Ecological status monitoring and assessment in Greek rivers: Do macroinvertebrate and diatom indices indicate same responses to anthropogenic pressures?

Ecological Indicators 101 (2019) 126–132

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Ecological status monitoring and assessment in Greek rivers: Do macroinvertebrate and diatom indices indicate same responses to anthropogenic pressures?

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Ioannis Karaouzas , Evangelia Smeti, Eleni Kalogianni, Nikolaos Th. Skoulikidis Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 46.7km Athens-Sounio Ave., Anavyssos 19013, Greece

A R T I C LE I N FO

A B S T R A C T

Keywords: Water pollution Metrics Water Framework Directive Mediterranean Ecological quality

Biotic indices based on macroinvertebrates and diatoms are regularly used for ecological status assessment in freshwaters, showing variable responses to anthropogenic pressures. In this work, we investigate the response of each biological quality element (BQE) index (i.e. diatoms and macroinvertebrates) to multiple stressors and compare their responses during ecological status assessments of seasonally variable river ecosystems. A total of 421 samples, collected during a four-year survey for the Greek National Water Monitoring Programme from 195 stream sites throughout Greece, were used. The correlation between macroinvertebrate (HESY2), diatom (IPS) and physicochemical (Ph) indices was tested and the environmental parameters affecting biotic indices were examined. All indices were positively intercorrelated, with IPS showing the highest correlation with the Ph index, confirmed by the fact that IPS was also driven by nutrient concentration and dissolved oxygen. HESY2 was driven by PO4, the extent of agriculture and urbanization, river typology, as well as sediment type and water temperature. The two BQEs, hence biological assemblages, respond differently to local and larger scale pressures; diatoms responded to reach-scale variations of water quality driven by large-scale drivers, such as land uses, to which macroinvertebrates responded. Moreover, macroinvertebrates depended on seasonality, whereas diatoms were independent of seasonality. Subsequently, when macroinvertebrates are absent, or haven’t yet recolonize a stream after drought, diatoms can reflect disturbance as they respond to water quality, rather than seasonality. Thus, in Mediterranean rivers, where flow variability is pronounced, the complementary use of both BQEs, although important for ecological status assessment, it could rely solely on the use of diatoms when macroinvertebrates are absent.

1. Introduction The EU Water Framework Directive (WFD) (Directive 2000/60/EC) requires member states to protect and improve the status of aquatic ecosystems and to restore all water bodies with less than good ecological status. The WFD demands bioassessments to be expressed as a ratio (Ecological Quality Ratio, EQR) at a five-scale system between one (High) and zero (Bad). According to the WFD, aquatic organisms (benthic macroinvertebrates, macrophytes, diatoms, phytoplankton and fish) are used as biological quality elements (BQEs) to assess the ecological quality status of European surface water bodies. During bioassessment, most of the biological groups are used, as each group presents specific responses to various pressures, related to their habitat requirements and life-cycle (Sabater et al., 2007; Johnson and Hering, 2009; Johnson and Ringler, 2014).

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Twenty-eight European countries reported, in a questionnaire survey, on 297 assessment methods applied to rivers (30%), coastal waters (26%), lakes (25%) and transitional waters (19%); the majority was based on benthic invertebrates (26%) and to a less extend to phytobenthos (10% – Birk et al., 2012). Diatoms and benthic invertebrates have long been used to assess anthropogenic pressures in rivers due to their sensitivity to different degrees of stress and at different time scales (Birk et al., 2012). More specifically, diatoms can detect eutrophication gradients and have fast responses to environmental changes due to their small generation times (e.g. Morin et al., 2016). On the other hand, benthic invertebrates are more sensitive to organic pollution and general degradation, as well as to past stress events (e.g.; Cairns and Pratt, 1993; Chainho et al., 2008; Hering et al., 2006). Apart from better detecting different pressures, the two groups vary in their performance assessing different stream types. For example,

Corresponding author. E-mail address: [email protected] (I. Karaouzas).

https://doi.org/10.1016/j.ecolind.2019.01.011 Received 7 June 2018; Received in revised form 3 January 2019; Accepted 5 January 2019 1470-160X/ © 2019 Published by Elsevier Ltd.

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laboratory, after filtration through 0.45 μm pore size membrane filters, nutrients were determined (APHA, 1980) by a Skalar San++ Continuous Flow Analyzer (SA5000, 2015, Breda, Netherlands). Geographical data (i.e. catchment area) were obtained by GIS and topographic maps. The basin has been digitised with the contribution of ArcView program (GIS package) and topographic maps of scale 1:25 000. Water flow data were obtained during sampling using a Water Flow meter OTT C20, while discharge was calculated based on the velocity – area method (Buchanan and Somers, 1976). Land uses of the sampling sites were based on CORINE 2012 database (European Environmental Agency, 2012).

diatom assemblages have been shown to better predict nutrient enrichment and habitat degradation in lowland streams, whereas invertebrates are better predictors of disturbance in mountainous streams across Europe (Johnson and Hering, 2009). The responses of the two groups were always correlated; thus the group that best represents quality in each stream type could be used over the other (Johnson and Hering, 2009). In Greece, the use of macroinvertebrates in assessing water quality of rivers dates back to the late 90s (Lazaridou-Dimitriadou et al., 1999; Karaouzas et al., 2018a), being relatively recent compared to other countries. On the other hand, ecological quality assessment using diatoms has been limited to a few sporadic case studies in some rivers (Montesanto et al, 1999; Iliopoulou-Georgudaki, et al., 2003; Ziller and Montesanto, 2004). Until now, there were no large scale biological data from diatom assemblages, highlighting a knowledge gap and the need of more samples for a more complete evaluation of the applicability of diatoms in detecting pollution gradients in Greek rivers (Solak and Àcs, 2011). The present study is the first that focuses on both biological groups in ecological quality assessment across the national river network of Greece. In this work, we explore relationships between ecological quality indices developed and used in the framework of the Greek National Water Monitoring Programme. We particularly examine the relationships of physicochemical indices with diatom and macroinvertebrate biotic indices. We further investigate the response of the biological quality index of each group (diatoms and macroinvertebrates) to different environmental factors, including physicochemical conditions and land use as well as the weight of each stressor on these biotic indices. Through this approach we aimed to assess differences in the response of the biological indices corresponding to the two BQEs, and to compare these responses during ecological status assessment of seasonally variable river ecosystems.

2.3. Diatom and Macroinvertebrate sampling Sampling of benthic diatoms followed the standard “EN 13,946 (European Committee for Standardization, 2003). During sampling, 5–7 submerged cobbles were collected from sun-lit areas and brushed to obtain biofilm material. In the laboratory, samples were digested using the hot hydrogen peroxide method, to remove any organic matter. Permanent slides were prepared with Naphrax. Up to 400 valves were counted and identified at the species or subspecies level in each sample, using phase-contrast light microscopy (Nikon Eclipse Ci-L) at 1000× magnification. Benthic macroinvertebrates were collected with the semi-quantitative 3-min kick/sweep method, plus a 1-min effort when bank vegetation existed, using a 250 mm × 230 mm, D-shaped pond net (0.9 mm mesh size) (EN 27828:1994). During the 3-min sampling, all microhabitats were covered proportionally according to the matrix of possible river habitats. Sampling was conducted biannually, during high flow (spring) and low flow (summer/autumn). Taxa were identified at the family level, except for Ostracoda, Hydracarina, Aranae and Oligochaeta (apart from Tubificidae) and the relative abundance of each taxon was determined.

2. Materials and methods 2.4. Ecological status indices 2.1. Study area For the evaluation and classification of the physicochemical (Ph) quality of the sites, the Nutrient Classification System (NCS) was used (Skoulikidis et al., 2006). The index equally considers chemical quality based on nutrient concentrations nitrate (NO3), nitrite (NO2), ammonium (NH4) and phosphate (PO4). In addition, dissolved oxygen (DO) concentration is considered. Biological quality based on benthic diatom assemblages was assessed using the IPS (Specific Pollution Sensitivity index– CEMAGREF, 1982) which takes into consideration the sensitivity of the species to pollution, together with its relative abundance. IPS has been used for the quality assessment of rivers during the Greek National Water Monitoring Programme and ecological quality boundaries have been adapted to national standards based on the intercalibration exercise in the WFD (Smeti and Karaouzas, 2016). Biological quality based on benthic macroinvertebrate communities was assessed using the HESY2 (Hellenic Evaluation System 2; Lazaridou et al., 2018). HESY2 is the Ecological Quality Ratio of an observed value of the existing Hellenic Evaluation System (HES; Artemiadou and Lazaridou, 2005) to the expected median reference value of the same river type (RM); it refers to the abundance and diversity/richness of benthic macroinvertebrates (family level) and their tolerance to pollution, being standardized against habitat diversity richness (Lazaridou et al., 2018).

A total of 421 samples from a nation-wide database that includes 195 stream sites throughout Greece were used in the current study (Fig. 1). The dataset comprised sites from all ecological quality gradients (including reference sites) and river typology. At each sampling site information of physicochemical and hydromorphological data, as well as biotic data (e.g. benthic macroinvertebrate abundance, number of families, etc.) were available. Samples covered a wide temporal variability and were collected biannually in spring and summer during a four-year survey for the Greek National Water Monitoring Programme (summer 2012–summer 2015). The database covered the Mediterranean intercalibration river types; (1) small rivers with mixed geology and highly seasonal flow (R-M1 type), (2) medium rivers with mixed geology and highly seasonal flow (R-M2 type), (3) large rivers with mixed geology and highly seasonal flow (R-M3 type), (4) small/ medium mountainous rivers with non-siliceous geology and seasonal flow (R-M4 type) and (5) small temporary rivers with mixed geology (R-M5 type) (for more details see EC, 2005; Birk et al., 2013) with 90, 138, 46, 89 and 58 samples, respectively for each river type. 2.2. Physicochemical sampling and environmental data Physicochemical water quality parameters, i.e. dissolved oxygen (D.O. mg L−1), water temperature (°C), conductivity (μS cm−1), and pH were measured in-situ using a Portable multiparameter Aquaprobe AP200 with a GPS Aquameter (Aquaread AP 2000) at each site. Prior to macroinvertebrate and diatom sampling, water samples were also collected to provide baseline water chemistry data on nutrient concentrations, namely nitrate (NO3, mg L−1), nitrite (NO2, mg L−1), ammonium (NH4, mg L−1) and phosphate (PO4, mg L−1). In the

2.5. Data analysis Spearman rank correlations between the ecological indices (IPS – Ph, HESY2 – Ph, IPS-HESY2) were performed for all samples together, as well as, within each river type, aiming in finding possible type-specific linkages between indices. To understand the drivers of ecological status based on different biotic groups, i.e. diatoms (IPS) and 127

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Fig. 1. Map of Greece illustrating the stream sampling sites and land uses according to CORINE Land Cover (European Environmental Agency, 2012).

regression analysis was applied and the relative importance of each significant variable was calculated. Statistical analysis was carried out in R v.3.3.3 (R Core Team, 2017). Package lme4 v.1.1-12 (Bates, et al., 2015) was used for mixed effect models and package MuMIn v.1.15.6 (Barton 2016) was used for the model selection. Package relaimpo v.2.2-3 (Grömping, 2006) was used to calculate relative importance of significant variables.

macroinvertebrates (HESY2), linear mixed-effects models were applied, with year of sampling as a random factor, to account for pseudo-replication during different year campaigns. The explanatory variables tested were nutrients (NO3, NO2, NH4, PO4), water temperature, conductivity, pH, dissolved oxygen (DO), mean flow, sediment type (coarse or fine), vegetation coverage, catchment area and land use (agriculture and urban). These variables were selected since they have been reported to affect benthic riverine communities. All variables were tested for linearity and some abiotic variables were log transformed prior to use. Seasonality (spring, summer) and river type (R-M1 to R-M5) were considered as Fixed Factors. Possible two-way interactions between the two fixed factors and the continuous variables were also considered (Table 2, global model). Model selection was based on the Akaike Information Criterion (AIC), whereby the model with the lowest AIC is retained, aiming in minimizing information loss and separating noise from structural information (Burhnam and Anderson 1998). For the selection of the best model, it is not the absolute value of AIC that is important, but rather the difference between the considered models (ΔAICi = AICi − AICmin). Models with ΔAIC < 2 are considered as equally explaining variation of the response variable, therefore, within this range, the most parsimonious and ecologically meaningful model was retained (Burhnam and Anderson 1998). Models with ΔAIC > 7 are not considered as adequately explaining variation. This criterion was further supported by the use of ‘weight of evidence’ (wi) of the selected model, i.e. the relative likelihood of the model, considered the data. The closest wi is to 1, the greater the likelihood that the selected model is the best. Furthermore, the ratio wmin/wj needs to be sufficiently higher than 2, to consider that the selected model better explains variation (Burhnam and Anderson 1998). Once the best model was selected, a multiple

3. Results Correlations between all indices were always positive (Table 1, Fig. 2). IPS index was significantly correlated with NCS index, irrespective of the river type, presenting the highest correlation in R-M2 Table 1 Spearman correlations between quality indices, i.e. Ph quality based on NCS and DO concentration, IPS based on diatoms, HESY2 based on benthic macroinvertebrates. Significance levels: *** < 0.001, ** < 0.01, * < 0.05. River Type TOTAL (n = 421) R-M1 (n = 90) R-M2 (n = 138) R-M3 (n = 46) R-M4 (n = 89) R-M5 (n = 58)

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Index IPS HESY2 IPS HESY2 IPS HESY2 IPS HESY2 IPS HESY2 IPS HESY2

Ph 0.481 0.350 0.419 0.298 0.579 0.429 0.382 0.206 0.459 0.448 0.430 0.318

IPS *** ***

0.381

***

0.448

***

0.316

***

0.383

**

0.355

***

0.398

**

*** ** *** *** **

*** *** *** *

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Fig. 2. Relationships between quality indices. Ph Q: Physicochemical Quality index, based on NCS IPS based on diatoms, HESY2 based on benthic macroinvertebrates.

second best model was chosen to better explain variation in HESY2 scores.

river type. HESY2 index showed the highest correlation in R-M4 river type, following R-M2 type and had no significant correlation with R-M3 river type. IPS and HESY2 were significantly correlated for all river types, with the highest correlation in R-M1 river type (Table 1). IPS variation could be explained by PO4 (explaining 23.5% of the total variation) and NH4 (explaining 6.1% of the total variation) concentrations, DO (explaining 2.7% of the total variation) and the extent of agriculture (explaining 7% of the total, Table 2). An increase in nutrient concentrations and agriculture negatively impacted IPS values and thus the diatom related biological quality (Fig. 3a, c and d). On the other hand, an increase in DO increased IPS values and thus biological quality (Fig. 3b). The year of sampling as a random factor did not explain any variation. The variables explaining IPS variation are consistent with the high correlation observed between IPS and Ph quality, as this is based on nutrients and DO. Most of the sites did not present a great variation in their abiotic parameters (clouds of points in Figs. 3, 4) and the trend of the pressures seems to be mainly driven by comparatively few sites forming a gradient of abiotic conditions. HESY2 variation was also negatively affected by PO4 (explaining 4.5% of the total variation) and the extent of agriculture (explaining 12.7% of the total variation) together with water temperature (explaining 2.7% of the total variation– Fig. 4a, c and d). In contrast to IPS, sediment type affected the biological quality based on macroinvertebrates, and HESY2 increased as the extent of coarse sediment increased (explaining 5.7% of the total variation– Fig. 4b). Urbanization also explained variation on HESY2 scores (1.5% of the total variation), but this depended on the river type (explaining 2.2% of the total variation), as R-M1 type rivers present a greater extend of urbanization (GLM, F4,416 = 5.17, p < 0.001). The year of sampling as a random factor did not explain any variation. In the case of HESY2, the difference between the AIC of the first two models was less than 2 (0.59). Furthermore, the second best model was more parsimonious (Table 2); the extra variable in the first model being seasonality, a variable that is strongly related to water temperature. Therefore, the

4. Discussion Several biological groups (macroinvertebrates, fish, macrophytes and diatoms) are used globally for monitoring and assessment of river ecological status (Sabater et al., 2007; Johnson and Hering, 2009; Johnson and Ringler, 2014; Pandey et al., 2017). In the case of Mediterranean rivers, where natural variability (i.e. seasonality, hydrology) is pronounced, the selection of the appropriate BQE in monitoring programmes in order to detect impacts of anthropogenic pressures is essential. In the current study, we showed that the two BQEs (diatoms and macroinvertebrates) respond differently to local and larger scale anthropogenic pressures; diatoms responded to reach-scale variations of water quality (i.e. nutrient concentrations) driven by large-scale drivers such as land use (i.e. agriculture and urbanization) to which macroinvertebrates responded. Moreover, diatoms were found to be independent of seasonality, in contrast to macroinvertebrates, and were affected by water chemistry, whereas macroinvertebrates were affected by a variety of pressures. Diatoms, compared to other biological groups, have fast growth rates that allow them to react faster to chemical changes, especially nutrient enrichment (e.g. Morin et al, 2016), detecting thus first steps of degradation (Schneider et al., 2012), as opposed to macroinvertebrates (Karaouzas et al., 2018b). Hence, diatom-based indices are usually dependent on chemical variables affecting water quality, while macroinvertebrate-based indices, apart from pollution, are more sensitive to changes affecting structural parameters; i.e. microhabitat composition, flow regime, etc. (Soininen and Könönen, 2004; Hering et al., 2006; Blanco et al., 2007). Our results show that the biological quality indices of the two groups were significantly correlated, however the extent of correlation varies depending on river type. The significant correlation between the 129

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two indices could be attributed to the fact that the vast majority of sampling sites are affected by mixed pollution sources, i.e. agriculture and municipal effluents (see Karaouzas, 2018). This correlation could be explained by the common main pressures affecting the two indices, namely agricultural land uses and phosphate concentration, which cumulatively accounted for 30.5% and 17.2% of the total variation of diatoms and macroinvertebrate assemblages, respectively. Biological quality indices of diatoms and macroinvertebrates have been reported to be from not correlated (in South Africa, de la Rey et al., 2004) to less correlated (in France, Prygiel, 1994) and to significantly correlated (in the UK, Kelly and Whitton, 1995). High correlation was also observed in other Mediterranean countries, were these two BQEs are regularly used. For example, in Italy the indices based on diatoms and macroinvertebrates were highly correlated, showing however complementary responses to environmental pressures (Torrisi et al., 2010). The case was similar for Portugal, with the indices of the two biotic groups showing 76% similarity in their responses in water quality assessments (Feio et al., 2007). However, responses of diatom and macroinvertebrate indices to anthropogenic pressures did not completely overlap in this study. Though, both indices were negatively affected by agricultural land use and phosphate enrichment, the diatom quality index further responded to ammonium and to dissolved oxygen concentrations (Table 2, Fig. 3), showing thus an additional response to eutrophication caused by municipal wastewaters, in agreement to previous studies (e.g. Hering et al., 2006; Potapova and Charles, 2007). The response of diatom indices to nutrient gradients has been reported previously (e.g. Hering et al., 2006; Triest et al., 2001; Rimet et al., 2005) and reflects the high association of diatoms to PO4 and thus agricultural land use. There is growing evidence that diatoms respond to other pressures besides nutrients, such as those related to organic and inorganic micropollutants, morphological and hydrological alterations (Leira and Sabater 2005; Feio et al., 2009). Even though these pressures were not tested in the present study, future assessment method should be directed towards integrating these pressures in indices construction (e.g. Almeida and Feio, 2012, Karaouzas et al., 2018b). Macroinvertebrate metrics are sensitive to land use changes, hydromorphological degradation on the microhabitat scale and general degradation gradients (Hering et al., 2006; Marzin et al., 2012). The macroinvertebrate quality index HESY2 responded to agriculture, urbanization, sediment type and river typology, as well as water temperature and PO4. These results are similar to Lazaridou et al. (2018) where HESY2, showed a significant correlation with PO4 concentrations, urban and agricultural areas in all intercalibration river types. The present study also suggested that water temperature, and subsequently seasonality, is an important factor affecting the macroinvertebrate quality index, contrary to the diatom quality index. This is in agreement with previous studies suggesting the relevance of seasonality in macroinvertebrates but not in diatoms (Almeida et al, 2014), giving an advantage to diatoms when assessing water quality during a wide time period. In sites where macroinvertebrates are less diverse or not found at all (e.g. due to substrate composition or pollution) diatoms are more reliable indicators (Beyene et al., 2009), since they are immobile and ubiquitous. In addition, they may respond faster to multiple pressures, such as water stress and pollution (Karaouzas et al., 2018b). Moreover, it has been shown that in lowland streams, where the habitat and substrate heterogeneity are naturally low, assessment by diatoms is more useful than assessments using invertebrates (Feio et al., 2007).

0.369 0.275 0.075 0.073 0.068 0.044 0.037 0.021 0.021 0 0.59 3.18 3.25 3.39 4.25 4.6 5.72 5.74 17 16 18 15 18 9 17 19 8 HESY2∼River type + Season + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + River type: % Urbanization + (1 |year) HESY2∼River type + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + River type: % Urbanization + (1 |year) HESY2∼River type + Season + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + River type: % Urbanization + Season: Water T + (1 |year) HESY2∼River type + % Agriculture + % Urbanization + PO4 + % coarse sediment + River type: % Urbanization + (1 |year) HESY2∼River type + Season + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + Mean Flow + River type: % Urbanization + (1 |year) HESY2∼Season + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + (1 |year) HESY2∼River type + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + Mean Flow + River type: % Urbanization + (1 |year) HESY2∼River type + Season + % Agriculture + % Urbanization + PO4 + Water T + % coarse sediment + Mean Flow + River type: % Urbanization + Season: Mean Flow + (1 |year) HESY2∼% Agriculture+% Urbanization + PO4 + Water T + % coarse sediment + (1 |year)

−210.1 −209.5 −206.9 −206.8 −206.7 −205.8 −205.5 −204.4 −204.4

0.803 0.106 0.067 0 4.04 4.95 7 6 6 IPS∼%Agriculture + PO4 + NH4 + DO + (1 |year) IPS∼%Agriculture + PO4 + DO + (1 |year) IPS∼%Agriculture + PO4 + NH4 + (1 |year)

−432.9 −428.8 −427.9

weight ΔAIC AIC

Macroinvertebrates

5. Conclusions

Diatoms

df

Global model: IPS/HESY2∼(1|year) + Season + River type + Water T + Conductivity + pH + NO2 + NO3 + NH4 + PO4 + DO + Mean Flow + % Urbanization + % Agriculture + % coarse sediment + % fine sediment + %Vegetation + Season: Water T + Season: Mean Flow + River type: %Urbanization + River type: %Agriculture

Table 2 Global model with all considered variables and meaningful two-order interactions. Year of sampling is considered as a random factor (1|year). Models with ΔAIC < 6 are presented. Models selected to best explain the ecological quality indices’ variation are in bold.

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Multiple pressures affect river ecosystems at different spatial and temporal scales thus the selection of more than one biological monitoring method with clearly identifiable responses is important. As the choice of the biological elements is based on the country implementing the WFD, it is crucial to define environmental gradients that affect each 130

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Fig. 3. Relationships between important environmental variables [a) NH4, b) DO c) PO4, d) % agriculture] and IPS. Line shows the positive or negative trend of the index. NH4, PO4 and DO values were log-transformed.

than seasonality.

biological group in order to better weight the most appropriate element. Monitoring programmes results can provide an adequate background, leading to a better selection of the most suitable biotic groups within a national dataset. The results of this work suggest that monitoring and assessment programmes in countries with highly variable flow (i.e. perennial – intermittent) can rely on the use of a diatom index, even when macroinvertebrates are absent (e.g. habitat characteristics), or haven’t yet recolonize a stream after drought, and still be confident that the diatom index will respond to water quality, rather

6. Note All authors declare no competing financial interest. Acknowledgements This study has been prepared in the framework of the Greek

Fig. 4. Relationships between important environmental variables [a) Water Temperature, b) % coarse sediment, c) PO4, d) % agriculture] and HESY2. Line shows the positive or negative trend of the index. PO4 is log-transformed. 131

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