Holistic assessment of hydropower hazards for European fishes

Beyond turbine mortality:

Holistic assessment of hydropower hazards for European fishes

D I S S ER TATI ON

zur Erlangung des akademischen Grades

Doctor rerum naturalium

(Dr. rer. nat.)

eingereicht an der Lebenswissenschaftlichen Fakultät der Humboldt-Universität zu Berlin

von

M.Sc. Ruben van Treeck

Präsidentin

der Humboldt-Universität zu Berlin

Prof. Dr.-Ing. Dr. Sabine Kunst

Dekan der Lebenswissenschaftlichen Fakultät der Humboldt-Universität zu Berlin

Prof. Dr. Dr. Christian Ulrichs

Gutachter/innen

1. Dr. Christian Wolter

2. Prof. Dr. Werner Kloas 3. Assoc. Prof. Dr. Jeffrey Tuhtan

Tag der mündlichen Prüfung: 24.01.2022

RUBEN VAN TREECK

BEYOND TURBINE MORTALITY:

HOLISTIC ASSESSMENT OF HYDROPOWER HAZARDS FOR EUROPEAN FISHES

Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department Fish Biology, Fisheries and Aquaculture

Institute of Inland Fis heries in Potsdam-Sacrow, Department Ecology of fish and waterbodies

Humboldt-University Berl in, Faculty of Life Sciences

Germany, January 202 2

https://doi .org/10.184 52/24 014

Content Summary ..................................................................................................................................... 7

Zusammenfassung........................................................................................................................ 9

Chapter 1: General introduction..................................................................................................11

1.1. Hydropower impacts on fishes...........................................................................................13

1.2. Assessing hydropower risks ...............................................................................................19

1.3. Scope of this thesis ...........................................................................................................21

Chapter 2: Fish species sensitivity classification for environmental impact assessment,

conservation, and restoration planning ........................................................................................23

Abstract ......................................................................................................................................24

2.1. Introduction .....................................................................................................................25

2.2. Material & methods ..........................................................................................................28

2.3. Results .............................................................................................................................34

2.4. Discussion ........................................................................................................................38

2.5. Limitations .......................................................................................................................42

2.6. Conclusions ......................................................................................................................42

Chapter 3: The European Fish Hazard Index – An assessment tool for screening hazard of

hydropower plants for fish ..........................................................................................................43

Abstract ......................................................................................................................................44

3.1. Introduction .....................................................................................................................45

3.2. Conceptual functioning of the European fish hazard Index and its principal components ......48

3.3. Technical implementation and mechanistic functioning of the EFHI .....................................53

3.4. Applicability and limitations...............................................................................................59

Chapter 4: Comparative assessment of hydropower risks for fishes using the novel European Fish

Hazard Index ...............................................................................................................................63

Abstract ......................................................................................................................................64

4.1. Introduction .....................................................................................................................65

4.2. Material and Methods.......................................................................................................67

4.3. Results .............................................................................................................................73

4.4. Discussion ........................................................................................................................77

4.5. Future adjustments ...........................................................................................................82

4.6. Conclusions ......................................................................................................................82

Chapter 5: General discussion .....................................................................................................83

5.1. Hydropower impacts, species susceptibility and their assessment .......................................85

5.2. The state of knowledge on ecology and hydropower...........................................................90

5.3. Methodological constraints of the EFHI ..............................................................................92

5.4. A note on the future of hydropower ..................................................................................94

5.5. Conclusion........................................................................................................................96

Appendix 1 ..................................................................................................................................97

Appendix 2 ................................................................................................................................ 105

Appendix 3 ................................................................................................................................ 111

References ................................................................................................................................ 117

Author affiliations ..................................................................................................................... 147

Acknowledgements ................................................................................................................... 149

Declaration of authorship .......................................................................................................... 151

Summary

7

SummaryWith the increasing pressure humans exert on freshwater ecosystems arises the necessity for means

of objective and systematic risk and impact assessments. In this thesis I developed two classification

systems to evaluate risks of anthropogenic activities for European freshwater fish populations.

The first is a sensitivity score that classifies a species’ resistance to and the recovery from an

anthropogenic stressor that would elevate the mortality of individuals in a population. It is based on

the species-specific, unique suite of life history traits that mediate a species’ resilience e.g., maximum

adult size, maturation metrics and fecundity, but also the migration type of species i.e., diadromous,

potamodromous and resident. Traits on a continuous scale were grouped using percentiles, binary

variables like migration type were treated nominally, and aggregated into a single score ranging from

1.58 (lowest sensitivity, calculated for Sabanejewia balcanica) to 4.42 (highest sensitivity, for

Petromyzon marinus), and for 168 species in total. Species with a high score are expected to resist a

stressor for a longer time or resist a stressor of higher magnitude, respectively, whereas a lower score

reflects a low individual resistance to a stressor but a rapid recovery after it is released. This score can

be used for risk and impact evaluations of anthropogenic activities on individual species or for the

response of a whole fish assemblage in various management activities like risk evaluations and impact

assessments, but also restoration or conservation efforts.

The second assessment system on which a particular focus is laid in this thesis is the European Fish

Hazard Index EFHI, an assessment tool for hydropower risks for fishes. It uses conceptual, empirical,

and modeled knowledge of the relative hazards of single hydropower components and offsets it with

the susceptibility of the affected fish species. In the EFHI, the sensitivity score described above is one

of the main factors determining specific susceptibility, along with body shape, swim bladder anatomy

and conservation concern. The tool evaluates hazards related to the four hydropower components i)

overall flow alterations, ii) entrainment and turbine mortality, iii) upstream fish passage and iv)

downstream fish passage and calculates species- and components specific hazard scores that are

ultimately aggregated into the final EFHI score ranging from 0 (no risk) to 1 (highest risk). It can be used

to compare the relative risk of multiple existing or planned hydropower plants in the same or in

different rivers, or to track improvements or deteriorations of a single hydropower plant over time,

and it is applicable across the bio-geographic range of all 168 species for which a sensitivity score could

be calculated.

Zusammenfassung

9

ZusammenfassungMit dem starken und stetig wachsenden Einfluss menschlicher Stressoren auf Süßwasserökosysteme

kommt die Notwendigkeit von Bewertungssystemen, die diese Einflüsse objektiv und systematisch

bewerten können. Diese Dissertation begleitet die Entwicklung zweier Klassifikationswerkzeuge, die

den Einfluss menschlicher Aktivitäten auf Europäische Süßwasserfischpopulationen abschätzen.

Das erste Bewertungsinstrument in dieser Dissertation ist eine Sensitivitätsklassifizierung für

europäische Süßwasserfischarten, die die Resistenz und das Erholungspotenzial einer Art gegen einen

Stressor widerspiegelt. Der Klassifizierungsalgorithmus basiert auf der Kombination and Aggregation

von Life History Traits, die für die Resilienz der Art entscheidend sind wie z. B. maximale Größe, Eintritt

in die Geschlechtsreife oder Reproduktionswerte, aber auch der potenzielle Migrationstyp (diadrom,

potamodrom und stationär). Diese Traits wurden einzeln je nach Typ und artspezifischer Ausprägung

Perzentil- und Nominalgruppen zugeordnet und diese Gruppen dann zu einem übergeordneten

Sensitivitätswert aggregiert, der von 1,58 (niedrigste, berechnet für Sabanejewia balcanica) bis 4,42

(höchste Sensitivität, für Petromyzon marinus) aufgespannt ist und für 168 Arten bestimmt werden

konnte. Von Arten mit einem hohen Wert ist eine höhere Resistenz gegenüber einem Stressor zu

erwarten, ein niedriger Wert hingegen deutet auf eine geringe Resistenz aber hohes

Erholungspotenzial nach Aufhebung des Stressors. Der Sensitivitätswert kann für die Bewertung

menschlicher Einflüsse auf einzelne Arten, aber auch Artgemeinschaften und in einer Vielzahl von

Szenarien genutzt werden wie z. B. Risikobewertungen und Umweltverträglichkeitsprüfungen, aber

auch in Schutz- und Revitalisierungsbemühungen Anwendung finden.

Das zweite Bewertungssystem, auf der in dieser Arbeit ein starker Fokus gelegt wird, ist der

Europäische Fischgefährdungsindex EFHI, ein Evaluationswerkzeug für Wasserkraftrisiken für Fische.

Dieser nutzt konzeptionelle, empirische und modellierte Daten zu den individuellen Risiken einzelner

Kraftwerksbausteine und verschneidet sie mit der artspezifischen Empfindlichkeit betroffener Fische.

Der Sensitivitätswert ist einer der Hauptfaktoren, die diese Empfindlichkeit bestimmen und wird um

weitere Informationen wie der Körperform oder Schwimmblasenanatomie ergänzt. Der EFHI bewertet

Risiken im Kontext von Abflussmanipulationen, Turbinenmortalitäten und Fischwanderhilfen und

berechnet für jede dieser Komponenten und betroffenen Arten einen individuellen Risikowert, der

dann zu einem Endergebnis zwischen 0 (kein Risiko) und 1 (höchstes Risiko) zusammengefasst werden.

Der EFHI kann das Risiko mehrerer Kraftwerke vergleichen oder das eines einzelnen, z. B. zur

Unterstützung von Verbesserungsmaßnahmen, und ist aufgrund der breiten Verfügbarkeit der

artspezifischen Sensitivität in ganz Europa anwendbar.

Chapter 1 – General introduction

11

Chapter 1: General introduction


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nthropogenic activities have long been identified as the key-driver of freshwater species

extinction, community shifts and biodiversity decline and related conservation efforts are

becoming an ever-pressing matter [1,2]. Detrimental activities include but are not limited to

hydro-morphological manipulations that cause habitat degradation or destruction [3], excessive

resource exploitation, pollution, invasive species, and climate change [1,4]. Of all biomes, freshwaters

are most affected, and the loss of biodiversity in particular is more severe in freshwaters than

anywhere else [5–8]. In fact, the continuous exploration of freshwater resources has contributed to an

unprecedented collapse in freshwater biodiversity over the last decades [5,6,9]. Among prevalent

stressors, habitat fragmentation and the interruption of river connectivity e.g., through dams, weirs,

and other barriers, constitute a major hazard [4,10–13], both in terms of quality and quantity: Recent

studies found that more than a million barriers are fragmenting Europe’s rivers [14]. In addition, the

increasing impacts of climate change let our society face challenges and trade-offs between the

necessity of reservoirs for the retention of water for drinking, irrigation and flood protection and the

release of water for carbon-free, renewable, and base-load electricity generation [15] on one side and

the equally critical conservation of ecologically healthy streams and rehabilitation of degraded ones

on the other [5]. However, the rate at which new dams are built in streams across the globe has never

been higher, and the installed capacity of hydropower plants is continuously increasing as well [16],

even in protected areas [17,18]. Due to both its benefits as well as its impacts hydropower is a

frequently studied subject of high economic, ecological, cultural, and political relevance. From an

energy perspective, hydropower is typically associated with economically beneficial traits like base-

load generation capacity, contribution to grid stability and flexibility, high efficiency, and cost

effectiveness [15,19]. However, the detrimental effects hydropower plants have on aquatic

ecosystems and biodiversity are manifold and comprehensive, and subject numerous studies

[4,12,18,20–25]. In the following section I will review, categorize, and outline hydropower-related

hazards and impacts with a sole focus on freshwater fishes.

A


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1.1. Hydropower impacts on fishes 1.1.1. Barriers

The central, most prominent element of every hydropower scheme is a dam or a weir. Although these

barriers are not exclusive to hydropower plants and regardless of their purpose, they always have the

same principal effect on fish, which is the isolation and segregation of resident populations into an

upstream and a downstream component, as well as a disruption of the river continuum [26,27] as the

natural migration corridor for fishes [28]. Dams act as migration barriers for migratory species and

render critical habitats inaccessible [29,30]. Unhindered upstream migration is of particular relevance

for diadromous species like salmonids, lampreys, some clupeids or sturgeons that only spawn in the

upper regions of rivers where hydraulic and geomorphic conditions support their eggs’ development

and provide larval habitats [31–34]. But also, migrations of potamodromous species are impaired by

dams [35,36]. This can result in reduced natural recruitment [37], differences in population structures

and species assemblages up- and downstream of the dam [11,38,39]. It can even cause whole fish

stocks to go extinct [40,41], unless habitat heterogeneity and availability in the system remains high

enough to support the native assemblage [42]. Furthermore, because dams act as bi-directional

nutrient traps, they can both reduce far-downstream fish biomass [43] and impair recruitment of

anadromous fish species in stream headwaters [44]. It is worth noting, that the mechanisms described

in this paragraph primarily impact population endpoints (i.e., recruitment) rather than individual fishes

(i.e., mortality).

1.1.2. Impoundments

Dams cause substantial alterations of the stream’s original discharge regime [15,45]. Reservoirs and

impoundments considerably slow down the streams’ current speed causing higher sedimentation

rates of finer particles, stratification, and potential oxygen depletion in the hypolimnion due to an

imbalance in aerobic production and consumption [46,47]. In principle, impoundments transform lotic

habitats into those with more lentic characteristics [48,49] that are unsuited for most lithophilic

species that require well aerated, fast flowing coarse gravel beds as spawning habitats [50]. In addition,

these conditions mean habitat loss for a range of rheophilic species [51–54], which reduces

recruitment rates and restricts population size [3,54] even though the number of spawners may

initially remain the same. Artificial impoundments also cause change in water quality [55], shifts in

biomass and ultimately, changes of species abundance and diversity relative to non-impounded

reaches downstream [48]. The altered environmental conditions in impoundments affect species-

specific length-frequency distributions, species richness [56] and species composition [57]. Abiotic

conditions in artificial impoundments were further associated with temperature-related changes of

growth patterns i.e., earlier age of maturity and smaller individual sizes [58]. Studies by Yang [59]


14

showed a reduced energy transfer efficiency in impoundments, suggesting potential energetic

bottlenecks of fish on higher trophic levels. Altered hydro-morphological conditions in impoundments

have caused increased predation, most likely due to the novel environment, lack of navigation cues for

diadromous species [51,53,60] and the resulting migration delay [29,61,62], and reinforced negative

impacts of introduced predators [63]. This has shown to promote local extinction of native and

proliferation of non-native species [64].

1.1.3. Flow manipulations

Usually, dams dampen high natural discharge amplitudes by cutting off flow peaks and increasing very

low discharges alike. As such, they completely alienate the natural discharge regime of a stream, with

flow fluctuations downstream being most problematic at all installations that do not release

approximately as much water through the dam (i.e., through the turbines, spill gates or sluices) as

would normally be discharged in the stream and culminating in diversion-type schemes as well as all

plants that cause hydropeaking. In diversion-type hydropower plants, the main purpose of the dam is

to divert water away from the main flow towards a more remote powerhouse where the water is

turbinated and returned to the original riverbed further downstream [15]. Often, only a fraction of the

original discharge remains in the residual water stretch, which then only supports a fraction of the

original fish population. This has caused species shifts and population declines [65–67] and as a result,

sometimes even rendered whole river stretches uninhabitable. Hydropeaking plants typically store

larger amounts of water and release it for electricity generation in times of peak demand, mostly in

the morning and evening [24,68]. Many species cannot cope with the manipulated flow alterations

induced by turbine operation which can lead to reduced food availability [69–71], erosion and habitat

loss due to periodical dewatering [71–76] and impaired egg development [71,75]. All of these factors

commonly result either in a reduced recruitment or increased direct mortality rate e.g., by stranding

[68,71,77,78], the latter primarily in smaller species or younger specimen with weaker swimming

performance [75,79,80]. If water shortage or pulse flows are not evident, manipulated flows can still

exert major pressures on fish, for example because new habitat types can emerge immediately

beneath the dam that support an increased accumulation of fishes [81], that, in turn, can attract

unnaturally high abundances of predators that may deplete already impaired stocks [29,82].

1.1.4. Upstream migration facilities

Upstream migration needs of fishes have generally received more attention and respective efforts to

increase passage rates date back longer than those for downstream migration [83], potentially because

of the high economic and cultural relevance of anadromous fish species and the higher visibility of

upstream disruption effects. However, fish in upstream migration facilities at dams and weirs show

highly varying passage rates between 0 and 100% [84–87], mostly due to the unique and highly


15

complex interaction between the species’ internal state and motivation to migrate, their anatomy and

swimming ability, ambient hydraulic conditions and type and design of the passage facility [88–91].

Commonly named passage success-determining factors of an upstream fish passage facility are

attraction efficiency (mediated by the position of the entrance and attraction flow (the magnitude and

direction of the outcoming flow relative to the flow of the stream) and suitability of the fish pass for

the target species in terms of flow rate and physical dimensions [84,86,88,91]. Failing upstream

passage success of fish result in excessive energy expenditure and migration delays [92–94] and thus,

delayed arrival at spawning events [93], and increased predation [95]. When hydropower plants are

aligned in cascades their cumulative barrier effect aggravates already significant delays, migration

failures and high mortalities and threatens the population as a whole [85,96–98].

1.1.5. Downstream migration facilities & turbine passage

Particularly, juveniles of anadromous and adults of catadromous guilds but also potamodromous

species require unobstructed downstream migration corridors and therefore, hydropower plants must

be equipped with fish guiding structures that facilitate downstream migration. However, development

of adequate downstream passage routes has not received the same scientific attention as upstream

passage [29,99]. All routes are inherently dangerous for fish and may cause substantial migration

delays [100–104] and associated mortalities as discussed for other impacts.

Spillways, mostly used to release excess water in times of high river discharge, can serve as effective

and comparably fish-friendly downstream paths through a hydropower plant with bypass efficiencies

of >90% [97]. However, water released through spillways, particularly from bigger heights, tends to

supersaturate with nitrogen and oxygen and, together with shear forces, pressure changes and blunt

trauma or abrasions can cause substantial damages and high mortality rates: up to 20% at a height of

<3 m, up to 40% at 10 m and up to 100% at 50 m [105–108], with larger fish being significantly more

susceptible to drop-induced injuries than smaller ones [109].

Sluice gates installed at hydropower plants are mostly opened to spill debris and may constitute

temporarily available pathways for downstream migrating fish. Because the hydraulic conditions

around an open (esp. undershot) gate act as a strong cue for migrating species sluices have proven

efficient in conveying e.g., European eels downstream [110]. However, undershot pathways may

expose passing fish to rapid pressure changes that by far exceed those at overshot routes [111],

causing high (up to 95%) mortality rates, especially for juveniles, small species, and those with

pressure-sensitive swim bladders [105,112,113] while passage efficiency remains low (<20%) [87].

Bypasses are downstream migration routes most often used in combination with deflection screens

[114]. This set-up is usually relatively simple, comprising concrete or metal chutes, slides or pipes that


16

are supplied with water of the forebay and that flush fish that enter them downstream. To be

operational and efficient, bypasses must be easily accessible, sufficiently dimensioned and supplied

with enough water (commonly measures as a proportion of the turbine flow rate), and the entrance

to the bypass should have a slightly higher current speed than the recommended approaching flow of

deflection screens [62,114]. Studies quantifying bypass mortalities are comparably scarce [105], but

documented bypass-related damages and mortalities were caused mainly by sheer forces, rapid

pressure changes, collisions, disorientation, and subsequent predation in the tailrace [115], however

at a generally lower level than at other downstream routes [105]. Bypass passage rates of fish showed

significant variation between 0 and 95% [102,116,117].

Trash racks are installed in front of turbine intakes to protect them from large debris like wood.

Normally, they feature vertical bars that – depending on design requirements – may be slightly

inclined. The clear width between the bars is usually very wide to minimize head loss and constitute a

substantial risk for larger fish that may get impinged and damaged by them when the approaching flow

is too high, during trash cleaner operations or when debris accumulates in the forebay [118]. Studies

investigating mortality rates of fishes due to trash racks are methodologically very challenging and

thus, scarce.

Deflection screens with much smaller clear widths that are installed behind or instead of trash racks

are mechanical and behavioral barriers that prevent fishes from entering the turbines. Fish deflection

screens come in a wide variety of designs e.g., vertically inclined with vertical bars and horizontally

angled screens with horizontal bars that mostly deflect fish mechanically, or horizontally angled

screens with vertical bars inducing an additional behavioral change that increases the deflection

performance up to 95% [114,117,119–123]. The purely mechanical deflection rate can be

approximated using empirical regressions by Ebel [123]: a gap width of 18 mm, for example, would

deflect fusiform fish of approximately ≥16 cm and eel of approximately ≥55 cm length, one of 15 mm

would lower these values to 13.6 and 48 cm. A trash rack with a clear width of 80 to 100 mm, that

older hydropower plants are commonly equipped with, is consequently passable for almost any native

species. When the approaching flow exceeds the recommended value of approximately 0.5 m/s fish

may be impinged in the screen and get damaged [29,62,114,122,124]. Typically, physical/behavioral

deflection screens and downstream bypasses form a functional unit [62,114,116,117,125] and are

usually not considered operational in absence of each other.

Turbine passage is probably the best-studied, most dangerous means for fish to move downstream

[105]. Turbines elevate the mortality of species, which directly affects the size and indirectly affects

the growth rate of a population because it reduces spawner biomass. This effect is most pronounced

in diadromous species that are prevented from reaching life-stage critical habitats and thus, do not


17

contribute to the spawning stock anymore [126,127]. Fishes are usually getting damaged or killed by

either one or a combination of i) abrupt pressure changes (barotrauma) ii) turbulent flow iii) shear

forces and iv) turbine blade strikes [91]. Mortality vary highly across and within turbine types: 1 to

7.7% were reported for “Very Low Head” (VLH) turbines [128,129], <2% in laboratory studies of the

new “RHT” blade design [130], 2% for the Alden turbine [128], 2 to 2.4% for the “Minimum Gap

Runner“ (MGR) [128,131], 0.1 to 25% for water (Zuppinger & Sagebien) wheels [129,132,133], 0 to

32.7% for Archimedes screws [100,128,129,132,134], 0.3 to 100% for Kaplan turbines [129,131,135–

139], 15 to >70% for Ossberger turbines [140], 4 to 100% for Francis turbines [129,132,135,138] and

100% for Pelton wheels [129]. Fish mortality increases with increasing rotational speed

[100,135,138,141,142] usually inversely correlates with turbine size, fish size [143–145] and hydraulic

head [29,135] i.e., with rapid decompression and lack of acclimation time [138,139,141,142,144,146–

149]. Further, mortality decreases with increasing turbine load [131,138,150] and depends on fish

behavior and species [123,145,151–153]. Even if direct mortality rates are not evident, fish may die

from their injuries later [150,154,155]. The rate of delayed mortality can be substantial and not

accounting for it might severely underestimate damage rates during field studies.

Despite causing potential damages and mortalities, unintended downstream entrainment in turbines

[152] can be a significant population impact factor not only for juveniles with weaker swimming

abilities or migratory species i.e., salmonid smolts [156,157], but also for potamodromous [158] and

even resident adult fish, mainly in fall and winter [159]. However, survival for smaller and juvenile fish

at turbine passage is often higher than for adults, and turbine entrainment may therefore contribute

to the persistence of downstream populations, albeit at the expense of populations upstream

[158,160]. Entrainment and mortality of drifting fish larvae are severely understudied and, as of yet,

have not been quantified.

1.1.6. Relative hazards of hydropower plants

Hydropower applies stress on fishes in many ways, depending on various physical and operational

components, and it affects multiple biological endpoints i.e., eggs, larvae, juveniles, and adults. Some

hydropower stressors like some types of flow manipulations or the dam itself exert a continuous press

disturbance and population recovery cannot commence until the stressor is lifted or mitigated [161].

Some fish species are only affected by a single hydropower stressor (e.g., direct mortality through

blade strike, or loss of rearing habitat) but others, like large and rheophilic gravel spawners, are

affected by more than one of them at the same time. The existence of a single HPP can result in

fundamentally different ecological impacts, depending on its location relative to others and to the

habitats of potentially affected species. If the plant forms a functional link with other installations in

the vicinity the relative response of an affected population to the single HPP will be very different than


18

if the HPP operates entirely independently. If an affected species is constrained in its dispersal, a single

HPP that is built into one of the species’ key habitats is very likely to extirpate the population,

regardless of its sensitivity or potentially high resilience, respectively. Ongoing research over the harm

of HPPs e.g., in the headwaters of streams [18], that by default only support a very limited number of

species [162,163] and that are naturally encountered by fewer species, points to the negative

consequences of hydropower allocation for habitat specialists like bullhead or trout.


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1.2. Assessing hydropower risks The high ecological, spatial, and mechanistic complexity of HPPs makes measuring, describing, and

predicting their actual impact on fish populations very challenging and in some cases, even almost

impossible, regardless of the knowledge about single site- or constellation-specific factors. This is due

to several reasons: First, the lack of information on the reference, that is, the undisturbed condition of

the system. The fundamental elements of many hydropower plants (i.e., dams or weirs) are fairly old,

and (at least in Europe) new hydropower plants are commonly built on top of existing infrastructure.

This imposes serious constraints on typical means of impact investigations like BA (before-after) or

BACI (before-after; control-impact) design approaches [164], unless the scientific objective is to assess

the additional impact or mortality factor of the hydropower plant compared to that of the already

existing dam. If construction work on a plant or rather, a dam, has not yet started studies applying

BACI designs could be used to investigate hydropower-related impacts before and after completion

e.g., by Almodóvar and Nicola [72], but if a particular stressor is already in place meaningful conclusions

about its impact could be very hard to come by: Although pressure-release studies, for example in the

context of dam removals or restoration [165,166], would indeed identify improvements (or, in fact,

deteriorations) from the prevalent condition previous to the construction or refurbishment, without

knowledge about the reference condition, however, these studies would merely describe the

“opportunistic” response of the ecosystem and not its resilience i.e., its proximity to the pre-

disturbance state.

Second, investigations on the impact of hydropower on fish populations and assemblages is biased

towards migratory (i.e., diadromous) species that express clearly distinguished, life stage-critical

habitat shifts. Species with a pronounced migration tendency like anadromous salmonids and

lampreys will by default always attempt to pass the hydropower plant to reach their spawning or

rearing grounds upstream. It would thus seem rather straightforward to set up a continuous

monitoring program in the vicinity of the hydropower plant counting e.g., adult salmonid spawning

runs and downstream migrating smolts or silver eels, and indirectly detect increased mortality or

reduced recruitment that can be traced back to the plant. These methods, however, would fail to

describe population dynamics of resident, non-migratory or potamodromous species that do not

express a clear, long-distance migratory behavior, migrate within the system (e.g., between their

spawning grounds and the hydropower plant) or even stay in the impoundment.

Furthermore, with the complexity of different hydropower-related stressors, their interactions and

summed impact on resident or migratory fish arise difficulties in predicting their impact in isolation or

in generic constellations, especially in relation to varying susceptibility of fish assemblages across sites.

Conclusions drawn from observations at one site are not necessarily valid at another. While the


20

constellation of a few hydropower components (e.g., turbine type and hydraulic head or turbine size,

rotational speed and flow rate) will remain relatively constant across sites and applications, others are

much more subject to either the operator’s intentions (e.g., operation modes), geo- and hydro-

morphologically imposed structural design decisions (e.g., plant type, stream and discharge, mode of

operation), spatial limitations (e.g., upstream migration facilities), composition and diversity of the

ambient fish community, and fish protection facilities installed (e.g., dimensions of fish deflection

screens and design or location of bypass systems). These elements can not only be combined in

hundreds of different ways, but they also interact uniquely with fish species and their life stages resp.

seasons; when diadromous guilds must migrate through a hydropower cascade, they are at higher risk

than in their non-migratory state, for example. Furthermore, a hydropower constellation may have

different implication for species of high conservation concern: Some streams constitute priority

migration routes for diadromous species, are exceptionally valuable as salmon (spawning) habitat,

have different management targets (i.e., must be ecologically improved or cannot be worsened) or are

protected as per the European eel regulation or the European habitats directive. These various

influences result in a highly dynamic and complex web of causal hierarchical relationships and hazard

projections that can best be disentangled using qualitative, rather than numerical approaches.

Qualitative assessments do not need numerical assumptions about population metrics like size,

recruitment, or mortality rates but rather, rely on conceptual knowledge about the direction of the

impact of a stressor on one side, and the response of the affected species on the other, and should

deliver much more meaningful insights facilitating approximation of hazards of generic hydropower

components for fish. Given the relevance of renewable electricity in context of the much-needed

decarbonization of our energy systems and subsequent, continuing development of hydropower

across Europe those means seem dearly needed. Until now, however, no such assessment

methodology existed.


21

1.3. Scope of this thesis This thesis follows the development of a hydropower risk assessment framework for hydropower

plants in European water bodies. The genesis of this framework was guided by two principles:

1. Consideration of the local fish assemblage

Species differ in their resilience to disturbances and respond along a gradient of life history traits

fostering rather resistance or recovery or both. Correspondingly, their populations’ intrinsic capacity

to buffer natural mortality of adult specimens differs and thus, accordingly also their response to

additional human-induced mortality. As a result, species have different levels of sensitivity to human-

induced adult fish mortality and will therefore recover faster or slower after a disturbance. Therefore,

as a first step, the resilience of European lamprey and fish species i.e., their sensitivity against adult

fish mortality has been classified based on relevant life history traits. This sensitivity classification of

species serves also assessing the resilience against other modes of human-induced mortality. However,

here it has been applied to operational and constellation specific risks of hydropower plants and

accordingly, implemented in the risk assessment framework. I compiled and evaluated life history

traits of 168 European freshwater fish species that control the species’ potential to resist (and to

recover after) a disturbance. These unique, species-specific suites of trait expressions were then used

to derive a sensitivity score. The score assigned to each species reflects the species’ relative

susceptibility to additional, “artificial” mortality and is applicable in various scenarios of human -

induced pressure on fishes, and particularly for the application in the context of hydropower plants. I

then embedded this score in the risk assessment framework. The development of this sensitivity

classification comprises the second chapter of this thesis [167].

2. Consideration of common standards and market-ready technology

Hydropower-related hazards in the shape of conceptual, empirical, and modeled knowledge were

compiled and translated into three broad risk categories for fish, “low”, “moderate” and “high”.

Hazards include upstream flow alterations in terms of impoundment capacity and current speed

through it, downstream flow alterations related to operation modes (i.e., hydropeaking and residual

flows) and plant design, hydraulic head and associated risks as a function of swim bladder type, turbine

entrainment and mortality as a function of fish length and size, discharge and turbine flow ratios and

turbine type, risks related to upstream fish passage and their admission flows and downstream fish

passages in context of deflection screens, their design and installation angle and bypass accessibility.

Algorithms calculating interactions were developed following literature reviews. The framework only

considers components with a technology readiness level (TRL, [168]) of at least 8. Because the

framework is designed in a modular way, components that reach TRL 8 can always be implemented at

a later stage. It allows for a detailed, objective and highly resolved scoring of site and constellation


22

specific hydropower-related risks for fish. The sensitivity metric documented in the first chapter

assumes an integral position in the framework, as it weighs hydropower-related hazards according to

the species’ susceptibility to disturbance. The fundamental development and arch itecture of the

European Fish Hazard Index EFHI is outlined in the third chapter of this thesis [169].

Extensive testing and application of the EFHI was conducted by analyzing EFHI’s component-specific

and final hazard scores with empirical fish mortality and environmental impact assessments of 7 small

hydropower plants in southern Germany. The tool performed as expected, accurately reflected the

described risks for fishes and provided valuable insights beyond turbine mortality. Testing also

revealed shortfalls in describing risks of uncommon plant constellations, turbine configurations or

sediment trapping. The validation and application of the EFHI is described in the fourth chapter of this

thesis [170].

Chapter 2 – Fish sensitivity classification for environmental impact assessment, conservation, and restoration planning

23

Chapter 2: Fish species sensitivity classification for environmental impact assessment, conservation, and restoration planning Ruben van Treeck, Jeroen Van Wichelen and Christian Wolter

Science of the Total Environment, 708, 2020.

https://doi.org/10.1016/j.scitotenv.2019.135173

https://doi.org/10.1016/j.scitotenv.2019.135173


24

Abstract Species conservation, river rehabilitation, stock enhancement, environmental impact assessment and

related planning tools require indicators to identify significant impacts but also mitigation success.

Since river systems are shaped by disturbances from floods and droughts, typical riverine fish species

should have evolved life history traits providing resilience against such disturbances. This study

compiled and analyzed resilience traits of European lampreys and fish species to derive a novel

sensitivity classification of species to mortality. We assembled life history traits like maximum length,

migration type, mortality, fecundity, age at maturity, and generation time of 168 species and created

a novel method to weigh and integrate all traits to generate a final sensitivity score from one (low

sensitivity) to three (high sensitivity) for each species. Large-bodied, diadromous, rheophilic and

lithophilic species such as sturgeons, sea trout, and Atlantic salmon usually appeared to have high

sensitivity to additional adult fish mortality, whereas small-bodied, limnophilic and phytophilic species

with fast generation cycles were of low sensitivity. The final scoring and classification of 168 European

lampreys and fish species according to their sensitivity can be easily regionalized by selecting the most

sensitive candidates according to the local species pool. This sensitivity classification has major

implications for advancing impact assessment, allowing better targeting of species for conservation

measures, benchmarking progress during rehabilitation and enhancing the objective evaluation of the

success of restoration projects.


25

2.1. Introduction Globally, we are experiencing higher species extinction rates than ever before [171]. Of all ecosystems,

freshwaters are among the most threatened in the world [1] and metrics of freshwater biodiversity

are changing at a more severe pace than in most affected terrestrial ecosystems [8]. Several

anthropogenic activities like overharvesting, introduction of non-native species, pollution, habitat

destruction and human-induced climate change have been identified as key components to species

declines [1,172–174]. There has been considerable research effort devoted to identifying the species

most prone to extinction. The resulting criteria [175] have classified species according to different

threat categories [176]. Numerous Red Data books at various spatial scales (ranging from regional to

global) correspond in the main finding that species at high risk are more likely to go extinct while co-

occurring but not threatened ones may prevail. Consequently, there is considerable variation among

sympatric species in their resilience against anthropogenic disturbances. Resilience is principally

mediated by resistance against and recovery from a disturbance [177]; however, we expect highly

resistant species to have a lower recovery potential and vice versa. For example, small-bodied fish

species often experience higher natural mortality rates corresponding to low resistance, which is

balanced by early maturation and high fecundity to support quick recovery. In contrast, large-bodied

species experience lower natural mortality corresponding to high resistance, but it takes the affected

population longer to recover once the stressor is relieved e.g., because of a long generation time [178].

The opposite of resilience against disturbances is the sensitivity of a species against mortality, with

most sensitive species being the least resilient ones.

Several different life history traits are likely to contribute to a species’ resilience respective sensitivity

and this study focuses on a number of functional traits in freshwater fish species. For one, b ecause

freshwater fish species are the most diverse vertebrate group [179] and freshwater biodiversity is

experiencing significant declines [180], but also, because especially river systems provide excellent

opportunity to study resilience [181,182]. Rivers are disturbance dominated systems, with floods and

droughts and the associated flow patterns, shear forces, variation in connectivity and varying habitat

suitability as significant stressors. According to Reice et al. [183], naturally disturbed ecosystems often

have the highest recovery rates. Fish species that evolved in such systems may be expected to have

evolved certain life history traits to cope with less predictable and harsh environmental conditions

[184]. These very same traits might therefore buffer them against human induced disturbances. As

such, exploring such ‘‘sensitivity traits” can help to inform conservationists and water managers on

population bottlenecks, thresholds and successful mitigation practices to better curtail biodiversity

loss in freshwaters.


26

Researchers often determine the species-specific response to disturbances causing mortality (the

degree of resistance) or recovery by analyzing their autecological attributes and life histories [185–

192]. Identifying traits that increase the individual potential to withstand stressors and analyzing how

the species-specific suites of life history traits link to their recovery can help predict how a particular

mortality factor acts upon the population as a whole. Life history traits are likely to play a particularly

pivotal role in determining species’ responses to anthropogenic activities and are thus frequently used

in impact analyses [193–195]. An increasingly popular way to utilize life history data is in individual-

based population models. These models are used to quantify the population response to

environmental stressors, be they anthropogenic or natural. Models of that type often are highly

resolved and may yield very accurate results but almost exclusively target well-studied flagship or

keystone species [196,197], such as sturgeons [198], salmon [199,200], northern pike [201] and other

species with comparable availability of biological data [202]. In contrast, for the majority of species,

biological and autecological data are too scarce and too heterogeneous for extensive application of

population viability models [203–205]. The need for reliable means of predicting likely responses of

freshwater systems to severe anthropogenic stressors, however, is as pressing as ever [206].

Therefore, this study’s main objective was to overcome this knowledge gap by using earlier concepts

of functional categorization of species based on coarser trait data e.g., by Winemiller [187] to derive a

classification of fish species according to their sensitivity to adult fish mortality. Conceptually, we base

our approach on the life history theory described by MacArthur and Wilson [207] as two distinct life

history strategies: K and r strategists invest either in somatic growth, thus increasing individual

resistance or in reproductive performance, resulting in quick recovery from population decline. We

hypothesize that fishes will show predictable resilience patterns based on the unique, species- specific

suite of 𝑟 and 𝐾 life history features: Species that have expressed strong resistance traits like high

longevity, large size and a low natural mortality will resist a disturbance longer than those that are

prioritizing traits associated with high recovery rates like high fecundity, short generation cycles and

high natural mortality. However, once a particular threshold is crossed, we suspect species of higher

resistance to take considerably longer to recover. In our analysis, we therefore equate high resistance

with high sensitivity to additional mortality caused by anthropogenic disturbances. We reviewed and

analyzed various traits of lamprey and fish species in European fresh waters, in particular life history

traits that provide resistance against and recovery from mortality [182,208]. We scored and processed

those traits identified as most informative and generated a final species-specific score that indicates a

species’ overall sensitivity against mortality resulting from its specific combination of resistance and

recovery traits.

In that final sensitivity classification, all species were ranked relative to each other in their sensitivity

to mortality. The main advantages of our system are that it includes many more species than available


27

population viability analyses and it can even be specifically applied to distinct mortality factors. The

sensitivity classification supports both the selection of target species for river rehabilitation and the

prediction of rehabilitation success. As such it might be used by water managers in river rehabilitation

planning to identify and prioritize restoration measures. The classification system also serves species

conservation by providing a set of highly sensitive candidates serving as target or flagship species.


28

2.2. Material & methods 2.2.1. Life history traits

The following life history traits were identified to determine the sensitivity of a species to

mortality (see Table 1 for a summary):

Table 1: Life history traits and metrics compiled and analyzed in this study.

No. Variable Description Unit Usage

1 𝐿𝑚𝑎𝑥 Maximum total length cm Sensitivity trait 1 2 𝑀 Migration type Categorical Sensitivity trait 2 3 𝑡𝑚 Age at maturity Years Sensitivity trait 3 4 𝑀𝑚 Mortality at maturity %/year Sensitivity trait 4 5 𝐹𝑚 Fecundity at maturity Eggs/female Sensitivity trait 5 6 𝑂𝑚𝑖𝑛 Minimum generation time Sensitivity trait 6a 7 𝑂𝑚𝑎𝑥 Maximum generation time Sensitivity trait 6b 8 𝑆𝑚𝑖𝑛 Minimum survival rate % survival/year Calculate 𝑂𝑚𝑖𝑛 9 𝑆𝑚𝑖𝑛 Maximum survival rate % survival/year Calculate 𝑂𝑚𝑎𝑥 10 𝐿∞ Asymptotic length cm VBGP, calculate 𝑀𝑚 11 𝐾 Growth rate 1/year VBGP, calculate 𝑀𝑚 12 𝑡0 Age at length 0 Years VBGP, calculate 𝑀𝑚 13 𝐿𝑚 Length at maturity cm Calculate 𝑊𝑚, 𝑀𝑚 14 𝑊𝑚 Weight at maturity gram Calculate 𝐹𝑚 15 𝐹𝑟 Relative fecundity Eggs/gram body weight Calculate 𝐹𝑚

Variables 1 - 7 were directly included into the final sensitivity scoring; variables 8 and 9 were used in Eq. 7 and 8; variables 10 - 12 appear in Eq. 1, 2, 3, 4, 5, 12, 13 and 14; variables 13 and 14 were used in Eq. 1, 4, 6, 7, 8, 10, 12, 13 and 14, variable 15 was used in Eq. 6. VBGP = Von Bertalanffy Growth Parameter.

• Maximum total length (𝑳𝒎𝒂𝒙) in cm.

Large bodied species usually rank higher in food webs [209], face lower predation risk, disperse further

[210], utilize larger home ranges [211] and thus, experience a comparatively low natural mortality as

adults. This, in turn, makes the largest species most sensitive to anthropogenic factors increasing

additional mortality [185,187,212]. For this trait, we considered the maximum ever reported length

for each species.

• The migration type (𝑴) of a species as either diadromous, potamodromous, resident and

large-bodied or resident and small-bodied.

Diadromous fishes depend on migrations between marine and freshwater habitats. They are

considered the first most sensitive due to this highly specialized lifestyle [35,213]. Potamodromous

fish essentially migrate in freshwaters and were thus considered second sensitive.

• Age at first maturity (𝒕𝒎) in years.

The time at which an individual becomes mature strongly influences its reproductive potential. A young

age at first maturity enables fast adaption to changing environments due to a high population turnover

and thus leads to a higher recovery rate [185,187]. Consequently, we ranked species with increasing

age at first maturity as more sensitive. Due to sex differences in this trait only age at first maturity of


29

females was considered, which takes longer in most species. In case of several reported values the

median was used.

• Mortality at maturity (𝑴𝒎).

𝑀𝑚 =𝐿𝑚

𝐿∞

−1.5×𝐾 Equation 1

𝑙𝑡 = 𝐿∞(1 − 𝑒−𝐾(𝑡−𝑡0)) Equation 2

The recovery rate of a fish population increases with increasing 𝑀𝑚 [190]. We therefore included this

powerful indicator for resilience in our analysis and assumed a decreasing sensitivity of species with

increasing 𝑀𝑚. We computed 𝑀𝑚 following the approach of Hutchings and Kuparinen [190] shown in

Equation 1. This equation contains the species’ length at maturity (𝐿𝑚), their asymptotic length (𝐿∞,

the theoretically possible length if they grew indefinitely) and their growth rate (𝐾), expressed as 1

𝑦𝑒𝑎𝑟,

at which they approach 𝐿∞. 𝐿∞ and 𝐾 are components of the von Bertalanffy Growth Function [214],

shown in Equation 2. Here, 𝑙𝑡 is the length of a fish at time (𝑡), and 𝑡0 is the species’ hypothetical age

at length zero, which is an artificial parameter. Since these parameters vary widely even between

populations, we only considered values calculated as follows:

• 𝐿∞ was derived from 𝐿𝑚𝑎𝑥 using the empirical relationship shown in Equation 3 established by

Froese and Binohlan [215].

• 𝐾 and 𝑡0 were calculated using a converted version of the Von Bertalanffy growth function by

Bertalanffy [214] in a stepwise procedure suggested by Froese and Binohlan [216]: In Equation

4, 𝑡0 was initially set to zero and the resulting 𝐾 was inserted in Equation 5 by Pauly [217]. This

new 𝑡0, in turn, was inserted in Equation 4 to obtain an updated estimate for 𝐾. The updated

𝐾 was then used in the reiteration of Equation 5 to obtain a new 𝑡0. This process was repeated

until the calculated values for 𝐾 remained stable. We explored the plausibility of all three Von

Bertalanffy growth parameters by comparing them with literature values.

𝐿∞ = 100.04+1.98×log10 𝐿𝑚𝑎𝑥 Equation 3

𝐾 = − ln1−𝐿𝑚

𝐿∞𝑡𝑚−𝑡0

Equation 4

𝑡0 = −1 × 10−0.39−0.28×log10 𝐿∞−1.04×log10 𝐾 Equation 5

• Fecundity (𝑭𝒎).

𝐹𝑚 = 𝐹𝑟 × 𝑊𝑚 Equation 6

The reproductive potential of an organism is a critical fitness factor and component of most life history

models [212,218]. The total fecundity of a fish, however, is highly variable and changes with factors


30

like individual state of nutrition [219,220], stress level [221] and body size. We therefore estimated 𝐹𝑚

by multiplying the less variable relative fecundity (𝐹𝑟, the number of eggs per gram of total fish weight)

by the females’ weight at first maturity (𝑊𝑚), as shown in Equation 6. The Allis shad Alosa alosa, for

example, has a relative fecundity of 200 eggs per gram body weight. Its weight at maturity is roughly

3800 g, which results in a total of 760,000 eggs at its time of maturity. When studies directly reported

values for 𝐹𝑚 for a species without the need to approximate them, we added those to the data pool,

too. We ranked species with a higher reproductive potential less sensitive.

• Minimum and maximum generation time 𝑶.

𝑂𝑚𝑖𝑛 =(𝑆𝑚𝑖𝑛×𝐹𝑚)

𝑡𝑚 Equation 7

𝑂𝑚𝑎𝑥 =(𝑆𝑚𝑎𝑥×𝐹𝑚)

𝑡𝑚 Equation 8

This metric was generated using Equation 7 and Equation 8 and accounts for the reproductive potential

𝐹𝑚 of a species, its age at first maturity 𝑡𝑚 and the juvenile survival rate 𝑆, which, according to most

studies, is extremely low. However, parental care was shown to increase offspring survival. Despite a

variety of parental care strategies (e.g., nest building and guarding) with potentially differing net

effects on offspring survival, we could only consider parental care as a binary trait (yes or no) in our

analysis. Based on several empirical results [222–226], the annual juvenile survival rates were set at

0.1–2% in case of no or unknown parental care and 0.5–2% with parental care. Both upper (𝑆𝑚𝑎𝑥) and

lower (𝑆𝑚𝑖𝑛) survival rates were used for calculating the maximum (𝑂𝑚𝑎𝑥) and minimum (𝑂𝑚𝑖𝑛)

generation time.

Table 1 summarizes the traits and metrics that we used in this study.

2.2.2. Data compilation

We searched the literature using Web of Science, Google and Google Scholar as well as more specified

repositories like Fishbase [227] or the Freshwater Information Platform

(www.freshwaterinformationplatform.eu). By using uniform search strings for each piece of life history

information and species of interest we ensured a systematic workflow. A typical search string used in

Google and Google Scholar was of the structure:

‘‘allintitle: Genus OR specific name OR genus + specific name biology OR life history”.

To maximize the number of species in our analysis, we included not only peer-reviewed literature and

textbook data, but also grey literature, project reports, personal communications, and online content,

for example hosted by angling associations following observations by Smialek et al. [228]. To overcome

the constraints of missing data, we either drew analogies from better studied species of the same

genus or family or we employed empirical regressions to obtain plausible estimates, as follows:

http://www.freshwaterinformationplatform.eu/


31

• We estimated missing data for 𝐿𝑚𝑎𝑥 by adding 15% and 10% to reported standard and fork

lengths, respectively, according to Beckman, Özaydin and Taskavak [229,230] and others.

• We estimated missing data for 𝑡𝑚𝑎𝑥 by using 𝑡𝑚 in a re-arranged equation (Equation 9) by

Froese and Binohlan [215]. If 𝑡𝑚 was not reported for a species either, we estimated 𝑡𝑚𝑎𝑥 by

drawing analogies from closely related species of the same genus or family.

• We approximated missing weight-at-age data by extrapolating existing weight-at-age series or

by converting existing length-at- age series using species-specific length-weight regressions.

These were usually reported as shown in Equation 10, following Eberhardt and Ricker [231].

𝑡𝑚𝑎𝑥 = 100.55+0.96×log10 𝑡𝑚 Equation 9

𝑊 = 𝑎 × 𝐿𝑏 → log10 𝑊 = log10 𝑎 + 𝑏 × log10 𝐿 Equation 10

• In Equation 10, 𝑊and 𝐿 denominate the total weight and length of a fish; 𝑎 and 𝑏 are

coefficients, of which 𝑏 is comparable across similarly shaped individuals but typically varies

widely across species, populations, and sexes. When the sex of the sample population was

provided, we used length-weight regressions for females only.

• We estimated missing data on total and relative fecundity using available fecundity-at-age or

fecundity-at-length regressions and length-weight regressions. In case of no data, analogies

from closely related species were used.

2.2.3. Generating trait-specific sensitivity groups

The resulting, initial data base contained observations and estimates for the traits 𝐿𝑚𝑎𝑥, 𝑡𝑚𝑎𝑥, 𝐿𝑚, 𝑡𝑚,

𝑀𝑚, 𝐹𝑚, 𝐹𝑟, length-weight regressions, 𝑂𝑚𝑖𝑛, 𝑂𝑚𝑎𝑥, and the three von Bertalanffy growth parameters

𝐿∞, 𝐾, 𝑡0 for each of 120 lamprey and fish species. We determined sensitivity groups trait-specifically:

For each trait, we divided the species-specific observations into five classes of equal size using 20-

percentiles, from highest (5) to lowest (1) sensitivity. Finally, we assigned all species to one of five

sensitivity classes for each life history trait.

2.2.4. Generating the species-specific sensitivity score

We used weighted averaging to combine the trait-specific sensitivity scores to a species-specific

sensitivity class as follows:

• Scores of 𝑀𝑚, 𝑡𝑚; 𝐹𝑚 and 𝐿𝑚𝑎𝑥 each were weighted by a factor of 1.

• The migration type of the species was scored 1-fold when it was resident, 3-fold when it was

potamodromous and 5-fold when it was diadromous, respectively.

• Scores of 𝑂𝑚𝑖𝑛 and 𝑂𝑚𝑎𝑥 each were weighted by a factor of 0.5 to account only once for the

trait ‘‘generation time”.


32

We summed the weighted scores and divided them by the number of traits. This yielded the final

species-specific sensitivity score rounded to integer that is inversely related to species’ resilience

(Appendix 1, Table A1_1).

2.2.5. Validating the classification score

Because of the very heterogeneous empirical data and a substantial need for surrogate estimates, we

evaluated the potential effect of data handling on the sensitivity classification of species. To do this,

we generated a second database for the identical set of species with all estimates of 𝐿𝑚, 𝑡𝑚, 𝑀𝑚, 𝐹𝑚,

𝑂𝑚𝑖𝑛 and 𝑂𝑚𝑎𝑥 solely calculated for female specimens based on 𝐿𝑚𝑎𝑥 and 𝑡𝑚𝑎𝑥 using the empirical

relationships shown in Equations 11–14, established by Froese and Binohlan [215].

𝑡𝑚 = 101.05×𝑙𝑜𝑔10 𝑡𝑚𝑎𝑥−0.58 Equation 11

𝐿𝑚(𝑎𝑙𝑙) = 100.9×log10 𝐿∞−0.08 Equation 12

𝐿𝑚(𝑚𝑎𝑙𝑒𝑠) = 100.89×log10 𝐿∞−0.10 Equation 13

𝐿𝑚(𝑓𝑒𝑚𝑎𝑙𝑒𝑠) = 100.95×log10 𝐿∞−0.12 Equation 14

We validated the calculated estimates of all variables by conducting Pearson product-moment

correlation analyses between them and their observed counterparts. We also re-calculated trait-

specific sensitivity groups and species’ sensitivity scores as descr ibed above. After having done that,

we conducted another correlation analysis between the two species’ sensitivity classifications resulting

from both the second ‘‘calculated” and the first ‘‘observed” database using Spearman-rank

correlations and Gwet’s [232] AC coefficient of agreement across all group memberships of the species.

The agreement of the species-specific scoring between the two datasets was high (Figure 1 and

Appendix 1, Table A1_1, Figure A1_1 and Figure A1_2 a-e). Only seven species (5.8%) each deviated

>0.5 on their sensitivity score. A total of 90 species (75%) were assigned the same final sensitivity class,

and 16 (~13.3%) and 14 (~11.7%) species scored one class less and one class more sensitive in the

‘‘calculated” than the ‘‘observed” data set. The number of species per class between the two data sets

differed to some extent, with the highest numerical deviation in the moderate sensitivity class and the

lowest in the high sensitivity class. This phenomenon was strongly influenced by the number of species

per class and disappeared when we weighted the deviation index according to the total number of

species per group (see Table 2). We statistically compared life history traits as well as the final

classification based on ‘‘calculated” and ‘‘observed” data. The correlation between the calculated and

observed variables age, length, and fecundity at maturity as well as the minimum and maximum

generation time was high with correlation coefficients of ρ = 0.76 (p < 0.0001) for age, ρ = 0.91 (p <

0.0001) for length and ρ = 0.74 (p < 0.0001) for fecundity at maturity, ρ = 0.91 (p < 0.0001) for minimum

and ρ = 0.85 (p < 0.0001) for maximum generation time. The final sensitivity scores were highly

significantly correlated (Spearman’s Rho 0.84, p < 0.0001) as well. Gwet’s [232]s AC coefficient scored


33

0.86 at a confidence level of 95%. Because of these results we added another 48 species to the

database for which only reported data on 𝐿𝑚𝑎𝑥, 𝑡𝑚𝑎𝑥, 𝐹𝑟, length-weight regressions and the Von

Bertalanffy growth parameters could be obtained. After calculating the missing trait values, we re-ran

the sensitivity analysis with a total of 168 species. Because 𝐿𝑚𝑎𝑥 and 𝑡𝑚 were usually reported as

integers or common fractions, the derived variables contained many identical values resulting in some

percentile splits through a cluster of identical values. Whenever that happened, we shifted the

threshold manually to the next biggest data gap either above or beneath the cluster to ensure the

correspondence within groups. The analysis produced a total number of 168 species classified

according to their sensitivity to additional mortality in adult females. Table 3 provides details on the

classification values and thresholds. Data analysis was done using R [233], v. 3.5.1, packages ‘‘rel”, v.

1.3.1., ‘‘ggpubr”, v. 0.2.2.

Table 2: Agreement in species numbers between the "observed" and the "calculated" data set.

Sensitivity High Moderate Low

Observed 23 65 32 Calculated 26 57 37 Difference 3 8 5 Deviation 0.06 0.07 0.07 x̅=0.07

The deviation index was calculated by dividing the numerical difference between the group sizes by the sum of the two groups. Values close to zero indicate very high, values close to 1 very low correspondence between group sizes.


34

2.3. Results We developed a classification of 168 European freshwater lamprey and fish species based on their

sensitivity to adult female fish mortality. Table 4 summarizes the relative data availability per trait,

using common metrics of exploratory statistics. Of the 168 species, 32 (~19%), 80 (~48%) and 56 (~33%)

were assigned to the ‘‘high”, ‘‘moderate”, and ‘‘low” sensitivity class, respectively (Appendix 1, Table

A1_2). All species of the Acipenseridae, the closely related non-native Mississippi paddlefish Polyodon

spathula and the European eel Anguilla anguilla were represented in the high sensitivity class. The

majority of salmonid species appeared highly sensitive as well except grayling Thymallus thymallus,

which was assigned to the moderate sensitivity class. Of the Centrarchidae, the smallmouth bass

Micropterus dolomieu was assigned to the high, while the largemough bass M. salmoides and the

pumpkinseed Lepomis gibbosus to the moderate sensitivity class. Assigned only to the moderate

sensitivity class were species of the families Centrarchidae, Cottidae, Ictaluridae, Mugilidae,

Xenocyprididae as well as the single species of the Loricariidae, Nemacheilidae, Odontobutidae,

Osmeridae, Pleuronectidae, Siluridae, Syngnathidae and Tincidae. Spread out across all sensitivity

classes were the three families Petromyzontidae, Cyprinidae and Leuciscidae, with the sea lamprey

Petromyzon marinus European, Italian, Iberian and Andalusian barbel Barbus barbus, B. plebejus, B.

bocagei and B. sclateri, blue bream Ballerus ballerus, ide Leuciscus idus, nase Chondrostoma nasus,

roach Rutilus heckelii and Danube bleak Alburnus chalcoides scoring high and Achondrostoma

salmantinum, Squalius torgalensis, Iberochondrostoma lusitanicum (Leuciscidae) scoring low. The

Clupeidae are located in the lower range of the sensitivity scale with Alosa alosa and A. immaculata

scoring moderate and A. tanaica and Clupeonella cultriventris low. Only in the low sensitivity class

appeared four species of the Acheilognathidae, Atherinidae, Poeciliidae and Umbridae (Figure 2 and

Appendix 1, Table A1_2). Spawning guilds were distinguished into lithophilic/litho-pelagophilic,

phytophilic/phyto-lithophilic, and ‘‘others”. “Others” contained ariadnophilic, ostracophilic,

pelagophilic, polyphilic, psammophilic, speleophilic, and viviparous species and appeared most often

in the moderate sensitivity class. Lithophils were the most dominant guild in the highest sensitivity

class, while phytophils/phyto-lithophils were proportionally most abundant in the low sensitivity class

(Figure 3). Species were additionally classified into rheophilic, limnophilic (including marine/estuary-

dwellers) and eurytopic guilds. Rheophilic species were most abundant in the high, limnophilic in the

low sensitivity class, respectively. Eurytopic species were most abundant in the moderate sensitivity

class (Figure 4).


35

Figure 1: Overall agreement (Rho = 0.84) between the two sensitivity scores of the ‘‘calculated” and ‘‘observed” data set. Points indicate single species, colors their family membership. Asterisks in the legend indicate families with only one species. N = 120 species of 29 families.

Table 3: Traits and metrics with their lowest and highest value and percentile thresholds used in the extended data set containing 168 species.

Sensitivity class

𝑳𝒎𝒂𝒙 (cm)

𝑴𝒎 (%)

𝒕𝒎 (years)

𝑭𝒎 (eggs/female)

𝑶𝒎𝒊𝒏 (Offspring/female/year)

𝑶𝒎𝒂𝒙 (Offspring/female/year)

Highest >88.6–800 <27.2–4.9 >4.5–17.5 (>4–17.5)

<574.3–14.1 <0.6–0.01 <7–0.17

High >46.6–88.6 <39.6–27.2

>3.5–4.5 (>3.27–4)

<1805.7–574.3 <1.3–0.6 <15.5–7

Moderate >25–46.6 <51–39.6 >2.5–3.5 (>2.35–3.27)

<5237–1805.7 <3.4–1.3 <32.37–15.5

Low >15–25 <65.4–51 >2–2.5 (>2.04–2.35)

<74733.6–5237 <16.1–3.4 <232.9–32.37

Lowest 5–15 (5–14.8)

89.8–65.4 0.35–2 (0.35–2.04)

985628.5–74733.6

1232–16.1 4928.1–212.9

𝐿𝑚𝑎𝑥=maximum body length, 𝑀𝑚=mortality at maturity, 𝑡𝑚=age at maturity, 𝐹𝑚 =fecundity at maturity, 𝑂𝑚𝑖𝑛 and 𝑂𝑚𝑎𝑥 =number of minimum and maximum offspring surviving the larval stage per female and year. Underlined values in parentheses are the adjusted thresholds at the next biggest data gap below or above the original percentile threshold when it cut through a cluster of identical values.

Table 4: Raw data availability per trait for each of the 168 species analyzed.

Life history traits

Data availability 𝑡𝑚𝑎𝑥 𝐿𝑚𝑎𝑥 𝑡𝑚 𝐿𝑚 LWR 𝐹𝑟 Total number of “observed” data points 156 168 572 383 249 435 Number of species with missing information 12 0 5 15 24 37 Calculated substitutes used 9 0 5 15 0 0 Substitute analogies used 3 0 0 0 24 37

𝑡𝑚𝑎𝑥=maximum lifespan, 𝐿𝑚𝑎𝑥=maximum body length, 𝑡𝑚=age at maturity, 𝐿𝑚=length at maturity, LWR=length-weight-regressions, 𝐹𝑟 =relative fecundity. For 𝑡𝑚𝑎𝑥 and 𝐿𝑚𝑎𝑥 only the biggest single observation was considered. Some of the LWR data are means of multiple entries of length-weight tables on Fishbase. The actual number of observations for species with a ‘Fishbase’ reference can therefore be higher.

1.50

2.00

2.50

3.00

3.50

4.00

4.50

1.50 2.00 2.50 3.00 3.50 4.00 4.50

Sen

siti

vity

(ca

lcu

late

d)

Sensitivity (observed)

Acheilognathidae* Acipenseridae Anguillidae* Atherinidae* Centrarchidae Clupeidae

Cobitidae Cottidae* Cyprinidae Esocidae* Gasterosteidae Gobiidae

Gobionidae Ictaluridae Leuciscidae Loricariidae Mugilidae Nemacheilidae*

Odontobutidae* Osmeridae* Percidae Petromyzontidae Pleuronectidae* Polyodontidae*

Salmonidae Siluridae* Tincidae* Umbridae* Xenocyprididae


36

Figure 2: Distribution of species within families and across the sensitivity classes High, Moderate and Low. Numbers within bars indicate the number of species per sensitivity class and family, asterisks behind the family name highlight families with only once species. N=168 species of 31 families.

Figure 3: Relative proportion of selected spawning guilds of 168 species within the 3 sensitivity classes High (N = 32), Moderate (N = 80) and Low (N = 56). ‘‘Others” include ariadnophils, ostracophils, pelagophils, polyphils, psammophils, speleophils and vivipars. Respective species numbers per sensitivity class and spawning guild are shown above bars. The black line and the numbers in parentheses indicate the total number of species per sensitivity class.

9 1 4 5 1 102

3 3 25

1 4 2 229

4 55 1

42 7

21 7 8

184

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40

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34

16

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High (32) Moderate (80) Low (56)

Nu

mb

er o

f sp

ecie

s

Rel

ativ

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rop

ort

ion

(%)

Sensitivity

Lithophils Phyto/phyto-lithophils Others Sum


37

Figure 4: Relative proportion of main habitat preference guilds of 168 species within the 3 sensitivity classes High (N = 32), Moderate (N = 80) and Low (N = 56). Respective species numbers per sensitivity class and habitat guilds are shown above bars. The black line and the numbers in parentheses indicate the total number of species per sensitivity class.

25

29 20

1

2016

6

31 20

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High (32) Moderate (80) Low (56)

Nu

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(%)

Sensitivity

Rheophils Limnophils Eurytopics Sum


38

2.4. Discussion 2.4.1. Species classification

We developed a novel scoring scheme that combines various life history traits determining resistance

and recovery potential of a species into a single, species-specific sensitivity score of both native and

established non-native European freshwater fish species. This sensitivity score reflects the species’

susceptibility to mortality of adults in a population. Our scheme has the great advantage that all

available, even very heterogeneous data of reasonable quality could be used, and various

combinations of resistance and recovery traits considered. Because all single traits were scored

separately, the resulting data could be statistically treated depending on their distribution and

heterogeneity and the species rank-ordered relative to each other for each specific trait. This approach

allowed us to include also lower quality trait estimates as long as the relation between species (e.g.,

lower or higher number of eggs) remained relatively constant. It is the first of its kind incorporating a

large number of freshwater fish species from all across Europe. Because our approach does not require

extensive knowledge about population parameters, we were able to include many species that could

not be considered in previous population risk assessments. By statistically confirming the very high

agreement between the trait variables age, length, and fecundity at maturity as well as the final

classification scores of 120 species based on ‘‘observed” and ‘‘calculated” life history traits, we could

extend our species pool even further and estimate the sensitivity for 168 species in total. The sensitivity

classification offers a versatile tool that is easy to apply for a range of stakeholders and supports the

selection of target species for river rehabilitation, stock enhancement and prediction of species-

specific responses to disturbances. It also serves conservation actions by providing a set of highly

sensitive candidates serving as umbrella species for migratory, habitat and spawning guilds.

Longevity, large size, long generation time, large size and late age at maturity as well as diadromous

migration were all considered life history traits that increase the sensitivity to human induced

mortality. Therefore, it is no surprise that the high sensitivity class is dominated by large-bodied

migratory species: sturgeons, salmonids, the sea lamprey, eel, and some riverine cyprinids like nase

Chondrostoma nasus, Danube bleak Alburnus chalcoides, ide Leuciscus idus, Rutilus heckelii and

common barbel Barbus barbus. This corresponds well with the long-lasting, slowly progressing

programs to reintroduce Atlantic salmon Salmo salar [234–236] and sturgeons [237,238] in Europe,

the ongoing decline of the European eel stock [239] and the low improvement of river fishes in

European waters [240]. But the sensitivity classification is not primarily driven by single dominant traits

like body size, as indicated by the result for the wels Silurus glanis. S. glanis is classified as of moderate

sensitivity, but at the same time is very large, matures late and has a very low natural mortality. These

traits alone could render it highly resistant and hence, sensitive to anthropogenic disturbance but are

offset by a high fecundity, parental care, and comparably high recruitment rates. Due to its low


39

demands toward flow regime and spawning substrate (it is eurytopic and phytophilic), it dwells in a

broad range of habitats, up to a point where it is even considered invasive in parts of Europe [241].

Higher sensitivity scores also have a higher relative proportion of lithophilic species (Figure 3). This

agrees very well with recent assessments of the mostly moderate and poorer ecological quality of

European rivers [240]. Lithophilic species have rather specific requirements for spawning habitat

conditions (e.g., the composition of spawning gravel, water depth, temperature, and current velocity),

which makes them very susceptible to hydro-morphological alterations and degradations. Because

early life stages constitute the major bottleneck for population growth [242], such disturbances have

critical consequences on the reproductive success of lithophilic species [243–246]. Consequently, it is

not surprising that most European gravel spawners are considered endangered [22]. The high

sensitivity class also includes the highest relative proportion of rheophilic species (Figure 4) and this,

too, matches observations of declines of specialist guilds [247]. However, it must be noted that species

of the rheophilic and lithophilic guild are also represented in the moderate and low sensitivity class

(Figure 3 & Figure 4). This indicates a broad variety of recovery potential among species of the same

habitat guild and has implications for river rehabilitation planning and benchmarking.

In the moderate and low sensitivity class the relative proportion of rheophilic and lithophilic species

decreases while the proportion of limnophils, phytophils and most importantly, eurytops increases.

The dominance of these guilds on the lower end of the sensitivity scale is highly plausible as well. It

has been repeatedly shown [247–250], that eurytopic species rather benefit from degraded conditions.

Accordingly, roach Rutilus rutilus and perch Perca fluviatilis (both only moderately sensitive) are even

considered indicator species for water quality impairment [251] and habitat degradation [252],

respectively. Species in the lower sensitivity classes mostly express a trait combination of small size

and short life expectation, rapid growth, early maturation and high mortality rates, which results in

high population turnover and occasionally very high recruitment facilitating fast recovery [184,253–

255]. As a consequence, we expect these species to be less resistant, but due to the high recovery

potential less sensitive against anthropogenic disturbances.

A significant result is the presence of one and 20 limnophilic and six and 31 eurytopic species in the

high and moderate sensitivity class respectively. Among them are the whitefish Coregonus maraena,

several barbel species and the Adriatic sturgeon Acipenser naccarii. This suggests that the ecological

guild approach can sometimes be insufficient to predict the biotic response of species to e.g., habitat

improvement, when it does not consider their intrinsic recovery potential at the same time. This was

also confirmed by Aarts et al. [247], and calls for different means of assessing specific population

behavior, especially of data-poor species, in response to anthropogenic activities.


40

2.4.2. Application

The species sensitivity classification developed here provides a tool for conservation and restoration

planning and also benchmarking that goes beyond habitat preferences and guild membership ship of

species. Combining resistance and recovery traits within a single scoring system allows for

differentiating within habitat guilds between species of high resistance and low recovery potential and

vice versa, which is relevant in predicting species’ response to degradation and rehabilitation. For

example, within the lithophilic i.e., gravel spawning fishes, species of low sensitivity and high recovery

potential are expected to respond faster to the provision of spawning gravel, a common river

rehabilitation measure, compared to highly sensitive species. Correspondingly, highly sensitive species

should be primarily protected and in focus of conservation actions, while species of low sensitivity

could be easier enhanced by rehabilitation measures. By providing guild-specific spawning and habitat

conditions water managers would not only aid to conserve highly sensitive species but also greatly

accelerate the recovery of less-sensitive species that are expected to recover much faster than the

highly sensitive ones after a disturbance-induced population collapse.

The sensitivity classification offers a versatile tool that is easy to apply for a range of stakeholders and

supports the selection of target species for river rehabilitation, stock enhancement and prediction of

species-specific responses to disturbances. Substantial enhancement of a population of a highly

sensitive species would definitively indicate rehabilitation success. In the same sense, the most

sensitive species might be selected as target species to implement the most comprehensive

rehabilitation measures. The system also supports conservation actions by making use of these highly

sensitive candidates serving as umbrella species for habitat protection and enhancement. The success

of using umbrella- or keystone species in conservation programs has been demonstrated several times

[256,257]. Conversely, if prevalent umbrella- or keystone species disappear in an affected water body,

the classification system makes it easier to predict which species will follow. Regardless of its

application, the detailed scoring and extensive species coverage facilitate success evaluation on a

highly resolved, temporal scale. If the reference community assembly is known the sensitivity score

can also be part of a ‘‘snapshot metric” to evaluate the conservation value of a certain water body at

a given time. To that end, the metric may include abundance, species number and a processed specific

sensitivity score of a sample, similar to other diversity metrics, described by e.g., Peet [258] or to the

Fish Region Index [259]. The use of this metric would even enable an objective comparison of two or

more independent water bodies whose reference communities differ in species richness if a coefficient

correcting for species numbers is included.

Further, our system allows for easy regionalization of its diagnostic capabilities by choosing the most

sensitive species from the classification that occurs in the regional species pool. Because of that, the


41

application of the sensitivity classification is not limited to a specific biogeographic region, catchment,

or country within Europe, which sets it apart from other common fish-based approaches surrounding

freshwater assessment policies. We therefore argue that using the sensitivity score developed here in

synergy with habitat guilds more comprehensively captures the response pattern of riverine fish

communities to degradation, rehabilitation, and conservation of river systems.

This new method provides an opportunity to overcome common problems occurring in restoration

and rehabilitation tasks everywhere:

• Due to a lack of knowledge of the species’ life history, very little is known about their intrinsic

resistance or recovery capacity; something that we can resolve by combining resistance and

recovery traits to a single score.

• A lack of means to evaluate the success of rehabilitation measures can be overcome, even

across many different bio-geographic regions, by using increasingly sensitive species as

benchmarks.

• Prioritizing conservation and rehabilitation measures can be streamlined by offsetting

ecological guilds and conservation concerns with the sensitivity score presented here.


42

2.5. Limitations Many physico-chemical pressures like eutrophication or depleted water quality appear as press or

ramp disturbance i.e., they just increase and stagnate in amplitude and may remain in that state for

an extended period of time, during which recovery cannot occur [161]. Even though our system

provides a baseline indication on how severe a species would react to a ramp disturbance (we expect

species of the highest sensitivity to withstand greater magnitudes) our system is not suited to identify

the exact nature of the disturbance. Furthermore, species might respond to disturbances by

adaptation, which was not considered here. For example, cyprinids of the genus Carassius and the

bitterling Rhodeus amarus, have evolved extraordinary resistance against anoxic conditions lasting for

weeks [260]. Consequently, the sensitivity score does not constitute a universally valid scale that

indicates a population’s unconditional performance but should be considered in a site- and situation-

specific context to provide the most comprehensive and meaningful information. Therefore,

discrepancies cannot be excluded between the sensitivity classification of a species here and its

conservation status according to the Habitats Directive (92/43/EEC) or the European Red Data Book

[261].

2.6. Conclusions Our approach is based on the analysis and compilation of resilience traits and provides a species -

specific ranking score of sensitivity to mortality of adults from a population. The classification is

intended as a diagnostic tool for rehabilitation planning and identifies particularly sensitive species to

streamline conservation efforts and less sensitive ones to benchmark restoration success. The results

agree well with other research and can be used across biogeographic regions in all of Europe and

independent of the precise nature of the disturbance.

Chapter 3 – The European Fish Hazard Index – An assessment tool for screening hazard of hydropower plants for fish

43

Chapter 3: The European Fish Hazard Index – An assessment tool for screening hazard of hydropower plants for fish Ruben van Treeck, Johannes Radinger, Richard A. A. Noble, Franz Geiger and Christian Wolter

Sustainable Energy Technologies and Assessments, 43, 2021.

https://doi.org/10.1016/j.seta.2020.100903



44

Abstract Hydroelectricity is critical for decarbonizing global energy production, but hydropower plants affect

rivers, disrupt their continuity, and threaten migrating fishes. This puts hydroelectricity production in

conflict with efforts to protect threatened species and re-connect fragmented ecosystems. Assessing

the impact of hydropower on fishes will support informed decision-making during planning,

commissioning, and operation of hydropower facilities. Few methods estimate mortalities of single

species passing through hydropower turbines, but no commonly agreed tool assesses hazards of

hydropower plants for fish populations. The European Fish Hazard Index bridges this gap. This

assessment tool for screening ecological risk considers constellation specific effects of plant design and

operation, the sensitivity and mortality of fish species and overarching conservation and

environmental development targets for a river. Further, it facilitates impact mitigation of new and

existing hydropower plants of various types across Europe.


45

3.1. Introduction Mitigating impacts of climate change while simultaneously meeting growing energy demands

necessitates the transition to decarbonized renewable energy [262]. Globally, hydropower is the major

source of renewable energy, with a total installed capacity of 1308 GW [263] and production of 4306

TWh in 2019 [263], of which around 8% (348 TWh) were generated within the European Union [264].

Hydropower counts as key technology of the decarbonized energy sector, because of its highly

predictable base-load generation, high stability, flexibility, and as most efficient large-scale mode to

store energy [19]. Not surprisingly, >1000 large hydropower plants (HPPs) have been built within the

last 50 years and >3000 are planned within the next decade [265–268]. In addition, >80,000 small HPP

with a capacity <1 MW are existing or under construction; a number is predicted to triple if total

generation potential is developed [269]. But this vast majority of small HPPs produce just 13% of global

hydroelectricity, while 87% is produced by just 9% of the HPPs (typically large plants) [270]. Irrespective

of their size, HPPs have substantial impacts on fish [42,65,66,271], with small schemes inherently

having the highest impacts relative to their capacity. Their dams fragment rivers and block up -and

downstream fish migrations [29]. Their impoundments alter flow regimes, sediment transport and

sorting resulting in the loss of suitable habitats for riverine species [16,272], also in the tail water [71].

HPPs themselves cause direct injury and mortality of fish e.g., by sheer forces, pressure changes and

collision with fixed or moving parts during both turbine and spillway passage [105]. The impacts of

HPPs cause strong conflicts with conservation needs of freshwater biodiversity, in particular of riverine

and migratory fish species, as they are most affected by hydropower dams [29]. Correspondingly,

habitat loss and hydro-morphological alteration had been identified as prime impact on European

water bodies [273] compromising environmental targets not only of the Water Framework Directive

(2000/60/EC), but also of the Habitats Directive (92/43/EEC) and the Eel Regulation (1100/2007/EC).

Consequently, reconnecting fragmented freshwater habitats is explicitly mentioned in the European

Union’s Biodiversity Strategy for 2030 [274]. Very obviously, there is a trade-off between the

generation of renewable hydroelectricity and the adverse environmental effects of HPPs on river

ecosystems. Therefore, it is important to evaluate hydropower impacts on river ecology to prevent

potentially severe environmental disruption for a rather limited power generation at the expense of

threatening freshwater biodiversity [275,276]. Consequently, sustainable hydropower development

and operation, and especially the environmentally sensitive management of small HPPs [265], has to

account for hazards for affected fish populations that cause injuries and elevated mortality rates. To

date, however, neither environmental impact assessment of hydropower is commonly agreed on, nor

does a tool exist that allows highly resolved impact assessment of various hydropower set-ups on the

ambient fish assemblage across Europe. Despite of a broad variety of HPP types and individual design

specifications, turbine passage constitutes the main hazard, especially for larger fishes [151].


46

Numerous studies have attempted to quantify turbine mortality rates and found them highly variable

[135,136,277–279]. Reportedly common mediators were species identity [148], body size [151],

individual behavior [152], turbine type [135,137,138,142] and operating condition [280]. Mortality

risks are species-specific and depend on the autecological characteristics of the species’ life history,

and so are the individual implications for long-term population development. For example, turbine

mortality correlates with the size and shape of the fish [137], the probability to encounter an HPP is

influenced by a species’ migratory behavior, the entrainment probability by its swimming performance

as well as migratory behavior [158] and the impact of an interrupted river continuum is most

pronounced for migratory species [281]. Generally, species that are long-lived and have a slow

generation turnover are more susceptible to individual mortality [282], while at the same time, smaller

species with rapid reproduction rates and high natural mortality rates might recover faster following

individual losses due to mortality [167]. Furthermore, independent of their intrinsic sensitivity to HPP-

related mortality, species require different management and prioritization related to their

conservation value and protection. This is mostly because of different regional protection levels (e.g.,

IUCN red list listings of species or species groups), ecological aspects, management considerations

(e.g., international agreements of stakeholders) or individual, site-specific conservation targets (e.g.,

EU habitats directive). Correspondingly, a risk-based assessment framework needs to consider species-

specific sensitivities and conservation statuses to evaluate the environmental impact of HPPs

appropriately. More than half of all HPPs worldwide have either already undergone, or will soon

require, upgrades and modernization [283], a process accelerated by some national legislatives that

demand nationwide re-licensing of all older HPPs e.g., in Sweden between 2022 and 2036 [284].

Therefore, the needs for comprehensive assessment of the overall hazard of HPPs on riverine fish

communities and for prioritizing mitigation efforts have never been higher. Thorough evaluations of

hydropower-induced hazards on fish species and communities are essential for efficient management

and conservation schemes and to supporting informed decisions during the planning, commissioning,

and operation of HPPs. While previous hazard assessments of hydropower projects provided

important insights, they have mostly been restricted to the description of observed, direct

consequences at a given study site e. g., [92,135,143,152,285] and commonly lack broader

applicability. Therefore, the hydropower sector needs a comprehensive assessment framework to

evaluate the hazards of hydropower schemes for fish assemblages without the necessity for detailed

site-specific mortality studies or population models. The framework should assess the hazards of one

hydropower constellation relative to others as a function of (1) the specific operational, constructional,

and technical characteristics of an HPP and (2) the ambient fish community, the sensitivity of species

and their specific mortality risk. Here we introduce the European Fish Hazard Index (EFHI), an

assessment tool for screening the risks of hydropower for fish. It has been developed by transferring


47

conceptual knowledge about hydropower-related hazards for fishes into a means of evaluating site-

specific risks. The EFHI is designed as a modular framework that offsets the relative hazard of a specific

planned or existing HPP with the susceptibility of the local fish assemblage. It explicitly considers

specific autecological characteristics of species in the target fish community [167] as well as their

conservation value and the specific management targets of the ambient water body. Via the selection

of candidate species, the tool can be adjusted to local environmental conditions and conservation

objectives and is applicable across all European biogeographic regions. At the same time, the EFHI is

comprehensive and sufficiently versatile to be applied to a wide range of HPP designs in various stream

types. In addition to a comprehensive description of the technical implementation and the ecological

reasoning of the EFHI we also demonstrate the tool’s applicability related to hypothetical scenarios.


48

3.2. Conceptual functioning of the European fish hazard Index and

its principal components The European Fish Hazard Index (EFHI) integrates (1) species-specific sensitivities of the ambient fish

community derived from species’ life history traits and conservation value and (2) the specific

operational, constructional, and technical characteristics of an HPP. As the principal mechanism, the

overall EFHI score increases with increasing severity of operational, constructional, and technical

hazards of the HPP and with increasing sensitivity of the affected, ambient species community.

Consequently, highest EFHI scores can be expected for HPPs associated with high overall hazards (e.g.,

mortality risks) installed in streams with numerous sensitive or conservation-critical species (see Box

1 for an example application). Additionally, the EFHI also considers measures that mitigate hazards

posed by the HPP-specific components (e.g., state-of-the-art fish guiding systems or ecologically

augmented migration facilities).

3.2.1. Hazards of hydropower to fish

Using extensive literature reviews, expert consultations and the consortium of 26 partners from

science to operators within an European Horizon 2020 project (https://www.fithydro.eu/) we

identified five constructional, technical and operational aspects of hydropower schemes that directly

affect fish mortality: (1) upstream and downstream flow alterations; (2) entrainment risk; (3) turbine

mortality; (4) upstream fish passage and (5) downstream fish passage (Figure 5).

https://www.fithydro.eu/


49

Figure 5: Mechanistic model of the components of the European Fish Hazard Index. Rectangles = input parameters; ellipses = derived variables; open circle = final index. The “risk scores” box represents the process of transferring risk classes, spec ies sensitivities and anatomies into adjusted and unadjusted hazard- and species- specific risk scores as shown in Table 5. These are then aggregated to the EFHI.

The impact of an HPP on a river’s natural discharge pattern strongly depends on the plant’s type and

mode of operation i.e., how it stores and releases water over time [16,79,286]. Generally, all barriers

create impoundments depending on their height and the river slope, which alter otherwise free-

flowing river sections and their associated habitat structures and substrates [52,287,288]. Specifically,

storage-type HPPs feature large reservoirs and retain large volumes of water which is released during

defined time periods. A storage-type plant is therefore associated with large reservoir fluctuations,

aggravated by seasonal drawdowns as well as alterations and shifts of the natural hydrologic regime

depending on the specific release scheme. Moreover, impoundments with storage capacity exceeding

the river’s discharge of several months or even years form a rather stagnant water body with lake-like

vertical temperature and oxygen profiles, fine sediment accumulations and changes in nutrient

availability [49,289]. Habitat and flow alterations caused by dams and their associated reservoirs,

threaten fish populations especially of rheophilic guilds and cause community shifts [290–292].

Moreover, impoundments cause disorientation in fish because of the lack of migration cues such as

attraction flow [98,293] and might even induce morphological adaptations in migratory fish species

[294].

Downstream of HPPs, two major flow alterations primarily impact river fish populations: hydropeaking

and residual flows. Hydropeaking is the rapid and frequent fluctuation in discharge and water level


50

resulting from intermittent electricity production [68], which was found having severe impacts on river

fish [71] including stranding and displacement [68,77], dewatering and degradation of habitats [295],

increasing water turbidity and reducing food uptake of fish [68,73,75,78,296]. Water abstraction and

lack of residual flow occur mainly in diversion-type HPP, where the dam does not create the desired

hydraulic head but serves to divert (usually) the majority of the discharge over a penstock to a distant

powerhouse, where it gets turbinated and released to the original river further downstream. Often at

length’ of tens of kilometers the original river section gets widely dewatered receiving only a reduced

residual flow that often lacks seasonal variability depending on the proportion of water abstracted.

This manipulated flow is a major stressor on aquatic biota and causes shifts in fish communities and

declines in abundance flow [65,67,297,298].

Compared to other hazards, turbine-related mortality is probably the best studied impact at HPPs. It

is generally caused by either one or a combination of the four factors i) abrupt pressure changes ii)

turbulent flow iii) shear forces and iv) turbine blade strikes [299]. Despite various attempts to deflect

fishes from entering the turbines or to increase their fish friendliness, mortality caused by turbine

passage remains a major issue. Depending on operating condition, turbine design and fish species a

combination of pressure changes, shear stress and strike directly affects the fish. Blade strikes

dominate at smaller turbines with higher rotational speed [137,141,300]. Predicting when and where

a fish gets hit by a physical part of the turbine remains difficult but can be modeled most accurately

for Kaplan and Francis turbines [301,302] as a function of fish length, rotational speed of the turbine,

number of blades and decreasing space between blades [303,304]. In larger plants with bigger

turbines, the importance of pressure-related injuries affecting fish survival increases

[137,139,144,146,149]. Previous studies indicated an increase of pressure-related fish mortality with

barrier height [29] due to the more severe pressure changes during passage [139,144,147,305].

Generally, the susceptibility to pressure-related injuries varies with species: Barotrauma (primarily

caused in Kaplan and Francis turbines) seems to be more pronounced in physoclistous than in

physostomous species and least problematic for species without a swim bladder [280,306–308]. In our

framework we acknowledge numerous technical advancements towards less harmful turbine types

and operation modes, which can lower fish mortalities and reduce the overall hazard of an HPP. More

detail on fish mortality across turbine types is provided by Dewitte and David [309].

On their way downstream, fish generally choose a migration route based on its respective discharge

[310,311]. Therefore, the amount of water that is passing through the turbines compared to what is

released through alternative pathways such as e.g., bypasses, fishways, spillways or sluice gates is a

significant predictor of fish being entrained into the turbine. Accordingly, discharge ratios between

turbine and all alternative pathways might serve as suitable proxy for a baseline turbine entrainment

risk of a fish.


51

Successfully preventing fishes from entering the turbines and subsequently guiding them downstream

unharmed is critical component of “fish friendly” installations and highly relevant for (re)licensing

HPPs. The risk of entrainment and related turbine mortality can be significantly reduced by

implementing technical fish guidance structures (FGS) subsequently facilitated by downstream

bypasses [123,285,312] and are extensively described by USFWS and Ebel [91,123]. Highly suitable FGS

comprise vertically inclined bar racks or angled (=installed at a horizontal angle) bar racks both with

fish guiding efficiencies of ≥80% [122,313]. However, research is devoted to even more advanced FGS

with vertically oriented bars like Louvers, modified and curved bar racks. These have significantly

reduced hydraulic losses at increased fish guiding performance of >95% [119,121,314] combined with

nearly no injuries or mortalities as observed at regular FGS [312,313]. Common fine screens act as

physical barrier for fish being only permeable for individuals leaner than the gap width of the screens.

However, while narrower spacing prevents smaller and thus probably more fish from passing through,

the overall efficiency of finest screens might be reduced due to sub-optimal hydraulic conditions and

excessive accumulation of floating debris [313]. Previous studies indicated FGS with <20 mm gap width

and a functional bypass protected large proportions of migrating fish [116,313,315,316]. Louvers,

modified or curved bar racks perform even better as FGS with significantly wider gap widths of up to

50 mm [119,121,314]. Other deflection devices like optical, acoustic, electric, or other barriers have

either not been conclusively studied or not sufficiently proven reliable in the long-term [29,312,316–

318] and thus, were not considered here.

Furthermore, given the overwhelming evidence of the importance of unhindered fish migration

[88,319,320] the presence of an upstream fish migration facility is considered an indispensable

element of a modern HPP. Successful upstream migration of fish at barriers is achieved by fishways

and thus, a lack thereof constitutes a major hazard for fish populations. Numerous types of fishways

exist; however, their passage efficiency is highly variable and strongly determined by their design (size,

openings, slope, flow velocities, head), location relative to the barrier, and attraction to migratory

species [84,91,124,321], as well as their admission flow [322]. Discharge in a fishway usually ranges

between <1% and 5% of the average stream discharge [323] with relatively higher proportions being

necessary for smaller streams [322]. Some types like nature-like fishways promise higher overall

efficiency and lower mortality for both upstream and downstream migrating fishes [324,325]

especially for small species and juveniles, whose needs are often overlooked when designing upstream

migration facilities [321]. Furthermore, nature-like fishways can provide additional fish-ecological

benefits like juvenile habitats, shelter, or spawning grounds [326,327].


52

3.2.2. Fish species and community sensitivity

As general principle, the EFHI considers the hazards caused by HPPs in relation to the unique

characteristics of the ambient, site-specific fish community. In our tool, the fish community is

characterized by the sensitivity of species to additional mortality, which has been derived from several

species-specific life history traits and conservation aspects [167]. It has repeatedly been shown that a

species’ unique suite of life history traits reflects its individual performance in resisting mortality [193–

195] and recovering from abundance drops [208]. van Treeck et al. [167] analyzed indicative life history

traits and derived a single species-specific score that reflects a species’ overall sensitivity against

mortality resulting from its specific combination of resistance and recovery traits. This sensitivity

classification of European lampreys and fishes has been implemented into the EFHI. In addition, species

and populations might be threatened or considered of conservation concern for various reasons. These

include (but are not limited to) legally protected species at different levels from regional to

international, red listed fish species e.g., by the IUCN [328], species of conservation or management

concern as well as target species for environmental improvements e.g., according to the Water

Framework Directive in Europe. The European eel is explicitly considered, because its stock

improvement is specifically targeted by the EU Eel Regulation as well as all species of common

conservation concern listed in the EU Habitats Directive, because many of them are migratory and, by

default, highly threatened by HPPs. By selecting respective species of interest, all regional conservation

targets can be explicitly implemented in the EFHI assessment.


53

3.3. Technical implementation and mechanistic functioning of the

EFHI The EFHI (v2.1.3, November 2020) is programmed in and compatible with Microsoft Excel (2016

Professional Plus or newer) and available online (Zenodo repository, DOI:

https://doi.org//10.5281/zenodo.4250761). The EFHI requires users to input information about the: i)

HPP’s main dimensions, turbine specifications, operating conditions and fish migration and protection

facilities; ii) target species and iii) stream reach. Impacts of the above discussed HPP-specific hazards

are classified according to defined thresholds derived from conceptual knowledge or model results,

and subsequently categorized into “high”, “moderate” or “low” risk classes. These categories are then

cross-tabulated and contrasted to the rounded integer value of the species’ biological sensitivity

(obtained from van Treeck et al. [167]) to produce a numerical score for each hazard and species as

shown in Table 5. Individual hazard scores can take values of 0, 0.25, 0.5, 0.75 or 1, with higher scores

corresponding to more severe hazards. Up to five target species are selected by the user to best reflect

the local fish assemblage, conservation concerns and river region. Selected species can be manually

assigned to the highest sensitivity class, regardless of their original score, to account for regional

conservation concerns or environmental targets for species and water bodies. Subsequently, all single

hazard scores are aggregated to the EFHI. The modules and workflow as well as the input and output

variables of the EFHI are presented in Figure 5 and Table 6 and discussed in the following section.

Table 5: Generating the numerical impact scores for a specific hazard and species contributing to the calculation of the EFHI.

Hazard classification Target species sensitivity 4 3 2

High 1 0.75 0.5 Moderate 0.75 0.5 0.25 Low 0.5 0.25 0

Target sensitivity score from 2 (low) to 4 (high) obtained from [167].

3.3.1. Flow alterations

HPPs may exert impacts up- and/or downstream of their dam and at different magnitudes. Upstream

flow alterations i.e., impoundment effects are most pronounced in large, reservoir-type HPPs. Here,

we assessed the impact of upstream flow alterations as dependent on storage capacity, which is often

described relative to the average net inflow per time period [329,330]. In our framework, reservoirs

with a storage capacity exceeding average annual net inflow were always scored as high risk. Schemes

with smaller reservoirs and impoundments were further discriminated into two categories: Those that

still cause substantial storage and reduce flow velocity through the impoundment below 0.5 m/s were

classified moderate risk and those maintaining a continuous, minimum average flow velocity of ≥0.5

m/s were classified low risk. Downstream flow alterations can be attributed to two distinct stressors:

hydropeaking and water abstraction. Because hydropeaking inherently results in a completely altered

discharge regime with severe impacts on stream biota, this operation mode was always scored high

https://doi.org/10.5281/zenodo.4250761


54

risk. The hazard of water abstraction, particularly problematic in residual river stretches of diversion

schemes, was scored by applying recommendations for sustaining full ecological functionality i.e.,

environmental flows, following Tharme [331]: If the discharge in the residual river stretch is <10% of

the mean annual low flow the risk was scored high. Residual flows of less than or equal to 10% of the

stream’s mean annual flow were scored moderate, and >10% of its mean annual flow scored low risk,

respectively. The overall downstream risk was assigned according to the higher score. Upstream and

downstream hazard classes were aggregated into the total flow alteration hazard using the same

principle.

3.3.2. Entrainment risk

The combined flow rate of the installed turbines (corresponding to the total generation capacity at a

location) was used to derive a proxy of the entrainment risk for fish. This risk describes the baseline

probability of fish passing through the turbine(s) rather than taking any other route downstream and

was estimated from the quotient of the installed total turbine flow rate (Qinstalled_total) over mean

river discharge Qmean. Turbine entrainment was divided into three classes based on thresholds

provided in Table 6. To account for the fish-deflecting effect of potential FGS, entrainment risk score

was individually adjusted for every installed turbine and turbine mortality hazard score as follows: In

a first step, gap width of the FGS and length-to-width ratios of the five target species were used to

derive the maximum length 𝐿𝑚𝑎𝑥 of both eel-like and non-eel-like (e.g., fusiform) species that could

pass the screen. For that purpose, Ebel [123] provided empirically derived estimates of the relationship

(ratio 𝑏) between a fish’s width and length, whose grand average is 𝑏 = 0.11 for fusiform and 𝑏 =

0.03 for eel-like body shapes, respectively. Accordingly, the maximum length 𝐿𝑚𝑎𝑥 of a fish that can

pass the screen is

𝐿𝑚𝑎𝑥 = 𝑔𝑎𝑝 𝑤𝑖𝑑𝑡ℎ

𝑏 Equation 15

For example, an FGS with a gap width of 20 mm is therefore permeable for fusiform species of 𝐿𝑚𝑎𝑥 =

18 cm and eel-like species of 𝐿𝑚𝑎𝑥 = 67 cm. In a second step, 𝐿𝑚𝑎𝑥 was compared to the common

adult length (𝐿𝑐𝑜𝑚𝑚𝑜𝑛; obtained from Fishbase [227]), of the selected species and processed as

follows: If 𝐿𝑐𝑜𝑚𝑚𝑜𝑛 of a species was shorter than 𝐿𝑚𝑎𝑥 the entrainment hazard score remained

unchanged. If 𝐿𝑐𝑜𝑚𝑚𝑜𝑛 was larger than 𝐿𝑚𝑎𝑥 the installed fine screen would lower the entrainment

risk for adults. In that case, the entrainment score was adjusted by weighing it with the factor 𝐿𝑚𝑎𝑥

𝐿𝑐𝑜𝑚𝑚𝑜𝑛.

This ratio assumes values closer to one when 𝐿𝑚𝑎𝑥 is relatively large and values closer to zero when

𝐿𝑚𝑎𝑥 is relatively small. This ratio was then used as an additional weighing factor in the assessment of

the turbine mortality hazard.


55

3.3.3. Turbine mortality hazard

The assessment of turbine-specific mortality risk was based on the three components i) model-based

turbine blade strike rates for Francis and Kaplan turbines and similar types, ii) literature-informed,

empirical mortality rates for Archimedes screws, Kaplan very-low-head (VLH), Ossberger, Pelton,

Pentair Fairbanks, and water wheels, and iii) empirical turbine barotrauma mortality rates. Following

an established classification by Wolter et al. [108], we scored mortality rates of i) and ii) between 0%

and 4% as low, between >4% and 8% as moderate and >8% as high risk. To evaluate HPPs with more

than one turbine, the EFHI assigns risk scores of i), ii) and iii) for each individual turbine and species as

shown in Table 5 and subsequently, aggregates the relative contribution of each turbine to the overall

mortality rate weighted by the turbines’ relative flow rates. For all turbine types other than Kaplan and

Francis, the score of i) was directly summed up across all installed turbines, for Kaplan and Francis

turbines the risk scores of ii) and iii) were aggregated before being summed up across all installed

turbines. The summed scores were only allowed to reach a maximum value of 1, regardless of their

actual value and multiplied with the weighing factor of the entrainment risk score. The result comprises

the overall turbine mortality score.

3.3.4. Blade strike models

Fish mortality of Kaplan, Kaplan bulb, Kaplan minimum gap runner and Francis turbines has been

frequently studied in the field [136,148,150,277,280,332,333], which allowed the development of

various blade strike models producing reliable outputs for these turbine types. We explicitly used blade

strike models to assess turbine mortality for these turbine types (Appendix 2) allowing for more

standardized estimates depending on detailed turbine characteristics. We applied the frequently used

blade-strike model by Montén [303] that calculates the probability of a fish striking a blade depending

on the fish’s length and the relative space between blades 𝑠𝑟𝑒𝑙_𝑚𝑖𝑑. This accounts for the angle of the

blades that might be variable depending on discharge e.g., in Kaplan turbines. The blade angle of

Kaplan turbines can be estimated from inflow velocity, velocity of the runner blades and the head.

Models for Francis turbines use a two-dimensional projection of the velocity characteristics (i.e., a

velocity parallelogram) at the turbine entrance [123,301,303,334]. Technical details on how to

estimate relevant variables leading to the calculation of β are described in Appendix 2. Based on the

relative spacing of the turbine blades 𝑠𝑟𝑒𝑙_𝑚𝑖𝑑 and the fish length 𝐿, the strike mortality (in %) of Kaplan

and Francis turbines can be calculated following Montén [303] as:

𝑀𝑀𝑜𝑛𝑡𝑒𝑛 = 0.5×𝐿

𝑆𝑟𝑒𝑙_𝑚𝑖𝑑× 100 Equation 16

For the calculation of 𝑀𝑀𝑜𝑛𝑡𝑒𝑛 we used 𝐿𝑚𝑎𝑥 from Equation 15 (for 𝐿) which refers to the body length

of fish that can pass the FGS. Analogous to Wolter et al. [108] and the risk scoring of the generic turbine


56

risk we classified mortality rates for Francis and Kaplan turbines [303] as low for rates between 0% and

4%, moderate (between >4% and 8%) and high (rates of >8%), respectively.

3.3.5. Empirical turbine mortality

For turbine types that cannot be assessed with Montén’s mortality model, empirical data were used

to establish hazard classes. Lowest mortality rates were reported for water wheels (<1%; [335]),

Pentair Fairbanks (<1%; [128,336]), and very-low-head (VLH; <4%; [128,336,337]) turbines. These

turbine types were therefore classified as low risk. Comparably higher mortality rates are caused by

Archimedes screws (<8%; [108]), which are considered moderate risk types, accordingly. Very high

mortality rates (>99%) were reported for Ossberger [140,279] and Pelton turbines [137], which are

consequently classified as high risk.

3.3.6. Barotrauma mortality

Because the modelled mortality rates are based solely on blade strike probability, they were

complemented with the risk for pressure-related injuries i.e., barotrauma, which was derived from the

specific hydraulic head. We scored barrier heights of <2 m as low, 2 - 10 m as moderate and >10 m as

high risk based on empirical data by Wolter et al. [108]. However, the susceptibility to barotrauma is

highly species-specific: Physoclistous fish (e.g., Percidae) are most at risk, because they cannot

acclimatize to pressure changes and release gas quickly enough during decompression, followed by

physostomous fish with an open swim bladder (e.g., Salmonidae) and species without swim bladder,

the latter considered of very low susceptibility to barotrauma [144,305–308]. We used these

differences to adjust the barotrauma risk score according to quantitative model observations by Wilkes

[305] as follows: i) 50% increase for physoclistous, ii) unchanged for physostomous, and iii) zero for

species without a swim bladder. Energy converters operating at atmospheric pressure induce no

barotrauma risk e.g., water wheels, screws.

3.3.7. Upstream fish passage

A major factor affecting the effectiveness of upstream fish passage facilities is their discharge relative

to the mean discharge of the river, with higher values increasing passage success [322]. Here we used

two linear regressions to determine the minimum recommended discharge in an upstream migration

facility (UMF) as a function of the total installed flow rate derived from Dewitte and David [309].

𝑄𝑈𝑀𝐹_𝑜𝑝𝑡 = −0.0801 × 𝑄𝑚𝑒𝑎𝑛 + 5.0008 Equation 17

𝑄𝑈𝑀𝐹_𝑜𝑝𝑡 = −0.0006 × 𝑄𝑚𝑒𝑎𝑛 + 3.014 Equation 18

Equation 17 was applied to plants with an installed flow rate of <25 m³/s that require a discharge in

the UMF between 3% and 5% of that value, with smaller installations requiring proportionally more


57

water to be functional. Equation 18 was applied to UMFs in larger installations that require

proportionally less water e.g., between 3% and 1%. User input values for the discharge in the UMF as

well as interpolated discharge values were rounded to one decimal point and compared: If the

discharge in the UMF was equal to or higher than the calculated recommendation, the risk class was

scored low. A discharge ≤50% of the calculated recommendation was considered a risk to full

functioning of the fish pass and scored moderate. Discharges <50% of the calculated recommendation

or no UMF altogether were both considered inadequate passage facilities and scored high risk. The

superior passage performance of nature-like fishways was considered by lowering both the upstream

and downstream passage score by 20% each, assuming an FGS was in place appropriately preventing

turbine passage (𝑔𝑎𝑝 𝑤𝑖𝑑𝑡ℎ < 𝐿𝑚𝑎𝑥 × 𝑏).

3.3.8. Downstream fish passage

The hazards related to downstream migrating fish were assessed as follows: Angled bar racks, Louvers,

modified and curved bar racks at a horizontal installation angle of ≤45°, or vertically inclined bar racks

with an inclination of ≤45°, all in combination with downstream bypasses accessible across the whole

water column have proven highly efficient [122,123,338], and were therefore scored as low risk. Less

ideal are FGS installed at larger horizontal or rather steep vertical angle and with vertical bars: While

those indeed prevent fishes from entrainment, they can also cause substantial injuries or mortalities

due to impingement or sheer force [122,151,313,338]. Furthermore, the efficiency of simple bypasses

located at the bottom or surface of the water body seems to be highly variable [116,122,125,339].

Therefore, vertically inclined bar racks of >45° as well as FGS at a horizontal angle of >45° and any

constellation without fully accessible bypass were scored as moderate risk. The total absence of an

FGS or a downstream bypass was scored as high risk. Analogous to the treatment of the upstream

hazard section, we reward the bi-directional performance of a nature-like fishway with a reduction of

the downstream passage hazard score by 20%.


58

Table 6: Risk classification of the main HPP-specific hazards contributing to the calculation of the EFHI.

Hazard type Evaluated attribute Risk class

Low Moderate High

Flow alterations Differentiated alteration of flow US and DS

Both, US, and DS flow alterations are

low

One, US or DS flow is altered moderately, the other is low

Either one, US or DS flow is highly altered

Entrainment risk Discharge ratio (𝑄installed_total/

𝑄mean )

≤0.5 >0.5-<1 ≥1

Turbine mortality Blade strike models MMonten<4% MMonten=4%-8% MMonten>8%

Empirical turbine mortality

Water wheel

Pentair Fairbanks

Kaplan VLH

Archimedes screw

Ossberger

Pelton

Barrier height (Barotrauma mortality)

<2 m 2-10 m >10 m

Upstream passage UMF presence & characteristics

Existing and Q ≥ recommended

Existing and Q up to 50% < recommended

Missing or Q < 50%

recommended

Downstream passage

FGS presence & characteristics

FGS Installation angle ≤45° and DS bypass across whole water column

FGS installation angle >45° or DS bypass not fully accessible

Missing FGS or missing DS bypass

Acronyms: US=Upstream; DS=Downstream; Q=Flow rate; M=Blade strike mortality; VLH=Very Low Head turbine; UMF=Upstream migration facility; FGS=Fish guidance structure.

3.3.9. Aggregation of the final EFHI

Both the adjusted and unadjusted species-specific scores of the single HPP-specific hazards were

aggregated into an index by calculating the arithmetic mean. The tool provides the possibility to

explore the effects of alternative HPP-related components and constellations. By simulating changes

to a range of measures with an immediate effect on the hazard classes, unadjusted and adjusted risk

scores and the final EFHI, the EFHI facilitates achieving a desired risk class, respectively identifying

configurations considered likely to be least harmful for fish.


59

3.4. Applicability and limitations Box 1: Example application of calculating the EFHI.

To showcase the performance of the EFHI, it was applied to a scenario of a small-scale, low-head (2.3

m) run-of-the-river HPP generating about 0.15 MW in a small river with 7.5 m3/s mean and 1 m3/s

mean low discharge. Flow velocity through the upstream impoundment is higher than 0.5 m/s. We

considered four different example configurations to illustrate the different outcomes as a result of the

sensitivity of the fish assemblage and the technical/operational HPP characteristics and to identify

potential management interventions (Figure 6 and Appendix 2, Figure A2_2, 1-4):

Assuming a baseline configuration, the HPP is operated with a Kaplan turbine (outer diameter = 2.0 m,

hub diameter = 0.76 m, 150 rpm, 4 blades) at an average flow rate that approximates mean river

discharge (7.5 m3/s). The considered lowland river fish community at the HPP location comprises of

small to large-bodied species of generally low sensitivity (sensu van Treeck et al. [167]): pike (Esox

lucius; sensitivity score = 2.8), white bream (Blicca bjoerkna; 2.8), perch (Perca fluviatilis; 2.8), roach

(Rutilus rutilus; 2.5), and bleak (Alburnus alburnus; 2.3). Fish protection is not applied in this baseline

scenario i.e., the HPP is equipped only with a vertically oriented trash rack of 100 mm gap width

(installation angle of 70°). Neither an up- nor a downstream fish migration facility or bypass is installed.

Accordingly, the resulting EFHI score is 0.64 indicating a moderate overall hazard given the low

sensitivity of the fish community (S3a). However, the EFHI for this HPP installation markedly increases

for a slightly different fish community comprising more sensitive species. For example, considering in

addition to pike and white bream the potamodromous Barbel (Barbus barbus; 4.1), the European eel

(Anguilla anguilla; 3.7) and Asp (Leuciscus aspius; 3.3), with the latter two being of specific

conservation concern (e.g. Eel regulation (Council Regulation (EC) No 1100/2007) and Annex II EU

Habitats Directive (92/43/EEC), respectively), the EFHI raises to 0.8, a high overall hazard score for the

same turbine configuration (S3b). The high EFHI can be lowered by the installation of or upgrading

certain fish protection measures. For example, an angled bar rack with a bar spacing of 20 mm (45◦

horizontally inclined) accompanied by a fully accessible downstream bypass and an upstream

migration facility with a recommended discharge of 0.33 m3/s (equivalent to 4.4% of the installed

capacity) lowers the EFHI for the same sensitive species community and turbine type to 0.49, a

moderate overall hazard (S3c). As a further improvement e.g., when refurbishing the HPP, the Kaplan

turbine might be replaced by a fish-friendlier Kaplan very-low-head unit with a lower overall mortality

risk, and the upstream migration facility is re-designed in a nature-like manner. In this mitigated

scenario, the EFHI further decreases to 0.32 for the same flow rate and rather sensitive species

community (S3d).


60

The European Fish Hazard Index (EFHI) detailed here constitutes a comprehensive, easily applicable,

transparent, and reproducible screening tool for environmental risk assessment of both existing and

planned hydropower plants and their effects on fishes. Thereby, the EFHI fills a methodological gap of

considerable importance in relation to several European and national legislative frameworks that has

not yet been addressed. In fact, the construction and operation of HPP is inevitably linked to impacts

on rivers’ hydro-morphology and fish assemblages and thus, development of renewable hydropower

may conflict with the environmental objectives of, for example, the EU Water Framework Directive,

Habitats Directive (92/43/EEC) and Eel Regulation, to mention just some European laws. Accordingly,

many existing hydropower installations are subjected to environmental impact assessment

(2014/52/EU). In addition, REN21, the global renewable energy community, estimated that a

significant amount of all HPPs worldwide will soon require upgrades and modernization [283].

Correspondingly, about 65% and 50% of small hydropower plants located in Western and Eastern

Figure 6: Example illustration of the principal mechanism of EFHI. The technical and operational parameters of a specific HPP pose a certain constellation-specific mortality risk to fish (1). With increasing sensitivity of the ambient species community, the EFHI increases (1 → 2). The EFHI decreases if the severity of operational, constructional, and technical hazards of the HPP are reduced, such as by the installation of or upgrading fish protection measures (2 → 3) and changing to a fish-friendlier turbine type and a nature-like upstream migration facility (3 → 4). Configurations at the bottom-left indicate no further management, while configurations at the top-right indicate priority for mitigation, respectively.


61

Europe, respectively, are >40 years old [270]. Therefore, the EFHI supports the environmental impact

assessment during the modernization of such HPPs by identifying relatively hazardous installations and

prioritizing plants to upgrade fist.

Given the comprehensiveness of the EFHI, it is different from other hydropower-related assessment

frameworks in many ways. For example, previous attempts were either assessing impacts on different

biological endpoints [340], running cost-benefit calculations [341], targeting only single species or life

stages [342,343], or aiming at maximization of electricity generation rates [344]. The EFHI addresses

the various potential impacts of HPP on the conservation-critical animal group of fishes built on

conceptual knowledge that is extracted from real case studies that can profitably operate even under

maximum-mitigation scenarios. This innovative assessment tool constitutes a step towards recognizing

and reducing the impacts of HPPs on fish and riverine environments.

There are also some limitations associated with the EFHI. First, as a risk screening tool it cannot replace

detailed hydropower site-specific empirical impact assessment studies of fish mortality. Especially the

mortality of large adult fish can be underestimated in Kaplan and Francis blade strike models [302] and

deterministic models often do not match prediction accuracy of stochastic approaches [345]. However,

such models rely on large amounts of data and could not be implemented while at the same time

maintaining its desired relative simplicity and universal applicability. Furthermore, the tool neither

informs nor provides input for fish population models and viability analyses and it does not reflect

seasonality of fish migrations, life-stage effects, water temperature and other trigger that affect fish

mortality [346–348]. Our methodology considers mainly the common adults of a species as assessment

target, and disregards potentially high hydropower-related mortality rates of eggs and larvae, which

makes it inappropriate for the estimation of detailed, long-term population effects since their risk

remains largely unquantifiable. Furthermore, a range of hazard parameters can be barely included in

an assessment tool: e.g., the vertical position of a fish in the water column approaching the turbine,

the actual angle of the blades in Kaplan turbines or their operation conditions (e.g., flow rates) over

the course of a year. Consequently, we used simplifications, modelled data, and data approximated

from empirical studies. Simplification applies to the mitigation measures considered by the tool, too,

because they only comprise approaches to lower fish mortality directly at or in the vicinity of an HPP.

Needs for appropriate river rehabilitation at the level of water bodies cannot be inferred from the EFHI

results. All parameter thresholds were derived as accurately as possible using empirical data, models,

expert judgement, and most-consensus schemes. But because of the high variability in construction

details, spatial arrangements, modes of operation and their various interaction effects, it was

impossible to derive a model sufficiently versatile to fully capture and precisely quantify the risk of

different HPP constellations. To account for the corresponding uncertainties, the numerical scoring of

the calculated EFHI was classified into just three hazard groups: “low”, “moderate” or “high” risk.


62

In summary, the EFHI can assess single- and multi-turbine set-ups in a variety of HPPs and streams. It

uses an optimized number of comparably easily obtainable input parameters, while all other relevant

parameters resulting from their complex interactions are modeled or approximated from literature

and empirical studies from similar systems. Regional biogeography and conservation concerns are

specifically considered by the selection of target species and account for rehabilitation planning,

protection requirements and other management decisions. The EFHI has implemented an inventory of

168 fish species native or established in European waters classified by sensitivity against mortality and

other relevant traits, and it supports mitigation planning by enabling the selection of potential

mitigation measures and measure combinations and assessing their effect on the hazard score.

The EFHI provides a robust, evidence-based, broadly applicable, and transparent screening tool for

systematic risk assessment of HPPs for fishes and as such, we expect it to be highly useful to a wide

range of stakeholders.

Chapter 4 – Comparative assessment of hydropower risks for fishes using the novel European Fish Hazard Index

63

Chapter 4: Comparative assessment of hydropower risks for fishes using the novel European Fish Hazard Index Ruben van Treeck, Johannes Radinger, Nicole Smialek, Joachim Pander, Jürgen Geist, Melanie Müller

and Christian Wolter

Sustainable Energy Technologies and Assessments, 51, 2022.




64

Abstract In context of transitioning towards renewable energy, hydroelectricity has gained global relevance.

However, hydropower plants have severe impacts on aquatic habitat and biota: Dams disrupt

migration routes of diadromous and potamodromous fish species, degrade the hydro-morphology of

streams and turbines cause high mortalities in fishes. To support risk assessment and mitigation, the

European Fish Hazard Index EFHI identifies potentially harmful constellations of existing and planned

hydropower plants adjusted to the reference fish assemblages of the affected stream sections. In this

study, we applied the EFHI to seven small, low-head hydropower plants of various types and compared

our results to those of extensive empirical fish mortality estimates independently conducted at the

same sites. We illustrate how hydropower hazards go beyond turbine mortality and that the EFHI

widely reflects site-specific risks of flow manipulations, entrainment, and upstream and downstream

fish passage. Based on the EFHI results we found that environmental impact assessments based on the

present fish community tend to underestimate hazards, particularly when the fish assemblage is

already degraded. We further examined the EFHI’s performance and identified some potential for

future implementations of new fish mortality models and novel, fish safer turbines.


65

4.1. Introduction Hydropower as a means of renewable energy has become increasingly relevant for the energy

transition towards a decarbonized electricity supply. However, this often comes at the expense of

disruption of free-flowing rivers, degraded ecosystems, and loss of aquatic species diversity

[1,4,65,105,270], fueling a debate about the environmental sustainability of hydropower [349]. This

holds especially true for small, low-head hydropower plants (HPPs) because of their severe impact on

the environment relative to their electricity output [42,271]. Their detrimental impacts are further

aggravated by cascading effects of multiple consecutive HPPs [275,350], as well as by some of their

technical features (e.g., fast rotating turbines) leading to high fish mortalities [100,135,138,141–

144,148]. Water wheels and recent turbine developments towards higher fish safety, such as the Very

Low Head (VLH) turbine or Archimedes screw [100,128,129,131–134,137,336,351,352], potentially

provide improvements. Yet, a holistic risk assessment of HPPs including their manifold impacts on

fishes as well as mitigation strategies is still missing. The European Fish Hazard Index EFHI [169]

provides this important keystone towards a holistic hydropower assessment. The EFHI is a freely

available, Microsoft Excel-based tool with a user-friendly interface (Appendix Figure A3_1 and Figure

A3_2) and available for download at https://zenodo.org/record/4686531. It scores risks related to

hydropower operation considering plant size and type, location in the stream, the ambient fish

assemblage and conservation constraints. The EFHI scores four main hazard components: i) the degree

of flow alterations imposed by the HPP upstream and downstream of the barrier, ii) the risk of fish

entrainment and subsequent turbine mortality, iii) risks related to impeded upstream fish passage and

iv) risks related to downstream fish passage routes [169]. As such, it is a systematic, universally

applicable, and transparent risk-screening framework for fishes in hydropower-affected environments

that goes beyond existing spatially, and biologically explicit, hydropower-related assessments (e.g., by

Ziv et al. [25]) as well as those primarily focusing on turbine blade strike or pressure-related injuries

(e.g., by Vowles et al. and Pracheil et al. [353,354]). The EFHI uses conceptual knowledge and empirical

models of hydropower-related hazards for fish and offsets both with i) site-specific information on

stream discharge patterns, ii) the ambient fish assemblage, iii) management targets, iv) species-specific

life history-mediated sensitivity scores to additional (i.e., artificial) mortality ranging from 2 (low

sensitivity) to 4 (high sensitivity) [167], and v) fish size and swim bladder anatomy. It then translates

these partly convoluted risk factors into an overall hazard score that is comparable within and across

installations and biogeographical regions running from 0 (no risk) to 1 (high risk). The EFHI can be used

in various scenarios e.g., to compare risks of multiple hydropower setups in the same stream, of similar

setups in different rivers or to track the hazard of a single HPP for fish over time (i.e., after upgrading

components or changing operation modes). This makes it a highly versatile tool to facilitate

comprehensive risk assessment conducted by operators, water authorities and other stakeholders.

https://zenodo.org/record/4686531


66

While the EFHI framework was developed to assess the impact of individual hydropower plants, an

assessment of cumulative effects of hydropower cascades considering population metrics may be a

logical next step for a more comprehensive assessment of impacts on entire river systems. Until then,

the usefulness of the EFHI is probably greatest for the comparative application across different

hydropower types and (bio)geographical regions, such an assessment has, to the best of our

knowledge, not yet been performed. Consequently, this study applied the EFHI to seven small, low-

head hydropower plants of various types and contrasted the tools’ results to the findings of extensive

empirical fish mortality estimates conducted at the same sites. The main objective of this study was to

examine the applicability, usefulness, and performance of the EHFI in comparison to commonly applied

empirical estimates of fish mortality at HPPs. Particularly, we aim to: i) investigate how and to what

extent the EFHI and its hazard components flow alterations (“FLOW”), entrainment and turbine

mortality (“ETM”), upstream fish passage (“US”) and downstream fish passage (“DS”) add to or go

beyond typical impact assessments; ii) assess the correspondence between EFHI scores and empirical

fish mortality rates as well as to reveal mechanistic factors for deviations, and iii) identify potential

improvements of the EFHI and its components.


67

4.2. Material and Methods

4.2.1. Test cases

To test how the EFHI performs under real-world conditions it was applied at seven small (<1 MW), low-

head HPPs and analyzed in context of already published results of empirical fish mortality assessments

independently conducted between 2014 and 2016. All studied HPPs are located in Bavaria, Germany

in small, low mountain range, subalpine or alpine rivers: Au at the river Iller [355], Baierbrunn at the

Isar [356], Baiersdorf-Wellerstadt at the Regnitz [357], Eixendorf at the Schwarzach [358], Heckerwehr

at the Roth [359], Höllthal at the Alz [360] and Lindesmühle at the Fränkische Saale [361] (Figure 7).

The fish community in these rivers is largely dominated by rheophilic salmonids and cyprinids.

Figure 7: Study sites of the project on innovative and conventional hydropower production in Bavaria, Germany, comprising seven hydropower stations (filled circles).

As part of the empirical studies, fish mortality (direct and delayed after 96 hours) was assessed at each

site using both standardized fish release and capture of fish naturally passing through turbines at


68

varying turbine loads. During standardized experiments, defined numbers of fish were released at

different positions in front of the turbine intakes and re-captured after turbine passage with stow-fyke

nets directly at the turbine outlet. Fishes were checked for damages prior to their release and after re-

capture. Methodological details are given by Mueller et al. [362]. In addition, damage rates of fish

migrating through up- and downstream passage facilities, flow and discharge patterns, the degree of

hydro-morphological degradations and reference fish communities were reported. The latter describe

the anthropogenically uninfluenced fish community, mark the rehabilitation target for a stream section

and are used for conservation planning and ecological status assessment in the Water Framework

Directive monitoring [363].

4.2.2. Parametrization of single EFHI components

For each site, the following technical information was obtained for parameterizing the EFHI: turbine

specifications, hydraulic head, stream discharge metrics, hydropower-related flow manipulations, fish

protection and passage facilities as summarized in Table 7. The EFHI evaluates four hazard components

that collectively contribute to the final EFHI score: FLOW, ETM, US and DS (Figure 8). For more details

of the tool’s mechanistic behavior, the implemented models, and empirical data we refer to van Treeck

et al. [169].

The EFHI FLOW component independently evaluates up- and downstream flow manipulations. For the

upstream part of FLOW, the impoundment storage capacity is assessed relative to the daily average

inflow and regarding current velocities within the impoundment. The downstream FLOW part assesses

the HPP operation regarding hydropeaking and discharge modifications of the annual mean and low

flows and, in the case of diversion-type plants, potential residual flows. Data on upstream

impoundment capacities were available for Au [355] and approximated using aerial photographs or

recalculated from mean flow rates of the stream and the turbines for the other sites. Current velocities

within the impoundments were extracted from the field studies for all seven HPPs.

The EFHI calculates the ETM risk by applying blade strike models for Kaplan-type and Francis turbines

and empirical mortality observations for other turbine types. The modelled strike rate risk is

complemented with scores for barotrauma risks depending on the plant’s hydraulic head and the

species’ swim bladder anatomy. The turbine-specific mortality risk is translated into a score and offset

with the species’ basic probability of entrainment based on individual turbine flows in relation to

stream discharge. The entrainment risk is corrected in cases of installed fine screens by regressing fish

length based on width following Ebel [123]. The resulting maximum size of species passing through the

turbine is then used in the fish length-dependent blade strike models.


69

The US component is calculated by determining an upstream migration facility’s discharge proportion

of the turbine flow, which is then scored in relation to the stream discharge, since larger streams

require a relatively lower admission flow in the upstream migration facility compared to smaller ones.

The DS hazard component is assessed by determining the presence of a downstream bypass and its

accessibility for fish in the vicinity of a deflection screen. Furthermore, it rewards facilities allowing for

both up- and downstream migration by lowering the upstream risk score by an increment. For

example, HPP Baierbrunn is equipped with a pool pass and an additional rocky ramp. While the pool

pass was exclusively designed for upstream migration, the rocky ramp was constructed to provide a

safe corridor for downstream migration as well as to facilitate upstream migration for strong and weak

swimming species. In addition, the rocky ramp provides habitat for rheophilic species

[86,117,327,364–366].

Entrainment

risk &

turbine

mortality

Overall flow

alterations

Risk scores

US

passage

DS

passage

EFHI

Sensitivity

Hydraulic

head

Turbine flow

rate

Turbine type

DS flow

condition

Dam & turbines

SpeciesConservation

concern

Discharge

pattern

Stream

Shape

Length

Swim

bladder

Anatomy

FGS presence &

design

Bypass presence &

design

DS passage

FGS gap width

UMF presence &

design

US passage

US flow

condition

Fish assemblage

Cons.

concern

Figure 8: Mechanistic model of the EFHI parameterized by different groups of input parameters and the four resulting hazard components (light grey) as well as the final EFHI score (open circle). Obtained from van Treeck et al. [169].

4.2.3. Assemblage-specific calculation of EFHI

For each of the seven hydropower sites final EFHI scores and scores of EFHI’s four hazard components

FLOW, ETM, US and DS were calculated in three treatments, comprising the same physical parameters

but three different sets of fish species:

1. The five most dominant (relative abundance ≥5%) yet sensitive species of the actual, fish-

faunistic reference community (“reference set”; Appendix 3, Table A3_1). When two species

were equally sensitive in the reference assemblage, we prioritized that of higher dominance.


70

Because a risk assessment based on an already degraded biological component does not

provide meaningful management recommendations towards the “good ecological

status/potential” demanded by the Water Framework Directive, the reference assemb lage is

used by default with the EFHI. This treatment was used to establish the ultimate risk ranking

of the seven HPPs and the results were analyzed in context of Mueller et al.’s [355–361]

empirical impact assessments.

2. The five most abundant species of the actual fish assemblage determined by electrofishing

(“sample set”; Appendix 3, Table A3_2). These species were evaluated regarding their

mortality rates in turbine entrainment events and therefore, comprise a baseline of the HPP

risks for the current fish assemblage. The sample sets were used to comprehend fish-ecological

observations in the EFHI and juxtapose them with results of the reference sets. Further, they

were used to determine the difference in HPP risk level for the reference compared to the

current fish assemblage, and to calculate the single contribution of the target fish sensitivity

on the final EFHI score.

3. Species used in the standardized fish release experiments (“release set”; Appendix 3, Table

A3_3). This set comprises the eight most frequently used species in each standardized release

experiment by Mueller et al. [355–361], European eel (Anguilla anguilla), common nase

(Chondrostoma nasus), brown trout (Salmo trutta), common perch (Perca fluviatilis), European

grayling (Thymallus thymallus), common barbel (Barbus barbus), common roach (Rutilus

rutilus) and Danube salmon (Hucho hucho), of which the five most abundant were used in the

EFHI. The EFHI results of the release set were compared to the results of the standardized

release turbine mortality experiments by Mueller et al. [355–361].

4.2.4. Data analysis

To estimate whether the EFHI reflected the environmental conditions around the seven HPPs, the

results of the reference set were interpreted and compared with the comprehensive reports on the

empirical fish mortality estimates by Mueller et al. [355–361].

Paired two-sample permutation tests (asymptotic general independence tests, AGIT [367]), were used

with a p-value adjustment that controls for false discovery rate (FDR [368]) to test for differences in

EFHI hazard scores and average fish sensitivities between the sets. Pearson correlation coefficients

were calculated for the entire data set and separately for the single sets to quantify pairwise

relationships of EFHI’s hazard components. In addition, multiple factor analysis (MFA, R-package

‘FactoMineR’, [369]) was used to analyze the multivariate characteristics of the EFHI hazard scores

dataset and to visualize potential multicollinearity between single hazard scores. MFA is a

generalization of the principal component analysis [370] to reduce the multidimensional attribute

space of the variables EFHI, FLOW, ETM, US and DS, which were calculated with three sets of species,


71

into a smaller set of principal components. Observed mortality estimates were extracted from the field

studies by Mueller et al. [355–361] and their correlation coefficients determined with the EFHI results

of the release and sample sets. To explore the impact of species sensitivity on the risk scores linear

models were calculated and tested against a null model by means of ANOVAs.

To identify hydropower components and sites of particularly high risk for the reference community,

the numerical divergence of each pair of risk scores between the reference and sample set within sites

was calculated. The result was then correlated with both the sensitivity of the sample and the

reference set to test whether high calculated risks were related to a high divergence (e.g., degradation)

of the current fish assemblage compared to the reference, or to disproportionally sensitive reference

communities, respectively.

All EFHI calculations were conducted using the European Fish Hazard Index EFHI , version 2.1.8

(https://zenodo.org/record/4686531, [169]). Statistical analyses were conducted in R, version 4.0.4

[233]. For calculating pairwise permutation tests the package ‘coin’, version 1.4-1 [367], was used.

Linear models were calculated using the package ‘lme4’ [371]. The statistical significance threshold for

all analyses was 𝑝 = 0.05.

https://zenodo.org/record/4686531


72

Table 7: Data of the seven hydropower plants for EFHI calculations. Except for one case, impoundment capacities of the plants were determined using aerial photographs. If not stated otherwise, turbine specifications and hydraulic head apply to all installed turbines. DAI = daily average inflow; Screw = Archimedes screw turbine; VLH = very low head turbine; VSP = vertical slot pass; HBR = horizontal bar rack; VBR = vertical bar rack; * = approximated; ** = upstream migration facility works bi-directional; 1 = run run-of-river setup; residual water discharge was defined equal to annual mean discharge; 2 = built into the residual water stretch of a HPP further downstream; discharge in residual stretch was used vicariously as annual average and low stream discharge.

Category Parameter Au Baierbrunn Baiersdorf Eixendorf Heckerwehr Höllthal Lindesmühle

Stream discharge parameters

Impoundment capacity <DAI <DAI <DAI >DAI <DAI <DAI <DAI Impoundment current speed (m/s) <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 Annual mean/low discharge (m³/s) 46.6/9.45 19/82 34.8 /15.1 4.32/1.33 3.17/0.87 51.5/19.3 12.3/2.97 Discharge residual water stretch (m³/s) 46.61 191 9.1 4.321 3.171 12 12.31

Turbines Number and type 2x VLH 1x VLH 2x Kaplan 1x Kaplan 1x Screw 2x Screw 1x Kaplan

1x Kaplan

Design flow rate (m³/s) 2x 27 14.5 2x 16 4.5 5 2x 9 1x 18

10.8

Hub/outer diameter (m) (Kaplan only) - - 0.76/2 0.38/1 0.53/1.5 0.95/2.5 0.53/1.5 Rotational speed (RPM) (Kaplan only) - - 150 333 212 100 212 Number of blades (Kaplan only) - - 4 4 3 4 3 Hydraulic head (m) 2 4 2.3 5 2.5 2 2.8

Fish protection and passage facilities

Upstream passage type VSP Pool pass Rocky ramp

VSP Pool pass

- Screw Pool pass Nature-like

Upstream passage discharge (m³/s) 1* 4.2 0.3

1 - 0.015 1* 0.33*

Protection screen type HBR - VBR VBR - - HBR Screen bar spacing (mm) 270 120 15 20 120 2x 150

1x 20 15

Downstream bypass No Yes Yes Yes No No Yes** Bypass fully accessible - No No No - - No


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4.3. Results Among all studied HPPs, the final EFHI score ranged between 0.27 (low risk; Baiersdorf; sample set)

and 0.75 (high risk; Eixendorf; reference set, Table 8, Table 9 and Appendix 3, Table A3_4). EFHI scores

varied substantially between the three species sets as indicated by a paired permutation test (two-

sided asymptotic general independence test AGIT, maxT = 3.26, p < 0.01). Pairwise post hoc tests

revealed significant differences in the final EFHI score between the sample set (mean EFHI = 0.47) and

the release set (mean EFHI = 0.60; AGIT, Z = 2.36, adjusted p < 0.05) and between the sample set and

the reference set (mean EFHI = 0.59; Z = 2.43, adjusted p < 0.05). Differences in EFHI scores between

reference and release sets were not significant (Z = 0.40, adjusted p > 0.1).

Scores of the four hazard components varied considerably between the different sets, too (Table 8 and

Table 9). Statistically significant differences between species sets were found for FLOW (AGIT, maxT =

3.38, p < 0.01), US (maxT = 3.35, p < 0.01) and DS (maxT = 3.35, p < 0.01). Differences in ETM were not

statistically significant between the three sets of species used (maxT = 1.37, p > 0.1).

Correspondingly, the average sensitivity of species differed among the three sets (AGIT maxT = 3.36, p

< 0.01, Table 8 and Table 9). Average sensitivity of the species set in release sets (mean = 3.5) deviated

clearly from that in the sample sets (mean = 2.7; Z = 2.55, adjusted p < 0.05), but was similar to those

of the reference sets (mean = 3.4; Z = 1.36, adjusted p > 0.1). Generally, sensitivities of species of the

release sets were more similar to those of the reference sets compared to sensitivities of the sample

sets (Figure 9).


74

Figure 9: Average species’ sensitivity in release sets (purple), sample sets (green) and reference sets (blue) vs. the respective final EFHI scores (n = 7 each). Red horizontal lines indicate thresholds to moderate and high-risk classes, respectively, purple vertical lines indicate areas of moderate and high species’ sensitivity.

An ANOVA comparing linear models of risk scores of FLOW against the null model revealed a

dependence on average species sensitivity across all sets (F = 10.28, p < 0.01). In contrast, scores of

ETM, US, DS and the final EFHI score did not improve the explanatory power of the null model

(F = 0.03, p = 0.87; F = 0.78, p = 0.39; F = 3.35, p = 0.08; F = 2.93, p = 0.1, respectively).

A strong positive correlation was found between EFHI and US scores in all species sets (all R > 0.97; p

< 0.01, Figure 10) and between EFHI and ETM scores in the reference species set (R = 0.86, p < 0.05,

Figure 10). Among the single components, significant pair-wise correlations were detected between

ETM and FLOW (R = 0.86, p < 0.05) and ETM and US (R = 0.78, p < 0.05) in the reference sets (Figure

10). The MFA indicated similar patterns of (multi)-collinearity across the four hazard components and

the final EFHI score with ETM and US scores rather contributing to the first axis (56% explained

variance) and the DS to the second axis (30% explained variance, see Appendix Table A3_5 and Figure

A3_3).

Au

Baierbrunn

Baiersdorf

EixendorfHeckerwehr

Höllthal

Lindesmühle

Au

Baierbrunn

Baiersdorf

Eixendorf

Heckerwehr

Höllthal

Lindesmühle

Au

Baierbrunn

Baiersdorf

Eixendorf

Heckerwehr

Höllthal

Lindesmühle

0.25

0.35

0.45

0.55

0.65

0.75

2.40 2.60 2.80 3.00 3.20 3.40 3.60 3.80 4.00

EFH

I

Average sensitivity

Sample Reference Release


75

Figure 10: Correlation metrics, scatter plots and histograms of hazard components, final EFHI scores and observed mortalities of release (purple), reference (blue) and sample (green) treatments.

Final EFHI scores were not significantly correlated with observed mortalities (p > 0.1) and similarly,

ETM scores did not correlate with observed mortalities either (all p > 0.1). However, some pronounced,

yet statistically insignificant trend was found between the ETM score, and the mortality rates observed

in the standardized release experiments, particularly when compared to the index values based on the

sample species set (R = 0.57, p = 0.18).

Divergence between risk scores of the reference set vs. sample set were highest at Baiersdorf

(cumulated divergence of 0.99) and lowest at Baierbrunn (0.23). With the exception for ETM,

divergence was significantly negatively correlated with the respective sensitivity of sample sets (FLOW:

R = -0.79, p < 0.05; US: R = -0.82, p < 0.05; DS = 0.82, p < 0.05 and EFHI: R = -0.76, p < 0.05; Figure 11)

but was not related with the sensitivity of the reference sets.


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Figure 11: Correlation of the divergence of FLOW (a), US (b) and DS (c; n = 7 each) between reference and sample sets and the average species’ sensitivity of the sample set.

Table 8: Results of the seven hydropower plants using the same species as in the standardized release experiments (“release”) and species caught upstream of the plant (“sample”). Displayed are the hazard scores for the single EFHI-components: overall flow alterations (FLOW), entrainment and turbine mortality (ETM), upstream fish passage (US), downstream fish passage (DS), final EFHI score (EFHI) and the related observed mortality rates.

Site Set FLOW ETM US DS EFHI Observed mortality

Average sensitivity

Au Release 0.65 0.40 0.65 0.90 0.65 5.7 3.8 Sample 0.50 0.25 0.50 0.75 0.50 44 2.9

Baierbrunn Release 0.60 0.35 0.28 0.48 0.43 19.3 3.5 Sample 0.55 0.30 0.24 0.44 0.38 31 3

Baiersdorf Release 0.63 0.45 0.38 0.63 0.52 20 3.6 Sample 0.35 0.26 0.10 0.35 0.27 50 2.5

Eixendorf Release 0.80 0.59 0.80 0.55 0.69 24.6 3.3 Sample 0.65 0.71 0.65 0.40 0.60 NA 2.5

Heckerwehr Release 0.60 0.60 0.85 0.85 0.73 12.8 3.4 Sample 0.45 0.45 0.70 0.70 0.58 28 2.7

Höllthal Release 0.60 0.42 0.60 0.85 0.62 6.8 3.4 Sample 0.45 0.47 0.45 0.70 0.52 29 2.9

Lindesmühle Release 0.63 0.58 0.50 0.50 0.55 42 3.6 Sample 0.45 0.64 0.36 0.36 0.45 69 2.6

Table 9: Calculated results of reference runs for overall flow alterations (FLOW), entrainment and turbine mortality (ETM), upstream fish passage (US), downstream fish passage (DS), and the final EFHI score (EFHI) and average species sensitivity.

Site FLOW ETM US DS EFHI Average sensitivity

Au 0.55 0.3 0.55 0.8 0.55 3.08 Baierbrunn 0.6 0.35 0.28 0.48 0.43 3.38 Baiersdorf 0.6 0.3 0.35 0.6 0.46 3.30 Eixendorf 0.8 0.86 0.8 0.55 0.75 3.17 Heckerwehr 0.6 0.6 0.85 0.85 0.73 3.27 Höllthal 0.6 0.53 0.6 0.85 0.65 3.38 Lindesmühle 0.65 0.56 0.52 0.52 0.56 3.75

0.00

0.05

0.10

0.15

0.20

0.25

0.30

2.4 2.6 2.8 3.0

Div

erge

nce

a

2.4 2.6 2.8 3.0

Average sensitivity

b

2.4 2.6 2.8 3.0

c


77

4.4. Discussion The expected expansion of hydropower and the ongoing decline of freshwater biodiversity require a

standardized, transparent, and evidence-based assessment of its impacts [349]. Therefore, the

assessment of potential risks of HPPs for fishes is paramount to its sustainable development. This study

exemplarily demonstrates at seven small hydropower plants in Germany how the European Fish

Hazard Index EFHI by van Treeck et al. [169] can facilitate and augment such risk screening. We tested

how accurately the final EFHI and its hazard components FLOW, ETM, US and DS reflect the results of

the empirical impact assessments on-site and explored mechanistic, framework-related, and ecological

factors when they did not. Overall and to a great extent, the tool captured risks that have been

documented in detailed, prior on-site impact assessments by Mueller et al. [355–361]. In addition, the

application of the EFHI also identified key areas for further adjustments of its mechanistic behavior.

4.4.1. Results of EFHI calculations

Final EFHI scores and scores of the hazard components FLOW, ETM, US and DS strongly differed across

sites, with the overall lowest (“moderate”) risk calculated at Baierbrunn and the overall highest

(“high”) risk at Eixendorf. Because the final EFHI score is calculated by aggregating the scores of the

four hazard components, we discuss their scoring results individually.

Highest FLOW scores (0.8) were calculated for Eixendorf, which is the only HPP with an upstream

storage exceeding daily average inflow, and slow current speeds through the impoundment. Since

hydro-morphological mechanisms downstream cannot compensate a high upstream risk and vice

versa, the higher risk class of the two components determines the final FLOW hazard. Indeed,

Eixendorf’s FLOW classification matched field observations conducted in 2015, when the hydropower

plant was built on top of an existing weir. Fish samplings upstream and downstream of the plant before

and after construction, showed lower than expected abundances of rheophilic fish species including

common gudgeon (Gobio gobio), European dace (Leuciscus leuciscus), common nase (Chondrostoma

nasus) and common barbel (Barbus barbus), as well as of rheophilic invertebrates [358]. Furthermore,

comparative analyses of the aquatic assemblages up- and downstream of the dam revealed no

significant change after construction of the HPP, indicating a persisting, detrimental effect of the

impoundment caused by the weir [358]. Downstream of Eixendorf the presence of ubiquitous fish

species like common perch (Perca fluviatilis) indicate ecological degradation [252]. The reasons for that

are closely related to the ecological deficits upstream and downstream: a lack of shallow littoral zones

as fish nurseries and heavy embankments. In addition, the reservoir traps sediment (i.e., gravel) that

is subsequently missing downstream [358]. This leads to habitat displacement and ecological

degradation [9,10,24,372,373], which cannot be fully captured by the EFHI, because it assesses

downstream risks based on stream discharge and turbine flow rate patterns and ratios only.


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The lowest FLOW scores (0.55) were calculated for Au. At this site, the original weir was replaced by

an inflatable rubber weir [355]. Its specific design allows for more dynamic management of high and

low flows and permits the downstream transport of sediment and dead wood. The improved hydro-

geomorphological conditions support a range of rheophilic species and their reproduction [355].

Despite the presence of rheophilic and lithophilic species, the assemblage still deviates from the

reference community, with some reference species such as European dace (Leuciscus leucisucs),

common nase (Chondrostoma nasus) and common gudgeon (Gobio gobio) still absent. The difference

in average sensitivity between sample set (2.9) and reference set (3.1) was small. Given that rheophilic

and lithophilic guilds are commonly more sensitive than eurytopics or phytophilics [167] we conclude

that Au’s FLOW scores accurately describe prevalent conditions.

The highest US score (0.85) was calculated for Heckerwehr. Studies show that one of the most

important metrics mediating the upstream passage success of a fish way is the amount of water going

through it [322]. At Heckerwehr, the installed upstream fish migration screw is supplied with only 0.3%

of the flow rate of the installed Archimedes screw, which is less than 15 times the recommended EFHI

value for a stream of that size. A standardized assessment of the screw’s passage performance

following Ebel et al. [374] confirmed the screw’s insufficiency, even if not explicitly attributed to

disproportionally low discharge [359].

The lowest US risks (0.28) were calculated for Baierbrunn. The risk rating was driven by the high

proportion of the turbine flow rate (29%) supplied to the passage facilities, the ecological benefit of

the additional rock ramp and its improved downstream fish passage [86,117,327,364–366]. The

beneficial effect of the rock ramp was also directly confirmed by Mueller et al. [356] at Baierbrunn.

However, due to EFHI’s properties, potential performance issues regarding excessive discharge or

current speeds in passage facilities as observed by Mueller et al. [356] were not reflected in the

framework.

Fish mortality in turbines and risks for fish due to flow alterations or upstream fish passage are

physically isolated from each other and therefore, mechanistically independently implemented in the

tool. In contrast to other components, this does not apply to ETM and DS because turbine passage

comprises one (albeit the most dangerous) downstream route for fish [105]. However, substantial

research was devoted to the development of turbines that are safer for fish [100,128–132,134]. In

HPPs running such improved turbines, dedicated downstream bypasses might become partly

superfluous if fish can safely migrate through the turbines [137]. If this was universally true, it would

principally annul the need for narrow spaced protection screens and dedicated downstream bypasses

at the turbine intake. However, so far there is too little evidence from only few installations of these

novel turbine types regarding the safety for fish under routine operation. Despite lower fatal blade


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strike rates in larger or more advanced turbines, fish may still experience significant physiological

impacts like barotrauma and other injuries that are not easily detectable or manifest with delay only

[104,154,375,376]. Populations may also suffer from elevated mortalities due to predation of

potentially highly disoriented fish post-passage [108,139,144,146,149,152,375]. Four of the seven

studied HPPs were equipped with more advanced turbines like VLH (Au and Baierbrunn [355,356]) and

Archimedes screws (Heckerwehr and Höllthal, although the latter also operated a Kaplan runner

[359,360]). Neither of these setups featured dedicated downstream bypasses, and all screws were only

protected by coarse trash racks with bar spacing between 120 and 270 mm. The missing protection

screens and bypasses caused high DS scores that ultimately, resulted in negative relationships with

ETM scores. This, in turn, caused relatively high final EFHI scores for HPPs with otherwise low ETM

scores.

This risk equality highlights the necessity to interpret EFHI results in the context of its sub scores, but

it also shows how EFHI goes beyond empirical mortality estimates. Even at low mortality rates, the

absolute mortality caused by a turbine can become high if fishes are not additionally prevented from

entering. Accordingly, a large Kaplan turbine equipped with a very fine screen and operating at high

load but with only a moderate flow rate compared to the stream discharge, could yield the same ETM

score as a highly advanced VLH turbine with documented mortality rates of ≤4% [128,129,169]. Only

if passage through a particular turbine would indeed be the safest downstream route, final EFHI scores

would overestimate the total risk of the plant. To date, however, operational, and accessible bypasses

are considered the safest downstream route for fishes and risks of turbine passage remain dominant

[105,135], even in more advanced turbines.

4.4.2. Interpretation of EFHI results in context of observed mortalities

At the seven hydropower plants, the final EFHI score did not significantly correlate with empirically

observed mortalities because it aggregates hazards beyond mortality (i.e., FLOW, US, and DS). This was

regardless of the assessment methodology i.e., examining natural entrainment vs. standardized

releases of fishes. However, due to the lack of standardization in natural entrainment trials [362] their

results are generally less comparable across sites.

Empirical mortality rates higher than expected by the EFHI were observed at Baierbrunn and Baiersdorf

which could be attributed to an atypical operation of the installed VLH turbine [356], and the use of

arithmetic means of observed mortalities [357], respectively. At Baiersdorf the averaged mortality rate

was notably high because of a low number of eels tested with an exceptionally high observed mortality.

Unexpectedly high mortality rates of up to 69% were also observed in natural entrainment

experiments at Lindesmühle [361], despite a 15 mm fine screen which should prevent most fishes ≥15

cm total length from entering the Kaplan turbine. Mueller et al. [361] explained these high mortality


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rates by an unknown share of already damaged or dead fish, low recapture rates that increase

statistical uncertainty, dominance of pressure-susceptible physoclistous species and low turbine loads

during the experiments. These are all common challenges of empirical fish mortality investigations at

HPPs, but only fish anatomy and turbine load factor into the EFHI score, which might explain some

deviation from empirical observations. In contrast, mortality rates in the Archimedes screws at Höllthal

and Heckerwehr were lower than expected by the EFHI. In screws, factors like fish length, runner size

and rotational speed might have the opposite effect than in Kaplan or Francis turbines, as smaller

specimens are more likely to get impinged between runner and trough [100,137,377]. So far, mortality

models mainly exist for Francis and Kaplan turbines as well as some Kaplan-derivates (e.g.,

[130,301,345,378]), while size and species-specific models of damage rates for other turbine types are

yet to be developed. In the EFHI, their risks are only implemented as static, empirical information of

low (VLH) and moderate risk (Archimedes screw) and cannot provide the same resolution and accuracy

as mortality models.

4.4.3. Mechanistic diagnosis

We observed that overall, the EFHI framework and its risk scores accurately reflected the

environmental conditions of the hydropower constellations investigated. However, the hazard scoring

results did not only depend on impact exerting (i.e., hydropower) factors but also on the resilience of

fishes against those impacts and the complex interaction between these two factors. This holds true

for all EFHI components but particularly for those considering individual life history traits beyond the

sensitivity score (the ETM component, for example, considers more than 10 individual input variables

per fish [169]). The relative impact strength of species sensitivity vs. hydropower components is

therefore not easily visible. The conducted analyses revealed a strong dependence of FLOW on the

respective average species’ sensitivity, and significant correlations of the risk score divergence

between sample and reference sets on the sample species’ sensitivity, with exception for ETM, which

is most likely due to its exceedingly complex mechanistic behavior. The role of sensitivity for the risk

scoring emphasizes the importance of explicitly considering fish-ecological and fish conservation

aspects in context of risk evaluations. Most importantly, however, it highlights the great potential for

underestimating hydropower risks in more degraded streams independent from the reference species’

sensitivity. The same physical components of an HPP might yield different risk levels for fish, depending

on their susceptibility to anthropogenic stress, a phenomenon that was clearly visible when comparing

sample and reference sets: The significantly lower sensitivity of the sample (average across all sites

2.7) compared to the reference set (average 3.3) was driven by the lower abundance of sensitive,

rheophilic, lithophilic and diadromous species that potentially would occur under natural conditions.

This community shift from the reference was generally well-reflected in the reports by Mueller et al.

[355–361], and most pronounced in our calculations for Lindesmühle. At this site the specific reference


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fish community comprises among others European eel (Anguilla anguilla), European grayling

(Thymallus thymallus), brown trout (Salmo trutta), common barbel (Barbus barbus) and common nase

(Chondrostoma nasus) and has an average sensitivity of 3.75. The most abundant species in the

sample, however, were common roach (Rutilus rutilus), common gudgeon (Gobio gobio), Eurasian

ruffe (Gymnocephalus cernua), European dace (Leuciscus leuciscus) and common perch (Perca

fluviatilis), with an average sensitivity of 2.63 (decline of 29.8%). This degradation was partly reflected

in low-risk scores of FLOW, US, DS and the final EFHI, but not at all in the ETM score, which was the

second highest of all HPP scored. The high ETM risk was caused by high numbers of small species, two

of them physoclistous, in the sample set, that could physically pass the deflection screen and enter the

turbine, which caused a full weighing of the subsequently high-risk turbine passage with calculated

blade strike rates of >9% [169]. The ETM score was particularly high for common gudgeon (G. gobio)

and independent of individual sensitivity or calculated blade strike rates and matched the very high

observed turbine mortality rates at HPP Lindesmühle discussed earlier. However, often small-scaled

HPPs replace existing weirs in already hydro-morphologically degraded stream systems [9], which

makes an evaluation of the additional ecological effects of hydropower difficult. While it was beyond

the scope of this study to investigate the causes for the diverging species assemblages, it is noteworthy

that habitat and up- and downstream flow alterations, as well and limited connectivity, have frequently

impacted stream fish species communities, often resulting in a loss of the most sensitive, rheophilic

species [29,50,52,53,72,295].


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4.5. Future adjustments Reasons for deviations between calculated in the EFHI and actual impacts found in field surveys can be

manifold and often require more detailed follow-up analyses. Besides this, some potential

improvements of the EFHI were identified, which might be implemented in future revisions of the tool.

This includes further implementation of non-standard turbine configurations and their potential

effects on fish mortality rates. For example, a refined turbine selection process issuing warnings when

typical turbine installation and operating parameters are exceeded could help fine-tuning the turbine

risk scoring. In addition, the turbine risk classification based on empirical data implemented in the EFHI

(e.g., for Archimedes screws) should be replaced by modelled fish mortalities, because of their better

scoring performance. Therefore, further mortality models for other turbine types should be

implemented as soon as they become available.

Finally, the performance of the downstream component DS could be enhanced. If the claimed better

performance of the novel, more fish-safe turbine types becomes empirically evidenced under routine

operation, these turbines can be implemented as a separate downstream passage route in the DS

component. Then it will be reflected in the DS score if passage of a particular fish safer turbine results

in lower mortality than alternative downstream routes.

4.6. Conclusions The EFHI performed well in detecting and describing hydropower-related risks such as those exerted

by flow manipulations, turbine entrainment and mortality, downstream passage across alternative

routes and upstream passage. It was shown that EFHI’s resolution is sufficiently high to reliably

combine and discriminate its constituent components according to the unique conditions of a given

HPP while still considering the sensitivity of the target fish species pool. With its four hazard

components and the broad selection of target species implemented (168 native European species),

the EFHI can effectively support risk assessment and mitigation of hydropower components and

constellations throughout Europe. The EFHI hazard scoring will be further improved by implementing

both novel, fish-safer turbine types and mortality models for turbines other than Kaplan and Francis,

as soon as they become available.

Chapter 5 – General discussion

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Chapter 5: General discussion


84

ith the global boom in hydropower development [268] comes the unconditional

necessity for means of evaluating its environmental impact in an objective, comparable,

and transparent manner. The European Fish Hazard Index (EFHI) that I developed in this

thesis is the first hydropower assessment framework of its kind and could be validated in a variety of

hydropower applications across central Europe. This thesis results from and outlines the development

of the EFHI and its principal components: In Chapter 1, I summarize the diverse hazards of HPP

projects, and I review and quantify the impacts that these hazards can have on fishes. In Chapter 2, I

lay the focus on the autecology of fishes and their different susceptibility (=sensitivity) to

anthropogenic pressures that ultimately, increases the mortality rates of species [167]. This sensitivity

to mortality is depicted as a score that is unique for each species, and I used it in the EFHI to weigh

stressors of hydropower plants (HPPs) according to the species that are affected by it. In Chapter 3, I

outline the development of the EFHI and the implementation of the sensitivity score as part of EFHI’s

core mechanistic behavior [169]. Although I developed the EFHI under consideration of evidence-

based effects of technical (i.e., hydropower) components and their interaction with biotic (i.e., fish)

factors, a systematic test and diagnosis of EFHI’s performance across different hydropower setups, bio -

geographic regions and operational scenarios was only conducted in Chapter 4: I validated the EFHI in

context of a series of comprehensive hydropower impact assessments and identified both strengths

and weaknesses in its scoring capacity [170]. In this last Chapter 5 of my thesis, I discuss the findings

of the Chapters 1, 2, 3 and 4, summarize their contribution to the scientific community and explore

persistent knowledge gaps that could be addressed in future projects.

W


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5.1. Hydropower impacts, species susceptibility and their

assessment In Chapter 1 I illustrated how diverse HPPs can be, both in the way they are designed and built but also

in the way they impact fishes. While these impacts go far beyond mortality or injuries inflicted by

turbine passage alone (e.g., through blade-strikes or rapid pressure changes), turbine mortality is still

a major threat for fishes and therefore, the assessment of risks related to turbines demands particular

attention. In the most frequently used Kaplan and Francis turbines, individual mortality increases with

smaller and faster spinning units, because the temporal and spatial gap between blades becomes too

small to pass uninjured. It follows that fish size (i.e., length) is a critical predictor for mortality in a

turbine, with bigger (i.e., longer) fish being more susceptible than smaller ones. Besides blade strike,

reaction turbines like Kaplan or Francis turbines can cause substantial, additional pressure-related

injuries due to a very rapid, strong decrease in turbinated water pressure. Fishes with a closed

pneumatic duct (physoclist species) are most susceptible to these changes, while those with an open

pneumatic duct (physostomous species) or without any swim bladder balance pressure faster or are

unaffected entirely, respectively. The severity of these injuries correlates with hydraulic head, which

can be roughly approximated by the height of the dam.

To protect fishes from the turbines, many HPPs installed bar racks at the turbine intakes to either

physically deflect fishes or to induce a behavioral aversion response. Gap width of bar racks and the

angle at which they are installed relative to the stream flow are important predictors for deflection

success, and deflected fishes must subsequently have access to alternative downstream routes.

Dedicated downstream routes assume a variety of shapes: Some are made for both downstream and

upstream migrating species alike, others are just operational one way. Some are continuously

accessible; others (like spillways or trapping systems) must be opened periodically. Some migration

facilities work better than others or work for a larger number of species, or even provide additional

habitat for a range of rheophilic species to offset some of the lost habitat in the impoundment, but

they require more ground space, which is not always available. Other downstream routes, like

undershot gates, have shown to cause mortalities as high as some turbines. To migrate upstream,

fishes need access to easily detectable and passable facilities, which are majorly dependent on the

appropriate generation of attraction flows and the amount of water supplied to the facility. But the

individual internal state and behavior of the passing fish, its urge to migrate, the current speed, slope,

and water levels and even the time of day or the weather determine passage success in an upstream

migration facility, as well. The diversity of failure causes regarding the performance of upstream

migration facilities demands high levels of expertise, thoroughness, and site-specificity in the design

and installation process.


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The hydro-morphological impact of an HPP can already be perceptible far upstream of the dam and

the powerhouse. In reservoir-type HPPs with huge impoundments, in which the stream water is slowed

down, stacked and thermically layered, the original river is altogether transformed into a lentic

environment. This displaces spawning, rearing, and feeding habitats of rheophilic and gravel spawning

species which, in turn, directly relates to a decline in recruitment. Furthermore, it can also cause an

additional migration delay of diadromous species that are already impeded by the dam. Flow

alterations induced by hydropower operation do not just affect the upstream, but also the

downstream area of HPPs, mainly in the form of water scarcity, which is a particularly severe problem

in diversion-type HPPs, or of a fast oscillation between water shortage and excess, and the ramping

rates between these two stages, which is common in hydropeaking plants that primarily generate

electricity in times of peak demand.

At the end of Chapter 1, I elaborated why the necessary research about hydropower can be challenging

and results of hydropower impact studies do not have universal validity. A major reason for that is the

different biological susceptibility of fishes to anthropogenic stressors. That is why I developed a

classification system that aligns 168 European fish species according to their sensitivity to

anthropogenic stress on a dimension-less scale from one (least sensitive) to five (most sensitive) in

Chapter 2. The sensitivity score reflects the species’ fundamental capabilities to resist to and recover

from stressors that affect the size of a population, and it is therefore applicable in various scenarios: It

can be used in context of risk and impact assessments of hydropower operation and other hydro-

morphological manipulations (e.g., dredging, straightening or other forms of engineering work),

fishing, water abstraction, in-stream manipulation, thermal pollution but also in restoration and

rehabilitation projects. For example, the unexpected absence of high-sensitive species in a sample

would indicate the relative severity of a stressor and could support the development of mitigation

measures. Similarly, an increasing or stable abundance of high-sensitive species could indicate a

recovery period of the system and an absence of a stressor, respectively. Considering the sensitivity

score in these scenarios could therefore support targeted restoration efforts.

Particularly sensitive species belonged notably frequently to rheophilic and lithophilic/phyto-

lithophilic guilds and were often either potamodromous or diadromous migrants. Most of the sensitive

species are relatively large, grow old, mature late and are of moderate fecundity relative to their

weight. In addition to their sensitivity, traits like a large body size have also facilitated a certain

economic interest in exploiting the respective stocks in the past, which has caused changes in growth

rates and accelerated population declines or even extinction of many stocks in the last century. But

sensitive species can also be relatively small, like the bullhead Cottus sp. or some members of the

genera Telestes and Phoxinus. These less intuitive classification results are of exceptionally high

importance: The focus of conservation efforts is often laid on iconic flagship species only [379], for


87

which the sensitive European eel, sturgeons or Atlantic salmon are highly qualified candidates [380–

382]. In contrast, smaller species are frequently overlooked in conservation and management efforts,

and can therefore be of high extinction risk as a result [383]. This elevated risk in itself should already

be reason enough to pay close attention to the ecological requirements of these species, but their

disappearance can also have far-ranging implications for the functioning of ecosystems or the stability

of communities that, due to the general lack of knowledge about their biology, are hardly predictable

and could be severely underestimated [384]. Furthermore, many management and conservation

frameworks and projects target functional and ecological guilds rather than individual species [385–

387]. While this approach often boosts the understanding of ecological processes and provides

straightforward results that can easily be operationalized by stakeholders, it falls short when it comes

to predict the individual response of populations to an impact or a restoration activity within the guild.

I could show that based on their life history, members of one guild can be of very different sensitivity,

especially in guilds of relatively broad definition i.e., rheophilic guilds. It is therefore conceivable that

a purely guild-targeted management approach is too coarse to produce the intended effect, and the

species-specific response to the management being of varying success, which is depending on the

individual sensitivity of the targeted guild’s members. An additional consideration of the sensitivity of

the species e.g., as a weighted average, or a range between the lowest and highest sensitive member,

could augment these applications by providing a higher resolved picture that facilitates better planning

of management efforts and a more accurate prediction of their results.

In this comprehensive life history analysis, many European fish species were considered for the first

time, and the classification results were surprising for some of these species. The inclusion of this knew,

sometimes less-intuitive knowledge makes the sensitivity score a valuable, highly versatile tool to

understand fish-ecological responses to a wide range of human-induced pressures and can help to

derive meaningful management recommendations across many bio-geographic regions.

In Chapter 3, I used the sensitivity score as a weighing factor of hydropower-induced stressors in the

European Fish Hazard Index EFHI. I identified four major risk clusters of HPPs, and individually

implemented them in the EFHI. These risks relate to i) flow manipulations up- and downstream of the

dam, ii) unintended turbine entrainment and subsequent turbine mortality, iii) insufficient upstream

fish passage facilities and iv) insufficient downstream fish passage facilities through the HPP. Risks are

evaluated individually for up to five most sensitive species. In addition, I included life history data that

was exceptionally relevant for a hydropower risk assessment like fish size and swim bladder anatomy,

as well as the conservation concern of species. Thus, a high-sensitive species community elevates the

overall risk classification independent of hydropower components, and more hazardous hydropower

components contributes to a higher risk class independent of the species’ sensitivity. If both the risk


88

of hydropower components and species sensitivity increase, their relative contribution results in an

even higher overall risk score. The risk score calculated by the EFHI can be used in four fundamental

ways: i) Compare similar HPPs in streams with species of different sensitivity. This can support decision

makers in identifying streams of higher fish-ecological value and restrict hydropower operation or

incentivize conservation, ii) compare different HPPs in the same stream section e.g., to identify the

least harmful hydropower constellation for the reference fish assemblage, iii) compare different HPPs

with differing species sensitivities e.g., to prioritize upgrading or removing of a plant and iv) compare

the risk classification of a single HPP after upgrading or changing components. The assessment

algorithms are based on comprehensive, highly resolved data (i.e., strike models for Kaplan and Francis

turbines) but also make use of rather generalized information in cases of knowledge gaps. Those gaps

occurred in all four hazard clusters but were most abundant in the assessment of flow alteration and

upstream migration risks. The high complexity of the framework required a systematic test and

validation of the EFHI under different hydropower scenarios, which I conducted in Chapter 4.

I tested the tool by applying it at seven small hydropower plants in the state of Bavaria, Germany, and

compared the classification results of the four hazard components flow alteration, entrainment, and

turbine mortality and up- and downstream fish passage and the final risk score to the results of

extensive empirical impact assessments conducted at the same sites. The EFHI detected prevalent risks

fairly well, and deviations in EFHI’s risk evaluation and the in-situ impact assessment could largely be

accounted for. The study also demonstrated, that if a hydropower risk assessment would be based on

the actually occurring species pool, it would severely underestimate the risk level for the actually native

(reference) assemblage. This could be attributed to the low abundance of sensitive species in

ecologically degraded stream systems like the ones impacted by hydropower operation, and the high

abundance of lower-sensitivity generalists. I showed that this effect was independent of the sensitivity

of the reference assemblage, which suggests that an underestimation of hydropower risks in lowland

rivers with a generally higher abundance of low- or moderately sensitive generalists is as likely as it is

in regions where the physical conditions of the stream support higher abundances of highly sensitive

habitat specialists. Chapter 4 also helped to identify shortfalls of the EFHI that could be addressed by

the implementation of additional modules or algorithms in future iterations of the software. The first

and perhaps most important one is the detection of risks related to sediment trapping up- and

downstream of the dam. Hydro-morphological manipulations of streams usually result in altered

sediment transport capacities of streams. A change of sediment composition has wide-ranging fish-

ecological implications, and a module that estimates the potential of sediment trapping e.g., by

considering the approximate gradient of the stream section, the stream type, and the occurrence of

lithophilic species, could potentially augment EFHI’s FLOW component. Further, the EFHI could be

upgraded by implementing an algorithm that calculates risks related to excess in discharge in upstream


89

migration facilities. Other potential upgrades relate to the installed turbines at HPPs e.g., to detect

variations in turbine mortality caused by non-standard configurations. In some HPPs, turbines operate

below design discharge or uncommonly in terms of hydraulic head and rotational speed, which can

cause higher mortality rates than expected for the specific constellation. A refined turbine selection

process in the EFHI input window that issues a warning whenever typical turbine installation and

operating parameters are exceeded would help fine-tune the turbine risk assessment. Moreover,

mortality or strike models of turbines should replace empirical risk classes as soon as they are available.

A great strength of the EFHI is its ETM component that considers many hydropower-, stream-, and

biological variables to calculate risks for fishes to get unintentionally entrained and killed in turbines.

For Kaplan and Francis turbines, the risk is majorly dependent on strike models that, in turn, use stream

discharge, the length of the fish, dimensions, flow rates, rotational speed and number of blades of the

turbines and other parameters for up to 13 turbines simultaneously. This provides a highly resolved

insight into the risk of turbine passage for 168 species. Hazard ranking of other turbine types is based

on empirical fish mortality observation and classified according to median and 75 percentile mortality

rates. Due to the lack of mortality models, critical biological parameters like fish length and swim

bladder anatomy and highly relevant physical factors like turbine flow rate, dimension and rotational

speeds were not considered in detail. I therefore argue that a revision of the ETM component,

following the release of new mortality or strike models for turbine types other than Kaplan or Francis,

is of high priority. Finally, a powerful improvement of the EFHI could be a conceptual revision of the

ETM and DS modules. Because turbine passage comprises one of many routes downstream it should

be handled as such, independent of the embedded complex risk calculation. If turbine passage would

be of very low risk e.g., because the turbines are particularly safe for the respective reference

assemblage, and other downstream routes would be riskier e.g., because bypasses are of objectively

poor design, it should be reflected in the DS score. However, to achieve this relative ranking,

substantial empirical and conceptual data about the risk of different downstream routes is required.


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5.2. The state of knowledge on ecology and hydropower Many stressor-impact relationships between anthropogenic disturbances and fishes are reasonably

well-studied. Fish-ecological research has been conducted in almost every conceivable manifestation

of human-induced pressures ranging from fishing, hydropower operation, water abstraction and

hydro-morphological alterations, over thermal, organic, and chemical pollution, fine sediments, to

climate change, invasive species and many more. Furthermore, in all these fields, research has targeted

different ecological levels e.g., ecosystems, community ecology, population ecology or autecology, and

here, too, the resulting cumulative knowledge is immense. Several databases, books, meta-analyses

and reviews containing and discussing life history traits, patterns, or strategies of freshwater fishes,

were set up and published [203,205,227,228,388–395]. In addition, Chapter 1 contributes to the

quantity and diversity of hydropower-related research. A search query for scientific publications

containing the word "hydropower" or "hydroelectricity" or "hydroelectric" or "hydro-electric" or

"hydro-electricity" or “hydropeaking” in combination with "fish" or "fishes" on Google Scholar yielded

more than 600 hits by the time of writing this thesis. All of this suggests rich availability of critical

knowledge relevant for conservation and management of freshwater ecosystems impacted by

hydropower operation. However, I learned that the availability of knowledge is evidently heavily

biased towards very few species or guilds of high cultural, societal, or economic interest, and that even

fairly ubiquitous or well-known fish species appear severely understudied: Several fundamental,

ecologically critical parameters like maturation or reproduction metrics of many European freshwater

species have only been reported anecdotally, or the knowledge is circumstantial or not available at all.

Because populations of the same species express life history traits differently, depending on

environmental factors like latitude [396,397], the transferability of life history traits across bio-

geographic regions can be rather limited. Clearly, there is a strong need for basic research about many

(aut)ecological aspects of freshwater fishes, particularly in light of the rate at which freshwater

biodiversity and freshwater habitats disappear [5,6,398–400]. Without this fundamental knowledge,

management and conservation attempts in anthropogenically impacted freshwater ecosystems face

substantial risk to fail. For the application in the sensitivity score of Chapter 2, however, the

approximation of life history traits has proven sufficiently accurate to calculate sensitivity scores for

168 species [167]. The strength of the sensitivity classification is the alignment of species traits relative

to each other rather than absolute, which allowed using also rather coarse trait data. The lack of

comprehensive, species-specific knowledge beyond those of iconic heritage was also evident in

context of research about their response to hydropower operation. While the search query

“allintitle: hydropower OR hydroelectricity OR hydro-electric OR hydro-electricity OR hydroelectric OR

hydropeaking AND salmon OR salmonid OR salmonids”,


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for example, still produced 231 hits at the time of writing this thesis; replacing all “salmon” terms and

its variations with “trout” resulted in only 82, with “eel” in 32, with “grayling” in eight, with “barbel”

in three and with “burbot” in two hits. Replacing them with “nase” produced only one hit and with

“chub” and “dace” produced no match whatsoever. And while the lack of matches in a search query

arguably does not necessarily imply a lack of related research interest, I still consider this a striking

result, given that all of the above-mentioned species are comparably sensitive members of rheophilic

and lithophilic guilds [167], of high relative abundance in many European reference fish assemblages

and, at least in parts, of particular conservation concern. The lack of knowledge about the interaction

of specific hydropower components and species is even more severe [148] and the transferability of

results is once more restricted by the diversity of metrics and biological endpoints used in studies

[401]. An accurate prediction of risks or impacts is further impeded by the different behavior of

individuals or species in response to specific hydropower components [152,402,403]. And other than

in a laboratory or a model, it is impossible to control for the countless factors influencing strength and

shape of these impacts in real-world conditions, let alone predict every possible interaction with other

components.


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5.3. Methodological constraints of the EFHI The previously discussed information bias illustrates the difficulties that arose during the development

of the EFHI, particularly because it is designed to be a near-universally applicable hydropower risk

assessment system. Because of that and because of other methodological constraints, there might

always be a mismatch between the detection of hydropower risks in the EFHI and their manifestation

into actual impacts: While the EFHI scoring mostly uses empirically derived average risk levels (except

for ETM), the specific physical, maintenance and turbine discharge conditions might differ resulting in

deviating impacts on fish. On one hand, I expect the potential mismatch between risk and impact

particularly likely to occur in EFHI components of lower mathematical complexity (e.g., the clusters

that calculate upstream risks or overall flow alterations) and least likely in those involving a comparably

rich amount of highly resolved data, like the ETM component. On the other hand, a data-rich model is

inadvertently flawed by its attempt to capture as many scenarios as possible. And while it has the

potential to deliver highly specific results in some cases, it can also be more “off” in others. In highly

specific systems, a less accurate scoring approach might – on average – produce more reliable risk

scores. In a typical application of the EFHI it is therefore advisable to focus on the species -specific

scores of the single hazard components in addition of the final EFHI score, too. This transparency across

multiple computational steps is one of the advantages of the EFHI, because it provides comparably

detailed insight into potentially problematic constellations for every hazard component and species

and facilitates the quest for less hazardous alternatives. In fact, these 20 components- and species-

specific hazard scores can even be of higher analytic value than the final EFHI score itself: It is the

arithmetic mean of the four component-specific hazard scores which, in turn, are aggregated from the

20 component- and species-specific hazard scores. In the final EFHI score, both balancing and

disproportionally influential factors are masked alike, whereas the single components’ scores reveal

the subtle but sometimes critical unique interactions between one or more target species and the

hydropower components that otherwise might have only been detected by comprehensive field

surveys. This diagnostic use of the EFHI can be very important, because especially risk scores of the

ETM cluster are the result of highly complex calculations, and their a priori prediction is almost

impossible: A species might be small, so its strike mortality rate is low. At the same time, a larger share

of its population can pass through a fine screen of a given size and be exposed to the high-risk

environment in the turbines, which is mathematically considered and penalized in the ETM score, too.

If a species does not have a swim bladder the risk for pressure-related injuries is neglectable, but if it

is very long it is more likely to experience blade strike. And a species of lower sensitivity can have a

higher turbine mortality risk than one of higher sensitivity because it fits through the installed

deflection screen, or because its risk of barotrauma is higher due to a different swim bladder anatomy.


93

The possibility to assign particular conservation concern to a species or a stream section in the EFHI

can help to account for some of these uncertainties in life history trait expressions of species or their

actual response to hydropower pressures, respectively. Ultimately, though, the declaration of

conservation concern only elevates the risk level of the HPP for that species or the stream section, and

the assessment results do not necessarily reflect the true ecological conditions on site. I therefore re-

emphasize, that a risk evaluation by the EFHI cannot substitute an actual, site-specific environmental

impact assessment but rather points to areas of potential concern with constellation-specifically

varying accuracy. These areas can subsequently undergo a thorough impact assessment that are based

on the results of actual field campaigns.


94

5.4. A note on the future of hydropower Since its first appearance, hydroelectricity has played an important role for developing and developed

nations and, considering the global significant unexploited hydropower potential, is expected to

maintain a dominant role in electricity supply in the foreseeable future [265,268].

Hydropower plants are commonly distinguished into two categories based on their generation

capacity, and they each serve a different strategic purpose. The construction of small HPPs with

installed capacities of <1 MW was and remains a common means of electrifying remote, rural or small

settlements, particularly in developing nations and nowadays, small HPPs are therefore abundant

worldwide [269,404–406]. However, their high environmental impact relative to their capacity and the

rich biodiversity in developing regions, where small HPPs are numerously built, are consistent causes

for ongoing conflicts in context of the exploitation, conservation and management of freshwater

ecosystems [20,269,271,275,276,407–410]. In contrast, large HPPs, some with installed capacities

equal to or even exceeding thermal (coal or nuclear) power plants, have a lower relative environmental

impact when corrected for capacity, and are much rarer in numbers (both in existence and

development) [408]. Large HPPs are often used as imposing displays of a nation’s power and socio -

economic growth [405,407,411] and are supposedly better suited to meet renewable energy demands

of a changing world [412,413], because they are hypothesized to provide additional benefits to

mitigate impacts of climate change e.g., the creation of reservoirs for drinking or irrigation [413]. Their

negative impact on the population directly near the dam, however, has been repeatedly

underestimated [408].

In the future, climate change will cause substantial variation in hydroelectric generation capacity in

many regions of the world [408,413–416]. In areas of heavily reduced precipitation and less predictable

runoff, a significant decrease in hydropower potential and hydroelectric generation rates may render

HPPs less profitable [413,416–421]. In contrast, HPPs in regions of higher predicted precipitation might

even increase their hydropower potential and generation rates in the future [416,418,419]. However,

both large and small HPPs alike are and will keep exerting substantial stress on already threatened and

degraded, and often even protected ecosystems [14,18,266,268,422,423]. To operate future HPPs in

a sustainable, cost effective and societally accepted way, their environmental and societal risks and

impacts should be contrasted to their economic gain in a very thoughtful and comprehensive manner

[19,424,425], and negatives outcomes of either environmental or societal impact assessments should

be capable of stopping an HPP from being built [408]. Existing environmental regulations like the EU

Water Framework Directive should be implemented more strictly [426] and ambiguous paragraphs

granting exemptions of the non-deterioration principle due to an “overriding public interest” [427]

might need to be carefully revised. The development of fish-safe turbines and more holistic


95

hydropower concepts whose environmental considerations reach beyond the directly affected stream

section, concepts that do not require an imposing dam to to operate [408], and initiatives to remove

rather than refurbish old or outdated dams and HPPs should be positively re-enforced, and their

ecological and societal benefits publicly showcased. And an increased environmental awareness of the

society about the ecological impacts of hydropower and their potentially high contribution to

greenhouse gas emissions will undoubtedly contribute to the public discussion over a more sustainable

hydropower operation, too.


96

5.5. Conclusion In the European Union, regional, national, and international conservation and management

regulations provide the legal framework in which anthropogenic activities in streams take place. To

achieve water framework directive targets and comply with the EU Habitats Directive, assessment

tools are needed to evaluate, describe, and predict the impact these activities can have on generic

freshwater fish assemblages. The species sensitivity classification facilitates impact prediction based

on the species’ unique suite of life history traits and supports decision making and restoration and

conservation efforts. Species of high sensitivity are typically large, grow old and mature late, and have

a high individual resistance to anthropogenic stresses that cause mortality. A post-pressure population

recovery of high-sensitive species is expected to occur at a substantially lower pace than that of a low-

sensitive species that is typically characterized by fast generation cycles and relatively small adult size.

The sensitivity classification is implemented as a simple score that is assigned to a total of 168

European freshwater fish species. This score is used in the European Fish Hazard Index EFHI to weigh

given hydropower hazards of the four major assessment clusters i) flow manipulations; ii) entrainment

and turbine mortality; iii) upstream fish passage and iv) downstream fish passage. The EFHI calculates

a risk score between 0 (no risk) and 1 (highest risk) for common hydropower constellations with up to

five species and 13 turbines simultaneously. It considers the biological susceptibility of species and

their potential conservation concern or that of the affected stream section. The EFHI can be used to

systematically prioritize installation, refurbishment or even removal of HPPs and as such, augment

objective decision making. Its risks scores can be related to environmental objectives in context of

hydropower potential assessments, predicted power generation, operation, and construction costs to

achieve more balanced feasibility estimates of existing and future hydropower projects and their

specific contribution to renewable energy supply. The EFHI is developed in a modular way that can be

continuously improved considering new scientific evidence. Tools like the species’ sensitivity score and

the EFHI offer a hands-on means of holistic, comparable, and objective evaluation that can be used by

a wide range of stakeholders, and I am convinced that a continuous development of these assessment

approaches by an international and interdisciplinary scientific community could mitigate many

management and conservation conflicts.

Appendix 1

97

Appendix 1

Table A1_1: Biological sensitivity of 120 European freshwater fish species of 24 families, calculated using “calculated” and “observed” life history traits.

Number Family Species “Observed” Sensitivity Score Class

“Calculated” sensitivity Score Class

1 Petromyzontidae Petromyzon marinus 4.42 High 3.08 Moderate 2 Cyprinidae Barbus barbus 4.25 High 3.92 High 3

Acipenseridae Acipenser gueldenstaedtii 4.00 High 4.17 High

4 Salmonidae Salmo salar 4.00 High 3.42 Moderate 5 Cyprinidae Luciobarbus sclateri 4.00 High 4.00 High 6 Acipenseridae Acipenser ruthenus 3.92 High 4.08 High 7 Cyprinidae Luciobarbus bocagei 3.92 High 3.25 Moderate 8 Acipenseridae Acipenser stellatus 3.83 High 3.83 High 9 Anguillidae* Anguilla anguilla 3.83 High 3.83 High 10 Salmonidae Hucho hucho 3.75 High 3.75 High 11 Salmonidae Salvelinus fontinalis 3.75 High 4.42 High 12 Polyodontidae* Polyodon spathula 3.75 High 3.92 High 13 Acipenseridae Acipenser baerii 3.67 High 3.75 High 14 Acipenseridae Acipenser nudiventris 3.67 High 3.75 High 15 Acipenseridae Huso huso 3.67 High 3.75 High 16 Acipenseridae Acipenser oxyrinchus 3.67 High 3.67 High 17 Acipenseridae Acipenser sturio 3.67 High 3.67 High 18 Salmonidae Coregonus maraena 3.67 High 3.67 High 19 Salmonidae Oncorhynchus mykiss 3.67 High 3.25 Moderate 20

Salmonidae Salmo trutta (anadromous) 3.58 High 3.58 High

21 Centrarchidae Micropterus dolomieu 3.58 High 4.08 High 22 Salmonidae Salmo obtusirostris 3.58 High 3.50 High 23 Leuciscidae Leuciscus idus 3.50 High 3.67 High 24 Leuciscidae Chondrostoma nasus 3.42 Moderate 3.25 Moderate 25

Salmonidae Salmo trutta (resident) 3.42 Moderate 4.17 High

26 Leuciscidae Ballerus ballerus 3.42 Moderate 3.67 High 27 Cyprinidae Barbus plebejus 3.42 Moderate 4.00 High 28 Salmonidae Salmo marmoratus 3.42 Moderate 2.75 Moderate 29 Clupeidae Alosa fallax 3.33 Moderate 3.58 High 30

Centrarchidae Micropterus salmoides 3.33 Moderate 3.83 High

31 Leuciscidae Rutilus meidingeri 3.33 Moderate 3.58 High 32 Leuciscidae Abramis brama 3.33 Moderate 3.17 Moderate 33 Salmonidae Salmo labrax 3.25 Moderate 2.50 Moderate 34 Leuciscidae Squalius cephalus 3.25 Moderate 3.42 Moderate 35 Tincidae* Tinca tinca 3.25 Moderate 2.92 Moderate 36 Leuciscidae Rutilus heckelii 3.25 Moderate 2.83 Moderate 37

Leuciscidae Pseudochondrostoma polylepis 3.25 Moderate 3.83 High

38 Cyprinidae Barbus tauricus 3.25 Moderate 3.17 Moderate 39 Mugilidae Mugil cephalus 3.17 Moderate 3.17 Moderate 40

Xenocyprididae Ctenopharyngodon idella 3.17 Moderate 3.00 Moderate

41 Xenocyprididae

Hypophthalmichthys nobilis 3.17 Moderate 3.00 Moderate

42 Xenocyprididae

Mylopharyngodon piceus 3.17 Moderate 3.00 Moderate

43 Salmonidae Thymallus thymallus 3.08 Moderate 2.92 Moderate 44 Leuciscidae Alburnus chalcoides 3.08 Moderate 3.42 Moderate 45 Leuciscidae Telestes souffia 3.00 Moderate 2.67 Moderate

Appendix 1

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46 Loricariidae Lota lota 3.00 Moderate 2.83 Moderate 47 Siluridae* Silurus glanis 3.00 Moderate 3.17 Moderate 48 Leuciscidae Leuciscus aspius 3.00 Moderate 2.92 Moderate 49 Cottidae* Cottus gobio 3.00 Moderate 2.83 Moderate 50 Leuciscidae Rutilus frisii 3.00 Moderate 3.00 Moderate 51 Clupeidae Alosa alosa 3.00 Moderate 3.00 Moderate 52 Percidae Perca fluviatilis 2.92 Moderate 2.50 Moderate 53

Leuciscidae Scardinius erythrophthalmus 2.92 Moderate 2.58 Moderate

54 Centrarchidae Lepomis gibbosus 2.92 Moderate 3.25 Moderate 55 Cyprinidae Cyprinus carpio 2.83 Moderate 3.33 Moderate 56 Esocidae* Esox lucius 2.83 Moderate 3.33 Moderate 57 Leuciscidae Pelecus cultratus 2.83 Moderate 2.42 Low 58 Leuciscidae Phoxinus phoxinus 2.83 Moderate 3.00 Moderate 59 Osmeridae* Osmerus eperlanus 2.83 Moderate 3.00 Moderate 60 Pleuronectidae* Platichthys flesus 2.83 Moderate 2.83 Moderate 61 Leuciscidae Squalius pyrenaicus 2.83 Moderate 2.67 Moderate 62 Percidae Sander lucioperca 2.83 Moderate 2.67 Moderate 63 Leuciscidae Rutilus rutilus 2.83 Moderate 2.50 Moderate 64 Mugilidae Chelon ramada 2.83 Moderate 3.00 Moderate 65

Mugilidae Planiliza haematocheila 2.83 Moderate 2.83 Moderate

66 Gobiidae

Neogobius melanostomus 2.83 Moderate 3.00 Moderate

67 Leuciscidae Leuciscus leuciscus 2.75 Moderate 3.17 Moderate 68 Cobitidae Misgurnus fossilis 2.75 Moderate 2.33 Low 69 Leuciscidae Vimba vimba 2.75 Moderate 3.25 Moderate 70 Leuciscidae Ballerus sapa 2.75 Moderate 3.00 Moderate 71 Petromyzontidae Lampetra planeri 2.75 Moderate 2.83 Moderate 72 Cyprinidae Carassius auratus 2.75 Moderate 2.42 Low 73

Xenocyprididae Hypophthalmichthys molitrix 2.67 Moderate 2.33 Low

74 Gobiidae

Pomatoschistus microps 2.67 Moderate 2.42 Low

75 Cyprinidae Carassius gibelio 2.67 Moderate 2.33 Low 76 Mugilidae Chelon aurata 2.67 Moderate 2.50 Moderate 77 Cobitidae Cobitis narentana 2.58 Moderate 2.50 Moderate 78 Nemacheilidae* Barbatula barbatula 2.58 Moderate 2.08 Low 79 Gobiidae Ponticola kessleri 2.58 Moderate 2.25 Low 80 Percidae Zingel zingel 2.50 Moderate 2.33 Low 81 Leuciscidae Blicca bjoerkna 2.50 Moderate 2.08 Low 82

Percidae Gymnocephalus cernua 2.50 Moderate 2.67 Moderate

83 Gasterosteidae

Gasterosteus aculeatus 2.50 Moderate 3.00 Moderate

84 Leuciscidae

Achondrostoma salmantinum 2.50 Moderate 2.50 Moderate

85 Cobitidae Cobitis elongatoides 2.50 Moderate 1.92 Low 86 Odontobutidae* Perccottus glenii 2.50 Moderate 2.25 Low 87 Cobitidae Cobitis elongata 2.50 Moderate 3.42 Moderate 88

Leuciscidae Achondrostoma arcasii 2.50 Moderate 2.92 Moderate

89 Gobiidae

Mesogobius batrachocephalus 2.42 Low 2.75 Moderate

90 Leuciscidae Rutilus pigus 2.42 Low 2.25 Low 91 Gobiidae Ponticola syrman 2.42 Low 2.33 Low 92 Leuciscidae Squalius carolitertii 2.33 Low 2.33 Low 93

Petromyzontidae Eudontomyzon mariae 2.33 Low 2.33 Low

94 Clupeidae Alosa immaculata 2.33 Low 2.33 Low 95 Gasterosteidae Pungitius pungitius 2.33 Low 2.25 Low

Appendix 1

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96 Gobiidae Padogobius bonelli 2.33 Low 2.83 Moderate 97 Ictaluridae Ameiurus melas 2.33 Low 2.75 Moderate 98 Gobionidae Gobio gobio 2.25 Low 2.25 Low 99 Ictaluridae Ameiurus nebulosus 2.25 Low 1.58 Low 100 Acheilognathidae* Rhodeus amarus 2.25 Low 2.00 Low 101 Cobitidae Cobitis calderoni 2.25 Low 2.25 Low 102 Cobitidae Cobitis taenia 2.25 Low 2.25 Low 103 Leuciscidae Alburnus alburnus 2.25 Low 2.00 Low 104

Clupeidae Clupeonella cultriventris 2.25 Low 2.50 Moderate

105 Cobitidae Cobitis paludica 2.25 Low 2.42 Low 106 Atherinidae* Atherina boyeri 2.25 Low 2.17 Low 107 Percidae Zingel streber 2.17 Low 2.17 Low 108 Cyprinidae Carassius carassius 2.17 Low 2.08 Low 109 Gobionidae Pseudorasbora parva 2.17 Low 2.58 Moderate 110 Cyprinidae Barbus meridionalis 2.08 Low 2.75 Moderate 111

Petromyzontidae Eudontomyzon lanceolata 2.08 Low 1.92 Low

112 Leuciscidae Leucaspius delineatus 2.08 Low 2.25 Low 113 Gobiidae Neogobius fluviatilis 2.08 Low 1.67 Low 114

Leuciscidae Iberochondrostoma lemmingii 2.08 Low 2.58 Moderate

115 Leuciscidae Squalius svallize 2.00 Low 2.17 Low 116 Percidae Sander volgensis 2.00 Low 2.00 Low 117

Leuciscidae Alburnoides bipunctatus 1.92 Low 2.00 Low

118 Umbridae* Umbra krameri 1.75 Low 2.08 Low 119 Cobitidae Sabanejewia baltica 1.58 Low 2.25 Low 120

Cobitidae Sabanejewia balcanica 1.58 Low 1.58 Low

Appendix 1

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Table A1_2: Biological sensitivity, habitat preference and spawning guild of 168 European freshwater fishes of 31 families, calculated using the complete data set.

No. Family Species Sensitivity Score Class

Habitat preference

Spawning guild

1 Petromyzontidae Petromyzon marinus 4.42 High rheophil lithophil 2 Cyprinidae Barbus barbus 4.25 High rheophil lithophil 3 Salmonidae Salmo salar 4.17 High rheophil lithophil 4 Cyprinidae Luciobarbus sclateri 4.00 High eurytop lithophil 5 Acipenseridae Acipenser ruthenus 3.92 High rheophil lithophil 6 Salmonidae Hucho hucho 3.92 High rheophil lithophil 7 Salmonidae Salvelinus fontinalis 3.92 High rheophil lithophil 8 Cyprinidae Luciobarbus bocagei 3.83 High eurytop lithophil 9 Salmonidae Coregonus maraena 3.83 High limnophil lithophil 10

Acipenseridae Acipenser gueldenstaedtii 3.83 High rheophil lithophil

11 Salmonidae

Salmo trutta (anadromous) 3.75 High rheophil lithophil

12 Polyodontidae Polyodon spathula 3.75 High rheophil lithophil 13 Cyprinidae Barbus plebejus 3.75 High rheophil lithophil 14 Salmonidae Salmo marmoratus 3.75 High rheophil lithophil 15 Acipenseridae Acipenser naccarii 3.67 High eurytop lithophil 16 Acipenseridae Acipenser nudiventris 3.67 High rheophil lithophil 17 Acipenseridae Acipenser stellatus 3.67 High rheophil lithophil 18 Acipenseridae Acipenser sturio 3.67 High rheophil lithophil 19 Acipenseridae Huso huso 3.67 High rheophil lithophil 20 Acipenseridae Acipenser oxyrinchus 3.67 High rheophil lithophil 21 Salmonidae Oncorhynchus mykiss 3.67 High rheophil lithophil 22 Anguillidae Anguilla anguilla 3.67 High eurytop pelagophil 23 Centrarchidae Micropterus dolomieu 3.58 High eurytop lithophil 24 Salmonidae Salmo labrax 3.58 High rheophil lithophil 25 Leuciscidae Alburnus chalcoides 3.58 High rheophil lithophil 26 Leuciscidae Ballerus ballerus 3.58 High rheophil lithophil 27 Leuciscidae Chondrostoma nasus 3.58 High rheophil lithophil 28 Salmonidae Salmo trutta (resident) 3.58 High rheophil lithophil 29 Salmonidae Salmo obtusirostris 3.58 High rheophil lithophil 30 Leuciscidae Leuciscus idus 3.58 High rheophil phyto-lithophil 31 Leuciscidae Rutilus heckelii 3.58 High eurytop phytophil 32 Acipenseridae Acipenser baerii 3.50 High rheophil lithophil 33

Cyprinidae Luciobarbus microcephalus 3.42 Moderate eurytop lithophil

34 Leuciscidae Alburnus mento 3.42 Moderate limnophil lithophil 35 Petromyzontidae Lampetra fluviatilis 3.42 Moderate rheophil lithophil 36 Centrarchidae Micropterus salmoides 3.42 Moderate limnophil phyto-lithophil 37 Tincidae Tinca tinca 3.42 Moderate limnophil phytophil 38 Clupeidae Alosa fallax 3.33 Moderate rheophil litho-pelagophil 39

Xenocyprididae Ctenopharyngodon idella 3.33 Moderate eurytop lithophil

40 Leuciscidae Leuciscus aspius 3.33 Moderate eurytop lithophil 41

Xenocyprididae Hypophthalmichthys nobilis 3.33 Moderate eurytop lithophil

42 Leuciscidae Pelecus cultratus 3.33 Moderate eurytop pelagophil 43 Mugilidae Mugil cephalus 3.33 Moderate limnophil pelagophil 44 Percidae Sander lucioperca 3.33 Moderate eurytop phyto-lithophil 45

Leuciscidae Scardinius erythrophthalmus 3.33 Moderate limnophil phytophil

46 Leuciscidae Ballerus sapa 3.25 Moderate rheophil lithophil 47

Leuciscidae Pseudochondrostoma polylepis 3.25 Moderate rheophil lithophil

48 Cyprinidae Barbus tauricus 3.25 Moderate rheophil lithophil 49 Leuciscidae Abramis brama 3.25 Moderate eurytop phyto-lithophil 50 Centrarchidae Lepomis gibbosus 3.25 Moderate limnophil polyphil 51 Cottidae Cottus poecilopus 3.25 Moderate rheophil speleophil 52 Clupeidae Alosa alosa 3.17 Moderate rheophil litho-pelagophil 53 Loricariidae Lota lota 3.17 Moderate eurytop lithophil

Appendix 1

101


Habitat preference

Spawning guild

54 Leuciscidae Squalius cephalus 3.17 Moderate rheophil lithophil 55 Leuciscidae Phoxinellus alepidotus 3.17 Moderate rheophil lithophil 56 Salmonidae Thymallus thymallus 3.17 Moderate rheophil lithophil 57 Leuciscidae Telestes souffia 3.17 Moderate rheophil lithophil 58 Pleuronectidae Platichthys flesus 3.17 Moderate eurytop pelagophil 59

Xenocyprididae Mylopharyngodon piceus 3.17 Moderate eurytop pelagophil

60 Leuciscidae Rutilus frisii 3.17 Moderate eurytop phyto-lithophil 61 Leuciscidae Rutilus rutilus 3.17 Moderate eurytop phyto-lithophil 62 Cyprinidae Cyprinus carpio 3.17 Moderate eurytop phytophil 63 Cottidae Cottus gobio 3.17 Moderate rheophil speleophil 64 Leuciscidae Leuciscus leuciscus 3.08 Moderate rheophil lithophil 65 Leuciscidae Vimba vimba 3.08 Moderate rheophil lithophil 66 Percidae Perca fluviatilis 3.08 Moderate eurytop phyto-lithophil 67 Leuciscidae Rutilus meidingeri 3.08 Moderate limnophil phyto-lithophil 68

Xenocyprididae Hypophthalmichthys molitrix 3.00 Moderate eurytop lithophil

69 Leuciscidae Squalius pyrenaicus 3.00 Moderate eurytop lithophil 70 Leuciscidae Phoxinus phoxinus 3.00 Moderate rheophil lithophil 71 Leuciscidae Phoxinellus dalmaticus 3.00 Moderate rheophil lithophil 72 Mugilidae Chelon ramada 3.00 Moderate limnophil pelagophil 73 Mugilidae Planiliza haematocheila 3.00 Moderate limnophil pelagophil 74 Leuciscidae Rutilus virgo 3.00 Moderate rheophil phyto-lithophil 75 Esocidae Esox lucius 3.00 Moderate eurytop phytophil 76 Siluridae Silurus glanis 3.00 Moderate eurytop phytophil 77 Cobitidae Cobitis elongata 3.00 Moderate rheophil phytophil 78

Gobiidae Neogobius melanostomus 3.00 Moderate eurytop speleophil

79 Gobiidae

Mesogobius batrachocephalus 2.92 Moderate limnophil lithophil

80 Petromyzontidae Lethenteron zanandreai 2.92 Moderate rheophil lithophil 81 Petromyzontidae Lampetra planeri 2.92 Moderate rheophil lithophil 82 Cobitidae Misgurnus fossilis 2.92 Moderate limnophil phytophil 83 Mugilidae Chelon aurata 2.83 Moderate limnophil pelagophil 84

Petromyzontidae Eudontomyzon vladykovi 2.75 Moderate rheophil lithophil

85 Cyprinidae Carassius auratus 2.75 Moderate limnophil phytophil 86 Ictaluridae Ameiurus nebulosus 2.75 Moderate limnophil speleophil 87 Osmeridae Osmerus eperlanus 2.67 Moderate eurytop lithophil 88

Leuciscidae Pseudochondrostoma willkommii 2.67 Moderate rheophil lithophil

89 Percidae Zingel zingel 2.67 Moderate rheophil lithophil 90 Leuciscidae Blicca bjoerkna 2.67 Moderate eurytop phytophil 91 Gasterosteidae Gasterosteus aculeatus 2.67 Moderate eurytop phytophil 92 Leuciscidae Rutilus pigus 2.67 Moderate rheophil phytophil 93 Leuciscidae Achondrostoma arcasii 2.67 Moderate rheophil phytophil 94

Leuciscidae Pseudochondrostoma duriense 2.58 Moderate rheophil lithophil

95 Petromyzontidae Eudontomyzon danfordi 2.58 Moderate rheophil lithophil 96

Percidae Gymnocephalus schraetser 2.58 Moderate rheophil lithophil

97 Percidae Gymnocephalus cernua 2.58 Moderate eurytop phyto-lithophil 98 Cyprinidae Carassius gibelio 2.58 Moderate eurytop phytophil 99 Cobitidae Cobitis narentana 2.58 Moderate limnophil phytophil 100 Gobionidae Gobio gobio 2.58 Moderate rheophil polyphil 101 Gobiidae Pomatoschistus microps 2.50 Moderate limnophil ariadnophil 102 Clupeidae Alosa immaculata 2.50 Moderate eurytop lithophil 103 Leuciscidae Squalius tenellus 2.50 Moderate eurytop lithophil 104 Ictaluridae Ameiurus melas 2.50 Moderate limnophil lithophil 105

Leuciscidae Achondrostoma salmantinum 2.50 Moderate rheophil lithophil

106 Gobiidae Babka gymnotrachelus 2.50 Moderate eurytop phyto-lithophil

Appendix 1

102


Habitat preference

Spawning guild

107 Leuciscidae Alburnus alburnus 2.50 Moderate eurytop phyto-lithophil 108 Odontobutidae Perccottus glenii 2.50 Moderate limnophil phyto-lithophil 109 Leuciscidae Scardinius plotizza 2.50 Moderate eurytop phytophil 110

Leuciscidae Achondrostoma oligolepis 2.50 Moderate limnophil phytophil

111 Syngnatidae Syngnathus abaster 2.50 Moderate limnophil polyphil 112 Nemacheilidae Barbatula barbatula 2.50 Moderate rheophil psammophil 113 Gobiidae Ponticola syrman 2.42 Low eurytop ariadnophil 114 Leuciscidae Squalius aradensis 2.42 Low eurytop lithophil 115 Petromyzontidae Eudontomyzon mariae 2.42 Low eurytop lithophil 116 Leuciscidae Squalius illyricus 2.42 Low rheophil lithophil 117 Leuciscidae Squalius janae 2.42 Low rheophil lithophil 118 Clupeidae Clupeonella cultriventris 2.42 Low limnophil pelagophil 119 Gobiidae Knipowitschia caucasica 2.42 Low limnophil phytophil 120 Cyprinidae Carassius carassius 2.42 Low limnophil phytophil 121 Leuciscidae Squalius microlepis 2.42 Low limnophil phytophil 122 Cobitidae Cobitis paludica 2.42 Low limnophil phytophil 123 Cobitidae Cobitis taenia 2.42 Low rheophil phytophil 124 Gobionidae Gobio lozanoi 2.42 Low eurytop polyphil 125 Gobiidae Ponticola kessleri 2.33 Low eurytop lithophil 126 Leuciscidae Iberocypris alburnoides 2.33 Low eurytop lithophil 127 Leuciscidae Squalius zrmanjae 2.33 Low eurytop lithophil 128 Gobiidae Padogobius bonelli 2.33 Low eurytop phyto-lithophil 129 Percidae Sander volgensis 2.33 Low eurytop phyto-lithophil 130

Leuciscidae Achondrostoma occidentale 2.33 Low limnophil phytophil

131 Gasterosteidae Pungitius pungitius 2.33 Low limnophil phytophil 132 Cobitidae Cobitis elongatoides 2.33 Low rheophil phytophil 133

Percidae Romanichthys valsanicola 2.33 Low rheophil speleophil

134 Poeciliidae Gambusia holbrooki 2.33 Low limnophil viviparous 135 Leuciscidae Squalius carolitertii 2.25 Low eurytop lithophil 136 Leuciscidae Alburnoides bipunctatus 2.25 Low rheophil lithophil 137 Cyprinidae Barbus meridionalis 2.25 Low rheophil lithophil 138

Petromyzontidae Eudontomyzon lanceolata 2.25 Low rheophil lithophil

139 Acheilognathidae Rhodeus amarus 2.25 Low limnophil ostracophil 140 Leuciscidae Anaecypris hispanica 2.25 Low eurytop phyto-lithophil 141 Atherinidae Atherina boyeri 2.25 Low limnophil phytophil 142 Cobitidae Cobitis calderoni 2.25 Low rheophil phytophil 143 Leuciscidae Squalius svallize 2.17 Low eurytop lithophil 144

Gobionidae Romanogobio uranoscopus 2.17 Low rheophil lithophil

145 Gobionidae Pseudorasbora parva 2.17 Low eurytop phyto-lithophil 146 Percidae Zingel streber 2.08 Low rheophil lithophil 147

Leuciscidae Iberochondrostoma lemmingii 2.08 Low limnophil phyto-lithophil

148 Gobiidae

Knipowitschia longecaudata 2.08 Low limnophil phytophil

149 Leuciscidae Leucaspius delineatus 2.08 Low limnophil phytophil 150 Umbridae Umbra krameri 2.08 Low limnophil phytophil 151 Gobionidae Romanogobio vladykovi 2.08 Low rheophil psammophil 152 Gobiidae Neogobius fluviatilis 2.08 Low eurytop speleophil 153 Clupeidae Alosa tanaica 2.00 Low rheophil litho-pelagophil 154

Leuciscidae Petroleuciscus borysthenicus 2.00 Low eurytop lithophil

155 Gobionidae

Romanogobio benacensis 2.00 Low eurytop phyto-lithophil

156 Percidae Gymnocephalus baloni 2.00 Low rheophil phyto-lithophil 157 Gobionidae Gobio obtusirostris 2.00 Low eurytop polyphil 158 Gobionidae Romanogobio kessleri 2.00 Low rheophil psammophil

Appendix 1

103


Habitat preference

Spawning guild

159 Leuciscidae

Iberochondrostoma almacai 1.92 Low limnophil phyto-lithophil

160 Gobionidae Romanogobio belingi 1.92 Low rheophil psammophil 161 Leuciscidae Chondrostoma knerii 1.83 Low eurytop phytophil 162 Cobitidae Sabanejewia romanica 1.75 Low rheophil phytophil 163

Gobionidae Romanogobio albipinnatus 1.75 Low rheophil psammophil

164 Gobiidae

Proterorhinus semilunaris 1.75 Low eurytop speleophil

165 Leuciscidae Squalius torgalensis 1.67 Low eurytop lithophil 166

Leuciscidae Iberochondrostoma lusitanicum 1.58 Low limnophil phyto-lithophil

167 Cobitidae Sabanejewia baltica 1.58 Low rheophil phytophil 168 Cobitidae Sabanejewia balcanica 1.58 Low rheophil phytophil

Figure A1_1: Coherence of the sensitivity scores of the two test subsets. ‘Calculated’ on the y-axis refers to life history traits that were obtained using empirical equations and Lmax or tmax, 'observed' is the data set that consists of medians of reported literature values (with the exception of the VBGF-parameters). Data points aligned diagonally from / to /x indicate relatively consistent results of the sensitivity classification between calculated and observed database.

Appendix 1

104

Figures A1_2, a-e: Correlation of calculated (y-axes) with observed (x-axes) life history traits.

a) b)

e) d)

c)

Appendix 2

105

Appendix 2

Calculation of the blade angle β

For the calculation of blade strike mortality according to the model of Montén [303] it is necessary to

obtain the relative blade distance that considers both the absolute spacing between blades and the

blade angle β. This accounts for the fact, that the free space available for fish during passage (i.e.,

relative spacing srel_mid) is, because of the skewed inflow angle, narrower than the absolute distance

measured from edge to edge [303]:

𝑠𝑟𝑒𝑙𝑚𝑖𝑑= 𝑠𝑖𝑛(𝛽) ×

𝜋 × (𝑑𝑚𝑎𝑥 + 𝑑𝑚𝑖𝑛)

2 × 𝑧

with β = blade angle; dmax = outer diameter of the turbine [m], dmin = inner diameter of the turbine (i.e.,

hub dimensions) [m]; and z = number of blades [-]. Prerequisite for the calculation of srel_mid is thus

knowledge about the blade angle β that, in turn, can be calculated for Kaplan and empirically estimated

for Francis turbines and requires information on the blade velocity (at the runner midpoint), denoted

umid [m/s] and the water’s inflow velocity, denoted vinflow (axial in Kaplan, radial in Francis turbines)

[m/s]. The blade velocity u is determined primarily by the revolution rate (RPM) and the by the

circumference of the runner. As used in the strike mortality model of Montén [303], it refers to the

velocity at the midpoint of the blade between the outer and inner diameter umid and is calculated from:

𝑢𝑚𝑖𝑑 =𝜋 × (𝑑𝑚𝑎𝑥 + 𝑑𝑚𝑖𝑛) × 𝑛

120

with dmax = outer diameter of the turbine [m]; dmin = inner diameter of the turbine (i.e., hub dimensions)

[m]; and n=rotational speed (RPM) [revolutions per minute]. The water’s inflow velocity vinflow is the

velocity by which the prevailing amount of water flowing through the turbine is taken into the runner

through its entire inflow surface. The inflow is calculated perpendicular to the inflow surface which is

axial for Kaplan and radial inflow for Francis turbines and calculated as follows:

𝑣𝑖𝑛𝑓𝑙𝑜𝑤(Francis) = 𝑣radial =2 × 𝑄𝑡𝑢𝑟𝑏

𝜋 × (𝑑𝑚𝑎𝑥 + 𝑑𝑚𝑖𝑛) × 𝑉

𝑣𝑖𝑛𝑓𝑙𝑜𝑤(𝐾𝑎𝑝𝑙𝑎𝑛) = 𝑣𝑎𝑥𝑖𝑎𝑙 =4 × 𝑄𝑡𝑢𝑟𝑏

𝜋 × (𝑑𝑚𝑎𝑥2 − 𝑑𝑚𝑖𝑛

2 )

with Qturb=water inflow of the turbine (turbine capacity) [m³/s]; dmax = outer diameter of the turbine

[m]; dmin = inner diameter of the turbine (i.e., hub dimensions) [m]; and V = height of guide vanes of

Francis turbines [m]. Subsequently, the blade angle β can be calculated for Kaplan turbines using an

approach by [301]:

Appendix 2

106

tan(𝛽(𝐾𝑎𝑝𝑙𝑎𝑛)) =𝑣𝑖𝑛𝑓𝑙𝑜𝑤(𝐾𝑎𝑝𝑙𝑎𝑛)

𝑢 −𝑔 × 𝐻 × 𝜂

𝑢

with g = gravitational acceleration (9.81 m/s²); H = head [m]; u = blade velocity; and η = efficiency (η is

frequently assumed 0.9, but recent works shows better agreement of the approximations with details

CFD analysis for a value of 1 [301]. For Francis turbines, an empirical relationship involving calculated

variables umid and vinflow is used to estimate the blade angle β based on work by Montén [303] and [123]:

𝛽𝐹𝑟𝑎𝑛𝑐𝑖𝑠 = −26.951 × 𝑙𝑛 (𝑢𝑚𝑖𝑑

𝑣𝑖𝑛𝑓𝑙𝑜𝑤(𝐹𝑟𝑎𝑛𝑐𝑖𝑠)) + 88.348

Appendix 2

107

Figure A2_1: Unmitigated scenario and low-sensitive species pool.

Conservation concernno Conservation concern no Conservation concern no Conservation concern no Conservation concernno

2.8 Sensitivity 2.8 Sensitivity 2.8 Sensitivity 2.5 Sensitivity 2.3

Overall flow alterations low

Entrainment risk high

Turbine mortality high

Upstream passage high

Downstream passage high

Upstream migration facility (UMF)

is: no new: is: new: is: new:


is: 0.00 new: is: new:

1.0

0.7

Type of FGS

Discharge in UMF (m³/s)

100

To display the original EFPHI remove all

values you have added in this panel.see EFHI

0.7

moderate

0.65

Nature-like UMF

Please select no

0.8

Simulation of mitigating measures

EFHI class

Clear width of FGS (mm)

EFHI score

Horizontal installation angle of FGS (°)

please select

please selectVBR Please select

0

0.8 0.8

Downstream bypass

Downstream fish guiding system (FGS)

1.0

0.0

Rutilus rutilus Alburnus alburnusEsox lucius Blicca bjoerkna Perca fluviatilis

0.8

Sensitivity

0.3 0.30.3

1.0

0.8

1.0

Affected species

European Fish Hazard Index

0.8

0.5

Hazard classification

Conservation concern

If stream conservation concern is selected

all species are assigned a sensitivity

score of 4.5

0.5

Species-specific hazard score

0.8

0.3

0.8

1.0

0.8

yes

0.8 0.8 0.8 0.5

0.2

0.8

Aggregated scores

0.7Please select

Appendix 2

108

Figure A2_2: Unmitigated scenario and sensitive species pool.

Conservation concernno Conservation concern no Conservation concern no Conservation concern yes Conservation concernyes



Entrainment risk high


Upstream passage high

Downstream passage high





1.0

0.9

Type of FGS


100



0.9

high

0.82

Nature-like UMF

Please select no

1.0


EFHI class


EFHI score


please select

please selectVBR Please select

0

0.8 1.0

Downstream bypass


1.0

0.5

Anguilla anguilla Leuciscus aspiusEsox lucius Blicca bjoerkna Barbus barbus

1.0

Sensitivity

0.3 0.50.5

1.0

1.0

1.0

Affected species


1.0

1.0





score of 4.5

1.0


0.8

0.3

0.8

1.0

0.8

yes

0.8 0.8 1.0 1.0

0.4

1.0

Aggregated scores

0.9Please select

Appendix 2

109

Figure A2_3: Partly mitigated scenario and sensitive species pool.




Entrainment risk moderate


Upstream passage moderate

Downstream passage moderate


is: yes new: is: new: is: new:



1.0

0.4

Type of FGS


20



0.4

moderate

0.51

Nature-like UMF

Please select yes

0.5


EFHI class


EFHI score


please select

please selectABR Please select

45

0.3 0.5

Downstream bypass


1.0

0.5


0.1

Sensitivity

0.3 0.50.5

1.0

1.0

1.0

Affected species


1.0

0.5





score of 4.5

0.4


0.3

0.3

0.0

1.0

0.2

yes

0.3 0.3 0.5 0.5

0.4

0.5

Aggregated scores

0.3Please select

Appendix 2

110

Figure A2_4: Highly mitigated scenario and sensitive species pool.




Entrainment risk moderate

Turbine mortality moderate

Upstream passage low

Downstream passage moderate





0.4

0.3

Type of FGS


20



0.3

moderate

0.36

Nature-like UMF

Please select yes

0.4


EFHI class


EFHI score


please select

please selectABR Please select

45

0.2 0.4

Downstream bypass


0.5

0.5


0.1

Sensitivity

0.3 0.50.5

0.3

1.0

0.5

Affected species


0.5

0.4





score of 4.5

0.4


0.2

0.3

0.0

0.3

0.2

yes

0.2 0.2 0.4 0.4

0.4

0.5

Aggregated scores

0.3Please select

Appendix 3

111

Appendix 3

Table A3_1: Species used for EFHI calculations of the release set. At HPP Eixendorf only 4 species were used for the standardized release experiments. Red=conservation concern.

Release set HPP Au Baierbrunn Baiersdorf Eixendorf Heckerwehr Höllthal Lindesmühle

Sp. 1 Hucho hucho H. hucho Anguilla anguilla Rutilus rutilus Perca fluviatilis R. rutilus A. anguilla

Sp. 2 Chondrostoma nasus P. fluviatilis C. nasus Thymallus thymallus C. nasus Thymallus thymallus C. nasus

Sp. 3 Salmo trutta T. thymallus S. trutta S. trutta S. trutta H. hucho S. trutta

Sp. 4 A. anguilla Rutilus rutilus Perca fluviatilis P. fluviatilis T. thymallus Perca fluviatilis P. fluviatilis

Sp. 5 T. thymallus A. anguilla None H. hucho Barbus barbus B. barbus None

Table A3_2: Species used for EFHI calculations of the sample set.

Sample set HPP Au Baierbrunn Baiersdorf Eixendorf Heckerwehr Höllthal Lindesmühle

Sp. 1 Cottus gobio

Alburnoides bipunctatus

Alburnus alburnus A. alburnus Rutilus rutilus A. bipunctatus R. rutilus

Sp. 2 R. rutilus Phoxinus phoxinus A. bipunctatus

Squalius cephalus Pseudorasbora parva Barbus barbus Gobio gobio

Sp. 3

Thymallus thymallus B. barbus P. parva R. rutilus

Scardinius erythrophthalmus A. alburnus

Gymnocephalus cernua

Sp. 4 Perca fluviatilis R. rutilus R. rutilus P. fluviatilis Tinca tinca

S. erythrophthalmus Leuciscus leuciscus

Sp. 5 Blicca bjoerkna Chondrostoma nasus Salmo trutta A. bipunctatus Sander lucioperca L. leuciscus P. fluviatilis

Table A3_3: Species used for EFHI calculations of the reference set. Red=Species that were assigned conservation concern.

Reference set HPP Au Baierbrunn Baiersdorf Eixendorf Heckerwehr Höllthal Lindesmühle

Sp. 1 Thymallus thymallus Leuciscus leuciscus Barbus barbus Barbatula barbatula Gobio gobio Squalius cephalus Anguilla anguilla Sp. 2 S. cephalus S. cephalus S. cephalus S. cephalus L. leuciscus B. barbus T. thymallus Sp. 3 Gobio gobio B. barbus Perca fluviatilis Chondrostoma nasus S. cephalus T. thymallus Salmo trutta Sp. 4 C. nasus T. thymallus L. leuciscus L. leuciscus B. barbus C. nasus B. barbus Sp. 5 L. leuciscus C. nasus C. nasus B. barbus C. nasus L. leuciscus C. nasus

Appendix 3

112

Table A3_4: Risk scores, divergence scores, average species’ sensitivities and observed mortalities of the seven hydropower sites and the different treatments.

Treatment Site FLOW ETM US DS EFHI Sensitivity Mortality

Sample Au 0.5 0.25 0.5 0.75 0.5 2.85 44

Sample Baierbrunn 0.55 0.3 0.24 0.64 0.43 2.95 31

Sample Baiersdorf 0.35 0.26 0.1 0.6 0.33 2.48333333 50

Sample Eixendorf 0.65 0.71 0.65 0.4 0.6 2.53333333 NA

Sample Heckerwehr 0.45 0.45 0.7 0.7 0.58 2.7 28

Sample Höllthal 0.45 0.47 0.45 0.7 0.52 2.88333333 29

Sample Lindesmühle 0.45 0.64 0.36 0.36 0.45 2.63333333 69

Reference Au 0.55 0.3 0.55 0.8 0.55 3.08333333 NA

Reference Baierbrunn 0.6 0.35 0.28 0.68 0.48 3.38333333 NA

Reference Baiersdorf 0.6 0.3 0.35 0.85 0.53 3.3 NA

Reference Eixendorf 0.8 0.86 0.8 0.55 0.75 3.16666667 NA

Reference Heckerwehr 0.6 0.6 0.85 0.85 0.73 3.26666667 NA

Reference Höllthal 0.6 0.53 0.6 0.85 0.65 3.38333333 NA

Reference Lindesmühle 0.65 0.56 0.52 0.52 0.56 3.75 NA

Release Au 0.65 0.4 0.65 0.9 0.65 3.8 5.7

Release Baierbrunn 0.6 0.35 0.28 0.68 0.48 3.5 19.3

Release Baiersdorf 0.63 0.45 0.38 0.88 0.59 3.6 20.5

Release Eixendorf 0.8 0.59 0.8 0.55 0.69 3.3 24.6

Release Heckerwehr 0.6 0.6 0.85 0.85 0.73 3.4 12.8

Release Höllthal 0.6 0.42 0.6 0.85 0.62 3.4 6.43

Release Lindesmühle 0.63 0.58 0.5 0.5 0.55 3.6 42.9

Divergence Au 0.05 0.05 0.05 0.05 0.05 0.95 NA

Divergence Baierbrunn 0.05 0.05 0.04 0.04 0.05 0.55 NA

Divergence Baiersdorf 0.25 0.04 0.25 0.25 0.2 1.11666667 NA

Divergence Eixendorf 0.15 0.15 0.15 0.15 0.15 0.76666667 NA

Divergence Heckerwehr 0.15 0.15 0.15 0.15 0.15 0.7 NA

Divergence Höllthal 0.15 0.06 0.15 0.15 0.13 0.51666667 NA

Divergence Lindesmühle 0.2 -0.08 0.16 0.16 0.11 0.96666667 NA

Appendix 3

113

Table A3_5: Results of the multiple factor analysis (MFA).

Eigenvalues Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6

Variance 3.066 1.667 0.449 0.202 0.088 0.032

% of var. 55.709 30.295 8.159 3.662 1.591 0.584

Cumulative % of var. 55.709 86.003 94.162 97.824 99.416 100.000

Groups Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2

FLOW 0.427 13.935 0.178 0.421 25.284 0.173 0.233 51.860 0.053

ETM 0.718 23.435 0.510 0.177 10.646 0.031 0.148 32.924 0.022

US 0.911 29.708 0.829 0.078 4.690 0.006 0.013 2.980 0.000

DS 0.074 2.400 0.005 0.905 54.314 0.819 0.029 6.533 0.001

EFHI 0.936 30.522 0.871 0.084 5.066 0.007 0.026 5.703 0.001

Individuals Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2

Au -0.265 0.326 0.021 1.564 20.957 0.716 0.806 20.658 0.190

Baierbrunn -2.224 23.044 0.729 -0.920 7.254 0.125 0.827 21.742 0.101

Baiersdorf -2.229 23.155 0.808 -0.312 0.832 0.016 -0.716 16.298 0.083

Eixendorf 2.902 39.256 0.695 -1.827 28.591 0.275 0.475 7.168 0.019

Heckerwehr 1.728 13.916 0.518 1.485 18.896 0.383 -0.603 11.589 0.063

Höllthal 0.218 0.221 0.025 1.175 11.829 0.712 0.052 0.087 0.001

Lindesmühle -0.131 0.080 0.007 -1.165 11.640 0.576 -0.840 22.458 0.299

Continuous variables Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2

FLOW_Sample 0.530 3.669 0.280 -0.440 4.660 0.194 0.705 44.456 0.498

FLOW_Reference 0.639 5.351 0.409 -0.741 13.215 0.549 0.036 0.115 0.001

FLOW_Release 0.613 4.915 0.376 -0.555 7.409 0.308 0.286 7.289 0.082

ETM_Sample 0.742 6.776 0.550 -0.514 5.974 0.264 -0.231 4.502 0.054

ETM_Reference 0.891 9.770 0.793 -0.387 3.390 0.150 -0.091 0.700 0.008

ETM_Release 0.748 6.889 0.559 -0.238 1.283 0.057 -0.574 27.721 0.330

US_Sample 0.927 9.619 0.860 0.273 1.539 0.075 0.147 1.647 0.022

US_Reference 0.962 10.364 0.926 0.223 1.020 0.050 -0.125 1.195 0.016

US_Release 0.932 9.724 0.869 0.322 2.130 0.104 -0.043 0.139 0.002

DS_Sample 0.255 0.738 0.065 0.915 17.415 0.836 0.282 6.135 0.079

DS_Reference 0.306 1.060 0.094 0.933 18.134 0.871 -0.046 0.162 0.002

DS_Release 0.230 0.601 0.053 0.949 18.765 0.901 0.055 0.236 0.003

EFHI_Sample 0.923 10.153 0.852 0.186 0.756 0.035 0.227 4.210 0.052

EFHI_Reference 0.976 11.359 0.953 0.122 0.327 0.015 -0.099 0.805 0.010

EFHI_Release 0.869 9.010 0.756 0.426 3.983 0.182 -0.092 0.688 0.008

EFHI_Release 0.869 9.010 0.756 0.426 3.983 0.182 -0.092 0.688 0.008

Appendix 3

114

Figure A3_1: EFHI input window for the hydropower plant Au (reference set). Input data is required to calculate risks for flow alterations, upstream fish passage, downstream fish passage and turbine entrainment and mortality.

Appendix 3

115

Figure A3_2: EFHI output window for the hydropower plant Au (reference set). The five species have a sensitivity score between 2.6 (Gobio gobio) and 3.6 (Chondrostoma nasus), and no conservation concern was assigned. The score for overall flow alterations (FLOW) is 0.55, for turbine entrainment & mortality (ETM) 0.3, for upstream passage (US) 0.55 and for downstream passage (DS) 0.8. The resulting EFHI score is 0.55, equivalent to a moderate hazard.

Appendix 3

116

Figure A3_3: Biplot of the multiple factor analysis (MFA) containing the seven hydropower plants and their different turbine types VLH, screw, Kaplan, and Kaplan & screw, the three different treatments reference, release and sample, the four hazard components FLOW, ETM, US and DS and the final EFHI score.

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Author affiliations

147

Author affiliations

Ruben van Treeck Faculty of Life Sciences, Department of Biology,

Humboldt-Universität zu Berlin, Germany

Leibniz Institute of Freshwater Ecology and Inland

Fisheries, Berlin, Germany

Institute of Inland Fisheries in Potsdam-Sacrow

Christian Wolter Leibniz Institute of Freshwater Ecology and Inland


Jeroen Van Wichelen Research Institute for Nature and Forest, Brussels,

Belgium

Johannes Radinger Leibniz Institute of Freshwater Ecology and Inland


Richard Noble Hull International Fisheries Institute, Department of

Biological and Marine Sciences University of Hull,

United Kingdom

Franz Geiger Versuchsanstalt für Wasserbau und

Wasserwirtschaft, Technical University of Munich,

Germany

Jürgen Geist Aquatic Systems Biology Unit, Technical University of

Munich, Germany

Joachim Pander Aquatic Systems Biology Unit, Technical University of

Munich, Germany

Melanie Müller Aquatic Systems Biology Unit, Technical University of

Munich, Germany

Nicole Smialek Institute of Marine Ecosystem and Fisheries Science,

University of Hamburg, Germany

Acknowledgements

149

Acknowledgements This thesis would not exist without the tireless efforts of many great and amazing people that

surrounded and supported me during my time at IGB and the Humboldt University.

First and foremost, I would like to express my deepest gratitude towards my advisor Christian Wolter.

Christian is a walking encyclopedia of fishes and their ecology, and discussing a subject, learning from

his insanely concise writing, working together in the field, or just listening to his tales over a few beers

after a hard day of trawling definitely comprised my steepest ecological and academic learning curve

yet. I am thankful for his trust in me and my developing skills, his willingness to keep working together

for as long as we had projects to work on and the unbiased and open manner with which he treated

me throughout our collaboration. I am not yet fully convinced that the feeling is mutual, but I do

consider him a friend, and I intend to keep in touch.

I further would like to thank my academic and institutional surroundings for enabling and supporting

me: Werner Kloas, for officially supervising me, Kirsten Pohlmann for her kind and powerful support

through all kinds of hick-ups, Johannes Radinger for highly productive coffee breaks, stimulating

conversations and fantastic co-authorships, Johannes Graupner, for introducing me to his extensive

network and training my communication skills with a range of stakeholders, Jörg Freyhof, for

invaluable information and fascinating stories about the biology of fishes, Steffen Bader, for many

weeks of harmonic field work, Jörn Gessner, for a consistently merry office mood, Stefan Bednarz, for

keeping IGB’s wheels turning, his big heart, and all the friendly hallway chats, and Captain Hallermann,

for refining my navigation skills on our research vessel, always spontaneously assisting in random little

projects and consistently preparing the best Zanderfilet on the Oder river.

I am thankful for all the wonderful people that made much of my time at IGB feel like a magical fusion

of a scientific think tank and an endless summer camp, and without which I would probably not have

stayed. They will always have a special place in my heart: Felicie Dhellemmes, Sven Matern, Max

Birdsong, Kate Laskowski, Hendrik Monsees, Torsten Seltmann, Karin Meinikmann and many more.

I am thanking Ralf-Udo Mühle for our wonderful friendship and many hours with wine and great

conversations in the staff kitchen of the ecological field station Gülpe. His life experience has helped

me through many rough and smooth patches alike, and for many years. And I am thanking Holger

Baumann for kindling my passion for biology and being the most entertaining teacher and good friend.

Last but not least, a huge wave of thankfulness goes towards my family and in particular, my parents

Udo and Ute. They supported me since I was born, encouraged me to try out new ideas, directions,

and strategies in life and they were always there to fall back on when these ideas turned out bad. They

are the best parents I could have wished for, and I feel very privileged to have been raised by them.

Declaration of authorship/Selbstständigkeitserklärung

151

Declaration of authorship I hereby declare that this PhD thesis has been written only by the undersigned and that no sources

other than those indicated have been used. This thesis has not been submitted for a doctor’s degree

at any other institution. I am aware of the underlying doctorate regulations of the faculty this thesis is

submitted to, i.e., Faculty of Life Sciences of the Humboldt-University at Berlin.

Selbstständigkeitserklärung Hiermit erkläre ich, diese Dissertation selbständig und ohne unerlaubte Hilfe angefertigt zu haben. Ich

habe mich nicht anderwärts als Doktorand beworben. Ich erkläre die Kenntnisnahme der dem

Verfahren zugrunde liegenden Promotionsordnung der Lebenswissenschaftlichen Fakultät der

Humboldt-Universität zu Berlin.

Ruben van Treeck, Berlin, 27.01.2022

Holistic assessment of hydropower hazards for European fishes

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