American catfish

Ictalurus sp.

Northern pike

Esox lucius L.

European eel European grayling European perch

Pike-perch Prussian carp Pumpkinseed Rainbow trout Round goby Sea trout Silver bream Spirlin Stone loach Striped ruffe Sunbleak

Sander lucioperca L. Carassius gibelio B. Lepomis gibbosus L. Oncorhynchus mykiss W. Neogobius melanostomus P. Salmo trutta lacustris L. Blicca bjoerkna L. Alburnoides bipunctatus B. Barbatula barbatula L. Gymnocephalus schraetser L. Leucaspius delineatus H.

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Tench

Tinca tinca L.

Three-spined stickleback Topmouth gudgeon Ukrainian brook lamprey

Gasterosteus aculeatus L. Pseudorasbora parva T.& S. Eudontomyzon mariae B.

Vimba bream

Vimba vimba L.

Wels catfish

Silurus glanis L.

Western tubenose goby

Proterorhinus seminlunaris H. Telestes souffia R.

Western vairone White-eye bream

Ballerus sapa P.

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Table 2: Significant DistLModels for different geographical subregions, catchments, and stream sizes. SCAT = catchment upstream sampling site; SWB = catchment of the respective surface water body; VAR = riparian strip with river-specific width; 200 m = riparian width of 200m; TOC = total organic carbon; p/a = presence/absence; (n) = number of; MI = Macroinvertebrates, WWTP = wastewater treatment plants. D at a s et s

Sub-data sets

Bayern Lech

River catchments

Isar Inn Altmühl Naab Danube Regnitz Main

2 9 6 1 3 2 2 2 7 1 1 2 4 5 8 1 2 3 4

Explained AI variability Cc

17.4% 53.0% 45.7% 28.7% 42.0% 25.5% 9.0% 50.9% 31.6%

DistLModels

22 87. 8 99. 8

WWTP (n) (SCAT)** (10.7%), Riverbed dynamics (SCAT)** (1.7%), Fish pass (p/a) (SCAT)** (4.5%), Stagnant waters (SCAT)** (5.4%), Root crop (SCAT)** (3.0%) WWTP (n) (SCAT) (16.9%), Flood protection structures (SCAT)** (31.8%), Stagnant waters (SCAT)* (18.7%) Hydromorphological quality class (SCAT)** (14.7%), Fish pass (p/a) (SCAT)** 17 (15.4%), Grain (SCAT)* (12.4%), Root crop (SCAT) (5.6%), Filter feeder (MI)** 6.5 (19.5%) 20 Maize (VAR)** 28.7%) 3.1 85. Maize (200 m)** (42.0%) 7 19 Floodplain dynamics (SCAT) (4.6%), Overflowing ability (SCAT) (1.9%), Oilseed 3.7 (SCAT) (5.9%), Ephemeroptera (MI) (3.1%), Grazer (MI)* (11.4%) 46 Oilseed (VAR)* (4.6%), Forage crop (VAR)** (5.6%) 0.8 99. Forest (VAR)** (35.5%), Road network (VAR) (2.7%), Stagnant waters (VAR)* 5 (30.4%) 26 Cormorant roosts (n) (SCAT)** (17.8%), Biogas plants (n) (SCAT)** (21.5%), 2.9 WWTP (n) (SCAT)** (20.5%), Shredder (MI)** (13.9%) 40. Biogas plants (n) (SCAT)** (65.7%) 2

5

65.7%

Danube

1 1 6

14.2%

90 Industrial area (SCAT)** (6.3%), Root crop (SCAT)** (3.3%), Oilseed (SCAT)** 1.1 (7.8%)

Rhine/Main

4 5

24.9%

35 Flood protection structures (SCAT) (22.5%), Fish pass (p/a) (SCAT)** (7.3%), 7.5 Stagnant waters (SWB)** (10.1%), Grazer (MI)** (8.0%), Gatherer (MI) (1.8%)

Foothill of the Alps

7 8

10.5%

60 Root crop (SCAT)** (5.0%), Maize (SCAT)** 8.5% 6.0

Eastern low mountain ranges Southwestern low mountain ranges

3 5

23.1%

27 Biogas plants (n) (SCAT)** (18.5%), Stagnant waters (SCAT)* (9.2%), Root crop 0.0 (SCAT)** (9.5%)

7 5

16.9%

59 Floodplain dynamics (SCAT) (1.2%), Grazer (MI)** (5.5%), Root crop (VAR)* 4.9 (2.8%), Road network (VAR)* (3.8%), Stagnant waters (VAR)** (5.3%)

Streams

1 8 6

7.7%

Medium-sized rivers

6 3

26.7%

Large rivers

2 9

42.6%

Major drainage systems

Saale

Geographical subregions Stream size

n

14 Industrial area (SCAT)* (1.4%), Stagnant waters (SCAT)** (2.8%), Root crop 68. (SCAT)** (2.8%), Ephemeroptera (MI) (0.8%) 9 Hydromorphological quality class (SCAT)** (10.0%), Grassland (SWB)** 48 (15.1%), Grain (SWB)** (14.8%), Grove (SWB)** (7.6%), Stagnant waters 3.5 (SWB) (2.0%) 21 WWTP (n) (SCAT)** (31.9%), Riverbed dynamics (SCAT)** (10.8%), Grain 0.1 (SCAT)** (15.8%), Grassland (SCAT)** (28.3%)

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63

Highlights 

Effects of multiple stressors fish community composition were quantified



Strongest impact of impoundments, agricultural land use and wastewater



Identified stressors and their relative impact varied between spatial scales



Conservation management should prioritize erosion, hydromorphology and wastewater



Fish conservation measures need to be adapted to regional stressors

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