Bartłomiej Błaszczyszyn
email: bartek.blaszczyszyn@ens.fr

Paul Mühlethaler
email: paul.muhlethaler@inria.friskander.banaouas@inria.fr

Skander Banaouas

Coexistence of radio networks using Aloha

Keywords: Ad hoc networks, slotted Aloha, transmission probability, Cognitive radio, Poisson point process, shot-noise, SINR, stochastic geometry

In this paper we use an analytical model to study how two radio networks : a primary network and a secondary network can coexist within the same area. We use a very simple protocol, Aloha, for both radio networks, and tools borrowed from stochastic geometry to model the performance of our two networks. We show that the primary network and the secondary network can adapt their transmission parameters simultaneously to achieve the following goal : the primary network maintains its performance with a maximum and fixed degradation whereas the secondary network maximizes its transmission throughput. In practice this involves the primary network adapting its transmission power and the secondary network its transmission probability. We also study the gain in performance when the secondary network nodes only transmit when their receivers are at minimum distance from any transmitter nodes in the primary network (constrained distance deployment).

We obtain the following result : when there is no constraint used for the secondary network (free deployment), it can offer a low (but not completely negligible) density of successful transmissions when the capture threshold in primary network T1 is small. If we optimize the density of successful transmissions for the selected nodes in the secondary network which are transmitting to receivers at a minimum distance from any node in the primary network then the per node throughput for these selected nodes can be very significantly increased. However with this optimization the total network throughput for the secondary network is also slightly reduced. If we use a constrained distance deployment for the secondary network (only nodes whose receivers at a minimum distance from the primary network) the per node throughput can be very significantly increased and the total network throughput for the secondary network is increased.

I. INTRODUCTION

The aim of this paper is to show that it is possible to share the same medium with a very simple Medium Access Control mechanism (MAC): Aloha. We obtain this result using simple models based on stochastic geometry. More precisely we show that if we adapt the transmission power of the primary and secondary networks and if we also use different CDMA codes, the performance of the primary network can be maintained while the secondary network can provide its users with an acceptable service for short range communication.

A. Related Work

Most of the studies which deal with cognitive radio try to optimize criteria based on the Signal over Interference-and-Noise Ratio (SINR) [START_REF] Chiang | Power control by geometric programming[END_REF][START_REF] Luo | Analysis of iterative waterfilling algorithm for multiuser power control in digital subscriber lines[END_REF][START_REF] Tan | Solving nonconvex power control problems in wireless networks: Low sir regime and distributed algorithms[END_REF][START_REF] Yu | Dual methods for nonconvex spectrum optimization of multicarrier systems[END_REF]. These papers aim to achieve optimizations using classical techniques : relaxation, convex optimization, etc. But it is difficult to apply these results in decentralized systems. However very few of these studies consider real medium access mechanisms, the focus being on adapting the power of a node or selecting a given channel to optimize a global (or local) criterion. In nearly all the papers dealing with cognitive radio and resource allocation, the temporal aspect is not addressed: the decisions that the algorithms provide are supposed to hold for long periods. Similarly the spatial aspect of the problem is seldom considered even though new results allow the interference to be exactly computed under a few but rather general assumptions [START_REF] Baccelli | An Aloha Protocol for Multihop Mobile Wireless Networks[END_REF][START_REF] Baccelli | Stochastic analysis of spatial and opportunistic Aloha[END_REF].

In this paper we study the coexistence of two networks which use slotted Aloha as a MAC protocol. We show that we can extend an existing model to compute the probability of successful transmissions in both networks. We study how the two networks can coexist and show that we can have reasonable performances if the primary network adapts its transmission power and the secondary network adapts its transmission probability. We also show that if the secondary network accepts a constraint on its deployment, it can greatly improve its performance. However in this case the primary network must increase its transmission power to maintain its performance.

B. Modeling Assumptions

The primary network is allowed to use a much greater transmission power than the secondary network. The primary network also uses CDMA codes. Since we use slotted Aloha, we can assume the primary network to be precisely synchronized. Thus, with CDMA codes we can assume that the Signal over Interference Ratio (SIR) T 1 required to accept a packet is small compared to 1, whereas a typical value for SIR without using CDMA codes is T 1 = 10.

II. MODELING AND PROBLEM FORMULATION

A. Two Network Model 1) Primary and Secondary Network Nodes:

In this paper we consider two coexisting networks called respectively primary and secondary network, each of which is represented by the so called Poisson Bipole model proposed in [START_REF] Baccelli | An Aloha Protocol for Multihop Mobile Wireless Networks[END_REF]; see also [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF]Chapter 16]. The nodes of these two networks are distributed on the infinite plane according to two independent, homogeneous, planar Poisson point processes (P.p.p.) of intensity λ 1 and λ 2 nodes per unit surface area (say per square meter) respectively. Each node of each of these two networks is wishing to transmit a packet to its own dedicated receiver located within a distance r a , a = 1, 2, for the primary and secondary networks, respectively. These receivers are not part of the Poisson point processes. Despite its drawbacks, mostly related to the modeling of the locations of receivers, this Poisson Bipole network model is reasonable, convenient and seems to be widely accepted (cf [START_REF] Ganti | Spatial and Temporal Correlation of the Interference in ALOHA Ad Hoc Networks[END_REF][START_REF] Weber | Transmission Capacity of Wireless Ad Hoc Networks With Outage Constraints[END_REF]).

Using the formalism of the theory of point processes, we can say that a joint snapshot of primary and secondary network nodes can be represented by two independently marked Poisson point processes (P.p.p) ( Φ1 , Φ2 ) with Φ = {(X a i , y a i )}, a = 1, 2, where the locations of nodes Φ a = {X a i } form a homogeneous P.p.p. of intensity of λ a and where the marks y a i denote the locations of the receivers for nodes X a i . We assume here that one receiver is associated with only one transmitter and that, given Φ 1 , Φ 2 , the vectors {X a i -y a i } are i.i.d with |X a i -y a i | = r a . 2) Wireless Channel Model: We assume that whenever node X ∈ Φ a (a = 1, 2) transmits a packet with some power P , the signal that is propagated and reaches any given location y on the plane with power equal to P F/l(|X -y|), where | • | denotes the Euclidean distance on the plane, l(•) is some deterministic path-loss function of the distance and F is a nonnegative random factor of unit mean representing the variations in the received power due to channel fading. An important special case, which is our default assumption in this paper, consists in taking a path-loss function of the form l(u) = (Au) β for A > 0 and β > 2.

(2.1)

Regarding the distribution of the random variable F , called for simplicity fading, in this paper we will consider only the special case of Rayleigh fading that corresponds to the exponential distribution of F , whose mean is assumed to be E[F ] = 1. More precisely, we will assume that for any pair: emitter X, location y of a potential receiver, an independent copy F = F X,y is used to model the fading throughout the transmission of this packet.

3) Slotted Aloha MAC: We assume that both networks use the slotted Aloha MAC scheme and are synchronized to the same time slots (this assumption can be easily relaxed later) but are however tuned differently. By different tuning we mean different emitted power and different medium access probability. More precisely, let us assume that each node of the network a (a = 1, 2), at a tagged time slot, independently tosses a coin with some bias p a and sends its packet using power P a in this time slot if the outcome is heads and does not transmit otherwise.

4) Successful Transmission:

It is natural to assume that transmitter X a i , successfully transmits a given packet to its receiver y a i if

SIR = P a F/l(|X a i -y a i |) Ī ≥ T a , (2.2)
where P a is the emitted signal power, T a is some signalto-interference (SIR) threshold for network a and where Ī is the total interference suffered by the receiver y a i . In the two-network model this total interference can be naturally expressed as the sum Ī = I 1 + I 2 of the interferences created by the concurrent transmissions of the primary and secondary

networks . For (a, b) ∈ {1, 2} 2 I b = X b j ∈Φ b ,X b j =X a i P b F X b j ,y a i /l(|X b j -y a i |)1 I(X b j transmits) .
(2.3) Note that taking (2.2) as the successful transmission condition, we ignore the external noise. This is a reasonable assumption if the noise is significantly smaller than the mean interference power.

B. Problem Formulation

We denote by p a cov , a = 1, 2 the probability of successful transmissions (coverage probability) in a given time slot by a typical node of the network a, provided it is scheduled for the transmission by the respective Aloha MAC. Consequently, the density of successful transmissions (defined as the expected number of such transmissions per node of the respective network a) can be expressed as d a = p a p a cov . The main objective of this paper is to study the maximization of the density of successful transmissions in the secondary network given a constraint (a lower bound) on the coverage probability in the primary network. This optimization will be achieved by some tuning of the power P 1 of the primary users and the Aloha MAC transmission probability p 2 used by the secondary users 1) Optimization without Deployment Control: We assume that the Aloha MAC parameter p 1 is fixed. Under these assumptions, p a cov (P 1 , p 2 ), and d a (P 1 , p 2 ) denote respectively the coverage probability and the density of successful transmissions in the network a = 1, 2, when the primary users emit with power P 1 and the MAC probability in the secondary network is fixed to p 2 . Note that when the secondary users do not emit at all (p 2 = 0), we have only the primary-network interference I = I 1 and thus the SIR condition (2.2) for a = 1 is invariant with respect to the power P 1 used by the primary nodes. Consequently p 1 max.cov := p 1 cov (1, 0) is the (maximal) coverage probability that the primary users can achieve when no secondary nodes are allowed to transmit.

The aforementioned optimization problem can now be formalized as follows. For a given δ > 0, representing an acceptable degradation of the coverage probability in the primary network, we want to find the maximal density of successful transmissions d2 that the secondary network can achieve

d2 := max 0≤p2≤1 d 2 (P 1 , p 2 ) : p 1 cov (P 1 , p 2 ) ≥ (1 -δ)p 1 max.cov for some P 2 < ∞ (2.4)
and the respective optimal tuning p2 of the Aloha MAC probability p 2 of the secondary network for which the maximum (2.4) is attained. We will denote by P1 the minimal power the primary nodes have to use in order to maintain their coverage probability at the required level when the secondary users implement p2 . Thus P1 is such that p 1 cov ( P1 , p2 ) = (1 -δ)p 1 max.cov . 2) Secondary Network with Exclusion Zones: Besides the optimization of the secondary network described above, we study if the obtained density of successful communications can be further improved by controlling the deployment of the secondary network nodes. We investigate the following simple geographic inhibition rule on the deployment of the secondary network: only transmitters in the secondary network whose receivers are closer than some given separation distance R to any of transmitter in the primary network can transmit.

III. PROBLEM ANALYSIS

The optimization problem formulated in the previous section can be explicitly solved for an unconstrained deployment of the secondary network. In what follows we will first present this solution. Then we show some approximations of the solution when the primary and secondary network users are geographically separated.

A. Optimization without Deployment Control

The problem consists in an optimal tuning of the Aloha MAC probability of the secondary users in order to maximize their density of successful transmissions, while the primary users react by fixing their transmission power so as not to decrease their coverage probability by more than δ × 100%.

The following result gives the explicit solution in the case of the (unconstrained) Poisson deployment of the secondary network.

Proposition 3.1: Assume the unconstrained Poisson distribution of the secondary nodes. Then the maximal density of successful transmission d2 in this network, defined in (2.4), is achieved for p2 = 1

r 2 2 T 2/β 2 K(β)λ 2 1 + λ1p1r 2 1 T 2/β 1 K(β) -log(1-δ) (3.5) and P1 = P 2 T 1 T 2 r 1 r 2 β λ 1 p 1 r 2 1 -log(1 -δ)T 2/β 1 K(β) -β/2 (3.6)
where K(β) is a constant given by :

K(β) = 2πΓ(2/β)Γ(1 -2/β)/β . (3.7)
Proof: Using the known explicit result for the coverage probability in the Poisson Bipole network (see e.g. [START_REF] Baccelli | Stochastic analysis of spatial and opportunistic Aloha[END_REF] or [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF]Chapter 16]) and the fact that the interferences I 1 and I 2 created by the primary and secondary networks (for some given tuning of these networks) are independent, we obtain the following probabilities of successful transmission p a cov = p a cov (P 1 , p 2 ) :

p 1 cov = exp -r 2 1 T 2/β 1 K(β)λ 1 p 1 × exp(-r 2 1 T 1 P 2 P 1 2/β K(β)λ 2 p 2 (3.8) p 2 cov = exp -r 2 2 T 2/β 2 K(β)λ 2 p 2 × exp -r 2 2 T 2 P 1 P 2 2/β K(β)λ 1 p 1 . (3.9)
Fixing p 2 and solving

p 1 cov ( P1 , p 2 ) = (1 -δ)p 1 cov (1, 0) in P1 we obtain P1 = T 1 r 2 1 λ 2 K(β)p 2 -log(1 -δ) β/2 P 2 . (3.10)
Using the above value of P1 in d 2 ( P1 , p 2 ) = p 2 p 2 cov ( P1 , p 2 ) and maximizing this expression in p 2 , we obtain (3.5). Finally inserting this expression in (3.10) we obtain (3.6).

B. Optimization of the selected secondary users.

Now we turn our attention in the optimization of the density d ′ 2 of successful transmissions (per node) of secondary users taking into account only users X 2 i ∈ Φ 2 whose receivers are such that

|y 2 i -X 1 j | ≥ R for all X 1 j ∈ Φ 1 .
But in contrast to the situation depicted in Section III-C the all the secondary users still transmit.

For such secondary users, the corresponding conditional interference produced by the primary users can be evaluated as in Figure 1. The interferers in the primary network are necessarily in the shaded area. Thus the coverage area for these secondary users can be simply computed; the shot-noise is just truncated. The coverage probability for the selected nodes in the secondary network is p ′ 2 cov given below:

p ′ 2 cov (P 1 , p 2 ) = exp -r 2 2 T 2 P 1 P 2 2 β K β, R r 2 P 2 P 1 T 2 1 β λ 1 p 1 × exp -r 2 2 T 2/β 2 K(β)λ 2 p 2 , (3.11) where K(β, a) = ∞ a x x β + 1 dx .
The density d ′ 2 of successful transmissions (per node) for secondary users whose receivers are at least R meters away from any primary user is given by the following equation:

d ′ 2 (P 1 , p 2 ) = p 2 p ′ 2 cov (P 1 , p 2 ) (3.12)
This density will be evaluated numerically in Section IV where we study the benefit for the nodes in the secondary network whose receivers are at a distance of at least R from any nodes in the primary network.

C. Secondary network deployed outside exclusion zones

We now study the performance when the secondary network is deployed outside exclusion zones as described in Section II-B2. We consider the nodes in the secondary network whose receiver nodes are at distance of at least R from any nodes in the primary network as in Section III-B. But in contrast, we consider that only these nodes in the secondary network are allowed to transmit.

The spatial intensity of the secondary users is equal to λ 2 e -πλ1R 2 , where the exponential expression corresponds to the mean fraction of the surface of the plane not covered by the spherical Boolean model of intensity λ 1 and disk radius R (i.e. one minus the so called volume fraction of the Boolean model).

In the following we assume that

• the interferences produced by the nodes in the primary and secondary network are independent, • the interference produced by the nodes of the secondary network is equivalent to the interference produced by a Poisson process of spatial intensity λ 2 e -πλ1R 2 . These assumptions are validated in Section IV by comparing the results of the model with those of simulations. We obtain the following probabilities of successful transmission p

′′ a cov = p ′′ a cov (P 1 , p 2 ) p ′′ 1 cov = exp -r 2 1 T 2/β 1 K(β)λ 1 p 1 × exp(-r 2 1 T 1 P 2 P 1 2/β K(β)e -πλ1R 2 λ 2 p 2 p ′′ 2 cov = exp -r 2 2 T 2 P 1 P 2 2/β K β, R r 2 P 2 P 1 T 2 1/β λ 1 p 1 × exp -r 2 2 T 2/β 2 K(β)λ 2 p 2 e -πλ1R 2 . (3.13)
Fixing p 2 and solving p

′′ 1 cov ( P1 , p 2 ) = (1 -δ)p ′′ 1
cov (1, 0) in P1 we obtain:

P1 = P 2 T 1 r 2 1 λ 2 K(β)p 2 e -πλ1R 2 -log(1 -δ) β/2 . (3.14) If we use P 1 = P1 in p ′′ 2 cov we can numerically optimize d ′′ 2 = p 2 p ′′ 2
cov versus the transmission probability in the secondary network p 2 . This optimization is presented in Section IV-C and compared with simulation results.

IV. RESULTS

We use β = 4 and the following parameters for the primary and secondary network:

• λ 1 = 0.0001, p 1 = 1, r 1 = 100 m and T 1 varies from 0.01 to 10, • λ 2 = 0.01, r 2 = 10 m and T 2 = 10. We adopt P 2 = 10 mW.

To control the performance reduction of the primary network we use δ = 0.05. We set R = 55 m when we constrain the deployment of the secondary network.

As stated in Section II-B, the primary users compute P 1 in order to maintain their reception probability and the secondary users optimize their transmission probability taking into account the transmission power P 1 computed by the primary users.

A. Optimization without deployment Control

First, to validate our model, we compute the (per node) density of successful transmissions for the secondary network versus the transmission probability p 2 . The comparison beween our model and the simulations are given in Figure 2. We note a very good matching between the model and the simulations. The maximum density of successful transmissions per node is 0.0022 which is roughly a tenth of the maximum density of successful transmissions of the secondary network when this network operates alone, i.e., 0.024. In Figure 3, we compute the power that the primary network must use to keep its reception probability greater than 1 -δ times the reception probability of the primary network when it operates without the secondary network. This power is computed versus the capture threshold T 1 in the primary network. We observe that the increase in this power remains limited. However in Figure 4, we compute the maximun density of successful transmissions per node that the secondary network can obtain versus the capture threshold T 1 . This density of successful transmissions per node could be acceptable for T 1 = 0.01, it is roughly one tenth the value of the density of successful transmissions per node ( i.e. 0.024) when there is no primary network. When T 1 becomes larger, the density of throughput that the secondary network exhibits tends to be very small.

B. Optimization of the selected secondary users

In Figure 5, we compute the density of successful transmissions (per node) for the selected secondary users versus the transmission probability p 2 . The primary network still computes P 1 to maintain its reception probability. The maximum of this density of successful transmissions is 0.0042 reached for p 2 = 0.0078. We have run simulations which confirm this maximum value. We observe that this density of successful transmissions is double for the selected secondary users; in the default configuration this density of successful transmissions is around 0.002 as shown in Figure 4. However if we compute the total throughput obtained by the selected secondary users, we find a total throughput of 0.0042 × λ 2 exp(-πλ 1 R 2 ) = 0.000016 transmissions per surface unit. This value is slightly smaller than the total throughput 0.000022 obtained within the whole network in the default configuration.

C. Secondary network deployed outside exclusion zones

We use equation 3.13 to we compute the density of successful transmissions (per node) when the secondary network is deployed outside exclusion zones. The maximum of this (per node) density of successful transmissions is 0.011 and it is reached for p 2 = 0.021. Simulations confirm this value and justify the assumptions used in the analytical model. We notice that the (per node) density of successful transmissions is roughly half the (per node) density of successful transmissions when the secondary network operates alone. The total network throughput is 0.011 × λ 2 exp(-πλ 1 R 2 ) = 0.000042 transmissions per surface unit. Thus the constraint on its deployment also allows the secondary network to obtain roughly twice the total throughput than in the free deployment. The power P 1 that the primary network must use to maintain its reception probability is 550 mW which is an acceptable value.

D. Other optimizations

The model presented in this paper allows many other optimizations to be conducted. They are not presented in this paper for reasons of space.

V. CONCLUSION

We have shown that simple models can predict the behaviour of two coexisting networks using Aloha as a MAC protocol. The primary network can maintain its performance by using CDMA to reach a small capture threshold and adapting its transmission power. If the secondary network only optimizes its transmission probability, it obtains a small density of successful transmissions. If the secondary network is deployed outside exclusion zones, it can greatly improve the per node density of successful transmissions and also its total throughput.

1 Fig. 1 .

 11 Fig.1. Approximation to compute the Laplace transform of the shot-noise created by the primary network to the secondary network. The shaded area is where nodes of the primary network can be located.

Fig. 2 . 1 P 1 Fig. 3 .

 2113 Fig. 2. Density of successful transmissions for the secondary network versus transmission probability p 2 . Comparison between the analytical model and simulations

Fig. 4 .

 4 Fig. 4. Density of successful transmissions for the secondary network versus the capture threshold T 1 when the secondary network is freely deployed

Fig. 5 .

 5 Fig. 5. Density of successful transmissions for the selected nodes in the secondary network versus p 2


J E Sánchez-Moyano

I García-Asencio

J C García-Gómez

Dpto Fisiología

Zoología

Spatial and temporal variation of the

Keywords: Heavy metal, Odiel estuary, Tinto River, macrofauna, recovery, South-Western Spain

come

INTRODUCTION

The Tinto-Odiel estuary (SW Spain) is one of the most polluted areas in the world, with extremely high concentrations of heavy metals in the sediments [START_REF] Nelson | Heavy metal anomalies in the Tinto and Odiel River and estuary system, Spain[END_REF][START_REF] Ruiz | Trace metals in estuarine sediments from the South-western Spanish Coast[END_REF][START_REF] Sainz | Influence of the very polluted inputs of the Tinto-Odiel system on the adjacent littoral sediments of southwestern Spain: A statistical approach[END_REF] and very acidic waters (pH 2-4) [START_REF] Elbaz-Poulichet | Metal biogeochemistry in the Tinto-Odiel rivers (Southern Spain) and in the Gulf of Cadiz: a synthesis of the results of TOROS project[END_REF]. Both rivers flow through the Iberian Pyrite Belt, one of the most important mining areas in Western Europe, which have been worked since the Phoenician and Roman times. The estuarine zone includes an area of salt marsh and, since the 1960s, a heavily industrialised urban area. This industrial activity includes phosphate fertilizer plants, oil refinery, power plants and other chemical industries.

Furthermore, in this area exists an important port activity that has supposed the construction of a long breakwater, causing an interruption of the littoral sedimentary fluxes, and the periodic dredging of the bottom. However, it is one of the more important wetlands for migrating birds from southern Europe together to the nearby Doñana National Park. This zone was declared as a biosphere reserve by UNESCO's MAB Programme in 1983, Natural Protected Area in 1989 by Andalusia Government, and is a RAMSAR site and a Special Zone for Birds Protection in the European Union.

To minimize the industrial impact on this system, between 1986 and 1998, the Environmental Agency of the Government of Andalusia established the [START_REF] Usero | Contaminación metálica y toxicidad en los sedimentos de los ríos Tinto y Odiel[END_REF]. Since then, the evolution of the estuary has been studied from the chemical point of view, in relation to the origin, variation and nature of the heavy metals [START_REF] Elbaz-Poulichet | Metal biogeochemistry in the Tinto-Odiel rivers (Southern Spain) and in the Gulf of Cadiz: a synthesis of the results of TOROS project[END_REF][START_REF] Ruiz | Trace metals in estuarine sediments from the South-western Spanish Coast[END_REF][START_REF] Bermejo | Spatial variations of heavy metals contamination in sediments from Odiel river (Southwest Spain)[END_REF][START_REF] Sainz | Influence of the very polluted inputs of the Tinto-Odiel system on the adjacent littoral sediments of southwestern Spain: A statistical approach[END_REF]; among others). There are some works related to distribution and migrations of birds (e.g., [START_REF] Sánchez-Moyano | Monitoring of marine benthic communities and taxonomic resolution: an approach through diverse habitats and substrates along the Southern Iberian coastline[END_REF]) and ecophysiology of salt-marsh macrophytes (e.g., [START_REF] Nieva | Field variability of invading populations of Spartina densiflora Brong. in different habitats of the Odiel Marshes (SW Spain)[END_REF]), but there are few studies related to the contamination effects on organisms, except some on Ostracoda and Foraminifera [START_REF] Ruiz | Ostracoda and foraminifera as short-term tracers of environmental changes in very polluted areas: the Odiel Estuary (SW Spain)[END_REF][START_REF] Ruiz | Impact of millennial mining activities on sediments and microfauna of the Tinto River estuary (SW Spain)[END_REF], and ecotoxicology [START_REF] Luque | Metals in halophytes of a contaminated estuary (Odiel Saltmarshes, SW Spain)[END_REF][START_REF] Morillo | Biomonitoring of trace metals in a minepolluted estuarine system (Spain)[END_REF]. On subtidal soft-bottom macrofauna there is only one investigation previous to the implementation of the Correction Plan [START_REF] Cano | Macrobentos endofaunal de la Ría de Huelva[END_REF] and a sampling survey enclosed in a general study of the Gulf of Cadiz communities [START_REF] Drake | Macrobenthic Community Structure in Estuarine Pollution Assessment on the Gulf of Cádiz (SW Spain): is the Phylum-level Meta-analysis Approach Applicable?[END_REF].

The soft-bottom macrofauna is one of the key components of the food web of estuaries and is considered a key element of many marine and estuarine monitoring programmes (Ysebaert and Herman 2002). An extensive literature has described the relationships between the benthic estuarine community and the effect of contaminants (e.g., [START_REF] Pearson | Macrobenthic succession in relation to organic enrichment and pollution on the marine environment[END_REF][START_REF] Warwick | Comparing the severity of disturbance: A metaanalysis of marine macrobenthic community data[END_REF][START_REF] Dauvin | Effects of heavy metal contamination on the macrobenthic fauna in estuaries: The case of the Seine estuary[END_REF].

However, estuaries are stressful environments due to the interaction of local physical, geological, chemical and biological factors (Saiz-Salinas and González-Oreja 2000; [START_REF] Dauvin | Recent changes in estuarine benthic and suprabenthic communities resulting from the development of harbour infrastructure[END_REF]) and, as a consequence, the estuarine macrofauna communities exhibit high resistance to pollution [START_REF] Boesch | Response to stress in marine benthic communities[END_REF]. This often makes difficult the interpretation of the effects of disturbance on the animal communities and confounds the impacts of anthropogenic activity on estuarine biotic integrity [START_REF] Rakocinski | Macrobenthic responses to natural and contaminant-related gradients in northern Gulf of Mexico estuaries[END_REF][START_REF] Dauvin | Recent changes in estuarine benthic and suprabenthic communities resulting from the development of harbour infrastructure[END_REF][START_REF] Dauvin | Effects of heavy metal contamination on the macrobenthic fauna in estuaries: The case of the Seine estuary[END_REF].

The temporal scale of sampling is a key issue for monitoring programmes in the coastal zone [START_REF] Comín | Spatial and temporal scales for monitoring coastal aquatic ecosystems[END_REF]). Many authors argue that the temporal variability of environmental parameters remain high but the changes in the community can be undetected, so that the structure of the communities remain stable for long periods of time [START_REF] Govaere | Benthic communities in the Southern Biight of the North Sea and their use in ecological monitoring[END_REF][START_REF] Livingston | Field sampling in estuaries: the relationship of scale to variability[END_REF][START_REF] Turner | Are soft-sediment communities stable? An example from a windy harbour[END_REF]. Some aspects on environmental disturbance can be identified only if there are sufficient data to show long-term trends which are not usually comparables with short-time scale fluctuations.

Without a long-term perspective, natural variations in community structure could be mistakenly attributed to anthropogenic disturbance [START_REF] Thrush | Impacts on soft-sediment macrofauna: the effects of spatial variation on temporal trends[END_REF][START_REF] Paz | Response of intertidal macrobenthic communities to long term human induced changes in the Eo estuary (Asturias, Spain): Implications for environmental management[END_REF].

The main aim of the present study was to contribute to the evaluation of the environmental quality of the Odiel-Tinto system in function of the spatial-temporal variation of subtidal macrofauna communities. This work spanned a sampling period of 4 years (and 3 sampling events) although our future research objective will be to establish a long-term monitoring programme of the soft-bottom macrofauna that allows us to see if the corrective measures established since 1986 have resulted in remarkable improvement of this environment.

MATERIAL AND METHODS

The Odiel and Tinto Rivers have 128 and 92 km in length, a drainage basin area around 2300 and 1680 km 2 and an average annual water discharge of 405 and 160 Hm 3 , respectively. To the west of the Huelva city, the Odiel River is a well mixed estuary and is divided in numerous channels and islands. The Tinto River also makes up a less extensive mixed estuary to the east of Huelva before the rivers confluence in the Padre Santo Channel. This channel is directed toward south-east along 13 km until the mouth, in the Atlantic Ocean.

Sampling was undertaken during ebb tide in the summer of 1998, 2000 and 2002 at 8 subtidal stations: 3 in Odiel River, 1 in Tinto River and 4 in Padre Santo Channel (fig. 1). The criterion of this spatial design was covering all the subtidal environments of the study area. At each station, six replicates samples (five for biological analysis and one for sediment analysis) were taken with a 0.05 m 2 van Veen grab. Each replicate was sieved in seawater through a mesh of 0.5 mm, fixed with 4% formalin and stained with Bengal rose. Macrofauna was sorted and, whenever possible, identified to family level.

Identification of animals to taxonomic levels equal to or even higher than family has been used in many benthic studies [START_REF] Herman | On the use of meiofauna in ecological monitoring: Who needs taxonomy?[END_REF][START_REF] Warwick | A comparison of some methods for analysing changes in benthic community structure[END_REF][START_REF] Vanderklift | Effect of reducing taxonomic resolution on ordinations to detect pollution-induced gradients in macrobenthic infaunal assemblages[END_REF][START_REF] Pagola-Carte | Concordance degrees in macrozoobenthic monitoring programmes using different sampling methods and taxonomic resolution levels[END_REF]) and has been found to be sufficient to determine changes in the composition of the soft-bottom benthic macrofauna (Sanchez-Moyano et al. 2006).

For sediment analysis, granulometry was assessed following the Boyoucos method [START_REF] Boyoucos | The hydrometer method for making mechanical analysis of soils[END_REF], and organic matter percentage was obtained as weight loss by ignition at 450 ºC for 24 h (mean value of 3 replicates per station). The other sediment parameters were measured by laboratories of the Environmental Agency of the government of Andalusia (South Spain): total organic carbon (TOC) was determined by EPA 415.1; fats and hydrocarbons were measured by extraction and FT-IR spectrophotometry; total nitrogen in the sediment was assessed via Kjedahl digestion; phosphate was measured using UV visible spectrophotometry; and the metal contents were measured by SM 3111 A and B for Cd, Zn, Cu and Cr, and EPA 245.1 for Hg. The index of geoaccumulation (I geo ) has been used as a relative measure of metal pollution in the sediments for Cr, Cu and Zn according to the regional background established by [START_REF] Ruiz | Trace metals in estuarine sediments from the South-western Spanish Coast[END_REF] for unpolluted sandy and silty-clayey sediments. I geo = log 2 (C n /1.5x B n ), where C n is the value of the element n and B n is the background data of that element. Following [START_REF] Ruiz | Trace metals in estuarine sediments from the South-western Spanish Coast[END_REF], the index values were divided in five groups: unpolluted (I geo <1); very low polluted (1<I geo <2); low polluted (2<I geo <3); moderate polluted (3<I geo <4); highly polluted (4<I geo <5); and very highly polluted (I geo >5).

For water analysis, a water sample per station was obtained close to the bottom by a vertical Alpha Van Dorn-style bottle. The following parameter was measured in situ: temperature, conductivity and salinity by conductivimeter WTW LF-323; pH by phmeter WTW 330i; and dissolved oxygen by oximeter WTW Univariate and multivariate analysis for environmental variables and macrofauna communities were performed using the PRIMER v 5.2.8 software package. Previously, the replicate data were pooled for the multivariate analysis. The macrofauna data were analysed to obtain the total number of taxa, abundance, evenness and Shannon diversity Index using neperian logarithms. Spatio-temporal differences for univariate variables were analysed by a two-way ANOVA, after verifying normality (Kolmogorov-Smirnov test) and homogeneity of variances (Barlett test). The data were log 10 (x+1) transformed prior to analysis. Homogenous groups were separated by a Student-Newman-Keuls (SNK) test set at the 5% significance level. Temporal differences for environmental variables were analysed by one-way ANOVA.

Affinities between stations and/or samplings were established using MDS (nonmetric multidimensional scaling) analysis with the taxa abundance (transformed by the fourth root). The validity of the ordination was verified with the Kruskal stress coefficient. The differences in community composition were tested with the nonparametric ANOSIM test [START_REF] Clarke | Statistical design and analysis for a 'biological effects' study[END_REF]. Percentage of similarity analysis (SIMPER) [START_REF] Clarke | Non parametric multivariate analyses of changes in community structure[END_REF]) was used to determine the taxa involved in grouping of the different stations and/or samplings. This analysis, based on the matrix of similarity in taxa abundance obtained from the Bray-Curtis index, calculates the contribution of each taxa to either the dissimilarity between groups of stations (discriminatory taxa) or the similarity within a group (typical taxa). Sediment and water variables (transformed by log x+1) were examined using principal components analysis (PCA).

The relationship between the physical environment and macrofauna assemblages was analysed by BIOENV and canonical correspondence analysis (CCA). BIOENV analysis consists of comparing, through the harmonic rank correlation coefficient of Spearman, the rank similarity matrix on species abundance and the rank similarity matrix obtained through Euclidean distances with the abiotic variables [START_REF] Clarke | A method of linking multivariate community structure to environmental variables[END_REF]. CCA is based on a unimodal response model that constrains the ordination axes to be linear combinations of the environmental variables that maximize the dispersion of sample or taxa scores [START_REF] Braak | Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis[END_REF][START_REF] Braak | Interpreting canonical correlation analysis through biplots of structure correlations and weights[END_REF]). In the ordinations, stations were represented as points and statistically significant environmental variables (after a Monte-Carlo permutation procedure) as arrows

RESULTS

Environmental variables

Water and sediment characteristics are showed in Table 1. Water parameters showed the natural trend of estuarine systems, e.g., increase of pH and decrease of salinity from inner to outer points. Sediments were dominated by silt and clay (<0.063mm) with the exception of the two nearest stations to the mouth (C3 and C4).

The values of organic matter in sediment showed a natural trend of decreasing from high estuary until the river mouth. Other parameters such as TOC and metal contents have showed a similar spatial pattern. In relation to metal contents, most of them have showed higher concentrations in the upstream stations, except Cd and Cr which have showed scarce spatial differences.

To test the differences between sampling events in sediment parameters, one-way ANOVA were used with the station values as replicates (Table 2). There was scarce temporal variation of the composition of the sediments in the entire study zone.

However, phosphate values have showed a great increment during the last sampling year (e.g., station O3 with 20.3 and 174.5 ppm in 1998 and 2002, respectively) while total nitrogen values have decreased in the same period (e.g., station O3 with 1023 and 23.43 ppm in 1998 and 2002, respectively).

In relation to the geoaccumulation index, the stations were classified as unpolluted or very low polluted for Cr (I geo <1 or1<I geo <2). However, during the all study period most of stations were highly or very highly polluted by Zn, and very highly polluted by Cu, except stations C3 andC4 in 1998 (unpolluted) andstation C4 in 2002 (low polluted).

PCA analysis, based on all measured parameters (water and sediment) and all samplings, is plotted in Fig. 2. The first two principal axes retained 61.6% of the variance (eigenvalues 8.5 and 3.8, respectively). The first principal component discriminated the stations mainly based on a gradient of pH (0.32), % organic matter (-0.29) and % of sand (0.30) and silt (-0.31), as stated by the eigenvectors. This axis separated stations according to a natural gradient from estuarine to marine environments. The second axis was influenced by other sediment parameters such as fats (0.39), hydrocarbons (0.39) and copper (0.29).

Macrofauna community

A total of 86 taxa were found in the studied area belonging to Phyla Annelida (27), Arthropoda (27 crustacean taxa), Mollusca (23), Echinodermata (3), Chordata (2), Cnidaria (1), Platyhelminthes (1), Nemertea (1) andPhoronidea (1 3). According to the SNK test (P<0.05), there were significant differences between outer stations (stations C3 and C4) and the rest by all the parameters except abundance (this parameter did not show a clear spatial or temporal trend). Besides, there was a pattern of increasing of diversity, evenness and taxa number since 1998 to 2002 samplings, mainly in the channel stations (C1 to C4).

A global MDS analysis of macrofauna community shows, independently of sampling period, the presence of two groups of stations: channel mouth stations (C3 and C4) and the more upstream points (Fig. 4). The first group is represented by the stations with more influence of the marine areas, sandy sediments and lower contents in sediment parameters. Meanwhile, the second group is comprised by stations with silty sediment and higher contents in the most of sediment parameters. Furthermore, a temporal pattern can be observed in MDS ordination from 1998 to 2002 samplings (it is indicated as a vertical arrow in Fig. 4).

The spatial-temporal differences in community composition were tested using a two-way crossed ANOSIM test. The test for stations gave a value of the global R= 0.57 (significance level of 0.1%) so that there were spatial differences across all samplings.

Nevertheless, the pairwise test showed no significant differences between stations O2 and O3 (R= 0.23, significance level= 3.5%) and between T1 and C1 (R= 0.14, significance level= 6.6%). For samplings, there were also global differences (R= 0.65, significance level= 0.1%), although R values for pairwise test showed no significant differences between 1998 and 2000 samplings (R= 0.38).

One-way ANOSIM tests for each sampling showed spatial differences at a significance level of 0.1% (1998: R= 0.40; 2000: R= 0.45; 2002: R= 0.66).

Homogeneous groups according to pairwise test are plotted in Fig. 5. At all samplings, the inner stations could be considered as a homogeneous group, meanwhile outer channel stations (C3 and C4) showed significant differences with the rest.

The SIMPER analysis gave the best discriminating taxa between the groups of stations or sampling periods identified in the multivariate analyses. Table 4 gives the contributions of taxa to discriminate between inner and channel mouth stations. In 1998 sampling, the differences (average dissimilarity =85.6%) were based on the presence at channel mouth stations of taxa such as the venerid and mactrid molluscs or the phyllodocid polychaetes, while inner stations were mainly characterised by the high abundance of the spionid polychaetes. In 2000 sampling, the differences (average dissimilarity =73.3%) were based on similar taxa to 1998 sampling: e.g., presence at channel stations of taxa such as the venerid and corbulid molluscs and orbiniid polychaetes, and again, high abundance of spionids (average abundance of 1054 individuals) and the anthurid crustaceans at inner areas. In 2002 sampling, the differences (average dissimilarity =71.2%) were based on a similar pattern to previous samplings again (e.g., high abundance of venerids and mactrids at outer areas), however, more taxa took part in these differences; there were higher abundance of cardiids at inner stations and spionids showed a more homogeneous distribution along study zone. In terms of temporal changes (Table 5), SIMPER distinguished only between inner and outer areas. In the first ones, the main differences among sampling periods were based on higher abundance of spionids and anthurids in 2000 (e.g., spionid average abundance of 193, 1054 and 248 individuals, respectively), and higher abundance of cardiids and the pectinariid polychaetes in 2002. At the outer channel area, the main differences were based on the presence of a greater number of taxa in 2000 and 2002 as opposed to 1998 sampling.

Relationship between environmental and macrofauna

The results of a global BIOENV analysis (all samplings pooled) indicated that the best correlations always occurred with % sand, organic matter, phosphates and TOC (maximum correlations of 0.44). Separately, a BIOENV with 1998 data showed that the best correlations occurred with others variables such as pH, TOC, Hg or organic matter (maximum correlation of 0.77 with pH, TOC, Hg and Cd). However, in 2000 and 2002 samplings, these best correlations were obtained with the variables related with granulometry and water characteristics (maximum correlations of 0.73 with pH and % sand in 2000, and of 0.79 with temperature, salinity, dissolved oxygen, fats and % silt and clay in 2002).

The global CCA analysis (Fig. 6) indicated a similar distribution of stations to MDS ordination: channel mouth stations (C3 and C4) and the upstream area. The environmental variables that best explained the observed community distributions were pH and % of sand toward channel mouth and heavy metals, % organic matter and salinity toward inner points. The second axis was influenced by hydrocarbons and phosphates and discriminated between 2002 sampling and the others two periods. The Monte-Carlo test was significant for both axes (P=0.01).

DISCUSSION

The natural gradients in salinity, granulometry and organic content have been described as the most important factors to explain the distribution and abundance of macrobenthic community in numerous estuarine ecosystems (Wolff 1983;Warwick et al. 1991;[START_REF] Attrill | The influence of drought-induced low freshwater flow on an upper-estuarine macroinvertebrate community[END_REF][START_REF] Rakocinski | Macrobenthic responses to natural and contaminant-related gradients in northern Gulf of Mexico estuaries[END_REF]Ysebaert et al. 2002;[START_REF] Mucha | Macrobenthic community in the Douro estuary: relations with trace metals and natural sediment characteristics[END_REF][START_REF] Sousa | Spatial subtidal macrobenthic distribution in relation to abiotic conditions in the Lima estuary, NW of Portugal[END_REF]). In general, The Tinto-Odiel system has shown typical water and sediment characteristics of estuarine areas at the spatial scale. This is corroborated through PCA analysis in which the stations were separated according to a natural gradient from estuarine to marine environment.

A main characteristic of Tinto-Odiel system is the high concentration of some heavy metals in sediment, in comparison to other European rivers [START_REF] Elbaz-Poulichet | Metal biogeochemistry in the Tinto-Odiel rivers (Southern Spain) and in the Gulf of Cadiz: a synthesis of the results of TOROS project[END_REF]. For example, the maximum value of Zn in the present study was 6378 ppm (Station C1 in 2000 sampling), which is much higher than 169 ppm in the Seine estuary, the most heavily contaminated French river [START_REF] Dauvin | Effects of heavy metal contamination on the macrobenthic fauna in estuaries: The case of the Seine estuary[END_REF] or >200 ppm in the Douro estuary (Portugal: [START_REF] Mucha | Spatial and seasonal variations of the macrobenthic community and metal contamination in the Douro estuary (Portugal)[END_REF]. In other geographically near estuary, the Guadiana River, the concentrations only reached maximum values of 149 ppm [START_REF] Sánchez-Moyano | Influencia del gradiente ambiental sobre la distribución de las comunidades macrobentónicas del estuario del río Guadiana[END_REF]. This last estuary, opposite to Tinto-Odiel, is located in an extensive agricultural zone, with few inhabitants and the main disturbances are moderate urban sewages. The same pattern was observed for other metals such as Cu: 2448 ppm in Tinto-Odiel system; 44.1 ppm in the Seine; 80 ppm in the Douro; and 20 ppm in the Guadiana. For both metals, the geoaccumulation indices showed highly or very highly polluted level (except at the mouth river stations). These data is in agreement with the results obtained in this zone by [START_REF] Ruiz | Trace metals in estuarine sediments from the South-western Spanish Coast[END_REF]. According to [START_REF] Ruiz | Stratigraphic sequence, elemental concentrations and heavy metal pollution in Holocene sediments from the Tinto-Odiel Estuary, southwestern Spain[END_REF], the origin of these heavy metals is industrial discharges (70-80%), acid-mine drainage (20-30%) and minor contributions from urban effluents, while near the mouth, the sediments are periodically dredged and have not had sufficient time to accumulate a high metal content, by which they show very low geoaccumulation indices for all the metals. Furthermore, industrial dumping corrective measures have resulted in remarkable local improvement but have not had a significant effect on the global contamination in the estuary (Sainz et al. 2003).

In spite of the historical knowledge of the high level of contamination of the Tinto-Odiel system, there is only a single study on the structure of macrofauna communities [START_REF] Cano | Macrobentos endofaunal de la Ría de Huelva[END_REF]. All estuaries are characterized by a soft-bottom macrobenthic communities impoverished in relation to those from marine sediments (Wolff 1983;Warwick et al. 1991;[START_REF] Rakocinski | Macrobenthic responses to natural and contaminant-related gradients in northern Gulf of Mexico estuaries[END_REF][START_REF] Peeters | Contribution of trace metals in structuring in situ macroinvertebrate community composition along a salinity gradient[END_REF] and this can difficult the interpretation of the effects of pollution on the structure of the animal communities. However, it has been demonstrated that the human impact increases this impoverishment in species diversity, for example, [START_REF] Marques | Human impact assessment on the subtidal macrobenthic community structure in the Mondego Estuary (Western Portugal)[END_REF] observed that the subtidal macrofauna in the Mondego Estuary appeared to be clearly impoverished compared to other Portuguese estuary much less exposed to human impacts.

In the present study, and according to MDS and CCA analysis, the different stations were distributed according to a natural gradient from estuarine to marine environments. Independently of sampling year, the structure of the community has been mainly determined by granulometry and the organic matter content in sediment, which are considered as the major structuring factors for the natural macrobenthic distribution pattern together with salinity (Warwick et al. 1991;Ysebaert et al. 2002;[START_REF] Mucha | Macrobenthic community in the Douro estuary: relations with trace metals and natural sediment characteristics[END_REF][START_REF] Sousa | Spatial subtidal macrobenthic distribution in relation to abiotic conditions in the Lima estuary, NW of Portugal[END_REF]. Community variables such as Shannon Diversity or taxa number showed the typical trend of estuaries, with a progressive increment towards the mouth river, but in a wide interval (e.g., H'= 0.96 and only 2 families at O1 until H'= 2.84 and 46 families at C4), which seem to demonstrate a strong stress on communities, at least at inner areas. High concentrations of heavy metals have been associated with low number of taxa and diversity in other estuaries [START_REF] Mucha | Spatial and seasonal variations of the macrobenthic community and metal contamination in the Douro estuary (Portugal)[END_REF]. In fact, high concentrations of metals, together with the granulometric composition and water characteristics, has been one of the main factors in the ordinations of stations through Odiel-Tinto system, although it is not possible to determine separately the effect from each one of these factors. The macrobenthic community was dominated by polychaetes and, especially, by small size opportunistic taxa such spionids; while the crustaceans, the most sensitive marine animal group to pollution [START_REF] Warwick | Evidence for the effects of metal contamination on the intertidal macrobenthic assemblages of the Fal estuary[END_REF][START_REF] Dauvin | Effects of heavy metal contamination on the macrobenthic fauna in estuaries: The case of the Seine estuary[END_REF], were the least abundant.

Recovery of marine ecosystems from pollution is inevitably a long-term process [START_REF] Hawkins | Recovery of polluted ecosystems: the case for long-term studies[END_REF]. A four year sampling period appears to be too short-term to establish definitive conclusions on possible improvements of the system after the implementation of corrective measures. This is particularly true for grossly polluted estuaries which are organically enriched from sewage discharges, and receive heavy metals and other contaminants [START_REF] Matthiessen | Contaminants and their effects on estuarine and coastal organisms in the United Kingdom in the late twentieth century[END_REF][START_REF] Essink | Response of an estuarine ecosystem to reduced organic waste discharge[END_REF]Gonzalez-Oreja and Saiz-Salinas 2003). Compared to results obtained in 1980-81 from [START_REF] Cano | Macrobentos endofaunal de la Ría de Huelva[END_REF], the diversity values or richness were very similar to those obtained in 1998. However, an increase of values of diversity index or number of taxa was observed since 1998 to 2002, and this "improvement" has been more notable during the last sampling period. The main difference between 1980-81 study and the present work has been a drastic decrease of the nereidid polychaetes (especially Hediste diversicolor), which were replaced by small size spionids. Hediste diversicolor is described as a species indifferent to pollution [START_REF] Pearson | Macrobenthic succession in relation to organic enrichment and pollution on the marine environment[END_REF], even in grossly polluted sediment (Gonzalez-Oreja and Saiz-Salinas 2003), so that it is difficult to attribute some cause to this particular change in the community composition.

Since 2000 sampling, other changes have been a high abundance of the anthurid isopods (exclusively Cyathura carinata) and greater richness and abundance of crustaceans. According [START_REF] Warwick | Comparing the severity of disturbance: A metaanalysis of marine macrobenthic community data[END_REF], the structure of estuarine softbottom communities generally shifts with environmental stress to one that is less dominated by crustaceans and more dominated by polychaetes or oligochaetes.

Furthermore, pericarid crustaceans appeared to be sensitive to sediment contamination, except some opportunistic amphipod such as Corophium [START_REF] Rakocinski | Macrobenthic responses to natural and contaminant-related gradients in northern Gulf of Mexico estuaries[END_REF].

Parallel, a high abundance of the cardiid molluscs (group represented here exclusively by Cerastoderma edule) occurred in the inner zone since 2000 sampling (with peak of abundance in 2002 period). The exclusion or restriction of some species of bivalves, such as C. edule, in the Fal estuary (UK) has been attributed in part to a strong copper and zinc pollution [START_REF] Matthiessen | Contaminants and their effects on estuarine and coastal organisms in the United Kingdom in the late twentieth century[END_REF], just as it happened in Odiel-Tinto estuary.

Considering all information, some changes have been noticed along the sampling period. There were (1) slight increment in richness and diversity; (2) higher presence of molluscs and crustaceans in the inner zones; (3) a more homogeneous spatial distribution of opportunistic taxa (e.g. Spionidae); (4) a higher number of taxa characterizing the differences among the estuarine sectors. However, the period is too short to conclude that these changes are the consequence of a true improvement of the environment or to natural cycles. In this sense, it would be necessary the establishment of a long-term monitoring programme to study the evolution of the macrofauna communities to state if the corrective measures could achieve a future remarkable improvement of this environment. This programme should focus on the study of macrobenthic community structure and on selected parameters (e.g., sediment composition, organic content, salinity and Cu and Zn) which have been the major structuring factors for these communities. ) of the taxa identified in the Odiel-Tinto estuary in each station and sampling period. 6 CCA analysis plot for all stations and sampling periods from selected parameters of water and sediment: pH, salinity (Sal), % of sand, heavy metals (Hg, Zn, Cu and Cd), hydrocarbons (Hydc.), phosphates (Phosp.), organic matter content (OM) and total organic carbon (TOC). The percentage of variability explained by the axis is given

  ). Abundance for each station and sampling is presented in Appendix 1. Polychaetes were the dominant group in all stations and samplings, mainly the abundance of the Spionidae. Spatial and seasonal variations of number of taxa, Shannon diversity index and Pielou's evenness are plotted in Fig. 3. These univariate parameters have showed a general pattern of increasing toward the channel mouth, except for abundance at upstream stations (O1 to T1) during 2000 sampling (by the contribution of the polychaetes Spionidae) and station O3 during 2002 (2520 individuals m -2 of the molluscs Cardiidae). Taxa number and Shannon diversity have ranged at a wide interval (e.g., 2 and 46 families and 0.96 and 2.84 of diversity index at stations O1 and C4, respectively, during 2002 sampling). The spatio-temporal differences of these univariate parameters were tested by two-way ANOVAs (Table

Fig. 4 Fig. 5

 45 Fig.4MDS ordination for all stations and sampling periods, using Bray-Curtis similarities on taxa abundance. Horizontal and vertical arrows show the spatial and temporal pattern of station distributions

  Figure1

Table 1 :

 1 Values of sediment and water parameters in each station and sampling period.

Table 2 :

 2 Results of the one-way ANOVA for the temporal differences of sediment parameters. The homogeneous groups according to the SNK test (P<0.05) are indicated with a continuous line. ns= not significant. Degrees of freedom= 2.

	Parameter	MS	MS error	F	P	Homogeneous group
	% Organic matter	0.24	0.11	2.19	ns	-
	% Silt and clay	0.04	0.53	0.08	ns	-
	TOC	0.06	0.06	1.11	ns	-
	Cd	0.01	0.06	0.19	ns	-
	Zn	0.06	0.23	0.28	ns	-
	Cu	0.12	0.49	0.24	ns	-
	Cr	0.15	0.16	0.94	ns	-
	Hg	0.04	0.14	0.32	ns	-
	Fats	0.26	0.06	4.26	<0.03	1998 2000 2002

Table 3 :

 3 Results of the two-way ANOVA for the spatio-temporal differences of univariate parameters. ns= not significant.

	Parameter	Df	MS	MS error	F	P
	Abundance
	Station	7	0.86	0.16	5.26	0.001
	Year	2	1.26	0.16	7.68	0.001
	Iteration	14	0.69	0.16	4.23	0.0003
	Taxa number
	Station	7	0.56	0.33	16.64	0.00001
	Year	2	0.46	0.33	13.74	0.00001
	Iteration	14	0.12	0.33	3.44	0.0003
	J
	Station	7	0.27	0.007	4.01	0.001
	Year	2	0.38	0.007	5.68	0.005
	Iteration	14	0.008	0.007	1.27	n.s.
	H'
	Station	7	0.15	0.01	14.45	0.00001
	Year	2	0.11	0.01	10.16	0.0001
	Iteration	14	0.27	0.01	2.54	0.006

Table 4 :

 4 Average abundance (Av. Abund.) of the most relevant taxa of the stations located in the inner and outer areas in each sampling. Taxa are listed in decreasing order according to its contribution to the average of the dissimilarity (Av.

	Diss.) between areas.
		INNER	OUTER
	Taxa	Av.Abund	Av.Abund	Av.Diss	Ratio	Contrib%	Cum.%
	1998
	Average dissimilarity = 85.6
	Veneridae	0	426.7	8.1	2.3	9.4	9.4
	Phyllodocidae	0	320	7.6	3.4	8.9	18.3
	Mactridae	0	193.3	6.2	1.8	7.2	25.5
	Orbiniidae	0	36.7	4.9	5.8	5.8	31.3
	Spionidae	193.3	10	4.9	1.8	5.7	37
	Capitellidae	0	10	3.6	5.4	4.2	41.2
	Dexaminidae	0	10	3.5	7.5	4.1	45.3
	Phoronidea	3.3	23.3	3.4	1.6	3.9	49.3
	2000
	Average dissimilarity = 73.3
	Spionidae	1054.4	55.3	3.4	1.9	4.6	4.6

Table 5 :

 5 Interannual variation of the average abundance (Av. Abund.) of the most relevant taxa of the stations located in the inner and channel mouth stations. Taxa are listed in decreasing order according to its contribution to the average of the dissimilarity (Av. Diss.) between sampling periods.

	Taxa	Av.Abund	Av.Abund	Av.Diss	Ratio	Contrib%	Cum.%
	INNER STATIONS
		1998	2000	(Average dissimilarity = 69.24)
	Anthuridae	0.00	132.22	7.38	2.68	10.66	10.66
	Nereididae	1.11	60.00	5.84	2.65	8.43	19.09
	Spionidae	193.33	1054.44	5.09	1.46	7.35	26.44
	Oligochaetes	88.89	32.22	4.99	1.90	7.20	33.65
		1998	2002	(Average dissimilarity = 72.78)
	Cardiidae	5.56	486.00	9.82	1.59	13.49	13.49
	Pectinariidae	0.00	94.00	6.85	1.43	9.42	22.90
	Spionidae	193.33	248.00	5.24	1.61	7.19	30.10
	Nephtyidae	11.11	24.67	4.49	1.08	6.17	36.27
		2000	2002	(Average dissimilarity = 69.56)
	Spionidae	1054.44	248.00	5.86	1.43	8.42	8.42
	Nereididae	60.00	0.67	5.24	2.53	7.53	15.95
	Anthuridae	132.22	4.67	5.17	1.46	7.43	23.38
	Pectinariidae	0.00	94.00	4.64	1.48	6.67	30.05
	Capitellidae	36.67	0.00	4.20	1.45	6.03	36.09
	Oligochaetes	32.22	0.00	4.17	1.82	6.00	42.08
	Cardiidae	28.89	486.00	3.78	1.13	5.43	47.52

ACKNOWLEDGEMENTS

We thank to Dr. Francisco Estacio and Emilio García-Adiego for their assistance in the field and in the laboratory; the crew members of the ship AMA 6 (Mariano Campoy, Blas Brito and José María Ávila), and Consejería de Medio Ambiente of the Government of Andalusia for financial support (projects OG-104/01, OG-013/03).

Echinoidea