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Assessing the ecological stress in a Garonne River stretch, southwest France
Ecological Indicators 67 (2016) 466–473
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Short communication
Assessing the ecological stress in a Garonne River stretch, southwest France M. Ching Villanueva a , Alonso Aguilar Ibarra b,∗ a b
IFREMER Centre de Bretagne, Laboratoire de Biologie Halieutique, Z.I. Pointe du Diable, BP 70, 29280 Plouzané, France Ecole Nationale Supérieure Agronomique de Toulouse, 1, Avenue de l’Agrobiopôle BP 107, Auzeville-Tolosane 31326, France
a r t i c l e
i n f o
Article history: Received 26 January 2015 Received in revised form 15 February 2016 Accepted 19 February 2016 Available online 25 April 2016 Keywords: Ecological stress Fish assemblages Garonne River ABC index Flow regime Water quality Jackknife estimations
a b s t r a c t In order to assess the level of ecological stress caused by the pollution from local disturbances in a stretch of the Garonne River, France, we applied the Abundance-Biomass Comparison (ABC) index, using fish assemblages. Data were collected in a 10-year span (1992–2002) in a reference site and in two pollutionexposed sites. The ABC index mean value in the reference site (S1) was 0.03 ± 0.002 (95% Confidence Interval – CI); for the polluted sites (S2 and S3), the values were −0.09 ± 0.002 (95% CI) and −0.12 ± 0.002 (95% CI), respectively. The ABC index showed that, besides flow variations, both downstream sites are statistically different (p < 0.05) from the reference site, but all three seem to be under moderate stress. Furthermore, we related our ABC scores to water quality and flow regime variables in the reference site and one of the polluted sites by means of a cluster analysis. The results showed that, in the reference site, the ABC scores are closely related to the flow regime, while in the polluted site, downstream a urban area, ABC is related to water quality variables such as phosphates and total phosphorous. We argue that ecological indicators can help decisions on environmental damage liability. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Lotic ecosystems provide valuable environmental services, including water for agricultural production, tourism, leisure, fish˜ et al., 2013; Gilvear et al., 2013). These eries and navigation (Acuna ecosystems are, nevertheless, under the pressure of industrial and urban effluents, erosion, runoff from agricultural activities, and changes in river morphology (Ibarra, 2005). Such disturbances generate ecological stress. Ecological stress, due to either natural or anthropogenic activities, provokes diverse physiological responses on an individual level and, in chronic cases, affects biological community structure (Rambouts et al., 2013). Warwick (1986) introduced a simple method for determining ecological stress by means of the Abundance-Biomass Comparison (ABC) index. Under free-stress conditions, the fish community is assumed to be approaching equilibrium and the biomass is dominated by few large species, each represented by few individuals. But as stress becomes more severe, fish communities become increasingly dominated numerically by
∗ Corresponding author at: Environmental Economics Research Unit D-209, Instituto de Investigaciones Económicas, UNAM, Circuito Mario de la Cueva, Ciudad Universitaria, Mexico City 04510, Mexico. Tel.: +52 55 56 23 01 00x42431. E-mail addresses: [email protected] (M.C. Villanueva), [email protected], [email protected] (A.A. Ibarra). http://dx.doi.org/10.1016/j.ecolind.2016.02.049 1470-160X/© 2016 Elsevier Ltd. All rights reserved.
small species. The scores of the index are negative in heavily stressed conditions, near zero in moderately stressed situations, and positive for no stress. The ABC index has proved useful for assessing communities of benthic invertebrates (Warwick, 1986; Beukema, 1988; Meire and Dereu, 1990; Warwick and Clarke, 1994; Rakocinski et al., 2000), as well as riverine (Coeck et al., 1993; Penczak and Kruk, 1999; Pinto et al., 2006), estuarine (Villanueva, 2004; Cerfolli et al., 2013) and marine fishes (Yemane et al., 2005). The pressures of industrial activities and recreational demands imposed on aquatic ecosystems have resulted in biological stress at different levels of organization, especially on fish populations (Crook et al., 2015). In fact, fish are reckoned as good indicators of an ecosystem’s ecological state (Lasne et al., 2007; Pont et al., 2007; Rambouts et al., 2013). As in many other basins, the Garonne River is an interesting example for analyzing the interaction between economic activities and ecological indicators (Baque, 2006). Our interest in studying this area is because the Garonne basin is one of the most important basins in the world (Revenga et al., 1998) as it contains a river network with a diversity of habitats comprising several ecoregions which range from the mountains to the Gironde estuary. Furthermore, since a number of human activities are related to rivers in this basin (Baque, 2006), we try to understand how these activities can be conciliated with new concepts of water quality and ecosystem health (Carr and Neary, 2008). The European Water Framework Directive (EWFD) requires a chemical and ecological assessment of European waters by 2015 and needs
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Fig. 1. Study area in the stretch on the Garonne river. A reference site (S1) and two downstream (S2 and S3) sites were sampled. The star shows the effluent discharge coming from St. Gaudens town and the paper mill. Source: Ibarra (2004).
an integrated, risk-based management approach to achieve this goal (Brack et al., 2009). Aside from using chemical status concepts to reduce excessive water contaminations, another of its leading principles is the reflection of ecological state based on ecological quality ratios. In fact, it acknowledges that studying and regenerating biotic communities is essential for sustainable development (Reyjol et al., 2014). Thus, this paper aims to present a case study for assessing the level of ecological stress of a fish community in a stretch of the Garonne River, southwest France, by means of the ABC index. Another major purpose of the study is to present a jackknifing methodology for a multi-year application for this index, and its quantitative relationship with water quality and flow regime variables. This paper further develops the preliminary results reported by Ibarra (2004). 2. Methods 2.1. Area of study The area under study is situated near the town of Saint Gaudens in the Department of Haute-Garonne, in the piedmont of the Pyrénées range, southwest France, and has a special interest for environmental policy for several reasons. On the one hand, important economic activities provide social benefits such as commerce, tourism, sportfishing, hiking, hydropower generation and most notably, paper pulp production by the CDRA-TEMBEC paper mill. The paper mill has a relevant role in the local economy but also has an environmental cost as it uptakes water in order to produce white pulp and then discharges the used water into the Garonne River. Indeed, paper mill effluents affect physicochemical and ecological dynamics of both riverine and marine living resources (Ihejirika et al., 2011; Kanu and Achi, 2011; Dey and Das, 2013; Negi and Rajput, 2013) and can also induce pathologies on human populations (Lee et al., 2002). In 1995, a waste treatment plant was set up as a secondary treatment for biodegradable substances coming from the paper mill and from the city of Saint Gaudens. Once the plant was working, the paper mill increased in 68% its annual production from 1996 onwards and was required to monitor effluent impacts on both water quality and fish populations in the river (Lim, 2000). On the other hand, rivers are environmental assets that need to be conserved and protected because they provide ecosystem services such as biodiversity maintenance, microclimate regulation, flood control and amenities values (Postel and Thompson, 2005). The river in this sector has an average width of 50 m, with a substrate dominated by pebbles and gravel (80%) and
about 20% of blocks and flagstones, and a rather rapid flow. This stretch of the Garonne River is defined by Reyjol et al. (2001) as a transition between a Salmoniform-dominated assemblage and a Cypriniform-dominated assemblage. Ibarra et al. (2005b) demonstrated that fish assemblages form, in fact, nested patterns in an aggregated hierarchy in the whole Garonne river system. 2.2. Field collections Electrofishing of fish populations was carried out in the stretch between Saint Gaudens and Beauchalot towns during late summer (i.e. early and mid-September) in 1992 and 1994–2002 using a fixed Heron-type group (tension: 600–900 V and intensity: 1–2 A) in three sampling sites: one upstream (site 1) of the paper mill effluent serving as the reference site, and two about eight km (site 2) and nine km (site 3) downstream (Fig. 1). This scale of analysis allows a more accurate interpretation of the ABC index performance. All three sampling sites were chosen in non-regulated flowing sectors. It should be noted that sampling site 3 was located several km upstream from the present location due to changes after hydrological events in 1995. However, this new sampling site was chosen as having the most similar conditions to the former site (Lim, 2000). Each sampling site was fished twice with successive passages at a constant fishing effort according to the DeLury method, following the recommendations of Laurent and Lamarque (1974). Total weight and total length of fish were recorded before putting them back to the river. Fish biomass and fish abundance were thus calculated by the De Lury method using Winfish© software (Segura, 1998). Appendix A shows fish species composition for the three sampling sites during the study period. The resulting data set (available as supplementary material) was used to evaluate the ABC method. 2.3. Index computation Warwick (1986) had introduced a simple method for determining the presence of stress in a given area based on abundance distribution. Later, Clarke (1990) suggested the calculation of W (termed after Warwick) as: S
W=
(Bi − Ai )
i=1
50(S − 1)
= ABC index
(1)
where Bi is the biomass value and Ai is the abundance (or individual) rank value of each species rank (i) and Ai and Bi do not necessarily refer to the same species. S is the number of species in the sample.
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Table 1 Names and codes of water quality and flow variables in the Garonne river stretch at Saint-Gaudens town for the period 1992–2013. Sources: (1) Adour-Garonne Basin Information System and (2) HYDRO database. Variable
Unit
Variable code
Source
Mean flow Discharge Runoff curve number Chemical Oxygen Demand Biological Oxygen Demand Dissolved Oxygen Oxygen saturation rate Ammonium Nitrites Nitrates Total Phosphorous Phosphates Minimum pH Maximum pH Temperature
m3 /s l/s/km2 mm mg/l mg O2 /l mg O2 /l % mg/l mg/l mg/l mg/l mg/l U pH U pH ◦ C
FLOW DISCH CN COD BOD DISSO2 %O2 NH4 NO2 NO3 totP PO4 MINpH MAXpH TEMP
(1) (1) (1) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2)
In a biomass–abundance plot, when the biomass curve is above the abundance curve, it indicates a low disturbed ecosystem; whereas the inverse orientation of curves suggests a stressed or disturbed ecosystem. In cases when the two curves overlap, a value of W (i.e. ABC index) close to 0 indicates moderate perturbations (Warwick, 1986). We applied a jackknife resampling method (Manly, 1998) for reporting the ABC index scores. This is a straightforward technique which allows to estimate any bias in the samples, and to also examine the stability of the estimates (Fried and Stampfer, 2006). It consists in considering each observation (i.e. biomass and abundance data) as a unique piece of information, repeating the estimation n − 1 times. In other words, we repeated the computation of the ABC index omitting one observation in turn. The pseudo-values (i.e. jackknifed estimations) computed by this technique have the feature of being normally distributed variables and therefore, the corresponding mean, standard deviation and confidence intervals are good estimators of the index (Magurran, 1998: 42). Nevertheless, we performed non-parametric Kolmogorov–Smirnov tests (Lehmann, 2006) for verifying normality of both the pseudo-values and ABC index estimates. Please refer to the supplementary spreadsheet material where the whole data set and jackknifed computations are given. 2.4. Environmental variables Information on both water quality and flow regime data was obtained from two different sources. On the one hand, the AdourGaronne Basin Information System (SIEAG: www.adour-garonne. eaufrance.fr; last accessed on January 11, 2016) provided the annual mean data on water quality for the Garonne River stretch at SaintGaudens town (Sampling site code: RNDE-05181800), and for the Garonne River sector at Labarthe Inard (Sampling site code: RNDE5181000) from 1992 through 2013. Both sampling sites correspond to sites 1 and 2, respectively, of this study. On the other hand, annual means of flow regime variables were retrieved from the HYDRO database (www.hydro.eaufrance.fr; last accessed on January 29, 2016) for the Garonne River stretch at Saint-Gaudens town (sampling site code: O0200020) from 1992 through 2013. It corresponds to the reference site (S1) of this study. No information was available on flow for the other sites (S2 and S3). However, Poff and Allan (1995) pointed out that flow conditions within a range of 15 km can be considered as representative of hydrological conditions downstream a gauging station. The variables analyzed are shown in Table 1. Thus, the information from both databases (i.e. SIEAG and HYDRO) coincided in both space and time with our sampling sites
S1 and S2, for which statistical analyses were performed for linking the ABC index estimates with water quality and flow regime variables. The analysis was conducted as follows: First, since the variables mean flow (FLOW) and discharge (DISCH) were highly correlated (i.e. Spearman correlation coefficient >0.99), the variable DISCH was set aside from statistical analyses. Second, the annual mean values for the period 1992–2002 of the 12 physico-chemical variables, the two flow regime variables and the jackknifed ABC index estimates of sampling sites S1 and S2, were standardized with mean 0 and standard deviation 1. This standardization is needed in multivariate analysis when variables are measured in different units (Everitt and Dunn, 1991). Third, cluster analyses and the corresponding dendrograms were produced. The principle of cluster analysis consists in creating a classification obtained by incorporating pairs of the closest elements in a hierarchy of groups in the form of trees or dendrograms. The importance of dendrograms is that they give an idea of the number of classes actually present in the population under study. The Ward’s Method with squared Euclidean distance was applied since it is a clustering technique that takes into account the variance in order to optimize at each step the pairwise clustering (Everitt and Dunn, 1991). Dendrograms were produced with and without the flow regime variables (i.e. FLOW and CN). This was done for data harmonization, such that they were exactly the same for both sampling sites (S1 and S2), whereas the other water quality variables were site-specific. Fourth, as fish sampling stopped after the end of the project in 2002, there are no recent data. We reckon, nevertheless, that the unavailability of recent information can be considered as a technical limitation but not a scientific one. Hence, statistical tests (i.e. one-way ANOVAs) were performed to show by how much the most closely related variables to the ABC index estimates in both sampling sites S1 and S2, changed in the period 2003–2013 with respect to the period 1992–2002. 3. Results and discussion The ABC curves for the three sampling sites (1992–2002) in the Saint Gaudens-Beauchalot sector are shown in Appendix B. According to a one-factor ANOVA test of the ABC scores for the three sites (normality previously confirmed with a Kolmogorov–Smirnov test – see supplementary material), both sites 2 and 3 present higher stress than the upstream reference site. It is important to note that along the 10-year span the ABC index fluctuated for all three sites, meaning that natural conditions have contributed to stress fish assemblages in this river stretch (Reyjol et al., 2001). Only a few similar studies have been conducted in polluted European rivers. For example, Coeck et al. (1993) found an annual ABC index score of – 13.69 in a sampling site under heavy pollution and physical disturbance, whereas an unpolluted and unregulated stretch scored 9.74. The study of Penczak and Kruk (1999) obtained a value of −13.23 in an overfished river (Pilca River) in Poland. In contrast to these studies, we applied the ABC method over a 10-year span, showing moderate stress in the sector analyzed. Our observed values for the ABC index indicate that sites 2 and 3 seem to be under higher stress than the reference site (Fig. 2). The trend for both downstream sites was clearly downwards after the paper pulp production increased in 1996, and in spite of the waste treatment plant implementation in 1995 (Table 2). Although raw wastewater pollution can be an additional cause of perturbation on fish communities in the study sites, the actual and specific effect of the paper mill as stress will remain, nevertheless, as a hypothesis because the paper mill discharge effluent converges with the sewage effluent from St. Gaudens town. Furthermore, our sampling sites were located adjacent urban areas, and the regulated
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Table 2 Jackknifed ABC index estimations for the Garonne River sampling sites. Year
1992 1994 1995 1996 1997 1998 1999 2000 2001 2002
Site 1
Site 2
Site 3
Mean
Conf. Interval (5%)
Mean
Conf. Interval (5%)
Mean
Conf. Interval (5%)
−0.039 0.093 −0.048 0.069 0.118 0.024 −0.019 −0.047 −0.058 0.184
0.001 0.001 0.004 0.000 0.002 0.000 0.002 0.003 0.002 0.003
−0.087 0.112 −0.086 0.009 −0.051 −0.103 −0.161 −0.067 −0.286 −0.223
0.002 0.001 0.002 0.001 0.002 0.002 0.001 0.001 0.002 0.004
−0.261 −0.029 −0.131 −0.162 0.044 −0.184 −0.046 −0.127 −0.228 −0.079
0.001 0.002 0.001 0.002 0.001 0.001 0.000 0.001 0.001 0.001
Table 3 One-way ANOVAS between 1992–2002 and 2003–2013 periods for each variable in sampling sites S1 and S2. Only p-values are shown; n.d. = no data.
Fig. 2. Boxplots showing the jackknifed ABC index estimations for the three sampling sites. Boxplots with the same letter represent no statistical difference (p < 0.05) between sites.
flow sector contributes, no doubt, to modifying fish dynamics in this stretch. In spite of all these influences, the recorded level of observed stress is still moderate. Indeed, while there is a statistical difference between the upstream reference and the two downstream sites, all values fluctuate around a value of zero (Fig. 2). Fig. 3a clearly shows that the estimates of the ABC index are more closely related to the flow regime variables than to water quality variables in the reference site. In fact, fish diversity in rivers is influenced by both natural and anthropogenic factors. For example, the flow regime is a major driver of fish population dynamics (Poff and Allan, 1995; Murchie et al., 2008; Poff and Zimmerman, 2010; Mims and Olden, 2012) and biodiversity (Iwasaki et al., 2012). Fish community perturbations due to hydrological flow regime modifications were also observed by Murchie et al. (2008) on their comparative study of regulated rivers. When FLOW and CN variables are left out, the dendrogram shows that the ABC index is not linked to another group at a distance shorter than 8 (Fig. 3b). In contrast, the ABC index is grouped along with variables such as PO4 and totP at a shorter in the dendrograms of Fig. 3c and d. Hence, independently of the flow regime, the ABC index is related to pollution variables in sampling site S2. Hence, the ABC index gave us a rapid insight into the ecological consequences of both water pollution and flow regulation. This result suggests that pollution and other stressors are stronger than auto-restorative capabilities of the river, at least in the sector studied. It can also be noted, that the ABC index demonstrated to be a useful indicator of pollution, since it was related to variables that indicate either urban or agricultural pollution in the sector under
Variable
S1
S2
FLOW CN COD BOD DISSO2 %O2 NH4 NO2 NO3 totP PO4 MINpH MAXpH TEMP
0.918 0.195 0.025 0.381 0.630 0.059 0.000 0.000 0.590 0.003 0.023 0.003 0.604 0.295
n.d. n.d. 0.686 0.043 0.087 0.001 0.000 0.000 0.401 0.010 0.037 0.004 1.000 0.537
study (Fig. 3c, d). In this case, the urban areas, most notably Saint Gaudens town, do have an influence on fish assemblages. Agricultural pollution is generally characterized by nitrates (NO3 ), nitrites (NO2 ) and phosphates (PO4 ) and can be found downstream of agricultural plots due to runoff. In fact, Ibarra et al. (2005a) demonstrated that non-point source pollution has a negative influence on fish guilds in the whole Garonne basin. Although ecological conditions in this stretch might have changed since the samplings were made and no other field collections were performed after the period considered in this study, this work was able to provide the state of the ecosystem during that time. Elsewhere, other studies have validated the use of the ABC index on samples collected several years in advance of the analysis (e.g. Coeck et al., 1993; Pinto et al., 2006) and were useful in establishing ecosystem ecological reference points for future studies. In fact, fish assemblages have proven to be useful indicators of environmental quality for long-term and large spatial scales (Ibarra, 2005; Yemane et al., 2005). With respect to sampling site S1, Table 3 shows that the flow regime variables had no statistically significant difference (p > 0.05) when comparing survey dates (1992–2002) with the 10 years aftermath (2003–2013). Hence, if the ABC index in a 10-year span (1992–2002) was closely related to the flow regime in sampling site S1 (Fig. 3a), then, it should be so for the following period (i.e. 2003–2013), as suggested by Poff and Zimmerman (2010) for rivers elsewhere. In the case of water quality variables, some of the closest variables to ABC index (according to dendrograms of Fig. 3), presented a statistically significant difference with the ABC index scores (p < 0.05). This was the case of NO2 , totP and PO4 in both sampling sites (Table 3). This might be due to the improvement of river water quality throughout the years thanks to water pollution control in France, which has been strongly focused on giving
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Fig. 3. Dendrograms (Ward’s Method with Squared Euclidean distance) for ABC index scores, water quality and flow regime variables, for the Garonne river stretch at St. Gaudens town (1992–2002). (a) Reference site S1 with flow regime variables; (b) reference site S1 without flow regime variables; (c) sampling site S2 with flow regime variables; (d) sampling site S2 without flow regime variables.
financial aid to clean technologies for reducing the amount of wastes in ecosystems (LeRoch and Mollard, 1996). Presumably, the ABC index could also reflect this improvement along these years in the Garonne River stretch under study. The ABC index has both advantages and drawbacks. An advantage is that it clearly shows contrasting patterns of the entire species abundance distribution (Magurran, 1998). A practical disadvantage of this method was pointed out by Tokeshi (1993), since two types of abundance data are necessary to be collected, which is costly and time consuming. Furthermore, collection of samples should be undertaken in a rigorous and standardized manner as the method is sensitive to natural variations. Finally, potential distinction of pollution characteristics and sources from plotted curves is not possible using this method. Yet, ecological indicators such as the ABC index can help decisions on the performance of industries and municipalities obtaining such aid. For example, a further step in this investigation would be to estimate the environmental damage in monetary terms of the urban and paper pulp effluents in this stretch. Hence, ecological stress might eventually guide environmental damage liability
assessments. Aside from this, cumulative impacts of anthropogenic perturbations enlisted above are, in fact, occurring in the river stretch under study. Hence, disentangling the effects of both natural and anthropogenic-driven disturbances remains as another opportunity for further investigation.
Acknowledgements This study was part of the dissertation thesis of AAI, and was partly sponsored by the Mexican-French Cooperation Program Conacyt-Sfere (Contract No. 131742) and the Association Toulousaine d’Ichthyologie Appliquée. A number of people were involved in sampling surveys, both students and personnel of the École Nationale Supérieure d’Agronomie de Toulouse, specially Puy Lim and Francis Dauba. We thank José F. Frey Aguilar for helping with data processing. The comments of three anonymous reviewers notably improved a former draft.
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Appendix A. Species list and presence in each sampling site (1992–2002) Scientific name
Common name
Alburnus alburnus Barbatula barbatula Barbus barbus Chondrostoma toxostoma Cottus gobio Esox lucius Gobio gobio Lampetra planeri Leuciscus cephalus Leuciscus leuciscus Perca fluviatilis Phoxinus phoxinus Rutilus rutilus Salmo trutta fario
Bleak Stone loach Barbel Toxostome Bullhead sculpin Pike Gudgeon Brook lamprey Chub Dace Perch Minnow Roach Brown trout
Site 1
Site 2
Site 3
1 1
1 1 1
1 1 1 1
1 1 1 1
1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
Appendix B. ABC plots (i.e. k-dominance curves) for each sampling site (1992–2002) Figs. B1–B3.
Fig. B1. ABC plots (k-dominance curves) for sampling site S1 (1992–2002). Grey line: cumulative % biomass. Black line: cumulative % abundance. Means and confidence intervals (5%) of ABC index are shown.
Fig. B2. ABC plots (k-dominance curves) for sampling site S2 (1992–2002). Grey line: cumulative % biomass. Black line: cumulative % abundance. Means and confidence intervals (5%) of ABC index are shown.
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Fig. B3. ABC plots (k-dominance curves) for sampling site S3 (1992–2002). Grey line: cumulative % biomass. Black line: cumulative % abundance. Means and confidence intervals (5%) of ABC index are shown.
Appendix C. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2016. 02.049.
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Short communication
Assessing the ecological stress in a Garonne River stretch, southwest France M. Ching Villanueva a , Alonso Aguilar Ibarra b,∗ a b
IFREMER Centre de Bretagne, Laboratoire de Biologie Halieutique, Z.I. Pointe du Diable, BP 70, 29280 Plouzané, France Ecole Nationale Supérieure Agronomique de Toulouse, 1, Avenue de l’Agrobiopôle BP 107, Auzeville-Tolosane 31326, France
a r t i c l e
i n f o
Article history: Received 26 January 2015 Received in revised form 15 February 2016 Accepted 19 February 2016 Available online 25 April 2016 Keywords: Ecological stress Fish assemblages Garonne River ABC index Flow regime Water quality Jackknife estimations
a b s t r a c t In order to assess the level of ecological stress caused by the pollution from local disturbances in a stretch of the Garonne River, France, we applied the Abundance-Biomass Comparison (ABC) index, using fish assemblages. Data were collected in a 10-year span (1992–2002) in a reference site and in two pollutionexposed sites. The ABC index mean value in the reference site (S1) was 0.03 ± 0.002 (95% Confidence Interval – CI); for the polluted sites (S2 and S3), the values were −0.09 ± 0.002 (95% CI) and −0.12 ± 0.002 (95% CI), respectively. The ABC index showed that, besides flow variations, both downstream sites are statistically different (p < 0.05) from the reference site, but all three seem to be under moderate stress. Furthermore, we related our ABC scores to water quality and flow regime variables in the reference site and one of the polluted sites by means of a cluster analysis. The results showed that, in the reference site, the ABC scores are closely related to the flow regime, while in the polluted site, downstream a urban area, ABC is related to water quality variables such as phosphates and total phosphorous. We argue that ecological indicators can help decisions on environmental damage liability. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Lotic ecosystems provide valuable environmental services, including water for agricultural production, tourism, leisure, fish˜ et al., 2013; Gilvear et al., 2013). These eries and navigation (Acuna ecosystems are, nevertheless, under the pressure of industrial and urban effluents, erosion, runoff from agricultural activities, and changes in river morphology (Ibarra, 2005). Such disturbances generate ecological stress. Ecological stress, due to either natural or anthropogenic activities, provokes diverse physiological responses on an individual level and, in chronic cases, affects biological community structure (Rambouts et al., 2013). Warwick (1986) introduced a simple method for determining ecological stress by means of the Abundance-Biomass Comparison (ABC) index. Under free-stress conditions, the fish community is assumed to be approaching equilibrium and the biomass is dominated by few large species, each represented by few individuals. But as stress becomes more severe, fish communities become increasingly dominated numerically by
∗ Corresponding author at: Environmental Economics Research Unit D-209, Instituto de Investigaciones Económicas, UNAM, Circuito Mario de la Cueva, Ciudad Universitaria, Mexico City 04510, Mexico. Tel.: +52 55 56 23 01 00x42431. E-mail addresses: [email protected] (M.C. Villanueva), [email protected], [email protected] (A.A. Ibarra). http://dx.doi.org/10.1016/j.ecolind.2016.02.049 1470-160X/© 2016 Elsevier Ltd. All rights reserved.
small species. The scores of the index are negative in heavily stressed conditions, near zero in moderately stressed situations, and positive for no stress. The ABC index has proved useful for assessing communities of benthic invertebrates (Warwick, 1986; Beukema, 1988; Meire and Dereu, 1990; Warwick and Clarke, 1994; Rakocinski et al., 2000), as well as riverine (Coeck et al., 1993; Penczak and Kruk, 1999; Pinto et al., 2006), estuarine (Villanueva, 2004; Cerfolli et al., 2013) and marine fishes (Yemane et al., 2005). The pressures of industrial activities and recreational demands imposed on aquatic ecosystems have resulted in biological stress at different levels of organization, especially on fish populations (Crook et al., 2015). In fact, fish are reckoned as good indicators of an ecosystem’s ecological state (Lasne et al., 2007; Pont et al., 2007; Rambouts et al., 2013). As in many other basins, the Garonne River is an interesting example for analyzing the interaction between economic activities and ecological indicators (Baque, 2006). Our interest in studying this area is because the Garonne basin is one of the most important basins in the world (Revenga et al., 1998) as it contains a river network with a diversity of habitats comprising several ecoregions which range from the mountains to the Gironde estuary. Furthermore, since a number of human activities are related to rivers in this basin (Baque, 2006), we try to understand how these activities can be conciliated with new concepts of water quality and ecosystem health (Carr and Neary, 2008). The European Water Framework Directive (EWFD) requires a chemical and ecological assessment of European waters by 2015 and needs
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Fig. 1. Study area in the stretch on the Garonne river. A reference site (S1) and two downstream (S2 and S3) sites were sampled. The star shows the effluent discharge coming from St. Gaudens town and the paper mill. Source: Ibarra (2004).
an integrated, risk-based management approach to achieve this goal (Brack et al., 2009). Aside from using chemical status concepts to reduce excessive water contaminations, another of its leading principles is the reflection of ecological state based on ecological quality ratios. In fact, it acknowledges that studying and regenerating biotic communities is essential for sustainable development (Reyjol et al., 2014). Thus, this paper aims to present a case study for assessing the level of ecological stress of a fish community in a stretch of the Garonne River, southwest France, by means of the ABC index. Another major purpose of the study is to present a jackknifing methodology for a multi-year application for this index, and its quantitative relationship with water quality and flow regime variables. This paper further develops the preliminary results reported by Ibarra (2004). 2. Methods 2.1. Area of study The area under study is situated near the town of Saint Gaudens in the Department of Haute-Garonne, in the piedmont of the Pyrénées range, southwest France, and has a special interest for environmental policy for several reasons. On the one hand, important economic activities provide social benefits such as commerce, tourism, sportfishing, hiking, hydropower generation and most notably, paper pulp production by the CDRA-TEMBEC paper mill. The paper mill has a relevant role in the local economy but also has an environmental cost as it uptakes water in order to produce white pulp and then discharges the used water into the Garonne River. Indeed, paper mill effluents affect physicochemical and ecological dynamics of both riverine and marine living resources (Ihejirika et al., 2011; Kanu and Achi, 2011; Dey and Das, 2013; Negi and Rajput, 2013) and can also induce pathologies on human populations (Lee et al., 2002). In 1995, a waste treatment plant was set up as a secondary treatment for biodegradable substances coming from the paper mill and from the city of Saint Gaudens. Once the plant was working, the paper mill increased in 68% its annual production from 1996 onwards and was required to monitor effluent impacts on both water quality and fish populations in the river (Lim, 2000). On the other hand, rivers are environmental assets that need to be conserved and protected because they provide ecosystem services such as biodiversity maintenance, microclimate regulation, flood control and amenities values (Postel and Thompson, 2005). The river in this sector has an average width of 50 m, with a substrate dominated by pebbles and gravel (80%) and
about 20% of blocks and flagstones, and a rather rapid flow. This stretch of the Garonne River is defined by Reyjol et al. (2001) as a transition between a Salmoniform-dominated assemblage and a Cypriniform-dominated assemblage. Ibarra et al. (2005b) demonstrated that fish assemblages form, in fact, nested patterns in an aggregated hierarchy in the whole Garonne river system. 2.2. Field collections Electrofishing of fish populations was carried out in the stretch between Saint Gaudens and Beauchalot towns during late summer (i.e. early and mid-September) in 1992 and 1994–2002 using a fixed Heron-type group (tension: 600–900 V and intensity: 1–2 A) in three sampling sites: one upstream (site 1) of the paper mill effluent serving as the reference site, and two about eight km (site 2) and nine km (site 3) downstream (Fig. 1). This scale of analysis allows a more accurate interpretation of the ABC index performance. All three sampling sites were chosen in non-regulated flowing sectors. It should be noted that sampling site 3 was located several km upstream from the present location due to changes after hydrological events in 1995. However, this new sampling site was chosen as having the most similar conditions to the former site (Lim, 2000). Each sampling site was fished twice with successive passages at a constant fishing effort according to the DeLury method, following the recommendations of Laurent and Lamarque (1974). Total weight and total length of fish were recorded before putting them back to the river. Fish biomass and fish abundance were thus calculated by the De Lury method using Winfish© software (Segura, 1998). Appendix A shows fish species composition for the three sampling sites during the study period. The resulting data set (available as supplementary material) was used to evaluate the ABC method. 2.3. Index computation Warwick (1986) had introduced a simple method for determining the presence of stress in a given area based on abundance distribution. Later, Clarke (1990) suggested the calculation of W (termed after Warwick) as: S
W=
(Bi − Ai )
i=1
50(S − 1)
= ABC index
(1)
where Bi is the biomass value and Ai is the abundance (or individual) rank value of each species rank (i) and Ai and Bi do not necessarily refer to the same species. S is the number of species in the sample.
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Table 1 Names and codes of water quality and flow variables in the Garonne river stretch at Saint-Gaudens town for the period 1992–2013. Sources: (1) Adour-Garonne Basin Information System and (2) HYDRO database. Variable
Unit
Variable code
Source
Mean flow Discharge Runoff curve number Chemical Oxygen Demand Biological Oxygen Demand Dissolved Oxygen Oxygen saturation rate Ammonium Nitrites Nitrates Total Phosphorous Phosphates Minimum pH Maximum pH Temperature
m3 /s l/s/km2 mm mg/l mg O2 /l mg O2 /l % mg/l mg/l mg/l mg/l mg/l U pH U pH ◦ C
FLOW DISCH CN COD BOD DISSO2 %O2 NH4 NO2 NO3 totP PO4 MINpH MAXpH TEMP
(1) (1) (1) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2)
In a biomass–abundance plot, when the biomass curve is above the abundance curve, it indicates a low disturbed ecosystem; whereas the inverse orientation of curves suggests a stressed or disturbed ecosystem. In cases when the two curves overlap, a value of W (i.e. ABC index) close to 0 indicates moderate perturbations (Warwick, 1986). We applied a jackknife resampling method (Manly, 1998) for reporting the ABC index scores. This is a straightforward technique which allows to estimate any bias in the samples, and to also examine the stability of the estimates (Fried and Stampfer, 2006). It consists in considering each observation (i.e. biomass and abundance data) as a unique piece of information, repeating the estimation n − 1 times. In other words, we repeated the computation of the ABC index omitting one observation in turn. The pseudo-values (i.e. jackknifed estimations) computed by this technique have the feature of being normally distributed variables and therefore, the corresponding mean, standard deviation and confidence intervals are good estimators of the index (Magurran, 1998: 42). Nevertheless, we performed non-parametric Kolmogorov–Smirnov tests (Lehmann, 2006) for verifying normality of both the pseudo-values and ABC index estimates. Please refer to the supplementary spreadsheet material where the whole data set and jackknifed computations are given. 2.4. Environmental variables Information on both water quality and flow regime data was obtained from two different sources. On the one hand, the AdourGaronne Basin Information System (SIEAG: www.adour-garonne. eaufrance.fr; last accessed on January 11, 2016) provided the annual mean data on water quality for the Garonne River stretch at SaintGaudens town (Sampling site code: RNDE-05181800), and for the Garonne River sector at Labarthe Inard (Sampling site code: RNDE5181000) from 1992 through 2013. Both sampling sites correspond to sites 1 and 2, respectively, of this study. On the other hand, annual means of flow regime variables were retrieved from the HYDRO database (www.hydro.eaufrance.fr; last accessed on January 29, 2016) for the Garonne River stretch at Saint-Gaudens town (sampling site code: O0200020) from 1992 through 2013. It corresponds to the reference site (S1) of this study. No information was available on flow for the other sites (S2 and S3). However, Poff and Allan (1995) pointed out that flow conditions within a range of 15 km can be considered as representative of hydrological conditions downstream a gauging station. The variables analyzed are shown in Table 1. Thus, the information from both databases (i.e. SIEAG and HYDRO) coincided in both space and time with our sampling sites
S1 and S2, for which statistical analyses were performed for linking the ABC index estimates with water quality and flow regime variables. The analysis was conducted as follows: First, since the variables mean flow (FLOW) and discharge (DISCH) were highly correlated (i.e. Spearman correlation coefficient >0.99), the variable DISCH was set aside from statistical analyses. Second, the annual mean values for the period 1992–2002 of the 12 physico-chemical variables, the two flow regime variables and the jackknifed ABC index estimates of sampling sites S1 and S2, were standardized with mean 0 and standard deviation 1. This standardization is needed in multivariate analysis when variables are measured in different units (Everitt and Dunn, 1991). Third, cluster analyses and the corresponding dendrograms were produced. The principle of cluster analysis consists in creating a classification obtained by incorporating pairs of the closest elements in a hierarchy of groups in the form of trees or dendrograms. The importance of dendrograms is that they give an idea of the number of classes actually present in the population under study. The Ward’s Method with squared Euclidean distance was applied since it is a clustering technique that takes into account the variance in order to optimize at each step the pairwise clustering (Everitt and Dunn, 1991). Dendrograms were produced with and without the flow regime variables (i.e. FLOW and CN). This was done for data harmonization, such that they were exactly the same for both sampling sites (S1 and S2), whereas the other water quality variables were site-specific. Fourth, as fish sampling stopped after the end of the project in 2002, there are no recent data. We reckon, nevertheless, that the unavailability of recent information can be considered as a technical limitation but not a scientific one. Hence, statistical tests (i.e. one-way ANOVAs) were performed to show by how much the most closely related variables to the ABC index estimates in both sampling sites S1 and S2, changed in the period 2003–2013 with respect to the period 1992–2002. 3. Results and discussion The ABC curves for the three sampling sites (1992–2002) in the Saint Gaudens-Beauchalot sector are shown in Appendix B. According to a one-factor ANOVA test of the ABC scores for the three sites (normality previously confirmed with a Kolmogorov–Smirnov test – see supplementary material), both sites 2 and 3 present higher stress than the upstream reference site. It is important to note that along the 10-year span the ABC index fluctuated for all three sites, meaning that natural conditions have contributed to stress fish assemblages in this river stretch (Reyjol et al., 2001). Only a few similar studies have been conducted in polluted European rivers. For example, Coeck et al. (1993) found an annual ABC index score of – 13.69 in a sampling site under heavy pollution and physical disturbance, whereas an unpolluted and unregulated stretch scored 9.74. The study of Penczak and Kruk (1999) obtained a value of −13.23 in an overfished river (Pilca River) in Poland. In contrast to these studies, we applied the ABC method over a 10-year span, showing moderate stress in the sector analyzed. Our observed values for the ABC index indicate that sites 2 and 3 seem to be under higher stress than the reference site (Fig. 2). The trend for both downstream sites was clearly downwards after the paper pulp production increased in 1996, and in spite of the waste treatment plant implementation in 1995 (Table 2). Although raw wastewater pollution can be an additional cause of perturbation on fish communities in the study sites, the actual and specific effect of the paper mill as stress will remain, nevertheless, as a hypothesis because the paper mill discharge effluent converges with the sewage effluent from St. Gaudens town. Furthermore, our sampling sites were located adjacent urban areas, and the regulated
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Table 2 Jackknifed ABC index estimations for the Garonne River sampling sites. Year
1992 1994 1995 1996 1997 1998 1999 2000 2001 2002
Site 1
Site 2
Site 3
Mean
Conf. Interval (5%)
Mean
Conf. Interval (5%)
Mean
Conf. Interval (5%)
−0.039 0.093 −0.048 0.069 0.118 0.024 −0.019 −0.047 −0.058 0.184
0.001 0.001 0.004 0.000 0.002 0.000 0.002 0.003 0.002 0.003
−0.087 0.112 −0.086 0.009 −0.051 −0.103 −0.161 −0.067 −0.286 −0.223
0.002 0.001 0.002 0.001 0.002 0.002 0.001 0.001 0.002 0.004
−0.261 −0.029 −0.131 −0.162 0.044 −0.184 −0.046 −0.127 −0.228 −0.079
0.001 0.002 0.001 0.002 0.001 0.001 0.000 0.001 0.001 0.001
Table 3 One-way ANOVAS between 1992–2002 and 2003–2013 periods for each variable in sampling sites S1 and S2. Only p-values are shown; n.d. = no data.
Fig. 2. Boxplots showing the jackknifed ABC index estimations for the three sampling sites. Boxplots with the same letter represent no statistical difference (p < 0.05) between sites.
flow sector contributes, no doubt, to modifying fish dynamics in this stretch. In spite of all these influences, the recorded level of observed stress is still moderate. Indeed, while there is a statistical difference between the upstream reference and the two downstream sites, all values fluctuate around a value of zero (Fig. 2). Fig. 3a clearly shows that the estimates of the ABC index are more closely related to the flow regime variables than to water quality variables in the reference site. In fact, fish diversity in rivers is influenced by both natural and anthropogenic factors. For example, the flow regime is a major driver of fish population dynamics (Poff and Allan, 1995; Murchie et al., 2008; Poff and Zimmerman, 2010; Mims and Olden, 2012) and biodiversity (Iwasaki et al., 2012). Fish community perturbations due to hydrological flow regime modifications were also observed by Murchie et al. (2008) on their comparative study of regulated rivers. When FLOW and CN variables are left out, the dendrogram shows that the ABC index is not linked to another group at a distance shorter than 8 (Fig. 3b). In contrast, the ABC index is grouped along with variables such as PO4 and totP at a shorter in the dendrograms of Fig. 3c and d. Hence, independently of the flow regime, the ABC index is related to pollution variables in sampling site S2. Hence, the ABC index gave us a rapid insight into the ecological consequences of both water pollution and flow regulation. This result suggests that pollution and other stressors are stronger than auto-restorative capabilities of the river, at least in the sector studied. It can also be noted, that the ABC index demonstrated to be a useful indicator of pollution, since it was related to variables that indicate either urban or agricultural pollution in the sector under
Variable
S1
S2
FLOW CN COD BOD DISSO2 %O2 NH4 NO2 NO3 totP PO4 MINpH MAXpH TEMP
0.918 0.195 0.025 0.381 0.630 0.059 0.000 0.000 0.590 0.003 0.023 0.003 0.604 0.295
n.d. n.d. 0.686 0.043 0.087 0.001 0.000 0.000 0.401 0.010 0.037 0.004 1.000 0.537
study (Fig. 3c, d). In this case, the urban areas, most notably Saint Gaudens town, do have an influence on fish assemblages. Agricultural pollution is generally characterized by nitrates (NO3 ), nitrites (NO2 ) and phosphates (PO4 ) and can be found downstream of agricultural plots due to runoff. In fact, Ibarra et al. (2005a) demonstrated that non-point source pollution has a negative influence on fish guilds in the whole Garonne basin. Although ecological conditions in this stretch might have changed since the samplings were made and no other field collections were performed after the period considered in this study, this work was able to provide the state of the ecosystem during that time. Elsewhere, other studies have validated the use of the ABC index on samples collected several years in advance of the analysis (e.g. Coeck et al., 1993; Pinto et al., 2006) and were useful in establishing ecosystem ecological reference points for future studies. In fact, fish assemblages have proven to be useful indicators of environmental quality for long-term and large spatial scales (Ibarra, 2005; Yemane et al., 2005). With respect to sampling site S1, Table 3 shows that the flow regime variables had no statistically significant difference (p > 0.05) when comparing survey dates (1992–2002) with the 10 years aftermath (2003–2013). Hence, if the ABC index in a 10-year span (1992–2002) was closely related to the flow regime in sampling site S1 (Fig. 3a), then, it should be so for the following period (i.e. 2003–2013), as suggested by Poff and Zimmerman (2010) for rivers elsewhere. In the case of water quality variables, some of the closest variables to ABC index (according to dendrograms of Fig. 3), presented a statistically significant difference with the ABC index scores (p < 0.05). This was the case of NO2 , totP and PO4 in both sampling sites (Table 3). This might be due to the improvement of river water quality throughout the years thanks to water pollution control in France, which has been strongly focused on giving
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Fig. 3. Dendrograms (Ward’s Method with Squared Euclidean distance) for ABC index scores, water quality and flow regime variables, for the Garonne river stretch at St. Gaudens town (1992–2002). (a) Reference site S1 with flow regime variables; (b) reference site S1 without flow regime variables; (c) sampling site S2 with flow regime variables; (d) sampling site S2 without flow regime variables.
financial aid to clean technologies for reducing the amount of wastes in ecosystems (LeRoch and Mollard, 1996). Presumably, the ABC index could also reflect this improvement along these years in the Garonne River stretch under study. The ABC index has both advantages and drawbacks. An advantage is that it clearly shows contrasting patterns of the entire species abundance distribution (Magurran, 1998). A practical disadvantage of this method was pointed out by Tokeshi (1993), since two types of abundance data are necessary to be collected, which is costly and time consuming. Furthermore, collection of samples should be undertaken in a rigorous and standardized manner as the method is sensitive to natural variations. Finally, potential distinction of pollution characteristics and sources from plotted curves is not possible using this method. Yet, ecological indicators such as the ABC index can help decisions on the performance of industries and municipalities obtaining such aid. For example, a further step in this investigation would be to estimate the environmental damage in monetary terms of the urban and paper pulp effluents in this stretch. Hence, ecological stress might eventually guide environmental damage liability
assessments. Aside from this, cumulative impacts of anthropogenic perturbations enlisted above are, in fact, occurring in the river stretch under study. Hence, disentangling the effects of both natural and anthropogenic-driven disturbances remains as another opportunity for further investigation.
Acknowledgements This study was part of the dissertation thesis of AAI, and was partly sponsored by the Mexican-French Cooperation Program Conacyt-Sfere (Contract No. 131742) and the Association Toulousaine d’Ichthyologie Appliquée. A number of people were involved in sampling surveys, both students and personnel of the École Nationale Supérieure d’Agronomie de Toulouse, specially Puy Lim and Francis Dauba. We thank José F. Frey Aguilar for helping with data processing. The comments of three anonymous reviewers notably improved a former draft.
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Appendix A. Species list and presence in each sampling site (1992–2002) Scientific name
Common name
Alburnus alburnus Barbatula barbatula Barbus barbus Chondrostoma toxostoma Cottus gobio Esox lucius Gobio gobio Lampetra planeri Leuciscus cephalus Leuciscus leuciscus Perca fluviatilis Phoxinus phoxinus Rutilus rutilus Salmo trutta fario
Bleak Stone loach Barbel Toxostome Bullhead sculpin Pike Gudgeon Brook lamprey Chub Dace Perch Minnow Roach Brown trout
Site 1
Site 2
Site 3
1 1
1 1 1
1 1 1 1
1 1 1 1
1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
Appendix B. ABC plots (i.e. k-dominance curves) for each sampling site (1992–2002) Figs. B1–B3.
Fig. B1. ABC plots (k-dominance curves) for sampling site S1 (1992–2002). Grey line: cumulative % biomass. Black line: cumulative % abundance. Means and confidence intervals (5%) of ABC index are shown.
Fig. B2. ABC plots (k-dominance curves) for sampling site S2 (1992–2002). Grey line: cumulative % biomass. Black line: cumulative % abundance. Means and confidence intervals (5%) of ABC index are shown.
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Fig. B3. ABC plots (k-dominance curves) for sampling site S3 (1992–2002). Grey line: cumulative % biomass. Black line: cumulative % abundance. Means and confidence intervals (5%) of ABC index are shown.
Appendix C. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2016. 02.049.
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