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Analyzing the (mis)behavior of Shannon index in eutrophication studies using field and simulated phytoplankton assemblages
Ecological Indicators 11 (2011) 697–703
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Original article
Analyzing the (mis)behavior of Shannon index in eutrophication studies using field and simulated phytoplankton assemblages Sofie Spatharis a,∗ , Daniel L. Roelke b,c , Panayiotis G. Dimitrakopoulos d , Giorgos D. Kokkoris a a
Department of Marine Sciences, University of the Aegean, University Hill, 81100 Mytilene, Greece Department of Wildlife and Fisheries Sciences, Texas A&M University, 2258 TAMUS, College Station, TX 77843-2258, United States Department of Oceanography, Texas A&M University, 2258 TAMUS, College Station, TX 77843-2258, United States d Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, University Hill, 81100 Mytilene, Greece b c
a r t i c l e
i n f o
Article history: Received 22 March 2010 Received in revised form 20 September 2010 Accepted 26 September 2010 Keywords: Phytoplankton diversity Productivity Log series Random fraction Simulated assemblages Unimodal Water Framework Directive
a b s t r a c t The Shannon index of diversity H is a commonly used metric in ecology. The tendency of this index to show a unimodal relationship with productivity has been the subject of several studies. In the present work, the behavior of H and three related ecological indices (Simpson, Hill, and Evenness) was investigated using phytoplankton assemblages along a eutrophication gradient. We used both natural and simulated assemblages, whereby the comparison enabled us to assess the role of environmental ‘noise’ on index behavior. We developed simulated assemblages based on phytoplankton distributions predicted by two model types: the log series statistical model and the random fraction niche-based model. Using field data, H and the related Simpson index showed expected unimodal relationships with eutrophication. The same unimodal relationships were reproduced with simulated assemblages. Comparing the simulations with natural assemblages along a eutrophication gradient showed that there was much unexplained variance in the real-world data, suggesting that these diversity indices are sensitive to stochastic processes. An analysis of the simulated assemblages using relative abundance distributions suggested that increasing H and Simpson index values in the low range of the eutrophication gradient were due to increasing species richness, and that decreasing index values in the high range of the eutrophication gradient were due to decreasing evenness. In addition, this analysis revealed how assemblages of identical H values arose from contrasting community structures found in the low- and high-range of eutrophication. The high variability and non-linearity of the Shannon and Simpson indices along a eutrophication gradient suggests that these measures of diversity are inappropriate for use as water quality monitoring assessment tools. Indeed, when calculating ecological quality ratios that are employed by the European Water Framework Directive, unreliable (non-monotonic) predictions of water quality resulted. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Changes in community structure are often quantified through a number of non-parametric ecological indices expressing diversity, evenness, and dominance. The Shannon index (H ), based on information theory, is used extensively in ecology. Among its early users, Margalef (1958) was the first to apply it to phytoplankton assemblages. Since then, H has been applied broadly in studies that include univariate measures to assess disturbance effects on phytoplankton assemblages (Ismael and Dorgham, 2003; Buyukates and Roelke, 2005; Livingston, 2007), in comparative studies of diversity (Vadrucci et al., 2003; Nuccio et al., 2003), to investigate diversity–productivity relationships (Irigoien et al., 2004; Duarte et al., 2006), and as a water quality assessment tool (Karydis and
∗ Corresponding author. Tel.: +30 22510 36835; fax: +30 22510 36809. E-mail address: [email protected] (S. Spatharis). 1470-160X/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2010.09.009
Tsirtsis, 1996; Osowiecki et al., 2008). Recent research is focused on different estimation approaches and the mathematical properties of H (Chao and Shen, 2003; Keylock, 2005; Jost, 2006; Camargo, 2008). The most appealing attributes of this index is that it accounts for species richness and evenness, and does not give weight to rare species (Washington, 1984). Diversity indices that are easily applied and interpreted are appealing for water quality assessment. Such indices are important for large-scale monitoring and assessment efforts, such as the European Water Framework Directive (WFD, 2000/60/EC). In the framework of policy development, investigators are urged to assess the suitability of existing indices rather than develop new ones (Diaz et al., 2004; Borja and Dauer, 2008). In community ecology, this has stimulated research towards the assessment of a number of existing indices (Washington, 1984; Mouillot and Wilson, 2002; Lamb et al., 2009). For phytoplankton studies in particular, H was assessed using field samples (Karydis and Tsirtsis, 1996; Beisel and Moreteau, 1997; Danilov and Ekelund, 1999), experi-
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mental replicates (Robinson and Sandgren, 1983), and computer simulated communities (Boyle et al., 1990). These studies reached contrasting conclusions, however. Some studies supported the use of H under certain circumstances, e.g., when evaluating diversity changes due to disturbance (Robinson and Sandgren, 1983), while other studies indicated limitations of this approach, as evidenced by inconsistent trends along gradients of eutrophication (Karydis and Tsirtsis, 1996; Danilov and Ekelund, 1999). Since policy-related issues typically involve nutrient loading, most of the negative criticism regarding use of H as a monitoring and assessment tool focused on coastal eutrophication (Robinson and Sandgren, 1983; Danilov and Ekelund, 1999). Factors shaping phytoplankton assemblages, and resulting H , are many and interdependent. They include competition for light and nutrients, and various other biotic stresses (Irigoien et al., 2004; Buyukates and Roelke, 2005; Spatharis et al., 2007). In addition, environmental ‘noise’ associated with seasonality, extreme weather events, hydrodynamic circulation, and patchiness influence phytoplankton assemblages (Karydis, 1992). Consequently, H is subject to stochasticity, resulting in less certainty when using H to elucidate processes influential to phytoplankton assemblages (Karydis and Tsirtsis, 1996; Mouillot and Wilson, 2002). A complementary approach for this purpose involves use of simulated assemblages, which can incorporate the influence of environmental factors on assembly. In contrast to field data, phytoplankton assemblages generated mathematically are free of ‘noise’. Consequently, a biological index calculated from simulated phytoplankton assemblages is undistorted and is more easily assessed along environmental gradients. Previous studies have shown that phytoplankton assemblages can be successfully simulated using statistical models, such as the lognormal and log series distributions (Tsirtsis et al., 2008), and stochastic, niche-based models, such as Tokeshi’s random fraction (Spatharis et al., 2009). The aim of the present study is to assess the behavior of H along a eutrophication gradient characteristic of the oligotrophic conditions of the Eastern Mediterranean. The index is initially assessed using an extensive database of naturally occurring phytoplankton assemblages where eutrophication is approximated using chlorophyll a concentrations, total sample abundance, and biovolume. To provide a mechanistic explanation of the index behavior and assess the influence of environmental noise, comparisons are made between natural and simulated phytoplankton assemblages, where the simulated assemblages are generated using deterministic log series and stochastic random fraction models. Finally, the efficacy of using H as a water quality assessment tool is investigated within the framework of the European Water Framework Directive (WFD, 2000/60/EC).
2. Methodology 2.1. Field samples To further assess the relationship between H and eutrophication, a database of 889 samples comprising phytoplankton species-abundance data was used. This dataset originates from annual collections from coastal areas of the Aegean Sea, Eastern Mediterranean: the island of Lesvos (Tsirtsis, 1995; Arhonditsis et al., 2000; Spatharis et al., 2007), the Gulf of Saronikos (Ignatiades et al., 1992) near the Metropolitan area of Athens, and the island of Rhodos (Vounatsou and Karydis, 1991). This region covers a wide productivity range, typical of the oligotrophic Eastern Mediterranean (chl a in the range 0.01–8.80 g/L and phytoplankton abundance from 960 to 9,905,980 cells/L), which is mainly affected by anthropogenic activities (agriculture, urbanization, tourism) in the coastal zone resulting in nutrient loading. Sampling effort
and the method for phytoplankton enumeration were uniform throughout the dataset, enabling direct comparisons of diversity among samples. Detailed information on the datasets was provided in Spatharis et al. (2008). The relationship between H and eutrophication was additionally assessed using species-biovolume data from a dataset of 188 samples from the Gulf of Kalloni, in the island of Lesvos (Spatharis et al., 2007). The eutrophication gradient was expressed in terms of chl a (g/L) and abundance (cells/L), and biovolume (m3 /L) in the case of Kalloni Gulf. 2.2. Ecological indices For the purposes of the present study, H (Shannon and Weaver, 1949) and three relevant indices were assessed along a eutrophication gradient. The Simpson’s index (Simpson, 1949; Krebs, 1999) was considered because previous studies commented on its close relation to H (Washington, 1984; Ludwig and Reynolds, 1988). Since both H and Simpson indices are influenced by a combination of species richness and evenness of populations, species richness (Hill, 1973) and a measure of evenness (Pielou, 1975) were also considered. These four indices are commonly applied in community ecology and aquatic studies (Washington, 1984; Karydis and Tsirtsis, 1996) and their application in the current study aims to provide an integrated view of structural changes to phytoplankton assemblages along a eutrophication gradient. Formulations for the indices were: S n
i
H = −
i=1
D=1−
n
s
× ln
(pi )2 ,
ni , n
Shannon
Simpson
(1)
(2)
i=1
N0 = S, J=
H ln S
Hill ,
Evenness
(3) (4)
where S was the number of species in a sample or a population, n was the total number of individuals in a sample, ni was the number of individuals of species i in a sample, pi was ni /n (the fraction of individuals belonging to species i in a sample or a population). 2.3. Simulated assemblages The log series (Fisher et al., 1943) is a commonly applied deterministic, statistical model for the description of community structure on both terrestrial and marine ecosystems (Magurran, 2004). This model assumes that the highest number of species in a community is represented by one individual, therefore most of the species are rare and few species are dominant. The log series model was shown to provide a good fit to 78.5% of phytoplankton field data (698 out of 889 phytoplankton samples) (Spatharis and Tsirtsis, 2010). On the other hand, the random fraction model is one of the six niche-based models developed by Tokeshi (Tokeshi, 1990; Tokeshi, 1993; Tokeshi, 1996; Tokeshi, 1999) which use a simple rule-based approach to simulate community assembly, and focus on the ecological explanation of the patterns observed in relative abundance distributions (RADs). The random fraction model assumes that a species entering an assemblage will target a niche occupied by another species irrespective of niche size; consequently, the niche division process is random (Tokeshi, 1999). The model assumes a relatively even RAD when presented in the form of a rank–abundance plot. This model was successfully fitted to different groups of organisms in both freshwater and marine ecosystems (Tokeshi, 1990, 1999; Fromentin et al., 1997; Fesl, 2002; Anderson and Mouillot, 2007). For phytoplankton in particular, this model
Log relative % abundanc e
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Random fraction
Log series
699
a
100
100
80 10 60 1
40 20
0.1
0 0.01
3 1
3
5
7
9
11
13
15
4
17
5
6
7
Abundance (Log cells/L)
Species rank
b
consistently fit with natural phytoplankton assemblages (Spatharis et al., 2009). The generation of simulated assemblages was based on the two models (log series and random fraction) using the cell number (N) as a proxy. In order to simulate a realistic phytoplankton structure based on phytoplankton assemblages from the field, the two models were based on the true relationship between species richness (S) and abundance (N) from the 889 field data. During the final stage of simulation, when abundances were distributed to species, the role of dominant species was also considered in the two models by incorporating a relation between the abundance of the most dominant species and the total abundance in a sample. For each simulated assemblage, the abundance of the most dominant species was subtracted from the total abundance, and the remaining abundance was distributed to the rest of the species according to the distribution predicted by the two models. Finally, given the range of abundance N (103 to 107 cells/L) and the step of increase (103 cells/L) along the simulated productivity range, the structure of each assemblage was generated, including the different species and the distribution of abundance to species. Details on this methodology were given in Spatharis and Tsirtsis (2010). The RADs of two 18-species phytoplankton assemblages generated by the two models are presented in the rank–abundance plot of Fig. 1. The accuracy of the simulated assemblages to represent field assemblages was tested by correlating the results of each of the four indices (H , D, N0 , and J) calculated from simulated assemblages with indices calculated from natural phytoplankton assemblages. Within the European Water Framework Directive (WFD, 2000/60/EC), the assessment must be presented in a standardized way as an ecological quality status, translating the results of the ecological quality ratio. This ratio expresses the relationship between observed values and reference condition values; its numerical value lies between 0 and 1. At high status, the reference condition may be regarded as an optimum where the EQR is close to 1. Based on the results of the simulated communities and a methodology presented in detail by Spatharis and Tsirtsis (2010) the four indices (H , D, N0 , and J) were further assessed developing a five-level water quality classification scheme. The ecological quality ratios of the five quality levels were calculated for each index by standardizing their values to fit the 0–1 range. 3. Results After standardization (scaled between 0 and 100), values of H using field data from the six coastal areas in the Aegean Sea showed unimodal relationships along a eutrophication gradient, as expressed by three different proxies (Fig. 2). Specifically, when using H calculated from population densities and eutrophication expressed as total assemblage abundance (Fig. 2a), ∼27%
Standardized H'
100 Fig. 1. Theoretical relative abundance distributions (RADs) of two 18-species assemblages as predicted by the statistical log series and the stochastic random fraction models.
80 60 40 20 0 5
6
7
8
9
10
11
Biovolume (Log μm3/L)
c
100 80 60 40 20 0 0
1
2
3
4
5
Chl a (μg/L) Fig. 2. Trend of Shannon index H (standardized) calculated on (a) 889 field assemblages of species abundances from six coastal areas of the Aegean across the cell number range, (b) 188 field assemblages of species biovolumes from the Gulf of Kalloni across the biovolume range, and (c) 188 field assemblages of species biovolumes from the Gulf of Kalloni across the chl a range.
of the variability could be explained using a unimodal curve fit (p < 0.01). Similarly, when using H calculated from population biovolumes, and eutrophication expressed as total assemblage biovolume (Fig. 2b), ∼27% of the variability was again accounted for (p < 0.01). A significant although weaker (R2 = 0.120, p < 0.01) unimodal relationship was observed when H was calculated from population biovolumes and eutrophication expressed as chl a (Fig. 2c). Very similar trends (not presented) were observed for the Simpson index (D), calculated on field data, regarding the respective relationships. The H , D, Hill N0 , and evenness J indices, calculated from simulated phytoplankton assemblages showed varied trends along the eutrophication range (expressed as abundance). Again, after standardization, H and D showed characteristic unimodal relationships when using simulated assemblages from log series and random fraction models (Fig. 3a and b). An increase of H and D at low eutrophication ranges is observed when the abundance is low (below 104 cells/L). At higher eutrophication levels, H and D decrease reaching minima when assemblage abundance is very high. This relationship was slightly more pronounced with H than D. Monotonic trends were determined with N0 (increasing) and J (decreasing) along the eutrophication gradient (Fig. 3c and d). Overall, the trends of indices along the eutrophication range appeared smoother for the random fraction than the log series model, which
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a
100
Standardized H'
700
80
C: H'=1.42 B: H' max. 60 40
A: H'=1.42
20 D: H' min. 0 3
4
5
6
7
b
10000000
Abundance (cells/L)
Abundance (Log cells/L)
1000000
A: H'=1.42
100000
B: H' max. C: H'=1.42
10000
D: H' min.
1000 100 10 1 1
5
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13
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21
25
29
33
Species rank Fig. 4. Graph a shows the position of four characteristic assemblages on the unimodal Shannon curve calculated on simulated data from the random fraction model: Assemblages A and C correspond to the same exact H value (HA = HC = 1.42) whereas assemblages B and D correspond to the minimum and maximum H val = 1.74, Hmin = 0.99). Graph b presents the corresponding ues respectively (Hmax rank–abundance distributions of the four assemblages.
Fig. 3. Trends of four indices calculated on simulated phytoplankton assemblages based on the log series and the random fraction models across the eutrophication range expressed as assemblage abundance (103 to 107 cells/L).
oscillated somewhat. This oscillation was due to the allocation of species to octaves in the log series model, where for specific cell number ranges, the number of species remained constant while the number of cells and consequently dominance increased. Simulated assemblages (by the log series and random fraction models) showed significant correlations with field assemblages based on all four indices (H , D, N0 , and J), indicating an agreement between simulated and field data (Table 1). However, based Table 1 Correlations between index values calculated on phytoplankton data from the 889 field samples and those from simulated data generated by the two models (random fraction and log series). The index values have not been standardized prior to analysis. Model
Index
Spearman coefficient
Random fraction
Shannon H Simpson D Hill N0 Evenness J
0.242** 0.426** 0.671** 0.757**
Log series
Shannon H Simpson D Hill N0 Evenness J
0.202** 0.423** 0.519** 0.747**
**
Statistically significant correlation at the 0.01 level.
on comparisons of field with simulated data by both models, the amount of unexplained variability was high for H and D indices, reaching around 80% and 60% respectively. These high percentages are probably due to the marked ‘noise’ (variability) observed in the index values when calculated on field data (Fig. 2). Using simulated data from the random fraction model, it was possible to reconstruct and visualize the simulated assemblages corresponding to characteristic H values (Fig. 4a). Assemblages A and C give the same value (H = 1.42), but originate from the low- and high-ranges of eutrophication, i.e., from either side of the maximum H (Fig. 4a). However, these assemblages are markedly different since assemblage A has a total abundance of 102 cells/L (corresponding to six species) whereas assemblage C has an abundance of 1.6 × 106 cells/L (corresponding to 26 species) (Table 2). Moreover, important structural differences between the two assemblages are stressed in the rank–abundance distributions of Fig. 4b. Assemblage A is less speciose and not characterized by dominance. On the other hand, assemblage C is more speciose and characterized by higher dominance, which can be visualized by the larger differences in abundance among the first ranks/species. Although dominance is higher, the distribution of the remaining ranks/species appears even. Assemblage B represents the trade-off point between increasing species richness and decreasing evenness (increasing dominance) giving the maximum H value. Finally, assemblage D, although having the maximum N0 , shows the minimum H value since dominance weighs much more at the high eutrophication range. Within the ecological quality ratio scale (0–1) five ecological quality status classes were defined to establish the five-level classification scheme: ‘Bad’, ‘Poor’, ‘Moderate’, ‘Good’ and ‘High’
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701
Table 2 Abundance (cells/L) and the corresponding values of the Shannon H , Hill N0 , and evenness J indices calculated on four simulated assemblages generated by the log series and Hmin are the and random fraction models. HA and HC are two assemblages that correspond to the same Shannon value at either side of the hump-back curve, and Hmax maximum and minimum Shannon values respectively observed across the simulated eutrophication gradient. Model
Assemblage
Abundance
Shannon
Hill
Evenness
Random fraction
HA Hmax HC Hmin
1,000 32,000 1,601,000 10,000,000
1.42 1.74 1.42 0.99
6 15 26 31
0.79 0.63 0.44 0.29
Log series
HA Hmax HC Hmin
1,000 8,000 351,000 10,000,000
1.75 2.06 1.75 0.92
6 12 22 28
0.98 0.83 0.57 0.28
(WFD, 2000/60/EC). Samples representing “High” ecological status, achieve ratio values near 1, while those representing ‘Bad’ ecological status will yield values close to 0. Calculation of the ecological quality ratios of the four indices under consideration (Table 3) showed that H and D indices present an initial increase and then a decrease from the high to the bad quality status. On the other hand, evenness J and Hill N0 showed a monotonic trend from the “High” to the “Bad” ecological quality status. 4. Discussion A unimodal relationship was observed in the diversity–productivity relationship regardless of the data type used to calculate H (species abundances and species biovolumes) or the parameter used to express eutrophication (chlorophyll a concentrations, assemblage abundance, and assemblage biovolume). This is consistent with previous studies that investigated diversity–productivity relationships (Vadrucci et al., 2003; Irigoien et al., 2004; Duarte et al., 2006). Although factors which control the diversity–productivity relationship are not fully understood (Kassen et al., 2000; Striebel et al., 2009), it was suggested that nutrient limitation at low productivity favored strong competitors at the expense of less competitive species (Tilman et al., 1982) and that other biotic factors, such as selective grazing and competition for light, resulted in low diversity at high productivity (Irigoien et al., 2004). While the unimodal diversity–productivity relationship is supported strongly in theory, real-world observations based on the Shannon H index showed this relationship to be weak. For example, the scatter-plot of H (calculated on species-biovolume data) with biovolume showed that the area below the unimodal curve was filled with data points, a reality often encountered in field data (Rosenzweig and Abramsky, 1993; Waide et al., 1999; Interlandi and Kilham, 2001; Schmid, 2002). This suggests that H is sensitive to environmental ‘noise’ associated with seasonality, extreme weather events, hydrodynamic circulation, and patchiness (Karydis, 1992). In addition, transient dynamics (sensu Hastings, 2004) resulting from the interaction of phytoplankton with trophic levels operating on multiple timescales may be another factor
contributing to the large variance in H along the eutrophication gradient. Our evaluation of D showed the same trends with productivity as did H , supporting previous observations showing the close relationship between H and D (Washington, 1984). Because many diversity index values were obtained at a given productivity level, the utility of H and D as diagnostic tools is limited. Our use of simulated phytoplankton assemblages free of environmental noise (Boyle et al., 1990; Mouillot and Wilson, 2002) allowed us to explore changes in assemblage structure expressed by ecological indices (H , D, N0 , and J) across the eutrophication spectrum. We achieved this by considering an important aspect of phytoplankton assemblage structure, dominance (Bruno et al., 1983; Gould and Wiesenburg, 1990). Focusing on H , its measurement is a function of both species richness and evenness; therefore, it is impossible to determine the relative importance of these components from the index value alone (Ludwig and Reynolds, 1988). However, it was possible to evaluate the relative importance of species richness and evenness along the eutrophication gradient by employing H complemented with visualization of rank–abundance distributions of simulated assemblages. By following this approach, we showed that the same values of H resulted from species-poor phytoplankton assemblages with no dominance as with speciesrich assemblages dominated by one or two species. In other words, the unimodal relationship was influenced by the initial increase in species richness (N0 ) that was eventually masked at abundances higher than 104 cells/L when community evenness (J) decreased. Our observations of increased species richness and eventual dominance with nutrient enrichment in our simulated assemblages are consistent with attributes of natural assemblages (Tilman, 1982; Bruno et al., 1983; Gould and Wiesenburg, 1990; Spellerberg, 1993). Regarding water quality assessment, particularly for the implementation of recent policies such as the Water Framework Directive (WFD, 2000/60/EC), the monotonicity of tested indices is a prerequisite since the categorization of ecological quality status in five categories is performed in a rank order using the ecological quality ratios. In contrast to the evenness J and Hill N0 indices, the EQRs for Shannon H and Simpson D indices do not provide an accurate classification into the five ecological quality categories (Table 3) since these indices do not show a monotonic relationship
Table 3 Water quality classification schemes and the corresponding ecological quality ratios for evenness J, Hill N0 , Shannon H , and Simpson D indices based on the classification criteria of the Water Framework Directive (WFD, 2000/60/EC). Index
High
Good
Moderate
Low
Bad
Evenness J EQR Evenness J Hill N0 EQR Hill N0 Shannon H EQR Shannon H Simpson D EQR Simpson D
0.98–0.88 1 6–10 1 1.75–2.03 1 0.18–0.16 1
0.88–0.71 0.79 10–15 0.76 2.03–1.92 2.47 0.16–0.22 1.11
0.71–0.60 0.44 15–20 0.47 1.92–1.79 1.89 0.22–0.27 0.78
0.60–0.50 0.21 20–23 0.18 1.79–1.56 1.21 0.27–0.36 0.50
0.50–0.27 0 23–28 0 1.56–0.90 0 0.36–0.64 0
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with eutrophication. Water quality classification must be based on robust tools able to synthesize the information from more than one metric or ecological quality elements, reducing in this way possible errors and misleading results. The use of Shannon and Simpson indices on phytoplankton assemblages is not suggested for water quality classification, whereas other indices such as Menhinick’s and evenness indices (Karydis, 1996; Karydis and Tsirtsis, 1996) or multimetric indices (Borja et al., 2004; Marques et al., 2009; Spatharis and Tsirtsis, 2010) are more appropriate metrics for this purpose. 5. Conclusions In the present study, the behavior of Shannon and the related Simpson indices was assessed along a eutrophication gradient using field and simulated phytoplankton assemblages. The main conclusions of the study are: (a) the same index values can be obtained at low and high ranges along the eutrophication gradient due to structural changes in phytoplankton assemblages; (b) H and D values showed high variability across the eutrophication gradient, likely due to a synergy of factors theoretically unaccounted for in these indices (Irigoien et al., 2004; Buyukates and Roelke, 2005; Spatharis et al., 2007); and (c) combined, these attributes of H and D limit their application as water quality assessment diagnostic tools along eutrophication gradients, as evidenced by non-monotonic ecological quality ratio values determined within the framework of the European Water Framework Directive. References Anderson, B.J., Mouillot, D., 2007. Influence of scale and resolution on niche apportionment rules in saltmeadow vegetation. Aquat. Biol. 1, 195–204. 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. Beisel, J.N., Moreteau, J.C., 1997. A simple formula for calculating the lower limit of Shannon’s diversity index. Ecol. Model. 99, 289–292. Borja, A., Franco, J., Muxika, I., 2004. The biotic indices and the Water Framework Directive: the required consensus in the new benthic monitoring tools. Mar. Pollut. Bull. 48, 405–408. Borja, A., Dauer, D.M., 2008. Assessing the environmental quality status in estuarine and coastal systems: comparing methodologies and indices. Ecol. Indic. 8, 331–337. Boyle, T.P., Smillie, G.M., Anderson, J.C., Beeson, D.R., 1990. A sensitivity analysis of 9 diversity and 7 similarity indices. Res. J. Water Pollut. C. 62, 749–762. Bruno, S.F., Staker, R.D., Sharma, G.M., Turner, J.T., 1983. Primary productivity and phytoplankton size fraction dominance in a temperate North-Atlantic estuary. Estuaries 6, 200–211. Buyukates, Y., Roelke, D., 2005. Influence of pulsed inflows and nutrient loading on zooplankton and phytoplankton community structure and biomass in microcosm experiments using estuarine assemblages. Hydrobiologia 548, 233–249. Camargo, J.A., 2008. Revisiting the relation between species diversity and Information Theory. Acta Biotheor. 56, 275–283. Chao, A., Shen, T.J., 2003. Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample. Environ. Ecol. Stat. 10, 429–443. Danilov, R., Ekelund, N.G.A., 1999. The efficiency of seven diversity and one similarity indices based on phytoplankton data for assessing the level of eutrophication in lakes in central Sweden. Sci. Total Environ. 234, 15–23. Diaz, R.J., Solan, M., Valente, R.M., 2004. A review of approaches for classifying Benthic habitats and evaluating habitat quality. J. Environ. Manage. 73, 165–181. Duarte, P., Macedo, M.F., da Fonseca, L.C., 2006. The relationship between phytoplankton diversity and community function in a coastal lagoon. Hydrobiologia 555, 3–18. Fesl, C., 2002. Niche-oriented species-abundance models: different approaches of their application to larval chironomid (Diptera) assemblages in a large river. J. Anim. Ecol. 71, 1085–1094. Fisher, R.A., Corbet, A.S., Williams, C.B., 1943. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58. Fromentin, J.M., Dauvin, J.C., Ibanez, F., Dewarumez, J.M., Elkaim, B., 1997. Long-term variations of four macrobenthic community structures. Oceanol. Acta 20, 43–53. Gould, R., Wiesenburg, D., 1990. Single-species dominance in a subsurface phytoplankton concentration at a Mediterranean Sea front. Limnol. Oceanogr. 35, 211–220. Hastings, A., 2004. Transients: the key to long-term ecological understanding? Trends Ecol. Evol. 19, 39–45.
Hill, M.O., 1973. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432. Ignatiades, L., Karydis, M., Vounatsou, P., 1992. A possible method for evaluating oligotrophy and eutrophication based on nutrient concentration scales. Mar. Pollut. Bull. 24, 238–243. Interlandi, S.J., Kilham, S.S., 2001. Limiting resources and the regulation of diversity in phytoplankton communities. Ecology 82, 1270–1282. Irigoien, X., Huisman, J., Harris, R.P., 2004. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–867. Ismael, A.A., Dorgham, M.M., 2003. Ecological indices as a tool for assessing pollution in El-Dekhaila Harbour (Alexandria, Egypt). Oceanologia 45, 121–131. Jost, L., 2006. Entropy and diversity. Oikos 113, 363–375. Karydis, M., 1992. Scaling methods in assessing environmental quality: a methodological approach to eutrophication. Environ. Monit. Assess. 22, 123– 136. Karydis, M., 1996. Quantitative assessment of eutrophication: a scoring system for characterizing water quality in coastal marine ecosystems. Environ. Monit. Assess. 41, 233–246. Karydis, M., Tsirtsis, G., 1996. Ecological indices: a biometric approach for assessing eutrophication levels in the marine environment. Sci. Total Environ. 186, 209–219. Kassen, R., Buckling, A., Bell, G., Rainey, P.B., 2000. Diversity peaks at intermediate productivity in a laboratory microcosm. Nature 406, 508–512. Keylock, C.J., 2005. Simpson diversity and the Shannon–Wiener index as special cases of a generalized entropy. Oikos 109, 203–207. Krebs, C.J., 1999. Ecological Methodology, second ed. Addison Wesley Publishers, California. Lamb, E., Bayne, E., Holloway, G., Schieck, J., Boutin, S., Herbers, J., Haughland, D., 2009. Indices for monitoring biodiversity change: are some more effective than others? Ecol. Indic. 9, 432–444. Livingston, R., 2007. Phytoplankton bloom effects on a gulf estuary: water quality changes and biological response. Ecol. Appl. 17, S110–S118. Ludwig, A.J., Reynolds, J.F., 1988. Statistical Ecology: A Primer on Methods and Computing. J. Wiley and Sons, New York. Magurran, A.E., 2004. Measuring Biological Diversity. Blackwell Publishing, Oxford. Margalef, R., 1958. Information theory in ecology. Gen. Syst. 3, 36–71. Marques, J.C., Salas, F., Patricio, J., Teixeira, H., Neto, J.M., 2009. Ecological Indicators for Estuarine and Environmental Assessment: A User Guide. Wit Press, UK. Mouillot, D., Wilson, J.B., 2002. Can we tell how a community was constructed? A comparison of five evenness indices for their ability to identify theoretical models of community construction. Theor. Popul. Biol. 61, 141–151. Nuccio, C., Melillo, C., Massi, L., Innamorati, M., 2003. Phytoplankton abundance, community structure and diversity in the eutrophicated Orbetello lagoon (Tuscany) from 1995 to 2001. Oceanol. Acta 26, 15–25. Osowiecki, A., Lysiak-Pastuszak, E., Piatkowska, Z., 2008. Testing biotic indices for marine zoobenthos quality assessment in the Polish sector of the Baltic Sea. J. Mar. Syst. 74, S124–S132. Pielou, E.C., 1975. Ecological Diversity. Wiley InterScience, New York. Robinson, J.V., Sandgren, C.D., 1983. The effect of temporal environmental heterogeneity on community structure—a replicated experimental study. Oecologia 57, 98–102. Rosenzweig, M.L., Abramsky, Z., 1993. How are diversity and productivity related? In: Ricklef, R.E., Schluter, D. (Eds.), Species Diversity in Ecological Communities: Historical and Geographical Perspectives. University of Chicago Press, USA. Schmid, B., 2002. The species richness-productivity controversy. Trends Ecol. Evol. 17, 113–114. Shannon, C.E., Weaver, W., 1949. The Mathematical Theory of Communication. University of Illinois Press, Urbana, IL. Simpson, E.H., 1949. Measurement of diversity. Nature 163, 688. Spatharis, S., Tsirtsis, G., 2010. Ecological quality scales based on phytoplankton for the implementation of water framework directive in Eastern Mediterranean. Ecol. Indic. 10, 840–847. Spatharis, S., Mouillot, D., Do Chi, T., Danielides, D.B., Tsirtsis, G., 2009. A niche-based modeling approach to phytoplankton community assembly rules. Oecologia 159, 171–180. Spatharis, S., Mouillot, D., Danielides, D.B., Karydis, M., Do Chi, T., Tsirtsis, G., 2008. Influence of terrestrial runoff on phytoplankton species richness-biomass relationships: a double stress hypothesis. J. Exp. Mar. Biol. Ecol. 362, 55–62. Spatharis, S., Tsirtsis, G., Danielidis, D., Do Chi, T., Mouillot, D., 2007. Effects of pulsed nutrient inputs on phytoplankton assemblage structure and blooms in an enclosed coastal area. Estuar. Coast. Shelf Sci. 73, 807–815. Spellerberg, I.F., 1993. Monitoring ecological change. Cambridge University Press, England. Striebel, M., Behl, S., Stibor, H., 2009. The coupling of biodiversity and productivity in phytoplankton communities: consequences for biomass stoichiometry. Ecology 90, 2025–2031. Tilman, D., 1982. Resource Competition and Community Structure. Princeton University Press, New Jersey. Tilman, D., Kilham, S.S., Kilham, P., 1982. Phytoplankton community ecology—the role of limiting nutrients. Annu. Rev. Ecol. Syst. 13, 349–372. Tokeshi, M., 1990. Niche apportionment or random assortment—species abundance patterns revisited. J. Anim. Ecol. 59, 1129–1146. Tokeshi, M., 1993. Species abundance patterns and community structure. Adv. Ecol. Res. 24, 111–186. Tokeshi, M., 1996. Power fraction: a new explanation of relative abundance patterns in species-rich assemblages. Oikos 75, 543–550.
S. Spatharis et al. / Ecological Indicators 11 (2011) 697–703 Tokeshi, M., 1999. Species Coexistence: Ecological and Evolutionary Perspectives. Blackwell Science. Tsirtsis, G., Spatharis, S., Karydis, M., 2008. Application of the lognormal equation to assess phytoplankton community structural changes induced by marine eutrophication. Hydrobiologia 605, 89–98. Tsirtsis, G.E., 1995. A simulation model for the description of a eutrophic system with emphasis on the microbial processes. Water Sci. Technol. 32, 189– 196. Vadrucci, M.R., Vignes, F., Fiocca, A., Basset, A., Santarpia, I., Carrada, G.C., Cabrini, M., Umani, S.F., 2003. Space-time patterns of co-variation of biodiversity and primary production in phytoplankton guilds of coastal marine environments. Aquat. Conserv. 13, 489–506.
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Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Original article
Analyzing the (mis)behavior of Shannon index in eutrophication studies using field and simulated phytoplankton assemblages Sofie Spatharis a,∗ , Daniel L. Roelke b,c , Panayiotis G. Dimitrakopoulos d , Giorgos D. Kokkoris a a
Department of Marine Sciences, University of the Aegean, University Hill, 81100 Mytilene, Greece Department of Wildlife and Fisheries Sciences, Texas A&M University, 2258 TAMUS, College Station, TX 77843-2258, United States Department of Oceanography, Texas A&M University, 2258 TAMUS, College Station, TX 77843-2258, United States d Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, University Hill, 81100 Mytilene, Greece b c
a r t i c l e
i n f o
Article history: Received 22 March 2010 Received in revised form 20 September 2010 Accepted 26 September 2010 Keywords: Phytoplankton diversity Productivity Log series Random fraction Simulated assemblages Unimodal Water Framework Directive
a b s t r a c t The Shannon index of diversity H is a commonly used metric in ecology. The tendency of this index to show a unimodal relationship with productivity has been the subject of several studies. In the present work, the behavior of H and three related ecological indices (Simpson, Hill, and Evenness) was investigated using phytoplankton assemblages along a eutrophication gradient. We used both natural and simulated assemblages, whereby the comparison enabled us to assess the role of environmental ‘noise’ on index behavior. We developed simulated assemblages based on phytoplankton distributions predicted by two model types: the log series statistical model and the random fraction niche-based model. Using field data, H and the related Simpson index showed expected unimodal relationships with eutrophication. The same unimodal relationships were reproduced with simulated assemblages. Comparing the simulations with natural assemblages along a eutrophication gradient showed that there was much unexplained variance in the real-world data, suggesting that these diversity indices are sensitive to stochastic processes. An analysis of the simulated assemblages using relative abundance distributions suggested that increasing H and Simpson index values in the low range of the eutrophication gradient were due to increasing species richness, and that decreasing index values in the high range of the eutrophication gradient were due to decreasing evenness. In addition, this analysis revealed how assemblages of identical H values arose from contrasting community structures found in the low- and high-range of eutrophication. The high variability and non-linearity of the Shannon and Simpson indices along a eutrophication gradient suggests that these measures of diversity are inappropriate for use as water quality monitoring assessment tools. Indeed, when calculating ecological quality ratios that are employed by the European Water Framework Directive, unreliable (non-monotonic) predictions of water quality resulted. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Changes in community structure are often quantified through a number of non-parametric ecological indices expressing diversity, evenness, and dominance. The Shannon index (H ), based on information theory, is used extensively in ecology. Among its early users, Margalef (1958) was the first to apply it to phytoplankton assemblages. Since then, H has been applied broadly in studies that include univariate measures to assess disturbance effects on phytoplankton assemblages (Ismael and Dorgham, 2003; Buyukates and Roelke, 2005; Livingston, 2007), in comparative studies of diversity (Vadrucci et al., 2003; Nuccio et al., 2003), to investigate diversity–productivity relationships (Irigoien et al., 2004; Duarte et al., 2006), and as a water quality assessment tool (Karydis and
∗ Corresponding author. Tel.: +30 22510 36835; fax: +30 22510 36809. E-mail address: [email protected] (S. Spatharis). 1470-160X/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2010.09.009
Tsirtsis, 1996; Osowiecki et al., 2008). Recent research is focused on different estimation approaches and the mathematical properties of H (Chao and Shen, 2003; Keylock, 2005; Jost, 2006; Camargo, 2008). The most appealing attributes of this index is that it accounts for species richness and evenness, and does not give weight to rare species (Washington, 1984). Diversity indices that are easily applied and interpreted are appealing for water quality assessment. Such indices are important for large-scale monitoring and assessment efforts, such as the European Water Framework Directive (WFD, 2000/60/EC). In the framework of policy development, investigators are urged to assess the suitability of existing indices rather than develop new ones (Diaz et al., 2004; Borja and Dauer, 2008). In community ecology, this has stimulated research towards the assessment of a number of existing indices (Washington, 1984; Mouillot and Wilson, 2002; Lamb et al., 2009). For phytoplankton studies in particular, H was assessed using field samples (Karydis and Tsirtsis, 1996; Beisel and Moreteau, 1997; Danilov and Ekelund, 1999), experi-
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mental replicates (Robinson and Sandgren, 1983), and computer simulated communities (Boyle et al., 1990). These studies reached contrasting conclusions, however. Some studies supported the use of H under certain circumstances, e.g., when evaluating diversity changes due to disturbance (Robinson and Sandgren, 1983), while other studies indicated limitations of this approach, as evidenced by inconsistent trends along gradients of eutrophication (Karydis and Tsirtsis, 1996; Danilov and Ekelund, 1999). Since policy-related issues typically involve nutrient loading, most of the negative criticism regarding use of H as a monitoring and assessment tool focused on coastal eutrophication (Robinson and Sandgren, 1983; Danilov and Ekelund, 1999). Factors shaping phytoplankton assemblages, and resulting H , are many and interdependent. They include competition for light and nutrients, and various other biotic stresses (Irigoien et al., 2004; Buyukates and Roelke, 2005; Spatharis et al., 2007). In addition, environmental ‘noise’ associated with seasonality, extreme weather events, hydrodynamic circulation, and patchiness influence phytoplankton assemblages (Karydis, 1992). Consequently, H is subject to stochasticity, resulting in less certainty when using H to elucidate processes influential to phytoplankton assemblages (Karydis and Tsirtsis, 1996; Mouillot and Wilson, 2002). A complementary approach for this purpose involves use of simulated assemblages, which can incorporate the influence of environmental factors on assembly. In contrast to field data, phytoplankton assemblages generated mathematically are free of ‘noise’. Consequently, a biological index calculated from simulated phytoplankton assemblages is undistorted and is more easily assessed along environmental gradients. Previous studies have shown that phytoplankton assemblages can be successfully simulated using statistical models, such as the lognormal and log series distributions (Tsirtsis et al., 2008), and stochastic, niche-based models, such as Tokeshi’s random fraction (Spatharis et al., 2009). The aim of the present study is to assess the behavior of H along a eutrophication gradient characteristic of the oligotrophic conditions of the Eastern Mediterranean. The index is initially assessed using an extensive database of naturally occurring phytoplankton assemblages where eutrophication is approximated using chlorophyll a concentrations, total sample abundance, and biovolume. To provide a mechanistic explanation of the index behavior and assess the influence of environmental noise, comparisons are made between natural and simulated phytoplankton assemblages, where the simulated assemblages are generated using deterministic log series and stochastic random fraction models. Finally, the efficacy of using H as a water quality assessment tool is investigated within the framework of the European Water Framework Directive (WFD, 2000/60/EC).
2. Methodology 2.1. Field samples To further assess the relationship between H and eutrophication, a database of 889 samples comprising phytoplankton species-abundance data was used. This dataset originates from annual collections from coastal areas of the Aegean Sea, Eastern Mediterranean: the island of Lesvos (Tsirtsis, 1995; Arhonditsis et al., 2000; Spatharis et al., 2007), the Gulf of Saronikos (Ignatiades et al., 1992) near the Metropolitan area of Athens, and the island of Rhodos (Vounatsou and Karydis, 1991). This region covers a wide productivity range, typical of the oligotrophic Eastern Mediterranean (chl a in the range 0.01–8.80 g/L and phytoplankton abundance from 960 to 9,905,980 cells/L), which is mainly affected by anthropogenic activities (agriculture, urbanization, tourism) in the coastal zone resulting in nutrient loading. Sampling effort
and the method for phytoplankton enumeration were uniform throughout the dataset, enabling direct comparisons of diversity among samples. Detailed information on the datasets was provided in Spatharis et al. (2008). The relationship between H and eutrophication was additionally assessed using species-biovolume data from a dataset of 188 samples from the Gulf of Kalloni, in the island of Lesvos (Spatharis et al., 2007). The eutrophication gradient was expressed in terms of chl a (g/L) and abundance (cells/L), and biovolume (m3 /L) in the case of Kalloni Gulf. 2.2. Ecological indices For the purposes of the present study, H (Shannon and Weaver, 1949) and three relevant indices were assessed along a eutrophication gradient. The Simpson’s index (Simpson, 1949; Krebs, 1999) was considered because previous studies commented on its close relation to H (Washington, 1984; Ludwig and Reynolds, 1988). Since both H and Simpson indices are influenced by a combination of species richness and evenness of populations, species richness (Hill, 1973) and a measure of evenness (Pielou, 1975) were also considered. These four indices are commonly applied in community ecology and aquatic studies (Washington, 1984; Karydis and Tsirtsis, 1996) and their application in the current study aims to provide an integrated view of structural changes to phytoplankton assemblages along a eutrophication gradient. Formulations for the indices were: S n
i
H = −
i=1
D=1−
n
s
× ln
(pi )2 ,
ni , n
Shannon
Simpson
(1)
(2)
i=1
N0 = S, J=
H ln S
Hill ,
Evenness
(3) (4)
where S was the number of species in a sample or a population, n was the total number of individuals in a sample, ni was the number of individuals of species i in a sample, pi was ni /n (the fraction of individuals belonging to species i in a sample or a population). 2.3. Simulated assemblages The log series (Fisher et al., 1943) is a commonly applied deterministic, statistical model for the description of community structure on both terrestrial and marine ecosystems (Magurran, 2004). This model assumes that the highest number of species in a community is represented by one individual, therefore most of the species are rare and few species are dominant. The log series model was shown to provide a good fit to 78.5% of phytoplankton field data (698 out of 889 phytoplankton samples) (Spatharis and Tsirtsis, 2010). On the other hand, the random fraction model is one of the six niche-based models developed by Tokeshi (Tokeshi, 1990; Tokeshi, 1993; Tokeshi, 1996; Tokeshi, 1999) which use a simple rule-based approach to simulate community assembly, and focus on the ecological explanation of the patterns observed in relative abundance distributions (RADs). The random fraction model assumes that a species entering an assemblage will target a niche occupied by another species irrespective of niche size; consequently, the niche division process is random (Tokeshi, 1999). The model assumes a relatively even RAD when presented in the form of a rank–abundance plot. This model was successfully fitted to different groups of organisms in both freshwater and marine ecosystems (Tokeshi, 1990, 1999; Fromentin et al., 1997; Fesl, 2002; Anderson and Mouillot, 2007). For phytoplankton in particular, this model
Log relative % abundanc e
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Random fraction
Log series
699
a
100
100
80 10 60 1
40 20
0.1
0 0.01
3 1
3
5
7
9
11
13
15
4
17
5
6
7
Abundance (Log cells/L)
Species rank
b
consistently fit with natural phytoplankton assemblages (Spatharis et al., 2009). The generation of simulated assemblages was based on the two models (log series and random fraction) using the cell number (N) as a proxy. In order to simulate a realistic phytoplankton structure based on phytoplankton assemblages from the field, the two models were based on the true relationship between species richness (S) and abundance (N) from the 889 field data. During the final stage of simulation, when abundances were distributed to species, the role of dominant species was also considered in the two models by incorporating a relation between the abundance of the most dominant species and the total abundance in a sample. For each simulated assemblage, the abundance of the most dominant species was subtracted from the total abundance, and the remaining abundance was distributed to the rest of the species according to the distribution predicted by the two models. Finally, given the range of abundance N (103 to 107 cells/L) and the step of increase (103 cells/L) along the simulated productivity range, the structure of each assemblage was generated, including the different species and the distribution of abundance to species. Details on this methodology were given in Spatharis and Tsirtsis (2010). The RADs of two 18-species phytoplankton assemblages generated by the two models are presented in the rank–abundance plot of Fig. 1. The accuracy of the simulated assemblages to represent field assemblages was tested by correlating the results of each of the four indices (H , D, N0 , and J) calculated from simulated assemblages with indices calculated from natural phytoplankton assemblages. Within the European Water Framework Directive (WFD, 2000/60/EC), the assessment must be presented in a standardized way as an ecological quality status, translating the results of the ecological quality ratio. This ratio expresses the relationship between observed values and reference condition values; its numerical value lies between 0 and 1. At high status, the reference condition may be regarded as an optimum where the EQR is close to 1. Based on the results of the simulated communities and a methodology presented in detail by Spatharis and Tsirtsis (2010) the four indices (H , D, N0 , and J) were further assessed developing a five-level water quality classification scheme. The ecological quality ratios of the five quality levels were calculated for each index by standardizing their values to fit the 0–1 range. 3. Results After standardization (scaled between 0 and 100), values of H using field data from the six coastal areas in the Aegean Sea showed unimodal relationships along a eutrophication gradient, as expressed by three different proxies (Fig. 2). Specifically, when using H calculated from population densities and eutrophication expressed as total assemblage abundance (Fig. 2a), ∼27%
Standardized H'
100 Fig. 1. Theoretical relative abundance distributions (RADs) of two 18-species assemblages as predicted by the statistical log series and the stochastic random fraction models.
80 60 40 20 0 5
6
7
8
9
10
11
Biovolume (Log μm3/L)
c
100 80 60 40 20 0 0
1
2
3
4
5
Chl a (μg/L) Fig. 2. Trend of Shannon index H (standardized) calculated on (a) 889 field assemblages of species abundances from six coastal areas of the Aegean across the cell number range, (b) 188 field assemblages of species biovolumes from the Gulf of Kalloni across the biovolume range, and (c) 188 field assemblages of species biovolumes from the Gulf of Kalloni across the chl a range.
of the variability could be explained using a unimodal curve fit (p < 0.01). Similarly, when using H calculated from population biovolumes, and eutrophication expressed as total assemblage biovolume (Fig. 2b), ∼27% of the variability was again accounted for (p < 0.01). A significant although weaker (R2 = 0.120, p < 0.01) unimodal relationship was observed when H was calculated from population biovolumes and eutrophication expressed as chl a (Fig. 2c). Very similar trends (not presented) were observed for the Simpson index (D), calculated on field data, regarding the respective relationships. The H , D, Hill N0 , and evenness J indices, calculated from simulated phytoplankton assemblages showed varied trends along the eutrophication range (expressed as abundance). Again, after standardization, H and D showed characteristic unimodal relationships when using simulated assemblages from log series and random fraction models (Fig. 3a and b). An increase of H and D at low eutrophication ranges is observed when the abundance is low (below 104 cells/L). At higher eutrophication levels, H and D decrease reaching minima when assemblage abundance is very high. This relationship was slightly more pronounced with H than D. Monotonic trends were determined with N0 (increasing) and J (decreasing) along the eutrophication gradient (Fig. 3c and d). Overall, the trends of indices along the eutrophication range appeared smoother for the random fraction than the log series model, which
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a
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Standardized H'
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C: H'=1.42 B: H' max. 60 40
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20 D: H' min. 0 3
4
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6
7
b
10000000
Abundance (cells/L)
Abundance (Log cells/L)
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B: H' max. C: H'=1.42
10000
D: H' min.
1000 100 10 1 1
5
9
13
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21
25
29
33
Species rank Fig. 4. Graph a shows the position of four characteristic assemblages on the unimodal Shannon curve calculated on simulated data from the random fraction model: Assemblages A and C correspond to the same exact H value (HA = HC = 1.42) whereas assemblages B and D correspond to the minimum and maximum H val = 1.74, Hmin = 0.99). Graph b presents the corresponding ues respectively (Hmax rank–abundance distributions of the four assemblages.
Fig. 3. Trends of four indices calculated on simulated phytoplankton assemblages based on the log series and the random fraction models across the eutrophication range expressed as assemblage abundance (103 to 107 cells/L).
oscillated somewhat. This oscillation was due to the allocation of species to octaves in the log series model, where for specific cell number ranges, the number of species remained constant while the number of cells and consequently dominance increased. Simulated assemblages (by the log series and random fraction models) showed significant correlations with field assemblages based on all four indices (H , D, N0 , and J), indicating an agreement between simulated and field data (Table 1). However, based Table 1 Correlations between index values calculated on phytoplankton data from the 889 field samples and those from simulated data generated by the two models (random fraction and log series). The index values have not been standardized prior to analysis. Model
Index
Spearman coefficient
Random fraction
Shannon H Simpson D Hill N0 Evenness J
0.242** 0.426** 0.671** 0.757**
Log series
Shannon H Simpson D Hill N0 Evenness J
0.202** 0.423** 0.519** 0.747**
**
Statistically significant correlation at the 0.01 level.
on comparisons of field with simulated data by both models, the amount of unexplained variability was high for H and D indices, reaching around 80% and 60% respectively. These high percentages are probably due to the marked ‘noise’ (variability) observed in the index values when calculated on field data (Fig. 2). Using simulated data from the random fraction model, it was possible to reconstruct and visualize the simulated assemblages corresponding to characteristic H values (Fig. 4a). Assemblages A and C give the same value (H = 1.42), but originate from the low- and high-ranges of eutrophication, i.e., from either side of the maximum H (Fig. 4a). However, these assemblages are markedly different since assemblage A has a total abundance of 102 cells/L (corresponding to six species) whereas assemblage C has an abundance of 1.6 × 106 cells/L (corresponding to 26 species) (Table 2). Moreover, important structural differences between the two assemblages are stressed in the rank–abundance distributions of Fig. 4b. Assemblage A is less speciose and not characterized by dominance. On the other hand, assemblage C is more speciose and characterized by higher dominance, which can be visualized by the larger differences in abundance among the first ranks/species. Although dominance is higher, the distribution of the remaining ranks/species appears even. Assemblage B represents the trade-off point between increasing species richness and decreasing evenness (increasing dominance) giving the maximum H value. Finally, assemblage D, although having the maximum N0 , shows the minimum H value since dominance weighs much more at the high eutrophication range. Within the ecological quality ratio scale (0–1) five ecological quality status classes were defined to establish the five-level classification scheme: ‘Bad’, ‘Poor’, ‘Moderate’, ‘Good’ and ‘High’
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701
Table 2 Abundance (cells/L) and the corresponding values of the Shannon H , Hill N0 , and evenness J indices calculated on four simulated assemblages generated by the log series and Hmin are the and random fraction models. HA and HC are two assemblages that correspond to the same Shannon value at either side of the hump-back curve, and Hmax maximum and minimum Shannon values respectively observed across the simulated eutrophication gradient. Model
Assemblage
Abundance
Shannon
Hill
Evenness
Random fraction
HA Hmax HC Hmin
1,000 32,000 1,601,000 10,000,000
1.42 1.74 1.42 0.99
6 15 26 31
0.79 0.63 0.44 0.29
Log series
HA Hmax HC Hmin
1,000 8,000 351,000 10,000,000
1.75 2.06 1.75 0.92
6 12 22 28
0.98 0.83 0.57 0.28
(WFD, 2000/60/EC). Samples representing “High” ecological status, achieve ratio values near 1, while those representing ‘Bad’ ecological status will yield values close to 0. Calculation of the ecological quality ratios of the four indices under consideration (Table 3) showed that H and D indices present an initial increase and then a decrease from the high to the bad quality status. On the other hand, evenness J and Hill N0 showed a monotonic trend from the “High” to the “Bad” ecological quality status. 4. Discussion A unimodal relationship was observed in the diversity–productivity relationship regardless of the data type used to calculate H (species abundances and species biovolumes) or the parameter used to express eutrophication (chlorophyll a concentrations, assemblage abundance, and assemblage biovolume). This is consistent with previous studies that investigated diversity–productivity relationships (Vadrucci et al., 2003; Irigoien et al., 2004; Duarte et al., 2006). Although factors which control the diversity–productivity relationship are not fully understood (Kassen et al., 2000; Striebel et al., 2009), it was suggested that nutrient limitation at low productivity favored strong competitors at the expense of less competitive species (Tilman et al., 1982) and that other biotic factors, such as selective grazing and competition for light, resulted in low diversity at high productivity (Irigoien et al., 2004). While the unimodal diversity–productivity relationship is supported strongly in theory, real-world observations based on the Shannon H index showed this relationship to be weak. For example, the scatter-plot of H (calculated on species-biovolume data) with biovolume showed that the area below the unimodal curve was filled with data points, a reality often encountered in field data (Rosenzweig and Abramsky, 1993; Waide et al., 1999; Interlandi and Kilham, 2001; Schmid, 2002). This suggests that H is sensitive to environmental ‘noise’ associated with seasonality, extreme weather events, hydrodynamic circulation, and patchiness (Karydis, 1992). In addition, transient dynamics (sensu Hastings, 2004) resulting from the interaction of phytoplankton with trophic levels operating on multiple timescales may be another factor
contributing to the large variance in H along the eutrophication gradient. Our evaluation of D showed the same trends with productivity as did H , supporting previous observations showing the close relationship between H and D (Washington, 1984). Because many diversity index values were obtained at a given productivity level, the utility of H and D as diagnostic tools is limited. Our use of simulated phytoplankton assemblages free of environmental noise (Boyle et al., 1990; Mouillot and Wilson, 2002) allowed us to explore changes in assemblage structure expressed by ecological indices (H , D, N0 , and J) across the eutrophication spectrum. We achieved this by considering an important aspect of phytoplankton assemblage structure, dominance (Bruno et al., 1983; Gould and Wiesenburg, 1990). Focusing on H , its measurement is a function of both species richness and evenness; therefore, it is impossible to determine the relative importance of these components from the index value alone (Ludwig and Reynolds, 1988). However, it was possible to evaluate the relative importance of species richness and evenness along the eutrophication gradient by employing H complemented with visualization of rank–abundance distributions of simulated assemblages. By following this approach, we showed that the same values of H resulted from species-poor phytoplankton assemblages with no dominance as with speciesrich assemblages dominated by one or two species. In other words, the unimodal relationship was influenced by the initial increase in species richness (N0 ) that was eventually masked at abundances higher than 104 cells/L when community evenness (J) decreased. Our observations of increased species richness and eventual dominance with nutrient enrichment in our simulated assemblages are consistent with attributes of natural assemblages (Tilman, 1982; Bruno et al., 1983; Gould and Wiesenburg, 1990; Spellerberg, 1993). Regarding water quality assessment, particularly for the implementation of recent policies such as the Water Framework Directive (WFD, 2000/60/EC), the monotonicity of tested indices is a prerequisite since the categorization of ecological quality status in five categories is performed in a rank order using the ecological quality ratios. In contrast to the evenness J and Hill N0 indices, the EQRs for Shannon H and Simpson D indices do not provide an accurate classification into the five ecological quality categories (Table 3) since these indices do not show a monotonic relationship
Table 3 Water quality classification schemes and the corresponding ecological quality ratios for evenness J, Hill N0 , Shannon H , and Simpson D indices based on the classification criteria of the Water Framework Directive (WFD, 2000/60/EC). Index
High
Good
Moderate
Low
Bad
Evenness J EQR Evenness J Hill N0 EQR Hill N0 Shannon H EQR Shannon H Simpson D EQR Simpson D
0.98–0.88 1 6–10 1 1.75–2.03 1 0.18–0.16 1
0.88–0.71 0.79 10–15 0.76 2.03–1.92 2.47 0.16–0.22 1.11
0.71–0.60 0.44 15–20 0.47 1.92–1.79 1.89 0.22–0.27 0.78
0.60–0.50 0.21 20–23 0.18 1.79–1.56 1.21 0.27–0.36 0.50
0.50–0.27 0 23–28 0 1.56–0.90 0 0.36–0.64 0
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with eutrophication. Water quality classification must be based on robust tools able to synthesize the information from more than one metric or ecological quality elements, reducing in this way possible errors and misleading results. The use of Shannon and Simpson indices on phytoplankton assemblages is not suggested for water quality classification, whereas other indices such as Menhinick’s and evenness indices (Karydis, 1996; Karydis and Tsirtsis, 1996) or multimetric indices (Borja et al., 2004; Marques et al., 2009; Spatharis and Tsirtsis, 2010) are more appropriate metrics for this purpose. 5. Conclusions In the present study, the behavior of Shannon and the related Simpson indices was assessed along a eutrophication gradient using field and simulated phytoplankton assemblages. The main conclusions of the study are: (a) the same index values can be obtained at low and high ranges along the eutrophication gradient due to structural changes in phytoplankton assemblages; (b) H and D values showed high variability across the eutrophication gradient, likely due to a synergy of factors theoretically unaccounted for in these indices (Irigoien et al., 2004; Buyukates and Roelke, 2005; Spatharis et al., 2007); and (c) combined, these attributes of H and D limit their application as water quality assessment diagnostic tools along eutrophication gradients, as evidenced by non-monotonic ecological quality ratio values determined within the framework of the European Water Framework Directive. References Anderson, B.J., Mouillot, D., 2007. Influence of scale and resolution on niche apportionment rules in saltmeadow vegetation. Aquat. Biol. 1, 195–204. 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. Beisel, J.N., Moreteau, J.C., 1997. A simple formula for calculating the lower limit of Shannon’s diversity index. Ecol. Model. 99, 289–292. Borja, A., Franco, J., Muxika, I., 2004. The biotic indices and the Water Framework Directive: the required consensus in the new benthic monitoring tools. Mar. Pollut. Bull. 48, 405–408. Borja, A., Dauer, D.M., 2008. Assessing the environmental quality status in estuarine and coastal systems: comparing methodologies and indices. Ecol. Indic. 8, 331–337. Boyle, T.P., Smillie, G.M., Anderson, J.C., Beeson, D.R., 1990. A sensitivity analysis of 9 diversity and 7 similarity indices. Res. J. Water Pollut. C. 62, 749–762. Bruno, S.F., Staker, R.D., Sharma, G.M., Turner, J.T., 1983. Primary productivity and phytoplankton size fraction dominance in a temperate North-Atlantic estuary. Estuaries 6, 200–211. Buyukates, Y., Roelke, D., 2005. Influence of pulsed inflows and nutrient loading on zooplankton and phytoplankton community structure and biomass in microcosm experiments using estuarine assemblages. Hydrobiologia 548, 233–249. Camargo, J.A., 2008. Revisiting the relation between species diversity and Information Theory. Acta Biotheor. 56, 275–283. Chao, A., Shen, T.J., 2003. Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample. Environ. Ecol. Stat. 10, 429–443. Danilov, R., Ekelund, N.G.A., 1999. The efficiency of seven diversity and one similarity indices based on phytoplankton data for assessing the level of eutrophication in lakes in central Sweden. Sci. Total Environ. 234, 15–23. Diaz, R.J., Solan, M., Valente, R.M., 2004. A review of approaches for classifying Benthic habitats and evaluating habitat quality. J. Environ. Manage. 73, 165–181. Duarte, P., Macedo, M.F., da Fonseca, L.C., 2006. The relationship between phytoplankton diversity and community function in a coastal lagoon. Hydrobiologia 555, 3–18. Fesl, C., 2002. Niche-oriented species-abundance models: different approaches of their application to larval chironomid (Diptera) assemblages in a large river. J. Anim. Ecol. 71, 1085–1094. Fisher, R.A., Corbet, A.S., Williams, C.B., 1943. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58. Fromentin, J.M., Dauvin, J.C., Ibanez, F., Dewarumez, J.M., Elkaim, B., 1997. Long-term variations of four macrobenthic community structures. Oceanol. Acta 20, 43–53. Gould, R., Wiesenburg, D., 1990. Single-species dominance in a subsurface phytoplankton concentration at a Mediterranean Sea front. Limnol. Oceanogr. 35, 211–220. Hastings, A., 2004. Transients: the key to long-term ecological understanding? Trends Ecol. Evol. 19, 39–45.
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