Jean-Louis Combes

Patrick Guillaumont

Gilles Nancy

Gilles Sanon

The trade-growth nexus in the developing countries: a quantile regression approach

Keywords: JEL Classification: C23, F13, 011 Quantile regression, trade-growth nexus, developing countries

This paper applies quantile regression techniques to investigate how the impact of trade openness on the growth rate of per capita income varies with the conditional distribution of growth. Using formal robustness analyses, we first identify robust variables affecting economic growth (investment, government balance, terms of trade, inflation, and population growth) which we then use as controls in the quantile regression estimations. Our findings suggest a heterogeneous trade-growth nexus: for both the short and the long run, the effect of openness on growth is higher in countries with low growth rates compared to those with high growth rates.

Introduction

The quest for growth-particularly in the developing and emerging countries-has resulted in a large volume of the literature since the early 1990s. Until the last two decades, it was a widely accepted view that among the driving factors of long-run growth, trade plays an important role in shaping economic and social performance. Policy recommendations based on export-led growth and trade liberalization have been at the heart of poverty reduction strategies for many years, and developing countries were encouraged to reduce trade barriers in order to benefit from their comparative advantage. Theoretical foundations of the positive links between trade openness strategies, growth and poverty reduction come at least from two sources. On the one hand, the neoclassical approach explains the gains from trade liberalization by comparative advantages, be they in the form of resource endowment (as in the Heckscher-Ohlin model) or differences in technology (as shown by the Ricardian model). On the other hand, the endogenous growth literature asserts that trade openness positively affects per capita income and growth through economies of scale and technological diffusion between countries. 2The empirical investigation of these theoretical foundations points to a large variation in the distribution of the benefits from trade openness to growth or to economic development. Some Asian and Latin American countries that managed to develop export-based strategies have been rewarded with high economic growth, while African countries have remained trailing behind despite efforts to emulate the export-led growth model. To explain this, one strand of the literature emphasizes the conditional aspect of the trade-growth link: trade openness may not be conducive to growth in the absence of an appropriate economic, social and political environment. Following [START_REF] North | Institutions, Institutional Change and Economic Performance[END_REF], some argue that institutional arrangements (governance and policies), market institutions (bureaucracy and competition) and social norms determine the degree to which trade openness contributes to higher income and growth (see, among others, [START_REF] Dollar | Institutions, Trade and Growth[END_REF]). Others, including [START_REF] Krugman | Rethinking International Trade[END_REF], state that the expansion of growth augments a country's income once the increase in the determinants of growth (capital, labor, education, and infrastructure) are taken into account, suggesting the possibility of various trade-growth relationships under different economic and social environments.

The hypothesis of a heterogeneous trade-growth nexus conditional on the country's structural characteristics has received some theoretical support. For instance, drawing on the experience of some developing countries, [START_REF] Devarajan | Trade Liberalization in Developing countries: Do Imperfect Competition and Scale Economies Matter?‖[END_REF] use a general equilibrium model to show that trade liberalization can be either welfare-augmenting or welfare-reducing in the presence of imperfect competition or increasing returns. Also, [START_REF] Young | Learning by Doing and the Dynamic effects of International Trade[END_REF] shows that growth can be higher for a country under autarky than under free trade, and [START_REF] Rassekh | The Interplay of International Trade, Economic Growth and Income Convergence: A Brief Intellectual History of Recent Developments[END_REF] provides an overview of theoretical models showing that growth effects from trade openness can be either positive or negative across countries. Despite the theoretical interest, however, empirical investigation of this hypothesis has not yet received enough attention.

In this paper, we pay special attention to the question of heterogeneity of the trade-growth link. More specifically, we test the conjecture that differences in the trade-growth nexus originate from fulfilled or unfulfilled internal preconditions, most of which relate to the domestic factors of economic growth (productive infrastructure, human capital, efficient investment, and factor productivity). Various methods have been used to account for non-linearities in the context of the trade-growth relationship, The remainder of the paper is structured as follows. Section 2 discusses the empirical framework by presenting the model and the econometric methodology in the context of the quantile regression approach, and the robustness analysis to identify the growth determinants. Section 3 presents the results from the quantile regressions and offers some policy recommendations. Section 4 contains some robustness tests and sensitivity analysis. Section 5 concludes.

The empirical framework

Our empirical investigation is centered around the specification of a growth regression which includes trade openness and other determinants as explanatory variables. This equation applies to a pool of countries i (i = 1,…, N) observed over T periods (t = 1,…, T) as follows:

it it it i it ε OPEN y Z c ) log( ( 1
)
where y is the real GDP per capita, OPEN is an indicator of trade openness and Z is a vector of contemporaneous and lagged values of growth determinants (including the lagged value of y). The double index it refers to a country i observed at time t, and c is a vector of individual fixed effects. 4We are interested in estimating Equation ( 1) in a way that identifies differences in the response of the per capita income growth rate to changes in trade openness5 and other growth determinants for countries at various points of the income distribution. First, the application of quantile regression techniques allows the investigation of possible parameter heterogeneity across the conditional distribution of growth rates. Second, to address the concern of model uncertainty in the specification of the variables Z in Equation ( 1), we conduct a formal robustness analysis using BMA. Finally, we address the endogeneity of the explanatory variables. We present all three parts of our approach below.

Quantile regression principle

The majority of the current empirical literature on the trade-growth nexus-based on ordinary least squares (OLS), generalized least squares (GLS), instrumental variables (IV), generalized method of moments (GMM), or matching estimators-assumes parameter homogeneity. 6Consider the linear model:

it it it v Y ' X (2)
where Y is the dependent variable and X is a vector of explanatory variables. The essential feature of a regression analysis is to examine the manner in which a set of explanatory variables affects the conditional distribution of a dependent variable. In the classical econometric techniques (OLS, GLS, IV, and GMM), the component around which the dependent variable randomly fluctuates is the conditional mean , / X Y E . However, assuming homogeneous elasticities across countries could lead to an over-fitting of conclusions based on the global population of economies to a particular subset of interest. Speaking differently, an empirical regularity found (say, a beneficial impact of openness on growth) may differ in its relevance for certain subsets of countries if there is a heterogeneity bias underlying the data.

In our investigation, the use of quantile regressions improves upon the usual techniques by uncovering patterns in which the effect of trade openness, conditional on other growth determinants, varies across countries. Unlike the classical approach-which amounts to estimating the conditional mean of the conditional distribution of Y-the quantile estimator is employed on different quantiles of the conditional distribution.7 Let ) (y F be the cumulated distribution function of Y. The th quantile of Y is defined as the smallest y satisfying y F

. In a regression context, it can be shown that finding amounts to obtaining the following estimator of :

it it it T i it v H H v v v 1 ) ( , min arg ˆ1 (3)
where it v is the vector of residuals with positive values and 0 otherwise, it v is the vector of negative residuals and 0 otherwise. Therefore, we have as many estimators of (and quantile regression estimates ) as values of ) 1 , 0 ( by changing the -representative‖ individual. The latter can be the mean (as in OLS), the median ( 5 . 0 ) or any other quantile. Koenker and Bassett (1978) derive asymptotic normality results for the quantile regression and show that:

1 2 ) ( ) 1 ( , 0 ˆJ s N T (4)
where

T J T / lim ' X X (5) )) ( ( / 1 1 F f s (6)
While the estimation of is quite simple and requires the use of simplex algorithms (see D'Orey and [START_REF] D'orey | Algorithm AS 229: Computing Regression Quantiles[END_REF]), the estimate of the residual variance and the standard error of the estimated parameters is more complicated, since it requires the estimation of the unknown probability distribution function of y and its derivative. The latter is necessary to estimate the quantile density function s , also called the sparsity function. Computation of the coefficient covariance matrices is an important part of quantile regression analysis and various approaches are available: bootstrap resampling methods, direct methods based on Siddiqui difference coefficients and kernel density (see [START_REF] Koenker | Confidence Intervals for Regression Quantiles‖ in Asymptotic Statistics[END_REF][START_REF] Bassett | Tests of Linear Hypotheses and l1 Estimation[END_REF][START_REF] Koenker | Confidence Intervals for Regression Quantiles‖ in Asymptotic Statistics[END_REF]).

We use the quantile regression methodology to estimate [START_REF]Robust standard errors in parentheses. *** significant at 1%, ** significant at 5%[END_REF] and investigate the possibility of differences between the elasticities of high-and low-growth countries. From a methodological viewpoint, Koenker (2004) points out that quantile regressions in panel data with a large number of cross-sectional units and relatively short time periods may result in a large number of individual fixed effects and can alter the estimation of the other parameters of interest. Various methods have been proposed to address this problem. For instance, [START_REF] Chay | Evaluating the Impact of the 1964 Civil Rights Act on the Economic Status of Black Men Using Censored Longitudinal Earnings Data‖[END_REF] suggests implementing quantile regressions on several cross-sections, and Koenker (2004) proposes the use of penalty methods in order to regularize or shrink these individual effects towards a common value. In this paper, we adopt a methodology that combines the preceding procedures by shrinking the individual fixed effects to a subset of them that are significant. This implies considering many different combinations of the fixed effects, dropping those which are significant and keeping the others in the regressions yielding the highest -corrected‖ pseudo-R-squares (that is a pseudo-R-squares penalized by the number of regressors). This iterative procedure yields better results than shrinking all the fixed effects to a common intercept.

We pay particular attention to the selection of the control variables Z in (1), as well as concerns of possible endogeneity of the regressors (see Sections 2.2 and 2.3 below, respectively). In particular, we are interested in the causal link between openness and growth, more specifically in the behavior of the coefficient in Equation ( 1) for three quantiles ( 75 . 0 , 5 . 0 , 25 . 0

). In addition, we examine the manner in which the trade-growth nexus differs at the tails of the conditional distribution by considering graphs corresponding to different quantiles between the 10th and the 90 th . Finally, we investigate how openness alters the conditional distribution of growth in developing countries, by simulating an openness shock and comparing the growth probability density function using the original data and the distribution of the forecasted level of growth that emerges following the simulated shock.8

Selection of the control variables

Model uncertainty-initially pointed out by [START_REF] Leamer | Specification Searches: Ad Hoc Inference with Non-experimental Data[END_REF] and later elaborated by [START_REF] Brock | Growth Empirics and Reality[END_REF]-arises because the lack of clear theoretical guidance and trade-offs on the choice of regressors result in a broad number of possible specifications, and often contradictory conclusions. A coherent mechanism to address the problem of model uncertainty is provided by BMA.9 BMA involves averaging over all possible combinations of predictors when making inferences about the quantities of interest. Unlike the classical approach which conditions on a single model, in BMA no single model is assumed to be the -true‖ model. Instead, all possible models are assigned different probabilities based on the researcher's prior beliefs using the posterior model probabilities as weights. The lack of consensus on the key determinants of growth has recently led some scholars to formally incorporate model uncertainty through the use of BMA in the empirical growth analysis. Fernández, Ley and Steel (2001a) (henceforth, FLS), [START_REF] Brock | Growth Empirics and Reality[END_REF], Brock et al. (2003), Sala-í-Martin, Doppelhofer, and[START_REF] Sala-Í-Martin | Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach[END_REF] (henceforth, SDM), [START_REF] Tsangarides | Growth Empirics under Model Uncertainty. Is Africa Different?[END_REF], [START_REF] Masanjala | A Rough and Lonely Road to Prosperity: A Re-Examination of Sources of Growth in Africa Using Bayesian Model Averaging[END_REF], and [START_REF] Mirestean | Growth Determinants Revisited[END_REF] have used BMA techniques to investigate cross-country growth determinants.

We apply both the FLS and SDM techniques to identify robust growth determinants Z for [START_REF]Robust standard errors in parentheses. *** significant at 1%, ** significant at 5%[END_REF]. Note that the analysis is implemented at the conditional mean of the distribution-and not on each quantile-because our main purpose is the identification of the differentiated effect of openness on growth given the same set of explanatory variables across the quantiles. 10 If trade openness is found to be a robust determinant of growth and if the control variables are allowed to differ across countries and/or quantiles, it would be very difficult to identify whether we are finding different effects of openness on growth due to the presence of different control variables, or the impact of openness.11

To identify the robust growth determinants, we use the data set of [START_REF] Tsangarides | Growth Empirics under Model Uncertainty. Is Africa Different?[END_REF] which covers a cross-section of countries using data on five-year averages within the period 1960-2005. This data set is different to that used in the later regression analysis in two ways, namely, the longer time period (1960-2005 versus 1980-2006) and the use of five-year averages rather than the annual data we employ in our regression analysis. The use of these different data sets deserves some justification. The use of the longer time-series for the BMA analysis gives us a data set with more variation both across and within countries, which should help in obtaining the true robust growth determinants. The use of five-year averages rather than annual data is intended to remove the importance of shocks from the data, allowing us to concentrate on the underlying determinants of growth. In our regression analysis however we use annual data, which allows us to include dynamic effects in our model and account for endogeneity. Given that one may expect that the determinants of short-(e.g., annual) and medium-run (e.g., five-year averaged) growth are likely to be similar, the use of data at these different frequencies need not be seen as a crucial problem in our study.

We consider two simulations of 22 potential explanatory variables to arrive to a set of robust growth determinants. The two simulations differ (marginally) in the set of determinants to further examine robustness, mainly the inclusion of the Sub-Saharan Africa dummy, which is an attempt to control for potential heterogeneity in the sample. The list of robust variables is virtually unchanged between the two simulations.

Table 7 in Appendix A presents the results of our robustness simulations. A variable is identified as a robust determinant if (i) the data favors its inclusion (e.g., its posterior inclusion probability is higher than 0.50, the unconditional prior for each variable), and (ii) the estimated coefficients have stable signs (e.g., the -sign certainty‖ is above 0.90). Appendix A provides some background on BMA and outlines our methodology, while Appendix B gives a description of the determinants used and their sources. The results can be summarized as follows. First, we identify a robust effect of the -Solow determinants‖, namely, population growth and investment, and the initial level of per capita GDP capturing conditional convergence, as well as a few policy variables, such as trade openness and the government's balance as share of GDP. It is indeed reassuring that the openness variable (our key variable of interest) is identified as a robust determinant, which further underlines its importance. In addition, while we identify life expectancy, the tropical area dummy, and ethnicity as robust, in the end, we drop these three variables because of data availability and due to the fact that some represent fixed effects. Finally, there is tentative robustness evidence for inflation rate and terms of trade (as in some cases conclusions vary with the choice of the prior), but we chose to keep them in the list of robust variables. 12 In the end, we retain seven variables, namely initial per capita GDP, log of real investment as share of GDP, government's balance in percentage of GDP, openness, population growth, terms of trade growth, and inflation rate.

Endogeneity of the explanatory variables

A typical concern in growth regressions is the issue of endogeneity. Indeed, some of the explanatory variables identified in Section 2.2 (namely, openness, investment, and government balance) could potentially be endogenous, and ignoring this potential endogeneity may bias the results. Earlier work in the quantile regression literature has examined situations where the explanatory variables were potentially endogenous. For example, [START_REF] Amemiya | -2 Stage Least Absolute Deviations Estimators[END_REF], [START_REF] Powell | The Asymptotic Normality of Two-stage Least Absolute Deviations Estimators[END_REF], and [START_REF] Chen | Two-Stage Regression Quantiles and Two-Stage Trimmed Least Squares Estimators for Structural Equations Models[END_REF] propose two-stage quantile estimators. Other approaches rely on IV and GMM quantile estimators, 13 and control functions. 14 To address the issue of endogeneity, we apply a two-stage quantile approach, where a quantile regression is implemented in both the first-stage estimation of the explanatory variables that are potentially endogenous (namely, openness, investment, and government balance) using the least absolute deviation, and in the second stage for the initial equation after plugging in the fitted values of the variables using first stage estimates. For our special variable of interest, openness, we get the required fitted values from a gravity model which we run separately. 15 For the period of our analysis , we follow [START_REF] Frankel | Does trade cause growth?‖[END_REF] and regress bilateral trade on a vector consisting of various gravity variables: distance, land border, membership of regional trade agreement, and dummy variables indicating bilateral free trade agreements, historical ties, sharing of a common currency. 16 Specifically, we estimate:

ijt ijt k ijt u b a T W ' 0 ) log( (7)
where ijt T stands for the value of bilateral trade between countries i and j at time t, ijt W is a vector of k gravity variables and ijt u is an error term. Appendix C presents the results of the gravity regressions. Following the literature, we consider several estimators (pooled OLS, pooled OLS with country effects à la [START_REF] Anderson | Gravity with Gravitas: A Solution to the Border Puzzle[END_REF], panel country-pair fixed effects, and Hausman-Taylor 17 ), to examine the robustness of the results. Estimated coefficients are in line with 12 As discussed later excluding them from the list of robust variables does not make a difference to the quantile regression results. 13 See, e.g., [START_REF] Abadie | Instrumental Variables Estimation of the Effect of Subsidized Training on the Quantiles of Trainee Earnings[END_REF], [START_REF] Honoré | On the Performance of Some Robust Instrumental Variables Estimators[END_REF], Chernozhukov and[START_REF] Chernozhukov | An IV Model of Quantile Treatment Effects[END_REF]2006), Chernohukov et al. (2007), and [START_REF] Sakata | Instrumental Variable Estimation Based on Conditional Median Restriction[END_REF]. 14 See [START_REF] Lee | Endogeneity in Quantile Regression Models: A Control Function Approach[END_REF]. 15 The other potentially endogenous variables, namely investment and government balance, are instrumented using their respective own lagged values in level and first-difference and by keeping only the lags that appeared to be significant in the first-stage regressions. We also consider the lagged values of the first-difference of the endogenous variable, as is current wisdom in the IV regressions. 16 For a complete description of the model structure, see [START_REF] Mirestean | Growth Determinants Revisited[END_REF]. 17 See [START_REF] Hausman | Panel data and unobservable individual effects[END_REF].

results in the literature, and confirm the importance of the -gravity variables‖ (including GDP, distance, currency union and free-trade area participation) on bilateral trade. Both OLS and countryfixed effects are likely to be biased, so we focus our attention on the country-pair fixed effects and the Hausman-Taylor estimates. While the Hausman-Taylor estimates are able to identify the effect of time-invariant determinants (which drop out with the country-pair fixed effects) and can control for the endogeneity of certain determinants, the estimated coefficients are sometimes sensitive to the choice of instruments. In the end, we derive our fitted values for openness from the country-pair fixed effects.18

Empirical results from quantile regressions

The data set for the quantile regressions includes annual data for 1980-2006 from a sample of 75 developing countries: 27 from Africa, 11 from Asia and Pacific, 25 from Central and Latin America, 9 Middle East and Arab countries and 3 from Europe (see Appendix D). We choose to focus on this sample as there is enough variation within the developing countries to allow meaningful analysis and also to allow for comparisons with results in the literature. To summarize our empirical approach, the explanatory variables are those selected from the robustness analysis described in Section 2.2.19 Also, as described in Section 2.3, to control for the possible endogeneity of trade openness we retain the fitted values of bilateral trade obtained from the gravity estimates, while for the two other endogenous variables, we consider the first-stage quantile fitted values of government balance and investment.

Estimation results

Our growth equation is estimated as an error-correction model in order to allow for both short-and long-run impacts. The short-run effects are measured by the coefficients of the contemporaneous exogenous variables. The long-run impact is measured by the sum of the coefficients related to the contemporaneous and lagged variables divided by the coefficient of the real GDP. Table 1 reports the estimation results for the whole period, with the 25 th quantile of growth distribution representing countries with low-growth rates and the 75 th quantile those with high growth rates. First, the impact of openness on the per capita GDP growth rate in the two-stage quantile regressions is very different between low-growth and high-growth countries, as the estimated 25 th quantile coefficient is at least twice that of the 75 th quantile. In addition, openness is associated with a 3-8 percent increase in growth in the short run, while in the long-run growth varies less than proportionally in the highgrowth countries (0.75), but more than proportionally in the low-growth countries (1.6). In other words, in the short run, a 1% increase in openness leads to a 0.03% increase in the real GDP in the high-growth countries, and this effect is more than doubled in the low-growth countries (the elasticity being equal to 0.08%).

A similar conclusion holds for the long-run impact where the elasticities fall in between 0.75 (highgrowth countries) and 1.6 (low-growth countries). These results suggest that the effect of openness is stronger for low-growth countries, both in the short and long run. Furthermore, comparing the twostage least-sum of absolute deviations (LAD) regression (50 th quantile) with GMM, it seems that the latter over-estimates the influence of trade in the short run, but under-estimates its impact in the long run. The other coefficients tend to be fairly similar across quantiles, with the notable difference of the variables capturing economic policies, namely inflation and government balance. Inflation is not significant for high-growth countries, while it significantly affects growth in low-growth countries.

Like trade, the effect of fiscal policy is roughly doubled when we compare the high-and low-growth countries. This would mean that the trade-growth nexus is stronger in those countries where the economic policies also drive the economic growth. Trade per se is not a factor of growth, but it is complementary to the effects played by the other determinants.

[Insert Table 1 here]

Figure 1 reports the impact of trade at different quantiles, showing graphically how the impact of openness falls in size as we move to higher quantiles, with the coefficient dropping by about 60 percent until the 40 th percentile and falling less slowly there after as growth increases. Interestingly, we see that the decline in the impact of openness on growth is steeper for the lowest quantiles and less strong for the highest ones. This would mean that the impact of trade on growth is stronger in the very poor countries, suggesting that the benefits from openness are the strongest for countries that have the smallest growth rates.

[Insert Figure 1 here] Next, we repeat the analysis of quantile regressions on two subperiods, namely 1980-1995 and 1996-2006. We break the sample period in this fashion to examine changes in the trade policy and liberalization during this period. In particular, the first subperiod of 1980-1995 was characterized by policies of trade and liberalization in many developing countries under structural adjustment programs supported by international financial institutions and the so-called -Washington consensus‖. However, up until the first half of the 1990s, these policies led to only short-lived recoveries in the Latin American countries and a few take-offs in Africa. During the second subperiod of the midnineties onwards there was a shift in the approach: trade policies were complemented by reforms putting a stronger focus on other macroeconomic and social policies including productivity-boosting reforms, spending on social programs, improving the investment climate, and the strengthening of institutions.

The results reported in Tables 2 and3 show that the conclusions obtained for the whole period in Table 1 are mainly driven by those of the second subperiod 1996-2006 (Table 3). For the subperiod 1980-1995, we find little evidence of any statistically significant correlation between trade openness and growth (Table 2). These results are in line with the stylized facts and empirical studies showing that trade openness based on first generation market-oriented policies have not lead to any economic boom in the developing countries (see [START_REF] Rodrik | Trade Policy and Economic Performance in Sub-Saharan Africa[END_REF], [START_REF] Easterly | The Lost Decades: Developing Countries' Stagnation in Spite of Policy Reforms 1980-1998[END_REF]World Bank (2005)).

[Insert Tables 2 and3 here] Overall, our finding that openness affects low-growth countries more than high-growth ones challenges the conclusions of most of the earlier cross-section and time-series studies that reveal either very little evidence of any statistical impact of trade on growth in the recent decades, or in some cases, a negative influence. 20 We believe there are two (possibly reinforcing) reasons for this difference, the heterogeneous effect across quantiles and the focus of the analysis. As already discussed, quantile regressions reveal relationships that may otherwise be masked in the case where parameter heterogeneity is ignored, as typically done in earlier literature. In addition, most of the earlier studies focus on trade liberalization and its effect on developing countries, essentially testing the validity of the -Washington consensus‖. Failure to find a statistical link between liberalization and growth may reflect the results in Table 2, and the (now) rather well-established fact that trade policies have not played a significant role in growth performance of many developing countries in Africa, Latin America and even Asia (see [START_REF] Stiglitz | Development Policies in a World of Globalization[END_REF]). 21From an economic viewpoint, the differences in the trade impact on growth for low-growth and highgrowth countries may be explained by different internal preconditions, mainly relating to the domestic factors of economic growth such as productive infrastructure, human capital, investment and factor productivity. For instance, considering investment, [START_REF] Greenaway | Does Trade Liberalisation Promote Economic Development?[END_REF] shows that countries which have increased investment following liberalization benefited from trade liberalization, while the liberalization impact on growth is negative for countries characterized by an investment slump.

Similarly, [START_REF] Wacziarg | Trade Liberalization and Growth: New Evidence‖[END_REF] report higher growth effects of liberalization for countries which experienced lower levels of trade taxes, higher levels of human capital, and higher import levels. Another important factor that may be seen as a key determinant of the heterogeneity of the trade-growth relationship has been highlighted by [START_REF] Foster | The Impact of Trade Liberalisation on Economic Growth[END_REF] and relates to the private sector's response to trade reforms. This point has to be linked to crises since reforms are frequently undertaken during periods of crisis, a fact that may weaken the positive effects of liberalization on growth. Finally, other factors that may be at play for explaining this heterogeneity have been investigated by [START_REF] Rodrik | Trade Policy and Economic Growth: A Skeptic's Guide to the Cross National Evidence[END_REF] and relate to the country's size, the level of development and comparative advantages. In summary, while various factors may explain our finding of a different trade-growth nexus for low-and high-growth countries, our results are in line with those of [START_REF] Foster | The Impact of Trade Liberalisation on Economic Growth[END_REF] suggesting that countries which benefited most from trade liberalization are those with lower per capita growth rates.

Effect of a trade shock on growth

To study the consequences of the heterogeneous response of growth to trade openness, we examine how the growth distribution changes when all countries in the sample are hit by the same trade shock. Such shocks affecting trade are exogenous, common to all countries, and can be either positive or negative. They can be of various types including changes in the prices of goods, suppression of trade barriers, and a drop in foreign demand caused by a world recession. We first compute the conditional probability density function for the growth rate of per capita GDP, using the estimated coefficients of (1) for different quantiles from the 10 th up to the 90 th . Then, the obtained distribution is -shocked‖ by one standard error of the trade openness variable (the fitted variable from our gravity model).

Figures 2 and3 show the growth conditional distribution corresponding to a situation with no shock on trade (solid lines) and the corresponding conditional distributions when the economies are -perturbed‖ with an exogenous shock to openness (darker lines), first for a positive shock and then a negative shock. The analysis reveals that a negative openness shock is associated with a more pronounced dispersion of the growth distribution and a reduction in the mean as the conditional distribution moves to the left. This suggests that countries in the lower tail are affected more by negative trade shocks, and their relative position in the income distribution widens resulting in a divergence effect. In contrast, a positive shock induces an increase in the mean and a reduction in the dispersion of the distribution, as the conditional distribution becomes leptokurtic. This suggests a catching-up effect between low-and high-growth countries and as a consequence, low-growth countries get closer to high-growth countries, suggesting convergence. Our results show that an exposure to a common trade shock affects growth in a heterogeneous manner. This has implications in the context of the current international crisis. A negative shock coming from the (already observed) drop in demand from developed countries may penalize low-growth countries more than the highgrowth ones.

Robustness and sensitivity tests

This section discusses the results of a number of robustness and sensitivity tests including tests for varying effects, omitted variables, and interaction effects. First, although our estimated short-run and long-run impact of trade openness on growth suggests varying effects across quantiles (see Figure 1), we formally test whether the coefficients estimated for the various quantiles are statistically different. Table 4 reports the results of the Wald tests for the whole period 1980-2006. 22 The quantiles in rows are those against which the null assumption of equal slopes is tested, while the quantiles in columns refer to those that are estimated. The lower and upper triangular matrices contain the p-values of the test of slope equality for the short-run impact and the long-run impact, respectively. Statistically significant differences (lower p-values) suggest heterogeneity. 23 Indeed, the reported results confirm that the quantile regressions estimate different impacts of openness on growth away from the middle portion of the sample. However, the heterogeneity is stronger for the long-run coefficients, since low p-values are found only for couples of extremely low and high quantiles.

[Insert Table 4 here] Second, we conduct tests of omitted variables on two policy variables (government balance as share of GDP and inflation), the investment share, and terms of trade. Table 5 reports the constrained regressions and the p-values corresponding to the tests for the whole period 1980-2006. The omission of a variable is rejected for low p-values. We see that all the series-with the exception of the terms of trade for the 75 th quantile regression-are relevant explanatory variables in the regressions, and excluding them may bias the results. In particular, comparing the constrained regressions with those in Table 1, we see that the former often lead to an underestimation of the long-run effect of growth on trade openness for the low-growth countries (but have no effect for the high-growth countries).

[Insert Table 5 here]

Finally, we examine whether interacting trade openness with the control variables affects the results reported in Table 1. 24 The interactions capture the indirect effects of trade on growth through the channels of investment, macroeconomic policies, and terms of trade. The investment channel may account for effects including productive resource availability or an indirect way of taking into account competitiveness. Estimates in Tables 6a and6b suggest that the trade coefficients in both the short and long run increase significantly when the indirect effect of trade on growth, via the investment share, is considered in the regression (Column ( 1)). For the high-growth countries, the impact of trade on growth is more than tripled and the magnitude of the coefficients increases slightly in the case of the low-income countries. With this new variable, we still find a stronger effect for the lowest quantiles. In Columns ( 2) and ( 3), we report the results corresponding to the interaction of trade and policy variables. High fiscal deficits and/or loose monetary policy yielding high inflation can hinder conditions conducive to trade by failing to attract capital required for production and export, and decreasing competitiveness. Our estimates in Tables 6a and6b show that the interacted effects of policy variables do affect the trade-growth nexus in the long run, as there is a significant negative effect of inflation in the low-growth countries and the overall long-run coefficient decreasing from 1.6 to 1. Tables 6a and6b also report tests of omitted variables for the crossed terms. The results, for Columns ( 2) and ( 3), suggest that the indirect effect of trade through the inflation rate does matter in the low-growth countries, while government balance is the key policy channel in the high-growth economies. Finally, interacting trade with the terms of trade does not alter significantly the order of magnitude of the trade coefficients.

[Insert Tables 6a and6b here]

Conclusion

The -heterogeneity‖ hypothesis has recently re-opened the debate in the literature investigating the trade-growth nexus. In this context, the empirical literature follows two strands. One is related to the question of omitted conditional variables in the classical regressions linking trade to growth. Another focuses on the application of more appropriate econometric tools to investigate the possibility of heterogeneous responses. In this paper, we attempt to control for both of these concerns, by applying quantile regression analysis, while controlling for identified robust growth determinants.

This paper shows that the response of growth performance to trade openness varies among the developing countries, thus providing empirical evidence of parameter heterogeneity. These results are in contrast to some earlier literature relying on mean-based approaches which mask the real effect and may sometimes lead to contradictory conclusions. Our findings suggest that, after conditioning on the robust growth determinants, openness has a higher impact on growth among low-growth countries relative to high-growth countries, with significantly larger short-run and long-run effects. We also show that trade openness shocks have an impact on the dispersion of international distribution of growth. We identify significant changes in the conditional growth distribution as a response to openness shocks, with positive shocks resulting in improvements in the distribution (higher mean income and smaller dispersion) and convergence effects, and negative shocks resulting in lower growth and wider dispersion around the mean. In terms of policy implications, our findings suggest that, on the one hand, low-growth countries could benefit the most from increasing trade openness, which would also help their convergence to the highest growth economies in the long run. On the other hand, increased trade dependency may raise vulnerability in situations of decreasing trade. As a particular point, Crespo-Cuaresma and Doppelhofer ( 2007) introduce a Bayesian Averaging of Thresholds approach for assessing the impact of robust threshold effects in cross-country growth equations.

Real GDP(- 1), and two-stage quantile estimations in columns ( 2)-( 4). All regressions include country dummies; t -statistics in brackets. *** significant at 1%, ** significant at 5%, * significant at 10%. 2. The sparsity function is computed using a kernel method (the results are robust to other methods such as bootstrap or Siddiqui).

3. The short-run impact is given by the openness coefficient; the long-run impact is given by the absolute value of the ratio of openness to the lagged value of per capita GDP growth rate. 4. Instruments used both for GMM and in the first-stage quantile regressions are lagged values of the endogenous and explanatory variables as well as lagged values of their first difference. Defining parameter priors in the case of SDM reduces to defining only k . That is the only prior that is arbitrarily specified in the simulations, so robustness checks of the results are estimated by changing the value of this parameter. For this reason, we estimate our simulations with values of 5, 8, 11, 15 k . On the other hand, FLS define priors for the parameters that are common to all models, namely the intercept, the scale parameter, and the slope coefficients. For the intercept and the scale parameter improper priors are assumed, while for the slope parameters we experiment with a variety of g-priors (specifically 1-9) as discussed in Fernández et al. (2001b).

Posterior densities can be calculated for using (1.1). Further, using (1.2) we can estimate the posterior inclusion probability of a variable. It is the sum of all posterior probabilities of all the regressions including the specific variable (regressor), and it serves as a ranking measure to see how much the data favors the inclusion of a variable in the regression. It is calculated as:

0 (inclusion) ( 0 | ) ( | ) k kj P p D p M D (1.4) If ( 0 | ) ( 0) kk k p D p k
then the variable has high marginal contribution to the regression model.

Next, conditional on a variable's inclusion, we compute the sign certainty probability, a measure of the robustness of the sign of the coefficient. It estimates the probability that (conditional on inclusion) the coefficient is on the same side of zero as its mean and is calculated as: We base our robustness estimations on the inclusion probability and the sign certainty. 26In particular, we proceed as follows. From a set of 22 or 24 potential regressors we apply both the FLS and SDM estimations to derive (inclusion) P and (sign certainty) P

. This is repeated for nine values of g-priors for FLS and five values of k ( 5, 8, 11, 15 k ) in the case of SDM. Then results are -aggregated‖ from both simulations and then summarized using our specified rule where a variable is identified as -robust‖ if (inclusion) 0.50 P and (sign) 0.90 P

. Finally, we combine results from all three simulations to arrive at the set of robust growth determinants used in [START_REF]Robust standard errors in parentheses. *** significant at 1%, ** significant at 5%[END_REF]. 1. For a list of regressors and their definitions and sources see Appendix B. 2. Variables in bold are classified as "robust" using the authors' defined criterion for robustness: P(sign)≥0.90 and P(inclusion)≥0.50. 3. Each of the simulations 1-3 were estimated using several priors. For FLS, priors 1 through 9 were used as defined in Fernandez et al. (2001b). For SDM, priors of kbar=5, 8, 11, 15 with kbar as defined in [START_REF] Sala-Í-Martin | Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach[END_REF].

Possible Simulations Using 22-24 Regressors

Appendix A,

3 .

 3 Instruments used in the first-stage quantile regressions are lagged values of the endogenous and explanatory variables as well as lagged values of their first difference. 4. The last line shows the p -value of the Wald tests for the omitted variables, respectively Government balance and inflation (Column (1)), Investment share (Column (2)) and terms of trade (Column (3)).

Figure 2 .

 2 Figure 2. Growth conditional distribution (positive trade shock).

Figure 3 .

 3 Figure 3. Growth conditional distribution (negative trade shock).

Table 1 . GMM and Two-Stage Quantile Regressions 1980-2006 Dependent variable: per-capita real GDP growth

 1

	GMM		Two-stage quantile regressions
		0.5
	(1)	(2)	(3)	(4)

Table 2 . GMM and Two-Stage Quantile Regressions 1980-1995 Dependent variable: per-capita real GDP growth

 2

	GMM		Two-stage quantile regressions
		0.5
	(1)	(2)	(3)	(4)

Table 3 . GMM and Two-Stage Quantile Regressions 1996-2006 Dependent variable: per-capita real GDP growth

 3 Lower triangular matrix: short-run effects. Upper triangular matrix: long-run effects. The null hypothesis of slope equality for the openness variables across quantiles is rejected when the p -value lies below 1%, 5% or 10%.

	GMM		Two-stage quantile regressions
	(1)	(2)	(3)

Table 4 . Tests for Varying Effects of Trade Openness on Growth across Quantiles (p values of the Wald test)

 4 -statistics in brackets. *** significant at 1%, ** significant at 5%, * significant at 10%. 2. The sparsity function is computed using a kernel method (the results are robust to other methods such as bootstrap and Siddiqui).

	Real GDP(-1)	-0.06 ***	-0.06 ***	-0.03 ***	-0.05 ***	***	-0.02 ***
		(-7.31)	(-7.45)	(-4.64)	(-4.82)	(-5.74)	(-3.63)
	Investment share	0.08 ***	-	0.07 ***	0.06 ***	-	0.07 ***
		(3.16)		(2.53)	(4.36)		(4.92)
	Government balance	-	0.32 ***	0.36 ***	-	0.09	0.27 **
			(2.51)	(2.65)		(1.11)	(2.12)
	Population growth	-0.04 ***	-0.05 ***	-0.06 ***	-0.04 ***	-0.04 ***	-0.06 ***
		(-7.98)	(-2.06)	(-6.12)	(-3.44)	(-3.59)	(-4.97)
	Terms of trade growth	0.01 *	0.01 **	-	0.02	0.00	-
		(1.85)	(2.22)		(1.66)	(1.37)
	Inflation	-	-0.02 ***	-0.03	-	-0.01 *	-0.02
				(-0.91)		(-1.82)	(-0.89)
	Openness from gravity	0.08 ***	***	0.07 ***	0.04 **	0.04 ***	0.02 *
		(4.91)	(5.45)	(4.53)	(2.40)	(2.84)	(1.74)
	Investment share(-1)	-0.01	-	-0.01	-0.02 *	-	-0.03 **
		(-0.82)		(-0.39)	(-1.75)		(-2.49)
	Government balance(-1)	-	0.00	-0.02	-	0.06	-0.02
			(0.00)	(-0.13)		(0.82)	(-0.22)
	Population growth(-1)	0.00	0.01	-0.00	0.01	0.01	-0.01
			(0.28)	(-0.51)	(0.44)	(0.90)	(-0.79)
	Terms of trade growth(-1)	0.01	0.02 ***	-	0.02	0.01	-
		(0.64)	(5.13)		(1.66)	(1.08)
	Inflation(-1)	-	0.01 **	0.01	-	0.01	0.01
			(2.17)	(1.06)		(0.63)	(0.80)
	Openness from gravity(-1)	-0.01	-0.04 *	-0.04 **	-0.00	-0.01	-0.01
		(0.01)	(1.76)	(-2.39)	(-0.17)	(-0.56)	(-0.99)
	Pseudo R²	0.23	0.24	0.23	0.22	0.22	0.22
	Impact of trade on growth
	Short run	0.08	0.09	0.07	0.04	0.04	0.02
	Long run	1.33	0.83	0.00	0.80	0.80	1.00
	Omitted (p -value)	0.00	0.00	0.00	0.01	0.00	0.11
	Notes:
	1. t

Table 5 . Constrained Regressions and Tests of Omitted Variables Two-stage quantile regressions 1980-2006 Dependent variable: per-capita real GDP growth

 5

	(2)	(3)

Table 6a . Two-Stage Quantile Regressions with Interaction Terms (25th Quantile) Dependent variable: per-capita real GDP growth (25th quantile)

 6a

		(1)	(2)
	Real GDP(-1)	-0.06 ***	-0.05 ***	-0.05 ***	-0.05 ***
			(-4.74)	(-5.20)	(-5.39)
	Investment share	0.06	0.06 ***	0.06 ***	0.06 ***
		(3.53)	(4.21)	(4.12)	(3.76)
	Government Balance	0.1	0.22	0.15	0.17
		(0.66)	-1.68	(1.24)	(1.42)
	Population growth	-0.05 ***	-0.05 ***	-0.05 ***	-0.05 ***
		(-4.08)	(-3.91)	(-4.09)	(-4.02)
	Terms of trade growth	0.00	0.00	0.00	0.00
		(0.22)	(0.37)	(0.85)	(0.54)
	Inflation	-0.02	-0.02 **	-0.02 **	-0.02
		(-1.56)	(-2.03)	(-2.77)	(-1.42)
	Trade(openness from gravity)	0.11 **	0.03 *	0.04 **	0.03 **
		(2.75)	(1.94)	(2.61)	(2.16)
	Trade x investment share(-1)	0.04 **	-	-	-
		(2.23)
	Trade x Gvt balance(-1)	-	-0.06	-	-
			(-0.67)
	Trade x Inflation(-1)	-	-	-0.01 **	-
				(-2.65)
	Trade x TOT growth(-1)	-	-	-	0.00
					(0.06)
	Investment share(-1)	0.05	-0.03 *	-0.03 **	-0.02
		(1.26)	(1.89)	(-2.03)	(-1.31)
	Government balance(-1)	-0.02	-0.08	0.02	-0.02
		(-0.17)	(-0.58)	(0.25)	(0.18)
	Population growth(-1)	0.01	0.007	0.01	0.01 *
		(1.68)	(0.49)	(1.35)	(1.75)
	Terms of trade growth(-1)	0.02	0.02 **	0.02	0.02
		(1.28)	(1.97)	(1.65)	(0.55)
	Inflation(-1)	0.01	0.01	-0.02	0.01
		(0.92)	(0.82)	(-1.25)	(0.92)
	Trade(-1) (Openness from gravity(-1))	0.00	-0.01	-0.01	-0.01
		(0.07)	(0.37)	(-0.73)	(-0.47)
	Trade(-1) x investment share(-2)	0.01	-	-	-
		(0.49)
	Trade(-1) x Gvt balance(-2)	-	-0.00	-	-
			(-0.04)
	Trade(-1) x Inflation(-2)	-	-	0.00	-
				-0.70
	Trade(-1) x TOT growth(-2)	-	-	-	0.00
					(-0.70)
	Pseudo R²	0.25	0.24	0.24	0.23
	Impact of trade on growth Short run	0.15	0.03	0.03	0.03
	Long run	2.50	0.60	0.60	0.60
	Omitted (p -value)	0.04	0.62	0.00	0.91
	Notes:
	See notes for Table 5.

Table 6b . Two-Stage Quantile Regressions with Interaction Terms (75th Quantile) Dependent variable: per-capita real GDP growth (75th quantile)

 6b

	Figure 1.
	(1)	(2)	(3)	(4)

Impact of trade on growth at different quantiles 95% confidence interval.

	Impact of trade on growth at different quantiles and 95% confidence interval
	.16
	.12
	.08
	.04
	.00
	-.04
	0.0 0.1 0.2	0.3 0.4	0.5	0.6 0.7	0.8 0.9	1.0
			Quantile

Table 7 : List of Variables Used in the Growth Regressions

 7

	Simulation 1: 24 regressors		Simulation 1: 22 regressors		Simulation 1: 22 regressors
	FLS	SDM	FLS	SDM	FLS	SDM
	prior=1-9	kbar=5,8,11,15	prior=1-9	kbar=5,8,11,15	prior=1-9	kbar=5,8,11,15

Examples of seminal papers on these topics are[START_REF] Grossman | Innovation and Growth in the Global Economy[END_REF] Helpman (1991a, 1991b), Rivera-Batiz and Romer (1991),[START_REF] Barro | Technological Diffusion, Convergence and Growth[END_REF][START_REF] Sala-Í-Martin | -I Just Ran Two Million Regressions[END_REF][START_REF] Eaton | International Technology Diffusion: Theory and Measurement[END_REF].

[START_REF] Foster | The Impact of Trade Liberalisation on Economic Growth[END_REF] proxies trade using a liberalization dummy variable and uses a growth model specification that is rather ad hoc and ignores model uncertainty. Within the context of growth,[START_REF] Cunningham | The Distributional Heterogeneity of Growth Effects: Some Evidence[END_REF], and[START_REF] Mello | Growth Equations: A Quantile Regression Exploration[END_REF] examine convergence and the effects of growth determinants/policy variables using quantile regressions. Quantile regressions are also employed by[START_REF] Barreto | Under performers and Over Achievers: A Quantile regression Analysis of Growth[END_REF] to identify the determinants of growth for under-achieving countries relative to those for over-achievers, and[START_REF] Osborne | The Sources of Growth at Different Levels of Development[END_REF] who uses panels of five-year averages to examine how a number of growth determinants (including openness) differ across quantiles.

We do not include time dummies since our equation is estimated as an error-correction model. Accordingly, the cyclical variations are captured by the short-run dynamic components.

In our empirical application, the trade openness variable is measured by bilateral trade forecasted from a gravity equation. By this construction, this variable captures the multidimensional nature of openness by taking into account transportation costs, trade barriers, and the rest of the usual determinants.

For a review of empirical works on trade and growth, refer to[START_REF] Billmeier | Trade Openness and Growth: Pursuing Empirical Glasnost[END_REF].

Quantile regressions have been introduced byKoenker and Bassett (1978). See also the references quoted in Footnote 9.

This technique allows the examination of how a uniform change in trade openness can affect the dispersion of economic growth. For surveys of quantile regression methods, see[START_REF] Buchinsky | Recent Advances in Quantile Regression Methods: A Practical Guideline for Empirical Research[END_REF],[START_REF] Koenker | Confidence Intervals for Regression Quantiles‖ in Asymptotic Statistics[END_REF][START_REF] Hallock | Quantile Regression[END_REF] Koenker (2005).

Seminal contributions to BMA include those of[START_REF] Raftery | Bayesian Model Selection in Social Research[END_REF], and[START_REF] Raftery | Bayesian Model Averaging for Linear Regression Models[END_REF].

In addition to the BMA and the Extreme Bounds Analysis, other methods have been proposed in the literature to identify robust determinants and are based on model selection criteria. See, e.g.,[START_REF] Hendry | We Ran One Regression[END_REF] or on the Least Absolute Shrinkage and Selection Operator (LASSO) estimator[START_REF] Zou | The Adaptive LASSO and its Oracle Properties[END_REF].

Note that[START_REF] Barreto | Under performers and Over Achievers: A Quantile regression Analysis of Growth[END_REF] and[START_REF] Crespo-Cuaresma | The Determinants of Regional Economic Growth by Quantile[END_REF] aim at investigating how the set of robust growth determinants varies across quantiles. In particular,[START_REF] Barreto | Under performers and Over Achievers: A Quantile regression Analysis of Growth[END_REF] consider an extreme bounds analysis similar to[START_REF] Levine | A Sensitivity Analysis of Cross-country Growth regressions[END_REF] to show that the determinants of growth are not the same for under-performing countries and over-achieving ones.[START_REF] Crespo-Cuaresma | The Determinants of Regional Economic Growth by Quantile[END_REF] investigate growth across European regions and show that, even if a determinant is robust across regions, its impact on growth may considerably differ across the quantiles.

The predicted values from country-pair fixed effects and Hausman-Taylor estimates were very highly correlated (0.95-0.98), so the choice between the two estimators has virtually no impact on the quantile regression results.

Strictly speaking, our quantile regressions also include the first-lagged values of our dependent variable, as well as those of the explanatory variables. While we may reasonably think that if a variable is found to be robust it may also be the case for the corresponding lagged variable, it would be interesting to implement the BMA analysis by including these lagged variables to rigorously check this assumption.

See Rodrik and Rodriguez (1999),[START_REF] Undp | A World of Development Experience[END_REF], and[START_REF] Wacziarg | Trade Liberalization and Growth: New Evidence‖[END_REF].

Another reason may be that the liberalization variable (usually an indicator variable) cannot identify the differences in the extent of trade liberalization within countries.

Similar results are available for the subperiods upon request.

The equality of slopes is rejected when the p-values are lower than 1%, 5% or 10%.

These results need to be interpreted with caution as the interaction variables have not been tested in the BMA, and hence, even when interactions are found relevant they may not be robust in the BMA sense. On this(continued…)

result, in this case, countries that stand to gain the most from increased trade in the long run are also in danger of being hurt the most in the short run.

We thank the authors for making their Fortran and Gauss codes available. See the Journal of Applied Econometrics Data Archive for FLS, and http://www.econ.cam.ac.uk/faculty/doppelhofer/ for SDM.

In addition, conditional means and variances may be computed, as discussed in FLS and SDM. We are not interested in inference based on BMA per se, so we only focus on these two measures.

Appendix A Selection of the Control Variables

This Appendix provides a summary of the BMA approach and presents briefly the calculated quantities and summary statistics used. Further details of the FLS (Fernández, Ley and Steel, 2001a) and SDM [START_REF] Sala-Í-Martin | Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach[END_REF]) can be found in the original papers. 25 Suppose that the parameter space can be divided into K regions (models) where is the quantity of interest. Let D denote the data. Then Bayesian inference about is constructed using Bayesian Model Averaging (BMA), based on the posterior distribution: Using Bayes' theorem, the posterior model probabilities are obtained using:

In the spirit of Bayesian inference, prior distributions need to be specified for the parameters k , and the models j M , the specification of which differs between FLS and SDM. Assuming that we have k variables, there are 2 k K possible sampling models, so the space of all possible models is :

1,..., 2 k j Mj 

. When there is no preference for a specific model 12 1 ( ) ( ) ... ( )

K p M p M p M K

, which is the specification in FLS. However, SDM assume that the model probabilities are not equal: assuming that each variable has an equal inclusion probability, the prior probability for model j M is:

where k is the total number of regressors, k is the researcher's prior about the size of the model, j k

is the number of included variables in model j M , and k k is the prior inclusion probability for each variable.


Nikolaus O Elsner
email: nikolaus.elsner@uni-hamburg.de

Sabine Jacobsen

David W Thieltges

Karsten Reise

Alien parasitic copepods in mussels and oysters of the Wadden Sea

Keywords: Introduced species, Mussels, Mytilicola, Oysters, Parasites, Wadden Sea

Molluscan intestinal parasites of the genus Mytilicola, specifically M. intestinalis, were initially introduced into bivalves in the North Sea in the 1930s. It was presumably introduced from the Mediterranean with ship-fouling mussels, then attained epidemic proportions in Mytilus edulis in the 1950s, and is now widely established in the North Sea region. Mytilicola orientalis was co-introduced with Pacific oysters to France in the 1970s and in the southern North Sea in the early 1990s. Its main host Crassostrea gigas has massively invaded the Wadden Sea with a concomitant decline in mussels. To explore whether introduced mytilicolid parasites could play a role in the shifting dominance from native mussels to invasive oysters, we analyzed 390 mussels and 174 oysters collected around the island of Sylt in the northern Wadden Sea. We show that M. intestinalis has a prevalence >90% and a mean intensity of 4 adult copepods in individual mussels with >50 mm shell length at all sheltered sites. By contrast, none were found in the oysters. However, at one site we found M. orientalis in C. gigas with a prevalence of 10% and an intensity of 2 per host individual (August 2008). This constitutes the most northern record in Europe for this Pacific parasite until now.

Alignments of partial sequences of the mitochondrial cytochrome oxidase I (COI) gene and the nuclear internal transcribed spacers (ITS) and 18S rDNA-sequences, each show a distinct difference between the two species which confirms our morphological identification. We suggest that the high parasite load in mussels compared to oysters may benefit the continued expansion of C. gigas in the Wadden Sea.

Introduction

Invasions of alien species have become a major process of change in marine coastal ecosystems [START_REF] Ruiz | Global invasions of marine and estuarine habitats by non-indigenous species: mechanisms, extent, and consequences[END_REF][START_REF] Reise | Are aliens threatening European aquatic coastal ecosystems?[END_REF]Rilov and Crooks 2009).

Parasites may have two different effects on this global process. On the one hand, introduced species often escape from their native parasites during transport and when founder populations are rather small [START_REF] Torchin | Escape from parasites[END_REF]. In addition, parasites with complex life cycles seldom encounter all necessary hosts in a recipient ecosystem and thus are rarely co-introduced [START_REF] Torchin | Introduced species and their missing parasites[END_REF], and native parasites tend to infest alien hosts only at low numbers [START_REF] Krakau | Native parasites adopt introduced bivalves of the North Sea[END_REF]. This parasite release is assumed to generally facilitate invasive hosts.

On the other hand, with transfers of organisms for aquaculture and to some extent also with ships or the aquaria trade, diseases and parasites have been introduced with occasionally disastrous effects when they infest naïve (previously unexposed) host species in a recipient coastal region [START_REF] Cook | Non-native aquaculture species releases: implications for aquatic ecosystems[END_REF][START_REF] Minchin | Characterizing vectors of marine invasion[END_REF][START_REF] Pillay | Aquaculture and the environment[END_REF]). Particularly, oyster stocks have been strongly affected by introduced disease agents [START_REF] Andrews | A review of introductions of exotic oysters and biological planning for new importations[END_REF]Wolff and Reise 2002). Many of these parasites mature and reproduce on or in a single host, and spores or larvae disperse to the next host individual (direct life cycle). This makes their invasion more likely compared to parasites with complex life cycles which depend on the presence of all hosts to become established. Parasitic copepods of the genus Mytilicola exemplify the type of invasive parasites with direct life cycles. They inhabit the guts of molluscs and disperse via larval stages to infect other host individuals [START_REF] Lauckner | Diseases of Mollusca: Bivalvia[END_REF][START_REF] Gotto | Commensal and parasitic copepods associated with marine invertebrates: keys and notes for the identification of British species[END_REF]). In European seas, M. intestinalis has been described from the mussel Mytilus galloprovincialis in the Adriatic Sea by [START_REF] Steuer | Mytilicola intestinalis n.gen. n.sp. aus dem Darm von Mytilus galloprovincialis Lam[END_REF]. On the Atlantic coast, it appeared in M. edulis in England and in the German Bight in the 1930s [START_REF] Caspers | Über Vorkommen und Metamorphose von Mytilicola intestinalis STEUER (Copepoda paras.) in der südlichen Nordsee[END_REF][START_REF] Korringa | On the ecology and distribution of the parasitic copepod Mytilicola intestinalis STEUER[END_REF]) and is believed to have been imported with mussels fouling ship hulls [START_REF] Korringa | On the ecology and distribution of the parasitic copepod Mytilicola intestinalis STEUER[END_REF]).

However, for British waters Eno et al. (1997) have not included M. intestinalis as an introduced species, based on a pers. comm. by M. Gee "that its biology suggests that it is a boreal species" (p.12). This remark seems to be derived from results in [START_REF] Gee | Stages in the life history of Mytilicola intestinalis STEUER, a copepod parasite of Mytilus edulis (L.), and the effect of temperature on their rates of development[END_REF] showing that development can be completed at temperatures below 18°C albeit at slow rates. We follow [START_REF] Korringa | On the ecology and distribution of the parasitic copepod Mytilicola intestinalis STEUER[END_REF] who assumes it as very unlikely that this highly conspicuous parasite has been overlooked in North Sea mussels prior to the 1930s, and thus regard M. intestinalis as an introduced rather than being a cryptogenic species.

From 1949 to 1951 an epidemic was observed at the North Sea coast, and M. intestinalis was blamed for massive mortality in M. edulis with a severe impact on the mussel fishery [START_REF] Korringa | On the ecology and distribution of the parasitic copepod Mytilicola intestinalis STEUER[END_REF]. Although this parasite evidently can harm its host (e.g. [START_REF] Meyer | Beiträge zur Epidemiologie und Physiologie des parasitischen Copepoden Mytilicola intestinalis[END_REF]Williams 1969;[START_REF] Mann | The influence of Mytilicola intestinalis (Copepoda parasitica) on the development of the gonads of Mytilus edulis. Rapp PV[END_REF][START_REF] Theisen | Mytilicola intestinalis STEUER and the condition of its host Mytilus edulis L[END_REF], doubts have been raised as to whether M. intestinalis has been the causal agent for the observed mortality in the mussel epidemic or as to whether it merely had spread coincidentally at that time (Detlefsen 1975;[START_REF] Lauckner | Diseases of Mollusca: Bivalvia[END_REF].

A second Mytilicola species, M. orientalis has been described by [START_REF] Mori | Mytilicola orientalis, a new species of parasitic Copepoda[END_REF] from the Pacific oyster Crassostrea gigas and mussels in the Inland Sea of Japan, and this species was first introduced with its host C. gigas to the Pacific coast of North America in the 1930s and to France in the 1970s [START_REF] Katkansky | Distribution and effects of the endoparasitic copepod, Mytilicola orientalis on the Pacific oyster, Crassostrea gigas, on the Pacific coast[END_REF][START_REF] His | Observations préliminaires sur la présence de Mytilicola orientalis MORI (1935) chez Crassostrea gigas THUNBERG dans le Bassin d'Arcachon[END_REF][START_REF] His | Mytilicola orinetalis Mori: son action sur les huitres du bassin d'Arcachon au sours de l'été et de l'automne[END_REF][START_REF] Lauckner | Diseases of Mollusca: Bivalvia[END_REF][START_REF] Grizel | (ed) Fiches d'identification des maladies et parasites des poissons, crustaces et mollusques[END_REF]. On the European Atlantic coast it has spread further to the Netherlands and Ireland [START_REF] Stock | Copepoda (Crustacea) associated with commercial and non-commercial Bivalvia in the East Scheldt, The Netherlands[END_REF][START_REF] Holmes | Two Exotic Copepods Imported into Ireland with the Pacific Oyster Crassostrea gigas (Thunberg) The Irish Naturalists[END_REF], and has also been introduced with C. gigas into the Mediterranean Sea [START_REF] Clantzig | Invertebrates d'introduction récente dans les lagunes méditerranéennes du Languedoc-Roussillon (France)[END_REF]. Although it was initially feared that M. orientalis could initiate an epidemic similar to the one assumed with M. intestinalis in 1949-51, this has not yet been observed, and harm to the host C. gigas seems to be small (Deslous-Paoli and Heral 1988;DeGrave et al. 1995;[START_REF] Steele | Impact of the copepod Mytilicola orientalis on the Pacific oyster Crassostrea gigas in Ireland[END_REF].

In the northern Wadden Sea, the typical hosts for these two parasites cooccur. While the mussels M. edulis are native, the oysters C. gigas have recently been introduced. Regular imports of young Pacific oyster C. gigas for sea ranching at Sylt island have occurred since 1986, and the oysters subsequently invaded mussel beds in that area [START_REF] Reise | Pacific oysters invade mussel beds in the European Wadden Sea[END_REF][START_REF] Diederich | Introduced Pacific oysters (Crassostera gigas) in the Northern Wadden Sea: invasion accelerated by warm summers?[END_REF]. The question arose as to whether C. gigas could displace the native mussels [START_REF] Diederich | Differential recruitment of introduced Pacific oysters and native mussels at the North Sea coast: coexistence possible?[END_REF][START_REF] Diederich | High survival and growth rates of introduced Pacific oysters may cause restrictions on habitat use by native mussels in the Wadden Sea[END_REF][START_REF] Nehls | Wadden Sea mussel beds invaded by oysters and slipper limpets: competition or climate control?[END_REF]. Direct competition between mussels and oysters for space and food has been suggested (Troost 2009), but it also seems likely that indirect competition mediated by differential parasite loads among the two host species (apparent competition) could be another contributing factor [START_REF] Krakau | Native parasites adopt introduced bivalves of the North Sea[END_REF].

The aim of our study was to investigate mussels and oysters for the presence of the two parasitic copepod species and to quantify the respective infection levels in their hosts. To test the selectivity of the infestation, we experimentally investigated the host specificity of M. intestinalis in mussels and oysters. Due to ambiguous features of species identification mentioned in the literature [START_REF] Stock | Copepoda (Crustacea) associated with commercial and non-commercial Bivalvia in the East Scheldt, The Netherlands[END_REF][START_REF] Gotto | Commensal and parasitic copepods associated with marine invertebrates: keys and notes for the identification of British species[END_REF] we sequenced parts of the nuclear rDNA and one mitochondrial gene to genetically verify species determination based initially on morphological features only.

Material and methods

Sampling area

The 390 individuals of Mytilus edulis and 174 of Crassostrea gigas examined in this study were sampled at 6 sites around the island of Sylt in 2007 and 2008 (Fig. 1, Tables 1 and2). The intertidal sampling sites in the north and in the south are disconnected by a dam from the island to the mainland since 1927. Sampling site 3 at the exposed beach refers to mussels attached to groins. All other mussels and the oysters collected were living on sediment in the low tidal zone and at one site in the shallow subtidal zone. The subtidal mussels collected in August 2007 were dredged at a depth of 1-2 m, while the ones collected in May 2008 were collected by hand at a depth of about 0.5 m during extreme low tide. Regarding the two sites sampled for both mussels and oysters, the oyster bank was approximately 500 m from the mussel bank at site 2, whereas mussels and oysters were cooccurred in a mixed assemblage at site 6. For every host individual, the infestation of Mytilicola spp. was recorded to determine the mean prevalence (% of host individuals infected) and the mean intensity (mean number of parasite individuals per infected host individuals) of the two copepods in each host population. Mussels were divided into three shell length categories: small (<30 mm), medium (30 mm to 50 mm) and large (>50 mm; cp. [START_REF] Meyer | Beiträge zur Epidemiologie und Physiologie des parasitischen Copepoden Mytilicola intestinalis[END_REF]). These categories have previously been shown to be positively correlated with prevalence and intensity of M. intestinalis (e.g. Detlefsen 1975). Due to the fact that the last recruitment event in C. gigas had occurred in summer 2006, small oysters were not available and random samples were taken.

After mussels and oysters were opened, the visceral mass was taken out and squeezed between two glass plates. Only adult stages of Mytilicola spp. were counted. These are readily detected due to their size and intensive red colour.

To detect statistically significant differences in infection levels, prevalence and intensity data were compared using Chi-Square-Test and analysis of variance (ANOVA), respectively.

Infection experiment

In order to test host specificity of M. intestinalis with regard to M. edulis and C. gigas a pilot study was conducted from the beginning of September to the beginning of November 2008. We infected previously uninfected individuals (medium-size mussels from site 3 and oysters from site 2, recipient mussels and oysters) by keeping them together in treatments with highly infected large blue mussels (from site 2, donor mussels) for two months. Recipient mussels were marked and could readily be distinguished by their size. The average rate of infection of recipient mussels and oysters and donor mussels was determined before the experiment commenced with samples of n=30 for each host species (see Tab. 1 and 2, samples from 2008). Epibionts of mussels were removed prior to the experiment.

Twelve aquaria (20 l) were used, each containing 10 donor mussels. Recipient mussels and oysters were exposed to infected mussels separately to prevent mutual interference. Therefore, 6 treatments were used for the infection of mussels and 6 for the infection of oysters. Of the recipient mussels and oysters, 6 individuals were added to each treatment. The bivalves were kept in oxygenated sea water filtered with a gravel filter. Every day, 3 l of each tank were renewed.

Once every week, the whole tank was filled with fresh filtrated sea water.

Previously uninfected mussels and oysters (n=20 each) were kept as a control in unfiltered seawater. Water temperature remained at 15°C during the entire experiment. After two months, all individuals were simultaneously taken out of the water and stored at 4°C until dissection within 2 weeks. Cooled storage is assumed to have no effect on the intensity of the infestation [START_REF] Dethlefsen | Zur Parasitologie der Miesmuschel (Mytilus edulis L., 1758)[END_REF].

Genetic species differentiation

For the genetic differentiation of the species, the DNA of 5 specimens of Mytilicola intestinalis and 2 of M. orientalis was extracted using the Qiagen DNeasy Blood and Tissue Kit. From this extracted DNA, the sequences of the 18S rDNA, the internal transcribed spacers (ITS) of the rDNA and sequences of the 5´part of the mitochondrial cytochrom c oxidase I (COI) gene were determined after amplification using Polymerase chain reaction (PCR). Due to its length, the 18S sequence was divided into 3 overlapping parts which were separately amplified using 3 sets of forward and reverse primers. All primers used are specified in Table 3. The annealing temperature was set to 57°C for the 18S primers and to 50°C for the COI and ITS primers. The PCR products were purified using the Qiagen QIAquick PCR Purification Kit and sent to Eurofins MWG Operon for sequencing. The three parts of the 18S sequence were manually rejoined to one sequence of 1728 base pairs based on the overlapping regions. The sequences are available in GeneBank under the accession numbers HM775187 -HM775206.

The 18S rDNA, the COI and the ITS sequences of M. intestinalis and M. orientalis were inserted into an alignment using ClustalX [START_REF] Higgins | CLUSTAL: a package for performing multiple sequence alignment on a microcomputer[END_REF]. Based on this alignment, the percentage of differing bases was calculated.

All three genomic regions were determined in both M. orientalis individuals analysed. Regarding M. intestinalis, COI and ITS sequences were obtained from all five individuals while for the 18S sequencing, only two individuals were used.

Results

Infection levels in mussels and oysters

Almost all mussel populations were infected with M. intestinalis and all mussels were free of M. orientalis (Tables 1 and2). Prevalence and intensity were generally high in large mussels (>90% and approx. 4 parasites/host), only medium mussels from the exposed shore of the island (site 3) had very low infection values (Table 1). In contrast, oysters were free of M. intestinalis and M. orientalis was found only at a single sampling site (Fig. 1, site 6; 54.7917°N, 8.3056°E). At this site, infection levels of M. intestinalis in mussels were significantly higher than of M. orientalis in oysters (Fig. 2; prevalence: χ 2 =45.3; intensity: F 59 =44.3, p<0.001).

Experimental infections

This pilot experiment yielded no infection of oysters but a noticeable infection of previously uninfected mussels (recipient mussels). Approximately half of the recipient mussels in each aquarium were infected (mean prevalence 45.6±7.2%, n=35) with one or two M. intestinalis in each mussel (mean intensity 1.3±0.3).

After the experiment, the donor mussels used for the infection had a prevalence of 93.3% and an intensity of 5.3±3.5. (The control mussels, previously uninfected but kept in unfiltered seawater, unexpectedly showed similar infection rates (prevalence 30.0% and intensity 1.7±0.9). Again, oysters remained uninfected.) Genetic species differentiation A comparison of the M. intestinalis and M. orientalis sequences obtained showed notable differences regarding intra-and interspecific variation. Intraspecifically, no sequence variation existed between the analysed individuals of M. intestinalis (based on five individuals for the COI and ITS sequences and two individuals for the 18S region). For the two M. orientalis individuals analysed, intraspecific variations were found in all three sequences, although they are well under 1% (Table 4). The interspecific differences are much higher. Regarding the conserved 18S sequence, they exceed 1%. For COI and ITS, both sequence markers with higher mutation rates, differences above 10% were determined.

During our investigations, we noticed that morphological identification features mentioned by [START_REF] Stock | Copepoda (Crustacea) associated with commercial and non-commercial Bivalvia in the East Scheldt, The Netherlands[END_REF] and [START_REF] Gotto | Commensal and parasitic copepods associated with marine invertebrates: keys and notes for the identification of British species[END_REF] are problematic. The caudal rami are said to be strongly divergent in M. intestinalis and almost parallel in M. orientalis. According to our observations, this feature is not reliable because Mytilicola can actively pull on the caudal rami causing them to lie parallel or to spread them out until they lie at a 180 degree angle to each other. We found the dorsal appendages to be the most suitable feature for identification: they are short and rounded in M. intestinalis and distinctly pointed and elongated in M. orientalis (Fig. 3).

Discussion

In the northern Wadden Sea we found a heavy infestation of native mussels Mytilus edulis by the intestinal mytilicolid copepod Mytilicola intestinalis, indicating that this introduced parasite has become well established since the first record in 1970 [START_REF] Dethlefsen | Zur Parasitologie der Miesmuschel (Mytilus edulis L., 1758)[END_REF], while still absent in 1950 and 1965 [START_REF] Meyer | Beiträge zur Epidemiologie und Physiologie des parasitischen Copepoden Mytilicola intestinalis[END_REF][START_REF] Theisen | Mytilicola intestinalis Steuer in Danish Waters 1964-65[END_REF]. Prevalence and intensity in 2003-04 [START_REF] Thieltges | Macroparasite community in molluscs of a tidal basin in the Wadden Sea[END_REF][START_REF] Thieltges | Parasites in the northern Wadden Sea: a conservative ecosystem component over 4 decades[END_REF] and in 2007-08 (this study) have been at a similarly high level. In the invasive Pacific oyster Crassostrea gigas we found no M. intestinalis. However, at a single site in the south of the island of Sylt, far away from the local oyster culture lot in the north of the island, we found for the first time oysters infected with M. orientalis. The nearest previously reported occurrence is the Rhine Delta in The Netherlands [START_REF] Stock | Copepoda (Crustacea) associated with commercial and non-commercial Bivalvia in the East Scheldt, The Netherlands[END_REF], approximately 570 km along the North Sea shore in south-western direction. We speculate that this parasite may have just arrived at this northern most location for this Pacific species in Europe, although this site has not been screened previously. Thus, the sweeping invasion of C. gigas in the northern Wadden Sea presumably took place in the absence of this parasite burden.

The presence of both parasitic copepods was confirmed by the molecular analyses. [START_REF] Avise | Phylogeography: The history and formation of species[END_REF] suggests < 1% for intraspecific and > 1% for interspecific COI variation. We found differences of > 1% even in the conserved 18S sequence while COI sequences differed between M. intestinalis and M. orientalis by around 16%. Thus, even when the limit of 1% for species differentiation based on COI variation may be low, our results reveal a clear genetic distinction between both species and point to a not very close phylogenetic relationship. This is supported by a comparison with a GeneBank entry of the 18S sequence of a third species of the Mytilicolidae, Trochicola entericus (Acc-No. AY627006). Differences between M. intestinalis and T. entericus (1.32%) were higher than between M. intestinalis and M. orientalis (max. 1.26%). [START_REF] Lauckner | Diseases of Mollusca: Bivalvia[END_REF] reviewed the host spectra of M. intestinalis and M. orientalis, listing a number of bivalve species and even some gastropods. Mytilids may even occasionally harbour both species concurrently in southern France [START_REF] His | Observations préliminaires sur la présence de Mytilicola orientalis MORI (1935) chez Crassostrea gigas THUNBERG dans le Bassin d'Arcachon[END_REF]. However, M. intestinalis has not been recorded from Pacific oysters, except in a study by [START_REF] Aguirre-Macedo | Diversity of metazoan parasites of the introduced oyster species Crassostrea gigas in the Exe Estuary[END_REF] who refer to this species occurring throughout the year in C. gigas collected at the Exe estuary in southern England. This is in contrast to our observations since we did not find M. intestinalis in the 174 oysters screened for parasites, although all oysters were collected at sites where M. intestinalis occurred abundantly in mussels, often found in aggregates with the oysters. By referring to the above mentioned problem in species identification, we suggest that the only myticolid copepod in the intestine of C. gigas is M. orientalis.

Our experimental infections support our field observations that oysters are unsuitable as a host for M. intestinalis, as we yielded a high prevalence in mussels whereas the oysters remained free of this parasite. (The unexpected infection of mussels kept separate from mussels with M. intestinalis was most likely due to the fact that the water supply system for the flow-through tanks of the experiment has its incurrent siphon not far away from a subtidal mussel bed which is heavily infected by M. intestinalis.) Nevertheless, this pilot experiment may indicate that Pacific oysters are not infected by M. intestinalis. Unfortunately, we could not get enough C. gigas with M. orientalis to conduct the reverse infection experiment.

That M. orientalis was encountered only at a single site in C. gigas near the southern tip of the island was unexpected because the only oyster farm in the region is near the northern tip of the island, and this company regularly imports young oysters from Irish sources where M. orientalis has already been reported to infest C. gigas [START_REF] Holmes | Two Exotic Copepods Imported into Ireland with the Pacific Oyster Crassostrea gigas (Thunberg) The Irish Naturalists[END_REF][START_REF] Steele | Impact of the copepod Mytilicola orientalis on the Pacific oyster Crassostrea gigas in Ireland[END_REF]. The sites in the north and south of Sylt are separated by a dam connecting the island with the mainland (see Fig. 1). Natural dispersal has to take a route all along the exposed shore of the island where C. gigas does not occur, and this amounts to a distance of at least 50 km, mostly against the residual currents. Thus, maybe it is more likely that M. orientalis has arrived in the northern Wadden Sea by spreading from the south within a C. gigas population which originates from cultures in the delta area of The Netherlands. Site 6 is located in an area with intensive mussel cultures with seed mussels taken from various sources. Pacific oysters could have been transferred together with mussels.

According to the literature, M. intestinalis has little or no effects on mussel growth and reproduction performance when intensity is low but negative effects are evident at intensities of 3 and higher [START_REF] Meyer | Beiträge zur Epidemiologie und Physiologie des parasitischen Copepoden Mytilicola intestinalis[END_REF][START_REF] Gee | Stages in the life history of Mytilicola intestinalis STEUER, a copepod parasite of Mytilus edulis (L.), and the effect of temperature on their rates of development[END_REF][START_REF] Theisen | Mytilicola intestinalis STEUER and the condition of its host Mytilus edulis L[END_REF]. Likewise, M. orientalis does not harm its host C. gigas at low intensities [START_REF] Steele | Impact of the copepod Mytilicola orientalis on the Pacific oyster Crassostrea gigas in Ireland[END_REF], but has a negative effect at intensities of 3 and more [START_REF] Odlaug | The effect of the copepod, Mytilicola orientalis upon the Olympia oyster, Ostrea lurida[END_REF][START_REF] Katkansky | Distribution and effects of the endoparasitic copepod, Mytilicola orientalis on the Pacific oyster, Crassostrea gigas, on the Pacific coast[END_REF][START_REF] Deslous-Paoli | Mytilicola orientalis Mori, Crassostrea gigas Thunberg's parasite in the basin of Marennes-Oleron: impact on the condition and biochemical composition of oysters during rearing[END_REF]. At the sampling site where both parasites occur, M. intestinalis has an intensity of about 4 in M. edulis, while that of M. orientalis was only about 2 in C. gigas. Thus, mussels are likely to suffer from its intestinal parasite while the oysters are probably not. This suggests that the invasive oysters may have a competitive advantage over the native mussels in the Northern Wadden Sea as a result of their unequal parasite burdens. However, differential loads with parasitic copepods are not the only competitive advantage of oysters. They may also be less susceptible to predators (crabs, shrimp, starfish), presumably due to their firm attachment, flat shape when small and with a sturdy shell when larger (own observation). And they also show low burdens of trematode parasites which regularly infect the native mussels [START_REF] Krakau | Native parasites adopt introduced bivalves of the North Sea[END_REF]. The differential infestation of native mussels and invasive oysters with mytilicolid parasites may thus have contributed to the success of the oyster invasion. However, this difference may be attributed to the fact that M. orientalis has arrived very recently, six decades after the introduction of M. intestinalis in the North Sea region and nearly four decades after M. intestinalis was first found where we now have encountered M. orientalis. Future studies will have to show whether M. orientalis can attain the same level of infestation in C. gigas as M. intestinalis does in M. edulis.

Legends

Figure 1 :

 1 Figure 1: Sampling sites of mussels (squares) and oysters (asterisks) around the North Sea island of Sylt.1 Ellenbogen, 2 List, 3 Kampen, 4 Leghörn, 5 Munkmarsch, 6 Puan Klent. Gray shaded areas indicate the intertidal zone

Figure 2 :Figure 3 :

 23 Figure 2: Prevalence and intensity of Mytilicola intestinalis in its host Mytilus edulis and of M. orientalis in its host Crassostrea gigas at sampling site 6 in August 2008 (see Fig. 1, Tab. 1 and 2). n=30 for each. *statistically significant difference

  Figure 1

Table 1 :

 1 Crassostrea gigas investigated for intestinal copepods with locality (see Fig.1), date, number, shell length (mean ± standard deviation) prevalence (% of host individuals infected) and intensity (mean ± standard deviation). Only Mytilicola orientalis was found in C. gigas

	Site	Date	Oysters n	Shell length(mm)	Prevalence (%)	Intensity ± SD
	1	May 2008	30	95.3 ± 28.4	0.0	0.0
	2	August 2007	19	80.8 ± 26.5	0.0	0.0
	2	August 2008	55	91.0 ± 21.5	0.0	0.0
	4	May 2008	10	128.1 ± 20.0	0.0	0.0
	5	May 2008	30	107.0 ± 24.7	0.0	0.0
	6	August 2008	30	106.2 ± 22.1	10.0	1.7 ± 0.6

Table 2 :

 2 Mytilus edulis investigated for intestinal copepods with locality (see Fig.1), date, number, shell length (mean ± standard deviation) prevalence (% of host individuals infected) and intensity (mean ± standard deviation). Only Mytilicola intestinalis was found in M. edulis *subtidal, all others intertidal

	Site	Date	Mussels n	Shell length (mm)	Category	Prevalence (%)	Intensity ± SD
	2	August 2007	30	24,1 ± 3,9	small	33.33	1.60 ± 0.97
	2	August 2007	30	38,1 ± 5,1	medium	63.33	4.05 ± 2.98
	2	August 2007	30	57,6 ± 6,4	large	90.00	4.48 ± 3.32
	2*	August 2007	30	72,7 ± 6,4	large	93.33	8.07 ± 4.75
	2*	May 2008	30	70,3 ± 8,1	large	86.67	3.81 ± 2.95
	2*	August 2008	120	68,0 ± 8,1	large	93.30	5.28 ± 3.53
	3	August 2007	30	35,7 ± 3,1	medium	3.33	1.0 ± 0.18
	3	May 2008	30	32,7 ± 1,6	medium	0.0	0.0 ± 0.0
	6	August 2007	30	60,1 ± 4,2	large	93.33	4.18 ± 3.25
	6	August 2008	30	66,5 ± 3,7	large	96.67	3.72 ± 2.76

Table 3 :

 3 Name and sequence of primers used for PCR amplification and sequencing

	Primer name	Sequence 5'  3'	Reference
	COI f	GGTCAACAAATCATAAAGATATTGG	Folmer et al. 1994
	COI r	TAAACTTCAGGGTGACCAAAAAATCA Folmer et al. 1994
	ITS1 f	TCCGTAGGTGAACCTGCGG	White et al. 1990
	ITS4 r	TCCTCCGCTTATTGATATGC	White et al. 1990
	18S f	TACCTGGTTGATCCTGCCAG	Huys et al. 2006
	18S 554 f	AAGTCTGGTGCCAGCAGCCGC	Huys et al. 2006
	18S 1150 f (p2)	ATTGACGGAAGGGCACCACCAG	Huys et al. 2006
	18S 614 r	TCCACCTACGAGCTTTTTAACC	Huys et al. 2006
	18S 1282 r	TCACTCCACCAACTAAGAACGGC	Huys et al. 2006
	18S r	TAATGATCCTTCCGCAGGTTCAC	Huys et al. 2006

Table 4 :

 4 Inter-and intraspecific sequence differences determined in M. intestinalis and M. orientalis. For interspecific values, minimal (min) and maximal (max) values are shown

	Sequence Base pairs	interspecific (%, min / max)	M. intestinalis intraspecific (%)	M. orientalis intraspecific (%)
	18S	1744	1.20 / 1.26	0.0	0.06
	COI	628	16.08 / 16.40	0.0	0.64
	ITS	515	14.17	0.0	0.19

Acknowledgements

We thank our colleagues at the Wadden Sea Station Sylt of the Alfred Wegener Institute for Polar and Marine Research, especially Christian Buschbaum and Manulea Krakau, for inspiring discussions. Dagmar Lackschewitz is thanked for providing us with overlooked references. Elisabeth Herre and Reimer Magens are thanked for technical support. Tobias Dolch kindly provided the map of Sylt. Troost K (2009) Pacific oysters in Durch estuaries. Ponsen & Looijen, Ede. Diss. Univ. Groningen, NL White TJ, Bruns T, Lee S, Taylor JW (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. Pp. 315-322 In: PCR Protocols: A Guide to Methods and Applications, eds. Innis MA, Gelfand DH, Sninsky JJ, White TJ. Academic Press, Inc., New York Williams C S (1969) The effects of Mytilicola intestinalis on the biochemical composition of mussels. J Mar Biol Assoc UK 49:161-173 Wolff WJ, Reise K (2002) Oyster imports as a vector for the introduction of alien species into northern and western Euopean coastal waters. Pp. 193-205. In: Leppäkoski et al. (eds).

Invasive Aquatic Species of Europe. Kluwer Academic Publ, Netherlands


Pierre Maret
email: pierre.maret@univ-st-etienne.fr

Laurent Vercouter
email: laurent.vercouter@emse.fr

E L Morr
email: celmorr@auk.edu.kw

Christo El Morr
email: elmorr@yorku.ca

Web Intelligence and Virtual Communities

Keywords: e-Health, e-Collaboration, PACS and Health Information Systems integration

Web Intelligence is a multidisciplinary area dealing with exploiting data and services over the Web, to create new data and services using Information and Communication Technologies (ICT) and Artificial Intelligence (AI) techniques. The link to Networking and Virtual Organisations (VOs) is obvious: the web is a set of nodes, providing and consuming data and services; the permanent or temporary ties and exchanges in-between these nodes build the so-called virtual organizations; and the ICT and AI techniques contribute to the process and automate (or partly automate) communication and cooperation processes. One could also speak about Virtual Communities (VCs), slightly putting more focus on the topic or the shared objectives of the participants (nodes, users), or about Virtual Enterprises (VEs), emphasizing then more generally the structure in-between node. VCs, VOs, VEs… The point is not about the vocabulary, rather it is about research projects that tend to increase and improve the use of the Internet to make people communicate with each other in the real life, by means of advanced networked numeric tools and software.

Many concerns should be addressed in the Web Intelligence and Virtual Community area; the papers selected for this special issue address some of these concerns, namely:

• The services and coordination of services in the topic addressed by van't Klooster, van Beijnum, Pawar, Sikkel, Meertens and Hermens. The ageing of the western population (including Japan and Australia), the individualistic way of life, and the cost of care services, constitute strong intencives to provide support for elderly, for instance, by participating in communities where they can find friendship, or get support at appropriate times from their neighbours, relatives or medical care providers.

• The information extraction and the exploitation within VCs of the context data collected from mobile sensors is the issue addressed by Lopez, Shuzo and Yamada. They pursue the concrete example of people suffering from the metabolic syndrome and who can be supervised and effectively supported by homogeneous or heterogeneous groups of healthcare professionals (physicians, psychologists, nurses, etc.). The processes used depend on the context of each of the participants in the VC.

• The Communication protocols dedicated to VCs is the issue addressed by King and Kawash. An XML-based publish/subscribe protocol as been implemented and is described in their paper. Their objective is to encourage information exchanges and interconnections between virtual communities.

This selection of papers covers a representative excerpt of the contribution of the Web Intelligence field for Virtual Communities. This is however a broad field addressing other issues such as multi-agent and knowledge modelling, communities of objects, privacy preservation or social and psychological aspects. Thus, structural and coordinated efforts are carried out at different organizational levels to address these different issues.

Internationally this is conducted for instance in the Web Intelligence Consortium, and in France we can cite the Rhône-Alpes regional project "Web Intelligence", as well as the creation of specialized MSc. degrees in Web Intelligence (e.g. University of Saint Etienne).

While many aspects are still under investigation and others need to be taken into consideration, Web Intelligence is situated to play a vital role in the next generation internet applications. Indeed, it is promising to permit more streamlined experience for users and organizations, relying on data analysis and AI techniques that tap into the mine of data collected throughout the communication and cooperation processes.