Widespread natural intraspecific variation in tissue stoichiometry of two freshwater molluscs: Effect of nutrient enrichment

Ecological Indicators 66 (2016) 583–591

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Widespread natural intraspecific variation in tissue stoichiometry of two freshwater molluscs: Effect of nutrient enrichment Yongjiu Cai a , Qingju Xue a , Jun Xu b , Lu Zhang a , Zhijun Gong a,∗ , Kumud Acharya c a b c

Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430070, China Division of Hydrologic Sciences, Desert Research Institute, NV 89119, USA

a r t i c l e

i n f o

Article history: Received 13 November 2015 Received in revised form 3 February 2016 Accepted 4 February 2016 Keywords: Ecological stoichiometry Homoeostasis Mollusca Eutrophication Shallow lakes

a b s t r a c t A central premise of ecological stoichiometry is that consumers maintain relatively fixed elemental composition in their bodies, a process known as elemental homoeostasis. Although nutrient enrichment is a ubiquitous problem facing many freshwater lakes around the world, intraspecific variation in elemental composition of freshwater invertebrates and its relationship with nutrient loading have not been well addressed. Here, we examined carbon:nitrogen:phosphorus (C:N:P) stoichiometry of two widely distributed molluscs, Corbicula fluminea and Bellamya aeruginosa, from several subtropical shallow lakes across a nutrient gradient. Our results showed that these two species exhibited substantial natural intraspecific variation in tissue stoichiometry which can reach or even exceed the values among different freshwater taxa investigated before. Our results suggest that tissue P content presents the greatest variations, followed by N content, and lowest in C content. Tissue P content ranged about three-fold (0.56%–1.65%) and five-fold (0.41%–2.28%) for B. aeruginosa and C. fluminea, respectively. Correspondingly, N content ranged from 5.16% to 12.06% and from 6.47 to 11.36%, respectively. Tissue %P, C:P and N:P ratios were strongly correlated with PO4 3− -P and/or chlorophyll-a in the water column. Tissue N and P contents of B. aeruginosa and P content of C. fluminea increased with increasing lake trophic levels (mesotrophic to eutrophic to hypertrophic). These results suggest that the two molluscs can adjust their tissue stoichiometry in relation to nutrient enrichment. Relaxing the assumption of strict homeostasis may help them cope with potential stoichiometric constraints. The results provide additional clues to why these two species are successful invaders and widely distributed. © 2016 Published by Elsevier Ltd.

1. Introduction Ecological stoichiometry focuses on understanding how the structure and function of ecosystems are influenced by the balance of chemical elements (carbon:nitrogen:phosphorus [C:N:P] ratios) and energy between the requirements by organisms and local availability in their environment (Elser et al., 2000). A central premise of ecological stoichiometry is that consumer elemental composition is relatively independent of food resource nutrient content, a process known as elemental homoeostasis. Elements consumed in excess of the consumer’s requirements are excreted or respired, while those limiting in supply are retained. In this context, mismatches between the elemental requirements of a consumer and

∗ Corresponding author at: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China. Tel.: +86 25 86882184; fax: +86 25 57714759. E-mail address: [email protected] (Z. Gong). http://dx.doi.org/10.1016/j.ecolind.2016.02.022 1470-160X/© 2016 Published by Elsevier Ltd.

the supply of elements in its diet can constrain its growth and reproduction, which in turn affect a range of ecological, biogeochemical and physiological processes (El-Sabaawi et al., 2012b; Elser and Urabe, 1999). A first step in understanding these influences is to identify patterns of variation in the elemental composition of the organisms that may influence ecosystem processes. It is therefore important to characterize the natural variation that exists among individuals in their body stoichiometry and to understand factors that generate it. To date, studies of organismal stoichiometry have mostly focused on characterizing variability and drivers among taxonomic and functional groups (Cross et al., 2003; Evans-White et al., 2005; Small and Pringle, 2010). In contrast, relatively few studies have examined the extent and causes of intraspecific variability in organismal stoichiometry in natural ecosystems (Frost et al., 2005a). A growing body of evidence suggests, however, that intraspecific variability in organismal stoichiometry is greater than was previously thought (Bertram et al., 2008; El-Sabaawi et al., 2012a,b; González et al., 2011; Small et al., 2011). Therefore, consumer

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body stoichiometry can often track diet or environmental stoichiometry (Frost et al., 2005b; Goloran et al., 2015; Schade et al., 2003, 2005). For example, zooplankton C:P ratio is strongly correlated to the seston C:P ratio (Hessen et al., 2005). A field study also found that invertebrates across multiple trophic levels in the high-P stream had significantly higher P content and lower C:P compared to the low-P stream (Small and Pringle, 2010). That study found that between-stream variation in P content of a given taxon greatly exceeds in-stream variation among different taxa. Nutrient enrichment is a ubiquitous problem in many freshwater lakes and may lead to increased N and P contents of both seston and periphyton (Frost and Elser, 2002; Qin et al., 2007; Sterner et al., 2008; Liao et al., 2014), which constitute the base of lake food webs. If deviation from strict homeostasis is a widespread phenomenon in freshwater invertebrates, then larger variations in nutrient loading could lead to extensive natural intraspecific variations in stoichiometric (C:N:P) composition of freshwater invertebrate such as molluscs. Still, it is less clear how tissue stoichiometry varies along nutrient gradient for many freshwater molluscs (Cross et al., 2005a; Naddafi et al., 2012; Small et al., 2009; Small and Pringle, 2010). One way to elucidate this question is to assess tissue C:N:P stoichiometry of freshwater molluscs among lakes with different nutrient loading (mainly P and N), as higher contents of these may affect tissue stoichiometry via altered food elemental composition (Naddafi et al., 2009). To better understand how nutrient loading affects freshwater invertebrate stoichiometry, the natural variation in elemental (C, N and P) stoichiometry is quantified in two molluscs, the deposit feeder Bellamya aeruginosa (Gastropoda) and the suspension feeder Corbicula fluminea (Bivalvia), from several lakes that vary widely in nutrient concentrations. C. fluminea mainly feeds on phytoplankton and particulate organic matter (Sousa et al., 2008) while B. aeruginosa grazes on periphyton off sediments and macrophytes (Chen and Sung, 1975). We chose these two species because they are widely distributed in Chinese lakes, rivers and reservoirs, suggesting that they have a wide variation in habitats and environmental conditions. In addition, C. fluminea is an invasive species for many freshwater ecosystems in North America, South America and Europe (Sousa et al., 2008), indicating this species is able to change tissue stoichiometry in response to the variation in elemental composition of their food in different environments (Naddafi et al., 2009, 2012). In this study, we measured tissue elemental stoichiometry of B. aeruginosa and C. fluminea collected from subtropical shallow lakes across a nutrient gradient. We attempted to address the following questions: (1) What is the natural variation in tissue C:N:P stoichiometry of these two molluscs in lakes with different nutrient loading? (2) Whether tissue C:N:P stoichiometry is related to nutrient gradient of the lakes? We predicted that increases in nutrient concentrations in the medium they live in would result in increased body tissue N and P contents, and decreased tissue C:N and C:P ratios. Besides, according to their feeding habit, we hypothesized that elemental composition of B. aeruginosa is significantly correlated with nutrients in surface sediment, while elemental

composition of C. fluminea will show a significant correlation with nutrients in the water column. 2. Methods 2.1. Sample collection and laboratory analysis Specimens of B. aeruginosa and C. fluminea were collected from 34 subtropical freshwater lakes in China (Table S1). These lakes are all shallow but vary greatly in surface area. Nutrients and chlorophyll a (Chl-a) concentrations in water column showed substantial variations among lakes (Table 1). The studied lakes ranged from mesotrophic to hypertrophic. For each lake, one to nine sites located in the pelagic zone were sampled in summer (July or August) from 2007 to 2009. Molluscs were collected by modified Peterson grab and were kept in water for 12 h without food to allow clearance of guts. Shell length of B. aeruginosa and C. fluminea were measured using a vernier caliper. To reduce the effect of body size on tissue stoichiometry, we selected similar sized B. aeruginosa and C. fluminea (Table 2 and Fig. S1). Specimens were then stored separately (individually) in a freezer (−20 ◦ C) prior to further analyses. Foot-muscle tissue samples were separated from each specimen with a scalpel and then freeze-dried to constant weight. To prepare for elemental analysis, muscle tissue samples were ground to fine powder with a mortar and pestle. Subsample masses of 500–2000 ␮g were used for elemental analysis. C and N contents of each specimen were measured with an elemental analyzer (EuroVector EA3000, Italy), and P content was measured as phosphate after hot hydrolysis with potassium persulfate (Naddafi et al., 2012). C:N, C:P, and N:P ratios were reported on molar units, and C, N, and P content were calculated as per gram dry mass. 2.2. Measurement of environmental parameters In our study lakes, phosphorus often was the limiting factor for primary production (Wang and Wang, 2009). Hence, we considered Chl-a, total phosphorus (TP) and orthophosphate (PO4 3− -P) in water column as proxies for pelagic productivity, while total phosphorus in surface sediment (TPs) as proxy for benthic productivity. Total nitrogen in water column (TN) and surface sediments (TNs) and other water physicochemical parameters were also measured (Table 1). Concurrent to the collection of molluscs, a 2-L water sample was taken about 25 cm above the lake bed at each sampling site to measure TN, TP and Chl-a based on the standard methods (APHA, 2012). Conductivity and transparency were measured in situ using YSI 6600 sensor and Secchi disk, respectively. Also, a short sediment core at each site was collected using a gravity sampler equipped with a perspex tube (11 cm in diameter). The upper 3 cm of the cores was extruded and transferred into polyethylene bags. Sediment samples were freeze dried and then ground with a mortar and pestle. TPs and TNs in sediments were determined by subsampling approximately 30 mg of the dried sediment from each site. Next, 25 ml of distilled water was added and the samples were analyzed, using a combined persulphate digestion followed by spectrophotometric analysis (Ebina et al., 1983).

Table 1 Descriptive statistics of environmental parameters for the 34 studied lakes. Environmental variables

Mean ± SD

Conductivity (␮s/cm) Secchi depth (m) TN (mg/L) TP (mg/L) Chl-a (␮g/L) TSI TNs (mg/kg) TPs (mg/kg)

266.6 0.76 1.04 0.066 12.92 57.6 2281.0 688.9

± ± ± ± ± ± ± ±

171.3 0.64 0.64 0.051 14.67 7.5 1673.1 483.9

Min

Max

CV (%)

61.5 0.20 0.36 0.013 1.22 40.2 223.7 101.5

696.0 3.10 3.12 0.314 77.30 75.1 8251.3 3645.2

64.26 84.22 61.61 76.10 113.56 13.02 73.35 70.24

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Table 2 Shell length (mm) and tissue stoichiometry descriptive statistics for B. aeruginosa and C. fluminea collected in shallow lakes along the Yangtze River. Species

Variable

Mean ± SD

Min

Max

Max/min

CV (%)

Bellamya aeruginosa (n = 89)

Shell length %C %N %P C:N C:P N:P

22.36 42.81 9.27 0.96 5.49 121.14 22.13

± ± ± ± ± ± ±

3.72 2.73 1.47 0.23 0.69 25.42 4.23

12.56 31.63 5.16 0.56 4.45 69.23 14.30

35.43 46.31 12.06 1.65 7.32 211.44 40.14

2.8 1.5 2.3 3.0 1.6 3.1 2.8

16.64 6.37 15.86 24.32 12.62 20.98 19.12

Corbicula fluminea (n = 36)

Shell length %C %N %P C:N C:P N:P

18.6 48.44 9.04 1.09 6.43 135.73 20.85

± ± ± ± ± ± ±

2.71 2.71 1.51 0.47 1.14 56.8 6.75

10.76 40.30 6.47 0.41 4.73 55.27 9.95

23.85 59.37 11.36 2.28 8.87 313.74 39.89

2.2 1.5 1.8 5.5 1.9 5.7 4.0

14.55 5.60 16.65 42.92 17.78 41.84 32.39

2.3. Statistical analysis Relationships among %C, %N, %P, C:N, C:P, N:P and body size were determined using Pearson correlation analysis. To test whether tissue elemental stoichiometry was associated with nutrient gradient, we correlated tissue stoichiometry to nutrient parameters in the water column (i.e., Chl-a, TP, PO4 3− -P, TN) and the surface sediments (TNs and TPs) using Pearson correlation analyses with shell length as a covariate to exclude the effect of body size. Since nutrient and Chl-a concentrations are highly variable over time, Spearman’s rank correlations between tissue stoichiometry and environmental variables was also examined. Furthermore, to examine the effect of nutrient enrichment on body tissue elemental stoichiometry, the studied lakes were grouped in different trophic levels based on trophic state index (TSI). The TSI is composed of Chl-a (␮g/L), TP (mg/L) and SD (m), and was calculated according to Carlson (1977) and Cai et al. (2002). The studied lakes belonged to mesotrophic, eutrophic and hypertrophic levels. Differences in tissue stoichiometry among lake trophic categories were tested using the t-test or one-way ANOVA following post hoc Tukey multiple comparisons. The normality and homoscedasticity of data were tested before all analyses. When necessary, log (x + 1) transformation was employed. All statistical analyses were performed using IBM SPSS 20.0. 3. Results 3.1. Environmental characterization The environmental variables related to trophic states of lakes varied greatly among sites (Table 1). There was a 15-fold difference

in Secchi depth, ranging from 0.20 to 3.10 m. Nutrient concentrations in the water column displayed substantial variations with TN ranging from 0.36 to 3.12 mg/L, TP from 0.013 to 0.314 mg/L. Chla concentrations ranged from 1.22 to 77.30 ␮g/L and presented the greatest variation with the highest coefficient of variation as 113.56%. The results of TSI indicated that 27.4%, 55.9% and 16.7% of the studied lakes were mesotrophic, eutrophic and hypertrophic, respectively. Nutrient content in sediments also varied greatly with TNs ranging between 223.7 and 8251.3 mg/kg, and TPs between 101.5 and 3645.2 mg/kg. 3.2. General patterns in tissue elemental composition Tissue elemental stoichiometry of B. aeruginosa exhibited wide natural variation (Table 2). Tissue %C varied from 31.63% to 46.31%, %N from 5.16% to 12.06%, and %P about three-fold from 0.56% to 1.65%. C:P and N:P ratios also exhibited an almost three-fold variation, ranging from 69.23 to 211.44, and 14.30 to 40.14, respectively. C:N ratio ranged from 23 to 36. C. fluminea also displayed substantial intraspecific variation in their tissue stoichiometry (Table 2), with %P ranging over five-fold from 0.41% to 2.28%, %N from 6.47 to 11.36% and %C from 40.30% to 59.37%. In general, tissue elemental contents and ratios of these two species related to phosphorus element (i.e., %P, C:P and N:P) showed the highest variations, followed by %N and C:N ratio, and %C the least (Table 2). Tissue %N was strongly positively correlated with %P for both B. aeruginosa (r = 0.62, P < 0.01) and C. fluminea (r = 0.62, P < 0.01, Table 3), suggesting that animals that are good at acquiring and retaining nitrogen are also good at acquiring and retaining phosphorus. N:P ratio was negatively correlated with %P, but not for %N for these two species (Fig. 1). Body size had no effect on

Table 3 Pearson correlations between body size and tissue stoichiometry for B. aeruginosa and C. fluminea. Pearson correlation coefficients and significance level were given. Statistically significant correlations are in bold. Size Bellamya aeruginosa (n = 89)

Corbicula fluminea (n = 36)

%C 0.32

Size %C %N %P C:N C:P N:P

0.11 0.13 0 −0.12 0.02 0.10

Size %C %N %P C:N C:P N:P

0.14 −0.09 −0.30 0.17 0.34 0.33

0.81 0.44 −0.61 −0.15 0.21 0.40 0.10 −0.22 0.21 0.34 0.34

%N

%P

C:N

C:P

N:P

0.22 <0.01

0.99 <0.01 <0.01

0.26 <0.01 <0.01 <0.01

0.87 0.15 <0.01 <0.01 <0.01

0.37 0.05 0.23 <0.01 0.32 <0.01

0.62 −0.95 −0.42 0.13 0.58 0.54 0.44 −0.94 −0.50 −0.10

−0.60 −0.92 −0.66 0.07 0.21 <0.01 −0.51 −0.87 −0.86

0.47 −0.11 0.32 0.21 <0.01 <0.01 0.63 0.23

0.83 0.04 0.04 <0.01 <0.01 <0.01 0.89

0.05 0.04 0.57 <0.01 0.17 <0.01

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Fig. 1. Pearson correlations between tissue N:P with %N and %P for B. aeruginosa (solid circles) and C. fluminea (open triangles).

tissue C:N:P stoichiometry of B. aeruginosa, while significant correlations were detected between body size and C:P of C. fluminea (Table 3).

4. Discussion

3.3. Relationships between tissue stoichiometry and nutrient enrichment

The two species analyzed in this study exhibited substantial natural variation in tissue elemental stoichiometry. C content fell within the range of values for freshwater invertebrates investigated before (Fig. 4). However, ranges of %N and %P within-species reached or exceeded the values among different taxa. B. aeruginosa differed up to 130% in their tissue %N, and up to 200% in %P. Stoichiometric ratios were also highly variable with tissue C:N, C:P, N:P differing up to 60%, 200% and 180%, respectively. Similarly, C. fluminea also showed significant differences in %P and C:P differing up to 450% and 460%, respectively. High variability of N and P contents led to wide range of stoichiometry ratios. Overall, the intraspecific variations in tissue stoichiometry were as high as or higher than the interspecific variation for freshwater invertebrates estimated. Our results showed greatest variation in tissue P content, followed by N content, and lowest in C content. This result was not surprising. Such intraspecific differences in C, N, and P contents may link to differences in the body content of biomolecules among individuals (Anderson et al., 2004; Vrede et al., 2004). C:N:P ratios differences likely reflect, in part, the relative contribution of rRNA (P-rich) and structural molecules (C- and N-rich) to tissue composition. Phosphorus-rich rRNA is necessary for protein synthesis which directly influences growth rates (Elser et al., 2003). Prior studies found that faster growing organisms had higher P contents which was in turn correlated with rRNA (Elser et al., 2003; Acharya

For B. aeruginosa, partial Pearson correlation analysis indicated that almost all tissue stoichiometry were significantly correlated with PO4 3− -P and Chl-a concentrations (Fig. 2 and Table S2), with %P showing the highest correlations with PO4 3− -P and Chl-a. In addition, some elemental contents and ratios were strongly correlated with TN and TP in water column in Spearman correlation analyses (Table S2). In contrary to our expectation, no significant correlations were detected between tissue stoichiometry and nutrients in sediment. For C. fluminea, tissue %P, C:P and N:P were strong positively correlated with PO4 3− -P and/or Chl-a in both partial Pearson and Spearman correlation analysis. Almost all tissue stoichiometry of B. aeruginosa differed significantly among different trophic level lakes with the exception of tissue N:P (Fig. 3). In general, tissue %N and %P increased from mesotrophic lakes to hypertrophic lakes, and decreased C:N and C:P ratios. Compared with mesotrophic lakes, tissue %N and %P in hypertrophic lakes increased by 23% and 41%, respectively. For C. fluminea, tissue %P and C:P differed significantly between mesotrophic and eutrophic lakes with 41% increase in %P. There were no significant differences in other variables such as %N, C:N and N:P and C% (Fig. 3).

4.1. Intraspecific variation in tissue stoichiometry

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Fig. 2. Correlations between tissue stoichiometry and nutrient parameters for B. aeruginosa (solid circles) and C. fluminea (open triangle). Partial Pearson correlation coefficients were given.

et al., 2004). Most of these studies were based on Daphnia and the assumptions that most lakes are P limited factor. However, subsequent studies by Hessen et al. (2007) have shown that for other organisms such as rotifers, if higher P reflects increased allocation to P-rich rRNA to meet the protein synthesis demand, that may mean increased demand for N as well. Therefore, both P and N could limit growth. In our studies, B. aeruginosa and C. fluminea have high growth rates and reproductive capacity (Chen and Sung, 1975; Sousa et al., 2008), indicating their susceptibility to nutrient limitation. However, it is not clear if both N and P are limiting but since most of our lakes are N rich, P seems to limit the growth rate. Hence, it is likely the reason for the highest variations in P content across the nutrient gradient. Further studies are needed to verify these proposed relationships between C:N:P stoichiometry and biomolecular content for these two species. 4.2. Nutrient enrichment and tissue stoichiometry Our results indicated that the variation in tissue stoichiometry of these molluscs can partly be explained by productivity (i.e., Chl-a, PO4 3− -P). Such variability suggests that tissue stoichiometry can be regulated by nutrient enrichment supported by findings

from studies in both pelagic and benthic food webs (Cross et al., 2005b; Shimizu and Urabe, 2008). For example, temporal data on nutrient concentrations and tissue stoichiometry of zebra mussel in Lake Erken revealed a negative correlation between TP and tissue C:P (Naddafi et al., 2009). Spatial data also showed that zebra mussels in more productive lakes have lower tissue C:P and N:P (Naddafi et al., 2012). Small and Pringle (2010) also found that invertebrate consumers from the high-P study stream were significantly elevated in P content across multiple taxonomic and functional feeding groups. Thus, it is reasonable to expect a higher tissue N and P contents of B. aeuginosa and C. fluminea in higher trophic level lakes. A shortcoming of this study was that we didn’t measure elemental stoichiometry of food (seston and periphyton) for these two molluscs. Nevertheless, significant correlations with nutrient parameters and considerable differences among different trophic level lakes indicated stronger effects of nutrient enrichment on tissue stoichiometry. The Growth Rate Hypothesis suggests that there is a tight coupling between body P content, RNA allocation, and growth rate in consumers. It has been demonstrated that P enrichment can lead to increased growth rate observed in freshwater gastropods and bivalves (Fink and Elert, 2006; Garton and Johnson, 2000; Tibbets et al., 2010). In general, elevated P supply

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Fig. 3. Comparison of tissue stoichiometry (mean + SD) among different trophic level lakes for B. aeruginosa and C. fluminea. One-way ANOVA and t-test were employed for B. aeruginosa and C. fluminea, respectively. Different letters indicate significant differences among different trophic level lakes (post hoc Tukey test, P < 0.05). For C. fluminea, the only one lake belonging to hypertrophic level was combined into eutrophic lakes.

will increase allocation to P-rich ribosomal RNA which in turn promote growth rate. However, we did not measure the tissue RNA content. Future investigations and experiments should determine whether there is a relation between body P content, RNA allocation, and growth rate of these molluscs. The results support our hypothesis that tissue stoichiometry of C. fluminea was related to nutrient parameters in water column. It is not surprising that the bivalve mainly filter seston from water column (Hwang et al., 2004). In contrary to our expectation, tissue stoichiometry of B. aeruginosa did not correlate with nutrient parameters in the surface sediments, although they were significantly correlated with PO4 3− -P and Chl-a in the water column. The most likely reason for this result is a likely diet shift of B. aeruginosa in eutrophic lakes. The results of Vadeboncoeur et al. (2003) showed that phytoplankton were responsible for nearly 100% of primary production in Danish eutrophic lakes with TP > 0.1 mg/L. Eutrophication was characterized by a switch from benthic to pelagic

dominance of primary productivity. Low benthic production will lead to food limitation to zoobenthos. Carbon isotopic signatures of zoobenthos demonstrated increased exploitation of phytoplankton with increasing eutrophication (Vadeboncoeur et al., 2003). Several studies indicated that B. aeruginosa can switch to filter feeding when phytoplankton biomass is high (Han et al., 2010; Zhu et al., 2013). Thus, it is reasonable why tissue stoichiometry were strongly correlated to PO4 3− -P and Chl-a concentrations. A flexibility in foraging strategy of this snail may promote its predominance in eutrophic lakes. Further investigations should quantify relative importance of benthic primary production and pelagic primary production to diets of the snail across nutrient gradients. Another possible reason for a lack of significant correlations maybe nutrient content in sediments cannot fully represent dietary elemental stoichiometry. It has been found that only particulate organic matter (POM) and epipelon in the surface sediments are assimilated by snails (Han et al., 2010).

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Fig. 4. Comparisons of intraspecific variation in tissue stoichiometry for B. aeruginosa and C. fluminea with interspecific variation among different freshwater taxa. Data of freshwater invertebrates from Frost et al. (2003), Evans-White et al. (2005) and Liess and Hillebrand (2005).

4.3. Implications for distribution and invasion Our results showed that C:N:P stoichiometry of these two species was highly flexible along nutrient gradient. Ability of organisms to change their nutrient stoichiometry according to environmental conditions would be vital for their physiological fitness and invasion success (González et al., 2010). Current knowledge has shown that adaptive changes in stoichiometric traits (e.g., varying tissue nutrient content and growth rate) confer an adaptive advantage (Acharya et al., 2006; González et al., 2010; Jeyasingh et al., 2009). Recent meta-analyses indicate that invasive species are often successful in both low- and high-nutrient environments. In low-nutrient environments, organisms that are more able to modify their nutrient requirements and grow faster per unit nutrient use will have a competitive advantage. In high-nutrient

environments, invasive organisms will have higher growth rates and/or reproductive outputs compared to natives (González et al., 2010). Zebra mussel, a successful invasive shellfish, can change their tissue P content, C:P and N:P in ratio to lake trophic state and elemental composition of their food, and therefore can cope with potential nutrient stoichiometric constraints (Naddafi et al., 2009, 2012). A lack of imbalance in C:P and N:P ratios between seston (food) and zebra mussels allow them to tolerate potential P limitation, and thus maintaining higher growth rate and proliferate in many novel ecosystems (Naddafi et al., 2009). In our study, C. fluminea exhibited wide variations in nutrient stoichiometry. C. fluminea is one of the most successful invasive species in freshwater ecosystems, which are found in both nutrient rich and relatively nutrient-poor lakes (Sousa et al., 2008; Li et al., 2016). B. aeruginosa dominated in many freshwaters (e.g., rivers, lakes,

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reservoir) in China, and can occur abundantly both at clean headwater streams and hypertrophic lakes (Zhang et al., 2014). Ability of these two species to adjust tissue nutrient requirements may confer their greater competitive advantage to other organisms in novel ecosystems. For example, B. chinensis and B. japonicus have successfully invaded North America (Soes et al., 2011; Solomon et al., 2010), imposing serious ecological and economic impacts on invaded ecosystems. B. aeruginosa is similar to B. japonicus in both shell morphology (Fig. S2) and life history. However, lack of physiological fitness (e.g., growth rate, tissue condition index) data for supporting adaptive trait plasticity of these two species was a necessary compromise in this study. In future, relationships between C:N:P stoichiometry and life history traits (e.g., growth, development, reproduction) of these two species and other invasive organisms across natural and controlled nutrient gradients should be examined. In conclusion, our study revealed that tissue elemental stoichiometry of B. aeruginosa and C. fluminea varied greatly in relation to nutrient gradient. Such variability indicates that these two molluscs can modify tissue nutrient contents with regards to nutrient availability in the environment. This adaptive plasticity may help them to cope with potential nutrient stoichiometric constraints, enhancing their ability to exploit diverse habitats and novel ecosystems. The results improve our ability to understand the mechanisms contributing to invasion success and geographical distribution of organisms from stoichiometric principles. Acknowledgements This work was supported by the National Basic Research Program of China (grant 31300396, 31370477), Natural Science Foundation of Jiangsu Province (BK20131055), and the Program of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (grant NIGLAS2012135012). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2016. 02.022. References Acharya, K., Jack, J.D., Smith, A.S., 2006. Stoichiometry of Daphnia lumholtzi and their invasion success: are they linked? Arch. Hydrobiol. 165, 433–453. Acharya, K., Kyle, M., Elser, J., 2004. Biological stoichiometry of Daphnia growth: an ecophysiological test of the growth rate hypothesis. Limnol. Oceanogr. 49, 656–665. Anderson, T.R., Boersma, M., Raubenheimer, D., 2004. Stoichiometry: linking elements to biochemicals. Ecology 85, 1193–1202. APHA, 2012. Standard Methods for the Examination of Water and Wastewater, 22nd ed. American Public Health Association, Washington, DC. Bertram, S., Bowen, M., Kyle, M., Schade, J., 2008. Extensive natural intraspecific variation in stoichiometric (C:N:P) composition in two terrestrial insect species. J. Insect Sci. 8, 1–7. Carlson, R.E., 1977. A trophic state index for lakes. Limnol. Oceanogr., 361–369. Cai, Q.H., Liu, J.K., King, L., 2002. A comprehensive model for assessing lake eutrophication. Chin. J. Appl. Ecol. 13, 1674–1678. Chen, C.Y., Sung, K.P., 1975. A preliminary study on reproduction and growth of the snail, Bellamya aeruginosa (Veeve). Acta Hydrobiol. Sin. 5, 519–534 (in Chinese with English abstract). Cross, W.F., Benstead, J.P., Frost, P.C., Thomas, S.A., 2005a. Ecological stoichiometry in freshwater benthic systems: recent progress and perspectives. Freshw. Biol. 50, 1895–1912. Cross, W.F., Benstead, J.P., Rosemond, A.D., Wallace, J.B., 2003. Consumer-resource stoichiometry in detritus-based streams. Ecol. Lett. 6, 721–732. Cross, W.F., Johnson, B.R., Wallace, J.B., Rosemond, A.D., 2005b. Contrasting response of stream detritivores to long-term nutrient enrichment. Limnol. Oceanogr. 50, 1730–1739. Ebina, J., Tsutsui, T., Shirai, T., 1983. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res. 17, 1721–1726.

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