Scaled mass index shows how habitat quality influences the condition of four fish taxa in north-eastern Spain, and provides a novel indicator of ecosystem health

ALBERTO MACEDA-VEIGA(1), ANDY J. GREEN(2), ADOLFO DE SOSTOA(3)

(1) School of Biosciences, Cardiff University, CF10 3AX Cardiff, Wales, UK.

(2) Department of Wetland Ecology, Estación Biológica de Doñana-CSIC, ES-41092 Sevilla, Spain.

(3) Department of Animal Biology & Biodiversity Research Institute (IRBio), Faculty of Biology, University of Barcelona, ES-08028 Barcelona, Spain.

IN PRESS: FRESHWATER BIOLOGY

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SUMMARY

1. Natural and anthropogenic disturbances are key forces governing the structure and functioning of aquatic communities. Understanding how these factors shape organism performance can help to identify the most vulnerable species and develop effective management strategies. This is particularly important for ichthyofaunas with high endemicity and low diversity, such as those of the Iberian peninsula.

2. We explored the suitability of a novel and simple condition index, the scaled mass index (SMI), based on mass-length relationships, for analysis of the effects that abiotic and biotic pressures have on the body condition of four fish taxa widely distributed in Mediterranean rivers in north-eastern Spain: Brown trout (Salmo trutta), Iberian redfin barbel (Barbus haasi), Ebro barbel (Luciobarbus graellsii) and minnows (Phoxinus spp.). The SMI performed better in explaining spatial variation in body condition than the Fulton Index, a traditional method for fish studies.

4. For all taxa, anthropogenic stressors influencing water quality and physical habitat explained more variance in SMI than other factors. Variation partitioning and GLM approaches consistently showed that SMI increased with elevation, reduced concentrations of toxic nitrogenous compounds, and well preserved riparian canopy and natural channel morphology, despite the fact that three of the study taxa are in expansion and generally considered “tolerant”. In addition, the application of SMI to an independent fish data-set showed that SMI provides a novel indicator of ecosystem health which performs better than the current index of biotic integrity developed in this region.

5. We discuss the likely mechanisms behind the strong effects of habitat quality on SMI, and the implications for our understanding of tolerance. Incorporating SMI into studies of fish monitoring is likely to improve the value of fish studies as indicators of river quality and ecological change. Further studies should compare the response of SMI to specific fish health indicators such as parasite load, haematological assays and pollutant bioaccumulation to improve our understanding of the value of SMI as a non-lethal diagnostic procedure.

Keywords: Mediterranean rivers, disturbance, endemic fish, body condition, non-lethal procedures, hierarchical partitioning, tolerance range, conservation status

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IntroductionFreshwater ecosystems have suffered a long-history of anthropogenic disturbances that are

responsible for declining fish populations (Dudgeon et al., 2006). The main threats to native

ichthyofauna are habitat degradation such as pollution events, water abstraction or riparian

coverage removal, and the introduction of exotic species due to angling and aquaculture

practises (Elvira, 1995; Smith & Darwall, 2005; Clavero & Hermoso, 2011; Moyle et al., 2011).

However, the effects of these anthropogenic pressures on fish species might be masked or

modified by natural factors governing the performance of individuals and the structure of

aquatic communities (Gasith & Resh, 1999; Ferreira et al., 2007; Boix et al., 2011). Among

these natural factors, elevation is a key geographical feature governing river hydromorphology,

water chemistry and climatic constraints (i.e. temperature and precipitation regime) (Sevruk,

1997; Murphy et al. 2013). Precipitation regime is particularly restrictive in some regions like

the Mediterranean area where sudden flooding events and prolonged drought periods drive life-

histories and the structure of aquatic communities (Gasith & Resh, 1999; Magalhães et al.,

2006; Benejam et al., 2010; Boix et al., 2011). Although native fish species evolved under these

conditions, they may succumb to the synergistic effect of drought periods and anthropogenic

disturbances (Benejam et al., 2010; Maceda-Veiga et al., 2009). To increase our understanding

of how natural and anthropogenic disturbances shape aquatic communities and organism

performance, it is crucial to identify the most vulnerable species and the causes of decline.

Studies on the response of fish species to natural and anthropogenic pressures increase

our understanding of the ecology of the species involved, and are also relevant to international

legislation such as the Water Framework Directive in Europe (EU Commission, 2000), and

Water Act in USA (Adler et al., 1993). This legislation requires freshwater managers to

determine the ecological status of waterbodies, combining results from different sentinel species

(e.g. algae, macroinvertebrates, fish, macrophytes) (reviewed by Jørgensen et al., 2005). The

application of these protocols using fish as bioindicators was first attempted with the so-called

indices of biotic integrity (Karr et al., 1986). These protocols have a long tradition in fish

studies and diagnostics are based on comparing community features between reference and

polluted reaches (Karr et al., 1996; Hughes & Oberdorf, 1999; Sostoa et al., 2003; Jørgensen et

al., 2005). However, these indices are coarse diagnostic approaches at a community level that

typically fail to detect subtle effects at the organism level. At best, when these effects are

detected, it is likely to be too late to facilitate species conservation programmes and to prevent

local extinction (Adams et al., 2002; Jørgensen et al., 2005; Maceda-Veiga, 2013).

Despite their easy applicability, the diagnostic ability of these protocols is limited when

a reduced set of community variables is available, as in fish communities with low diversity, or

when it is difficult to determine reference conditions due to widespread habitat degradation

(Hughes & Oberdorf, 1999; Sostoa et al., 2003; FAME 2004; Ferreira et al., 2007; Moyle et al.,

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2011). More specific diagnostics can be obtained by the application of biomarkers (e.g.

physiological variables, cell responses), but many of these procedures are costly or require

euthanasia (Van deer Oost et al., 2003; Lavado et al. 2006; Maceda-Veiga et al., 2013).

Although some non-lethal sampling methods are being validated in wild freshwater fish (Santos

& Pacheco, 2002; Tavares-Dias & Moraes, 2007; Maceda-Veiga et al., 2013), the current

conservation status of freshwater ichthyofauna would benefit from the identification of other

powerful, easy, economic and non-lethal invasive tools (Maceda-Veiga, 2013).

The determination of animal’s body condition (CI) based on mass-length relationships

is one potential non-lethal procedure, and is indeed widely applied in many ecological studies

(reviewed by Stevenson and Woods, 2006; Peig & Green, 2009). A CI should serve as a reliable

indicator of the body condition of each individual, allowing comparison between individuals in

an unbiased manner. CIs are often based on an animal´s weight (W) while adjusting for

difference in size, which for fish is usually expressed as some measure of body length (L)

(García-Berthou, 2001; Vila-Gispert & Moreno-Amich, 2001; Cade et al., 2008; Ogle &

Winfield, 2009; Giannetto et al., 2012). Adjusting for size in an effective way is crucial to avoid

biases and misleading results, but is not a straightforward matter (Green 2001). Fulton´s

condition factor (CF=W/L3) attempts to correct for the mass-length relationship, and it is still

widely used in fish CI studies even though it violates several key assumptions (reviewed by

García-Berthou & Moreno-Amich, 1993; Peig & Green, 2010). Its continued popularity might

be explained by its simplicity (i.e. fixed scale exponent) and also because scientists tend to

specialise and to adopt the methods of their peers rather than look for alternative methods from

other disciplines (Peig & Green, 2009, 2010). Although the ANCOVA (Analysis of Covariance)

method has been proposed as a powerful alternative (García-Berthou, 2001; Vila-Gispert &

Moreno-Amich, 2001; Benejam et al., 2009), when the relatively strict statistical assumptions

underlying this method are violated, Fulton's condition factor seems to be adopted as the

simplest alternative and preferred to other methods with their own limitations (e.g. mass-length

residuals from ordinary least squares (OLS) regression, García-Berthou, 2001; Green, 2001;

Ogle & Winfield, 2009; Newmann et al., 2012). Furthermore, the ANCOVA procedure can

produce results which are difficult to interpret, with no possibility for direct comparisons

between studies (Cade et al., 2008; Peig & Green, 2009, 2010).

As previously demonstrated in amphibians, birds and mammals (Peig & Green, 2009,

2010, MacCracken & Stebbings 2012, Guillemain et al. 2013), the scaled mass index (SMI) is

a powerful new alternative CI method, but it has not previously been adopted in fish (but see

Appendix 4 in Peig & Green 2009). The SMI is a method for standardizing body mass for a

fixed length (chosen by the researcher), based on the scaling relationship between mass and

length and bearing in mind that both variables have sources of error (unlike methods relying on

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OLS regression). One benefit of the SMI is that it enables comparison of CI between studies, as

required for bioassessment.

The ichthyofauna of north-eastern of Spain typifies well the current conservation status

and characteristics of fish communities in other Mediterranean climatic areas (Elvira, 1995;

Clavero & Hermoso, 2011; Moyle et al., 2011). The geographical range of the species in this

region has decreased by 60% in recent decades, and many species are listed as threatened

(Doadrio, 2001; Smith & Darwall, 2005; Maceda-Veiga et al. 2010). The conservation concern

is acute for the Iberian ichthyofauna because of the high degree of endemicity, and low species

richness at a basin scale (Doadrio, 2001; Elvira, 1995, Maceda-Veiga, 2013). The present study

aimed to test and compare the suitability of the scaled mass index (SMI) and the Fulton's

condition factor (CF) for detecting a link between body condition and anthropogenic

disturbances in fish. Previous studies have suggested that body condition in fish decreased with

anthropogenic disturbances such as poor habitat quality or water quality deterioration.

Therefore, we expected the relative response of CIs between species would be driven by the

strength of their relationship with body condition, which in turn determines susceptibility to

such environmental stressors. Finally, we aimed to test the potential of SMI as an index of river

quality, especially for areas of low fish diversity where the fish community structure reduces the

applicability of biotic indices.

Methods

Study area

We gathered environmental and fish community data from our own surveys performed in north-

eastern Spain (Iberian Peninsula) from 2002 to 2008, mainly for the development of an index of

biotic integrity in this region. This data set comprised 430 sampling sites that involved all

Catalonian catchments from the Muga to Riudecanyes basins, plus the complete River Ebro and

part of the Garonne basin (Fig. 1). These sampling sites accounted for all river typologies

present in this region in terms of flow, riparian characteristics, geology and water quality, as

described in previous studies (e.g Maceda-Veiga & De Sostoa, 2011). There rivers are

characterised by a typical Mediterranean hydrological cycle in which peak flows occur in spring

or autumn with a summer drought that sometimes extends to mid-autumn. Our surveys

concentrated on low flow conditions from late summer to middle autumn because this is when

fish populations are more stable and can be properly sampled using electrofishing (see below).

In this regard, we excluded sites that could not be surveyed properly mainly because the reach

was not fully wadeable, so as to diminish possible biases in fish captures. In addition we

excluded sites surveyed within the breeding season to avoid any effect of gonad weight on CI.

For the brown trout, the breeding season is between November and January in these catchments,

and for the cyprinids between March and July (Sostoa et al., 1990).

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Environmental variables

Water quality was analysed prior to fish sampling using a digital multiparametric YSI ® sonde

556 MPS for temperature (ºC), conductivity (µS/cm) and pH, and the colourimetric test kit

VISOCOLOR® for ammonium (mg/l), nitrite (mg/l), nitrate (mg/l) and phosphate (mg/l)

concentrations. To characterise physical habitat quality, we incorporated into our data-set a total

of 17 variables from two widely used habitat quality indices in this region: the riparian

vegetation quality index QBR (Munné et al. 1998), and a version of the U.S. Rapid

Bioassessment protocol (Barbour et al., 1999) for Mediterranean rivers (see RBA in Maceda-

Veiga & De Sostoa, 2011). Briefly, RBA ranked 10 features of the local habitat (microhabitat

structure, river channelization, channel morphology, water flow, degree of silting, erosion of

river margins, macrophyte coverage, and the coverage and width of riparian canopy) on an

ordinal scale of 1-10 (score increases with quality). RBA includes more variables related to

physical habitat for fish than the QBR but both consider the quality of riparian vegetation. QBR

strongly declines with the presence of exotic plant species in the riparian vegetation whereas

RBA does not. Elevation was also incorporated into our set of environmental variables (see

statistical analysis), measured for the village nearest to the sampling site using Google Earth®.

Fish sampling

We followed an international standardised fish sampling method (CEN standards EN 14962 and

EN 14011). Fish were sampled by a single-pass electrofishing using a portable unit which

generated up to 200V and 3 A pulsed D.C in an upstream direction, covering the whole wetted

width of the 100-m long reaches surveyed at each location (see also Maceda-Veiga et al., 2010).

The location of each sampling site within a reach was selected in the field based on accessibility

and representativeness, including a variety of habitat types (pools, rifles and runs). The same

equipment was used across sites and the crew had a standardised time devoted to the

electrofishing passes according to their own experience and the reach features. All fishes were

collected with nets, placed in buckets, anaesthetised with MS-222® (Sigma-Aldrich), measured

(L, mm), weighed to the nearest 0.01 g, and returned alive to the river. When a large amount of

fish was captured, a random sample of 40 per species was used at each sampling site for

biometric measurements. The condition analyses only included those species with at least 10

specimens measured per location and with a high frequency of occurrence in our data-set. Thus,

the species selected were as follows: Brown trout (Salmo trutta, nspecimens=3153, nlocations=152),

Iberian redfin barbel (Barbus haasi, nspecimens=1780, nlocations=86), Ebro barbel (Luciobarbus

graellsii, nspecimens=3550, nlocations=133) and minnows (Phoxinus spp., nspecimens=3575, nlocations=121).

We measured fork length with the exception of trout in which total length was determined. The

uncertain taxonomical status of Phoxinus spp. is due to the description of new species of the

former P. phoxinus in Spain after some of our surveys were performed (Kottelat, 2007; Doadrio,

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com. pers.). A straightforward change in the nomenclature of these species is complicated

because it is subjected to basin translocations by anglers (Maceda-Veiga et al., 2010). The total

density of introduced fish species at each site was also incorporated to our set of explanatory

variables as another indicator of anthropogenic pressure. The introduced fish species with the

highest occurrence in were bleak (Alburnus alburnus), common carp (Cyprinus carpio),

pumpkinseed (Lepomis gibbosus) and rudd (Scardinius erythrophthalmus) with densities up to

8000, 3100, 5755 and 1000 individuals per m2. However, commonly introduced exotic fish

predators such as blackbass (Micropterus salmoides), European perch (Perca fluviatilis) and

wels catfish (Silurus glanis) were also present (see Maceda-Veiga et al., 2010 for details).

Statistical analysis

As previously performed (Maceda-Veiga & De Sostoa, 2011), Spearman rho coefficient was

used to remove redundant variables (|rho|>0.7) from the combined set of 17 habitat features

assessed in the two indices of habitat quality (i.e. QBR and RBA) (see Appendix S1 in

Supporting Information). Principal component analysis (PCA) was then applied as an indirect

ordination technique to describe the main sources of variation and relationships between the

retained habitat features plus the physico-chemical water quality variables. Continuous variables

were log-transformed to improve linearization (Sokal & Rohlf, 1995). The “varimax” rotation

method (“principal” function in R) was used to increase the interpretation of axes and the

number of PCA axes examined was determined by the Kaiser`s rule, which states that the

minimum eigenvalue should be 1 when correlation matrices are used (Legendre & Legendre,

1998).

Calculating body condition indices

The Scaled Mass Index (SMI) was calculated as an index of body condition following Peig &

Green (2009), and is calculated with the following formula:

Scaled mass index (SMI) =W i[ L0

Li ]bSMA

where Wi and Li are the weight and length of each specimen respectively, L0 is a suitable length

to which the CI values are standardized, and bSMA is the scaling exponent, i.e. the slope of a

standardised major axis (SMA) regression (also known as RMA or reduced major axis) of the

mass-length relationship. In our case, for L0 we used the arithmetic mean of the data-set

analysed for each fish species, but other descriptors such as the median or geometric mean can

be used. It is important to report the L0 value used, so that it can be applied to other datasets to

enable cross-study comparison.

To compute the bSMA, we followed the two-step procedure described by Peig & Green

(2009). First, a bivariate plot of W and L was performed to identify outliers that strongly distort

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the expected relationship (see Fig. 1 in Peig & Green, 2009). At this step, the criteria to remove

outliers was maximising the better refit of the regression line. Then, points located outside the

main trend were removed. Secondly, we applied an SMA regression (using the “lmodel2”

function in R) to log-transformed weight and length values to determine the slope of the fitted

line (i.e. bSMA). An alternative procedure to calculate the bSMA is to divide the slope from the

standard ordinary least squares regression of W on L (bOLS) by the Pearson’s correlation

coefficient r (see Fig. 1 in Peig & Green, 2009). Outliers removed from analyses during the

computation of bSMA were recovered to calculate the SMI for these individuals, with the

exception of those values that were clearly measurement errors (i.e. cases where the

combination of W and L were not credible and likely to be explained by typical errors such as

misplacing a decimal point or a zero).

Finally, SMI results were compared with those from the Fulton's condition factor

(CF=Wi / Li^3 x 105), chosen as the alternative methodological approach which applies a fixed

scaling exponent for all species. We were unable to use the ANCOVA method as an alternative

CI because of heterogeneity of slopes (i.e. an interaction between length and basin, p<0.05)

(García-Berthou, 2001; Vila-Gispert & Moreno-Amich, 2001).

Generalised linear models (GLMs)

The relative performance of the scaled mass index (SMI) and Fulton's condition factor (CF) as

CIs and as indicators of the effects of habitat quality was examined with a series of general

linear models (GLM) with basin, elevation, total density of introduced fish species and the main

stressor gradients from PCA analysis (i.e. PC1 and PC2) as fixed factors. As validated

previously in this region (e.g. Murphy et al. 2013), elevation was used as a surrogate of the

longitudinal position of the reach in the stream, and summarised the role of natural spatial

gradients in fish condition. Basin was included as a categorical variable and its importance is

consistent with well documented landscape-scale influences in riverine systems (Williams et al.

2003; Coulthard et al., 2005; Murphy et al., 2013). Log-transformation was applied to elevation,

total density of introduced species and SMI to reduce heterocedasticity and increase model

fitting. We also used partial η2 (partial eta squared) as a measure of effect size (i.e. importance

of factors). Similarly to r2, partial η2 is the proportion of variation explained for a certain effect

(effect SS/(effect SS + error SS)). Partial η2 has an advantage over η2 (effect SS/total SS) in that

it does not depend on the amount of source variation in the ANOVA design used because it does

not use the total sum of squares (SS) as the denominator (Tabachnick & Fidell, 2001).

We used Gaussian errors and identity link function in GLMs. An analysis of variance

(“anova” function in R) was employed as a measure of goodness of fit. As the modelling

approach employed lacks a true variation coefficient (i.e. R2), we calculated a pseudo-R2

coefficient as follows: (null deviance – residual deviance)/null deviance. Best models were

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selected using a manual stepwise backward deletion of non-significant terms from the full

global models containing elevation, the two PCA gradients (PC1 and PC2) and the density of

introduced fish species and interactions. In cases where there was no significant improvement in

the model but there was a graphically observed relationship between the deleted variable and

the standardised residuals of the final model, this variable was finally included. Durbin Watson

tests (function “dwtest” in R) were also conducted to evaluate autocorrelation in our models.

Hierarchical partitioning

To complement the results of GLMs and to test the robustness of our results, we performed a

hierarchical partitioning analysis (“hier.part” function in R) (Walsh & Mac Nally 2011). HP

measures the increase in goodness of fit of all models with a particular explanatory variable,

compared with the equivalent model without that variable. An advantage of this approach is that

it controls for collinearity among explanatory variables which, even at low levels, can cause

variance inflation and lead to erroneous conclusions (Graham 2003). The removal of redundant

variables is not a completely safety procedure because of the independent effects (Freckleton

2011). Compared to partial model approaches, this statistical procedure enables more robust

assessment of variable importance, and the contribution of a single variable is neither enhanced

nor masked by its correlation with other explanatory variables (i.e. it increases the accuracy in

the determination of the relative importance of each individual explanatory variable) (Mac

Nally, 2002, Murray & Conner, 2009). Other modelling criteria such as AIC and model

averaging are discouraged because collinearity results in biased parameter estimates

(Freckleton, 2011). However, a minor rounding error is also attributed to HP as the number of

explanatory variables increases, but this is unlikely in our case because the number of

explanatory variables is almost half the recommended safety limit (<8 variables) (Mac Nally,

2002; Walsh & Mac Nally, 2011). We assessed the significance of HP models using a

randomization test for hierarchical partitioning analysis (function “rand.hp” in R). All analyses

were performed in R (R Development Core Team, 2013) using the libraries stats, lmodel2,

MASS, psych, HH and hier.part. Significance in HP analysis was based on the upper 0.95

confidence interval, but it was reached at p<0.05 in the remaining statistical procedures.

Implications of the incorporation of SMI into bioassessment procedures

To assess the potential improvement provided by the incorporation of SMI in diagnostics for

ecosystem health, we used two independent data-sets (not previously used in our study) for B.

haasi (n=38 sampling sites, from Figuerola et al., 2012 and unpublished data) and L. graellsii

(n=20 sampling sites, from Maceda-Veiga & De Sostoa, 2011 and unpublished data). Spearman

rank correlation coefficients were calculated between the two fish CIs addressed in our study

(i.e. SMI and CF), the current index of biotic integrity developed in this region known as

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IBICAT (Sostoa et al., 2003), the above mentioned indices of habitat quality (i.e. QBR and

RBA), and general indicators of water quality such as ammonium and nitrate concentrations,

and conductivity as unspecific indicators of other salts (i.e. chlorides) that usually increase their

concentration in polluted freshwaters (Cañedo-Argüelles et al., 2013). We additionally

considered the incorporation into this analysis of variables related to the composition and

abundance (i.e. absolute and relative) of native and introduced fish species at each sampling

point. For L. graellsii we used a random data-set from different locations in different rivers,

whereas the B. haasi data-set came from a stream with a monospecific fish community so that

total fish density was equal to total B. haasi density. The inherent characteristics of these two

data-sets also allowed us to test and compare the response of fish CI and the other indicators in

rivers with a moderate (≤6 species) or low fish (<2 species) diversity as often observed in other

Mediterranean rivers (Maceda-Veiga et al., 2010).

Results

Environmental gradients

Information on water and habitat quality was summarised in a PCA analysis that produced four

significant axes, which explained 64.96% of overall variation (Table 1). PC1 (a water quality

deterioration gradient) accounted for 29.60% of the variation and included ammonium, nitrite,

nitrate, phosphates and conductivity. PC2 (physical habitat quality) explained 13.28% of

variation and included physical habitat variables (riparian coverage, habitat structure, channel

morphology). PC3 accounted for 11.18% of the variation and included pH and water

temperature, whereas PC4 accounted for 10.84% of the variation and included the percentage of

macrophytes. Based on the loading of environmental variables at each PCA gradient, we only

considered PC1 and PC2 as environmental stressor gradients. These gradients were,

respectively, negatively and positively correlated with elevation (elevation-PC1: Pearson’s r=-

0.32 p<0.001; elevation-PC2: r=0.21 p<0.001). A weaker but highly significant correlation was

also observed between the density of introduced fish species and elevation (Pearson´s r =-0.18,

p=0.001).

Defining body condition indices

Details of the morphometric variables measured in all four fish species are provided in Table 2.

The relationship between fish weight (M) and (L) was nonlinear for all fish species but

linearized by log-transformation (S. trutta: R2=0.97, B. haasi: R2=0.91, L. graellsii: R2=0.98, and

Phoxinus spp.: R2=0.87). In contrast to the Fulton's condition factor assumption, growth was

clearly non-isometric in fish species because bSMA values deviated somewhat from 3 in all

species (S. trutta: bSMA = 2.82, B. haasi: bSMA =2.52, L. graellsii: bSMA =2.75 and Phoxinus spp.:

bSMA = 1.83) and all confidence intervals for bSMA were below 3 (Table 2).

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Relationship between body condition indices and environmental variables

As the environmental stressors and the density of introduced fish species were significantly

correlated with elevation to some extent, it was necessary to disetangle this relationship and

analyse the independent and joint effects that these predictors have on condition indices. At

least one of the environmental stressor gradients from the PCA, i.e. PC1 and/or PC2, was

retained as significant, and explained the highest independent contribution to the variance in the

HP analyses for all species (Table 3). Together with these stressor gradients, elevation was also

retained as a significant independent contributor in S. trutta and L. graellsii based on the

randomization permutation test. In all models, the variation explained in SMI was always higher

than for the Fulton's condition factor (Table 3). None of the HP models retained basin or the

density of introduced fish species as significant variables (Table 3).

These results were mostly concordant with a GLM approach. The estimated explained

variation for full GLM models was as follows: S. trutta (SMI: pseudo-R2 = 0.36 and CF:

pseudo-R2 =0.24), B. haasi (SMI: pseudo-R2 = 0.63 and CF: pseudo-R2 =0.56), L. graellsii

(SMI: pseudo-R2 = 0.43 and CF: pseudo-R2 =0.31) and Phoxinus spp. (SMI: pseudo-R2 = 0.46

and CF: pseudo-R2 =0.72). SMI and CF were mostly concordant in the variables that achieved

significance in the models, and no significant autocorrelation was observed (dw.test, all cases

p>0.18) (Table 4). The contribution of each predictor to the final model determined by the

partial eta squared was concordant in most cases with the HP approach (Table 4). However, the

percentage of variation explained by GLM models was lower than the variation explained by

each independent contributor highlighted in HP. In any case, the highest explained variation

tended to be higher in SMI than in CF with the exception of Phoxinus spp. (Table 4). Given that

SMI was the index that generally accounted for the highest explained variation in the modelling

approaches, we generated simple scatterplots with fitted linear trend lines to visualise the

relationship between SMI, elevation and the environmental stressors that achieved significance

in the above mentioned analysis (Fig. 2).

Comparison of diagnostic approaches

SMI performed better based on correlation coefficients than CF in addressing either water

quality or physical habitat degradation in two independent data-sets analysed for B. haasi and L.

graellsii (Table 5, Fig. 3). SMI was poorly correlated with the current index of biotic integrity

(IBICAT), possibly because of the non-significant relationship between IBICAT and RBA

(r=0.32, P=0.17), QBR (r=0.36, P=0.12), conductivity (r=-0.10, P=0.75), ammonia (r=-0.20,

P=0.39) and nitrates (r=-0.05, P=0.84). Conversely, the percentage of native fish species in the

community was strongly correlated with IBICAT scores (r=0.84, P<0.001). The major causal

factor for SMI seemed to be RBA for L. graellsii (r=0.67, P=0.001) and QBR for B. haasi

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(r=0.76, P<0.001). However, nitrate concentration seemed to favour SMI in B. haasi (r=0.57,

P<0.001) while ammonia concentration showed opposing correlations with SMI in L. graellsii

(r=-0.56, P=0.01) and B. haasi (r=0.42, P=0.009). In addition, SMI was negatively correlated

with the total abundance of fish for B. haasi (r=-0.48, P=0.002, note B. haasi was the only

species present in this dataset).

Discussion

Defining body condition indices

The suitability of the scaled mass index (SMI) as a condition index is confirmed for the four fish

species analysed in our study. Although Fulton's condition factor (CF) is in widespread use, it is

based on simplistic assumptions of isometry which were violated in all our study species.

Furthermore, it did not perform as well as the SMI in detecting significant responses of fish to

environmental stressors. Although CF has previously been criticised (Stevenson & Woods

2006), it still performed better than SMI in one of our eight analyses, and also fared better than

several other CI methods when compared with SMI for small mammals (Peig & Green, 2010).

As previously suggested by Peig & Green (2010), this might be explained because the scaling

relationship assumed by the CF (i.e. W is proportional to L3) is reasonably close to the true

scaling relationship, since most bSMA values ranged between 2.5-2.9. The discrepancies observed

between CF and SMI in the current data-set observed in Phoxinus spp. may be related to the

unusually low scaling exponent of this species (1.83). The scaling exponent of Phoxinus spp.

may be subjected to bias in field studies owing to the relatively high contribution of water

droplets to the mass of this small fish (author’s observation). An additional explanation to the

low bSMA of Phoxinus spp. might be the uncertain taxonomical status of minnows considered in

this study. As the data for this study were collected before the description of a new minnow

species in the region, these results might have been influenced by the putative occurrence of

various species identified as the same species, each of which may differ in their shape and hence

their mass-length relationships (Kottelat & Freyhof, 2007). Human-made translocations due to

angling practices could also be a confounding factor, but this seems unlikely given the good

functioning of SMI in Luciobarbus graellsii or Salmo trutta which are also often translocated in

this study area (Maceda-Veiga et al., 2010).

The explanatory predictors also explained more variation in scaled mass index than in

CF, further suggesting that SMI is the better CI. Although the comparison of explanatory power

between studies based on different data-sets is challenging, the variation explained by SMI in

the current study was within the range 30 - 80% reported in other studies exploring the effects

of anthropogenic disturbances on fish, either using ANCOVA (e.g. Vila-Gispert & Moreno-

Amich, 2001; Oliva-Paterna et al., 2003; De Miguel et al. 2013) or CF (e.g. Clavero et al.,

2009; Figuerola et al., 2012; Lyssimachou et al., 2013). Unlike these alternative methods, the

12

functioning of SMI is based on the calculation of the weight of each individual at a standardized

body size, as indicated by L0 (Peig & Green, 2009). This allows a direct interpretation of the

results obtained by the SMA because it is a standardized weight measurement (in g) in contrast

to the results obtained from other approaches such as ANCOVA or CF (Peig & Green, 2009).

Cade et al. (2008) also provides a valid, but data-hungry method for studying fish condition,

although one which does not facilitate cross-study comparisons, and would therefore be harder

to integrate into biotic indices. Many previous studies of condition in fish, especially in North

America, have been based on standard length-weight relationships calculated with OLS

regression (e.g. Ogle & Winfield 2009, Neumann et al. 2012). Owing to their reliance on OLS,

these methods perform poorly compared to SMI (see Peig & Green 2010 for Relative condition

‘Kn’ and Relative mass ‘Wr’). When computing SMI, the parameter “L0” can be any value within

the range of L observations. In the current study we used the arithmetic mean of L for all

individuals collected, but our results would have been the same had we chosen the median or

some other measure. By using the same L0 value, condition can be directly compared between

studies (as required for bioassessment), so long as authors present their results in detail (Peig &

Green 2009, 2010).

Relationship between body condition indices and environmental variables

Anthropogenic modifications were found in this study to make the largest independent

contribution to fish condition in the four species, regardless of the effect of elevation. Many

studies have analysed the effects of natural and anthropogenic factors in governing the structure

of fish communities (e.g. species richness, abundance, invasions) (Rahel et al., 1991; Magalhães

et al., 2002, 2007; Boix et al., 2011). Briefly, an increase in the richness of native and

introduced fish, and a decrease in native abundance are observed along the upstream-

downstream gradient. Conversely, few studies have analysed the independent contribution of

elevation and anthropogenic modifications on organism performance, especially on body size

and condition (e.g. Carmona-Cabot et al., 2011; Murphy et al., 2013). In this regard, statistical

approaches such as hierarchical partitioning analysis used in the current study allow the

independent contribution of each explanatory variable to be disentangled (i.e. the effect of

collinearity is removed) (Mac Nally, 2002, Murray & Conner, 2009). Following this approach,

Murphy et al. (2013) also highlighted the effects of anthropogenic modifications over elevation

on the population size structure of the native Catalan chub (Squalius laietanus) in this study

area. Although direct effects of elevation on fish condition remain largely unknown, a trend in

life-history within species has been detected in which fish populations inhabiting cold-waters

tend to grow more slowly, mature later, have longer life-spans and allocate more energy to

reproduction than populations at lower latitudes, although there are discrepancies (Blanck &

Lamouroux, 2007; Carmona-Cabot et al., 2011; Budy et al., 2013). As elevation is used in these

13

studies as a surrogate of the spatial gradient in rivers, it is likely that CIs interact with either

other unmeasured environmental factors that correlate with elevation (e.g. water temperature,

distance to the sea, river slope) (Maceda-Veiga et al., 2010; Murphy et al., 2013), or biological

factors such as productivity, competition and predation that are also expected to change along

an elevation gradient (Vannote et al., 1980; Rahel et al., 1991). For instance, fish at lower

elevation are likely to be subjected to a higher risk of predation by birds and other predators or

competitors such as introduced fish species, and this is likely to reduce fish activity and

foraging intake rate (Allouche et al. 2001; Maceda-Veiga et al., 2010).

The response of SMI in the species analysed in the current study is consistent with

decreases reported elsewhere in fish CI under the effects of anthropogenic disturbances (e.g.

Oliva-Paterna et al., 2003; Benejam et al. 2010; De Miguel et al. 2013). However, of the four

species analysed, the response of CI to anthropogenic impacts has only previously been studied

in B. haasi. Figuerola et al., (2012) reported an increased in B. haasi CF associated with the

same habitat quality features such as habitat structure and river channel morphology assessed in

the current study. In the Mediterranean barbel (Barbus meridionalis), a species closely-related

to B. haasi, changes in CI using ANCOVA in relation to water quality or habitat structure have

also been observed (Vila-Gispert et al., 2000; Vila-Gispert & Moreno-Amich, 2001). Similarly,

in Sclater’s barbel Luciobarbus sclateri, a species from the south of Spain similar to L. graellsii,

ANCOVA also showed a reduction in CI associated with physical habitat deterioration such as

water flow reduction and absence of refugia (Oliva-Paterna et al., 2003). However, as for

elevation, variation in the response of SMI to anthropogenic modification might be directly or

indirectly affected by any factor that changes body condition and indeed body shape (e.g.

selection for streamlining), and therefore our analyses cannot tell us the mechanisms for the

strong effects we have detected for water quality or physical habitat degradation.

The observed decrease in CI under the effects of anthropogenic impacts suggests that all

four species are sensitive to anthropogenic disturbances. Interestingly, recent studies applying

average weighted models and ordination methods to fish abundances or occurrence categorised

three of these species as intolerant to water and habitat quality deterioration, L. graellsii being

the exception (Oberdoff et al., 2002; Maceda-Veiga & De Sostoa, 2011; Segurado et al. 2011).

This raises the question of which is the best approach to determine species susceptibility to

anthropogenic modifications, and/or what is the meaning of the tolerance categories in previous

studies. A species “completely tolerant” to anthropogenic disturbances does not really exist

since all species prefer living in good environmental conditions (see also Kennard et al. (2005)

and Maceda-Veiga & De Sostoa (2011)). Therefore, the health of all fish species is expected to

be affected by water and habitat deterioration, including widespread “invaders” and “tolerant”

species such as common carp (Cyprinus carpio) (Benejam et al. 2010) or eastern-mosquitofish

(Gambusia holbrooki) (Edwards & Guilette, 2002). Species tolerance should be defined based

14

on how a targeted species responds to the same environmental gradients compared to its peers

(Meador & Carlisle, 2002; Segurado et al. 2011; Maceda-Veiga et al. 2013). A relative

terminology should be applied to these tolerance categorisations, and the consequences of using

restrictive criteria is typified by two native species, B. meridionalis and S. laietanus in this study

area (Maceda-Veiga et al. 2013; Murphy et al., 2013). According to physiological and

histological markers, S. laietanus appeared to have fewer pathological responses to sewage

discharges than B. meridionalis (Maceda-Veiga et al. 2013). This suggests that S. laietanus can

be considered more tolerant to the water pollution in this river than B.meridionalis, which

agrees with previous tolerance classifications (Oberdoff et al., 2002; Maceda-Veiga & De

Sostoa, 2011; Segurado et al. 2011) that have been questioned by others (Murphy et al., 2013).

In the case of the reduction we observed in L. graellsii CI in response to anthropogenic

modifications, this should not be considered as incompatible with a “tolerant” category because

it may be that other species in the region are even more sensitive to these environmental

stressors.

Comparison of diagnostic approaches

The independent data-set used for addressing the improvements to be gained by the

incorporation of SMI into bioassessment studies also showed the negative tendency observed in

our large-scale study between SMI in L. graellsii and deterioration of habitat or water quality,

measured as the RBA index and ammonium concentration, respectively. Although this decrease

in CI of L. graellsii associated with poor water quality was not previously reported in the

literature, such an effect has been reported for the similar ecological species L. sclateri (Oliva-

Paterna et al., 2003). Because bioassessment procedures often portray fish as poor indicators of

water quality compared to other sentinel organisms such as diatoms and macroinvertebrates

(Jørgensen et al., 2005), our study illustrates the benefits of incorporating more refined

indicators in bioassessment procedures able to detect subtle changes in fish communities.

Indices of Biotic Integrity (IBIs) using fish as bioindicators can be strongly correlated with

other indicators of ecosystem health (Benejam et al. 2008). However, although we observed that

IBICAT (the current index of biotic integrity for our study area) decreased in association with

habitat and water quality deterioration, significance was not achieved in this index and no

variation was observed when applied to the monospecific community of B. haasi. This lack of

significance in IBIs might be attributed to subjectivity in the categorisation of certain IBICAT

variables (i.e tolerance ranges) or simply to the narrow range of variables available (i.e. low fish

diversity and high degree of endemicity) that diminish the diagnostic ability of these indices

when they are applied to Mediterranean fish communities, a limitation which is especially

evident for the B. haasi data-set (Ferreira et al., 2007; Segurado et al. 2011; Maceda-Veiga et

al., 2012; Figuerola et al., 2012). An additional explanation could be that, when exotic fish

15

species are present, IBIs are scored much lower (i.e. driven by exotic fish species occurrence)

and thus may have a poor relationship with water and habitat quality conditions, as suggested by

Benejam et al. (2008). In fact, we found that IBICAT was positively associated with the relative

abundance of native fish. We therefore urge caution when inferring a direct relationship

between low IBI scores and bad environmental conditions because of the potential confounding

effect of poor environmental conditions and/or the presence of introduced fish species (Benejam

et al., 2008).

The response of SMI in B. haasi was also significantly related to habitat or water

quality indicators, and increased with nitrates, conductivity and ammonium. Although a long-

term exposure either to ammonium or nitrate can lead to deleterious effects for fish (Noga 2003;

Camargo et al., 2006), we cannot assume that there is straightforward toxicity relationship

between these nitrogenous compounds and SMI in B. haasi given the possible temporal

fluctuations, especially in ammonium concentration (less stable than nitrates). Furthermore, the

tolerance range of this species to these compounds is unknown, whilst sewage effluent contains

a complex mixture of compounds each of which makes an unknown contribution to the SMI

response. Assuming that these compounds are not over the “no toxic effect level” for B. haasi, a

slight increase in ammonium and nitrate concentrations are likely to enhance ecosystem

productivity and, as SMI can be expected to indicate energetic reserves (Peig & Green 2009,

2010), the positive results could indicate increased food intake. Alternatively, our results might

also indicate that factors other than physico-chemical water quality are also responsible for an

increase in SMI, particularly habitat quality, which also showed a high correlation. In this

regard, QBR achieved the highest correlation with SMI in B. haasi and performed better than

RBA. Although these indices can be correlated, our results might be caused by the effect of the

introduction of exotic plants (e.g. Arundo donax, Platanus hispanica) on fish CI as QBR is

strongly reduced when exotic plants occur (Munné et al., 2003). In fact, these plant species

dominated the riparian coverage of some reaches in this stream (Aparicio 2003), and exotic

stands are likely to reduce the food supply for fish and thus their CI. Important prey items for B.

haasi such as aquatic and terrestrial invertebrates could be strongly depleted in reaches invaded

by A. donax, as in rivers with Mediterranean climatic conditions in the USA (Herrera and

Dudley, 2003). A high fish density was also associated with a decrease in SMI in this

monospecific fish community (of B. haasi) perhaps due to trophic competition which may be

intense in these small streams during drought periods (Aparicio 2003). In any case, although

different habitat indices have been found to be important for the two fish species analysed and

we have observed contrasting responses, e.g. for ammonium concentration in B. haasi and L.

graellsii, the direct comparison between these two species is not recommended because two

independent data-sets were analysed.

16

Conclusions

This study demonstrates the suitability of SMI for addressing condition in fish species

inhabiting Mediterranean rivers. However, the perfect method to determine ecosystem or fish

health does not exist, and it is the combination of indicators of impairment at different levels of

organisation (e.g. community, population, organism) that will give us the best diagnostic picture

(Adams et al., 2002; Pacheco & Santos, 2002; Van deer Oost et al., 2003; Jørgensen et al.,

2005; Maceda-Veiga, 2013). In fact, no CI should be assumed to accurately reflect ‘true

condition’ without analysing body composition and the response of the CI measurement and

specific indicators of disease or physiological disruption (Peig & Green, 2009). The SMI will be

confirmed as an ideal non-lethal diagnostic approach in wild and caged fish populations when it

has been further validated by comparing its response to other indicators of health impairment

such as parasite load, haematological assays and pollutant bioaccumulation.

Acknowledgements

We thank all the people that participated in field surveys and database management, and the

Catalan Water Agency (ACA), the “Confederación Hidrográfica del Ebro (CHE)” and the

Natural Parks of the “Collserola” and “Sant Llorenç del Munt i Serra de l’Obac” for financial

support. AMV is currently funded by a Marie Curie Fellowship (Para-Tox, PIEF-GA-2012-

327941).

17

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Table 1 Loadings for axes 1, 2, 3 according to PCA built with water physico-chemical variables and habitat quality features measured in rivers from north-eastern Spain. Bold values are considered high ≥ 0.4.

Environmental variables PC1 PC2 PC3 PC4Habitat structure -0.24 0.67 0.04 0.22Riparian coverage -0.09 0.79 0.08 -0.06Channel conservation -0.05 0.80 0.00 -0.16pH 0.03 0.02 0.92 0.04Temperature 0.27 -0.28 0.72 -0.04Ammonium 0.80 -0.08 -0.15 -0.09Nitrite 0.79 -0.13 -0.08 0.06Nitrate 0.76 -0.09 0.15 -0.02Phosphates 0.49 -0.23 -0.45 0.23Conductivity 0.43 -0.31 0.15 0.41Macrophytes -0.06 0.04 -0.05 0.90

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Table 2 Mean body length (L mm), weight (W g), Fulton condition factor (CF), scaled mass index (SMI) and standard deviations (SD), and details of the scaling exponents used to calculate the SMI in four native species of north-eastern Spain. The regression coefficients for standardised major axis regressions of W on L (bSMA) and the 95% confidence intervals are also shown. Mean length shown was used as Lo when calculating SMI. SMI is given in g, CF in g/cm3

Species n L±SD W±SD CF±SD SMI±SD bSMA bSMA (CI 95%)

S. trutta 3153140.9±60.

3 45.3±34.31.30±0.

344.3±20.

2 2.82 2.81-2.84

B. haasi 1780 92.0±34.5 15.7±17.11.42±0.

6 11.2±4.4 2.49 2.46-2.54

L. graellsii 3550129.3±24.

5 50.5±45.81.40±0.

247.1±20.

4 2.75 2.73-2.79

Phoxinus spp. 3575 54.3±13.9 2.3±1.91.16±0.

3 1.9±0.8 1.83 1.80-1.85

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Table 3 Independent contribution (%) of the environmental predictors to the explained variation of the hierarchical partitioning models performed on scaled mass index (SMI) and Fulton's condition factor (CF). Bold values indicated the highest independent contribution. The CI in bold indicates which CI accounted for the highest joint contribution of the environmental predictors that achieved significance (*). Significance was reached at the 95% confidence interval based on a randomized permutation test (rand.hp function, see methods). In all significant cases, CI increases with elevation, higher water quality and physical habitat quality (see Fig. 2).

Species CIBasin

Elevation

Water quality (PC1)

Physical habitat quality (PC2)

Introduced species

S. truttaSMI 2.64 30.30* 1.45 63.90* 1.66CF 3.44 38.39* 2.04 54.50* 1.61

B. haasiSMI

28.46 3.75 57.96* 7.56 2.26

CF28.6

3 8.02 54.69* 8.17 0.48

L. graellsiiSMI 2.00 30.37* 5.31 60.23* 2.08CF 7.19 6.77 6.81 78.48* 0.74

Phoxinus spp.

SMI 1.18 17 76.48* 3.43 1.91CF 8.11 5.72 57.92* 22.61 5.64

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Table 4 Results of the final GLM models for scaled mass index (SMI) and Fulton's condition factor (CF) that include the significant variables highlighted in previous full models with interactions. Partial eta squared (η2) and pseudo-R2 indicated, respectively, the weight of each predictor on the final model, and the proportion of variation explained by the model (see methods). The CI in bold is the one that achieved the highest proportion of variation explained.

Species Variables SS df Fp-

value η2 Pseudo-R2 (%)S. trutta SMI model 25

Elevation 0.16 1 6.42 0.0120.0

4

Physical habitat (PC2) 0.96 1 39.3<0.00

10.2

1

Residuals 3.6314

8

CF model 14

Elevation 0.34 1 5.74 0.0180.0

4

Physical habitat (PC2) 1.03 117.3

3<0.00

10.1

1

Residuals 8.7714

8

B. haasi SMI model 21

Water quality (PC1) 0.95 114.7

5<0.00

10.1

6Residuals 5.00 84

CF model 17

Water quality (PC1) 0.80 117.8

9<0.00

10.1

6Residuals 3.79 84

L. graellsii SMI model 24

Elevation 0.48 113.6

7<0.00

10.1

0

Physical habitat (PC2) 0.69 119.6

2<0.00

10.1

3

Residuals 4.5312

9

CF model 7

Physical habitat (PC2) 0.27 1 9.52 0.0030.0

7

Residuals 3.713

0

Phoxinus spp. SMI model 21

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Elevation x Water quality (PC1) 0.53 1

16.64

<0.001

0.13

Residuals 3.6911

7

CF model 25

Basin x Elevation 1.11 6 5.33<0.00

10.2

2Elevation x Water quality (PC1) 0.41 1

11.96

<0.001

0.10

Residuals 3.8511

1

Table 5 Spearman rank correlation coefficients between fish body condition indices (i.e. SMI

and CF), and habitat quality indices (i.e. QBR and RBA), the index of biotic integrity using fish

as bioindicators in this region (i.e. IBICAT) and some common abiotic and biotic variables

applied in water and fish community health diagnostics. Bold values indicated significance at

P<0.05.

Response of SMI Response of CFLuciobarbus graellsii data-set (n=20) r P value r P valueQBR 0.11 0.65 0.09 0.78RBA 0.67 <0.001 0.49 0.03IBICAT 0.23 0.32 0.01 0.60Native fish species abundance (ind/ha) 0.11 0.65 0.03 0.89Introduced fish species abundance (ind/ha) -0.07 0.76 0.04 0.85% Native fish species in abundance 0.20 0.40 0.03 0.88Ammonium concentration (mg/l) -0.56 0.01 -0.48 0.03Nitrate concentration (mg/l) -0.38 0.10 -0.26 0.28Conductivity (µS/cm) -0.13 0.56 -0.13 0.54

Barbus haasi data-set (n=38) r P value r P valueQBR 0.76 <0.001 0.70 <0.001RBA 0.68 <0.001 0.64 <0.001IBICAT nd nd nd ndNative fish species abundance (ind/ha) -0.48 0.002 -0.46 0.003Introduced fish species abundance (ind/ha) nd nd Nd nd% Native fish species in abundance nd nd nd ndAmmonium concentration (mg/l) 0.42 0.005 0.40 0.01Nitrate concentration (mg/l) 0.57 <0.001 0.54 <0.001Conductivity (µS/cm) 0.56 <0.001 0.55 0.003

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30

Fig. 1 Location of sites sampled for the current study in the north-eastern of Iberian Peninsula (Catalonia. Spain)

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Fig. 2 Scatterplots with fitted linear trends showing the relationship between mean scaled mass index (SMI, g) values and other variables at a given location for Salmo trutta (A), Barbus haasi (B), Luciobarbus graellsii (C) and Phoxinus spp. (D). Plots are shown for elevation and the environmental stressor gradients that achieved significance (except in the case of elevation for B. haasi) according to general linear models and hierarchical partitioning analysis. SMI values were not log-transformed so as to facilitate the interpretation of axes..

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Fig. 3 Bivariate relationships between SMI and ammonium concentration (A) and RBA (B) in L. graellsii, and ammonium concentration (C), nitrate concentration (D), conductivity (E), RBA (F), QBR (G) and native fish abundance (H) in B. haasi. Note that only significant relationships (from Table 5) are shown.

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Appendix S1 Bivariate correlation analyses between the 17 habitat features compiled from the RBA and QBR indices after Maceda-Veiga & De Sostoa (2011). Data are only presented for the strongest correlations observed (Spearman´s rho ≥0.7).

Habitat featuresSpearman's rho Pvalue

Riparian coverage Coverage structure 0.88<0.001

Riparian quality 0.81<0.001

Riparian coverage width 0.71<0.001

Riparian structure Riparian quality 0.83<0.001

Riparian coverage width 0.72<0.001

Riparian quality Riparian coverage width 0.70<0.001

Channel conservation Channelisation 0.71<0.001

Channel morphology 0.71<0.001

Habitat structure Flow 0.70<0.001

Erosion 0.70<0.001

Degree of siltation 0.70<0.001

Habitat diversity Flow 0.72<0.001

Channelisation Channel morphology 0.86<0.001

Flow Erosion 0.72<0.001

Degree of siltation 0.71<0.001

Degree of siltation Erosion 0.68<0.001

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Appendix S2. Mean body length (L mm), weight (W g), Fulton condition factor (CF), SMI and standard deviations (SD) of the independent data-sets for L. graellsii and B. haasi used for comparison with biotic indices.

Species n L±SD W±SD CF±SD SMI±SDL.graellsii 2082 103.1±55.15 51.6±68.28 1.37±0.12 44±7.38B. haasi 1178 119.46±26.48 40.8±23.76 1.46±0.11 32.5±2.80

Note that the scaling exponent (bSMA) was adapted for the computation of scaled mass index (SMI) in this new B. haasi data-set (bSMA=2.96, L0=131).

Appendix S3. Mean ammonium and nitrate concentrations (mg/l), conductivity (µS/cm), QBR and RBA scores, native species abundance (ind/ha), percentage of native species in the community, IBICAT scores, and the range of minimum and maximum values of the independent data-sets used for L. graellsii and B. haasi to validate the application of the scaled mass index (SMI) in relation to other bioassessment procedures.

Luciobarbus graellsii data-set (n=20) Mean Range(min-max)QBR 21 0-75RBA 64 35-100IBICAT 2 1-5Native fish species abundance 1992 0-16275Introduced fish species abundance 8078 11-92726% Native fish species in abundance 29 0-96Ammonium concentration 1.27 0-5Nitrate concentration 3.6 0-15Conductivity 1344 437-4108

B. haasi data-set (n=38) Mean Range(min-max)QBR 38 10-80RBA 90 66-100IBICAT 5 5Native fish species abundance 7181 178-25163Introduced fish species abundance 0 0% Native fish species in abundance 100 100Ammonium concentration 0.26 0-0.9Nitrate concentration 7.70 5-12Conductivity 929 534-1148

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