© 2017 E. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, Germany www.schweizerbart.de
DOI: 10.1127/fal/2017/0982 1863 - 9135/17/0982 $ 3.25
Development of a fish-based index to assess the
ecological status of oceanic-temperate streams in the
Northern Iberian Peninsula
Begoña Gartzia de Bikuña
1, Jesús Arrate
1, Aingeru Martínez
2, *, Alberto Agirre
Iker Azpiroz 3 and Alberto Manzanos 4
With 4 figures and 7 tables
Abstract: Multimetric indices based on fish assemblages assessing the ecological status of rivers have been widely
developed. Nevertheless, the heterogeneity of environmental conditions, biological assemblages, and human pres-
sures require the development of suitable indices for different regions. In the Iberian Peninsula, where there is a lack
of an accurate tool for monitoring systems under temperate oceanic conditions, indices are developed under a Medi-
terranean climate. Therefore, our goal was to develop and validate a multimetric index based on fish assemblages to
evaluate the ecological status of streams and rivers from the Northern Iberian Peninsula under a temperate oceanic
climate. For this, 147 streams (41 reference and 96 disturbed streams) were monitored from 2010 to 2014. At each
site, we calculated the stress level based on human alterations. For the index development, the rivers were grouped
into two large classes: salmonids and cyprinids. Ninety-one metrics were tested for their capability to discern stress
level. The index was calculated as the mean of five metrics for salmonid rivers: the percentage of density of benthic
species, the percentage of richness of species intolerant to contamination, the percentage of richness of species
intolerant to low oxygen concentrations, the percentage of insectivore density, and the percentage of Salmo trutta
density. As the mean of two metrics for cyprinid rivers, we considered the percentage of density of water column
species and the percentage of density of species intolerant to low oxygen levels. In salmonid rivers, the longitudinal
connectivity was evaluated by measuring the presence of Anguila anguila. The index was split into five ecological
status classes, ranging from 0 (bad ecological status) to 1 (high ecological status). This index was compared with
four other biological indices and its performance was validated for 793 study cases, thus demonstrating its suitabil-
ity for employment in biomonitoring works to assess the ecological status of stream and river ecosystems from the
Northern Iberian Peninsula under temperate oceanic conditions.
Keywords: multimetric index; fish assemblages; biomonitoring; temperate oceanic climate
Streams and rivers are among the most threatened
ecosystems in the world due to human activities
(Malmqvist & Rundle 2002). As these ecosystems
provide important services (Thorp et al. 2010), it is
crucial to understand the consequences of human per-
turbations to preserve and restore their integrity (Mey-
beck 2003). Therefore, a major challenge in freshwa-
ter ecology is to provide assessment and monitoring
tools for stream and river management.
1 Anbiotek S.L., Axpe Industrialdea, Ribera de Axpe 11 B-201, 48950 Erandio, Spain
2 Laboratory of Stream Ecology, Department of Plant Biology and Ecology, University of the Basque Country, P.O. Box 644,
48080 Bilbao, Spain
3 Ekolur S.L.L., Astigarrako Bidea, 2, 20180 Oiartzun, Spain
4 Basque Water Agency (URA), Orio 1-3, 01010 Vitoria-Gasteiz, Spain
* Correspondending author: firstname.lastname@example.org
EFundam. Appl. Limnol. 189/4 (2017), 315–327 Article
published online 16 February 2017, published in print March 2017
316 B. Gartzia de Bikuña et al.
Among the different biological communities inhab-
iting streams and rivers, fish assemblages are used as
tools for assessment of the ecological status of fresh-
water systems because of their response to anthropo-
genic pressures at wide temporal and spatial scales
(Hermoso et al. 2009; Maceda-Veiga et al. 2014; Stan-
field & Kilgour 2013); additionally, their capture and
identification are relatively easy (Karr 1981). A well-
developed method for assessing the ecological status
of streams and rivers based on fish assemblages is the
use of Indexes of Biotic Integrity (IBIs), based in the
pioneering work of Karr (1981). This methodological
approach requires the identification and characteriza-
tion of river type, the description of fish assemblages
from reference conditions for each river typology, and
the selection of biological attributes (metrics) to quan-
tify the differences between the observed assemblages
and the assemblages from reference conditions. The
original IBI (Karr 1981) presents 12 metrics grouped
in four important components of community ecology,
such as taxa richness, trophic and habitat niche, indi-
vidual health, and abundance.
Around the world, there are several versions of
the original IBI for modulating the characteristics de-
pending on the region and habitat type. The majority
of the existing versions include the original classifica-
tion of the metrics within the above mentioned four
categories. Nevertheless, the development and the ap-
plication of IBIs in the Iberian Peninsula present some
problems and difficulties due to the low number of
species and the great endemism that fish assemblages
present (Doadrio 2001). While the first attempt to ap-
ply an IBI in the Iberian Peninsula was the IBICAT
(Sostoa et al. 2003), it was not an accurate tool, since
it presented a low correlation with other biotic indices
based on different biological assemblages (Benejam et
al. 2008). Another index that has been tested in this
territory was the European Fish Index EFI (FAME
Consortium 2004), although it was associated with
several problems. Due to its limitations, a new ver-
sion called EFI+ was developed (EFI+ Consortium
2009), which was also an inaccurate tool in this region
(García-Berthou & Bae 2014). With the aim to solve
these limitations, some indices have been developed in
the last years by taking into account the special char-
acteristics of the Iberian fish assemblages (Aparicio et
al. 2011; Hermoso et al. 2010; Magalhães et al. 2008;
Sostoa et al. 2010). Despite the development of these
approaches, their implantation along the entire Ibe-
rian Peninsula is complicated due to the climatic (13
sub-climates under both oceanic and Mediterranean
conditions) and biological differences among different
regions within this territory. Thus, it is essential to de-
velop accurate tools for assessing the ecological status
of streams and rivers based on fish assemblages for the
different regions within the Iberian Peninsula. This is
needed mainly in a temperate oceanic climate, since
all the above mentioned indices have been developed
for Mediterranean regions.
Therefore, our goal was to develop a fish-based
index to assess the ecological status of streams from
the Northern Iberian Peninsula (located along the
Cantabrian mountain range), mainly in temperate
oceanic conditions. The Cantabrian Fish index (CFi)
was developed using 147 streams (41 references and
96 disturbed). Our aim was to develop a direct and
user-friendly tool which could be used by managers
and decision-makers to assess the ecological status of
streams under oceanic climatic conditions and close
regions (even under sub-Mediterranean conditions)
from the Northern Iberian Peninsula. For this reason,
we assessed the capability of this index to discrimi-
nate disturbed streams from reference ones and vali-
date its performance at 154 study sites during the years
1993 – 2009 (totaling 793 study cases).
Material and methods
The study of fish assemblages was carried out in 137 streams
from the Northern Iberian Peninsula (Fig. 1) during the period
2010 – 2014. All these streams were subject to an oceanic cli-
mate with cool (but not cold) winters and warm (but not hot)
summers, and with a mean annual rainfall of 1200 – 2000 mm,
distributed throughout the year. Despite the lack of a drought
period, the lowest precipitation occurs during summer. The
most common river alteration in this region is mixed contami-
nation due to organic and industrial waste and hydromorpho-
logical changes (Docampo & Gartzia de Bikuña 1993; Gartzia
de Bikuña & Docampo 1990).
At each study site, 12 environmental variables were char-
acterized at three spatial scales: site, reach (500 m long and
30 m width in each river bank), and catchment. At site level,
altitude (m), distance from the origin (m), distance to the sea
(m), Strahler order, wetted width (m), ecological flow during
the highest water flow (m3 s–1), ecological flow during the low-
est water flow (m3 s–1), annual mean air temperature (°C), and
annual mean precipitation (mm) were considered. At the reach
level, we evaluated the type of mineralization and slope (%),
and at the catchment scale, the watershed surface area (km2).
The ecological flows during the highest and lowest flows were
used to estimate the spatial magnitude of the rivers. These
data were calculated by Uragentzia (2015a). Wetted width was
measured in situ, data of the annual mean air temperature and
precipitation were obtained from shape layers of isotherms and
precipitation (Pandora Database) and computed via ArcGIS
(ESRI 2011); data of mineralization type were obtained from
Sanz de Galdeano & Madariaga (1992). The other data were
obtained from geographical information databases (CORINE
Land Cover) and computed via ArcGIS (ESRI 2011).
317Fish-based index to assess the ecological status of oceanic-temperate streams
As for the environmental variables, at each study site, the ex-
isting human pressure was characterized across three spatial
scales: site, reach, and catchment. At the site level, water phys-
icochemical properties were measured in situ. The physico-
chemical variables considered were oxygen content (mg l–1),
pH, ammonium (mg l–1), and nitrite (mg l–1). At the reach and
catchment scales, data of human pressures were obtained from
geographical information databases (CORINE Land Cover)
and computed via ArcGIS (ESRI 2011). At the reach scale
(500 m long and 30 m width in each river bank), the following
measurements were determined: percentage of urban land use
(%), percentage of agriculture (%), pressure by water abstrac-
tion, channelization, defense structures, occupation of public
property (%), number of dams and bridges, quality of riparian
forest (QBR index, Munné et al. 2003), and whether the flow
was regulated. At the catchment level, the punctual contamina-
tion (percentage of artificial land use) and diffused contamina-
tion (percentage of intensive agriculture) were calculated.
To determine the stressor gradient, a discrete score rang-
ing from 1 (lowest human pressure) to 5 (highest human pres-
sure) was assigned to each variable following the REFCOND
criteria (Wallin et al. 2003). Giving the same weight to each
variable, the stress level was calculated at each study site as a
mean value. Thus, the stress level ranged from 1 (lowest) to 5
(highest), following the methodology of REFCOND (Wallin et
al. 2003) and the specified criteria of the Central Baltic – GIG
(CB – GIG) classification (EC 2010). Based on human impact
records, the study sites were split into 41 reference sites (mean
stress value < 2) and 96 disturbed ones (mean stress value > 2).
As the presence and dominance of exotic species may occur
even in areas with good physicochemical water quality (Ken-
nard et al. 2006), and the impact of alien species may be as, or
more disruptive of the native fish fauna than adverse physical
and chemical conditions (Ganasan & Hughes 1998), the lack
of exotic species was mandatory to classify a site as reference.
Some sites were re-classified after expert criteria.
Fish assemblage sampling
Fish were sampled by electrofishing during low water flow (Au-
gust–September), following the CEN 14011 standard protocol
(CEN 2003). A single upstream pass was made, and block nets
were not used to enclose the sampling area. Sites were electro-
fished for reaches of 100 m in length, including all geomorphic
channel units present in the reach. All fish were anesthetized
with 2-phenoxyethanol, identified, counted, and released.
Fig. 1. Map of the study area and sampling sites used to develop the CFi.
318 B. Gartzia de Bikuña et al.
Stream typologies based on fish assemblages
The streams were classified into different typologies based on
differences of fish assemblages among the reference sites. Only
data of autochthonous species (eight species) were considered.
For this, a hierarchical cluster using Euclidean distance and the
Ward method was performed based on relative abundance us-
ing xlStat (Addinsoft 2007). In the analysis, the data of rare
autochthonous species presenting very low densities, such as
Achondrostoma arcasii Steindachner, Salaria fluviatilis Asso,
Squalius pyrenaicus Günther, and Barbus haasi Mertens, were
not considered. The data of Anguila anguila L. and Salmo salar
L. were neither considered at this point, since their presence
does not respond to the conditions from a determined stream
reach, but also to the longitudinal connectivity along the en-
tire watershed. Moreover, in some streams, local governments
are introducing juveniles of S. salar to recover the presence of
this species. Thus, four stream typologies arose (see Table 1):
type 1 A–Salmonids (T1A) located in the upper reaches, where
Salmo trutta fario L. is the dominant species; type 1 B–Salmo-
nids (T1B), where S. trutta fario and Phoxinus bigerri L are
dominants, with a high presence of Barbatula quignardi Bac-
escu-Mester; type 2 –Cyprinides (T2) located in medium-low
reaches, where Parachondrostoma miegii Steindachner and Lu-
ciobarbus graellsii Steindachner are the dominant species, with
a common occurrence of P. bigerri and B. quignardi; and type
3 –Cyprinides-Suprahaline (T3) located in the lowest reaches
near the sea, where marine species such as Platichthys flesus
L. and Chelon labrosus A. Risso are present.
The assignment of disturbed sites to stream typologies was
carried out by a discriminant analysis based on environmental
conditions performed using xlStat (Addinsoft 2007). Although
15 environmental conditions were calculated for each stream,
for the analysis, we only considered altitude, slope, distance
from the origin, and distance to the sea. Due to the orographic
and climatic characteristics of this territory, some environmen-
tal variables provide no information about the existing fish as-
semblages. As some small streams are located in low-land areas
very near the sea, stream size-related variables, such as width,
order, watershed surface, and ecological flow, were not consid-
ered. Other variables that may disrupt the analysis were precipi-
tation and temperature due to the variation along the altitude
and the East-West and North-South gradients. Furthermore,
variables such as mineralization and substrate type did not sig-
nificantly differ between typologies. Thus, among the 137 sites,
47 sites were assigned to T1A, 59 to T1B, 15 to T2, and 16 to T3.
Multimetric index development
For the index development, the four stream typologies were
grouped into two large classes: salmonids (T1A and T1B) and
cyprinids (T2 and T3).
In total, 91 metrics representing taxonomic and functional
composition, density, perturbation of sensitive species, and age
structure (Schmedtje et al. 2009) were selected. The develop-
ment of this index used a general approach comparing the fish
metrics with the calculated values of stress level. The capability
to discern impairment with respect to reference sites of stable
metrics was assessed. This capability was analyzed by evalua-
tion of the degree of inter-quartile overlap in a box-and-whisker
plot confirmed by standard linear models with Gaussian error
distribution followed by ANOVA, using the R statistical pro-
gram (version 2.11.1; R Development Core Team 2010). The
values of discriminating metrics were compared with stress lev-
els to evaluate the response of the metrics to stressor gradients.
For quantitative comparison of the discrimination ability of a
metric, each metric’s discrimination efficiency (DE; Green &
Swietlik 2000) was examined. The DE of a particular metric
measures the agreement between metric values and the refer-
ence status of a site. It is a numerical description of the degree
of separation between metric value distributions of reference
and impaired sites and is calculated as a percentage as follows:
DE = (a/b)*100, where a is the number of disturbed samples
scoring below the 25th percentile (for metrics that are expected
to decrease in value with increasing site impairment) or above
the 75th percentile (for metrics that are expected to increase in
value with increasing site impairment) of the reference dis-
tribution, and b is the total number of disturbed samples. The
redundancy among these metrics was tested using Spearman
correlation. Metrics showing a correlation coefficient r > 0.9
were considered redundant and metrics showing lower values
for correlation with stress level and DE were discarded. The se-
lected metrics for integration into the index showed a high cor-
relation with the stress level, high values of DE, and were not
intercorrelated. The selection of metrics to integrate the index
was carried out separately between salmonid streams (T1A and
T1B) and cyprinid streams (T2 and T3). Thus, the metrics se-
lected for salmonids were used to calculate the index in the T1A
and T1B types, and the metrics selected for cyprinids were used
to calculate the index in the T2 and T3 types. Prior to integrat-
ing metrics into the multimetric index, the values of selected
metrics were normalized to minimize the variance as follows:
Value = (metric result–lower anchor)/(upper anchor–lower an-
chor); where the lower anchor refers to the lowest value from
disturbed sites and the upper anchor refers to the 50th percentile
values (median) of reference sites.
Finally, the index was calculated as the average among the
scores of selected metrics. Thus, the values of the index ranged
from 0 to 1. As recommended by the European Water Frame-
work Directive (Wallin et al. 2003), the index was split into
five ecological status categories: high, good, moderate, poor,
and bad. The threshold between high and good was set using
the 25th percentile value of reference sites. The range of values
under this one was split into four equal-sized groups to deter-
mine the thresholds among good, moderate, poor, and bad cat-
egories. A last step to calculate the index in salmonid streams is
Table 1. Centroids of the classes derived from the hierarchi-
cal cluster using Euclidean distance and Ward method based
on autochthonous species. The river types are T1A (1 A–Sal-
monids), T1B (1 B–Salmonids), T2 (2 –Cyprinides) and T3 (3 –
Fish species River types
T1A T1B T2 T3
Salmo trutta fario 2.51 1.59 0.50 1.00
Phoxinus bigerri 0.81 1.71 1.17 1.63
Barbatula quignardi 0.13 0.71 1.13 0.00
Luciobarbus graellsi 0.00 0.13 2.00 0.50
Parachondostroma miegii 0.00 0.20 1.63 1.13
Gobio lozanoi 0.06 0.36 0.00 1.13
Platichthys flesus 0.00 0.00 0.00 2.13
Chelon labrosus 0.00 0.00 0.00 1.00
319Fish-based index to assess the ecological status of oceanic-temperate streams
needed to evaluate the longitudinal connectivity of the streams.
This is measured with the presence of A. anguila, a species that
presents a high resistance capacity to pollution, but, due to its
migratory behavior, is a good indicator of stream connectivity.
Thus, a lack or low density (< 6 ind. 100 m– 2) of A. anguila is
penalized with the deduction of 0.2 points (score of each metric
within the index, see below), and the status category must be
reassigned. To determine the discriminating capability of the fi-
nal multimetric index between reference and impaired sites, the
degree of inter-quartile overlap in the box-and-whisker plot was
evaluated, confirmed by standard linear models with a Gauss-
ian error distribution followed by ANOVA, using the R statisti-
cal program (version 2.11.1; R Development Core Team 2010).
The applicability of the CFi for evaluating the ecological status
of streams from the Northern Iberian Peninsula was examined
Table 2. Measured metrics and test values for sensitivity, correlation with stress level and discrimination efficiency (DE) for Sal-
monid streams. For sensitivity, the degree of inter-quartile overlap in the box-and-whisker plot (+ means no overlapping) and p
value after ANOVA tests are shown. For correlation with stress level, r2 and p after Pearson correlations are shown. The positive or
negative correlation between each metric and stress level are shown by ↑ and ↓, respectively. Out of the 91 calculated metrics, only
those having the power to discern between disturbed and reference sites are shown.
Metrics Sensitivity Correlation with stress level DE (%)
Box plot pr2PSense
Total density + < 0.01
Density autochthonous + < 0.01
Density intermediate tolerant to contamination + < 0.01
Density intolerants to contamination + < 0.01
% density intermediate tolerant to contamination + < 0.01 0.25 < 0.01 ↑74.7
% density intolerants to contamination + < 0.01 0.25 < 0.01 ↓25.3
Density tolerant to low O2+< 0.01
Density intermediate tolerant to low O2+ 0.04
% density intermediate tolerant to low O2+< 0.01 0.32 < 0.01 ↑72.8
% density intolerants to low O2+< 0.01 0.32 < 0.01 ↓27.2
Density water column species + 0.04
Density benthic species + < 0.01
% density water column species + < 0.01 0.32 < 0.01 ↓29.0
% Density benthic species + < 0.01 0.32 < 0.01 ↑72.8
Density rheophilics + < 0.01
Density eurytopics + < 0.01
% density eurytopics + < 0.01
Density detritivores + < 0.01
Density insectivores + 0.02
Density omnivores + < 0.01
% density detritivores + < 0.01 0.18 < 0.01 ↑51.2
% density insectivores + < 0.01 0.28 < 0.01 ↓43.3
Density S. trutta +< 0.01
Density P. bigerri +< 0.01
Density L. graellsii +< 0.01
Density P. miegii +< 0.01
Density L. graellsii + P. miegii +< 0.01
% density S. trutta +< 0.01 0.25 < 0.01 ↓25.3
% density P. miegii +< 0.01
% density L. graellsii + P. miegii +< 0.01 0.25 < 0.01 ↑55.3
% richness intermediate tolerant to contamination + < 0.01 0.28 < 0.01 ↑85.3
% richness intolerants to contamination + < 0.01 0.31 < 0.01 ↓38.2
% richness tolerant to low O2+
% richness intermediate tolerant to low O2+< 0.01 0.28 < 0.01 ↑79.7
% richness intolerants to low O2+< 0.01 0.27 < 0.01 ↓43.8
% richness water column species + < 0.01 0.28 < 0.01 ↓44.7
% richness benthic species + < 0.01 0.28 < 0.01 ↑79.7
% richness insectivores + < 0.01 0.27 < 0.01 ↓43.3
% richness omnivores + < 0.01
320 B. Gartzia de Bikuña et al.
Table 3. Spearman correlation test on metric redundancy for Salmonid streams. The correlation values among selected metrics are highlighted in bold.
% density intermediate
tolerant to contamination
% density intolerants to
% density intermediate
tolerant to low O2
% density intolerants to
Density water column
Density benthic species
% density detritivores
% density insectivores
% density S. trutta
% density L. graellsii +
% richness intermediate
tolerant to contamination
% richness intolerants to
% richness intermediate
tolerant to low O2
% richness intolerants to
% richness water column
% richness benthic
% richness insectivores
% density intermediate tolerant to contamination 1.00
% density intolerants to contamination – 1.00 1.00
% density intermediate tolerant to low O2 0.51 – 0.51 1.00
% density intolerants to low O2– 0.51 0.51 – 1.00 1.00
Density water column species – 0.51 0.51 – 1.00 1.00 1.00
Density benthic species 0.51 – 0.51 1.00 – 1.00 – 1.00 1.00
% density detritivores 0.38 – 0.38 0.73 – 0.73 – 0.73 0.73 1.00
% density insectivores – 0.43 0.43 – 0.89 0.89 0.89 – 0.89 – 0.83 1.00
% density S. trutta – 1.00 1.00 – 0.51 0.51 0.51 – 0.51 – 0.38 0.43 1.00
% density L. graellsii + P. miegii 0.41 – 0.41 0.85 – 0.85 – 0.85 0.85 0.84 – 0.95 – 0.41 1.00
% richness intermediate tolerant to contamination 0.58 – 0.58 0.87 – 0.87 – 0.87 0.87 0.61 – 0.76 – 0.58 0.72 1.00
% richness intolerants to contamination – 0.90 0.90 – 0.58 0.58 0.58 – 0.58 – 0.44 0.52 0.90 – 0.49 – 0.70 1.00
% richness intermediate tolerant to low O2 0.58 – 0.58 0.87 – 0.87 – 0.87 0.87 0.61 – 0.76 – 0.58 0.72 1.00 – 0.70 1.00
% richness intolerants to low O2– 0.58 0.58 – 0.87 0.87 0.87 – 0.87 – 0.61 0.76 0.58 – 0 .72 – 1.00 0.70 – 1.00 1.00
% richness water column species – 0.58 0.58 – 0.88 0.88 0.88 – 0.88 – 0.62 0.77 0.58 – 0.72 – 1.00 0.69 – 1.00 1.00 1.00
% richness benthic species 0.58 – 0.58 0.88 – 0.88 – 0.88 0.88 0.62 – 0.77 – 0.58 0.72 1.00 – 0.69 1.00 – 1.00 – 1.00 1.00
% richness insectivores – 0.50 0.50 – 0.84 0.84 0.84 – 0.84 – 0.70 0.87 0.50 – 0.82 – 0.88 0.61 – 0.88 0.88 0.87 – 0 .87 1.00
321Fish-based index to assess the ecological status of oceanic-temperate streams
in 154 study sites located in the same region during the years
1993 – 2009 (totaling 793 study cases). The discriminating capa-
bility between disturbed and reference sites (box-and-whisker
plot and lm) and the correct state classification power were
Furthermore, the CFi was validated with four other biologi-
cal indices to assess the ecological status of streams, as calcu-
lated for 98 streams during the period 2010 – 2014 (totaling 364
study cases). These indices were: 1) EFI+ (EFI+ Consortium
2009), based on fish assemblages and developed with the goal
to be applied across Europe, 2) ECP (Uragentzia 2012), based
on fish assemblages and applied in the Basque Country (a re-
gion of the Northern Iberian Peninsula), 3) MBi (Gartzia de
Bikuña et al. 2015), based on macroinvertebrate assemblages
and applied in the Basque Country, and 4) IPS (Coste 1982;
Uragentzia 2014), based on diatom assemblages.
Development of the index for Salmonid streams
Out of the 91 candidate metrics, 38 had the power
to discern between disturbed and reference sites. Of
these, 21 did not show a relationship with stress level.
Thus, redundancy analysis was carried out with the
17 metrics that were related with stress level (Table
2). The DE values ranged from 25.3 % (percentage of
density of intolerants to contamination and percentage
of S. trutta density) to 85.3 % (percentage of richness
of species with intermediate tolerance to contamina-
Table 4. Measured metrics and test values for sensitivity, correlation with stress level and discrimination efficiency (DE) for Cypri-
nid streams. For sensitivity, the degree of inter-quartile overlap in the box-and-whisker plot (+ means no overlapping) and p value
after ANOVA test are shown. For correlation with stress level r2 and p after Pearson correlation are shown. The positive or negative
correlation between each metric and stress level are indicated by ↑ and ↓, respectively. Out of the 91 calculated metrics, only those
having the power to discern between disturbed and reference sites are shown.
Metrics Sensitiveness Correlation with stress level DE (%)
Box plot pr2pSense
Density tolerant to contamination + 0.04
Density intolerants low O2+< 0.01 0.22 < 0.01 ↓56.0
% density intolerants low O2+< 0.01 0.17 < 0.01 ↓70.3
Density sestonics + < 0.01 0.22 < 0.01 ↓56.0
% density water column species + < 0.01 0.22 < 0.01 ↓93.4
% density benthic species + < 0.01 0.22 < 0.01 ↑41.8
Density insectivores + < 0.01 0.21 < 0.01 ↓50.5
Density P. bigerri +< 0.01 0.22 < 0.01 ↓52.7
% density P. bigerri +< 0.01 0.19 < 0.01 ↓69.2
Table 5. Spearman correlation test on metric redundancy for Cyprinid streams. The correlation values among selected metrics are
highlighted in bold.
Denisty intolerants low O2
Density water column species
Density P. bigerri
% density intolerants low O2
% density P. bigerri
% density water column species
% density benthic species
Density intolerants low O2 1.00
Density water column species 1.00 1.00
Density insectivores 0.96 0.96 1.00
Density P. bigerri 0.99 1.00 0.96 1.00
% density intolerants low O2 0.63 0.62 0.55 0.61 1.00
% density P. bigerri 0.64 0.63 0.55 0.63 0.98 1.00
% density water column species 0.55 0.56 0.47 0.54 0.92 0.91 1.00
% density benthic species – 0.55 – 0.56 – 0.47 – 0.54 – 0.92 – 0.91 – 1.00 1.00
322 B. Gartzia de Bikuña et al.
The selected metrics to integrate the index showed
high correlations with stress level, high values of DE
(Table 2), and were not intercorrelated (Table 3). Thus,
the percentage of density of benthic species, percent-
age of richness of species intolerant to contamination,
percentage of richness of species intolerant to low ox-
Table 6. Rating values and class boundaries for the CFi for each stream type. The river types are T1A (1 A–Salmonids), T1B (1 B–
Salmonids), T2 (2 –Cyprinides) and T3 (3 –Cyprinides-Suprahaline).
Class boundaries Rating values
T1A T1B T2 T3
High ≥ 0.85 ≥ 0.92 ≥ 0.88 ≥ 0.85
Good 0.84 – 0.64 0.91– 0.70 0.87– 0.67 0.84 – 0.65
Moderate 0.63 – 0.43 0.69 – 0.47 0.66 – 0.45 0.64 – 0.43
Poor 0.42 – 0.22 0.46 – 0.24 0.44 – 0.23 0.42 – 0.22
Bad ≤ 0.21 ≤ 0.23 ≤ 0.22 ≤ 0.21
Table 7. Relationship (r2 and p) after Pearson correlation between stress level and biotic-based indexes (CFi, EFI+, ECP, MBi and
IPS) for each river type. When the relationship is not statistically significant (p > 0.05), n.s. is shown. The river types are T1A (1 A–
Salmonids), T1B (1 B–Salmonids), T2 (2 –Cyprinides) and T3 (3 –Cyprinides-Suprahaline).
T1A T1B T2 T3
CFi 0.48 < 0.01 0.35 < 0.01 0.22 < 0.01 0.43 < 0.01
EFI+ 0.34 < 0.01 0.03 < 0.01 0.00 n.s. 0.03 n.s.
ECP 0.16 < 0.01 0.11 < 0.01 0.21 < 0.01 0.17 < 0.01
MBi 0.31 < 0.01 0.22 < 0.01 0.13 < 0.01 0.21 < 0.01
IPS 0.13 < 0.01 0.35 < 0.01 0.23 < 0.01 0.16 < 0.01
T1A T1B T2 T3
Fig. 2. Box–and-whisker plot for the CFi sensitivity test for each river type. The river types are T1A (1 A–Salmonids), T1B (1 B–Sal-
monids), T2 (2 –Cyprinides) and T3 (3 –Cyprinides-Suprahaline). Horizontal dark bars represent the median and the boxes denote
the interquartile range, whiskers represent the full data range within 1.5 times the interquartile range and circles denote extreme
323Fish-based index to assess the ecological status of oceanic-temperate streams
ygen concentration, percentage of insectivore density,
and percentage of S. trutta density finally shaped the
CFi. The metrics of the percentage of density of ben-
thic species were then transformed (1-value per unit).
Development of the index for Cyprinid streams
Out of the 91 candidate metrics, only 9 had the power
to discern between disturbed and reference sites (Table
4). Of these, only one did not show a relationship with
stress level. Thus, the redundancy analysis was carried
out with the eight metrics that were related with stress
level (see Table 5). The DE values ranged from 41.8 %
(percentage of density of benthic species) to 93.4 %
(percentage of density of water column species). Fi-
nally, two metrics, the percentage of density of water
column species and percentage of density of species
intolerant to low oxygen concentration, shaped the
CFi in this type of streams.
Quality classes and index validation
Prior to index calculation, all the metrics were rescaled
to values from 0 to 1, with the CFi, consequently,
showing the same range. The CFi range was split into
five quality classes for each stream type (Table 6) and
showed the capability to discriminate between dis-
turbed and reference sites (Fig. 2; ANOVA, p < 0.005),
as well as a negative relationship with stress level in
the four stream types (Table 7).
Of the 793 study cases used for validation, 559 were
correctly classified by the CFi (70 %) compared with
abiotic classification. The majority of inadequately
classified streams were initially designated as dis-
turbed, with the CFi indicating high or good ecological
status based on invertebrate assemblages. Only 9 cases
classified as reference based on abiotic characteristics
did not reach good quality. In T1A streams, 31 study
cases classified as reference by abiotic characteristics
did not reach maximum quality due to the longitudinal
discontinuity, measured as a lack or a low presence of
A. anguila. In any case, the CFi discerned between dis-
turbed and reference sites (Fig. 3; ANOVA, p < 0.05).
In general, the CFi was related with the other four
considered indices based on biological assemblages
(Fig. 4). Except for T2, CFi showed the highest degree
of relationship with the EFI+ index; in T2, the high-
est degree of relationship was with ECP. Despite the
existing relationships among indices, in general, CFi
showed a better correlation with stress level compared
with the other indices (Table 7).
T1A T1B T2 T3
Fig. 3. Box–and-whisker plot for the CFi sensitivity test to validate the applicability of the index in other study cases for each river
type. The river types are T1A (1 A–Salmonids), T1B (1 B–Salmonids), T2 (2 –Cyprinides) and T3 (3 –Cyprinides-Suprahaline). Hori-
zontal dark bars represent the median and the boxes denote the interquartile range, whiskers represent the full data range within 1.5
times the interquartile range and circles denote extreme values.
324 B. Gartzia de Bikuña et al.
Despite the numerous examples of multimetric indices
based on fish assemblages developed all around the
world (e.g. Karr 1981; Oberdorff & Hughes 1992; Mar-
zin et al. 2014; Melcher et al. 2007; Pont et al. 2007), all
indices vary in their core metrics due to the heteroge-
neity of environmental conditions, biological assem-
blages, and human pressures. In the Iberian Peninsula,
due to the peculiarities of fish assemblages (low num-
ber of species and great endemism; Doadrio 2001), the
application of multimetric indices through the study
of fish metrics developed in large projects such as EFI
(FAME Consortium 2004) and EFI+ (EFI+ Consor-
tium 2009), which tried to apply a unique index across
whole Europe, has presented problems and difficul-
T1A T1B T2 T3
0 0.5 1.0 1.5 0 0.5 1.0 1.5 0 0.5 1.0
0 0.5 1.0 1.5
R2= 0.50 R2= 0.00
R2= 0.20 R2= 0.31
R2= 0.13 R2= 0.23 R2= 0.47 R2= 0.12
R2= 0.17 R2= 0.10 R2= 0.22
R2= 0.07 R2= 0.13 R2= 0.06 R2= 0.23
Fig. 4. Relationships between CFi and the other biotic-based indexes (EFI+, ECP, MBi and IPS) for each river type. The river types
are T1A (1 A–Salmonids), T1B (1 B–Salmonids), T2 (2 –Cyprinides) and T3 (3 –Cyprinides-Suprahaline). Correlation lines are dis-
played when the relationships are significant (p < 0.05).
325Fish-based index to assess the ecological status of oceanic-temperate streams
ties. Therefore, in the last years, indices that take into
account local factors to be applied in small surface ar-
eas within the Iberian Peninsula, have been developed
(Aparicio et al. 2011; Hermoso et al. 2010; Magalhães
et al. 2008; Sostoa et al. 2010). Without exception, all
these indices were developed in Mediterranean re-
gions, which lacked an accurate tool to assess the eco-
logical status based on fishes in systems located under
oceanic climate conditions. Thus, the development of
the index presented here (CFi) tries to fill this gap.
The CFi, as the EFI+, was split into two sub-indi-
ces to separately assess the ecological status of salmo-
nid and cyprinid streams due to species and trait dif-
ferences. Although the metrics shaping the two types
of the CFi were different, they were selected because
they allowed discrimination between anthropogenic
influences and natural variability, and because they re-
flect human impairment, while having a good relation-
ship to the impacts.
Another condition to be matched by any metric is
the presence of low redundancy. In salmonid streams,
the percentage of S. trutta density showed the threshold
value to be considered as redundant with the percent-
age of richness of species intolerant to contamination.
Nevertheless, this metric was considered to shape the
index. This exception is not unique when construct-
ing multimetric indices (see Baptista et al. 2011; Gart-
zia de Bikuña et al. 2015), giving more attention to
the biological information than to the statistical tests.
Salmo trutta is the most widely distributed freshwa-
ter fish native to the Palearctic region and across most
of Europe, including the Iberian Peninsula, western
Asia, and parts of North Africa (Bernatchez 2001).
Moreover, despite the restocking with foreign trout
from central and northern Europe in the Iberian Penin-
sula in response to the reduction or total loss of natu-
ral populations, the populations from the Cantabrian
streams are considered autochthonous (Machordom et
al. 2000). This highlights the importance of this spe-
cies for the conservation of genetic diversity in this
region. Furthermore, of the local phylogenetic origin,
a trout-related metric (percentage of S. trutta density)
has been considered to shape the CFi, since this spe-
cies is sensitive to pollution and hydromorphological
alterations (Benejam et al. 2016; Vincze et al. 2015)
and dominant in the upper reaches of streams. In fact,
some headwater streams from this region are trout
monospecific. In these reaches, the CFi is not appli-
cable and the only metric to be applied is trout density
(see Uragentzia 2015b).
Apart from the streams where this index was de-
veloped, the CFi was able to discern between dis-
turbed and reference sites in other streams from the
same region in the Northern Iberian Peninsula (Can-
tabrian Mountain range). However, the ecological sta-
tus obtained by the CFi did not match in all cases the
reference-non reference classification derived from
the REFCOND (Wallin et al. 2003) based on abiotic
characteristics. In the majority of cases, these mis-
matches consisted of a classification by the CFi into
a high or good state in non-reference streams. In fact,
some human alterations do not have a significant ef-
fect on fish assemblages if some structural attributes
of stream ecosystems are maintained (Quinn 2005;
Wang et al. 2001). This fate highlights the limitations
in stream ecological state classification based on non-
biological aspects and the need to incorporate tools
based on biological assemblages in monitoring works.
Furthermore, ecological status classification based on
abiotic characteristics is restricted to delimited stream
reaches, ignoring the longitudinal connectivity of the
whole watershed. Obstacles in streambeds impede
movement and negatively affect the density of fishes
(Benejam et al. 2016). In this sense, the CFi takes into
account the degree of longitudinal connectivity by
measuring the presence of A. anguila (catadromous
species). Thus, reaches classified as reference based
on abiotic characteristics do not reach the maximum
value based on fish assemblages (lack or low density
of A. anguila) due to watershed discontinuity, a wide-
spread impact in the Iberian Peninsula with more than
8,500 obstacles in rivers (García de Jalón 2003). This
fate is important at the ecosystem level since the loss
of biodiversity alters the whole ecosystem functioning
(Cardinale et al. 2002). Thus, the use of indices based
on fish assemblages to assess the ecological status pro-
vides a deeper integrative view at the whole ecosystem
level than assemblages of more local distribution, such
as biofilm and macroinvertebrates.
In addition to the CFi validation, this index was
compared with other biological indices used or pro-
posed for application in streams and rivers within
the Cantabrian mountain region. In general, the four
considered indices showed positive relationships with
CFi, with the two indices based on fish assemblages
presenting higher correlation values. Nevertheless,
the explained variance did not exceed 50 % in the best
case (R2 ≤ 0.50, see Fig. 4). Moreover, in type T2,
the EFI+ was not significantly related to either CFi
or stress level. In addition, in the other stream types,
the EFi+ and ECP showed weaker responses to stress
level than CFi. This highlights the low applicability
of the previously proposed indices based on fish as-
semblages to assess the ecological status of streams
326 B. Gartzia de Bikuña et al.
in this region; it also underlines the need to develop
an accurate tool for this goal. The weak relationship
among CFi and other indices based on diatom (IPS)
and macroinvertebrate (MBi) assemblages was ex-
pected due to the response variability of the differ-
ent biological assemblages to different alterations at
temporal and spatial scales (Hering et al. 2006). Thus,
the complementary relationships among biotic indices
based on different assemblages demonstrate the need
to consider multiple organism groups to improve the
biotic integrity evaluation of streams (Hughes et al.
2009; Justus et al. 2010).
Overall, the CFi provides an integrative view of
stream ecosystem status based on fish assemblages
both in salmonid and cyprinid reaches. This index
not only takes into account organic pollution, but also
considers other kinds of alterations such as hydro-
morphology at the reach and whole watershed scale.
Therefore, it is a direct and user-friendly tool for man-
agers and decision-makers to employ in biomonitoring
works for assessing the ecological status of stream and
river ecosystems in the Northern Iberian Peninsula un-
der temperate oceanic conditions.
This study was supported by a contract undertaken with the
Basque Water Agency (URA). Aingeru Martínez was supported
by a grant from the University of the Basque Country.
Addinsoft, 2007: Stat, Analyse de données et statistique avec
MS Excel. – Addinsoft, New York.
Aparicio, E., Carmona-Catot, G., Moyle, P. B. & García-
Berthou, E., 2011: Development and evaluation of a fish-
based index to assess biological integrity of Mediterra-
nean streams. – Aquat. Conserv. Mar. Freshw. Ecosyst. 21:
324 – 337.
Baptista, D. F., de Souza, R. S. G., Vieira, C. A., Mugnai, R.,
Souza, A. S. & de Oliveira, R. B. S., 2011: Multimetric index
for assessing ecological condition of running waters in the
upper reaches of the Piabanha-Paquequer-Preto Basin, Rio
de Janeiro, Brazil. – Zoologia 28: 619 – 628.
Benejam, L., Aparicio, E., Vargas, M. J., Vila-Gispert, A. &
García-Berthou, E., 2008: Assessing fish metrics and biotic
indices in a Mediterranean stream: effects of uncertain native
status of fish. – Hydrobiologia 603: 197– 210.
Benejam, L., Saura-Mas, S., Bardina, M., Solà, C., Munné, A.
& García-Berthou, E., 2016: Ecological impacts of small hy-
dropower plants on headwater stream fish: from individual to
community effects. – Ecol. Freshw. Fish 25: 295 – 306.
Bernatchez, L., 2001: The evolutionary history of brown trout
(Salmo trutta L.) inferred from phylogeographic, nested
clade, and mismatch analyses of mitochondrial DNA varia-
tion. – Evolution (N. Y.) 55: 351– 379.
Cardinale, B. J., Palmer, M. A. & Collins, S. L., 2002: Species
diversity enhances ecosystem functioning through interspe-
cific facilitation. – Nature 415: 426 – 429.
CEN, 2003: Water quality. Sampling of fish with electricity. EN
14011. – European Committee for Standardization, Brussels.
Coste, M., 1982: Étude des méthodes biologiques d’apprécia-
tion quantitative de la qualité des eaux. – Rapp. Cemagref
QE Lyon-AF Bassin Rhône Méditerranée Corse.
Doadrio, I., 2001: Atlas y libro rojo de la ictiofauna continental
española. – NIMAM-CSCI, Madrid.
Docampo, L. & Gartzia de Bikuña, B., 1993: The Basque
method for determining instream flows in Northern Spain.
– Rivers 4: 292 – 311.
EC, 2010: CIS Guidance Document No. 14 “Guidance on the
Intercalibration Process 2008–2011.”
EFI+ Consortium, 2009: Manual for the application of the new
European Fish Index—EFI+. A fish-based method to assess
the ecological status of European running waters in support
of the Water Framework Directive.
ESRI, 2011: ArcGIS Desktop: Release 10. – Environmental
Systems Research Institute, Redlands, CA.
FAME Consortium, 2004: Manual for application of the Euro-
pean Fish index-EFI. Version 1.1.
Ganasan, V. & Hughes, R. M., 1998: Application of an index
of biological integrity (IBI) to fish assemblages of the rivers
Khan and Kshipra (Madhya Pradesh), India. – Freshw. Biol.
40: 367– 383.
García de Jalón, D., 2003: The Spanish experience in determin-
ing minimum flow regimes in regulated streams. – Can. Wa-
ter Resour. J. 28: 185 –198.
García-Berthou, E. & Bae, M. J., 2014: Aplicación de los peces
como indicadores en la cuenca del Ebro en cumplimiento de
la Directiva marco del agua. – Zaragoza.
Gartzia de Bikuña, B. & Docampo, L., 1990: Ecological impli-
cations of the analysis of the redox system of organic matter
in the streams of Vizcaya (Northern Spain). – Regul. Riv.
Res. Manage. 5: 329 – 340.
Gartzia de Bikuña, B., López, E., Leonardo, J. M., Arrate, J.,
Martínez, A. & Manzanos, A., 2015: Development of a mul-
timetric benthic macroinvertebrate index for assessing the
ecological condition of Basque streams (north of Spain). –
Fundam. Appl. Limnol. 187: 21– 32.
Green, J. & Swietlik, W., 2000: A Stream Condition Index (SCI)
for west Virginia wadeable streams. – Tetra Tech, Inc., Ow-
ings Mills, MD. www.littlekanawha.com/536_wv-index.pdf
Hering, D., Johnson, R. K., Kramm, S., Schmutz, S., Szoszkie-
wicz, K. & Verdonschot, P. F. M., 2006: Assessment of Euro-
pean streams with diatoms, macrophytes, macroinvertebrates
and fish: a comparative metric-based analysis of organism
response to stress. – Freshw. Biol. 51: 1757–1785.
Hermoso, V., Clavero, M., Blanco-Garrido, F. & Prenda, J.,
2009: Assessing freshwater fish sensitivity to different
sources of perturbation in a Mediterranean basin. – Ecol.
Freshw. Fish 18: 269 – 281.
Hermoso, V., Clavero, M., Blanco-Garrido, F. & Prenda, J.,
2010: Assessing the ecological status in species-poor sys-
tems: a fish-based index for Mediterranean Rivers (Guadiana
River, SW Spain). – Ecol. Indic. 10: 1152 –1161.
Hughes, S. J., Santos, J., Ferreira, M. T., Caraça, R. & Mendes,
A. M., 2009: Ecological assessment of an intermittent Medi-
terranean river using community structure and function:
evaluating the role of different organism groups. – Freshw.
Biol. 54: 2383 – 2400.
Justus, B. G., Petersen, J. C., Femmer, S. R., Davis, J. V. & Wal-
lace, J. E., 2010: A comparison of algal, macroinvertebrate,
and fish assemblage indices for assessing low-level nutrient
327Fish-based index to assess the ecological status of oceanic-temperate streams
enrichment in wadeable Ozark streams. – Ecol. Indic. 10:
Karr, J. R., 1981: Assessment of biotic integrity using fish com-
munities. – Fisheries 6: 21– 27.
Kennard, M. J., Pusey, B. J., Arthington, A. H., Harch, B. D. &
Mackay, S. J., 2006: Development and application of a pre-
dictive model of freshwater fish assemblage composition to
evaluate river health in eastern Australia. – Hydrobiologia
572: 33 – 57.
Maceda-Veiga, A., Green, A. J. & de Sostoa, A., 2014: Scaled
body-mass index shows how habitat quality influences the
condition of four fish taxa in north-eastern Spain and pro-
vides a novel indicator of ecosystem health. – Freshw. Biol.
59: 1145 –1160.
Machordom, A., Suarez, J., Almodovar, A. & Bautista, J. M.,
2000: Mitochondrial haplotype variation and phylogeog-
raphy of Iberian brown trout populations. – Mol. Ecol. 9:
Magalhães, M. F., Ramalho, C. E. & Collares-Pereira, M. J.,
2008: Assessing biotic integrity in a Mediterranean water-
shed: development and evaluation of a fish-based index. –
Fish. Manage. Ecol. 15: 273 – 289.
Malmqvist, B. & Rundle, S., 2002: Threats to the running water
ecosystems of the world. – Environ. Conserv. 29: 134 –153.
Marzin, A., Delaigue, O., Logez, M., Belliard, J. & Pont, D.,
2014: Uncertainty associated with river health assessment in
a varying environment: The case of a predictive fish-based
index in France. – Ecol. Indic. 43: 195 – 204.
Melcher, A., Schmutz, S., Haidvogl, G. & Moder, K., 2007:
Spatially based methods to assess the ecological status of
European fish assemblage types. – Fish. Manage. Ecol. 14:
453 – 463.
Meybeck, M., 2003: Global analysis of river systems: from
Earth system controls to Anthropocene syndromes. – Phil.
Transact. Roy. Soc. London B Biol. Sci. 358: 1935 –1955.
Munné, A., Prat, N., Sola, C., Bonada, N. & Rieradevall, M.,
2003: A simple field method for assessing the ecological
quality of riparian habitat in rivers and streams: QBR index.
– Aquat. Conserv. Mar. Freshw. Ecosyst. 13: 147–163.
Oberdorff, T. & Hughes, R. M., 1992: Modification of an index
of biotic integrity based on fish assemblages to character-
ize rivers of the Seine Basin, France. – Hydrobiologia 228:
Pont, D., Hugueny, B. & Rogers, C., 2007: Development of a
fish-based index for the assessment of river health in Europe:
the European Fish Index. – Fish. Manage. Ecol. 14: 427– 439.
Quinn, J., 2005: Effects of rural land use (especially forestry)
and riparian management on stream habitat. – N. Z. J. Forest.
49: 16 –19.
R Development Core Team, 2010: R: A Language and Environ-
ment for Statistical Computing. – R Foundation for Statisti-
cal Computing, Vienna.
Sanz de Galdeano, J. M. & Madariaga, C., 1992: Caracteri-
zación Hidrobiológica de la Red Fluvial de Alava y Gipuz-
koa. – Servicio Central de Publicaciones del Gobieno Vasco,
Schmedtje, U., Birk, S., Poikane, S., van De Bund, W. & Bonne,
W., 2009: Guidance document on the intercalibration process
2008 – 2011.
Sostoa, A. de, Caiola, N., Casals, F., García-Berthou, E., Alca-
raz Cazorla, C., Benejam Vidal, L., Maceda, A., Solà, C. &
Munné, A., 2010: Ajust de l’índex d’Integritat Biòtica (IBI-
CAT) basat en l’ús dels peixos com a indicadors de la quali-
tat ambiental als rius de Catalunya. – Dep. Medi Ambient i
Habitatge, General. Catalunya.
Sostoa, A. de, Casals, F., Caiola, N. M., Vinyoles, D., Sánchez,
S. & Franch, C., 2003: Desenvolupament d’un índex
d’integritat biòtica (IBICAT) basat en l’ús dels peixos com
a indicadors de la qualitat ambiental dels rius a Cataluña. –
Doc. tècnics l’Agència Catalana l’Aigua 203.
Stanfield, L. W. & Kilgour, B. W., 2013: How proximity of land
use affects stream fish and habitat. – Riv. Res. Appl. 29:
Thorp, J. H., Flotemersch, J. E., Delong, M. D., Casper, A. F.,
Thoms, M. C., Ballantyne, F., Williams, B. S., O’Neill, B. J.
& Haase, C. S., 2010: Linking ecosystem services, rehabilita-
tion, and river hydrogeomorphology. – Bioscience 60: 67–74.
Uragentzia, 2012: Red de seguimiento del estado ecológico de
los ríos de la CAPV. – Tomo de metodología.
Uragentzia, 2014: Protocolo de muestreo, análisis y evaluación
de organismos fitobentónicos en ríos vadeables.
Uragentzia, 2015a: Plan hidrológico. Parte española de la De-
marcación Hidrográfica del Cantábrico Oriental. – Revisión
Uragentzia, 2015b: Sistema de evaluación de la comunidad pis-
cícola en ríos de la CAPV.
Vincze, K., Scheil, V., Kuch, B., Köhler, H. R. & Triebskorn,
R., 2015: Impact of wastewater on fish health: a case study
at the Neckar River (Southern Germany) using biomarkers in
caged brown trout as assessment tools. – Environ. Sci. Pollut.
Res. 22: 11822 –11839.
Wallin, M., Wiederholm, T. & Johnson, R. K., 2003: Guidance
on establishing reference conditions and ecological status
class boundaries for inland surface waters. – CIS Work. Gr.
Wang, L., Lyons, J., Kanehl, P. & Bannerman, R., 2001: Impacts
of urbanization on stream habitat and fish across multiple
spatial scales. – Environ. Manage. 28: 255 – 266.
Manuscript received: 27 September 2016; Manuscript accepted: 03 February 2017.