WATER BODIES IN EUROPE

Global warming and potential shift in reference conditions:the case of functional fish-based metrics

Maxime Logez • Didier Pont

Received: 29 March 2012 / Accepted: 7 July 2012 / Published online: 1 August 2012

� Springer Science+Business Media B.V. 2012

Abstract The reference condition approach, advo-

cated by the Water Framework Directive, is the basis

of most currently used multimetric indices using

functional traits of fish species. The ecological status

of streams is assessed by measuring the deviation of

the observed trait values from the theoretical values of

reference conditions in the absence of anthropogenic

disturbances. While reference conditions serve as

baselines for ecological assessment, they vary with

natural environmental conditions. Therefore, global

warming appears to be a major threat to the use of

current indices for diagnosing future stream condi-

tions, as climate change is projected to modify

assemblage composition, suggesting that the func-

tional structure of fish assemblages will also be

affected. The main objectives of this study are to

assess the potential effect of climate change on the

trait composition of fish assemblages and the conse-

quences for the establishment of reference conditions.

The results highlight the relation between environ-

mental, especially climatic, conditions and functional

traits and project the effects of climate change on

trait composition. Traits based on species intolerance

are expected to be most negatively affected by the

projected climatic shift. The consequences for the

development of multimetric indices based on fish

functional traits are discussed.

Keywords IBI � Climate change � Riverine fish

assemblages � Functional trait � Reference condition �Water Framework Directive � Local species richness

Introduction

Reference conditions, as defined by the European

Water Framework Directive (WFD), serve as base-

lines to assess the ecological status of streams by

comparing the observed biotic assemblages with

theoretical assemblages in the absence of human

disturbance (Bailey et al., 1998; Hawkins et al.,

2010). The WFD also advocates that the ecological

assessment should integrate several biotic assemblage

descriptors, also called metrics (Hering et al., 2006),

into a synthetic index. These multimetric indices must

reflect the level of impairment of a given site (Karr &

Chu, 1999; Oberdorff et al., 2002; Pont et al., 2006,

2007). Several multimetric indices based on fish

assemblages (Oberdorff et al., 2002; Pont et al.,

2006, 2007, 2011; Argillier et al., this issue) use the

Guest editors: C. K. Feld, A. Borja, L. Carvalho & D. Hering /

Water bodies in Europe: integrative systems to assess

ecological status and recovery

Electronic supplementary material The online version ofthis article (doi:10.1007/s10750-012-1250-6) containssupplementary material, which is available to authorized users.

M. Logez (&) � D. Pont

Irstea, UR HBAN, 1 rue Pierre-Gilles de Gennes,

CS 10030, 92761 Antony, France

e-mail: maxime.logez@irstea.fr

123

Hydrobiologia (2013) 704:417–436

DOI 10.1007/s10750-012-1250-6

principles of the index of biotic integrity (IBI)

developed by Karr (1981), which compares observed

values of several assemblage descriptors with theo-

retical values that would be observed in ‘‘pristine’’

sites, a concept very close to that of reference

conditions (Karr, 1981). Since the first IBI, several

changes have been made to enhance the assessment

of ecological conditions with multimetric indices. One

of the main improvements was to consider metric

variability (Fausch et al., 1984; Karr & Chu, 1999;

Pont et al., 2006, 2007; Roset et al., 2007; Logez &

Pont, 2011) to disentangle the effects of impairment

from natural variability (Oberdorff et al., 1995, 2002;

Hughes et al., 1998; Stoddard et al., 2008). Neverthe-

less, the establishment of reference conditions remains

essential to develop and use accurate bioassessment

tools.

The historical approach was used by several authors

to define the reference conditions from past evidence

of system functioning (Swetnam et al., 1999; Muxika

et al., 2007). In contrast to this palaeoecological

approach, for most ecosystem types, reference condi-

tions are very often defined in a more pragmatic

manner (Stoddard et al., 2006; Hering et al., this

issue), and very often the minimally disturbed condi-

tions that are observed in a region are used (Logez &

Pont, 2011). All these approaches are based on the

assumption of relatively stable biotic communities,

so that the observed deviations consistently reflect

the effects of human disturbances. Comparing the

observed biota with reference values of a differently

functioning river, however, would lead to inaccurate

assessment due to inconsistencies in reference condi-

tion baselines. Nevertheless, with the projected mod-

ifications of climatic conditions, the assumption of

stable and consistent reference conditions is less likely

to be met. Temperature and precipitation are two

major components of hydrosystem functioning (Allan

& Castillo, 2007) which are strongly affected by

climatic changes (Webb & Nobilis, 1995; Webb,

1996; Webb & Nobilis, 2007; Albelk & Albek, 2009).

The consequences of global change, and in partic-

ular of temperature modifications, for riverine fish

assemblages are mostly studied by addressing fish

species distributions rather than assemblage structure.

Projected distributions using various gas emission

scenarios (IPCC, 2007) highlight a shift in fish species

distributions following the predicted changes in

climatic conditions (Buisson et al., 2008; Buisson &

Grenouillet, 2009; Grenouillet et al., 2011). Following

the theory of habitat filtering, local assemblages are

composed of species selected on their traits by the

environmental conditions (Keddy, 1992). Among the

environmental drivers, temperature was shown to be

the major determinant of the functional structure of

European fish assemblages (Logez et al., 2012a).

Therefore, global warming will likely affect both the

composition and the functional structure of fish

assemblages.

Functional traits which group species having

a common biological or ecological characteristic into

a single variable, e.g. species intolerant of habitat

degradation, are now commonly used to develop

bioassessment tools based on fish assemblages (Obe-

rdorff et al., 2002; Pont et al., 2006, 2007, 2009;

Logez & Pont, 2011). Ecological theory suggests that

these traits are directly or indirectly related to

ecosystem functioning (Lavorel & Garnier, 2002),

because of either their response to or their effect on

the ecosystem. Functional traits are assumed to react

to human pressure in a consistent way across a wide

range of streams and over a very broad spatial extent

(Pont et al., 2006, 2007). While patterns derived from

assemblage compositions are controlled by species

distribution areas and thus by biogeographical drivers

(Hoeinghaus et al., 2007), patterns derived from

functional traits are mostly related to the environmen-

tal conditions present (e.g. Lamouroux et al., 2002;

Goldstein & Meador, 2004; Statzner et al., 2004;

Bonada et al., 2007; Ibanez et al., 2009; Logez et al.,

2010). In addition to the projected shift in species

distribution, trait–environment relationships suggest

that fish assemblage structures will change to cope

with the new climatic conditions. It is hypothesised,

however, that functional traits will respond differently

to global change compared with species composition.

Two major approaches are used to control the

environmental variability in metrics. The type-specific

approach groups sites based on their environmental

similarities, whereas with the site-specific approach

the assemblage functional structure is directly pre-

dicted from environmental variables (Roset et al.,

2007). Some recent bioassessment methods use this

latter predictive approach to estimate the theoretical

value of a metric that should have been observed in the

absence of human pressures (Pont et al., 2006, 2007;

Hawkins et al., 2010; Logez & Pont, 2011). For

instance, the European Fish Index (EFI), a multimetric

418 Hydrobiologia (2013) 704:417–436

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index designed for European streams and based on ten

fish functional traits, used statistical models to predict

the expected metric values, in the absence of human

pressures, from the environmental conditions observed

at a given site (Pont et al., 2006, 2007; Bady et al.,

2009). The impairment level of a site, i.e. its deviation

from the reference conditions, is quantified by com-

paring the field-observed values with the predicted

theoretical values (Bailey et al., 1998). The scores

associated with each functional trait are thus indepen-

dent of the environmental conditions and comparable

among stream reaches. The predictive approach

explicitly takes into account the climatic conditions

in modelling the reference baselines for functional

traits, because climatic factors (e.g. mean air temper-

ature in July) are integrated as predictors into the

statistical models (Pont et al., 2006, 2007; Logez &

Pont, 2011). This makes it possible to test whether

climate change will limit the representativeness of

certain functional traits in European fish assemblages,

which could have important consequences for the use

of bioassessment tools based on such traits.

Fig. 1 Locations of the 1,548 sampling sites in the 13

ecoregions adapted from Illies (1978). Due to the low number

of sites occurring in some regions, we regrouped some

ecoregions into larger geographical areas: the Alps and Pyrenees

in Alps (ALPS); the Hungarian lowlands, Eastern plains and

Pontic province in the Eastern region (EAST); and the

Fennoscandian Shield and Borealic uplands in the Nordic region

(NOR). To take into account the specificity of the Mediterranean

areas (Gasith & Resh, 1999), we defined a Mediterranean region

(MED, coloured in dark grey) as Mediterranity level 1 of

Segurado et al.’s (2008) classification. The former Ibero-

Macaronesian (IBE) and Italy, Corsica and Malta ecoregions

(ITA) were thus divided into two distinct areas. The Baltic

province (BAL), the Carpathians (CAR), Great Britain (ENG),

Central plains (C.P), Central highlands (C.H), Western plains

(W.P) and Western highlands (W.H) remain unchanged

Hydrobiologia (2013) 704:417–436 419

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The aims of this study are to assess the effect of

climate change on eight functional traits widely used

in bioindication by: (1) assessing the relative influence

of environmental drivers (including climatic drivers)

on the field variability of functional traits, (2)

estimating the changes in functional traits in assem-

blages that are not or only slightly impacted, (3)

testing whether the trait sensitivity to climate change

differs among ecoregions and (4) studying the

potential shift in reference condition baselines, defined

as the deviation between the observed and theoretical

functional trait values.

The EFI? database integrating both natural envi-

ronmental and anthropogenic factors and the data from

the UK Met Office Hadley Centre for Climate

Prediction and Research allowed us to test the effects

of climate change on riverine fish assemblage for

different scenarios at the European scale.

Materials and methods

We used the same data set as Logez et al. (2012b). All

the data used, except the forecasted climate data, were

gathered during the European EFI? project (http://

efi-plus.boku.ac.at/downloads/EFI?%200044096%

20Deliverable%20D1_1-1_3.pdf). Data were gathered

from national fish surveys conducted by several lab-

oratories and governmental environmental agencies

(1974–2007, 95 % after 1990). The sites were sampled

using electrofishing methods either by wading or by

boat, depending on the stream depth. A total of 88

species were sampled, and all fish caught were iden-

tified to species level. To homogenise the sampling

effort among regions, only fish collected during the

first pass were considered.

Site selection

Sites were selected as being not impacted or only

slightly impacted by anthropogenic activities (Pont

et al., 2005) based on objective criteria (Stoddard

et al., 2008), because human alterations can modify

local assemblage composition and structure. This

selection is of major importance because all outcomes

will rely on the accuracy of the estimated trait–

environment relationships. The sites chosen had good

water quality, no or few modifications of the river

cross-section, river channel and water flow, no

impoundment, no or few alterations of the river banks

and bottom habitat and no major alteration to the

river’s connectivity (Logez & Pont, 2011; Logez

et al., 2012b).

To limit the spatial autocorrelation among sites,

which is a requirement for the application of gener-

alised linear models (GLMs; McCullagh & Nelder,

1989; Faraway, 2006), a 0.2-decimal degree latitude

and longitude grid (184–396 km2) was defined and

only one site per cell was randomly selected. A total of

1,548 sites were used (Fig. 1).

Table 1 Description of the eight functional traits considered

Trait Categories

Richness Local species richness (RICH)

Tolerance to oxygen Intolerant (O2INTOL): species

requiring more than 6 mg oxygen

per litre

Tolerance to habitat

degradation

Intolerant (HINTOL): species

intolerant to hydromorphological

degradations

Affinity for flow

velocity (habitat)

Rheophilic (RH): species preferring to

live in high-flow conditions

Eurytopic (EURY): species with broad

tolerance to flow conditions

Spawning habitat (RHPAR) species preferring to spawn

in running waters

(EUPAR) species without clear

spawning preferences

Reproduction Lithophilic (LITH): species spawning

exclusively on gravel, rocks, stones,

rubbles or pebbles and with

photophobic hatchlings

Table 2 Description of the four gas emission scenarios

Scenario Description

A1F1 Rapid economic growth, global population peaks in

mid-century and more efficient technologies with

intensive consumption of fossil energy

A2 Heterogeneous world, high population growth, slow

economic growth and slow technological changes

B1 Same global population as A1F1 but convergent

world with rapid changes in economic structure

toward a service and information economy

B2 Intermediate population and economic growth,

emphasising local solutions to economic, social

and environmental sustainability

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Table 4 Hierarchical partitioning of the reduction (R) of

deviance owing to the environmental factors to assess the

relative effect of each factor on functional traits: independent

(I), joint contribution (J) and the independent effect (%Ii)

associated with each environmental variable (alone or with

its quadratic term) expressed as a percentage of the total

independent effect I

Trait Total partition %Ii

R I J |I/J| UDA2 STP STP2 TJUL2 TDIF

RICH -3,096.8 -2,289.8 -807.1 2.8 62.4 – 7 12 18.6

O2INTOL -361.4 -320.5 -40.9 7.8 25.2 – – 44.4 30.4

HINTOL -419.9 -349.9 -70 5 41.5 – 3.1 27 28.4

EURY -1,954.4 -1,601.8 -352.7 4.5 67.6 4.7 – 17.2 10.5

RH -1,115.5 -882.1 -233.4 3.8 55.9 – 3.4 15.8 24.9

EUPAR -1,995.8 -1,455.3 -540.5 2.7 57.9 – 7.8 12.9 21.4

RHPAR -861.9 -613.1 -248.9 2.5 64.3 – 5.8 8.1 21.8

LITH -673.7 -528.3 -145.4 3.6 72.7 – – 12.8 14.6

The environmental variables: temperature in July (TJUL2 the sum of TJUL and its quadratic term), thermal amplitude between July

and January (TDIF), stream power (STP or STP2 the sum of STP and its quadratic term) and the upstream drainage area (UDA2 the

sum of UDA and its quadratic term)

Table 3 Overview of the current environmental conditions:

upstream drainage area (UDA), mean air temperature in July

(TJUL), mean thermal amplitude between July and January

(TDIF) and stream power (STP) and of the deviations between

current and projected climatic conditions for scenarios A1F1,

A2, B1 and B2 and periods 2020–2030 and 2050–2060

Dataset Scenario UDA TJUL TDIF STP

Current, mean (SD) 4.50 (1.80) 18.09 (2.16) 16.94 (4.30) 11.25 (1.54)

Range 0.00–11.51 11.30–25.10 8.40–28.80 3.95–17.43

2020–2030 A1F1 – 1.74 (0.38) -0.13 (0.71) -0.15 (0.21)

A2 – 1.68 (0.38) -0.16 (0.69) -0.15 (0.21)

B1 – 1.71 (0.41) -0.29 (0.65) -0.15 (0.22)

B2 – 1.73 (0.41) -0.30 (0.66) -0.13 (0.20)

2050–2060 A1F1 – 3.75 (0.93) 1.23 (1.14) -0.25 (0.25)

A2 – 3.11 (0.75) 1.03 (0.94) -0.22 (0.23)

B1 – 2.59 (0.65) 0.66 (0.71) -0.18 (0.22)

B2 – 2.66 (0.63) 0.66 (0.73) -0.16 (0.19)

Table 5 Average differences between the theoretical trait values predicted for the current and projected climatic conditions, for the

four scenarios and the two periods

Trait 2020–2030 2050–2060

A1F1 A2 B1 B2 A1F1 A2 B1 B2

RICH 0.0959 0.1046 0.0708 0.0721 -0.3264 -0.0912 0.0108 -0.0039

O2INTOL -0.3844 -0.3728 -0.3889 -0.3932 -0.8069 -0.6618 -0.5513 -0.5685

HINTOL -0.3224 -0.311 -0.3281 -0.3307 -0.75 -0.5954 -0.4844 -0.4997

EURY 0.2798 0.2776 0.2733 0.277 0.3495 0.3669 0.3435 0.3483

RH -0.271 -0.2575 -0.2841 -0.2863 -0.823 -0.6019 -0.4597 -0.4794

EUPAR 0.0458 0.05 0.0339 0.0348 -0.1038 -0.0098 0.0296 0.0225

RHPAR 0.0114 0.0131 -0.002 0.0001 -0.0987 -0.0216 0.0028 0.001

LITH 0.0521 0.0539 0.0432 0.0461 -0.0873 -0.005 0.0275 0.0272

Hydrobiologia (2013) 704:417–436 421

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Functional traits

A previous classification of biological and ecological

traits of European fish species (Noble et al., 2007) was

revised and completed during the European EFI?

project. We considered seven traits that are widely

used to assess the ecological status of streams based on

fish assemblages, together with local species richness

(Oberdorff et al., 2002; Pont et al., 2006, 2007, 2009;

Logez & Pont, 2011). These traits consider tolerance

Met

ric th

eore

tical

val

ues

Metric projected values

0 5 10 15 20 25

0 5 10 15 20

1 2 3 4 1 2 3 4

2 4 6 8 0 5 10 151

23

40

510

15

1 2 3 4 5 6

05

1015

2025

05

1015

201

23

54

6

1 2 3 4 5 6

01

23

42

46

81

23

45

6

cba

fed

hg

Summary of the four scenarios, period 2020–2030

Fig. 2 Relationships between predicted trait values for the

current (y-axis) and projected climatic conditions for

2020–2030 (average of the four scenarios) (x-axis): a RICH,

b O2INTOL, c HINTOL, d EURY, e RH, f EUPAR, g RHPAR,

h LITH (Table 1). Grey lines represent the y = x curve, and

dashed lines represent a general trend (Loess regression curve,

Hastie et al., 2009)

422 Hydrobiologia (2013) 704:417–436

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to dissolved oxygen concentration, tolerance to habitat

degradation, affinity to flow velocity, spawning habitat

and type of reproduction (Table 1); e.g., in the case of

affinity to flow velocity, the affinity or lack of affinity

of the 88 species was evaluated, and each species was

assigned to one of the different states (e.g. species are

either rheophilic or eurytopic) corresponding to

different species attributes (Logez et al., 2012a).

Therefore, traits did not overlap and provided specific

information on the functional structure of fish assem-

blages; e.g. eurytopic species do not include rheophilic

species (RH). The eight metrics were composed of the

05

1015

2025

01

23

4

05

1015

20

02

46

8

12

34

56

0 5 10 15 20 25 0 1 2 3 4 0 1 2 3 4

01

23

40 5 10 15 20 0 2 4 6 8 0 5 10 15

05

1015

1 2 3 4 5 6 1 2 3 4 5 6

12

34

56

Met

ric th

eore

tical

val

ues

Metric projected values

Scenario B1, period 2050–2060

cba

fed

hg

Fig. 3 Relationships between predicted trait values for the

current (y-axis) and projected climatic conditions for 2050–2060

under the B1 scenario (x-axis): a RICH, b O2INTOL, c HINTOL,

d EURY, e RH, f EUPAR, g RHPAR, h LITH (Table 1). Greylines represent the y = x curve, and dashed lines represent a

general trend (Loess regression curve; Hastie et al., 2009)

Hydrobiologia (2013) 704:417–436 423

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number of species at a site that share a given trait, e.g.

the number of RH.

Environmental factors

Four environmental factors were used (Logez et al.,

2012b) to explain the field variability of the eight

traits: upstream drainage area (UDA, km2), stream

power (STP, watt m-1), mean air temperature in July

(TJUL, �C) as a proxy for water temperature (Allan &

Castillo, 2007) and the thermal amplitude between

July and January (TDIF, �C). UDA reflects habitat

diversity and is a descriptor of the stream reach

position along the river network (Matthews, 1998;

01

23

40 5 10 15 20 25 0 1 2 3 4 0 1 2 3 4

1

02

46

8

0 5 10 15 20 0 2 4 6 8 0 5 10 150

23

40

510

15

05

1015

2025

05

1015

201

23

45

6

1 2 3 4 5 6 1 2 3 4 5 6

12

34

56

Met

ric th

eore

tical

val

ues

Metric projected values

Scenario A1F1, period 2050–2060

cba

fed

hg

Fig. 4 Relationships between predicted trait values for the

current (y-axis) and projected climatic conditions for 2050–2060

under the A1F1 scenario (x-axis): a RICH, b O2INTOL,

c HINTOL, d EURY, e RH, f EUPAR, g RHPAR, h LITH

(Table 1). Grey lines represent the y = x curve and dashed linesrepresent a general trend (Loess regression curve; Hastie et al.,

2009)

424 Hydrobiologia (2013) 704:417–436

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Oberdorff et al., 2001). STP is ‘‘the rate of potential

energy expenditure over a reach or STP per unit of

stream length’’ (Gordon et al., 2004) and reflects the

hydraulics at reach scale and integrates annual

precipitations (see Logez et al., 2012b for further

details). Temperature is a very well-known environ-

mental driver that influences both species distribution

and functional traits (e.g. Somero, 1997; Logez et al.,

2010, 2012a, b). UDA and STP were log-transformed

due to the skewness of their distributions.

To assess the effect of global change, we used four

different climate projections developed by the UK

Met Office Hadley Centre for Climate Prediction and

Research (Mitchell et al., 2004; Mitchell & Jones,

2005). These projections were averaged for the

2020–2030 (referred to as 2020) and 2050–2060

(referred to as 2050) periods. These projections are

based on four different socio-economic scenarios

proposed by the Intergovernmental Panel on Climate

Change (Nakicenovic & Swart, 2000), used in its

fourth and latest assessment report (IPCC, 2007):

A1FI, A2, B1 and B2 (Table 2). Projections were

derived and averaged from three global circulation

models (GCM): HadCM3 (Mitchell et al., 1998;

Gordon et al., 2000), CGCM2 (Flato & Boer, 2001)

and CSIRO-Mk2 (Hirst, 1999; Hirst et al., 2000). All

climatic data were extracted from the TYN SC 1.06

data set (Mitchell et al., 2004) with 100 9 100 grid

resolution (127–275 km2).

Ecoregions

To test whether the response to climate change could

differ among European regions and size of streams, we

used Illies’ ecoregions (Illies, 1978). Some regions

were grouped into larger geographic areas because of

their very low representations in the data sets: the

Fennoscandian Shield and Borealic uplands into the

‘‘Nordic’’ region, the Alps and Pyrenees into ‘‘Alps’’,

and the Eastern plains, Hungarian lowlands and Pontic

province into the ‘‘Eastern’’ region. To take into

account the specificity of the Mediterranean areas

(Gasith & Resh, 1999), we defined a ‘‘Mediterranean’’

region as Mediterranity level 1 of Segurado et al.’s

(2008) classification.

Statistical analyses

Due to the nature of the metrics (count data), log-linear

models (McCullagh & Nelder, 1989; Faraway, 2006)

were used to relate metric variations to environmental

conditions. Log-linear models use a non-normal

distribution for the model errors, and dependent

variables are linearly related to predictors through a

link function (the logarithm function for the Poisson

distribution; McCullagh & Nelder, 1989; Cameron &

Trivedi, 1998). Therefore, the models are formulated

as: log(Y) = a ?P

biXi, where Y is the dependent

variable (i.e. each metric), a is the intercept, and bi is

the ith parameter associated with environmental

variable Xi. The coefficients are estimated by maxi-

mising the likelihood (McCullagh & Nelder, 1989;

Faraway, 2006) rather than by ordinary least squares

as for linear models (Kutner et al., 2005; Montgomery

et al., 2006).

Environmental variables and their quadratic terms

(except for TDIF) were integrated into the models

to allow for nonlinear relationships, and the most

Table 6 Summary of average standardized deviation between observed and projected theoretical trait values (mean and SD) for

2020–2030 (see ‘‘Materials and methods’’) under the four scenarios

Trait A1F1 A2 B1 B2

RICH -0.031 (1.064) -0.033 (1.062) -0.022 (1.068) -0.023 (1.068)

O2INTOL 0.325 (1.034) 0.315 (1.033) 0.329 (1.035) 0.333 (1.034)

HINTOL 0.235 (1.046) 0.227 (1.043) 0.240 (1.045) 0.242 (1.045)

EURY -0.178 (1.031) -0.176 (1.031) -0.174 (1.030) -0.176 (1.031)

RH 0.140 (1.072) 0.133 (1.069) 0.147 (1.073) 0.148 (1.073)

EUPAR -0.028 (1.059) -0.031 (1.057) -0.020 (1.062) -0.021 (1.063)

RHPAR -0.007 (1.027) -0.008 (1.026) 0.001 (1.029) 0.000 (1.029)

LITH -0.032 (1.021) -0.033 (1.019) -0.026 (1.023) -0.028 (1.021)

Hydrobiologia (2013) 704:417–436 425

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relevant variables were selected with a stepwise

procedure based on Akaike’s information criterion

(AIC) (Logez & Pont, 2011). The complete models

were formulated as: log(metric) * UDA ? UDA2 ?

STP ? STP2 ? TJUL ? TJUL2 ? TDIF.

The relative effect of each environmental variable

selected on a given metric was assessed by the

hierarchical partitioning method (Chevan & Suther-

land, 1991; Mac Nally, 2000). This method estimates

the independent and joint contributions of each

−2 0 2 4 −2 0 2 4

−2 0 2 4 −2 0 2 4−2 0 2 4

−2 0 2 4

0.0

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a b c

d e f

g h

Den

sity

Standardized deviation between observed and theoretical metric values

Summary of the four scenarios, period 2020–2030

Fig. 5 Densities (kernel estimation; Hastie et al., 2009) of the

functional trait scores on the current environmental conditions

(in grey) and for 2020–2030 (average of the four scenarios)

(dashed lines): a RICH, b O2INTOL, c HINTOL, d EURY,

e RH, f EUPAR, g RHPAR, h LITH (Table 1)

426 Hydrobiologia (2013) 704:417–436

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explanatory variable to the models’ goodness of fit

by jointly considering all the possible models and

using the theorem of hierarchies. The reduction of

deviance was used as a measure of the models’

goodness of fit (Pont et al., 2005; Logez et al., 2012b).

Multicollinearity between environmental factors was

assessed by computing the general variance inflation

factor (GVIF) (Fox, 2002).

The shift in functional traits was assessed by

comparing the trait values predicted for the current

−2 0 2 4 −2 0 2 4 −2 0 2 4

−2 0 2 4 −2 0 2 4 −2 0 2 40.

00.

20.

40.

6

0.0

0.1

0.2

0.3

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0.5

0.0

0.2

0.4

0.6

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0.0

0.1

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0.5

−2 0 2 4 −2 0 2 4

0.0

0.1

0.2

0.3

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0.5

0.0

0.1

0.2

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0.0

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0.1

0.2

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Den

sity

Standardized deviation between observed and theoretical metric values

Scenario B1, period 2050–2060

cba

fed

hg

Fig. 6 Densities (kernel estimation; Hastie et al., 2009) of the

functional trait scores on the current environmental conditions

(in grey) and for 2050–2060 under the B1 scenario (dashed

lines): a RICH, b O2INTOL, c HINTOL, d EURY, e RH,

f EUPAR, g RHPAR, h LITH (Table 1)

Hydrobiologia (2013) 704:417–436 427

123

environmental conditions and for the projected cli-

matic conditions for the two periods and for the four

gas emission scenarios. The other physical compo-

nents were assumed to be constant. If climate change

does not have any effect on the functional fish

structure, the predictions should be distributed around

the y = x curve.

The potential effects of climate change on reference

condition baselines were assessed by computing the

standardised deviations between present observed

−2 0 2 4 −2 0 2 4 −2 0 2 4

−2 0 2 4 −2 0 2 4 −2 0 2 4

0.0

0.0

0.2

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0.0

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0.5

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0.2

0.3

0.4

0.5

0.0

0.1

0.2

0.3

0.4

Den

sity

Standardized deviation between observed and theoretical metric values

Scenario A1F1, period 2050–2060

cba

fed

hg

Fig. 7 Densities (kernel estimation; Hastie et al., 2009) of the

functional trait scores on the current environmental conditions

(in grey) and for 2050–2060 under the A1F1 scenario (dashed

lines): a RICH, b O2INTOL, c HINTOL, d EURY, e RH,

f EUPAR, g RHPAR, h LITH (Table 1)

428 Hydrobiologia (2013) 704:417–436

123

values and theoretical (predicted) trait values in the

absence of pressure (Pont et al., 2006), also called trait

scores, for the different climate scenarios. This

computation tests how an assemblage structure that

is currently observed in minimally disturbed condi-

tions (i.e. close to the reference condition) would be

considered in the future. For a given scenario, if the

future climatic conditions would lead to a lesser

representation of a given metric, the deviation

between observed and theoretical values increases

and thus the difference between the deviations com-

puted for the future and current climate is positive. For

example, if in a given site the observed RICH was 2

and the theoretical value was 3, then the deviation

between the observed and theoretical values is -1, for

the current climatic conditions. If climate change

decreased the theoretical value to 0, then the deviation

between observed and theoretical values equals 2 and

the difference between the two deviations, future

minus current, is 3 (i.e. 2 minus -1). On the other

hand, if the climate change led to a better represen-

tation of the metric in fish assemblages then a negative

difference would be observed between the deviations

computed for the future and current climatic condi-

tions. In summary, a positive difference means that the

metric is less represented in the future assemblage,

whereas a negative difference means a higher value of

the given metric in undisturbed assemblages.

We used Wilcoxon tests to compare the average

trait deviations computed for each period and each

Table 7 Summary of average standardized deviation between observed and projected theoretical trait values (mean and SD) for

2050–2060 (see ‘‘Materials and methods’’) under the four scenarios

Trait A1F1 A2 B1 B2

RICH 0.112 (1.170) 0.032 (1.127) -0.002 (1.104) 0.003 (1.108)

O2INTOL 0.683 (1.063) 0.560 (1.051) 0.466 (1.043) 0.481 (1.044)

HINTOL 0.548 (1.088) 0.435 (1.068) 0.354 (1.057) 0.365 (1.058)

EURY -0.223 (1.075) -0.234 (1.060) -0.219 (1.048) -0.222 (1.049)

RH 0.424 (1.167) 0.310 (1.131) 0.237 (1.107) 0.247 (1.111)

EUPAR 0.072 (1.165) 0.009 (1.126) -0.017 (1.103) -0.013 (1.108)

RHPAR 0.063 (1.064) 0.014 (1.045) -0.002 (1.039) -0.001 (1.039)

LITH 0.056 (1.068) 0.004 (1.046) -0.016 (1.037) -0.016 (1.036)

Table 8 Average deviation of trait scores (from the four scenarios) between projected climatic conditions for 2020–2030 and current

climatic conditions for each ecoregion (Fig. 1)

Ecoregion RICH O2INTOL HINTOL EURY RH EUPAR RHPAR LITH

ALPS -0.220 0.204 0.066 -0.212 -0.097 -0.177 -0.152 -0.192

BAL -0.165 0.360 0.215 -0.356 0.057 -0.216 -0.034 -0.108

C.H -0.194 0.293 0.148 -0.200 -0.033 -0.178 -0.104 -0.147

C.P -0.117 0.413 0.270 -0.350 0.134 -0.142 -0.036 -0.086

CAR -0.330 0.431 0.230 -0.361 -0.053 -0.350 -0.180 -0.221

ENG -0.137 0.059 -0.014 -0.104 -0.100 -0.096 -0.083 -0.120

EAST 0.345 0.664 0.581 -0.160 0.600 0.404 0.188 0.154

IBE -0.027 0.266 0.210 -0.131 0.128 -0.026 -0.002 -0.009

ITA 0.275 0.462 0.438 0.032 0.430 0.216 0.181 0.205

MED 0.260 0.334 0.344 0.033 0.370 0.205 0.177 0.203

NOR -0.410 0.029 -0.101 -0.337 -0.309 -0.456 -0.185 -0.244

W.H -0.088 0.309 0.202 -0.139 0.070 -0.072 -0.041 -0.072

W.P 0.036 0.370 0.298 -0.170 0.228 0.028 0.041 0.014

Hydrobiologia (2013) 704:417–436 429

123

scenario with the average deviations computed for the

current climatic conditions. Due to the multiple

comparisons testing the same null hypothesis (Shaffer,

1995), as the two samples come from populations in

the same location (Sokal & Rohlf, 1995), the P-values

were adjusted using the method proposed by Benja-

mini and Yekutieli (2001) and Dudoit & van der Laan

(2008). This method controls the false discovery rate

and is ‘‘more powerful, less conservative and better

limits the number of hypotheses inconsistently not

rejected’’ than classical Bonferroni correction (Logez

et al., 2010).

The potential differences in reference baseline shift

among ecoregions were tested using Kruskal–Wallis

tests, which are the nonparametric equivalent of one-

way analysis of variance (Sokal & Rohlf, 1995). The

P-values were adjusted using the same methodology

as for Wilcoxon tests.

Results

Data sets

The 1,548 stream reaches were mostly located in small

to medium-sized streams; 90 % of sites had UDA less

than 1,000 km2. The climatic conditions were highly

contrasted in terms of both temperatures and thermal

amplitude. TJUL ranged from 11.3 to 25.1 �C, and

TDIF varied between 8.4 and 28.8 �C. STP had a very

broad amplitude but lower variance than the other

environmental parameters (Table 3).

For the 2020–2030 period, the change in climatic

conditions was mostly visible on TJUL with an

average increase of 1.72 �C. At the same time, the

thermal amplitude was expected to decrease slightly,

suggesting that the temperature increase would be

greater in January than in July for this period. In

contrast, the increase of temperature projected for the

2050–2060 period would be higher in July than in

January, respectively, 3.03 and 2.13 �C (average of

the four scenarios). STP was on average expected to

decrease over time. The same climate change patterns

were predicted with the four scenarios (all Spearman

q[ 0.95); only the magnitudes were different, with

scenarios A1F1 and A2 predicting the greatest tem-

perature increases over 2050–2060 (Table 4).

Effect of environmental factors

Among the four environmental factors considered,

UDA, TJUL and TDIF were always selected by the

stepwise procedure, as were the quadratic terms of

UDA and TJUL. STP was selected for six traits and

was associated with its quadratic term for five traits

(Table 4). All GVIF values were lower than 2.34,

suggesting that multicollinearity was a limited

phenomenon.

The hierarchical partitioning supported this result:

the ratios between independent and joint contributions

Table 9 Average deviation of trait scores (from the four scenarios) between projected climatic conditions for 2050–2060 and current

climatic conditions for each ecoregion (Fig. 1)

Ecoregion RICH O2INTOL HINTOL EURY RH EUPAR RHPAR LITH

ALPS -0.290 0.385 0.180 -0.319 -0.062 -0.245 -0.226 -0.266

BAL -0.286 0.666 0.432 -0.678 0.169 -0.385 -0.079 -0.176

C.H -0.232 0.462 0.264 -0.275 0.026 -0.225 -0.151 -0.185

C.P -0.187 0.554 0.371 -0.517 0.186 -0.228 -0.085 -0.128

CAR -0.310 0.905 0.620 -0.542 0.243 -0.351 -0.188 -0.216

ENG -0.264 0.072 -0.039 -0.196 -0.189 -0.195 -0.176 -0.214

EAST 0.706 1.021 0.933 -0.030 1.021 0.764 0.354 0.345

IBE 0.118 0.604 0.519 -0.164 0.417 0.077 0.062 0.078

ITA 0.477 0.648 0.631 0.119 0.646 0.357 0.327 0.363

MED 0.720 0.634 0.680 0.330 0.784 0.517 0.480 0.555

NOR -0.714 0.033 -0.174 -0.590 -0.526 -0.800 -0.359 -0.412

W.H -0.099 0.473 0.326 -0.195 0.147 -0.088 -0.061 -0.086

W.P 0.080 0.548 0.454 -0.236 0.366 0.050 0.042 0.034

430 Hydrobiologia (2013) 704:417–436

123

(I/J) ranged between 2.5 and 7.8. UDA had the greatest

relative independent contributions (%Ii) to almost all

traits except O2INTOL, ranging from 25.2 to 72.7 %.

Temperatures (TJUL and TDIF) were the second most

important variables, and STP always had the lowest

relative independent contributions compared with the

other environmental factors (when this variable was

selected) (Table 4).

Change in functional traits

The four scenarios provided relatively similar results

for the 2020–2030 period; the average deviations

between the predicted values for the current and

projected climatic conditions were consistent among

scenarios. O2INTOL, HINTOL, EURY and RH

showed the largest differences among the predicted

trait values (Table 5). O2INTOL and HINTOL pre-

dictions for 2020–2030 were almost always under the

y = x curve (Fig. 2), suggesting that the representa-

tion of these traits would decrease. The average

deviation for RH was also negative (Table 5), but the

deviation from the current conditions seemed to

appear for high RH values (Fig. 2). In contrast to the

three former traits, EURY values for 2020–2030 were

on average higher than the predictions for the current

conditions (Table 5; Fig. 2).

Compared with 2020–2030, the results obtained

with the four scenarios were much more contrasted

over 2050–2060. The magnitude of responses was

greater for the ‘‘A’’ scenarios, especially for A1F1,

than for the ‘‘B’’ scenarios, but the response pattern

was similar (Table 5). The only noticeable difference

was for RICH, which was expected to decrease on

average under the A1F1 scenarios and to remain stable

under the three other scenarios. As for 2020–2030, the

four metrics that deviated the most from projected and

current climatic conditions were O2INTOL, HINTOL,

EURY and RH (Table 5; Figs. 3, 4). Under A1F1, the

deviations from the y = x curve seemed to occur for

lower predicted values of RH than for the 2020–2030

period (Fig. 2). Otherwise, the response patterns over

the two periods are relatively similar but more

scattered for 2050–2060.

Changes in reference condition baselines

The four scenarios provided very close trait scores

(standardised trait values, see ‘‘Materials and

methods’’ section) between current and climatic

conditions projected for 2020–2030 (Table 6), and

the patterns observed were very similar to those

observed for functional traits. O2INTOL and HINTOL

were expected to shift toward positive values on

average. The score distributions were very close for

the other traits (Fig. 5).

The patterns predicted for 2050–2060 seemed

amplified compared with 2020–2030: the magnitude

of the differences between trait scores increased.

O2INTOL and HINTOL displayed much more distinct

distributions from the current climatic conditions

(Figs. 6, 7), and the deviations among average trait

scores were more pronounced (Table 7). Not surpris-

ingly, the metric score deviations were higher for

the ‘‘A’’ scenarios than for the ‘‘B’’ scenarios. For

instance, distributions of RH scores for the current and

2050–2060 climates were close under the B1 scenario

(Fig. 6), whereas these two distributions were rela-

tively distinct under the A1F1 scenario (Fig. 7).

All Kruskal–Wallis tests were significant (adjusted

P-values \0.001), whatever the period and the

scenario considered, suggesting that the shift in the

reference condition baselines differ among ecore-

gions. Because the results from all scenarios were

comparable (only the magnitude of the response

differed, see Electronic Supplementary Material), we

present only the average values from the four scenar-

ios (Tables 8, 9). O2INTOL and HINTOL traits were

expected to decrease in almost all regions (positive

average deviation of trait scores), but the magnitudes

of the decline varied among regions. For the other

traits, the results were more contrasted among regions

(Tables 8, 9).

Discussion

The data used in this study allowed assessment of the

influence of environmental drivers on fish functional

traits and for accurate forecasting of species traits for

future decades. The projections showed that mainly

four traits will be highly affected by climate change:

species intolerant to habitat degradation (HINTOL),

species with high oxygen needs (O2INTOL), RH and

eurytopic species (EURY).

The main environmental driver of the eight traits

was the UDA, which could be explained by the type of

metrics considered in this study, as the eight traits used

Hydrobiologia (2013) 704:417–436 431

123

in this study were expressed by species number. Local

species richness is known to vary along the longitu-

dinal gradient (Horwitz, 1978; Vannote et al., 1980;

Hugueny, 1989; Oberdorff et al., 1993, 1995; Gren-

ouillet et al., 2004; Ibanez et al., 2009; Logez et al.,

2010), which could explain the importance of UDA for

these metrics. Moreover, these results are consistent

with previous studies which used environmental

variables reflecting the longitudinal gradient (e.g.

distance from source) to model metrics based on

functional traits and expressed in the number of

species (Pont et al., 2006, 2007; Logez et al., 2010).

The second major environmental factor was tem-

perature, expressed either in monthly temperature

(TJUL) or in thermal amplitude (TDIF). The climatic

changes, especially thermal changes, projected by the

four different gas emission scenarios (IPCC, 2007) are

expected to modify the functional structure of European

fish assemblages. This shift in functional structure can

be compared with the shift in distribution areas

projected using species distribution models (Xenopo-

ulos et al., 2005; Buisson et al., 2008, 2010; Buisson &

Grenouillet, 2009; Grenouillet et al., 2011). The rise in

observed temperature (Webb & Nobilis, 1995, 2007;

Webb, 1996) and expected temperature caused by

global change (IPCC, 2007) will negatively affect cold-

and cool-water species such as salmonids (Isaak et al.,

2012) and modify the composition of the species

assemblage (Xenopoulos et al., 2005; Graham &

Harrod, 2009). Compared with the current situation,

suitable climatic conditions for coldwater species

presence would be greatly limited (Isaak et al., 2012),

leading to extinction of local populations.

Coldwater species are very often intolerant to

both habitat degradation and low oxygen concentra-

tion (Halliwell et al., 1999; Zaroban et al., 1999;

Holzer, 2008; Logez & Pont, 2011). Therefore, the

projected decline of the two corresponding metrics,

HINTOL and O2INTOL, must be directly linked to

the projected decline of coldwater species under

climate warming. The same tendency is observed for

the RH metric, but to a lesser extent due to the

wider range of thermal preference of RH (Logez

et al., 2012b), including both salmonid and cyprinid

species. At the opposite extreme, the eurytopic

metric (EURY) tended to be better represented in

the predicted future undisturbed fish assemblage,

due to their ability to adapt to a wider range of

environmental conditions.

The traits related to reproduction (RHPAR, EU-

PAR, LITH) were not expected to be greatly affected

by climate change. This suggests either that only some

species will be affected in the same manner by climate

change depending on their thermal preferences (Buis-

son et al., 2008, 2010; Hering et al., 2009; Grenouillet

et al., 2011; Logez et al., 2012b) or that the same

function will be represented by other species with

higher thermal preferences but with similar character-

istics. Finally, local species richness (RICH) was also

expected to remain stable, suggesting replacement

of intolerant coldwater species with more eurytopic

species.

These results will have consequences for the

bioassessment of European streams based on fish

assemblages, especially when using most of the IBI-

type indices commonly used nowadays (Pont et al.,

2011). Most of these indices integrate several metrics

based on functional traits that will be impacted by

climate change. For the metrics that are expected to

be better (EURY) or slightly less (RH) represented

in assemblages, reference condition baselines would

have to be changed because they will become poorly

adapted to the future climate. Otherwise, under the

reference condition approach (Bailey et al., 1998), the

observed values would be compared with inconsistent

values, leading to misclassification of both impaired

and non-impaired sites. This redefinition of baseline

reference conditions could be partially obtained with

predictive fish indices (Pont et al., 2006, 2007)

including climatic conditions in the computation of

the predicted metric values in the absence of pressure.

For the intolerance-based metrics expected to be

highly negatively affected by climate change (HIN-

TOL, O2INTOL), a change in the baselines of

reference conditions could be considered as long as

global warming does not excessively reduce the

diversity of these functional groups. For example, in

view of a huge disappearance of cold-intolerant

species (Buisson et al., 2008; Lassale et al., 2008;

Buisson & Grenouillet, 2009), these metrics must not

be considered in future bioassessment tools, because

the metrics must reflect the faunal specificity of the

regional fauna (Karr, 1981, 1991; Logez & Pont,

2011). Including a metric which has little or no

representation in the region limits the discrimination

between impaired and unimpaired sites (Harris &

Silveira, 1999), which could lead to limited use of the

metric based on species intolerance, the most sensitive

432 Hydrobiologia (2013) 704:417–436

123

metric to human pressures (Fausch et al., 1984; Simon

& Lyons, 1995; Oberdorff et al., 2002; Joy & Death,

2004; Pont et al., 2006, 2007, 2009; Melcher et al.,

2007; Vehanen et al., 2010; Logez & Pont, 2011).

A final consequence would be reduced sensitivity of

fish-based indices to local anthropogenic alterations.

Depending on the ecoregion, different effects of

climate change were forecasted, suggesting that for

some ecoregions the current bioassessment tools will

more rapidly become inadequate, whereas for the

others these tools will remain consistent when used to

assess the ecology of streams. Climate change conse-

quences would depend on the shift in climatic

conditions projected for these regions.

All these results clearly stress the need to maintain

a biomonitoring network on undisturbed sites consid-

ered as reflecting present reference conditions. The

functional changes of European fish assemblages

could be missed and the reliability of the currently

used indices may not be assessed if monitoring is not

pursued.

Acknowledgments We are grateful to all the members of the

European EFI? project (contract number 044096, http://efi-

plus.boku.ac.at/), which provided all the data. This paper is a

result of the WISER (Water bodies in Europe: Integrative Sys-

tems to assess Ecological status and Recovery) project funded

by the European Union under the 7th Framework Programme,

Theme 6 (Environment including Climate Change) (contract no.

226273), http://www.wiser.eu.

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