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Ecological quality scales based on phytoplankton for the implementation of Water Framework Directive in the Eastern Mediterranean
Ecological Indicators 10 (2010) 840–847
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Ecological quality scales based on phytoplankton for the implementation of Water Framework Directive in the Eastern Mediterranean Sofie Spatharis *, George Tsirtsis Department of Marine Sciences, University of the Aegean, University Hill, 81100 Mytilene, Greece
A R T I C L E I N F O
A B S T R A C T
Article history: Received 2 October 2009 Received in revised form 28 December 2009 Accepted 17 January 2010
Structural changes of phytoplankton communities, often expressed through ecological indices, constitute one of the metrics for the implementation of the European Water Framework Directive (WFD). In the current study a thorough analysis of the efficiency of 22 ecological indices was performed and a small number was selected for the development of five-level water quality scales (High, Good, Moderate, Poor, and Bad). The analysis was performed on simulated communities free of the noise of field communities due to uncontrolled factors or stochastic processes. Two criteria were set for the sensitivity of indices, namely their monotonicity and linearity across the studied eutrophication spectrum. The whole procedure was based on the development of a five-level quality assessment scheme based on phytoplankton abundance. Among the indices tested, the Menhinick diversity index and three indices of evenness were the most efficient, showing consistency (monotonic behavior) and linearity and were therefore used for the development of quality scales for the WFD. An Integrated Phytoplankton Index (IPI) based on three phytoplankton metrics, chlorophyll a, abundance, and diversity is also proposed. The efficiency of these indices was evaluated for a number of sites in the Aegean, already classified in the past by various methods based on nutrient concentrations or phytoplankton data. The results indicate that the various phytoplankton metrics (chlorophyll a, abundance, and diversity) assessed or proposed in the current study, carry their own information showing differences in the final classification of areas. Therefore the establishment of synthetic indices as the IPI seems to be advantageous for the integrated assessment of coastal water quality in the framework of European policies as the WFD. ß 2010 Elsevier Ltd. All rights reserved.
Keywords: Community structure Diversity Ecological assessment Indices Log series distribution Model communities Sensitivity analysis
1. Introduction Surface waters including streams, rivers, lakes, estuaries, and coastal waters are under increasing ecological stress due to anthropogenic activities worldwide (UNEP, 1999), leading to the enforcement of major environmental policies, including the US Federal Water Pollution Control Act (2008), the US Oceans Act (2000), the European Water Framework Directive 2000/60 (EC, 2000), and the European Marine Strategy (EC, 2008). The implementation of the Water Framework Directive (WFD) in particular, both regionally and nationally, assumes the development of a five-level water quality classification scheme (High, Good, Moderate, Poor, and Bad) with the environmental objective to achieve good surface water status for all European waters by 2015 (EC, 2000). The levels are defined using the Ecological Quality Ratio (EQR) for a number of biological and chemical quality elements. Among the key biological elements specified for coastal
* Corresponding author. Tel.: +30 22510 36835; fax: +30 22510 36809. E-mail address: [email protected] (S. Spatharis). 1470-160X/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2010.01.005
waters, the only planktic element referred to in the WFD is phytoplankton. Phytoplankton is an efficient indicator of changes in nutrient loads, but is also effective in evaluating responses to many other environmental stressors, due to its fast population response to changes in water quality, hydrology or climate (Domingues et al., 2008). According to the European Directive (EC, 2000), phytoplankton metrics that are fundamental in defining and classifying the ecological status of surface waters are biomass (as chl a), community changes (composition and species abundance), and increase in the frequency and intensity of blooms. Phytoplankton has been considered as a water quality keyelement in many studies for coastal ecosystems. A synthetic approach for water quality assessment in the Atlantic Ecoregion (Basque country), involved the development of an integrated index based on chlorophyll a, phytoplankton total abundance (when exceeding 107 cells/L), and abundance of harmful phytoplankton species (Borja et al., 2004; Revilla et al., 2009; Vincent et al., 2002). For the North Sea and the Atlantic Ecoregions (British coastal waters), Devlin et al. (2007) developed an Integrated Phytoplankton Index taking into account phytoplankton biomass (as chl a), the frequency of elevated phytoplankton counts (individual species
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and total cell counts), and seasonal progression of functional groups. Regarding the coastal waters of the Eastern Mediterranean Ecoregion, one of the most oligotrophic marine environments around the world (Krom et al., 1991), chlorophyll a is the only parameter taken into consideration so far for WFD implementation (Simboura et al., 2005). The limits set by these authors are below 0.10 mg/L of chl a for High water quality, 0.10–0.40 for Good, 0.40– 0.60 for Moderate, 0.60–2.21 for Poor and above 2.21 for Bad water quality. Chlorophyll concentrations represent a simple and integrative measure of the phytoplankton community response to nutrient enrichment or succession (Devlin et al., 2007; Harding, 1994). However, community structure (i.e. the distribution of individuals to species) conveys different information by also considering heterotrophic species that are not represented in chlorophyll measurements (Domingues et al., 2008). Moreover, previous studies have demonstrated that an increase in chlorophyll a due to eutrophication is always accompanied by changes in phytoplankton community structure in terms of total abundance, species richness, and evenness (Tsirtsis and Karydis, 1998; Tsirtsis et al., 2008). For phytoplankton abundance, a four-level classification system was developed in the past for Eastern Mediterranean coastal waters (Kitsiou and Karydis, 2000). The limits set were <4160 cells/L for oligotrophy, 4160–31,399 cells/L for lower mesotrophy, 31,400–188,333 cells/L for higher mesotrophy, and >188,334 cells/L for eutrophication. However this scale has not been adjusted so far to the five-level system required by the WFD. Changes in community structure are often quantified through a number of indices which constitute popular tools in studies associated with community ecology and disturbance (Washington, 1984). The most important advantages provided by indices are the ability for direct comparisons between communities that have few or no species in common and the easiness of their application and interpretation (Magurran, 2004). Alternative methods to taxonomic-based indicators related to body size, abundance distribution among functional groups, functional diversity, and productivity descriptors have been found suitable for other biological quality elements (Mouillot et al., 2006). However, for phytoplankton, productivity and species-abundance data are considered so far the most effective means to explore changes in community structure (Mouillot et al., 2006). In the framework of new policies for water quality assessment, investigators should retain a parsimonious approach, assessing the suitability of existing indices rather than developing new ones (Borja and Dauer, 2008; Diaz et al., 2004). In addition, the evaluation of water quality status should ideally incorporate suitable multimetric indices (Borja and Dauer, 2008; Diaz et al., 2004; Domingues et al., 2008) taking into consideration as many of the fundamental attributes of phytoplankton as possible (i.e. biomass, community structure, and frequency of blooms). An important shortcoming in the development process of water quality assessment schemes is the inherent noise or variability of field data associated with seasonality, hydrodynamic circulation, grazing, and patchiness (Karydis, 1996) or stochastic processes (Mouillot and Lepretre, 2000). This variability masks and distorts the information associated with a particular stressor or process of interest (Karydis, 1992) and therefore is considered as undesirable, especially in water quality assessment schemes which are often based on annual means. An alternative approach in the assessment of water quality can be based on the use of simulated communities (Boyle et al., 1990; Mouillot and Wilson, 2002) that retain the main structural characteristics of natural communities. The elimination of noise in the simulated communities reveals the signal, and therefore supports the quantification of the community response to environmental stressors. Previous studies have demonstrated that phytoplankton communities can be successfully modeled
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using statistical distributions such as the lognormal and log series (Tsirtsis et al., 2008) along a wide spectrum of productivity, from oligotrophy to eutrophication, characteristic of Eastern Mediterranean coastal waters. The present study aims to assess the sensitivity of 22 ecological indices of phytoplankton diversity to efficiently detect eutrophic trends in Eastern Mediterranean coastal waters, and thus their applicability in the Water Framework Directive. The development and use of simulated phytoplankton communities covering a wide and continuous eutrophication spectrum enabled (a) the unbiased examination of the sensitivity (monotonicity and linearity) of the indices and (b) the development of coastal water quality classification scales using selected indices. Moreover a new, integrated index of water quality was developed taking into account three of the metrics proposed by the European Directive for phytoplankton, which is chlorophyll a, abundance, and community structure. The proposed classification schemes are finally evaluated with an extensive validation dataset from coastal areas of the Aegean Sea. 2. Methodology 2.1. Field data Available datasets from coastal areas of the Aegean Sea, Eastern Mediterranean were used to develop the proposed water quality classification schemes and assess their efficiency. Detailed information on the datasets, as well as previous assessment of the water quality of the coastal areas under consideration, is provided in Spatharis et al. (2008). For each dataset, information on chlorophyll a, nutrients, and species abundances existed on a monthly basis covering a full annual cycle and a total number of 816 samples was available for analysis. The areas under consideration cover a wide range of biomass (chl a in the range 0.01– 8.80 mg/L and phytoplankton abundance from 960 to 9,905,980 cells/L), mainly affected by anthropogenic activities (agriculture, urbanization, tourism) in the coastal zone. Summary statistics for chlorophyll a and nutrient concentrations are given in Table 1. More eutrophic areas according to previous assessments based on chl a and/or nutrient concentrations are the Inner Saronikos (Ignatiades et al., 1992; Karydis, 1996), the Kalloni Gulf (Spatharis et al., 2007a,b), the Mytilene port (Tsirtsis, 1995) and the Rhodos ports (Stefanou et al., 2000), whereas outer Saronikos (Ignatiades et al., 1992; Karydis, 1996) and the Gera Gulf (Arhonditsis et al., 2000) are considered as mesotrophic. Mytilene strait (Tsirtsis, 1995), Rhodos coastal (Stefanou et al., 2000) and Rhodos offshore (Vounatsou and Karydis, 1991) are characteristic of oligotrophy. Rhodos offshore waters in particular, located northwestern of the Island of Rhodos, Greece, are typical of the pristine undisturbed marine environment of the Eastern Mediterranean (Vounatsou and Karydis, 1991). Therefore, this site has been extensively used in the past to express reference conditions when developing water quality assessment scales for the Aegean Sea Table 1 Dataset information and mean annual values of chlorophyll a (mg/L) and dissolved nutrient concentrations (mM) for the coastal areas in the Aegean Sea. Site
Stations
Samples
Chl a
DIP
DIN
Rhodos offshore Rhodos coastal Mytilene strait Rhodos ports Gera Gulf Outer Saronikos Gulf Mytilene port Kalloni Gulf Inner Saronikos Gulf
R1–R5 RH1–2 & 6–10 M2 RH3–RH5 GG3–GG8 S3–S9 M1 K3–K8 S1, S2
145 84 67 36 89 168 39 140 48
0.10 0.10 0.33 0.44 0.84 0.63 1.01 1.19 1.65
0.002 0.002 0.002 0.002 0.007 0.004 0.003 0.003 0.020
0.064 0.083 0.122 0.293 0.053 0.148 0.166 0.301 0.354
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(octaves) of doubling intervals can be easily calculated (Tsirtsis et al., 2008). In the current study, a relationship was firstly established between S and N from the field data (816 samples), that is S = 6.241 log(N) 12.642; this relationship was found statistically significant (R2 = 0.480, P < 0.01) by applying linear regression analysis. Then given N (total abundance), S can be calculated and thereafter x and a from the ratio S/N. Finally, knowing the shape of the log series distribution (defined by x and a), the number of species corresponding to each abundance class can be calculated and total cells N can be allocated to species S. The important role of dominant species in phytoplankton communities was accounted by assigning a percentage of the total abundance to the most dominant species. This percentage was calculated from the field data. It was found that the abundance of the most dominant species (N1) is related with total abundance (N) by the equation log(N1) = 1.123 log(N) 0.968. This relation was sta-
(Ignatiades et al., 1992; Karydis, 1992, 1994, 1996; Karydis and Tsirtsis, 1996; Kitsiou and Karydis, 2000; Tsirtsis and Karydis, 1998; Simboura et al., 2005; Stefanou et al., 2000). 2.2. Development of simulated phytoplankton communities Statistical distributions including the lognormal and log series, allocating individuals in a community to species, can successfully model phytoplankton communities (Tsirtsis et al., 2008). In the current study, simulated phytoplankton communities were generated using the log series distribution (Fisher et al., 1943). The log series is characterized by two parameters, x and a, which can be calculated when the ratio S/N is known (Magurran, 2004), where S the species richness and N the total abundance of a sample. When the two parameters of the log series are known, the number of species in a community corresponding to abundance classes
Table 2 The 22 indices applied in the current study including 13 diversity, 7 evenness and 2 dominance indices. Index type
Index
Formula
Reference
Diversity indices
Margalef
S1 D¼ ln N
Margalef (1958)
Gleason Menhinick Simpson’s Shannon H0 Shannon D0 Hill N0 Hill N1 Hill N2 Odum Kothe Hurlbert McIntosh
Evenness indices
Evenness E1 Evenness E2 Evenness E3 Evenness E4 Evenness E5 Evenness E6 Redundancy
Dominance indices
Berger–Parker McNaughton
S ln N S D ¼ pffiffiffiffi N PS n ðni 1Þ D ¼ i¼1 i n ðn 1Þ S X ni n ln i H0 ¼ n n i¼1
D¼
0
D0 ¼ S ðS d Þ N0 = S N1 = exp(H0 ) 1 N2 ¼ Simpson0 s D S 1000 O¼ N Smax Si D¼ Smax ! S X N PIE ¼ p2i 1 N1 i¼1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PS 2 n i¼1 ni pffiffiffi M¼ n n H0 ln S expðH0 Þ E2 ¼ S expðH0 Þ 1 E3 ¼ S1 1=Simpson0 s D E4 ¼ expðH0 Þ ð1=Simpson0 s DÞ 1 E5 ¼ expðH0 Þ 1 0 E6 ¼ 1 d 0 H H0 R ¼ 0 max Hmax H0min E1 ¼
n1 n n þ n2 a¼ 1 n
B¼
The terms used in the formulas are given below: S = the number of species in a sample or a population. N = the number of individuals in a population or community. Ni = the number of individuals in species i of a population or community. n = the number of individuals in a sample from a population. ni = the number of individuals in a species i of a sample from a population. pi = ni/n = the fraction of a sample of individuals belonging to species i. Smax = the maximum number of species in a sample. n1, n2 = the number of individuals in the two most abundant species. PK j pi p j j 0 N! H0max ¼ ln S (Pielou, 1975), H0min ¼ N1 ln ðNSþ1Þ! (Pielou, 1975), and d ¼ i 6¼ j S (Camargo, 2008).
Ludwig and Reynolds (1988) Menhinick (1964) Ludwig and Reynolds (1988) Shannon and Weaver (1949) Camargo (2008) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Odum et al. (1960) Pielou (1975) Hurlbert (1971) McIntosh (1967)
Pielou (1975) Sheldon (1969) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Camargo (2008) Patten (1962)
Berger and Parker (1970) McNaughton (1967)
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tistically significant by applying regression analysis (R2 = 0.957, P < 0.01). More details about the modeling procedure have been given elsewhere (Tsirtsis et al., 2008). In the current study the total abundance of the phytoplankton communities was set in the range of 103 to 107 cells/L characteristic of the coastal waters of the Aegean. Twenty-two ecological indices expressing diversity, evenness, and dominance were calculated for the simulated communities using the cell number as a proxy, and their fit to the corresponding indices’ values of field communities was statistically evaluated. 2.3. Ecological Indices Ecological indices are characterized as diversity, evenness, or dominance indices, according to their mathematical formula weighting more to the species richness or evenness components of community structure. In the current study 13 diversity, 7 evenness, and 2 dominance indices commonly applied in community ecology and aquatic studies (Karydis and Tsirtsis, 1996; Washington, 1984) have been considered (Table 2). 2.4. Sensitivity analysis of ecological indices and development of water quality classification scales The sensitivity analysis of the 22 indices to detect structural changes of phytoplankton communities due to eutrophication and the development of coastal water quality classification schemes, were performed with the following step-by-step procedure: 1. A new five-level scale was developed for phytoplankton abundance according to the requirements of the WFD, based on an existing four-level scale (Oligotrophic, Lower Mesotrophic, Upper Mesotrophic, Eutrophic), proposed by Kitsiou and Karydis (2000). This scale was developed using three standard datasets from the Aegean coastal waters, already described above, characteristic of eutrophication (stations S1 and S2 in Saronikos Gulf), mesotrophy (stations S3 to S9 in Saronikos Gulf), and oligotrophy (stations R1 to R5 offshore of Rhodos Island). 2. 22 ecological indices were calculated on simulated phytoplankton communities of 103 to 107 cells/L, which is the observed range in Aegean coastal waters as mentioned above. For each index, six values were calculated by the model corresponding to the limits of abundance set in step 1. Thereafter the limits of each index were tested for monotonicity (consistent increase or decrease along the eutrophication spectrum). The monotonic behavior is considered as a prerequisite for an efficient water quality index, since the indices are used in a quantitative manner (Washington, 1984) and their limits have to follow a rank order. Indices that have shown non-monotonic behavior were considered inefficient for water quality assessment and were therefore excluded from further analysis. 3. The monotonic indices were then tested for linearity along the studied eutrophication spectrum. Linearity was set as an objective, considering that the linear behavior maximizes the distances between the limits, resulting to more equally spaced water quality scales. Linearity of the indices was tested by applying linear regression analysis and the significance of the departures from linearity was considered by calculating the studentized residuals (Zar, 1998). 4. Selected ecological indices that have shown monotonic and linear behavior were further used for the development of fivelevel water quality classification schemes for the WFD implementation. The Ecological Quality Ratios (EQRs) of the various quality levels were calculated for each index. Finally, a new Integrated Phytoplankton Index was developed from the
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average EQR values of selected indices in order to account for most of the phytoplankton metrics defined by the WFD (chl a, abundance, diversity). 3. Results The limits for phytoplankton abundance in the existing fourlevel scale for eutrophication proposed by Kitsiou and Karydis (2000) were <4160 cells/L for oligotrophy, 4160–31,399 cells/L for lower mesotrophy, 31,400–188,333 cells/L for higher mesotrophy, and >188,334 cells/L for eutrophication. Trying to develop a new five-level scale for WFD by modifying the above scale, a correspondence was set at first between the characterizations according to the eutrophication level and the WFD characterizations (High, Good, Moderate, Poor, and Bad). This was achieved by considering the mean chlorophyll a concentration of each group of stations used for the scaling by Kitsiou and Karydis (2000) according to the existing chlorophyll a scale for Eastern Mediterranean by Simboura et al. (2005). It was found that the characterizations oligotrophy, lower mesotrophy, higher mesotrophy, and eutrophication correspond to the High, Good, Moderate and Poor water qualities of the WFD. For oligotrophy or High water quality in particular, both authors used the same dataset (Rhodos offshore) to define reference conditions, also being used in the current study. Trying to develop a five-level quality scale for abundance complying with the WFD, a limit for Bad water quality was defined in the present study, always using the same dataset. The upper outlying values of the whole dataset were defined (49 values), that is values greater than the upper quartile of the dataset plus 1.5 times the interquartile range (Tukey, 1977). These upper outlying values were considered as characteristic of the Bad water quality of WFD (or dystrophy according to the eutrophication level notation), and their median (710,470 cells/L) was set as the limit between Poor and Bad water qualities. Simulated communities in the range of abundance of 103 to 7 10 cells/L were generated by the log series distribution. The rankabundance plot of a selected number of simulated communities of 12, 17, 23 and 31 species, being the minimum, lower quartile, median, and upper quartile respectively of species richness in the 816 field samples, is plotted in Fig. 1. The important role of the dominant species in phytoplankton communities is revealed, especially when abundance is high. Moreover, evenness is affected by the total abundance and species richness, being higher for communities characteristic of oligotrophy and showing a decreasing trend with eutrophication (as expressed by the increase in cell number). The goodness-of-fit of the simulated communities to field data (816 samples) was then tested. Twenty-two indices, expressing diversity, evenness, and dominance were calculated for both simulated and field communities of the same abundance, and
Fig. 1. Rank-abundance plots of simulated phytoplankton communities of 12, 17, 23 and 31 species, that is the minimum, lower quartile, median, and upper quartile respectively of species richness in the 816 field samples.
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Fig. 2. Monotonic and non-monotonic behavior of selected ecological indices along the cell number gradient (in log scale). Calculations were based on simulated speciesabundance data generated with the log series model.
Spearman rank coefficient was used as a measure of correlation. Significant correlations (P < 0.01) were found for all indices, the Spearman coefficients being in the range from 0.245 for evenness E4 to 0.979 for Odum index. Six values were calculated for each index on simulated phytoplankton communities; the minimum and maximum values (for 103 and 107 cells/L), and the values corresponding to the limits of the five-level scale defined for phytoplankton abundance above. The monotonic behavior of each index was checked by plotting its variability in the range of 103 to 107 cells/L, after standardization on a 0–100 scale, for comparative reasons. Most of the indices (14 out of 22) have shown monotonic behavior, that is a consistent trend of increase as Margalef’s index, or decrease as Menhinick’s
and evenness E1 indices (Fig. 2). The indices of Simpson, Shannon H0 and D0 , Hill N1 and N2, Hurlbert, and McIntosh were nonmonotonic presenting a characteristic hump-shape curve. As observed for Simpson and Hill N1 (Fig. 3), these indices increase when the abundance is low (below 104 cells/L) and then they gradually decrease reaching their minima at very high abundances. Evenness E4 presents the opposite trend, showing a gradual decrease and then a rise for abundances greater than 8 105 cells/L (Fig. 2). The above-mentioned nine non-monotonic indices were considered unreliable to be used for water quality assessment and were excluded from further analysis. The monotonic indices selected in the previous step were further tested for linearity by applying linear regression analysis.
Fig. 3. Test of linearity of selected monotonic indices along the water quality levels (H, High; G, Good; M, Moderate; P, Poor; B, Bad). Menhinick and evenness E2 indices do not present significant departures from linearity, whereas Odum and Redundancy deviate more than 2 standard deviations at one and two points, respectively.
S. Spatharis, G. Tsirtsis / Ecological Indicators 10 (2010) 840–847 Table 3 Assessment of the linearity of the 14 monotonic ecological indices: regression coefficient (R2), significance level, number of unusual residuals and their departure from linearity in terms of standard deviations. Index
R2
Sign. level
Studentized residuals
Deviation
Odum Kothe Gleason Margalef Menhinick Hill N0 Evenness E1 Evenness E2 Evenness E3 Evenness E5 Evenness E6 Redundancy Berger–Parker McNaughton
0.704* 0.925** 0.896** 0.905** 0.961** 0.996** 0.984** 0.959** 0.953** 0.952** 0.960** 0.984** 0.938** 0.928**
0.0367 0.0022 0.0042 0.0035 0.0006 0.0000 0.0001 0.0006 0.0008 0.0009 0.0005 0.0001 0.0015 0.0020
1 1 2 1 0 1 2 0 0 1 0 2 1 1
3.89 6.48 2.12, 2.06 2.25 – 2.37 2.28, 2.98 – – 3.17 – 2.28, 2.98 4.96 3.05
Significant departures from the linear behavior were checked by considering the studentized residuals that is observations which deviate significantly from the linear model fitted. These residuals are expressed in numbers of standard deviation and in case they exceed two, they can be considered as outliers and be removed from the analysis. Statistically significant linear behavior at P < 0.05 was observed for all indices (Table 3). Moreover, Menhinick, and evenness indices E2, E3, and E6 have shown no significant residuals, therefore their behavior can be considered as fully linear. The diversity indices of Hill N0, Margalef, Odum and Kothe, the evenness index E5, and the dominance indices Berger– Parker and McNaughton presented significant departures from the linear behavior with one value deviating more than 3 standard deviations. The rest of the indices, such as Gleason’s, evenness E1, and Redundancy have shown remarkable departures from linearity with one or two values deviating more than 2 standard deviations. A graphical presentation of the residuals for selected indices is shown in Fig. 3. Considering both the criteria of monotonicity and linearity, as well as the goodness-of-fit of the simulated to the field data, it seems that the most efficient ecological indices for the development of water quality classification schemes based on phytoplankton data are the diversity index of Menhinick and the evenness indices E2, E3, and E6. For these indices the Ecological Quality Ratios (EQRs) can be also calculated by standardizing their values to fit the 0–1 range. In order to synthesize the information on the water quality characterization based on chlorophyll a, phytoplankton abundance, and community structure, an Integrated Phytoplankton Index (IPI) is proposed by accounting the above metrics with equal weights. Diversity is expressed in terms of the Menhinick index, a mathematically simple index calculated from species richness and total abundance. The proposed integrated
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Table 5 Water quality assessment of coastal Aegean waters (5, High; 4, Good; 3, Moderate; 2, Low; 1, Bad) based on chlorophyll a (Simboura et al., 2005), phytoplankton abundance, two ecological indices’ (Menhinick and evenness E2) scales, and the proposed Integrated Phytoplankton Index (IPI). Site
Stations
Chl a
Cell no.
Menhinick
E2
IPI
Rhodos offshore Rhodos coastal Mytilene strait Rhodos ports Gera Gulf Outer Saronikos Gulf Mytilene port Kalloni Gulf Inner Saronikos Gulf
R1–R5 RH1–2 & 6–10 M2 RH3–RH5 GG3–GG8 S3–S9 M1 K3–K8 S1, S2
5 4 4 3 2 2 2 2 2
4 4 3 3 3 2 2 1 2
5 5 4 4 4 3 3 2 3
4 4 4 3 4 3 3 2 3
5 5 4 4 3 3 2 2 2
index can be easily calculated as the mean of the EQRs of the three metrics in each quality class, since these ratios are standardized in the 0–1 range. The scales developed for selected indices (Menhinick, E2, IPI) for the implementation of the Water Framework Directive in Eastern Mediterranean, along with the existing scales (a) of chlorophyll a for the Aegean waters (Simboura et al., 2005) and (b) of abundance by (Kitsiou and Karydis, 2000) as modified in the current study, are presented in Table 4. A summary of the water quality classification of several coastal sites in the Aegean according to the already existing and newly developed quality metrics is presented in Table 5. Although there is an overall agreement in the characterization of each site, some differences were observed, spanning one quality level at most. The classifications ranged between High and Low, none of the coastal areas classified in the Bad water quality, except from Kalloni according to the abundance criterion. Considering the differences between the various metrics, it seems that scales based on ecological indices are more optimistic compared to the chlorophyll a and abundance scales. This optimism is further reflected to the proposed synthetic IPI index, if it is compared with the already established chlorophyll a scale of Simboura et al. (2005) for coastal Aegean waters. 4. Discussion Many water quality assessment schemes have been developed on phytoplankton for the implementation of the WFD in European regional seas (Borja et al., 2004; Devlin et al., 2007; Revilla et al., 2009; Sagert et al., 2005), using biomass (chl a and abundance), composition, and the frequency and intensity of blooms. For Eastern Mediterranean, chlorophyll a is the only metric used so far for the WFD (Simboura et al., 2005). In the current study a fivelevel classification system was developed on phytoplankton abundance, as a modification of an existing four-level scale (Kitsiou and Karydis, 2000). The characterization of water quality with the abundance scale seems more conservative compared to
Table 4 Water quality classification schemes and the corresponding Ecological Quality Ratios for Menhinick and evenness E2 indices, and phytoplankton abundance (N). The existing scale for chlorophyll a by (Simboura et al., 2005) and the proposed Integrated Phytoplankton Index (IPI) are also shown. Index
High
Good
Moderate
Low
Bad
Menhinick EQRMenhinick Evenness E2 EQ REvenness E2 N EQRN Chl a EQRChl a IPI
0.19–0.15 1 0.96–0.77 1 1000–4160 1 <0.10 1 1
0.15–0.09 0.75 0.77–0.46 0.75 4160–31,400 0.99 0.10–0.40 0.96 0.90
0.09–0.05 0.38 0.46–0.30 0.33 31,400–188,334 0.96 0.40–0.60 0.82 0.72
0.05–0.03 0.13 0.30–0.21 0.12 188,334–710,470 0.73 0.60–2.21 0.73 0.53
0.03–0.01 0 0.21–0.09 0 710,470–10,000,000 0 >2.21 0 0
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chlorophyll a, however the differences span one quality level at most. This discrepancy is more or less expected since abundance and chlorophyll a are two different phytoplankton metrics (Domingues et al., 2008). Chlorophyll a, being a photosynthetic pigment, is indeed present in all phytoplankton cells, but it only represents a fraction of the whole biomass. Moreover, heterotrophic species, not represented in chlorophyll a measurements (Domingues et al., 2008), may form important peaks in abundance that may be associated with other forms of disturbance such as allochthonous and autochthonous organic inputs rather than eutrophication. Therefore taking into consideration community structure as a whole, seems a more representative measure of water quality than chlorophyll a alone. In the current study, simulated phytoplankton communities resembling the field communities and the changes they undergo due to eutrophication formed the basis for the evaluation of 22 indices. Simulation offers the opportunity to reduce the undesirable variability often encountered in field data due to uncontrolled factors (seasonality, patchiness, hydrodynamic circulation) or stochastic processes. Moreover it is possible to insert fully controlled perturbations, assess the response of indices and infer possible explanations for their behavior (Boyle et al., 1990). The log series model, which was applied, successfully reproduced the structure of phytoplankton communities of the Aegean coastal waters, in terms of diversity, evenness and dominance. The monotonic behavior of the tested indices was set as a prerequisite since the water quality categorization in WFD is performed in a rank order. Some indices showed non-monotonic behavior, among them the Shannon and Simpson indices, two of the most used measures of diversity in community ecology (Digby and Kempton, 1987). These indices showed a hump-shaped behavior that may be initially related to the increase in species richness (expressed by the Hill’s N0 index), which is later overturned for abundances higher than 103 cells/L by the decrease in the community evenness (expressed through the evenness indices). This index has been already widely criticized (Boyle et al., 1990; Karydis and Tsirtsis, 1996), mainly on the basis that its response is mostly a function of changes in species richness rather than in the relative abundance of species (Simboura et al., 2005). The sensitivity of indices to discriminate water quality can be further enhanced by the linear or close to linear, trend of change across the studied ecological gradient, since the average distance between the set of limits is maximized. Among the indices examined, a small number including the diversity index of Menhinick and the evenness indices E2, E3, and E6 has passed the screening procedure for both monotonicity and linearity. These four indices were considered as the most efficient to assess water quality for the implementation of the WFD. The scales developed on Menhinick index of diversity and evenness E2 were used to assess the water quality of several coastal sites in the Aegean. Between them, the Menhinick index renders a more optimistic characterization by classifying some sites of higher quality than the E2 index. The Menhinick index is a simple index based on species richness and total abundance, whereas evenness E2 includes the exponent of the Shannon index carrying the full information on the distribution of individuals to species (Washington, 1984). A new synthetic index for water quality assessment in the WFD is also proposed in the current study, considering that the evaluation of water quality should be also based on suitable multimetric indices (Borja et al., 2004; Domingues et al., 2008). For this multimetric, namely the Integrated Phytoplankton Index (IPI), three phytoplankton metrics were used that is chlorophyll a, abundance, and diversity (Menhinick index) with equal weights. The classification of areas with the IPI was consistent with the other metrics, but was more optimistic than chlorophyll a or total abundance, and more conservative than Menhinick’s diversity and
evenness E2 indices. It seems that the structure of phytoplankton communities is more robust to the changes due to eutrophication than the chlorophyll a content. Therefore it can be concluded that, although there is a consistency, each metric carries its own information and the use of multimetric indices combining various phytoplankton metrics is appropriate in the framework of integrated water quality assessment. The classification of the Aegean coastal waters according to the proposed metrics (ecological indices and IPI) is generally in agreement with previous studies on nutrients (Ignatiades et al., 1992; Primpas et al., 2008; Stefanou et al., 2000; Vounatsou and Karydis, 1991) and phytoplankton community data (Ignatiades et al., 1995; Karydis and Tsirtsis, 1996; Kitsiou and Karydis, 2000; Tsirtsis and Karydis, 1998). The Rhodos offshore waters, which were used as the reference site in the current study, were characterized as a typical oligotrophic environment in the past by many authors (Ignatiades et al., 1992; Karydis, 1992, 1994, 1996; Karydis and Tsirtsis, 1996; Kitsiou and Karydis, 2000; Tsirtsis and Karydis, 1998; Simboura et al., 2005; Stefanou et al., 2000), in agreement with the assessment by the IPI. Coastal sites such as Rhodos inshore (coastal and ports), the Gulf of Gera, the Strait of Mytilene and the outer Saronikos have been previously characterized as mesotrophic according to a three-level classification scheme (Arhonditsis et al., 2000; Karydis and Coccossis, 1990; Tsirtsis, 1995). The use of a five-level scheme in the current study allowed a more detailed discrimination in the field of mesotrophy and the above sites were characterized as of High, Good, and Moderate quality. Finally, sites such as the port of Mytilene, and the Inner Saronikos and Kalloni Gulfs are more heavily affected by anthropogenic activities and have been previously characterized as eutrophic based on both physicochemical and biotic data (Ignatiades et al., 1992; Simboura et al., 2005; Spatharis et al., 2007a,b), in agreement with the classification of Poor quality in the current study. Potential dissimilarities of one quality level at most may be related to the methodologies applied in the past which were based on a relative local scale, whereas the proposed metrics are suitable for assessments in the regional scale, since they were based on a large number of available data from many coastal areas in the Aegean coastal waters. The characterization of coastal water quality constitutes a critical issue for planning and decision making in Integrated Coastal Zone Management (Karydis and Coccossis, 1990). Therefore it must be based on robust tools able to synthesize the information from more than one metrics or quality elements, reducing in this way possible errors and misleading results. In the current study an effort has been made for the development of such qualitative tools for the Eastern Mediterranean Ecoregion. Further testing of the proposed quality scales, as well as of the proposed integrated index (IPI), need to be done in order to be established for water quality assessment in the WFD. Moreover, the application of the current screening methodology in a eutrophic west European area would be very interesting (higher chlorophyll and nutrient levels) and could contribute towards the enhancement of the ongoing research for the implementation of the Water Framework Directive in European waters. Acknowledgement The current study was conducted in the framework of the Program INTERREG IIIA, Gr-Cy (K.2301.001) co-funded by the European Union and national funds. References Arhonditsis, G., Tsirtsis, G., Angelidis, M.O., Karydis, M., 2000. Quantification of the effects of nonpoint nutrient sources to coastal marine eutrophication: applications to a semi-enclosed gulf in the Mediterranean Sea. Ecol. Model. 129, 209– 227.
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Ecological quality scales based on phytoplankton for the implementation of Water Framework Directive in the Eastern Mediterranean Sofie Spatharis *, George Tsirtsis Department of Marine Sciences, University of the Aegean, University Hill, 81100 Mytilene, Greece
A R T I C L E I N F O
A B S T R A C T
Article history: Received 2 October 2009 Received in revised form 28 December 2009 Accepted 17 January 2010
Structural changes of phytoplankton communities, often expressed through ecological indices, constitute one of the metrics for the implementation of the European Water Framework Directive (WFD). In the current study a thorough analysis of the efficiency of 22 ecological indices was performed and a small number was selected for the development of five-level water quality scales (High, Good, Moderate, Poor, and Bad). The analysis was performed on simulated communities free of the noise of field communities due to uncontrolled factors or stochastic processes. Two criteria were set for the sensitivity of indices, namely their monotonicity and linearity across the studied eutrophication spectrum. The whole procedure was based on the development of a five-level quality assessment scheme based on phytoplankton abundance. Among the indices tested, the Menhinick diversity index and three indices of evenness were the most efficient, showing consistency (monotonic behavior) and linearity and were therefore used for the development of quality scales for the WFD. An Integrated Phytoplankton Index (IPI) based on three phytoplankton metrics, chlorophyll a, abundance, and diversity is also proposed. The efficiency of these indices was evaluated for a number of sites in the Aegean, already classified in the past by various methods based on nutrient concentrations or phytoplankton data. The results indicate that the various phytoplankton metrics (chlorophyll a, abundance, and diversity) assessed or proposed in the current study, carry their own information showing differences in the final classification of areas. Therefore the establishment of synthetic indices as the IPI seems to be advantageous for the integrated assessment of coastal water quality in the framework of European policies as the WFD. ß 2010 Elsevier Ltd. All rights reserved.
Keywords: Community structure Diversity Ecological assessment Indices Log series distribution Model communities Sensitivity analysis
1. Introduction Surface waters including streams, rivers, lakes, estuaries, and coastal waters are under increasing ecological stress due to anthropogenic activities worldwide (UNEP, 1999), leading to the enforcement of major environmental policies, including the US Federal Water Pollution Control Act (2008), the US Oceans Act (2000), the European Water Framework Directive 2000/60 (EC, 2000), and the European Marine Strategy (EC, 2008). The implementation of the Water Framework Directive (WFD) in particular, both regionally and nationally, assumes the development of a five-level water quality classification scheme (High, Good, Moderate, Poor, and Bad) with the environmental objective to achieve good surface water status for all European waters by 2015 (EC, 2000). The levels are defined using the Ecological Quality Ratio (EQR) for a number of biological and chemical quality elements. Among the key biological elements specified for coastal
* Corresponding author. Tel.: +30 22510 36835; fax: +30 22510 36809. E-mail address: [email protected] (S. Spatharis). 1470-160X/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2010.01.005
waters, the only planktic element referred to in the WFD is phytoplankton. Phytoplankton is an efficient indicator of changes in nutrient loads, but is also effective in evaluating responses to many other environmental stressors, due to its fast population response to changes in water quality, hydrology or climate (Domingues et al., 2008). According to the European Directive (EC, 2000), phytoplankton metrics that are fundamental in defining and classifying the ecological status of surface waters are biomass (as chl a), community changes (composition and species abundance), and increase in the frequency and intensity of blooms. Phytoplankton has been considered as a water quality keyelement in many studies for coastal ecosystems. A synthetic approach for water quality assessment in the Atlantic Ecoregion (Basque country), involved the development of an integrated index based on chlorophyll a, phytoplankton total abundance (when exceeding 107 cells/L), and abundance of harmful phytoplankton species (Borja et al., 2004; Revilla et al., 2009; Vincent et al., 2002). For the North Sea and the Atlantic Ecoregions (British coastal waters), Devlin et al. (2007) developed an Integrated Phytoplankton Index taking into account phytoplankton biomass (as chl a), the frequency of elevated phytoplankton counts (individual species
S. Spatharis, G. Tsirtsis / Ecological Indicators 10 (2010) 840–847
and total cell counts), and seasonal progression of functional groups. Regarding the coastal waters of the Eastern Mediterranean Ecoregion, one of the most oligotrophic marine environments around the world (Krom et al., 1991), chlorophyll a is the only parameter taken into consideration so far for WFD implementation (Simboura et al., 2005). The limits set by these authors are below 0.10 mg/L of chl a for High water quality, 0.10–0.40 for Good, 0.40– 0.60 for Moderate, 0.60–2.21 for Poor and above 2.21 for Bad water quality. Chlorophyll concentrations represent a simple and integrative measure of the phytoplankton community response to nutrient enrichment or succession (Devlin et al., 2007; Harding, 1994). However, community structure (i.e. the distribution of individuals to species) conveys different information by also considering heterotrophic species that are not represented in chlorophyll measurements (Domingues et al., 2008). Moreover, previous studies have demonstrated that an increase in chlorophyll a due to eutrophication is always accompanied by changes in phytoplankton community structure in terms of total abundance, species richness, and evenness (Tsirtsis and Karydis, 1998; Tsirtsis et al., 2008). For phytoplankton abundance, a four-level classification system was developed in the past for Eastern Mediterranean coastal waters (Kitsiou and Karydis, 2000). The limits set were <4160 cells/L for oligotrophy, 4160–31,399 cells/L for lower mesotrophy, 31,400–188,333 cells/L for higher mesotrophy, and >188,334 cells/L for eutrophication. However this scale has not been adjusted so far to the five-level system required by the WFD. Changes in community structure are often quantified through a number of indices which constitute popular tools in studies associated with community ecology and disturbance (Washington, 1984). The most important advantages provided by indices are the ability for direct comparisons between communities that have few or no species in common and the easiness of their application and interpretation (Magurran, 2004). Alternative methods to taxonomic-based indicators related to body size, abundance distribution among functional groups, functional diversity, and productivity descriptors have been found suitable for other biological quality elements (Mouillot et al., 2006). However, for phytoplankton, productivity and species-abundance data are considered so far the most effective means to explore changes in community structure (Mouillot et al., 2006). In the framework of new policies for water quality assessment, investigators should retain a parsimonious approach, assessing the suitability of existing indices rather than developing new ones (Borja and Dauer, 2008; Diaz et al., 2004). In addition, the evaluation of water quality status should ideally incorporate suitable multimetric indices (Borja and Dauer, 2008; Diaz et al., 2004; Domingues et al., 2008) taking into consideration as many of the fundamental attributes of phytoplankton as possible (i.e. biomass, community structure, and frequency of blooms). An important shortcoming in the development process of water quality assessment schemes is the inherent noise or variability of field data associated with seasonality, hydrodynamic circulation, grazing, and patchiness (Karydis, 1996) or stochastic processes (Mouillot and Lepretre, 2000). This variability masks and distorts the information associated with a particular stressor or process of interest (Karydis, 1992) and therefore is considered as undesirable, especially in water quality assessment schemes which are often based on annual means. An alternative approach in the assessment of water quality can be based on the use of simulated communities (Boyle et al., 1990; Mouillot and Wilson, 2002) that retain the main structural characteristics of natural communities. The elimination of noise in the simulated communities reveals the signal, and therefore supports the quantification of the community response to environmental stressors. Previous studies have demonstrated that phytoplankton communities can be successfully modeled
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using statistical distributions such as the lognormal and log series (Tsirtsis et al., 2008) along a wide spectrum of productivity, from oligotrophy to eutrophication, characteristic of Eastern Mediterranean coastal waters. The present study aims to assess the sensitivity of 22 ecological indices of phytoplankton diversity to efficiently detect eutrophic trends in Eastern Mediterranean coastal waters, and thus their applicability in the Water Framework Directive. The development and use of simulated phytoplankton communities covering a wide and continuous eutrophication spectrum enabled (a) the unbiased examination of the sensitivity (monotonicity and linearity) of the indices and (b) the development of coastal water quality classification scales using selected indices. Moreover a new, integrated index of water quality was developed taking into account three of the metrics proposed by the European Directive for phytoplankton, which is chlorophyll a, abundance, and community structure. The proposed classification schemes are finally evaluated with an extensive validation dataset from coastal areas of the Aegean Sea. 2. Methodology 2.1. Field data Available datasets from coastal areas of the Aegean Sea, Eastern Mediterranean were used to develop the proposed water quality classification schemes and assess their efficiency. Detailed information on the datasets, as well as previous assessment of the water quality of the coastal areas under consideration, is provided in Spatharis et al. (2008). For each dataset, information on chlorophyll a, nutrients, and species abundances existed on a monthly basis covering a full annual cycle and a total number of 816 samples was available for analysis. The areas under consideration cover a wide range of biomass (chl a in the range 0.01– 8.80 mg/L and phytoplankton abundance from 960 to 9,905,980 cells/L), mainly affected by anthropogenic activities (agriculture, urbanization, tourism) in the coastal zone. Summary statistics for chlorophyll a and nutrient concentrations are given in Table 1. More eutrophic areas according to previous assessments based on chl a and/or nutrient concentrations are the Inner Saronikos (Ignatiades et al., 1992; Karydis, 1996), the Kalloni Gulf (Spatharis et al., 2007a,b), the Mytilene port (Tsirtsis, 1995) and the Rhodos ports (Stefanou et al., 2000), whereas outer Saronikos (Ignatiades et al., 1992; Karydis, 1996) and the Gera Gulf (Arhonditsis et al., 2000) are considered as mesotrophic. Mytilene strait (Tsirtsis, 1995), Rhodos coastal (Stefanou et al., 2000) and Rhodos offshore (Vounatsou and Karydis, 1991) are characteristic of oligotrophy. Rhodos offshore waters in particular, located northwestern of the Island of Rhodos, Greece, are typical of the pristine undisturbed marine environment of the Eastern Mediterranean (Vounatsou and Karydis, 1991). Therefore, this site has been extensively used in the past to express reference conditions when developing water quality assessment scales for the Aegean Sea Table 1 Dataset information and mean annual values of chlorophyll a (mg/L) and dissolved nutrient concentrations (mM) for the coastal areas in the Aegean Sea. Site
Stations
Samples
Chl a
DIP
DIN
Rhodos offshore Rhodos coastal Mytilene strait Rhodos ports Gera Gulf Outer Saronikos Gulf Mytilene port Kalloni Gulf Inner Saronikos Gulf
R1–R5 RH1–2 & 6–10 M2 RH3–RH5 GG3–GG8 S3–S9 M1 K3–K8 S1, S2
145 84 67 36 89 168 39 140 48
0.10 0.10 0.33 0.44 0.84 0.63 1.01 1.19 1.65
0.002 0.002 0.002 0.002 0.007 0.004 0.003 0.003 0.020
0.064 0.083 0.122 0.293 0.053 0.148 0.166 0.301 0.354
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(octaves) of doubling intervals can be easily calculated (Tsirtsis et al., 2008). In the current study, a relationship was firstly established between S and N from the field data (816 samples), that is S = 6.241 log(N) 12.642; this relationship was found statistically significant (R2 = 0.480, P < 0.01) by applying linear regression analysis. Then given N (total abundance), S can be calculated and thereafter x and a from the ratio S/N. Finally, knowing the shape of the log series distribution (defined by x and a), the number of species corresponding to each abundance class can be calculated and total cells N can be allocated to species S. The important role of dominant species in phytoplankton communities was accounted by assigning a percentage of the total abundance to the most dominant species. This percentage was calculated from the field data. It was found that the abundance of the most dominant species (N1) is related with total abundance (N) by the equation log(N1) = 1.123 log(N) 0.968. This relation was sta-
(Ignatiades et al., 1992; Karydis, 1992, 1994, 1996; Karydis and Tsirtsis, 1996; Kitsiou and Karydis, 2000; Tsirtsis and Karydis, 1998; Simboura et al., 2005; Stefanou et al., 2000). 2.2. Development of simulated phytoplankton communities Statistical distributions including the lognormal and log series, allocating individuals in a community to species, can successfully model phytoplankton communities (Tsirtsis et al., 2008). In the current study, simulated phytoplankton communities were generated using the log series distribution (Fisher et al., 1943). The log series is characterized by two parameters, x and a, which can be calculated when the ratio S/N is known (Magurran, 2004), where S the species richness and N the total abundance of a sample. When the two parameters of the log series are known, the number of species in a community corresponding to abundance classes
Table 2 The 22 indices applied in the current study including 13 diversity, 7 evenness and 2 dominance indices. Index type
Index
Formula
Reference
Diversity indices
Margalef
S1 D¼ ln N
Margalef (1958)
Gleason Menhinick Simpson’s Shannon H0 Shannon D0 Hill N0 Hill N1 Hill N2 Odum Kothe Hurlbert McIntosh
Evenness indices
Evenness E1 Evenness E2 Evenness E3 Evenness E4 Evenness E5 Evenness E6 Redundancy
Dominance indices
Berger–Parker McNaughton
S ln N S D ¼ pffiffiffiffi N PS n ðni 1Þ D ¼ i¼1 i n ðn 1Þ S X ni n ln i H0 ¼ n n i¼1
D¼
0
D0 ¼ S ðS d Þ N0 = S N1 = exp(H0 ) 1 N2 ¼ Simpson0 s D S 1000 O¼ N Smax Si D¼ Smax ! S X N PIE ¼ p2i 1 N1 i¼1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PS 2 n i¼1 ni pffiffiffi M¼ n n H0 ln S expðH0 Þ E2 ¼ S expðH0 Þ 1 E3 ¼ S1 1=Simpson0 s D E4 ¼ expðH0 Þ ð1=Simpson0 s DÞ 1 E5 ¼ expðH0 Þ 1 0 E6 ¼ 1 d 0 H H0 R ¼ 0 max Hmax H0min E1 ¼
n1 n n þ n2 a¼ 1 n
B¼
The terms used in the formulas are given below: S = the number of species in a sample or a population. N = the number of individuals in a population or community. Ni = the number of individuals in species i of a population or community. n = the number of individuals in a sample from a population. ni = the number of individuals in a species i of a sample from a population. pi = ni/n = the fraction of a sample of individuals belonging to species i. Smax = the maximum number of species in a sample. n1, n2 = the number of individuals in the two most abundant species. PK j pi p j j 0 N! H0max ¼ ln S (Pielou, 1975), H0min ¼ N1 ln ðNSþ1Þ! (Pielou, 1975), and d ¼ i 6¼ j S (Camargo, 2008).
Ludwig and Reynolds (1988) Menhinick (1964) Ludwig and Reynolds (1988) Shannon and Weaver (1949) Camargo (2008) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Odum et al. (1960) Pielou (1975) Hurlbert (1971) McIntosh (1967)
Pielou (1975) Sheldon (1969) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Ludwig and Reynolds (1988) Camargo (2008) Patten (1962)
Berger and Parker (1970) McNaughton (1967)
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tistically significant by applying regression analysis (R2 = 0.957, P < 0.01). More details about the modeling procedure have been given elsewhere (Tsirtsis et al., 2008). In the current study the total abundance of the phytoplankton communities was set in the range of 103 to 107 cells/L characteristic of the coastal waters of the Aegean. Twenty-two ecological indices expressing diversity, evenness, and dominance were calculated for the simulated communities using the cell number as a proxy, and their fit to the corresponding indices’ values of field communities was statistically evaluated. 2.3. Ecological Indices Ecological indices are characterized as diversity, evenness, or dominance indices, according to their mathematical formula weighting more to the species richness or evenness components of community structure. In the current study 13 diversity, 7 evenness, and 2 dominance indices commonly applied in community ecology and aquatic studies (Karydis and Tsirtsis, 1996; Washington, 1984) have been considered (Table 2). 2.4. Sensitivity analysis of ecological indices and development of water quality classification scales The sensitivity analysis of the 22 indices to detect structural changes of phytoplankton communities due to eutrophication and the development of coastal water quality classification schemes, were performed with the following step-by-step procedure: 1. A new five-level scale was developed for phytoplankton abundance according to the requirements of the WFD, based on an existing four-level scale (Oligotrophic, Lower Mesotrophic, Upper Mesotrophic, Eutrophic), proposed by Kitsiou and Karydis (2000). This scale was developed using three standard datasets from the Aegean coastal waters, already described above, characteristic of eutrophication (stations S1 and S2 in Saronikos Gulf), mesotrophy (stations S3 to S9 in Saronikos Gulf), and oligotrophy (stations R1 to R5 offshore of Rhodos Island). 2. 22 ecological indices were calculated on simulated phytoplankton communities of 103 to 107 cells/L, which is the observed range in Aegean coastal waters as mentioned above. For each index, six values were calculated by the model corresponding to the limits of abundance set in step 1. Thereafter the limits of each index were tested for monotonicity (consistent increase or decrease along the eutrophication spectrum). The monotonic behavior is considered as a prerequisite for an efficient water quality index, since the indices are used in a quantitative manner (Washington, 1984) and their limits have to follow a rank order. Indices that have shown non-monotonic behavior were considered inefficient for water quality assessment and were therefore excluded from further analysis. 3. The monotonic indices were then tested for linearity along the studied eutrophication spectrum. Linearity was set as an objective, considering that the linear behavior maximizes the distances between the limits, resulting to more equally spaced water quality scales. Linearity of the indices was tested by applying linear regression analysis and the significance of the departures from linearity was considered by calculating the studentized residuals (Zar, 1998). 4. Selected ecological indices that have shown monotonic and linear behavior were further used for the development of fivelevel water quality classification schemes for the WFD implementation. The Ecological Quality Ratios (EQRs) of the various quality levels were calculated for each index. Finally, a new Integrated Phytoplankton Index was developed from the
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average EQR values of selected indices in order to account for most of the phytoplankton metrics defined by the WFD (chl a, abundance, diversity). 3. Results The limits for phytoplankton abundance in the existing fourlevel scale for eutrophication proposed by Kitsiou and Karydis (2000) were <4160 cells/L for oligotrophy, 4160–31,399 cells/L for lower mesotrophy, 31,400–188,333 cells/L for higher mesotrophy, and >188,334 cells/L for eutrophication. Trying to develop a new five-level scale for WFD by modifying the above scale, a correspondence was set at first between the characterizations according to the eutrophication level and the WFD characterizations (High, Good, Moderate, Poor, and Bad). This was achieved by considering the mean chlorophyll a concentration of each group of stations used for the scaling by Kitsiou and Karydis (2000) according to the existing chlorophyll a scale for Eastern Mediterranean by Simboura et al. (2005). It was found that the characterizations oligotrophy, lower mesotrophy, higher mesotrophy, and eutrophication correspond to the High, Good, Moderate and Poor water qualities of the WFD. For oligotrophy or High water quality in particular, both authors used the same dataset (Rhodos offshore) to define reference conditions, also being used in the current study. Trying to develop a five-level quality scale for abundance complying with the WFD, a limit for Bad water quality was defined in the present study, always using the same dataset. The upper outlying values of the whole dataset were defined (49 values), that is values greater than the upper quartile of the dataset plus 1.5 times the interquartile range (Tukey, 1977). These upper outlying values were considered as characteristic of the Bad water quality of WFD (or dystrophy according to the eutrophication level notation), and their median (710,470 cells/L) was set as the limit between Poor and Bad water qualities. Simulated communities in the range of abundance of 103 to 7 10 cells/L were generated by the log series distribution. The rankabundance plot of a selected number of simulated communities of 12, 17, 23 and 31 species, being the minimum, lower quartile, median, and upper quartile respectively of species richness in the 816 field samples, is plotted in Fig. 1. The important role of the dominant species in phytoplankton communities is revealed, especially when abundance is high. Moreover, evenness is affected by the total abundance and species richness, being higher for communities characteristic of oligotrophy and showing a decreasing trend with eutrophication (as expressed by the increase in cell number). The goodness-of-fit of the simulated communities to field data (816 samples) was then tested. Twenty-two indices, expressing diversity, evenness, and dominance were calculated for both simulated and field communities of the same abundance, and
Fig. 1. Rank-abundance plots of simulated phytoplankton communities of 12, 17, 23 and 31 species, that is the minimum, lower quartile, median, and upper quartile respectively of species richness in the 816 field samples.
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Fig. 2. Monotonic and non-monotonic behavior of selected ecological indices along the cell number gradient (in log scale). Calculations were based on simulated speciesabundance data generated with the log series model.
Spearman rank coefficient was used as a measure of correlation. Significant correlations (P < 0.01) were found for all indices, the Spearman coefficients being in the range from 0.245 for evenness E4 to 0.979 for Odum index. Six values were calculated for each index on simulated phytoplankton communities; the minimum and maximum values (for 103 and 107 cells/L), and the values corresponding to the limits of the five-level scale defined for phytoplankton abundance above. The monotonic behavior of each index was checked by plotting its variability in the range of 103 to 107 cells/L, after standardization on a 0–100 scale, for comparative reasons. Most of the indices (14 out of 22) have shown monotonic behavior, that is a consistent trend of increase as Margalef’s index, or decrease as Menhinick’s
and evenness E1 indices (Fig. 2). The indices of Simpson, Shannon H0 and D0 , Hill N1 and N2, Hurlbert, and McIntosh were nonmonotonic presenting a characteristic hump-shape curve. As observed for Simpson and Hill N1 (Fig. 3), these indices increase when the abundance is low (below 104 cells/L) and then they gradually decrease reaching their minima at very high abundances. Evenness E4 presents the opposite trend, showing a gradual decrease and then a rise for abundances greater than 8 105 cells/L (Fig. 2). The above-mentioned nine non-monotonic indices were considered unreliable to be used for water quality assessment and were excluded from further analysis. The monotonic indices selected in the previous step were further tested for linearity by applying linear regression analysis.
Fig. 3. Test of linearity of selected monotonic indices along the water quality levels (H, High; G, Good; M, Moderate; P, Poor; B, Bad). Menhinick and evenness E2 indices do not present significant departures from linearity, whereas Odum and Redundancy deviate more than 2 standard deviations at one and two points, respectively.
S. Spatharis, G. Tsirtsis / Ecological Indicators 10 (2010) 840–847 Table 3 Assessment of the linearity of the 14 monotonic ecological indices: regression coefficient (R2), significance level, number of unusual residuals and their departure from linearity in terms of standard deviations. Index
R2
Sign. level
Studentized residuals
Deviation
Odum Kothe Gleason Margalef Menhinick Hill N0 Evenness E1 Evenness E2 Evenness E3 Evenness E5 Evenness E6 Redundancy Berger–Parker McNaughton
0.704* 0.925** 0.896** 0.905** 0.961** 0.996** 0.984** 0.959** 0.953** 0.952** 0.960** 0.984** 0.938** 0.928**
0.0367 0.0022 0.0042 0.0035 0.0006 0.0000 0.0001 0.0006 0.0008 0.0009 0.0005 0.0001 0.0015 0.0020
1 1 2 1 0 1 2 0 0 1 0 2 1 1
3.89 6.48 2.12, 2.06 2.25 – 2.37 2.28, 2.98 – – 3.17 – 2.28, 2.98 4.96 3.05
Significant departures from the linear behavior were checked by considering the studentized residuals that is observations which deviate significantly from the linear model fitted. These residuals are expressed in numbers of standard deviation and in case they exceed two, they can be considered as outliers and be removed from the analysis. Statistically significant linear behavior at P < 0.05 was observed for all indices (Table 3). Moreover, Menhinick, and evenness indices E2, E3, and E6 have shown no significant residuals, therefore their behavior can be considered as fully linear. The diversity indices of Hill N0, Margalef, Odum and Kothe, the evenness index E5, and the dominance indices Berger– Parker and McNaughton presented significant departures from the linear behavior with one value deviating more than 3 standard deviations. The rest of the indices, such as Gleason’s, evenness E1, and Redundancy have shown remarkable departures from linearity with one or two values deviating more than 2 standard deviations. A graphical presentation of the residuals for selected indices is shown in Fig. 3. Considering both the criteria of monotonicity and linearity, as well as the goodness-of-fit of the simulated to the field data, it seems that the most efficient ecological indices for the development of water quality classification schemes based on phytoplankton data are the diversity index of Menhinick and the evenness indices E2, E3, and E6. For these indices the Ecological Quality Ratios (EQRs) can be also calculated by standardizing their values to fit the 0–1 range. In order to synthesize the information on the water quality characterization based on chlorophyll a, phytoplankton abundance, and community structure, an Integrated Phytoplankton Index (IPI) is proposed by accounting the above metrics with equal weights. Diversity is expressed in terms of the Menhinick index, a mathematically simple index calculated from species richness and total abundance. The proposed integrated
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Table 5 Water quality assessment of coastal Aegean waters (5, High; 4, Good; 3, Moderate; 2, Low; 1, Bad) based on chlorophyll a (Simboura et al., 2005), phytoplankton abundance, two ecological indices’ (Menhinick and evenness E2) scales, and the proposed Integrated Phytoplankton Index (IPI). Site
Stations
Chl a
Cell no.
Menhinick
E2
IPI
Rhodos offshore Rhodos coastal Mytilene strait Rhodos ports Gera Gulf Outer Saronikos Gulf Mytilene port Kalloni Gulf Inner Saronikos Gulf
R1–R5 RH1–2 & 6–10 M2 RH3–RH5 GG3–GG8 S3–S9 M1 K3–K8 S1, S2
5 4 4 3 2 2 2 2 2
4 4 3 3 3 2 2 1 2
5 5 4 4 4 3 3 2 3
4 4 4 3 4 3 3 2 3
5 5 4 4 3 3 2 2 2
index can be easily calculated as the mean of the EQRs of the three metrics in each quality class, since these ratios are standardized in the 0–1 range. The scales developed for selected indices (Menhinick, E2, IPI) for the implementation of the Water Framework Directive in Eastern Mediterranean, along with the existing scales (a) of chlorophyll a for the Aegean waters (Simboura et al., 2005) and (b) of abundance by (Kitsiou and Karydis, 2000) as modified in the current study, are presented in Table 4. A summary of the water quality classification of several coastal sites in the Aegean according to the already existing and newly developed quality metrics is presented in Table 5. Although there is an overall agreement in the characterization of each site, some differences were observed, spanning one quality level at most. The classifications ranged between High and Low, none of the coastal areas classified in the Bad water quality, except from Kalloni according to the abundance criterion. Considering the differences between the various metrics, it seems that scales based on ecological indices are more optimistic compared to the chlorophyll a and abundance scales. This optimism is further reflected to the proposed synthetic IPI index, if it is compared with the already established chlorophyll a scale of Simboura et al. (2005) for coastal Aegean waters. 4. Discussion Many water quality assessment schemes have been developed on phytoplankton for the implementation of the WFD in European regional seas (Borja et al., 2004; Devlin et al., 2007; Revilla et al., 2009; Sagert et al., 2005), using biomass (chl a and abundance), composition, and the frequency and intensity of blooms. For Eastern Mediterranean, chlorophyll a is the only metric used so far for the WFD (Simboura et al., 2005). In the current study a fivelevel classification system was developed on phytoplankton abundance, as a modification of an existing four-level scale (Kitsiou and Karydis, 2000). The characterization of water quality with the abundance scale seems more conservative compared to
Table 4 Water quality classification schemes and the corresponding Ecological Quality Ratios for Menhinick and evenness E2 indices, and phytoplankton abundance (N). The existing scale for chlorophyll a by (Simboura et al., 2005) and the proposed Integrated Phytoplankton Index (IPI) are also shown. Index
High
Good
Moderate
Low
Bad
Menhinick EQRMenhinick Evenness E2 EQ REvenness E2 N EQRN Chl a EQRChl a IPI
0.19–0.15 1 0.96–0.77 1 1000–4160 1 <0.10 1 1
0.15–0.09 0.75 0.77–0.46 0.75 4160–31,400 0.99 0.10–0.40 0.96 0.90
0.09–0.05 0.38 0.46–0.30 0.33 31,400–188,334 0.96 0.40–0.60 0.82 0.72
0.05–0.03 0.13 0.30–0.21 0.12 188,334–710,470 0.73 0.60–2.21 0.73 0.53
0.03–0.01 0 0.21–0.09 0 710,470–10,000,000 0 >2.21 0 0
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chlorophyll a, however the differences span one quality level at most. This discrepancy is more or less expected since abundance and chlorophyll a are two different phytoplankton metrics (Domingues et al., 2008). Chlorophyll a, being a photosynthetic pigment, is indeed present in all phytoplankton cells, but it only represents a fraction of the whole biomass. Moreover, heterotrophic species, not represented in chlorophyll a measurements (Domingues et al., 2008), may form important peaks in abundance that may be associated with other forms of disturbance such as allochthonous and autochthonous organic inputs rather than eutrophication. Therefore taking into consideration community structure as a whole, seems a more representative measure of water quality than chlorophyll a alone. In the current study, simulated phytoplankton communities resembling the field communities and the changes they undergo due to eutrophication formed the basis for the evaluation of 22 indices. Simulation offers the opportunity to reduce the undesirable variability often encountered in field data due to uncontrolled factors (seasonality, patchiness, hydrodynamic circulation) or stochastic processes. Moreover it is possible to insert fully controlled perturbations, assess the response of indices and infer possible explanations for their behavior (Boyle et al., 1990). The log series model, which was applied, successfully reproduced the structure of phytoplankton communities of the Aegean coastal waters, in terms of diversity, evenness and dominance. The monotonic behavior of the tested indices was set as a prerequisite since the water quality categorization in WFD is performed in a rank order. Some indices showed non-monotonic behavior, among them the Shannon and Simpson indices, two of the most used measures of diversity in community ecology (Digby and Kempton, 1987). These indices showed a hump-shaped behavior that may be initially related to the increase in species richness (expressed by the Hill’s N0 index), which is later overturned for abundances higher than 103 cells/L by the decrease in the community evenness (expressed through the evenness indices). This index has been already widely criticized (Boyle et al., 1990; Karydis and Tsirtsis, 1996), mainly on the basis that its response is mostly a function of changes in species richness rather than in the relative abundance of species (Simboura et al., 2005). The sensitivity of indices to discriminate water quality can be further enhanced by the linear or close to linear, trend of change across the studied ecological gradient, since the average distance between the set of limits is maximized. Among the indices examined, a small number including the diversity index of Menhinick and the evenness indices E2, E3, and E6 has passed the screening procedure for both monotonicity and linearity. These four indices were considered as the most efficient to assess water quality for the implementation of the WFD. The scales developed on Menhinick index of diversity and evenness E2 were used to assess the water quality of several coastal sites in the Aegean. Between them, the Menhinick index renders a more optimistic characterization by classifying some sites of higher quality than the E2 index. The Menhinick index is a simple index based on species richness and total abundance, whereas evenness E2 includes the exponent of the Shannon index carrying the full information on the distribution of individuals to species (Washington, 1984). A new synthetic index for water quality assessment in the WFD is also proposed in the current study, considering that the evaluation of water quality should be also based on suitable multimetric indices (Borja et al., 2004; Domingues et al., 2008). For this multimetric, namely the Integrated Phytoplankton Index (IPI), three phytoplankton metrics were used that is chlorophyll a, abundance, and diversity (Menhinick index) with equal weights. The classification of areas with the IPI was consistent with the other metrics, but was more optimistic than chlorophyll a or total abundance, and more conservative than Menhinick’s diversity and
evenness E2 indices. It seems that the structure of phytoplankton communities is more robust to the changes due to eutrophication than the chlorophyll a content. Therefore it can be concluded that, although there is a consistency, each metric carries its own information and the use of multimetric indices combining various phytoplankton metrics is appropriate in the framework of integrated water quality assessment. The classification of the Aegean coastal waters according to the proposed metrics (ecological indices and IPI) is generally in agreement with previous studies on nutrients (Ignatiades et al., 1992; Primpas et al., 2008; Stefanou et al., 2000; Vounatsou and Karydis, 1991) and phytoplankton community data (Ignatiades et al., 1995; Karydis and Tsirtsis, 1996; Kitsiou and Karydis, 2000; Tsirtsis and Karydis, 1998). The Rhodos offshore waters, which were used as the reference site in the current study, were characterized as a typical oligotrophic environment in the past by many authors (Ignatiades et al., 1992; Karydis, 1992, 1994, 1996; Karydis and Tsirtsis, 1996; Kitsiou and Karydis, 2000; Tsirtsis and Karydis, 1998; Simboura et al., 2005; Stefanou et al., 2000), in agreement with the assessment by the IPI. Coastal sites such as Rhodos inshore (coastal and ports), the Gulf of Gera, the Strait of Mytilene and the outer Saronikos have been previously characterized as mesotrophic according to a three-level classification scheme (Arhonditsis et al., 2000; Karydis and Coccossis, 1990; Tsirtsis, 1995). The use of a five-level scheme in the current study allowed a more detailed discrimination in the field of mesotrophy and the above sites were characterized as of High, Good, and Moderate quality. Finally, sites such as the port of Mytilene, and the Inner Saronikos and Kalloni Gulfs are more heavily affected by anthropogenic activities and have been previously characterized as eutrophic based on both physicochemical and biotic data (Ignatiades et al., 1992; Simboura et al., 2005; Spatharis et al., 2007a,b), in agreement with the classification of Poor quality in the current study. Potential dissimilarities of one quality level at most may be related to the methodologies applied in the past which were based on a relative local scale, whereas the proposed metrics are suitable for assessments in the regional scale, since they were based on a large number of available data from many coastal areas in the Aegean coastal waters. The characterization of coastal water quality constitutes a critical issue for planning and decision making in Integrated Coastal Zone Management (Karydis and Coccossis, 1990). Therefore it must be based on robust tools able to synthesize the information from more than one metrics or quality elements, reducing in this way possible errors and misleading results. In the current study an effort has been made for the development of such qualitative tools for the Eastern Mediterranean Ecoregion. Further testing of the proposed quality scales, as well as of the proposed integrated index (IPI), need to be done in order to be established for water quality assessment in the WFD. Moreover, the application of the current screening methodology in a eutrophic west European area would be very interesting (higher chlorophyll and nutrient levels) and could contribute towards the enhancement of the ongoing research for the implementation of the Water Framework Directive in European waters. Acknowledgement The current study was conducted in the framework of the Program INTERREG IIIA, Gr-Cy (K.2301.001) co-funded by the European Union and national funds. References Arhonditsis, G., Tsirtsis, G., Angelidis, M.O., Karydis, M., 2000. Quantification of the effects of nonpoint nutrient sources to coastal marine eutrophication: applications to a semi-enclosed gulf in the Mediterranean Sea. Ecol. Model. 129, 209– 227.
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