Sandra Johnson
email: sandra.johnson@qut.edu.au

Fiona Fielding

Grant Hamilton

Kerrie Mengersen

An Integrated Bayesian Network Approach to Lyngbya majuscula Bloom Initiation

Keywords: Bayesian network, cyanobacteria, DOOBN, dynamic, IBN, Lyngbya majuscula, object oriented, OOBN

Blooms of the cyanobacteria Lyngbya majuscula have occurred for decades around the world. However, with the increase in size and frequency of these blooms, coupled with the toxicity of such algae and their increased biomass, they have become substantial environmental and health issues. It is therefore imperative to develop a better understanding of the scientific and management factors impacting on Lyngbya bloom initiation. This paper suggests an Integrated Bayesian Network (IBN) approach that facilitates the merger of the research being conducted by various parties on Lyngbya.

Pivotal to this approach are two Bayesian networks modelling the management and scientific factors of bloom initiation. The research found that Bayesian networks (BN) and specifically Object Oriented BNs (OOBN) and Dynamic OOBNs facilitate an integrated approach to modelling ecological issues of concern. The merger of multiple models which explore different aspects of the problem through an IBN approach can apply to many multifaceted environmental problems.

Introduction

Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide (Osborne et al., 2001;Arquitt and Johnstone, 2004;Dennison et al., 1999), including Moreton Bay in Queensland, Australia. Lyngbya grows on the sediment or over the seagrass, algae or coral (Dennison and Abal, 1999;Watkinson et al., 2005) and when the conditions are favourable, the algae goes through a rapid growth phase, resulting in a substantial increase in biomass, commonly referred to as a bloom [START_REF] Ahern K S, Ahern C R, Savige | Mapping the distribution, biomass and tissue nutrient levels of a marine benthic cyanobacteria bloom (Lyngbya majuscula)[END_REF]Hamilton et al., 2007c). Lyngbya blooms appear to be increasing in both frequency and extent (Dennison and Abal, 1999;Albert et al., 2005;[START_REF] Ahern K S, Ahern C R, Savige | Mapping the distribution, biomass and tissue nutrient levels of a marine benthic cyanobacteria bloom (Lyngbya majuscula)[END_REF], which can have major ecological (Stielow and Ballantine, 2003;Paul et al., 2005;Watkinson et al., 2005), health (Osborne et al., 2001;Osborne et al., 2007) and economic consequences (Dennison and Abal, 1999). It is therefore imperative to better understand the scientific and management factors that drive the initiation of L. majuscula blooms.

Deception Bay, located in Northern Moreton Bay in Queensland, Australia, has a history of Lyngbya blooms (Watkinson et al., 2005;[START_REF] Ahern K S, Ahern C R, Savige | Mapping the distribution, biomass and tissue nutrient levels of a marine benthic cyanobacteria bloom (Lyngbya majuscula)[END_REF] and forms a case study for this investigation. With its proximity to Brisbane, Australia's third largest city with an estimated population in 2004 of 1.78 million (ABS, 2004), it is a popular tourist destination. The many waterways feeding from intensive and rural agricultural activities into the Bay and its use for commercial and recreational fishing, put pressure on the marine environment and compound the issues resulting from a nuisance algal bloom (Dennison and Abal, 1999).

A modelling approach was required to identify the high priority research that needed to be undertaken into the poorly known features of Lyngbya initiation.

Therefore it was necessary to capture and represent all the available data and expert knowledge about the initiation of Lyngbya blooms in Deception Bay. This approach had to engage stakeholders, represent the available information at different spatial and temporal scales, identify scientific and management factors affecting Lyngbya initiation and quantify the factors and their inter-dependencies. Moreover, the stakeholders were particularly diverse comprising ecologists and scientists familiar with Lyngbya and the factors that affect its bloom, state and local government representatives, committee members of local organisations, as well as individuals with an active interest in Lyngbya, including a third generation local fisherman with decades of accumulated knowledge of Lyngbya blooms in the Bay.

There are several modelling approaches that could be considered for such a problem, including decision trees, stochastic petri nets and Bayesian networks. A decision tree has a "top-down approach". The first factor (root node) at the top of the tree is split according to the decision taken. Each subsequent node is then split in a similar way (Janssens et al., 2006). This approach lacked the ability to represent the many interactions between the factors which would be needed to model the initiation of a Lyngbya bloom. A stochastic petri net (SPN), also known as a place/transition net is used to model concurrent systems (Angeli et al., 2007). Implementing a SPN is not trivial, even with the use of bespoke software. It mandates some statistical knowledge as well as some familiarity with stochastic process theory and Monte Carlo simulation techniques (Goss and Peccoud, 1998). A Bayesian Network (BN) provides a graphical representation of key factors, which are represented as nodes in the diagram and their causal relationships with each other and with the outcome of interest (Borsuk et al., 2006;McCann et al., 2006;Jensen and Nielsen, 2007;Uusitalo, 2007) are depicted as directed links or arrows connecting a 'parent node' to a 'child node', resulting in a directed acyclic graph (DAG) (Saddo et al., 2005;Jensen and Nielsen, 2007;Uusitalo, 2007;Park and Stenstrom, 2008). BNs are better able to portray the complexity of the decision process and the many inter-dependencies between the factors of the decision process (Janssens et al., 2006). Moreover, they are visually appealing, easy to use, comprehend and interact with. For more detailed information about the advantages and disadvantages of BNs and comparisons with alternative statistical methods, we refer the reader to (Wilson et al., 2006;Uusitalo, 2007;[START_REF] Ahmed | A comparison of methods for assessing penetrating trauma on retrospective multi-center data[END_REF].

Bayesian networks have been used successfully to better understand and model many complex environmental problems (Bromley et al., 2005). They facilitate the representation of different management decisions and scenarios that may impact on the environmental issue being modelled and the consequences of these situations and actions (McCann et al., 2006;Uusitalo, 2007). However, the focus of many networks is often on a single aspect of the outcome, and multi-faceted inferential needs are most commonly addressed through multiple independent networks. This paper describes an approach to integrating diverse knowledge about Lyngbya bloom initiation in the Deception Bay area, by developing an Integrated Bayesian Network (IBN). The IBN comprises a series of BNs designed to conceptualize and quantify the major factors and their pathways contributing to the initiation of Lyngbya, from both scientific and management perspectives. In Figure 1 a unified modelling language (UML) use case diagram illustrates the conceptual processes of the Lyngbya IBN. To our knowledge an IBN approach has not previously been applied to Lyngbya bloom initiation.

(Place figure 1 here)

In Section 2 we describe the characteristics of a traditional BN, an object oriented BN (OOBN) and the natural progression to a dynamic OOBN (DOOBN). We then introduce the integrated BN approach (IBN) which consolidates the information held in various networks and models. We present the results of this approach in Section 3 by applying it to the initiation of Lyngbya blooms.

Methods

Bayesian Network (BN)

As described in Section 1, a BN visualizes knowledge about an ecological issue of interest with the important factors depicted as nodes in the network.

These nodes may be at different temporal and spatial scales and the data represented in the BN may originate from diverse sources such as empirical data, expert opinion and simulation outputs (Saddo et al., 2005;Borsuk et al., 2006;McCann et al., 2006;Jensen and Nielsen, 2007;Pollino et al., 2007;Park and Stenstrom, 2008). In the case of the Lyngbya network, the outcome of interest is the initiation of a Lyngbya bloom. (Saddo et al., 2005;Pollino et al., 2007;Park and Stenstrom, 2008).

(Place Table 1 here) Two important characteristics of a BN which also simplify probability calculations are directional separation (d-separation) and the assumption of the Markov property (Jensen and Nielsen, 2007). The criterion for dseparation was first proposed by (Pearl, 1988) and an alternative criterion was specified by Lauritzen et al., (1990). If nodes are d-separated then they are conditionally independent (Kjaerulff, 1995, Taroni et al., 2006). The Markov property means that the probability distribution of a variable depends only on its parents. Consequently from the multiplication law of elementary probability theory, the conditional independence (d-separation) and the Markov property enable the probability distribution of a BN with n nodes ( ) n X X ,... 1 to be factorized as follows:

( ) ( ) ( ) ∏ = = n i i i n X Pa X P X X P 1 1 ,... where ( ) i X Pa
is the set of parents of node i X

This greatly simplifies calculations of the joint probability distribution and allows us to focus on each node in turn to combine the expertise and data available for that node and its parents. The BNs described in this paper were developed as part of a larger study of the major factors and their pathways contributing to the initiation of Lyngbya blooms. They were constructed in close collaboration with a Lyngbya Science Working Group (LSWG) drawn from a range of disciplines and a Lyngbya Management Working Group (LMWG) drawn from local and state government and private organisations [START_REF] Abal | Healthy Waterways: Healthy Catchments -An Integrated Research/Management Program to Understand and Reduce Impacts of Sediments and Nutrients on Waterways in Queensland, Australia[END_REF].

The Science Network focused on nutrient and physical factors that were agreed by the LSWG to be the most influential contributors to the initiation of Lyngbya. To construct the Science Network, numerous meetings were convened to determine the most important factors that were believed to have an impact on the ecosystem surrounding Deception Bay. Once the initial structure was agreed upon, the factors were then clearly defined. This was necessary to ensure throughout the process all involved could refer to these definitions to agree that this was indeed the focus of that particular aspect. Waterways report (Fielding et al., 2007), and is available on the organisation's website.

Validation of the BN was assessed in three ways: through sensitivity analysis, outcomes comparison and scenario testing. Sensitivity analysis is a popular technique in mathematical modelling and the field of decision theory to investigate uncertainty in a model's parameters and their effect on the model output (Hamby, 1994;Coupe et al., 2000). For BNs this means studying the changes in the probabilities of the target node as a result of changes in the network's CPT values (Coupe et al., 2000). In the Science BN the probabilities of one node was varied, while keeping the others fixed, and then observing the changes in the probability of a Lyngbya bloom initiation.

Sensitivity analysis is considered crucial to model validation and for targeting further research (Hamby, 1994). It is performed on the BN model to reduce uncertainty in the target node and to identify those nodes that have the largest impact on the target node (Hamby, 1994;Coupe et al., 2000). Additional research effort can then be directed to the quantification of those nodes (Bednarski et al., 2004).

Outcomes comparison involves comparison between external data and model

predictions. In the case of the Lyngyba Science BN, no such external data were available since all known available data had been used to populate aspects of the BN. Moreover, for any observed Lyngbya outbreak, data were not available for the complete set of nodes in the BN model. As a result, a more limited outcomes comparison was undertaken through scenario testing, in which selected scenarios reflected known conditions associated with documented initiation or lack of initiation of Lyngbya outbreaks in the last 30 years.

Scenario testing is important to investigate model behaviour for different expert defined scenarios, assessing whether the model behaves as expected in light of past experience and in accordance with current credible research (Laskey, 1995;Bednarski et al., 2004). The expert team therefore nominated scenarios of interest and evidence was entered into the BN to represent these scenarios. The relevant nodes were updated to reflect the proposed scenario and this evidence was propagated through the BN to update the probability of a Lyngbya bloom initiation under those conditions (Laskey, 1995;Bednarski et al., 2004). For example, evidence of 'best practice' was entered into the BN by setting the Point sources node to low. Further sensitivity analysis was then performed on the other nodes in the BN to observe the sensitivity of the target node to changes in node probabilities for that scenario.

The Management Network focused on management inputs that potentially

Object Oriented Bayesian Network (OOBN)

The basic building block in an Object Oriented Bayesian Network (OOBN) is an object, which can be a physical or an abstract entity, or a relationship between two entities. Typically, an entity comprises one or more nodes in a BN that are related in a physical, functional or abstract sense. From a probabilistic point of view, the attributes (nodes and links) are encapsulated in an object and therefore d-separated from the rest of the network.

The definition of classes of objects in OOBNs enables a more generic, reusable network to be described, which can then be used in different contexts. A class is a generic network fragment and when this class is instantiated it is called an object. A class may be instantiated many times (Jensen and Nielsen, 2007). It is not uncommon for several classes to share common substructures. These subclasses can inherit many attributes and behaviours from the parent class, which they can then modify and enhance.

The parent class can be viewed as being more abstract than its subclasses with only the important details being retained, whereas subclasses define more specific attributes and behaviours. The ability to create subclasses that inherit properties from another class is a well known and very useful characteristic of object oriented modelling (Koller and Pfeffer, 1997).

Applying this object oriented approach to BN modelling, an OOBN can be instantiated within another OOBN. An instantiated OOBN is called an instance node and represents an instance of another network, which in turn could contain instance nodes. Connectivity between these OOBNs is achieved through interface nodes (input nodes and/or output nodes) (Hugin, 2007;Jensen and Nielsen, 2007). It is clear that OOBNs enable a more structured, hierarchical approach to modelling and consequently the construction of complex and dynamic models (Koller and Pfeffer, 1997;Hepler and Weir, 2008).

The groups of nodes defined in Section 2.1 for the Science Network formed the basis for the creation of OOBN sub-networks, for example the 'Dissolved Elements subnet' and 'Light subnet', respectively. Thereafter the interface nodes were identified and added to the sub-networks to facilitate the transfer of information and evidence into and out of the sub-nets. The OOBN subnetworks were then linked via the interface nodes to recreate the Lyngbya Science network. The new structure now facilitated the independent parallel development and interrogation of the sub-networks so that they could be reintegrated into the parent network when they were deemed to be complete.

Dynamic Object Oriented Bayesian Network (DOOBN)

The had the best predictive behaviour. Rainfall at a lag of one month was the only other variable that appeared in the top five identified models.

Integrated Bayesian Network (IBN)

We describe here the IBN for the probability of initiation of a Lyngbya bloom.

This network comprises two primary BNs, the Management Network and the

Science

Results

The static Science BN for initiation of Lyngbya is depicted in figure 4 The Science BN was also interrogated using management and climatic scenarios and analysing the effect on the probability of bloom initiation to changes in the various factors. The predicted changes in the probability of a

Lyngbya bloom initiation as a result of each of the seven most influential factors in isolation, is shown in Table 2. In a 'typical' year, as defined by the LMWG, the probability of a bloom initiation was reported as 28%; this increased significantly during a severe summer storm event to 42%, when light climate was optimal and rain-present was high. Bloom initiation was a predicted as a certainty (100%) when the available nutrient pool (dissolved)

was enough, temperature was high and light climate was optimal. However, when only the available nutrient pool (dissolved) was set to 'not enough', the probability of a bloom initiation dropped to 3%, but jumped to 80% when it was changed to 'enough'. Although bottom current climate was a key influential factor, changing only this factor caused the probability of bloom initiation to drop to 15% when the bottom current climate was 'high' and to increase to 43% when it was 'low'. This is a variation of 28% in the probability of a bloom initiation and although large, is clearly overshadowed by the 77% variation caused by changes in nutrient availability. Changing iron availability alone increased the probability of a bloom initiation from 21% to 37%.

Changing organics availability alone increased the probability of a bloom initiation from 25% to 31%.

(Place Table 2 here (Place Figure 7 here)

As can be seen in figure 8, the rainfall information for a particular month is bound to the Rain present input node in the Rainwater and Science model sub-network instances for that month and the Groundwater Amount and Land Run-off output nodes from one month bind to the Prev Groundwater and Prev Land Run-off input nodes of the following month, respectively.

(Place Figure 8 here)

The point and diffuse nutrient sources contributing to the Management Network for Lyngbya initiation included: aquaculture, composting, onsite sewage, poultry, waste disposal, waste water treatment plant, agriculture, artificial development, development and clearing, extractive industries, forestry, grazing, natural vegetation and stormwater. The sources and nutrients identified by the management committee are shown in Table 3.

An extract of the Management Network, which identifies and locates point and diffuse sources of nutrients for the Mellum Creek Sub-catchment, visually represented in Netica®, is shown in figure 9.

(Place Figure 9 here) Scenario modelling predicted higher probabilities of Lyngbya bloom initiation during the summer months and confirmed the temporal nature of Lyngbya bloom initiation. Incorporating this behaviour resulted in the DOOBN for Lyngbya bloom initiation (figure 7) being developed as outlined above with one month lag effects included for groundwater amount and land run-off.

As shown in figure 10 below, the BN predicts a sharp increase in the probability of initiation of a Lyngbya bloom from the end of spring (November)

to the first month of summer (December). The increased probability continued during the next two summer months, with a slight fall in autumn (March).

Although these predicted probabilities for Lyngbya bloom initiation are low, the increased trend in bloom initiation is clearly visible. When evidence of summer rainfall was added to these time slices we observed a more dramatic increase. For example, the probability of a bloom initiation was predicted as 52% when evidence of a summer rainfall event was entered into the December time slice. This compares to 42% in the original static annual BN model.

(Place Figure 10 here)

Discussion

This paper describes an Integrated Bayesian Network approach applied to the initiation of Lyngbya blooms. The aim was to present the exposition of BN methodology to a complex ecological problem such as Lyngbya bloom initiation and illustrate how it can be used to integrate models for different aspects of the same issue. We have illustrated the process that could be followed to integrate two static BNs and another type of model (such as the E2 model of the Whole of Catchment) to achieve an integrated BN. The IBN approach described here can also be used for investigating other features of this organism, such as growth, biomass and decay, through appropriate changes to the Science Network. as to significant contributing factors and their definitions and pathways which impact on this outcome, and identification and integration of information that allow quantification of these factors and impacts. The benefits of such an approach include a much greater specification of the issue at hand or research focus, buy-in from diverse stakeholders, consolidation and formalisation of information, an audit trail for decision-making and future research, and quantitative outcomes in the form of probability statements about the outcome of interest.

In the static Lyngbya BN (figure 4) similar factors were grouped together and colour coded as a visual aid. The nature of the Science BN enabled a simple conversion of the network to an OOBN (figure 6), with a sub-network for each group of factors and interface nodes providing the communication links to other OOBNs (figure 8). In the same way many complex BNs can be simplified by abstracting the network to a higher level to include sub-networks of logically grouped factors, which in turn can include other sub-networks, thereby having several levels of abstraction. An important feature of the OOBN sub-networks is that they can be developed simultaneously by the various expert groups who are responsible for them. When the sub-networks have been quantified, tested and ratified, they are integrated into the master network containing instances of those sub-networks.

The extension to a DOOBN not only improved prediction but also enhanced interpretability of the network. The inclusion of time-specific dynamics for temperature and water was more consistent with the conceptual framework of Lyngbya behaviour held by both science and management stakeholders.

Moreover, it is more straightforward to include expert opinion and data of a temporal nature in this expanded model. It is suggested that for other complex ecological systems, the additional complexity of a DOOBN is more than compensated for by the increased flexibility of representation of information and acceptability of the outputs.

  influence the delivery of nutrients to the Bay and was constructed through a series of meetings with the LMWG. The potential nutrient sources that were identified by the LMWG were split into point sources (coming from a relatively concentrated area e.g. waste water treatment plants) and diffuse sources (nutrients being contributed to the water catchment from a larger geographical area e.g. grazing land). The management model has evolved into a graphical representation of the catchment area showing the waterways and identifying the location of the sources within the catchment as well as their nutrient contributions. Participants of the LMWG identified the existing, committed and best practice management options for each source. The network was then quantified by assigning probabilities to each node to reflect the probability of low or high nutrient discharge for each source under each management option.

  temporal behaviour of a network can be represented by time slices, one for each period of interest. The resulting network, consisting of several OOBN time slices, is referred to as a dynamic OOBN (DOOBN)(Kjaerulff, 1995; Weber and Jouffe, 2006). Lyngbya blooms in Moreton Bay occur more frequently during the summer months when conditions are more favourable for bloom initiation(Watkinson et al., 2005). Additional statistical modelling was conducted byHamilton et al. (2007a) on the effects of temperature, rainfall and light on L .majuscula blooms and the importance of groundwater in stimulating Lyngbya blooms has been studied by[START_REF] Ahern | Investigating the potential for groundwater from different vegetation, soil and landuses to stimulate blooms of the cyanobacterium, Lyngbya majuscula, in coastal waters[END_REF] and was nominated by LSWG as a key node that may exhibit temporal behaviour.It was thus considered that the DOOBN would be better able to predict the probability of Lyngbya bloom initiation. A UML use case diagram illustrating the processes involved in creating this DOOBN, is shown in figure2.(Place Figure2here)The initial static Science BN model used annual averages for rainfall and temperature, but captured some temporal behaviour by introducing a node to represent the previous number of dry days. As directed by the LSWG the Lyngbya Science BN was extended to incorporate the temporal nature of L. majuscula to create a DOOBN with five time slices (one for each of the months of November to March). The DOOBN is therefore able to predict the probability of a Lyngbya bloom initiation by incorporating specific monthly data while also taking into account the influence of the previous month.UsingBayesian statistical modelling (Hamilton et al., 2007b) investigated the response of Lyngbya bloom initiation to temporal factors such as average minimum and maximum monthly temperature, monthly rainfall, average monthly solar exposure and average monthly clear sky (the inverse of cloud cover). One month time lags and interaction terms were also included for rain and minimum temperature. From a total of 890 models evaluated, the single term average minimum monthly temperature model (with an intercept term)

  Network described in Section 2.1, integrated with a Water Catchment simulation model, which was concurrently developed under the Lyngbya Programme. The IBN is conceived as a series of steps, in which the Management Network informs about nutrient discharge into the Deception Bay catchment, the Catchment model simulates the movement of these nutrients to the Lyngbya site in the Bay, and the Science model then integrates this nutrient information with other factors to determine the probability of initiation of a Lyngbya bloom.

Figure 3

 3 Figure 3 is a UML activity diagram detailing the processes of the IBN for

  with the nodes representing the factors identified by the LSWG as important in the initiation of a Lyngbya bloom. (Place Figure 4 here) Sensitivity analysis of this BN revealed that the seven most influential factors in the Science Network were (in decreasing order of influence): available nutrient pool (dissolved), bottom current climate, sediment nutrients, dissolved iron (Fe), dissolved phosphorous (P), light and temperature. Furthermore scenario modelling consistently identified available nutrient pool as the factor which most heavily influences the probability of initiation of a bloom. Point and diffuse sources deliver nutrients to the bay and this nutrient delivery is affected by management actions at the sources.

)

  Next the Science OOBN sub-network (figure5) was created from the static Lyngbya Science BN as outlined in Section 2.2, retaining all the key factors (with the exception of the No of prev dry days) and their CPTs from the static BN. As is characteristic of Object Oriented networks, the Science OOBN subnetwork includes instances of other sub-networks, shown in figure5as rectangles with rounded edges, such as the Wind subnet and the Turbidity subnet. Input nodes were added to the Science OOBN sub-network as placeholders for the real nodes, Temperature, Rain-present, Land Run-off Load and Ground Water Amount. The sub-networks were based on the groups created in the static Science BN to yield standalone networks capable of linking to other networks via the interface nodes (input and output nodes), or being instantiated in other networks. Importantly, providing the interface remains intact, these OOBN sub-networks can be further expanded without affecting the structure of any other networks linking to it. As a consequence we have a powerful concept of parallel development by independent expert teams while retaining the overall cohesive model. (Place Figure 5 here)In collaboration with the LSWG and based on the findings of(Hamilton et al., 2007a) as described in Section 2.3, the static Lyngbya Science network was adapted in the following manner to incorporate monthly rainfall and temperature data and the lag effect of rainfall on the amount of groundwater and land run-off. First, the lag effect of rainfall on groundwater amount and land run-off was replicated by creating a Rainwater OOBN sub-network as shown in figure6. In this OOBN, the Prev Groundwater and the Prev Land Run-off are input nodes (double edged eclipse with a broken outer line), which enable connectivity to the previous time slice's Ground Water Amount and Land Run-off nodes, respectively. The Rain -present input node enables the instances of the Rainwater OOBN to be bound to the rainfall relating to that instance, e.g. the November Rainwater OOBN instance will have November's rainfall bound to the Rain -present input node. The Ground Water Amount and Land Run-off Load output nodes (double edged eclipse with a solid outer line) make them visible to other networks and therefore allow them to be bound to input nodes in other networks.(Place Figure6here) Finally the DOOBN was created with five time slices (figure7), one time slice for each of the summer months (December to February), one for the end of spring (November) and one for the start of autumn (March) . Every time slice has an instance of the Rainwater and Science sub-networks as well as the temperature and rainfall nodes for that month. Data from the Bureau of Meteorology was used to quantify the DOOBN, as well as the information contained in the initial static BN.

Finally, the creationLegendsFigure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :Figure 7 :Figure 8 :

 12345678 Figure 1: UML use case diagram of the conceptual processes in the Lyngbya bloom initiation Integrated Network

Figure 9 :Figure 10 :

 910 Figure 9: Extract of the Management Network for Mellum Creek Subcatchment, a visual representation of the sub-catchment, showing point and

  These networks are currently being developed. The Integrated Network approach is also conceptually suitable for investigating other outcomes of interest that are impacted by nutrient outputs and water movement in a catchment.It is noted that it is beyond the scope of the present paper to provide an actual test of the utility of BNs for predicting cyanobacterial blooms. The paper therefore does not include a comparison of the predictions against classical multivariate techniques; a test of the BNs own output reliability, that is, whether the probabilistic estimate of the likelihood the BNs output is correct for the target data set; a clear presentation of exactly what data are being used; a sufficient amount of data to first build and refine the model on one data set and then test it on a previously unseen set of data. However the Science BN, which has been adopted by Healthy Waterways, will be validated through future data collected as part of the next phase of the Lyngbya project.More broadly, the general approach proposed in this paper is applicable to environmental or other outcomes involving both scientific and management considerations. Information arising from expert knowledge, data and research can be formally conceptualized and quantified through Science and Management Networks, and combined into an Integrated Network. Such an approach involves definition of the problem or outcome of interest, agreement

Table 2 :

 2 Changes to the probability of Lyngbya bloom initiation for key factors. All possible states for each of the nodes were assessed individually to ascertain the delta effect it had on the probability of a Lyngbya bloom initiation.

	Factor	Change in P(Bloom) (%)
	Available Nutrient Pool	77
	Bottom Current Climate	28
	Sediment Nutrient Climate	17
	Dissolved Fe	16
	Dissolved P	15
	Light Climate	14
	Temperature

Table 1: Conditional probability table for Bottom Current Climate node with

Acknowledgements

Financial assistance was provided by the Environmental Protection Agency and Australian Government through the South East Queensland Healthy Waterways Partnership, the ARC Centre for Dynamic Systems and Control, and QUT Institute for Sustainable Resources. We fully acknowledge the contributions of the Lyngbya Management Working Group and the Lyngbya Science Working Group. For helpful comments on the manuscript we acknowledge Kathleen Ahern, Barry Hart and an anonymous reviewer.


Heidi Pethybridge
email: heidi.pethybridge@csiro.au

Daniel Cossa

Edward C V Butler

Running title: Mercury in demersal sharks Mercury in 16 demersal sharks from southeast Australia: Biotic and abiotic sources of variation and consumer health implications

Keywords: Mercury, methylmercury, deep-sea, body distribution, maternal transfer, Squaliformes, Chimaeriformes, Carcharhiniformes

Total mercury (THg) and monomethylmercury (MMHg) concentrations were determined in the tissues of demersal shark (Order Squaliformes and the Families: Scyliorhinidae, Hexanchidae) and chimaera species (Families: Chimaeridae and Rhinochimaeridae) from continental shelf and slope waters off southeast Australia, including embryos, juveniles and adults. The distribution of THg in various tissues (muscle, liver, kidney, and skin), examined in ten species, shows higher levels in the muscle tissue (1.49 ± 0.47 mg kg -1 , ww), which accounted for between 59 -82 % of the total body burden of mercury and in the kidney (0.93 ± 0.14 mg kg -1 , ww) and liver (0.61 ± 0.25 mg kg -1 , ww) with lower levels observed in the skin (0.12 ± 0.06 mg kg -1 , ww). Additional THg determinations were performed in the muscle tissue of five other species allowing geographical and interspecific comparisons. Speciation analysis demonstrated that more than 90% mercury was bound in muscle tissue as MMHg with higher percentages (>95%) observed in sharks species occupying deeper environments. Species differences were observed. Highest THg levels in the muscle tissue (up to 6.64 mg kg -1 wet weight, ww) were recorded in Proscymnodon plunketi and Centrophorus zeehaani (mean values; 4.47 ± 1.20 and 3.52 ± 0.07 mg kg -1 , ww, respectively). Consistent with the ongoing paradigm on mercury bioaccumulation, we systematically observed THg concentrations increasing with animal size from the embryos to the larger sharks. Embryos of Etmopterus baxteri and Centroselachus crepidater had average levels 0.28 and 0.06 mg kg -1 (ww), while adult specimens reached 3.3 and 2.3 mg kg -1 (ww), respectively. THg concentrations in Australian sharks were compared with the same genus collected in other world regions. Levels were closer to data reported for East Atlantic than for the epicontinental Mediterranean margins. At a smaller geographical scale, the habitat effect on mercury concentration in sharks seems less clear. Squalid sharks occupying shelf waters showed higher mean mercury levels relative to their size (body weight, bw) than mid-slope species (0.4 -6.7 mg kg -1 bw and 0.3 -2.2 mg kg -1 bw, respectively). However, local regional differences (East and South Tasmania vs Victoria) in Hg levels were not detected for the majority of taxa examined. All species, with the exception of Figaro boardmani showed values greater than 0.5 mg kg -1 (ww) and all but four were above many international regulatory thresholds (1.0 mg kg -1 , ww).

INTRODUCTION

Mercury (Hg) is a volatile and highly toxic environmental contaminant present in marine systems. In marine organisms, it is most commonly found as monomethylmercury (MMHg), a chemical species formed in hypoxic and suboxic sediments and waters [START_REF] Cossa | The origin of methylmercury in open Mediterranean waters[END_REF]. The uptake and bioconcentration of MMHg by marine organisms is a very efficient process. This is largely due to the high permeability through biological membranes of certain neutral complexes [START_REF] Mason | Bioaccumulation of mercury and methylmercury[END_REF], and its high affinity for proteinaceous material, which retains the molecule within the muscle tissue. Hence, MMHg tends to concentrate in fish tissues as they age (and increase in size) [START_REF] Boudou | Mercury in the Food Web: Accumulation and Transfer Mechanisms[END_REF]. In addition, after this rapid uptake (predominantly from food for MMHg in aquatic animals), slow elimination mechanisms cause an increase in methylmercury concentration and proportion with increasing trophic position; a process known as biomagnification through the food chains [START_REF] Boudou | Mercury in the Food Web: Accumulation and Transfer Mechanisms[END_REF]. By this process, while MMHg constitutes less than 10 % of the THg in water [START_REF] Cossa | The Mediterranean mercury anomaly, a geochemical or a biological issue[END_REF], it constitutes more than 90% in high-order predators [START_REF] Bloom | On the chemical form of mercury in edible fish and marine invertebrate tissue[END_REF]. Consequently, mercury concentrations in marine fish appear to be governed mainly by size and trophic position.

Since the well-documented incidences of mercury exposure to human communities in Japan and Iraq, which resulted in severe toxic and teratogenic effects [START_REF] Harada | Minamata disease: Methylmercury poisoning in Japan caused by environmental pollution[END_REF], there has been widespread public concern over the consumption of mercury. Because of this, health managers face great challenges in their efforts to better understand how mercury is dispersed in the environment, and to protect the public from exposure to methyl-mercury through the consumption of fish. Currently, the Australian and New Zealand Food Standards Code (FSANZ) prescribes two separate maximum levels for mercury in seafood offered for commercial sale: 1.0 mg kg -1 (wet weight, ww) for fish that are known to contain high levels of mercury (including several large deep-sea fishes, rays and sharks) and 0.5 mg kg -1 (ww) for all smaller species of fish, as well as crustacean and mollusks (FSANZ, 2007). Similar regulations have been taken by other governmental authorities (e.g., EFSA 2008).

In contrast to near-shore organisms, limited data is available on mercury levels in demersal predators, such as sharks. Despite the increasing commercial market for flesh and use of sharks in the pharmaceutical market, few studies have investigated the distribution of mercury in internal tissues of sharks. As many of the sharks tested in this study are commonly consumed in local markets, data will be highly pertinent to health advisors and management boards. Data on some of these shark species are completely lacking or have not been investigated since the 1980's [START_REF] Walker | Mercury concentrations in edible tissues of elasmobranchs, teleosts, crustaceans and molluscs from south-eastern Australian waters[END_REF]. In this framework, the present study provides data on THg and MMHg levels in a range of consumed and non-consumed demersal sharks from continental shelf and slope waters off southeastern Australian and investigates variations in their mercury content and compartmentalization (organotropism). Furthermore, the study addresses questions of Hg over the life cycle, in, embryos, juveniles and size related bioaccumulation within adult sharks, and assesses the effects of habitat.

METHODS

Sample collection

A total of 227 individual sharks and chimaeras of 16 demersal species were collected from five sites in continental waters around eastern and southern Tasmania and south of Victoria and South Australia (Table 1). Samples were taken opportunistically as by-catch from local long-line and trawl fishing boats, between November 2004 and June 2006. Specimens were immediately frozen whole before being transported to CSIRO Marine Laboratories, and stored whole at -20ºC. All sharks were processed within 6 months of being captured, where species were identified, individually weighed and measured. All taxonomic names are based on the most recent literature [START_REF] Last | Sharks and rays of Australia[END_REF]. Stage of maturity was determined by examination of internal and external reproductive organs. Two 100-g portions of peduncle dorsal muscle tissue were collected in plastic zip-lock bags and stored at -20 ºC. Sections of liver, kidney, and skin were also removed from selected specimens to explore tissue distribution of THg. A constant focus was on obtaining homogenous (representative) tissue samples. To minimize the risk of contamination, samples were placed onto a polyethylene plate and plastic utensils were used and rinsed between each sampling with demineralised water (Milli-Q). All samples taken were freeze-dried and sheltered from light until analysis.

Mercury analysis

Total mercury (THg) determinations were carried out on 2-3 aliquots ranging from 10-50 mg of dried material and were directly analysed in an flameless Atomic Absorption Spectrophotometry (AAS) using an automatic mercury analyser (AMA-254, Altec) controlled by WinAMA software. In brief, samples were dried at 120 ºC and thermally decomposed at 550ºC under an oxygen flow to an elemental mercury gold trap (Au-amalgamator) and were quantified by cold-vapour AAS techniques, described in detail by [START_REF] Cossa | Dosage du mercure total and du Monométhylmercury dans les organismes et les sédiments marins. Méthodes d' analyse en milieu marin[END_REF]. The instrument was regularly calibrated using two sets of standard solutions prepared in 0.1% (m/v) K 2 Cr 2 O 7 and 0.6 % (v/v) HNO 3 . This method is also known as the US EPA N°7473. The accuracy and the reproducibility of the method were established using certified reference materials (DORM 2 from the National Research Council of Canada). The accuracy was within the uncertainties of the CRM (Table 1) and reproducibility better than 5%. The detection limit was0.007 mg kg -1 .

As MMHg is of greatest concern to organism health due to its toxic and bioaccumulative nature in marine food-webs it was determined for selected muscle tissue samples for 9 of 16 species of shark to confirm approximate percentage of THg. The method for the determination of MMHg in fish tissue is an adaptation for biota of the US EPA method N° 1630 described for waters. Briefly, tissue samples are digested using a NaOH/methanol solution. An ethylating agent is then added to the aqueous sample to form a volatile methyl-ethyl-mercury derivative. The ethylated species are then purged onto Tenax traps as a means of preconcentration and interference removal. The ethylated mercury species are then removed from the traps by heating (250°C) and separated using isothermal chromatography (70°C). The mercury species evolving from the chromatography column are destroyed by pyrolysis (800°C), releasing elemental mercury for detection by cold vapor atomic fluorescence spectroscopy (CVAFS) as described in [START_REF] Bloom | Determination of volatile mercury species at the picogram level by low temperature gas chromatography with cold vapor atomic fluorescence detection[END_REF]. We used an AFS Tekran, model 2500 for the detection. The detailed protocols are described in [START_REF] Cossa | Dosage du mercure total and du Monométhylmercury dans les organismes et les sédiments marins. Méthodes d' analyse en milieu marin[END_REF]. The method of determination includes the use of the standard addition technique with a MMHg solution in isopropanol. Method precision of MMHg determinations, estimated from 4 replicates of the CRM DORM-2, ranged between 8-15% (mean 14%) and its mean recovery was around 90% (Table 1). The detection limit is 0.004 ng.mg -1 .

Calculations of the relative tissue burden were based on a percentage of THg within a certain tissue relative to the THg in all tissues analysed. Relative contributions of the total body burden relative body burden) of THg (weight normalized) were calculated as the percentage of THg within the percent proportional tissue relative to the total body weight. Calculations were based on the species-specific weighed average distributions in percentage wet weight (ww) for all tissues (tissue-to-total body ratios) and the tissue-specific ratios derived from published and unpublished data. The proportional liver weight was between 6 to 30% and kidney 0.9 to 9.0 % of total body weight. Relative weights of the muscle compartment (45-65 % total body weight) were taken as the proportional carcass weights, which exclude the head and fins [START_REF] Daley | Catch analysis and productivity of the deepwater dogfish resource in southern Australia[END_REF]. Relative proportions of the skin (denticles) were more difficult to determine, but were estimated at 1.9 % based on the percentage calculation of a 30-cm 2 section taken from Centroselachus crepidater.

Relative tissue burden (%) = (THg in tissue compartment/ THg in all tissues assessed) x 100.

Relative body burden (%) = (100 x THg content in tissue compartment)/Reference tissue proportion.

Statistical methods

To assess interspecies, habitat effects, gender and collection-period variability in Hg levels, variance (ANOVA) and covariance (ANCOVA) analyses were performed after checking assumptions of normality and homoscedasticity of the data. Where homogeneity was violated, THg data was log-transformed to stabilize the variance, and the non-parametric Mann-Whitney U test was used. Where assumptions were met, a parametric t-test (assuming unequal variance) was applied to test for significant differences among samples. This included assessing the variations in total mercury, between species, sex, site, maturity and size. Where the number of individuals per species was low (<10), Kruskal-Wallis non-parametric tests were used to make statistical comparisons between species. To explore the relationship between Hg level and shark length, linear regression analysis was conducted for each species. In each test, p<0.05 was considered significant.

RESULTS

Levels of THg, MMHg in the muscle and other tissue distribution of THg

Total mercury levels in the muscle for 227 (16 taxa) demersal sharks were quantified and ranged between 0.21 to 6.64 mg kg -1 , ww (1.1-24.5 mg kg -1 dw), excluding embryos and juveniles, where levels ranged between 0.06-0.41 and 0.09-0.93 mg kg -1 , ww, respectively (Table 2). More than 84% of all adult individuals examined had mean mercury levels greater than or equal to the 1.0 mg kg -1 (ww) threshold level set by national health regulations of the region (FSANZ 2007). Only one species (non-commercial Figaro boardmani) had mean THg concentrations below 0.5 mg kg -1 (ww).

Highest concentrations were observed in Proscymnodon plunketi (6.64 ± 0.04 mg kg -1 , ww), Centrophorus zeehaani (4.72 ± 0.2 mg kg -1 , ww), and Squalus mitsukurii (3.74 ± 0.2 mg kg -1 , ww), where these values corresponded to the largest individuals assessed for those species. Methylmercury measurements taken in 13 individuals of 9 shark species showed that it accounts for the majority ( 91%) of THg in shark muscle tissue (Table 3). Lower MMHg concentrations were observed in shelf and upper-slope (90-95%) than in mid-slope species (>95%). All mercury determined in P. plunketi, E. baxteri and Dalatias licha was apparently MMHg as their estimated mean was indistinguishable from 100%. Such results could be accounted for by the relatively high standard errors arising from analytical imprecision in MMHg analyses (coefficient of variation of 13% according to results in Table 1) or inherent bio-variability.

Excluding the single juvenile tested, higher levels of THg were recorded in the muscle tissue (mean 1.49 ± 0.47 mg kg -1 , ww), followed by the kidney (0.93 ± 0.14 mg kg -1 , ww), liver (0.62 ± 0.25 mg kg -1 , ww) and skin (0.12 ± 0.06 mg kg -1 , ww), respectively (Table 4). Amongst all species, 68 % (range 59-82 %) of the relative total body burden of mercury was stored in the muscle tissue. The kidney accounted for 2.4 % (1-5 %), the liver 9.5 % (1-25 %) and the skin 0.2 % (from below the detection limit to 0.5). For most species, liver, kidney and skin tissue presents statistically lower quantities of mercury than muscle (p<0.01). Unusually large levels of THg were found in the kidneys of Chimaera lignaria (1.62 -1.97 mg kg -1 , ww; 4.8% total body burden) and E. baxteri (0.90 -4.16 mg kg -1 , ww; 4.8% total body burden).

Comparably high percentage THg burdens were also found in the livers the two Chimaera species, C. lignaria (0.68 -1.49 mg kg -1 , ww; 12.8 %) and Rhinochimaera pacifica (0.5 mg kg - 1 , ww; 24.8%). Low levels were observed in the liver and kidney of D. licha (0.7 and 0.9 % relative body burden, respectively) and in the liver of C. zeehaani (2.1 %). Relatively large variability was observed in the majority of tissues tested.

Influence of size and sex on THg of the muscle tissue

In general, THg in the muscle exceeded 1.0 mg kg -1 (ww) above a shark size of approximately 55 cm. On the basis of the ANCOVA, length differences accounted for 85%, 78%, and 71% of the total variation in mercury levels observed in C. zeehaani, Squalus mitsukurii and C. crepidater, respectively. Linear relationships were evident between THg and size for most species, with most regressions being significant with positive slopes, indicating that Hg level increases with increasing length (Figure 1) with lowest values found in embryos and juveniles. R-squared values ranged between 0.43 and 0.98 with more positive correlations observed in C. zeehaani (R 2 = 0.98, p<0.001), S. mitsukurii (R 2 = 0.88, p<0.001), C. crepidater (R 2 = 0.82, p<0.001), and Squalus acanthias (R 2 = 0.83, p<0.001) (Table 5). Analysis of the THg data by size and sex of shark were tested by ANOVA (where class size was >10 individuals) revealing significant positive correlations for most, but not all species (Table 6). Stage of maturity correlated with both the length of male and female individuals, and as such, also significantly influenced THg levels. In most species, females were larger and had higher mean total mercury levels than males, except in Deania calcea, S. mitsukurii and R. pacifica.

Sex significantly (ANOVA p<0.05) influenced THg levels in two species; E. baxteri and D. calcea (Table 6). However, when THg levels were normalized with size (using BMI; THg mg kg -1 /kg bm, ww), higher levels of mercury were in fact observed in males of an equivalent size to females (± 0.5cm).

Geographical, vertical distribution and inter-specific variations

Mercury concentration varied between species and taxon (Table 1) according to biology and environment (Table 6). No significant relationships between collection site and THg was observed for C. crepidater collected in the east and south off Tasmania, nor for sharks of different species collected in the Great Australian Bight and Tasmania. In contrast, some variation in THg concentrations was explained by habitat type, where species were separated by their vertical distribution (shelf and upper-slope vs mid-slope, Table 6). In general, higher mean concentrations of Hg in sharks appeared in deep-demersal species, although these were not seen to be highly significantly different (p = 0.058). To take away the effects of biological variation (including some effects of size and physiology), species from the Order Squaliformes were pooled together for comparisons of habitat and THg. Squalids occupying the shelf and upper-slope had higher mercury levels relative to their size (BMI) than mid-slope species (0.6 -2.5 and 0.3 -1.5 THg/kg bm, respectively). The exception was the mid-slope southern lantern sharks, E. baxteri which showed mean concentration levels of 1.41 THg/kg bm and were statistically higher than those of other mid-slope squalids (p=0.01). In comparison to other taxon groups, the two Scyliorhid species, deepwater catsharks F. boardmani and Apristurus sp. A, contained higher BMI levels than the averages of those Squalids occupying similar water bodies. The upper-slope Hexanchid species, Notorynchus cepedianus showed the lowest mercury levels relative to BMI (0.08 -0.1 THg/kg). Regardless of slope habitat, all juveniles displayed similar THg levels (BMI, 0.5 -0.9 THg/kg). Inter-annual and inter-seasonal variation in Hg level was investigated and all taxa examined showed no effect for either factor (Table 6). For many species, sample size limitations did not warrant inter-annual and/or regional comparisons.

DISCUSSION

The present studies brings new results on the mercury levels in several species of demersal sharks from the Australian waters, in relation to their development stage, size, sex and habitat.

In addition, it provides comment on the food safety implications of human consumption of the flesh of these animals.

Mercury and shark life-cycle

Basic information about the mercury pathway and dynamics within the shark may be extracted from the results on Hg tissue distributions (organotropism) and growth-related Hg variation (embryos, juveniles, size and sex Hg dependences).

Organotropism -In some marine organisms, methylmercury tissue concentrations are related to the uptake rate (gill, intestine), others to the demethylation and excretion rate (liver, kidney) [START_REF] Boening | Ecological effects, transport, and fate of mercury: a general review[END_REF]. In sharks, like most high-order marine predators, dietary mercury is incorporated in a dose-dependant manner [START_REF] Boudou | Mercury in the Food Web: Accumulation and Transfer Mechanisms[END_REF], and is accumulated in internal tissues.

Few studies have tested the partitioning of mercury in demersal Chondrichthyes. Horung et al., (1993) reported THg content in various tissues of five deepwater species (including Centrophorus granulosus, Galeus melastomus and Etmopterus spinax) from the eastern Mediterranean Sea. In all species, they found that higher levels of Hg were observed in the kidney and 'healthy' livers than in the muscle tissue, with highest (up to 11 times greater in C. granulosus) concentration being observed in unhealthy (very dark) livers. Similarly, in pelagic sharks, [START_REF] Boush | Mercury content in sharks[END_REF] showed that in sexed sharks (Garcharhinus limbatus), significantly higher levels were observed in the liver (0-5.5 mg kg -1 ) than the muscle tissue (0-2 mg kg -1 ), although the opposite was true for the tiger shark (Galeocerdo cuvieri, n=13), which displayed higher THg levels in the muscle tissue (0.1-0.65 mg kg -1 ) than the liver (0-0.25 mg kg -1 ), possibly illustrating different physiological capacities to process Hg between these species.

In this study, the highest mercury concentrations and more than half the total mercury burden were found in the muscle tissue. The kidney showed relatively high THg levels along with the liver, and minute amounts were found in the denticles (skin). This suggests that mercury is taken up through the diet rather than the environment, and is readily dispersed throughout the body and cumulatively stored in muscle tissue. In contrast, the digestive tissues (kidney and liver) are likely to be associated with metal metabolism, storage and elimination procedures. In species (and individuals) that have relativity higher Hg concentrations in the kidney (Chimaera lignaria and E. baxteri) and liver (R. pacifica and C. lignaria) than other species, it might suggest either recent exposure followed by concentration in the liver, or strong protein binding, perhaps in a similar manner as has been demonstrated with cadmium by [START_REF] Lucis | Cadmium as a trace element and cadmium binding components in human cells[END_REF].

Multiple factors are likely to have contributed to the higher individual variability and different trends in the tissue distribution of mercury observed in demersal sharks by Horung et al., (1993) than in this study. Firstly, although similarities within genera can be drawn, interspecies variability in metabolism and physiology are likely to be large. Secondly, sharks studied by Horung et al., (1993) were collected near a deep-water dumpsite, and thus, were atypical (affected by variable and elevated Hg concentrations from localised pollution).

Influence of size and sex -The linear relationships observed in almost all species demonstrates that larger, presumably older individuals accumulated higher levels of mercury. This pattern has been extensively exhibited in previous studies of sharks [START_REF] Forrester | Mercury content of spiny dogfish, Squalus acanthias, in the Strait of Georgia, British Columbia[END_REF][START_REF] Walker | Effects of species, sex, length and locality on the mercury content of school shark, Galeorhinus australis, and gummy shark, Mustelus antarcticus, from South-eastern Australian waters[END_REF][START_REF] Walker | Mercury concentrations in edible tissues of elasmobranchs, teleosts, crustaceans and molluscs from south-eastern Australian waters[END_REF][START_REF] Taguchi | Study of metal contents of elasmobranch fishes: Part 1. Metal concentration in the muscle tissues of a dogfish, Squalus mitsukurii[END_REF][START_REF] Hueter | Methylmercury concentration in shark muscle by species, size and distribution of sharks in Florida coastal waters[END_REF]. For C. crepidater analysed in this study, comparable Hg levels and a similar relationship between size and Hg levels was observed by [START_REF] Davenport | Mercury in blue sharks and deepwater dogfish from around Tasmania[END_REF] and [START_REF] Turoczy | Observations on Metal Concentrations in Three Species of Shark (Deania calcea, Centroscymnus crepidater, and Centroscymnus owstoni) from Southeastern Australian Waters[END_REF]. The accumulation of mercury with increasing size (fundamentally age) is likely to also reflect other ecological and physical factors, such as differential feeding ecology, and slow and inefficient elimination of mercury from deeper dwelling species [START_REF] Trudel | Modeling the elimination of mercury by fish[END_REF]. It is widely recognized that diet is the most important source of Hg exposure in marine top predators (e.g., [START_REF] Boening | Ecological effects, transport, and fate of mercury: a general review[END_REF]. Thus, it can be assumed that the wide range of mercury concentrations observed in this study were a consequence of distinct feeding habits, in addition to other species-specific parameters, such as metabolism and growth rates. Furthermore, data presented here suggest that through bioaccumulation, larger, older adults of these common species potentially contain excessive mercury burdens. Where variability was high in adult individuals, stage of maturity was positively correlated with increasing mercury levels. Ontonogentic variations in diet may also be a considerable factor, particularly where differences are observed between juveniles and adults. In contrast to length, similar THg levels were not often correlated with body weight. This indicates that there is a change in the proportion between size and weight to a certain degree, especially as the shark' s shape changes in relation to storage of energy and/or sexual maturity. Although aging, therefore, would have been better than weighing, information on the growth rates of demersal sharks is limited, Aging studies (from spines) of dogfish sharks around Tasmania [START_REF] Irvine | Comparing external and internal dorsal-spine bands to interpret the age and growth of the giant lantern shark, Etmopterus baxteri (Squaliformes: Etmopteridae)[END_REF] suggest that our specimens ranged between 15 and 48 years, excluding juveniles. Relationships between aging estimations and size of sharks are thought to be quite variable, possibly explaining some of the observed variability of mercury levels in sharks, particularly where juvenile and sub-adult specimens were also tested.

As a group, females in most species showed higher values than males; however, males that were of a similar length gave higher mean mercury levels. Such phenomena have been observed in S. mitsukurii [START_REF] Taguchi | Study of metal contents of elasmobranch fishes: Part 1. Metal concentration in the muscle tissues of a dogfish, Squalus mitsukurii[END_REF], and in Squalus megalops [START_REF] De Pinho | Total Mercury in Muscle Tissue of Five Shark Species from Brazilian Offshore Waters: Effects of Feeding Habit, Sex, and Length[END_REF]. Similar to most elasmobranchs, female demersal sharks have higher growth rates and are larger (sometimes up to 40%) than males [START_REF] Daley | Catch analysis and productivity of the deepwater dogfish resource in southern Australia[END_REF], displaying sexual dimorphism. This indicates that although males are smaller, they may be of a similar or older age group to larger females. Several species tested in this study have complex population structures, exhibiting segregation by sex, size and mature condition [START_REF] Daley | Catch analysis and productivity of the deepwater dogfish resource in southern Australia[END_REF][START_REF] Last | Sharks and rays of Australia[END_REF]. Differences in mercury levels in males and females may be caused by factors such as energetic requirements, maturation condition, mercury deposition, and transference to eggs and fetuses [START_REF] Walker | Effects of species, sex, length and locality on the mercury content of school shark, Galeorhinus australis, and gummy shark, Mustelus antarcticus, from South-eastern Australian waters[END_REF]. In sharks that are characterized by viviparous reproduction, due to the transfer of mercury to the fetus, females of some sharks may have lower concentrations of mercury than males; however this was not shown to be the case in this study.

Maternal transfer to embryos and juveniles -Because mercury levels increase as individuals grow larger, observed levels in embryos and juveniles can be viewed as the base concentration for the overall population, and indicate the transfer of mercury from maternal sources. Total mercury levels in embryos ranged from 4-10% of levels observed in their mothers, whose mercury levels were greater than 1.5 mg kg -1 . Marginally higher Hg levels were observed in E. baxteri than the sympatric squalid C. crepidater, although they are both viviparous and have similar estimated gestation periods [START_REF] Last | Sharks and rays of Australia[END_REF]. [START_REF] Walker | Effects of species, sex, length and locality on the mercury content of school shark, Galeorhinus australis, and gummy shark, Mustelus antarcticus, from South-eastern Australian waters[END_REF] suggested that transfer of mercury to developing ova and embryos may reduce mercury levels in mature females. In the present study, such relationships were not clearly shown with relatively similar mercury levels recorded in pregnant and non-pregnant females, despite the effects of maternal transfer of mercury to developing ova and embryos. Increased dietary needs, and thus, an increased uptake of MMHg, may be responsible for these similarities. [START_REF] Adams | Mercury levels in four species of sharks from the Atlantic coast of Florida[END_REF] studied mercury levels in embryos, neonates and juveniles of three Carcharhinid and one Sphyrnid sharks, which have placental viviparous reproduction. All species showed greater mean mercury levels (embryos 0.16-0.78, juveniles 0.17-1.7) than those reported in this study for both E. baxteri (embryos, 0.2-0.4 mg kg -1 , no juvenile data) and C. crepidater (embryo 0.06 mg kg -1 , juveniles 0.1-0.5 mg kg -1 ). The reproduction mode, combined with other physiological factors associated with family and habitat differences, conceivably explain why aplacental viviparous shark embryos contained the lowest mean mercury levels in this study. Squalids are viviparous, where nutrients are derived from an associated yolk sac; and thus, high concentrations would possibly be expected due to the direct interchange of nutrients (and presumable mercury) between the mother and embryo via the placenta. [START_REF] Jeffree | The accumulation of lead and mercury form seawater and their depuration by eggs of the spotted dogfish Scliorhinus canicula (Chondrichthys)[END_REF] showed that that the egg case of dogfish (Scyliorhinus canicula) is highly permeable to chemotoxic metals such as Hg and that there is much capacity for developing embryos to accumulate very high concentrations of Hg, such as was displayed in this study. In most viviparous sharks, developing oocytes take up large amounts of maternally derived vitellogenin, which consist of about 30% lipid (Pethybridge et al., in review) and thus lipid metabolism may play a role in transgenerational contaminant transfer [START_REF] Landrum | Influence of lipids on the bioaccumulation and trophic transfer of organic contaminants in aquatic organisms[END_REF].

This pathway would be particularly important for well-known lipophilic contaminants, such as DDT and PCBs but is unlikely to be as important for MMHg which preferentially forms bonds with thiol-containing molecules, such as proteins and amino acids [START_REF] Bloom | On the chemical form of mercury in edible fish and marine invertebrate tissue[END_REF]. In this regards, it seems vitellogenin (Vtg), a lipophosphoprotein yolk-granule precursor produced in the liver and taken up by developing oocytes [START_REF] Hamlett | Female reproductive system[END_REF] may have the ideal characteristics in which to transport MMHg to embryos by tightly bounding to sulfhydryl and other sulfur functional groups associated with these proteins.

In juveniles, mercury concentrations were found to be variable amongst species. For juveniles of S. acanthias, much lower Hg levels were found in larger specimens collected in the Celtic Sea [START_REF] Domi | Feeding ecology of five commercial shark species of the Celtic Sea through stable isotope and trace metal analysis[END_REF] than were reported in smaller specimens in this study. [START_REF] Greig | Mercury concentrations in three species of fish from North Atlantic waters[END_REF], found low Hg concentrations in the fetuses of S. acanthias, and concluded that in this species mercury does not transfer from the mother to the fetus. Such a conclusion suggests that the higher Hg levels recorded in the juveniles of S. acanthias in this study, compared with [START_REF] Domi | Feeding ecology of five commercial shark species of the Celtic Sea through stable isotope and trace metal analysis[END_REF], were accumulated after hatching and may be an effect of regional differences in diet.

Interspecific, local and regional Hg variations in sharks muscle tissue

Information regarding mercury in demersal sharks around the world is scarce. For comparisons with this study, only for the genera Centrophorus, Etmopterus, Squalus, Dalatias, Deania and Chimaera are some published data available (e.g., De Pinho et al., 1989;[START_REF] Hornung | Trace metal content in deep-water sharks from the eastern Mediterranean Sea[END_REF][START_REF] Davenport | Mercury in blue sharks and deepwater dogfish from around Tasmania[END_REF][START_REF] Turoczy | Observations on Metal Concentrations in Three Species of Shark (Deania calcea, Centroscymnus crepidater, and Centroscymnus owstoni) from Southeastern Australian Waters[END_REF]Storellii et al., 2002;and reviewed by Cossa and Coquery, 2005). Results from the present study are the first published for dogfish, Etmopterus baxteri, Proscymnodon plunketi, chimaeras, Rhinochimaera pacifica, and Chimaera lignaria, and deepwater catshark, Apristurus sp (A and C) and Figaro boardmani. In the following, we contend that mercury concentration in the shark muscle tissue, in addition to size and sex dependence (see subsequent sections), depends on the species and their habitats.

Mercury levels varied greatly amongst species, although noticeably all specimens from the Order Squaliformes had the highest levels of mercury recorded, and mean mercury levels strongly exceeding the 1.0 mg kg -1 (ww) threshold. In comparison, the families Scyliorhidae, Chimaeridae and Rhinochaeridae had relatively lower mean mercury levels (<1.0 THg mg kg -1 , ww). This is not surprising as these families share lower trophic levels [START_REF] Cortes | Standardized diet compositions and trophic levels of sharks[END_REF], and highlights that Hg uptake is strongly related to diet [START_REF] Domi | Feeding ecology of five commercial shark species of the Celtic Sea through stable isotope and trace metal analysis[END_REF]. However, the sevengill shark, N. cepedianus (Hexanchidae) had the lowest body mass index (BMI = 0.09) of any species, indicating low accumulative levels despite its known diet of high trophic status [START_REF] Cortes | Standardized diet compositions and trophic levels of sharks[END_REF]. Such results emphasize that multiple factors are important in the accumulation of mercury in sharks, including environment, physiology and ecological interactions.

Results of several studies around southeastern Australia [START_REF] Walker | Mercury concentrations in edible tissues of elasmobranchs, teleosts, crustaceans and molluscs from south-eastern Australian waters[END_REF][START_REF] Davenport | Mercury in blue sharks and deepwater dogfish from around Tasmania[END_REF][START_REF] Turoczy | Observations on Metal Concentrations in Three Species of Shark (Deania calcea, Centroscymnus crepidater, and Centroscymnus owstoni) from Southeastern Australian Waters[END_REF] show similar mean mercury levels in the muscle tissue to those reported in this study. Comparable elevated levels are also documented in several demersal sharks, globally [START_REF] De Pinho | Total Mercury in Muscle Tissue of Five Shark Species from Brazilian Offshore Waters: Effects of Feeding Habit, Sex, and Length[END_REF]Storelli et al., 2002). However, when comparing these results with those of our study, taxon and regional differences in Hg biomagnification do seem present. For example, Storelli et al. (2002) reported higher concentrations in similar sized specimens of the kitefin shark, D. licha collected from the Ionian Sea (Eastern Mediterranean Sea) than in those of this study. Similarly, in the Portuguese dogfish C. coelolepis, lower concentrations in the muscle were reported in specimens collected off south-eastern Australia (Davenport, 1995 and this study) than those of a similar size range collected from the Mediterranean [START_REF] Cossa | The Mediterranean mercury anomaly, a geochemical or a biological issue[END_REF]. This is also true for deepwater catshark species, for which higher concentrations have been observed in the Mediterranean (Horung et al., 1993), than in those presented in this study for Figaro boardmani (previously known as Galeus boardmani), while comparable levels have been reported in the Celtic Sea [START_REF] Domi | Feeding ecology of five commercial shark species of the Celtic Sea through stable isotope and trace metal analysis[END_REF]. Storelli and Marcotrigiano (2002) found that for similar sized specimens of blackmouth dogfish, Galeus melastomus, THg and MMHg concentrations differed substantially among those collected from the Adriatic Sea, the Ionian Sea and the Aegean Sea, demonstrating the strong affect that the environment has on mercury uptake. In D. calcea, similar levels were recorded in similar sized species collected from waters of the Mid-Atlantic Ridge [START_REF] Martins | Temporal and spatial changes in mercury concentrations in the North Atlantic as indicated by museum specimens of glacier lanternfish Benthosemal glaciale (Pisces: Myctophidae)[END_REF] to those collected off south-east Australia [START_REF] Davenport | Mercury in blue sharks and deepwater dogfish from around Tasmania[END_REF]Turoczy et al., 2000 and this study). In a first attempt, it can be suggested that margin waters from open oceanic environments such as the Australian and Celtic shelves, harbour sharks with lower mercury content than those from an epicontinental environment, such as the various basins of the Mediterranean Sea. However, this generalization is not yet supported by available environmental data. We also have to consider that food chain structure or growth rate differences between Mediterranean sharks and those from other oceanic regions might equally explain these observations. Indeed, the range of MMHg concentrations measured very recently in the water column of the Mediterranean Sea [START_REF] Cossa | The origin of methylmercury in open Mediterranean waters[END_REF], Tasmanian margin [START_REF] Cossa | Methylmercury distribution in the upper part of the Southern Ocean (SR3, Geotraces)[END_REF] and Celtic Sea (Cossa and Chou, unpublished) are similar (<0.01-0.15 ng L -1 ). Further studies of mercury cycling in the Mediterranean Sea, and the dynamics of the heavy metal in the region's food chains topped by demersal sharks, are required to establish the basis for their greater mercury burden in these waters.

At a smaller geographical scale (East and South Tasmania vs Victoria), the habitat effect on mercury concentration in sharks seems less clear. Differences between shelf/upper-slope species and mid-slope species are noted in our study, although differences between collections sites were not detected for the majority of taxa examined. A lack of variability between taxa and site are likely to indicate similar associations among predatory behavior, longevity, and mercury accumulation processes as has been suggested by [START_REF] Forrester | Mercury content of spiny dogfish, Squalus acanthias, in the Strait of Georgia, British Columbia[END_REF]. Furthermore, similarities in diet and base-level mercury concentrations, hydrodynamic patterns, and the movement of the migratory species are additional factors likely to exclude within regional differences in Hg biomagnification in the sharks reported in this study.

Deep-sea environments are considered to be the sink and final reservoir for contaminants [START_REF] Tatsukawa | Environmental monitoring: geochemical and biochemical behaviour of PCB's in the open ocean environment[END_REF]. Deep-sea species are long-lived, and tend to feed at higher trophic levels than their shallow-water counterparts Hence, they are exposed to higher levels of recycled elements for longer periods [START_REF] Gordon | Environmental and biological aspects of slope dwelling fishes of the North Atlantic[END_REF], and the corresponding accumulation of most trace metals is likely to be greater. In this study, higher concentrations appeared in deep-demersal species which may be related to the consumption of prey from different food chains. [START_REF] Sarica | Mercury transfer from fish carcasses to scavengers in boreal lakes: the use of stable isotopes of mercury[END_REF] found that marine predators with scavenging behavior have a greater potential to assimilate methylmercury. This seems true in regards to the high levels found in the demersal sharks reported in this study. Alternatively, differences between shelf/upper-slope species and mid-slope species may also be attributed to differences in lipid biochemistry and increasing lipid content in the liver with depth (Pethybridge et al., in review) which may affect the ability of the liver to demethylate mercury.

The absence of any long-term, seasonal or annual differences (summer, winter and 2005, 2006) for the majority of the taxa examined is consistent with the fact that all samples were considered of adult size, and presumably older than 20 years. In a 20-year accumulation time, a one-year sampling difference is a relatively short time interval in which natural or anthropological events could cause environmental changes. Additionally, sharks, being highorder predators with slow metabolisms, accumulate mercury over longer time periods than those occupying the lower trophic levels, leading to greater biomagnification of mercury. For example, [START_REF] Sager | Long-term variation in mercury concentrations in estuarine organisms with changes in releases into Lavaca Bay, Texas[END_REF] found elevated Hg levels in predatory fishes 30 years after a local Hgcontaminated water release was ended even though the Hg level of some fishes at lower trophic levels dropped below the safety level the next year.

Conclusions

Regardless of species, in all adults, mercury concentrations of the muscle tissue are high enough to cause potential health concerns for sustained and regular human consumption [START_REF] Harada | Minamata disease: Methylmercury poisoning in Japan caused by environmental pollution[END_REF] and are over the local (FSANZ 2007) and European Union legal limit (EFSA 2008) for mercury in fish of 1.0 and 0.5 mg kg -1 (ww), respectively. MMHg is the most toxic form of mercury for human consumption, and essentially accounts for the majority (>90%) of mercury found in fish muscle tissue (e.g., [START_REF] Bloom | On the chemical form of mercury in edible fish and marine invertebrate tissue[END_REF], as well as in the sharks studied here.

In other studies on sharks [START_REF] Walker | Effects of species, sex, length and locality on the mercury content of school shark, Galeorhinus australis, and gummy shark, Mustelus antarcticus, from South-eastern Australian waters[END_REF]Storelli et al., 2002), methylmercury has been recorded to represent between 50-100% (most exceeding 70%) of the total mercury content in muscle tissue.

This study has provided information on the contamination status of mercury in demersal Chondrichthyes, which are less studied than teleost fish. The longevity and slow growth rates of demersal sharks, considered in conjunction with their apex predator life-style contribute significantly to the accumulation of high concentrations of mercury that warrant public concern as many species reported here are still being fished and marketed. The variations of mercury concentrations among species are likely to be most affected by the trophic level at which they feed and physiological differences in metal assimilation and metabolic capacity in processing MMHg. Large-scale regional differences in Hg levels seem apparent in Chondrichthyes, which emphasis the important position of the environment and ecology in mercury biomagnification and also stimulate consideration of these high-order predators as environmental monitors. Further research, with increased and targeted sample sizes of certain ubiquitous species, size ranges, and locations would help elucidate the relationship of MMHg to the specific migratory habits and physiology of demersal sharks. * Proportion tissue burden (%) is the tissue-to-total body ratio for each specified tissue, and is taken from published (muscle: [START_REF] Daley | Catch analysis and productivity of the deepwater dogfish resource in southern Australia[END_REF] and unpublished data.

Table and figuresTable 1 .

 and1 Accuracy of the total mercury (THg) and monomethylmercury (MMHg) determinations using the DORM-2 Dogfish Muscle Certified Reference Material from the National Research Council of Canada. CRM's were analysed every 10-15 samples.

		Certified value*	Measured value*	Relative	Recovery
		(mg kg -1 , dry weight)	(mg kg -1 , dry weight)	error (%)	(%)
	THg	4.64 ± 0.26	4.60 ± 0.07	0.9	99
	MMHg	4.47 ± 0.32	4.03 ± 0.55	13.6	90
	* mean ± 95 percent tolerance limits.

Table 2 .

 2 Total mercury (THg) concentrations in muscle tissue of deep-sea sharks from southeast Australia. Results are presented as range (min -max) THg mg kg -1 (ww) (mean ± SD). THg Body Mass Index (BMI) is calculated as THg mg.kg

-1 per kg of body mass (ww). F: female; M: male.

Table 3 .

 3 Percentage (%) of MMHg (mg kg -1 dw) that accounts for THg (mg kg -1 dw). Values are taken as the mean from 1-4 replicates were performed on each individual. n: number of determination.

	Habitat
	Species (n)	Sex	THg	MMHg	MMHg/THg (%)
	Mid-slope
	C. crepidater (2)	F	7.5 -8.0	7.1 -7.8	94.6 -97.5
	C. owstoni	F	10.0	9.6	95.8
	D. licha	M	8.4	8.5	101.3
	E. baxteri	F	10.8	11.0	101.1
	P. plunketi (2)	M	17.5 -20.6	17.1 -21.3	97.5 -103.4
	Shelf and Upper-slope
	S. acanthias	F	6.0	5.6	93.3
	S. megalops	F	6.9	6.3	91.3
	S. mitsukurii	F	10.5	9.9	94.3

Table 4 .

 4 Range (min -max) of total mercury concentrations (mg kg -1 , ww) in the tissues of ten species of shark from south eastern Australia. Wet weight (ww) conversion factor is the % of total water content and was used to convert dry mass concentrations to wet mass. Relative tissue burden is calculated as the proportion of mercury (mg kg -1 , ww) within a certain tissue relative to all other tissues tested. Relative body burden is calculated as percent (%) THg in a tissue relative to the tissues contribution to the whole body weight. n: number of determination.

	Muscle	Liver	Kidney	Skin

Table 5 .

 5 Regression analysis of Log (THg) level as a function of size for demersal sharks species. Significant linear functions (Y= a + bx) were fitted. Y = Log (THg) level (mg kg -1 ww), x is total length (cm). The estimated intercept (a), slope (b), R 2 value and P value are listed by species.Equations based on all data, regardless of location and time of collection, except where outliers were present in the data set. n: number of determination.

	Species (n)	a	b	R 2	P-value
	C. crepidater (42)	1.10	0.015	0.82	>0.001
	C. lignaria (14)	0.67	0.007	0.68	0.012
	C. zeehaani (13)	1.92	0.029	0.98	>0.001
	E. baxteri (40)	0.27	0.009	0.43	0.010
	D. calcea (19)	-0.27	0.005	0.24	0.075
	S. mitsukurii (21)	0.64	0.061	0.88	>0.001
	S. acanthias (18)	2.29	0.054	0.83	>0.001
	S. megalops (15)	-0.61	0.009	0.06	0.171
	F. boardmani (11)	2.36	0.065	0.78	>0.001

Table 6 .

 6 Probability values for species-specific ANOVA results examining sex, year, season and location effect on THg levels. *P < 0.05, N/A: insufficient data for statistical treatment. Mid-slope species consist of E. baxteri, C. crepidater and D. calcea; Upper-slope species consists of S. acanthias and S. mitsukurii. All species includes all adult species collected in this study.

	Species / group	Sex

M vs. F Year 2005 vs. 06 Season S vs. W Location Tas vs. Vic Maturity 1,2 vs. >3 BMI (THg/kg) vs. Sex

  Log 10 (THg) concentration of shark muscle vs total length (cm) for four shark species: • Centroselachus crepidater, • Etmopterus baxteri, ˜ Squalus acanthias, • Squalus mitsukurii

		0.8
		0.6
		0.4
	ww)	0.2
	-1
	Log (TH mg kg	-0.4 -0.2 0.0
		-0.6
		-0.8
		-1.0
		30	40	50	60	70	80	90	100
					Total length (cm)
	Figure 1.
	C. crepidater	0.111		N/A	0.274	0.253	0.009 *	0.015 *
	D. calcea	0.040*		N/A	N/A	N/A	0.023 *	0.053
	E. baxteri	0.031*		0.326	0.356	N/A	0.012 *	0.01 *
	S. mitsukurii	0.093		N/A	N/A	N/A	0.032 *	0.022 *
	Mid-vs Upper-slope	0.127		N/A	0.124	0.058	0.072	0.032 *
	All species	0.346		0.427	0.267	N/A	0.041 *	0.029 *

Acknowledgments

We would like to thank Bernard Averty and Stephan Guedron for their training and assistance with mercury analyses. Thanks are expressed to the captain and crew of the 'Adriatic Pearl' for sample collection. We are grateful to Ross Daley for his helpful comments on demersal shark ecology and help with sample collection. The comments by two anonymous referees are appreciated in improving the text. This project was partly funded by the Goddard Sapin-Jaloustre Trust Fund in association with IFREMER Centre de Nantes, France.