Distribution of Polychaete Assemblage in Relation to Natural Environmental Variation and Anthropogenic Stress

ZAN Xiaoxiao, ZHANG Chongliang, XU Binduo, XUE Ying, and REN Yiping

College of Fisheries,Ocean University of China,Qingdao266003,P. R. China

Distribution of Polychaete Assemblage in Relation to Natural Environmental Variation and Anthropogenic Stress

ZAN Xiaoxiao, ZHANG Chongliang, XU Binduo*, XUE Ying, and REN Yiping

College of Fisheries,Ocean University of China,Qingdao266003,P. R. China

Polychaete are diverse species of the soft-bottom community, and are often used as indicators in environment monitoring programs. However, the effects of anthropogenic activities and natural environmental variation on polychaete assemblage are rarely addressed. The goals of this study are to identify the effects of natural environmental variation and anthropogenic stress on polychaete assemblage, and to explore the relationship between the polychaete assemblage structure and anthropogenic stress without considering the natural environmental variation. Based on the data collected from the surveys conducted in the tidal flat of Jiaozhou Bay, the relationship between polychaete assemblage structure and environmental variables was determined using multivariate statistical methods including hierarchical cluster analysis, multidimensional scaling (MDS) and canonical correspondence analysis (CCA). The results showed that the polychaete assemblage was dominated by two species,Amphictene japonicaandHeteromastus filiformis, and could be divided into two subgroups characterized by high and low species abundance. CCA illustrated that the natural environmental variables including water temperature and the distance from coast had primary effects on the polychaete assemblage structure; while stress of contaminants, such as As and Hg, had the secondary influences; and stress from the aquacultured species, mainlyRuditapes philippinarum, had a limited effect. Colinearity between the natural environmental variables and anthropogenic stress variables caused a critical divergence in the interpretation of CCA results, which highlighted the risk of a lack of information in environment assessment.Glycinde gurjanovae,Sternaspis scutataandEulalia bilineatamay serve as the ‘contamination indicators’, which need to be confirmed in future studies.

polychaete; tidal flat; anthropogenic stress; contaminant; aquaculture; multivariate analysis; canonical correspondence analysis (CCA)

1 Introduction

Polychaete are critical functional components in softbottom marine ecosystems (Hutchings, 1998), which contribute considerably to the marine benthic food webs and influence the biogeochemical processes and stability of sediment significantly (Aller, 1983; Rhoadset al., 1985; Yeunget al., 2013). These taxa contain both sensitive and tolerant species in an environmental gradient from pristine to heavily disturbed habitats (Pearson and Rosenberg, 1978; Pocklington and Wells, 1992), and they are relatively sedentary, thus can hardly migrate to avoid contaminants (Grayet al., 1992). Polychaete can serve as useful indicators of environment conditions (Gambi and Giangrande, 1986; Inglis and Kross, 2000; Samuelson, 2001; Neaveet al., 2013), and have been extensively applied in environment monitoring programs on coasts (Cremaet al., 1991; Solis-Weisset al., 2004), especially in organic contaminants monitoring projects (Giangrandeet al., 2005; Caiet al., 2013).

The effects of anthropogenic activities on the polychaete assemblages have been discussed extensively (e.g., Woodin, 1976; Kaiseret al., 1996; Beadmanet al., 2004; Neaveet al., 2013; Daffornet al., 2013). However, the natural environmental variables, such as sediment and hydrological conditions that influence the structure of biotic community significantly, are often ignored in some studies, making the interpretation of anthropogenic influences on community and ecosystem confusing (e.g., Pearson and Rosenberg, 1978; Warwick, 1986; Aschan, 1990; Lotzeet al., 2006; Halpernet al., 2008). Estuaries and coasts are sensitive to anthropogenic stress, and natural environmental gradient such as salinity, temperature and sediment are often highly heterogeneous, which impose additional challenges to effective ecological management (Aschan, 1990; Ferraroet al., 1991; Magalh?es and Barros, 2011).

Historically, the tidal flat of Jiaozhou Bay was a suitable habitat for many wild organisms, and over 300 benthic animal species were recorded (Liu, 1992). In recent years, the tidal flat has been suffering from agricultural and industrial pollutions, coastal engineering constructions, and aquaculture activities, such as bivalve cultiva-tion (Yanget al., 2006). Assessing the relationship between polychaete assemblage distribution and the anthropogenic stress and natural environmental variation will provide insights into the community successions, as well as improve the understanding of the ecological effect of human activities.

The objectives of this study are to reveal the relationship between polychaete species distribution and the disturbance from contaminants and aquaculture activities, and to test whether the interpretation of the relationship remained consistent with and without sufficient information of natural environmental variation. The roles of natural environmental variation and anthropogenic stress played in driving polychaete assemblage structure were identified by canonical correspondence analyses (CCA). The influence of colinearity among environmental variables was determined and removed through principal component analysis (PCA) by testing the robustness of CCA in dealing with colinearity. The correlations of individual species with anthropogenic stress were also explored for ecological interpretation of identified pattern.

2 Materials and Methods

2.1 The Studied Area and Data Collection

Jiaozhou Bay is a temperate and semi-closed bay in the western Yellow Sea of China, which has regular semidiurnal tides and a flat seabed (Liu, 1992). Dagu River runs into the Bay, bringing plenty of organic detritus and various contaminants discharged by the industrial and agricultural activities on the upper reaches of the river into Jiaozhou Bay (Hanet al., 2010). On the northwestern Jiaozhou Bay lies a broad tidal flat,i.e., an intertidal, non-vegetated, soft sediment wetland between mean high water and mean low water lines of spring tides (Dyeret al., 2000). The tidal flat is a suitable habitat for many wild lives. In the surveys carried out in early 1980s, about 300 benthic animal species have been recorded (Liu, 1992).

In February, May, August and November, 2009, the macrobenthos in the tidal flat of Jiaozhou Bay were investigated. Seven transects were set on the flat in the direction of tides, and three sampling stations each transect were predetermined in the supertidal, intertidal and subtidal zones, respectively, and there were 21 sampling stations in each month in this study (Fig.1).

Fig.1 Seven sampling transections set for polychaete survey in the tidal flat of Jiaozhou Bay.

In order to make sure that the sampling size results in reliable estimation of species composition, a simulation study was conducted, aiming to examine the effect of sample size on the species richness and the mean abundance of polychaete. The results indicated that the stability of the species composition increased when the sample size was enlarged, and the sample size (21 sampling stations, 84 samples in total in four months) currently used was believed to be enough for estimating mean abundance and species richness of polychaete species (Fig.2).

Fig.2 The effects of sample size on the abundance and species composition of polychaete.

The sediment (30 cm in thickness) was collected with a box corer of 25 cm×25 cm without disturbance. Three cubes of sediments (i.e., 1875 cm2in surface area) were collected each station and pooled together. The polychaete were sorted out with species identified by referring to the taxonomic standards.

Natural environmental variables including temperature (T), salinity (S), acidity (pH), dissolved oxygen (DO) and distance from coast (Dis) were obtained along with the polychaete sampling (Table 1). Contamination variables consisted of contents of heavy metals including mercury (Hg), copper (Cu), lead (Pb) and cadmium (Cr), and the non-metallic arsenic (As), dichloro-diphenyl-trichloroethane (DDT), benzene hexachloride (BHC), sulfide (Sul) and organic carbon (OC). Aquaculture variables representing aquaculture activity included the abundance indices of three aquacultured bivalve species,Ruditapes philippinarum,Potamocorbula laevisandSinonovacula constrictacultured in the tidal flat and shallow water of Jiaozhou Bay.

Table 1 Summary of measured environmental variables in the polychaete surveys in the tidal flat of Jiaozhou Bay

Temperature, salinity, pH and dissolved oxygen were measured using a portable measurement device (‘HANNA’Instruments), and the metal contaminant was measured by ICP-AES (Inductively Coupled Plasma Atomic Emission Spectrometry). The contents of arsenic, sulfide, and organic carbon were measured using chemical titration method. DDT and BHC were measured using gas chromatograph method. The sampling and analysis of the environmental variables followed the regulations of The Specification for Marine Monitoring (GB17378-2007) (State General Administration for Quality Supervision and Inspection of China and Quarantine of Standardization Administration of China, 2007).

2.2 Data Processing

The relationship between polychaete assemblage and environmental variables were examined with canonical correspondence analysis (CCA), a method of nonlinear multivariate gradient analysis using a unimodal model (ter Braak, 1986). There were five natural environmental variables, ten contamination variables and three aquaculture variables in the analysis, and survey season was an extra dummy variable considering the seasonality of the polychaete assemblage. The contamination and the aquaculture variables were log-transformed (log(X+1)) to reduce the skew in their distributions, and the abundance of polychaete was also log-transformed (log(N+1)) to deemphasize the influence of dominant species (ter Braak, 1986).

In order to identify the roles of the natural environmental variables and anthropogenic stress in structuring the polychaete assemblage, CCA based on all the 19 variables was used to explore the relationship between the polychaete assemblage and environmental variables. At the same time, CCA with 13 anthropogenic variables was applied to analyze the effect of lacking information of natural variability. In addition, partial-CCA was used to identify the residual effects of contamination and aquaculture by removing the variation of natural environmental variables; and Spearman’s correlation coefficient and principal component analysis (PCA) were used to examine the effect of collinearity among different environmental variables. Main PCs with the eigenvalues ＞1 were used to interpret the relationship between polychaete assemblage and environmental variables (Rakocinskiet al., 1997).

A forward selection procedure conducted by Monte Carlo permutation test was employed to identify the subset of environmental variables that significantly improved the explanation of CCA model (ter Braak, 1988). The primer variables driving the polychaete assemblage were identified in the CCA plot, and the correlations of individual species with contaminants and aquaculture variables were examined according to the ordination results (ter Braak, 1986; Kremen, 1992; Rakocinskiet al., 1997). These processes were conducted using CANOCO 4.5 software (ter Braak and Smilauer, 2002).

3 Results

3.1 Roles of Different Environmental Variables in Structuring Polychaete Assemblage

The 24 polychaete species belonging to 14 families were identified in the surveys in the tidal flat of Jiaozhou Bay.Heteromastus filiformisandAmphictene japonicawere the two most abundant species in the polychaete assemblage (Table 2).

Table 2 Taxa of polychaete occurring on the tidal flat of Jiaozhou Bay

(continued)

Cluster analysis and MDS showed that the polychaete assemblage in the flat of Jiaozhou Bay could be divided into 2 station groups. The composite species, especially in terms ofAmphictene japonicaandHeteromastus filiformis, in station group 1 and station group 2 had low and high abundance in each sampling station, respectively (Fig.3). The first CCA with all the environmental variables showed that the polychaete assemblage was mainly driven by the natural environmental variables including T, Dis, DO and survey seasons (Fig.4). T was highly correlated with the second axis of CCA diagram (r= -0.60); Dis had strong correlation with the first axis (r= 0.55); DO was strongly related with the second axis (r= 0.49). The influence of contaminant and aquaculture was minor, with As, Hg and Rp having secondary influence on the assemblage. Other variables showed low correlations with both CCA axes (Fig.4). The eigenvalues of the first two canonical axes were 0.45 and 0.34, and the cumulative percentage of the species-environment relationship explained by the two axes in CCA was 37.1%. Monte Carlo permutation test showed that only T, Dis, and As improved the model significantly (P＜ 0.05). The distribution of the sampling stations showed an aggregative pattern of seasonality, which indicated the strong seasonal variation in the environmental variables and polychaete assemblage.

Fig.3 Analysis of polychaete assemblage structure using hierarchical cluster and MDS methods. The labels in numbers represent sampling seasons and stations (e.g., in 0521, 05 represents the survey in May, and 21 means the first station in transect 2). a) Cluster analysis showed that the assemblage could be divided into 2 station groups; b) and c) MDS showed the similarity of species compositions. The dotted line in b split sampling stations into two subgroups corresponding to the cluster analysis; the size of the symbols in c represented the relative abundance ofAmphictene japonicaandHeteromastus filiformisat each station.

Without information of the natural environmental variables, the effects of anthropogenic stress on the polychaete assemblage showed similar patterns,i.e., Hg, As and Rp had primary influence on the structure of the assemblage (Fig.5). However, the eigenvalues of the first two canonical axes were 0.30 and 0.24, and the cumulative percentage of species-environment relationship explained by the two axes was 43.0%. The variables selected by Monte Carlo permutation tests showed that only Hg, As and Rp improved the CCA model significantly (P＜ 0.05).

Fig.4 Diagram of canonical correspondence analysis (CCA) with all 18 environmental variables. The environmental variables were expressed as arrows, species were indicated by triangles, and sampling stations were indicated by circles (survey months were marked in different colors). The environmental variables were listed in Table 1 while the abbreviations for species were shown in Table 2.

Partial CCA removed the influences of the significant natural environmental variables like T and Dis, and provided a different view of the species-environment relationships (Fig.6). The influences of Rp, As and Hg on the polychaete assemblage decreased significantly. The contamination variables, Hg, Pb, Sul and DDT had similar influences on the assemblage, and aquaculture variables had minor influence. The cumulative percentage of species-environment relationship of the first two axes improved a little to 44.5%, while the eigenvalues of the axes decreased to 0.275 and 0.179, respectively. Monte Carlo tests showed that only As had significant influence on the assemblage(P＜ 0.05).

Fig.5 Diagrams of CCA with only contamination and aquaculture variables. The environmental variables were expressed as arrows while species were indicated by triangles. The environmental variables were listed in Table 1 while the abbreviations of species were shown in Table 2.

Fig.6 The results of partial CCA treating the significant natural variables (temperature and distance from coast) as covariables. The environmental variables were expressed as arrows while species were indicated by triangles. The environmental variables were listed in Table 1 while the abbreviations of species were shown in Table 2.

3.2 Robustness of CCA to Colinearity

Significant correlations were revealed by Spearman’s correlation coefficients among variables, such as Cu, Zn and Pb, and among T, DO, pH among others (Table 3). PCA generated five composite variables with eigenvalue＞1, explaining 71.0% of the total variation (Table 4). The variables loaded differently on each PC. PC1 was highly loaded by T and DO; PC2 was primarily loaded by con-tamination variables, such as Zn and Pb; PC3 had high loading scores of S and Dis; PC4 and PC5 were highly loaded by the three aquaculture-related variables.

CCA was conducted using PCs as environmental variables to verify the former CCA analysis (Fig.7). PC1 and PC3 that mainly reflected the natural environmental gradient had a primary influence on the assemblage structure; PC2 and PC4 that were highly loaded by the contamination and aquaculture stress variables had minor influences. Eigenvalues of the first two axes were 0.273 and 0.249, respectively, and the cumulative percentage of speciesenvironment relationship increased to 66.5% when this method was used. The results were consistent with CCA using the individual environmental variables. Monte Carlo test showed that PC1, PC3 and PC4 improved the model significantly (P＜ 0.05).

Table 3 Correlation matrix of the 17 environmental variables obtained with Spearman method

Fig.7 Canonical correspondence analysis using the first 5 PCs from PCA. PC1 and PC3 had primary effects on the assemblage structure; PC2 and PC4 had minor influences.

3.3 Relationship between Distribution of Individual Species and Anthropogenic Stress

The effects of contamination and aquaculture variables on the distribution of individual polychaete species were examined based on the partial CCA (Fig.6), and the individual species was defined as ‘sensitive’ or ‘tolerant’species accordingly (ter Braak, 1986; Rakocinskiet al., 1997). The ‘potential indicator species’ to contaminants suggested in this study were in the lower quadrant of Fig.6, includingGg(Glycinde gurjanovae),Ss(Sternaspis scutata) andEb(Eulalia bilineata). On the contrary,Gs(Glycera subaenea),Ev(Eulalia virides), andMs(Mar-physa sanguinea) were suggested as ‘sensitive species’ to contaminants. The species located at the top right quadrant includingLh(Lumbrineris heteropoda),Nj(Neanthes japonica),Db(Diopatra bilobata) andGr(Glycera rouxii) were the tolerant species to the aquaculture variable Rp (Fig.6). The dominant speciesH. filiformiswas moderate to all anthropogenic variables, andA. japonicawas moderate to all contamination variables but sensitive to Rp.

Table 4 Principal component analysis of the 18 environmental variables

4 Discussion

This study revealed the relationships between polychaete assemblage distribution and the natural environmental factors, contaminants and stresses from aquaculture activities, respectively, in the tidal flat of Jiaozhou Bay. The natural environmental variables including water temperature and distance from coast had primary effects on the polychaete assemblage; stresses from contamination, such as As and Hg, had the secondary effect on the polychaete community structure; stresses from the aquaculture species, mainlyR. philippinarum, had a limited influence on the polychaete assemblage. The CCA without natural environmental information showed similar patterns with the results from the full-information model, but it was significantly different from the result of partial-CCA, in which the effects of aquaculture variables was mainly offset by water temperature and distance from coast, while the influence of contaminant variables was also reduced (Figs.5 and 6). In nature, the real effects of the highly correlated variables,e.g.,R. philippinarumand distance from coast on the polychaete assemblage in our study were difficult to identify unless specific experiments were conducted to define the cause-and-effect linkage. However, partial-CCA with natural environmental information was a conservative method considering the type I error, and the comparison of the two approaches provided meaningful insights into assessing the effects of anthropogenic stresses on the polychaete assemblage. It was suggested that the correlation among environmental variables could be properly dealt with in CCA (ter Braak, 1988). In our study, the CCA without natural environmental information showed similar patterns with the full-information model, and the CCA with PCA method showed a consistent pattern, which confirmed the robustness of CCA in calculation for dealing with collinearity (Figs.4, 5, and 7). However, it also illustrated that colinearity caused critical divergence in interpretation. Such a divergence was found in many other studies (Pearson and Rosenberg, 1978; Rakocinskiet al., 1997; Labruneet al., 2007). Here, we took a conservative opinion to make judgment of the CCA method, and highlighted the risk of lacking natural environmental information.

4.1 Gradient Analysis Methods

There have been many existing literatures on the topic of polychaete in the deep water of Jiaozhou Bay. For example, Bi and Sun (1998) explored the biomass, density, diversity and species composition of polychaete in Jiaozhou Bay, indicating that the factor governing the distribution of polychaete was sediment type, and the harvest ofR. philippinariumled to a low diversity of polychaete during summer. Wanget al. (2006a) examined the distribution of dominant polychaete concerning sediment habitat, and the relationship between quantitative distributions of benthic polychaete Annelida and environmental variables in Jiaozhou Bay (Wanget al., 2006b). These studies mainly focused on the polychaete in deep water of this bay. The relationship between polychaete and the natural environmental variation and anthropogenic stress in the tidal flat of Jiaozhou Bay was explored using the canonical corresponding analysis method. Indeed, diverse methods have been used to study the effects of pollution on biotic communities. As were reviewed by ICES (2004), the current methods on habitat quality assessment can be divided into three categories according to their complexities, including univariate indices (such as the Shannon-Wiener diversity and infauna trophic indices and abundance/biomass comparison curve) (Shannon and Weaver, 1949; Word, 1978; Warwick, 1986), multimetric indices (such as the biological quality index, the benthic index of biotic integrity and the benthic quality index) (Wilsonet al., 1985; Weisberget al., 1997; Rosenberget al., 2004), and multivariate methods (such as canonical correspondence analysis, multi-dimensional scaling and multivariate-AMBI (Kruskal and Wish, 1978; ter Braak, 1986; Clarke and Green, 1988; Borjaet al., 2004). However, Diazet al. (2004) and Borja and Dauer (2008) indicated that excessive indices were available presently, and suggested that testing the suitability of the existent indices was more important than developing new ones (Diazet al., 2004; Borja and Dauer, 2008). Multivariate methods were quite suitable for ecological analysis because of their capabilities of simplifying datasets with limited information loss (ICES, 2004). CCA was proved to be a powerful tool in estimating responses of assemblage to the anthropogenic stresses (Johnsonet al., 1993).

The heterogeneity of sediment and hydrology in a small scale might be the main reasons for the variation in the spatial distribution of polychaete (Archambault and Bourget, 1996; Mannino and Montagna, 1997; Kendall and Widdicombe, 1999; Fraschettiet al., 2005). It should be pointed out that the data we used to conduct CCA had their limitation in this study. The relationship between the assemblage and the environmental variables may be underlined by the heterogeneity in the spatial scale in our surveys. The natural environmental variables involved in the study only included hydrological variables without sediment characteristics. The sediment feature such as sediment type and particle size were measured simultaneously with other environmental factors, but the variation of sediment composition was limited. The mean particle size was 24 ± 9 μm, and the sorting coefficient (the ratio of the particle sizes corresponding to 25% and 75% percentage in the accumulate curve of sediment size frequency) was 2.1 ± 0.17. The sediment type was believed to be homogeneous and was excluded from analysis.

4.2 Effect of Contamination

Potential ‘contaminant indicator species’ suggested by our study consisted ofG. gurjanovae,S. scutataandE. bilineata, though these ‘indicator species’ need further verification.S. scutatawas a deep burrowing depositfeeder and had a low growth rate and a long life span, which was supposed to be the characteristics of pollution tolerant species (Salen-Picardet al., 2003). Although the other two species,G. gurjanovaeandE. bilineata, were mentioned in some studies (Jensen and Bender, 1972; Limet al., 2006; Zhanget al., 2013), their ecological features have not been studied explicitly.

The dominant species,H. filiformisresponded moderately to all the contamination variables. The species was a small-sized deposit feeder, and was abundant in the estuary with rich organic detritus (Freyet al., 1987). The ecological role ofH. filiformishas been widely examined (Aller and Yingst, 1985; Neira and H?pner, 1994; Mulsow and Landrum, 1995). It was an extreme pollution tolerant species (Rakocinskiet al., 1997), and was regarded as an opportunistic species in many studies (Buchananet al., 1974; Virnstein, 1979). In addition, Brage (1985) illustrated thatH. filiformiscould tolerate copper, and could live in the copper-polluted stations. However, there were some studies with different results,e.g., Olsgard (1999) showed that there was no significant correlation between sediment concentration of copper and abundance ofH. filiformis. A possible explanation for the divergence was that the range of copper concentration differed in these two studies. The copper in low content might not cause significant effect on most of the polychaete, while copper in high content could restrain the lives of many species, thus created wider ecological niche for tolerant species.

4.3 Effect of Aquaculture

Aquaculture variables, mainlyR. philippinarum, had an important effect on the polychaete assemblage in the normal CCA, but they had limited influence in partial CCA (Figs.4 and 5). The effect was mainly offset by water temperature and distance from coast due to the significant correlation mentioned above. Meanwhile, the relationship between polychaete and the variables was different from the former results. The dominant species,A. japonica, was sensitive to Rp in partial CCA but was not in the other CCA results. As the method revealed the correlation rather than the causality, the true effect remained to be analyzed. Despite the divergence,D. bilobatashowed positive correlation withR. philippinarumin the three CCA diagrams consistently.

Previous studies claimed that bivalve cultivation had a positive effect on polychaete. For instance, the biodeposition effect of clams increased food supplements for deposit feeding worms (Kaiseret al., 1996; Spenceret al., 1996; Hartstein and Rowden, 2004). However, many studies presented contrary evidences, illustrating that the intensive aquaculture activities led to the reduction in species richness. Woodin (1976) suggested that the suspension-feeding bivalves could have a negative effect on the recruitment of infaunal species due to the predation effect by filter feeding. The abundance of cirratulids declined strongly with increasing mussel surface area, suggesting that monocultures of mussels might alter the infaunal benthic community by providing a complex habitat, input of organic materials and larval removal through filter feeding (Beadmanet al., 2004; Dumbauldet al., 2009). In effect, bivalve aquaculture had both positive and negative effects on the polychaete assemblages, and the resultant influences would depend on the strength of the two sides in different spatial scales. We proposed the hypothesis that the effect of increasing food resources by bivalves might be strong in small scales, while the effects of reducing recruitment might be overwhelming in a larger scale. Further study is necessary to examine its validity.

Acknowledgements

This study was financially supported by the Public Science and Technology Research Funds Projects of Ocean (Nos. 201305030, 200805066). We are grateful to Prof. Jufa Chen from the Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, for providing the hydrological and sediment data for the surveys. We also appreciate Profs. Zishan Yu and Xiaoqi Zeng from the Ocean University of China for their helps in identifying some species that were difficult to be identified.

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(Edited by Qiu Yantao)

(Received April 21, 2014; revised July 14, 2014; accepted March 23, 2015)

? Ocean University of China, Science Press and Spring-Verlag Berlin Heidelberg 2015

* Corresponding author. Tel： 000086-532-82031319 E-mail： bdxu@ouc.edu.cn