THE FIDDLER CRAB, Uca pugnax L Lauren

Judith S Bergey
email: lbergey@pegasus.rutgers.edu

Weis

MOLTING AS A MECHANISM OF DEPURATION OF METALS IN

Introduction

The Atlantic marsh fiddler crab, Uca pugnax, is the most abundant of its genus on the eastern coast of the United States. From Cape Cod to Florida, they inhabit intertidal salt marshes in sheltered bays and estuaries and fill a major niche in these ecosystems. U. pugnax is a food source for a variety of organisms including birds, fish, and other crabs [START_REF] Crane | Fiddler Crabs of the World[END_REF]. In addition, their burrowing behavior stimulates the turnover of nutrients in the marsh sediment, aerates the soil, and allows deeper root penetration of marsh plants [START_REF] Bertness | Fiddler crab regulation of Spartina alterniflora production on a New England salt marsh[END_REF].

Crustaceans have an exoskeleton that must be shed in order to grow. During proecdysis a new cuticle is produced underneath the old one, which is subsequently shed (ecdysis). Calcium is reabsorbed from the exoskeleton just prior to molting, which softens the carapace so that it can be shed [START_REF] Greenaway | Calcium balance and moulting in the crustacea[END_REF]. After ecdysis has occurred, the stored calcium is utilized to harden the new cuticle.

Metals such as copper, lead, and zinc are commonly found in estuaries surrounding urban environments. Metals can be bound to chitin in exoskeletons of crustaceans [START_REF] Keteles | The contribution of ecdysis to the fate of copper, zinc, and cadmium in grass shrimp, Palaemontes pugio Holthius[END_REF]. These metals become associated with the calcium in the exoskeleton matrix. They may be absorbed to the surface of the exoskeleton or bind to the inner exoskeleton matrix after uptake [START_REF] Keteles | The contribution of ecdysis to the fate of copper, zinc, and cadmium in grass shrimp, Palaemontes pugio Holthius[END_REF]. Metals also accumulate in the hepatopancreas, and can be stored in granules and/or by binding to metallothioneins [START_REF] Carvalho | An experimental study on the bioaccumulation and turn over of polonium-210 and lead-210 in marine shrimp[END_REF][START_REF] Wallace | Alteration in prey capture and induction of metallothioneins in grass shrimp fed cadmiumcontaminated prey[END_REF].

Metal-rich granules are a mechanism for long term storage of essential and non essential metals, where as binding to metallothioneins, regulates internal metal concentrations on shorter time scales [START_REF] Brown | The form and function of metal-containing 'granules' in invertebrate tissues[END_REF][START_REF] Roesijadi | Metallothioneins in metal regulation and toxicity in aquatic animals[END_REF].

Metals can cause disruption of molting, limb regeneration, alteration of blood glucose levels, color changes, and impairment of reproduction in crustaceans [START_REF] Defur | Endocrine disruption in invertebrates: Endocrinology, testing, and assessment[END_REF]. [START_REF] Elumalai | Alterations of reproductive parameters in the crab Carcinus maenas after exposure to metals[END_REF] found that exposure to cadmium and chromium can alter the reproductive cycle of C. maenas, and that the combination of the two metals disrupted the reproductive cycle to a greater extent. Metals have been shown to inhibit regeneration and molting in fiddler crabs [START_REF] Callahan | Methyl-mercury effects on regeneration and ecdysis in fiddler crabs (Uca pugilator, U. pugnax) after short-term and chronic preexposure[END_REF][START_REF] Callahan | Methyl-mercury effects on regeneration and ecdysis in fiddler crabs (Uca pugilator, U. pugnax) after short-term and chronic preexposure[END_REF][START_REF] Weis | Effects of pollutants on molting and regeneration in Crustacea[END_REF]. Mercury can also cause sluggishness and lack of responsiveness as well as premature egg dropping in females [START_REF] Vernberg | Multiple Environmental Factor Effects on Physiology and Behavior of the Fiddler Crab, Uca pugilator[END_REF]. As larvae develop, their sensitivity to mercury increases and their swimming behavior is further altered [START_REF] Vernberg | Multiple Environmental Factor Effects on Physiology and Behavior of the Fiddler Crab, Uca pugilator[END_REF]. [START_REF] Vernberg | Multiple Environmental Factor Effects on Physiology and Behavior of the Fiddler Crab, Uca pugilator[END_REF] found that sublethal exposure to cadmium affects the development of the larvae and alters their metabolic rate. There has also been some evidence that fiddler crabs may adapt to chronic exposure to toxicants and become more resistant. This could potentially impact the rest of the ecosystem as the toxicants move up the food chain [START_REF] Callahan | Methyl-mercury effects on regeneration and ecdysis in fiddler crabs (Uca pugilator, U. pugnax) after short-term and chronic preexposure[END_REF].

Since some fraction of the body burden of metals is associated with the exoskeleton, molting may be a way for arthropods to depurate metals. Molting in insects has been shown to reduce body burdens of heavy metals [START_REF] Raessler | Accurate determination of Cd, Cr, Cu and Ni in woodlice and their skins-is moulting a means of detoxification?[END_REF]. However, in the estuarine grass shrimp, Palaemonetes pugio, molting did not substantially reduce the amounts of copper, zinc, and cadmium, because they were reabsorbed with the calcium during proecdysis [START_REF] Keteles | The contribution of ecdysis to the fate of copper, zinc, and cadmium in grass shrimp, Palaemontes pugio Holthius[END_REF]. Bondgaard and Bjerregarrd (2005) found that molting of the crab Carcinus maenas did not reduce cadmium levels.

Molting played a role in metal concentrations and their distributions in soft tissues and the exoskeleton in amphipods [START_REF] Weeks | The loss, uptake and tissue distribution of copper and zinc during the moult cycle in an ecological series of talitrid Amphipods (Crustacea:Amphipoda)[END_REF].

The purpose of the present study was to determine if fiddler crabs can reduce their overall body burdens of metals significantly as a result of molting. We studied two populations, one from a highly contaminated site and one from a relatively clean environment to determine if there were differences in the way copper, lead, and zinc were distributed in intermolt and postmolt individuals.

Study Sites

Piles Creek (PC) Piles Creek (PC) is located in Linden, New Jersey, and is a tributary of the Arthur Kill.

The site is surrounded by industrial sites, a sewage treatment plant, and a major highway.

Oil spills in the Arthur Kill and possible leachate from landfills have also been a source of organic contaminants [START_REF] Perez | Differences in prey capture in grass shrimp, Palaemonetes pugio, collected along an environmental impact gradient[END_REF]. This site has been the source of ecological research for over 20 years. Studies have been conducted on resident fish, shrimp, crabs, and ecology of the system (Santiago [START_REF] Bass | Some factors affecting size distribution and density of grass shrimp (Palaemonetes pugio) populations in two New Jersey estuaries[END_REF]. Elevated levels of organic contaminants and metals have been found in sediments and biota [START_REF] Weis | Effects of contaminants on behavior: Biochemical mechanisms and ecological consequences[END_REF].

Great Bay-Mullica River Estuary (TK)

The Tuckerton (TK) site is part of the Great Bay-Mullica River Estuary and part of the Jacques Cousteau National Estuarine Research Reserve in New Jersey. It is part of 3,500 acres of protected salt marsh. It is a non-industrialized site that is relatively clean and has also been monitored for many years of research [START_REF] Bass | Some factors affecting size distribution and density of grass shrimp (Palaemonetes pugio) populations in two New Jersey estuaries[END_REF].

Materials and Methods

Intermolt Crabs

Eighteen male intermolt crabs from each population were collected and preserved on ice in August 2006. Analysis of metals (lead, copper, and zinc) was performed at the University of Medicine and Dentistry of New Jersey. Crabs were dissected and separated into the soft tissues (including hypodermis) and the carapace. The soft tissue samples and carapaces were dried and put through a series of acid digestions. Cu, Pb, and Zn were analyzed by using a flame aspiration AAS in a Perkin-Elmer 603 Atomic Absorption Spectrophotometer. Dolt 2, dogfish liver was used as a standard reference material for all analyses.

Postmolt Crabs

Male crabs from both locations were collected and brought back to the lab in August 2006. Molting was accelerated by multiple autotomy, which was induced by pinching the merus of the crabs' legs with forceps [START_REF] Skinner | Loss of limbs as a stimulus to ecdysis in Brachyura (true crabs)[END_REF]. Two of the walking legs on each side of the crab and the large claw were removed. The crabs were then placed in individual containers with their native sediments and 18 ppt artificial seawater at a depth that just covered the crab. Crabs were maintained at room temperature with a 14/10 light cycle, and monitored until they molted. Water was changed daily and sediment was changed every three days. Immediately after molting, both the soft crab and its exuvium (molt) were frozen for metal analysis. Initially 25 crabs from each population were used. All 25 PC crabs and exuvia were viable but only 20 viable TK crabs and exuviae were collected. Each newly molted soft crab and exuvium was dried and put through a series of acid digestions. Samples were analyzed for Cu, Pb, and Zn by using a flame aspiration AAS in a Perkin-Elmer 603 Atomic Absorption Spectrophotometer. Dolt 2, dogfish liver was used as a standard reference material for all analyses. Standard reference material recovery ranged from 92.3% to 104.5%. A Wilcoxon Rank Sum Test was performed on the concentrations (µg/g) of each metal in the soft tissues and exuvia. An independent two sample t-test was performed on distributions of total body burden (µg) of each of the metals in the soft tissues and exoskeletons.

Results

Intermolt Crabs

Concentrations (µg/g) and proportions (ratios calculated from whole body burdens) of copper, lead, and zinc in the carapace and soft tissues during intermolt of the two populations are presented in Table 1. Both populations had significantly higher concentrations of Cu in their soft tissues than carapace (PC p < 0.001; TK p < 0.001).

The intermolt phase PC crabs had significantly higher concentrations of copper in their soft tissues (p = 0.003) than their conspecifics from TK. However, there was no significant difference in concentrations (µg/g) of copper in the carapaces (p = 0.235).

More copper was stored in the soft tissue than the carapace for both populations (PC 5.98:1 ratio; TK 2.26:1 ratio). However, PC crabs stored proportionally more copper in their soft tissues (t(34) = 2.04, p = 0.049) than their TK conspecifics.

Lead concentrations (µg/g) were not significantly different between soft tissue and carapace for PC (p = 0.728). However, significantly higher concentrations were found in the carapace than soft tissues for TK (p = 0.038). PC crabs also had significantly higher concentrations (µg/g) of lead in both their soft tissues (p = 0.011) and their carapace (p =0.003) than TK crabs. Slightly more Pb was stored in the carapace than the soft tissues during intermolt for both populations, PC (1:0.81 ratio); TK (1:0.39 ratio). However, TK crabs stored proportionally more lead in their soft tissues (t(34) = 3.27, p = 0.003) than their PC conspecifics.

Zinc concentrations (µg/g) were significantly higher in soft tissues than carapace for both populations (PC p < 0.001; TK p < 0.001). There was no significant difference in the concentrations (µg/g) of Zn the soft tissues (p = 0.517) and carapaces (p = 0.825) between populations. More Zn was stored in the soft tissues for both populations (PC 5.37:1 ratio, TK 1.94:1 ratio). There was also no significant difference (t(34) = 1.46, p = 0.155) in proportions of Zn stored in the soft tissues between populations.

Postmolt crabs

Both PC and TK crabs had significantly higher concentrations of Cu (µg/g) in the soft crab than the exuvium (PC < 0.001; TK < 0.001). Concentrations (µg/g) and proportions (ratios) of copper, lead, and zinc burden in the soft crab and exuvium immediately after molting are presented in Table 2. Significantly higher concentrations (µg/g) of Cu were found in the PC soft crabs (p < 0.001) and exuvia (p < 0.001) than TK crabs. More copper was stored in the soft crab than the exuvia for both populations (PC 9.97:1 ratio, TK 46.66:1 ratio). TK crabs stored proportionally more copper in their soft tissues (t(43) = 5.27, p < 0.001) than their PC conspecifics.

Both PC and TK crabs had significantly higher concentrations of Pb (µg/g) in the exuvia than the soft tissues (PC < 0.001; TK < 0.001). Significantly higher concentrations (µg/g) of Pb were found in the soft crab (p < 0.001) and the exuvia (p < 0.001) in the PC crabs than TK crabs. Post molt soft crabs contained more Pb than the exuvia in both populations (PC 1:0.33 ratio, TK 1:0.84 ratio). PC crabs stored proportionally more lead in their soft tissues (t(43) = 8.36, p < 0.001) than their TK conspecifics.

Both populations had significantly higher concentrations of Zn (µg/g) in the soft tissues than the exuvia (PC < 0.001; TK < 0.001). PC crabs had significantly higher concentrations (µg/g) of Zn in the soft crab (p = 0.002) and exuvia (p < 0.001) than TK crabs. More Zn was stored in the soft crab than the exuvia for both populations (PC 4.39:1 ratio, TK 14.47:1 ratio). PC crabs stored proportionally more zinc in their soft tissues (t(43) = 6.37, p < 0.001) than their TK conspecifics.

Crabs from both populations were able to reduce a large portion of their total body burden of lead as a result of molting (Figures 1 and2). PC crabs shifted significant amounts of Cu (t(41) = 5.41, p < 0.001) and Zn (t(41) = 2.76, p = 0.009) into the soft tissue prior to molting. However, they shifted Pb (t(41) = 4.89, p < 0.001) into the exoskeleton prior to molting. TK crabs shifted significant amounts of Cu (t(36) = 16.55, p < 0.001), Pb (t(36) = 5.39, p < 0.001) and Zn (t(36) = 15.56, p < 0.001) into the soft tissue prior to molting.

The average percent of the total body burden eliminated during intermolt for the contaminated PC crabs varied with each metal, 12% Cu, 76% Pb, and 22% Zn. The average percent of the total body burden eliminated during intermolt for the relatively clean TK crabs also varied with each metal, and was significantly lower than the conspecifics from PC, 3% Cu, 56% Pb, and 8% Zn.

Discussion

Our results indicate that fiddler crabs from more contaminated areas have higher concentrations of metals and they depurate a significant amount of certain metals (lead).

The proportions in which they store these metals (soft tissues/exoskeleton) varies with metal type.

We also discovered that the proportion of metals stored in the soft tissues and carapace does change between the intermolt and molt phases. Both populations of crabs shifted Cu and Zn from the carapace into the soft tissues prior to molting. This is similar to [START_REF] Engel | Metal regulation and molting in the blue crab, Callinectes sapidus: copper, zinc, and metallothionein[END_REF] who found that the distribution of Cu in the blue crab, Callinectes sapidus, changed prior to molt. Copper distributions in the lobster, Homarus gammarus, have also been shown to change just prior to molting with more copper being shifted into the soft tissues (Hagerman, 1983).

Previous studies indicate that molting as a mechanism for detoxification may vary depending upon the particular element and crustacean species. [START_REF] Keteles | The contribution of ecdysis to the fate of copper, zinc, and cadmium in grass shrimp, Palaemontes pugio Holthius[END_REF] found that molting in grass shrimp, Palaemonetes pugio, did not substantially reduce copper, zinc and cadmium levels, as they were reabsorbed along with Ca prior to ecdysis. In contrast, crabs from PC, the highly contaminated site, shifted a considerable amount of Pb from soft tissues to the exoskeleton prior to molt, thereby depurating a large amount of Pb. TK shifted crabs did the opposite and shifted Pb, as well as Cu and Zn, from the carapace into the soft tissues. Even though the fiddler crabs from the cleaner environment shifted some Pb into their soft tissues prior to molting they were also able to depurate a substantial amount of this non essential element.

Fiddler crabs can incorporate substantial amounts of lead into their carapace and eliminate them during molting, and the relative amount of depuration is greater in crabs from contaminated sites with higher body burdens. Molting in Uca pugnax appears to be a feasible mechanism for fiddler crabs to significantly reduce their overall body burden of lead.

The levels at which the crabs can depurate varies significantly with each metal.

Large fractions of lead are eliminated along with the shed exuvium, while very little Cu and Zn are eliminated with the exoskeleton. Copper and zinc are essential metals while lead has no known biological function [START_REF] Abel | Water Pollution Biology[END_REF]. Copper is part of hemocyanin, the blood pigment in decapod crustaceans and is instrumental in forming the new exoskeleton during ecdysis [START_REF] Keteles | The contribution of ecdysis to the fate of copper, zinc, and cadmium in grass shrimp, Palaemontes pugio Holthius[END_REF]. Zinc is a component of some enzymes, and can be effectively regulated in crustaceans and may be lost through excretion across the gills [START_REF] Bryan | Concentrations of zinc and copper in the tissues of decapod crustaceans[END_REF][START_REF] White | Zinc flux in Palaemon elegans (Crustacea, Decapoda): molting, individual variation and tissue distribution[END_REF].

It is uncertain how long this detoxification as a result of molting will last for crabs in the field. [START_REF] White | Zinc flux in Palaemon elegans (Crustacea, Decapoda): molting, individual variation and tissue distribution[END_REF] demonstrated that for some crustaceans, metals can be absorbed from the surrounding water through the soft new cuticle if molting occurs in contaminated water. [START_REF] White | Accumulation of cadmium by Palaemon elegans (Crustacea: Decapoda)[END_REF] found that although there was some loss of Cd (15%) from the shrimp, Palaemon elegans, as a result of molting, the new soft cuticle readily took up additional Cd from the water, and the exuvia took up large amounts after being shed into water with high concentrations of Cd. The metal-rich exuvium may also be consumed by the crab itself or other organisms within the estuary.

If consumed by other organisms in the estuary it could facilitate trophic transfer.

Figure 1 .

 1 Fig.1

Figure 2 .

 2 Figure 2. Metal distribution during the intermolt and postmolt for Tuckerton population.

Table 1 .

 1 Mean metal concentrations (ug/g) + SE and proportions of metal storage in tissue vs. carapace (ratios as a function of total body burden) in intermolt crabs from Piles Creek and Tuckerton. Asterisks indicate significantly higher concentrations when comparing soft tissues vs. carapace within the same population. ST Soft Tissue CP = Carapace

		Piles Creek	n = 18		Tuckerton	n = 18
		ST	CP	Ratio	ST	CP	Ratio
	Copper 639.69 + 39.28 * 152.71 + 11.29	5.98 518.89 + 31.09 * 136.66 + 4.54	2.26
	Lead	41.37 + 6.28	41.28 + 4.50	0.81	20.36 + 2.67	27.03 + 1.54 *	0.39
	Zinc	165.78 + 12.59 *	46.58 + 2.46	5.37 149.85 + 6.25 *	46.64 + 2.28	1.94

Table 2 .

 2 Mean metal concentrations (ug/g) + SE and proportions of metal storage in the soft crab vs. exuvium (ratios as a function of total body burden) in post-molt crabs from Piles Creek and Tuckerton. Asterisks indicate significantly higher concentrations when comparing soft tissues vs. exuvia within the same population. SC = Soft Crab EX = Exuvium

		Piles Creek	n = 25		Tuckerton	n = 20
		SC	EX	Ratio	SC	EX	Ratio
	Copper 306.46 + 18.07 *	78.53 + 6.48	9.97 194.37 + 8.14 *	16.72 + 2.53	46.44
	Lead	18.31 + 2.11	129.81 + 9.81 * 0.33	11.46 + 0.64	33.07 + 3.85 *	0.84
	Zinc	133.30 + 10.75 *	79.63 + 8.48	4.39 108.75 + 2.28 *	25.63 + 1.90	14.47

Acknowledgements

The authors would like to thank Dr. Peddrick Weis and Ted Proctor from the University of Medicine and Dentistry of New Jersey, Newark, for the use of their equipment and expertise in metals analysis. We would also like to thank the Rutgers University Marine Field Station, Tuckerton, NJ for funding support.


Jennifer Ayars

Yuan Gao

Atmospheric nitrogen deposition to the mullica river-great bay estuary

Keywords: nitrogen compounds, atmospheric nitrate and ammonium, deposition rate, coastal waters, eutrophication

HAL is

Introduction

An important portion of nitrogen (N) entering coastal waters is of atmospheric origin. Atmospheric nitrogen can reach coastal waters by direct deposition pathways: wet deposition by precipitation and dry deposition by both aerosol particles and gases, and it can also enter the coastal waters by indirect pathways through deposition first to the land surface and then subsequent runoff. Studies conducted along the east coast of the United States indicate that atmospheric nitrogen deposition represents a significant amount of the total nitrogen input to coastal waters [START_REF] Fisher | Atmospheric nitrogen deposition and the Chesapeake Bay estuary[END_REF][START_REF] Paerl | Isotopic characterization of atmospheric nitrogen inputs as sources of enhanced primary production in coastal Atlantic Ocean waters[END_REF][START_REF] Russell | Sources of nitrogen in wet deposition to the Chesapeake Bay region[END_REF][START_REF] Scudlark | Organic nitrogen in precipitation at the Mid-Atlantic U.S. coast -methods evaluation and preliminary measurements[END_REF][START_REF] Castro | Atmospheric nitrogen deposition to estuaries in the mid-Atlantic and northeastern United States[END_REF][START_REF] Whitall | Importance of atmospherically deposited nitrogen to the annual nitrogen budget of the Neuse River estuary, North Carolina[END_REF]. A large fraction of the atmospheric nitrogen input is in the form of inorganic nitrogen, mainly nitrate (NO 3 -) and ammonium (NH 4 + ). NO 3 -is formed through atmospheric reactions involving NO x, and emission sources for NO x include fossil fuel combustion from automobiles and industries, biomass burning, and other natural fixation processes. The formation of NH 4 + is mainly by the gas-to-particle conversion reactions of NH 3 in the atmosphere, and NH 3 is primarily derived from agricultural waste, fertilizers, industrial emissions, and decomposition of organic substances [START_REF] Seinfeld | Atmospheric Chemistry and Physics[END_REF][START_REF] Paerl | Connecting atmospheric nitrogen deposition to coastal eutrophication[END_REF].

Organic N can also contribute to a sizeable portion of the total wet deposition budget [START_REF] Cornell | Atmospheric input of dissolved organic nitrogen to the oceans[END_REF][START_REF] Whitall | Importance of atmospherically deposited nitrogen to the annual nitrogen budget of the Neuse River estuary, North Carolina[END_REF]. Recent measurements for coastal North Carolina indicated that ~30% of the total N in rainwater was organic N [START_REF] Peierls | Bioavailability of atmospheric organic nitrogen deposition to coastal phytoplankton[END_REF]. Although sources of organic nitrogen in the atmosphere are not well understood, certain contributions to atmospheric organic nitrogen from natural sources such as sea spray droplets and plant pollens have been noted [START_REF] Prospero | Atmospheric deposition of nutrients to the North Atlantic Basin[END_REF]. Another important component in the total atmospheric deposition could be dry deposition by gasphase nitrogen which has not been studied widely and is often omitted from calculations of the total N deposition. Recent studies from coastal New Jersey and North Carolina indicate that both oxidized N such as HNO 3 and reduced N such as NH 3 contribute significantly to the dry deposition of atmospheric nitrogen (CASTNET;Walker et al., 2004). Therefore, careful considerations of the contributions of these gas-phase N species to the dry deposition, as well as organic N, should be incorporated into future atmospheric N measurements.

Nitrogen is a major nutrient that controls primary productivity in many coastal waters, and atmospheric deposition is an important non-point source of both organic and inorganic nitrogen [START_REF] Ryther | Nitrogen, phosphorous, and eutrophication in the coastal marine environment[END_REF][START_REF] Vitousek | Nitrogen limitation on land and in the sea: How can it occur?[END_REF][START_REF] Nixon | Coastal marine eutrophication: a definition, social causes, and future concerns[END_REF][START_REF] Paerl | Coastal eutrophication in relation to atmospheric nitrogen deposition: current perspectives[END_REF][START_REF] Paerl | Anthropogenically-derived atmospheric nitrogen deposition, marine eutrophication and harmful algal bloom expansion: is there a link?[END_REF][START_REF] Seitzinger | Atmospheric inputs of dissolved organic nitrogen stimulate estuarine bacteria and phytoplankton[END_REF][START_REF] Rabalais | Nitrogen in aquatic ecosystems[END_REF][START_REF] Driscoll | Nitrogen pollution in the northeastern United States: sources, effects, and management options[END_REF][START_REF] Galloway | The nitrogen cascade[END_REF]. Excessive fluxes of nitrogen from atmospheric sources result in the eutrophication of many coastal waters in the United States, which is now considered the most widespread water quality problem [START_REF] Nixon | Coastal marine eutrophication: a definition, social causes, and future concerns[END_REF][START_REF] Carpenter | Nonpoint pollution of surface waters with phosphorous and nitrogen[END_REF].

This work focuses on the Mullica River-Great Bay Estuary to characterize the atmospheric inorganic nitrogen (NO 3 -and NH 4 + ) input from precipitation and associated with aerosols. The Mullica River-Great Bay Estuary is located along the southern New [START_REF] Kennish | Benthic macrofaunal community structure along a well-defined salinity gradient in the Mullica River-Great Bay Estuary[END_REF]. The waters in this region have traditionally been undisturbed and clean with little human impact. Most of the Mullica River watershed is sheltered from anthropogenic input because the upstream portions are part of the New Jersey Pinelands National Reserve, while the downstream portions are surrounded by federal and state wildlife refuges [START_REF] Psuty | The Mullica River-Great Bay National Estuarine Research Reserve: a unique opportunity for research, preservation, and management[END_REF][START_REF] Zampella | The Mullica River Basin: A Report to the Pinelands Commission on the Status of the Landscape and Selected Aquatic and Wetland Resources[END_REF]. This estuary, therefore, can serve as an excellent reference location to assess the health of impacted coastal ecosystems in New Jersey. To date, there has been little data collected to evaluate atmospheric nitrogen deposition to this ecosystem.

Methods

Sampling Collection

Atmospheric precipitation and aerosol samples were collected at Rutgers University Marine Field Station (RUMFS) in Tuckerton, New Jersey (39.59 o N, 74.34 o W) from March 2004 to March 2005. This site is located at the southern tip of a salt marsh peninsula across from the Little Egg Inlet in the Mullica River-Great Bay Estuary (Figure 1). Precipitation samples were collected by a MIC wet-only automatic precipitation sampler controlled by an onsite rain sensor (MIC Company, Richmond, Ontario, Canada). A total of 51 precipitation samples were collected on an event-basis throughout the year, using plastic reservoir sets that were previously cleaned and sealed in plastic bags until use. A precipitation event during this study was arbitrarily defined as the collection of small amounts of precipitation over a raining period that could last for a consecutive three to four days or one day of heavy precipitation. The annual precipitation rate during this study period was 107.5 cm and the average precipitation rate was 8.99 cm/month. After the precipitation sample was collected, the bottle with the unfiltered sample was labeled. A total of 22 aerosol samples were also collected using a Model 500EL high-volume aerosol sampler with a flow rate of ~ 1 m 3 min -1 (Aquaero Tech, Miami, Florida, USA). Whatman 41 and 48 cellulose filter papers (both with the same filtration efficiency of air particulate matter but different ash maximum) were used to collect the samples, and the sampling duration lasted between 6 and 24 hours. On average, two samples were collected per week from March 2004 to May 2004. After collection, the sample filter was unloaded, placed in a plastic bag and labeled. All samples were stored frozen until analysis.

Chemical Analysis

To prepare precipitation samples for analysis, the frozen samples were first thawed at room temperature. The pH of each sample was determined with a SevenEasy pH meter (Mettler Toledo, Germany). Each sample was subsequently filtered to remove particulate matter using a polycarbonate filter of 0.45 µm pore size along with a 250 ml Nalgene filtration system. To prepare aerosol samples for analysis, one-fourth of each sample filter was leached in 100 ml deionized water ultrasonically for ~ 1 hour. Then the samples were filtered following the same procedure as that used for precipitation sample preparation. The details of the method can be found in [START_REF] Gao | Atmospheric non-sea-salt sulfate, nitrate and methanesulfonate over the China Sea[END_REF]. The analysis of nitrate and ammonium in precipitation and aerosols was conducted at Montclair State University (MSU) with a Dionex 2500 Ion Chromatograph (IC) (Dionex Corporation).

The 2500 IC system was equipped with an IonPac AG11-HC 4mm guard column and an IonPac AS18 4mm analytical column for anions such as nitrate. An eluent mixture of 50% w/w sodium hydroxide (NaOH) and distilled water was used to elute nitrate and the other anions at a flow rate of 1 ml min -1 . Collection of field blanks, analyses of replicates and an interlaboratory comparison were conducted to assure the data quality. The IC detection limits were 0.05 mg L -1 for nitrate and 0.05 mg L -1 for ammonium. As a data QA/QC procedure, a total of five precipitation samples that were analyzed at MSU were also analyzed at a lab at Rutgers University Pineland Field Station in New Jersey, and the concentrations from the two labs agreed within 15%, although the samples were stored longer at the Pineland Field Station before analysis. Therefore, the different storage times could be a contributing factor to the difference in concentrations as a result of a chemical change in composition over time. There could also be a difference in results due to the age of each of the laboratory's IC's column and suppressor as well as different standard operating procedures and quality assurance practices procedures established at different laboratories.

Results and Discussion

Nitrate and Ammonium Concentrations in Precipitation and Aerosols

Figure 2 shows the concentrations of nitrate and ammonium in precipitation. The nitrate concentrations for precipitation were always higher than ammonium concentrations. The arithmetic means of nitrate and ammonium concentrations in precipitation were 2.3 mg L -1 and 0.42 mg L -1 , respectively. These results are comparable to other studies conducted along the east coast [START_REF] Russell | Sources of nitrogen in wet deposition to the Chesapeake Bay region[END_REF][START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF]. [START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF] found that the average nitrate concentration for precipitation was 1.8 mg L -1 , and the average ammonium concentration was 0.33 mg L -1 for Barnegat Bay, New Jersey. At Edwin B. Forsythe National Wildlife Refuge located in Atlantic County, New Jersey, the annual mean concentration in precipitation was 0.88 mg L -1 for nitrate, and 0.20 mg L -1 for ammonium in 2005, and in 2004, the nitrate and ammonium concentrations were 1.00 mg L -1 and 0.15 mg L -1 , respectively. The eight-year precipitation record (1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) shows the average concentrations of 0.99 mg L -1 for nitrate and 0.16 mg L -1 for ammonium at this location (NADP/NTN). At Washington

Crossing in Mercer County in New Jersey, the annual concentrations of nitrate and ammonium in precipitation were 1.53 mg L -1 for nitrate and 0.30 mg L -1 for ammonium in 2004 and 1.04 mg L -1 for nitrate and 0.21 mg L -1 for ammonium in 2005

(NADP/NTN). At the Chesapeake Bay, the volume-weighted average concentrations of nitrate and ammonium from wet deposition were 1.3 and 0.24 mg L -1 , respectively [START_REF] Russell | Sources of nitrogen in wet deposition to the Chesapeake Bay region[END_REF]. It is worth noting that there were variations in the average nitrate and ammonium concentrations for the above studies, and there are many factors contributing to the concentration differences, such as the sampling locations, the strength of emission sources, the year of the study, and the precipitation rates.

The values for nitrate and ammonium concentrations in aerosol samples for the spring are shown in Figure 3. The average nitrate and ammonium concentrations were 3.7 µg m -3 and 1.6 µg m -3 , respectively. Nitrate concentrations in the aerosol samples were higher than ammonium concentrations in this area. At Barnegat Bay, New Jersey, the nitrate concentrations in aerosol observed during a recent two-year measurement were also higher than ammonium concentrations, with average concentrations of 2.0 ± 0.89 µg m -3 for nitrate and 0.50 ± 0.28 µg m -3 for ammonium [START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF]. However, at Washington Crossing, an inland location in New Jersey, the average concentration of aerosol nitrate in 2004 was 1.2 µg m -3 , slightly lower than that for aerosol ammonium (1.5 µg m -3 ), and the six-year (2000)(2001)(2002)(2003)(2004)(2005) average concentrations for aerosol nitrate and ammonium at this location show the same feature (CASTNET). This feature also exists at other locations in the eastern United States. At Abington, Connecticut, the average aerosol concentrations for the period of 2000-2005 were 0.84 µg m -3 for nitrate and 1.1 µg m -3 for ammonium, and at Blackwater, Maryland for the same period, the average concentrations for aerosol nitrate and ammonium were 1.3 µg m -3 and 1.7 µg m -3 , respectively (CASTNET). The higher concentration of aerosol nitrate observed at coastal New Jersey may be attributed to a contribution of sea-salt nitrate in addition to nitrate from non-marine sources. A comparison table of the above studies conducted along the east coast is included in Table 1.

The average value of the pH in all precipitation samples was 4.32, with a range of 3.65 to 5.05, indicating the acidic nature in precipitation at this location. The cause of this acidity is likely anthropogenic emissions from the combustion of fossil fuels which release nitrogen oxides and sulfur dioxide into the atmosphere. These gas pollutants are the primary sources for acid deposition because they are later transformed into nitric and sulfuric acids which increase the acidity of precipitation [START_REF] Seinfeld | Atmospheric Chemistry and Physics[END_REF].

Figure 4 demonstrates a negative correlation between pH and nitrate and ammonium concentrations in precipitation: high nitrate and ammonium concentrations were related to low pH values. Acidic rainfall combined with high inorganic nitrogen concentrations could increase primary productivity when compared to more neutral rain [START_REF] Paerl | Enhancement of marine primary production by nitrogen-enriched acid rain[END_REF].

Atmospheric Flux Calculations

Based on the in situ measurements of nitrate and ammonium, the atmospheric flux via wet and dry deposition can be calculated using wet and dry deposition models for the Mullica River-Great Bay Estuary. Wet deposition involves the removal of atmospheric substances within clouds and below the cloud base mainly by precipitation [START_REF] Pryor | Particle dry deposition to water surfaces: processes and consequences[END_REF]. The wet deposition flux (F w ) (mg m -2 month -1 ) can be calculated by the following equation:

F w = 10 C r * P (1)
where C r is the concentration of nitrate or ammonium in precipitation (mg L -1 ), P is the precipitation rate (cm month -1 ), and 10 is a unit conversion factor. Dry deposition can be defined as the removal of gaseous and particulate species from the atmosphere without precipitation. Gas diffusion, impaction, and gravitational settling play a role in the dry deposition of particles. The particle dry deposition process depends primarily on the particle size. The atmospheric flux via dry deposition (F d ) (mg m -2 month -1 ) can be calculated by the following equation:

F d = (2.592 x 10 4 ) * C air * V d (2)
where C air is the concentration of nitrate or ammonium in the air (mg m -3 ), V d is the dry deposition velocity (cm s -1 ), and 2.592 x 10 4 is a unit conversion factor. The average dry deposition velocities are assumed to be 0.34 cm s -1 for aerosol nitrate and 0.19 cm s -1 for aerosol ammonium. These values were derived based on in situ measurements of three sets of aerosol particle-size distributions with a cascade impactor sampler at the same location conducted in an earlier study [START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF]; therefore, these V d values are consistent for the same geographical area, and this could limit uncertainty. On the other hand, due to a number of environmental factors (wind speed and direction, solar radiation, air temperature, relative humidity, etc.), which constantly change from time to time even within the same area, the use of the above V d values could involve substantial uncertainties. For this reason, we adopted the ±50% uncertainty associated with the average values assumed by [START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF]. We acknowledge that higher uncertainty levels with a range of 0.001 -2 cm s -1 are possible as suggested by other researchers [START_REF] Slinn | Predictions for particle deposition on natural waters[END_REF]Williams, 1982;Clark and Kremer, 2005). These deposition velocities were previously used within the same study region. There are uncertainties associated with the values of dry deposition velocity and these values can only serve as first approximations.

Seasonal Patterns of Atmospheric Nitrogen Deposition

Total wet deposition (nitrate + ammonium) appeared to be highest in the spring for the Mullica River-Great Bay Estuary, with the average rate being 1.33 kg-N ha -2 month -1 , which accounts for approximately 41% of the total wet deposition (Figure 5a).

Total dry deposition appeared to increase from March to May 2004: 0.089 kg-N ha -2 month -1 for March, 0.118 kg-N ha -2 month -1 for April, and 0.177 kg-N ha -2 month -1 for May, approximately a 20-30% increase per month (Figure 5b). However, this study could not determine the seasonal variation in dry deposition at this location due to limited aerosol sampling. The preliminary results suggest that the formation of nitrate and ammonium in the atmosphere could be promoted by increased air temperature and solar radiation intensity. Many studies have found that atmospherically deposited nitrogen varies seasonally, with the highest concentrations and deposition fluxes occurring during the warmer seasons [START_REF] Russell | Sources of nitrogen in wet deposition to the Chesapeake Bay region[END_REF][START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF][START_REF] Luo | Atmospheric deposition of nitrogen along the Connecticut coastline to Long Island Sound: a decade of measurements[END_REF][START_REF] Sheeder | Modeling atmospheric nitrogen deposition and transport in the Chesapeake Bay watershed[END_REF][START_REF] Whitall | Importance of atmospherically deposited nitrogen to the annual nitrogen budget of the Neuse River estuary, North Carolina[END_REF]. These seasonal patterns may be the result of a seasonal variation in the precipitation rates and the emission of nitrogen species into the atmosphere [START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF][START_REF] Sheeder | Modeling atmospheric nitrogen deposition and transport in the Chesapeake Bay watershed[END_REF]. The direction from which an air mass originates can also affect the deposition of nitrogen species. Meteorological parameters during the measurement period of this study indicate that a high atmospheric nitrogen deposition event is usually associated with the air masses having an inland origin, and vise versa, consistent with many previous observations [START_REF] Prospero | Atmospheric deposition of nutrients to the North Atlantic Basin[END_REF]Whitall et al., 2003;Butler et al., 2005;Clark and Kremer, 2005).

Annual Atmospheric Nitrogen Deposition

Based on the average monthly deposition rates, the annual wet deposition fluxes for nitrate and ammonium were estimated to be 6.21 kg-N ha -2 year -1 and 3.77 kg-N ha -2 year -1 , respectively, for the Mullica River-Great Bay Estuary. The dry deposition flux for this same region was estimated to be 0.219 kg-N ha -2 month -1 for nitrate and 0.169 kg-N ha -2 month -1 for ammonium. However, the dry deposition flux values only reflect the dry deposition of nitrogen for the spring due to the limited aerosol sampling. Assuming that there is no seasonal change for the remainder of the year, the annual dry deposition was roughly estimated to be 0.874 kg-N ha -2 year -1 for nitrate and 0.670 kg-N ha -2 year -1 for ammonium. These estimates may involve certain degrees of uncertainties since the dry deposition was not directly measured for the entire year and it is likely to change with season.

The above results are comparable to other studies (Table 2). At Washington Crossing in Mercer County, New Jersey, annual wet deposition of nitrate and ammonium was estimated to be 4.0 kg-N ha -2 year -1 and 3.23 kg-N ha -2 year -1 , respectively, in 2004.

Annual dry deposition was estimated to be 0.1 kg N ha -2 year -1 for nitrate and 0.5 kg N ha -2 year -1 for ammonium during the same year (CASTNET/NADP-NTN). In 2005 at Edwin B. Forsythe National Wildlife Refuge located in Atlantic County, New Jersey, annual wet deposition of nitrate and ammonium was estimated to be 9.83 kg N ha -2 year -1 and 2.22 kg N ha -2 year -1 , respectively (NADP/NTN). Annual dry deposition values were not available for this site.

Based on the above calculations, the total annual atmospheric deposition flux for nitrate was then calculated to be 7.08 kg ha -2 year -1 and 4.44 kg ha -2 year -1 for ammonium to the Mullica River-Great Bay Estuary. Nitrate deposition accounts for ~61% of the total inorganic nitrogen deposition to this estuary (Table 3). The surface area of the Mullica River-Great Bay Estuary is 4,160 ha (41.6 km 2 ) [START_REF] Kennish | Benthic macrofaunal community structure along a well-defined salinity gradient in the Mullica River-Great Bay Estuary[END_REF].

Consequently, the total atmospheric nitrate and ammonium directly deposited to this estuary is estimated to be 4.79 x 10 4 kg N year -1 . An estimate of the total atmospheric nitrate and ammonium deposited to the Mullica River's watershed is determined to be 1.69 x 10 6 kg N year -1 , based on the area of the watershed (147,500 ha) [START_REF] Zampella | The Mullica River Basin: A Report to the Pinelands Commission on the Status of the Landscape and Selected Aquatic and Wetland Resources[END_REF].

It is worth noting that the annual deposition estimates of atmospheric nitrate and ammonium from this study represents the atmospheric deposition for a specific year only (March 2004-March 2005). Due to the changes in precipitation rates, air pollution emission rates, etc. from year to year, interannual variability in deposition likely exists in this area. A recent two-year measurement of atmospheric nitrate and ammonium within the same region [START_REF] Gao | Atmospheric nitrogen deposition to Barnegat Bay[END_REF] shows that the annual wet deposition decreased ~2% for nitrate and almost 16% for ammonium from year one to year two. Therefore, it is necessary to establish a long-term record of atmospheric N deposition to a sensitive coastal ecosystem through multi-year atmospheric measurements.

Dissolved organic nitrogen (DON) in precipitation may contribute to the total atmospheric N deposition. Atmospheric DON has recently drawn increasing attention as a significant additional source of "new" N input to both coastal and oceanic regions [START_REF] Cornell | Atmospheric input of dissolved organic nitrogen to the oceans[END_REF][START_REF] Seitzinger | Atmospheric inputs of dissolved organic nitrogen stimulate estuarine bacteria and phytoplankton[END_REF]. Measurements from coastal North Carolina indicate that 30% of rainwater N concentrations and deposition occurred in organic form, and ~20-30% of atmospheric organic N was available for the uptake by primary producers on short time scales [START_REF] Peierls | Bioavailability of atmospheric organic nitrogen deposition to coastal phytoplankton[END_REF]. Studies conducted in Bay, Rhode Island, show that ~19% N in precipitation is organic N [START_REF] Nixon | An assessment of the annual mass balance of carbon, nitrogen, and phosphorus in Narragansett Bay[END_REF]. At Lewes, Delaware, organic N in precipitation accounts for ~ 23% of the total N [START_REF] Scudlark | Organic nitrogen in precipitation at the Mid-Atlantic U.S. coast -methods evaluation and preliminary measurements[END_REF]. Preliminary results obtained from the analysis of limited precipitation samples collected at Barnegat Bay show that DON accounts for ~20% of the total dissolved N in precipitation in this area (Gao, unpublished data). Earlier studies conducted in the same region show that DON accounts for an important portion of the total atmospheric N deposition, ~20% at New Brunswick and 7-12% at Barnegat Bay in New Jersey [START_REF] Seitzinger | Atmospheric inputs of dissolved organic nitrogen stimulate estuarine bacteria and phytoplankton[END_REF]. Clearly, DON in precipitation is an important component of the total atmospheric N over the coastal ocean. Due to the limited scope of this study, DON was not measured, and therefore the current estimate of total atmospheric wet deposition in this region, in particular bioavailable N, may be undervalued. Assuming that the portion of DON in wet deposition is the same as those mentioned above, the total atmospheric N fluxes by wet deposition from this study could be underestimated by ~10-20%.

Contribution of Atmospheric Deposition to the Annual Nitrogen Input to the

Mullica River-Great Bay Estuary

All atmospheric nitrogen deposited directly to the Mullica River-Great Bay

Estuary is available for use by primary producers. However, only a fraction of the nitrogen deposited on the watershed will actually reach the estuary. Most of the nitrogen will be retained in the watershed and/or utilized and denitrified during transport through the watershed. After utilizing a simple retention model outlined in [START_REF] Whitall | Importance of atmospherically deposited nitrogen to the annual nitrogen budget of the Neuse River estuary, North Carolina[END_REF] based on the land cover at the site, the amount of N actually reaching the Mullica River-Great Bay Estuary indirectly is estimated to be 5.07 x 10 4 kg N year -1 , approximately 97% is retained within the watershed. This value is considered a rough estimate because an average retention value for generalized land use types was applied to the entire watershed. Parameters, such as soil N content, soil type, land slope, elevation and vegetative type, which are land parcel-specific, determine true nutrient detention values [START_REF] Valigura | Atmospheric nutrient input to coastal areas: reducing the uncertainties[END_REF], Whitall et al., 2003). At the Neuse River Estuary in North Carolina, approximately 94% of atmospheric N was retained in the watershed. The land cover that dominates in the watershed is forest, similar to the Mullica River-Great Bay Estuary [START_REF] Whitall | Importance of atmospherically deposited nitrogen to the annual nitrogen budget of the Neuse River estuary, North Carolina[END_REF]. In Waquoit Bay, Massachusetts, 89% of atmospheric N is lost within the watershed; approximately 10% reaches the estuary. In this area, urban land such as residential areas cover approximately 39% of the land, with natural vegetation only covering 21% [START_REF] Valiela | Nitrogen loading from coastal watersheds to receiving estuaries: new method and application[END_REF].

The total amount of atmospheric inorganic nitrogen entering the Mullica River-Great Bay Estuary is 9.86 x 10 4 kg N year -1 , approximately 49% is the result from direct deposition and 51% from indirect deposition. Considering the limited surface area of this estuary compared with that of the watershed (a ratio of 1:35), the direct deposition of atmospheric nitrogen certainly accounts for a large portion of the total input of this nutrient to the estuary. This atmospheric nitrogen deposition data will be extremely valuable in the development of an annual nitrogen budget for the estuary.

Conclusions

Nitrogen is a major nutrient that controls primary productivity in many coastal waters, and atmospheric deposition is an important non-point source of both organic and inorganic nitrogen. This project was the first to quantify the atmospheric inorganic nitrogen (nitrate and ammonium) input to the Mullica River-Great Bay Estuary. The average concentrations of nitrate and ammonium in the precipitation samples were 2.3 mg L -1 and 0.42 mg, L -1 , respectively. The average aerosol concentrations were 3.7 µg m -3 for nitrate and 1.6 µg m -3 for ammonium in the spring. Atmospheric wet deposition of nitrogen appeared to vary with season; the highest flux occurred during the spring, with an average value of 1.33 kg-N ha -2 month -1 accounting for approximately 41% of the total annual wet deposition. This seasonal deposition could be influenced by the variation in emission source strength, precipitation rate, and the origin of the air masses.

The total (wet + dry) annual atmospheric deposition fluxes were 7.08 kg N ha -2 year -1 for nitrate and 4.44 kg N ha -2 year -1 for ammonium. The total atmospheric inorganic nitrogen directly deposited to this estuary was estimated to be 4.79 x 10 4 kg N year -1 . The estimated deposition of N to the Mullica River watershed was 1.69 x 10 6 kg N year -1 . Only ~3% of the inorganic N deposited to the watershed ultimately reaches the estuary. The total amount of atmospheric inorganic nitrogen entering the Mullica River-Great Bay Estuary is 9.86 x 10 4 kg N year -1 ; approximately 49% is the result from direct deposition and 51% from indirect deposition. High atmospheric nitrogen deposition may stimulate phytoplankton productivity in this coastal ecosystem. It is worth noting that these percentages are based on the calculations with nitrate and ammonium only. If gaseous N species and organic N were included, the values would likely change to some extent. Therefore, future fieldwork should include simultaneous measurements of gasphase N and organic N in addition to N in aerosol and precipitation phases, to improve the atmospheric N flux estimates.

ACKNOWLEDGEMENTS:

Special Inset shows location of the estuary with respect to the state of New Jersey [START_REF] Kennish | Benthic macrofaunal community structure along a well-defined salinity gradient in the Mullica River-Great Bay Estuary[END_REF].

Figure Captions Figure 1 .

 Captions1 Figure Captions

Figure 2 .

 2 Figure 2. Concentrations of nitrate and ammonium in precipitation samples collected at RUMFS from March 2004 to March 2005.

Figure 3 .

 3 Figure 3. Concentrations of aerosol nitrate and ammonium for samples collected at RUMFS from March to May 2004.

Figure 4 .

 4 Figure 4. Relationship between pH and nitrate and ammonium concentrations in precipitation (March 2004 to March 2005).

Figure 5

 5 Figure 5(a). Seasonal wet deposition of nitrate and ammonium at the RUMFS site from March 2004 to March 2005.

Figure 5

 5 Figure 5(b). Monthly dry deposition of nitrate and ammonium for the spring of 2004 at the RUMFS site.

  Figure1

Table 1 .

 1 thanks to the staff at Rutgers University Marine Field Station for help with this research, especially Gregg Sakowicz for his help with precipitation sample collection and Roger Holden for equipment installation. Thanks to Michael Kennish for valuable comments and Dennis Gray for assistance with cation analysis. This research was supported by a graduate research fellowship from the Estuarine Reserves Division of National Oceanic and Atmospheric Administration Grant number NA04NOS4200135. Comparison of nitrate and ammonium concentrations in precipitation and in aerosols.

			NO 3 -1	NH 4	+	NO 3	-1	NH 4	+
	Region	Collection Year	Precipitation Concentration (mg L -1 )	Precipitation Concentration (mg L -1 )	Aerosol Concentration (ug m -3 )	Aerosol Concentration (ug m -3 )
	Mullica
	River-Great Bay Estuary,	2004-2005	2.33	0.42	3.66	1.62
	NJ
	Barnegat Bay, NJ	1999-2001 a	1.8	0.33	2.04	0.50
	Edwin B.
	Forsythe National	2005 b	0.88	0.20	-		-
	Wildlife
	Refuge, Atlantic	2004 b	1.00	0.15	-		-
	County, NJ
		2005 c	1.04	0.21	-		-
	Washington
	Crossing, NJ	2004 c	1.53	0.30	1.2	1.5
	Chesapeake Bay	1993-1994 d	1.3	0.24	-		-
	Abington, CT	2000-2005 e	-	-		0.84	1.1
	Blackwater, MD	2000-2005 e	-	-		1.3	1.7
	a Gao, 2002
	b NADP/NTN

c CASTNET/NADP-NTN d Russell et al., 1998 e CASTNET

Table 2 .

 2 Comparison of annual wet and dry nitrogen deposition rates for nitrate and ammonium.

	Region	Collection Year	Wet Deposition NO 3 -1 (kg-N ha -2 year -1 )	Wet Deposition NH 4 + (kg-N ha -2 year -1 )	Dry Deposition NO 3 -1 (kg-N ha -2 year -1 )	Dry Deposition + NH 4 (kg-N ha -2 year -1 )
	Mullica
	River-Great Bay	2004-2005	6.21	3.77	0.874	0.670
	Estuary, NJ
	Washington
	Crossing, Mercer	2004 a	4.0	3.23	0.1	0.5
	County, NJ
	Edwin B.
	Forsythe National	2005 b	9.83	2.22	-	-
	Wildlife
	Refuge, Atlantic	2004 b	9.66	1.47	-	-
	County, NJ
	Wye, Maryland	1996 c	-	-	0.05	-

a CASTNET/NADP-NTN b NADP/NTN c AIRMoN

Table 3 .

 3 Annual wet and dry deposition rates for nitrate and ammonium for the Mullica River-Great Bay Estuary from March 2004 to March 2005.

				Wet Deposition	Dry Deposition	Total Deposition
				(kg-N ha -2 yr -1 )	(kg-N ha -2 yr -1 )	(kg-N ha -2 yr -1 )
	NO 3	-	6.21 (62%)	0.87 (56%)	7.08 (61%)
	NH 4	+	3.77 (38%)	0.67 (44%)	4.44 (39%)
	Total	9.98 (87%)	1.54 (13%)	11.52


B G Clynick
email: bgclynick@yahoo.com.au

Characteristics of an urban fish assemblage: distribution of fish associated with coastal marinas

Keywords: Artificial habitats, Estuaries, Fish, Marinas, Spatial scale

In the marine environment, artificial structures are not, in general, managed for their value as habitat and are often built with no a priori expectation as to the assemblages which may colonise them. It may, however, become increasingly important to consider the value of such structures as habitat when decisions are made with respect to the management of artificial structures. This study investigates the role marinas play as habitat for fish by examining the distribution of fish associated with these urban developments and how this distribution relates to the physical characteristics of marinas. Assemblages of fish associated with marinas in waterways around Sydney were sampled in August and December 2002 and May 2003. Counts were done around structures present at marinas, i.e. pontoons and pilings and in the open water immediately adjacent to these structures. Within marinas, spatial patterns were particular to the types of fish examined. Large mobile species, such as Acanthopagrus australis and Girella tricuspidata, moved between structures and the open water between them. Smaller species, such as Trachinops taeniatus and Microcanthus strigatus, were found only in the immediate vicinity of these structures. At a larger spatial scale, assemblages of fish varied markedly between different marinas. This variability could not, however, be attributed to the depth of water, the age or size of marinas, nor the type of material with which marinas were constructed (i.e. pontoons or jetties).

Introduction

Throughout the world, degradation and alteration of coastal ecosystems continues at an alarming rate (Hinrichsen, 1998). The extensive changes to marine habitats have included the addition of artificial structures in waterways surrounding coastal cities [START_REF] Burdick | The effects of boat docks on eelgrass beds in coastal waters of Massachusetts[END_REF][START_REF] Connell | Do urban structures influence local abundance and diversity of subtidal epibiota? A case study from Sydney Harbour, Australia[END_REF][START_REF] Thompson | Rocky intertidal communities: past environmental changes, present status and predictions for the next 25 years[END_REF][START_REF] Chapman | Paucity of mobile species on constructed seawalls: effects of urbanization on biodiversity[END_REF]. These range from pontoons, pier-pilings and jetties used in the construction of foreshore developments, such as marinas, to breakwaters and seawalls that are built to protect foreshores. For example, in Sydney Harbour, at least 50 % of the foreshore is now artificial seawalls [START_REF] Chapman | Paucity of mobile species on constructed seawalls: effects of urbanization on biodiversity[END_REF] and there are about 40 functioning marinas that service 35,000 registered recreational vessels [START_REF] Widmer | Factors affecting traffic and anchoring patterns of recreational boats in Sydney Harbour, Australia[END_REF]. Similar trends in the increase of artificial structures have occurred in other parts of the "developed" world, particularly Europe [START_REF] Smith | Marinas in Europe[END_REF] and the USA (Hollin, 1992).

Following this trend, current ecological research in urbanised coastal waters has examined whether built structures are creating novel habitats that support a different suite of animals and plants than do natural habitats, or, alternatively, if species can use artificial structures in lieu of natural habitat [START_REF] Glasby | Differences between subtidal epibiota on pier pilings and rocky reefs at marinas in Sydney, Australia[END_REF][START_REF] Chapman | Intertidal seawalls-new features of landscape in intertidal environments[END_REF][START_REF] Bulleri | Intertidal assemblages on artificial and natural habitats in marinas on the north-west coast of Italy[END_REF]. Artificial structures introduced to intertidal and subtidal habitats may replace natural hard substrata or soft sediment. Therefore, the creation of such habitats has the potential to alter the distribution, diversity and abundance of organisms in these environments [START_REF] Mcdonnell | Ecosystem structure and function along urban-rural gradients: an unexploited opportunity for ecology[END_REF][START_REF] Connell | Do urban structures influence local abundance and diversity of subtidal epibiota? A case study from Sydney Harbour, Australia[END_REF], although the degree to which they alter biodiversity will depend on the type of natural habitat most affected [START_REF] Bulleri | The introduction of artificial structures on marine soft-and hard-bottoms: ecological implications of epibiota[END_REF].

Much research has focused on artificial structures as fish attraction devices [START_REF] Molles | Fish species diversity on model and natural reef patches: experimental insular biogeography[END_REF][START_REF] Grant | Early development of Pendleton artificial reef[END_REF][START_REF] Carr | Artificial reefs: The importance of comparisons with natural reefs[END_REF][START_REF] Rilov | Fish assemblage on natural versus vertical artificial reefs: the rehabilitation perspective[END_REF] and countless studies have claimed that the presence of artificial structures can contribute to increasing fish biomass [START_REF] Bohnsack | Are high densities of fishes at artificial reefs the result of habitat limitation or behavioral preference[END_REF][START_REF] Fabi | Effects of fish community induced by installation of two gas platforms in the Adriatic Sea[END_REF]). Few studies have, however, documented patterns of the spatial distribution of fish assemblages associated with artificial structures. Coastal developments, such as marinas, add novel habitats, including pontoons, pilings and jetties, to the marine environment. Different types of artificial structures differ in a number of respects, especially in their orientation, depth and composition, which may influence the types of organisms inhabiting them [START_REF] Glasby | Orientation and position of substrata have large effects on epibiotic assemblages[END_REF][START_REF] Chapman | Intertidal seawalls-new features of landscape in intertidal environments[END_REF]. Recent studies that have compared intertidal and/or subtidal assemblages living on different types of artificial structures have generally found that each type of structure, e.g. pontoons, pilings, seawalls, is inhabited by a different assemblage of organisms (e.g. [START_REF] Glasby | Differences between subtidal epibiota on pier pilings and rocky reefs at marinas in Sydney, Australia[END_REF][START_REF] Glasby | Orientation and position of substrata have large effects on epibiotic assemblages[END_REF].

Such studies have, however, only dealt with organisms that live directly on the substratum [START_REF] People | Mussel beds on different types of artificial structures support different macroinvertebrate assemblages[END_REF]. The influence of these different types of structures on fish assemblages is not known.

This study investigates the role marinas play as habitat for fish in an urban coastal ecosystem. The first aim of this study was to identify spatial patterns in the distribution of fish within marinas to examine the potential effects of different components of habitat on the distribution of fish in these artificial habitats. Specifically, we tested the hypothesis that the composition of fish assemblages and the number of fish would differ among different habitats (pontoons, pilings and the open water immediately adjacent to these structures) within marinas.

To determine the generality of patterns, this hypothesis was tested at several locations and at several times. Furthermore, as the effects of habitat on fish may be dependent on the spatial scale examined, the second aim of this study was to determine if marinas that are constructed of different materials and thus provide different types of habitats, differ with respect to the associated fish assemblages. This was done by testing the hypothesis that marinas with pontoons, i.e. pilings with an adjacent pontoon, and marinas with jetties, i.e. pilings with no adjacent pontoon, are consistently different with respect to the types and abundance of fish associated with them.

Materials and methods

Study locations

Fish assemblages associated with marinas were sampled at four sheltered locations in each of two waterways in Sydney, NSW, Australia; Sydney Harbour and Pittwater (Fig. 1). The selection of marinas was based on the need for sufficient visibility to allow visual census. The marinas used in this study have all been in operation for over 20 years and all support at least 30 boats and some, more than 100 boats. Some are constructed of wooden jetties and wooden pilings. Others have floating pontoons and pylons. Therefore, not all habitats (specifically pontoons) were present at every marina (Table 1). All marinas were built over soft sediment at depths varying from 4 m to 12 m.

Experimental methods

To test hypotheses about the spatial distribution of fish within and among marinas at different times of the year, sampling was done in August 2002 (austral winter), December 2002 (austral summer) and May 2003 (austral autumn). Differences between the fish assemblages, the number of species of fish and the abundances of common species were examined at different spatial scales. At each marina, fish were sampled visually using stationary point counts within different areas of the marinas; under pontoons (if present), around pilings and in the open water between structures. Visual counts do have specific limitations, such as underestimating cryptic fish [START_REF] Sale | Correction of bias in visual transect censuses of coral reef fishes[END_REF] and under-or over-estimating 'diver-negative' and 'diver-positive' fish species, respectively [START_REF] Thresher | Comparative analysis of visual census techniques for highly mobile, reef-associated piscivores (Carangidae)[END_REF]. Potential biases, however, should be consistent among locations. Furthermore, stationary point counts were chosen as they are appropriate when estimating the abundance of fish in small areas (Connell et al. 1998). This technique involves visually counting fish in a defined area for a defined period of time.

Although the small size of area sampled may reduce the number of fish counted and be biased to more mobile species, it was deemed appropriate to enable a comparison between different habitats within a marina. Because the fish assemblages at different depths were likely to be different, counts around pilings and in open water were done at the surface and bottom. Fish were counted 1 m either side of the diver and 2 m in front in a semi-circle formation. This was the maximal area that could be sampled without counting fish that may be associated with adjacent habitats. Although the orientation of pontoons and pilings differed, comparable amounts of each habitat were sampled in each count. Surface counts were done from the surface to a depth of 2 m and bottom counts were done from the bottom to 2 m above the bottom. This depth was chosen to not confound surface and bottom counts. Four 3 minute counts (determined by a pilot study) were done in each habitat at the surface and at the bottom. The count began when the diver approached the piling/pontoon. Small cryptic species were counted at the completion of the count by slowly swimming around the structure and closely inspecting the substratum.

Statistical methods

Differences in fish assemblages among marinas and among habitats within marinas were tested using non-parametric multivariate analyses of variance (described as NP-MANOVA; [START_REF] Anderson | A new method for non-parametric multivariate analysis of variance[END_REF] using Bray-Curtis measures of dissimilarity [START_REF] Bray | An ordination of the upland forest communities of southern Wisconsin[END_REF] calculated from untransformed data. Significance of all NP-MANOVA analyses were determined from 999 permutations of the data. Because complex multivariate interactions make multi-factor designs difficult to interpret ecologically because of the many components that can contribute to differences, two separate analyses were done. First, for each sampling time and depth, two factor analyses were done to test for differences between the two estuaries and marinas within the estuaries. Second, at each depth, each marina was analysed separately to test for differences among habitats within marinas and among sampling times. Surface and bottom counts were analysed separately because these two depths were assumed to support different assemblages. To test for differences between marinas with jetties compared to those with pontoons, the assemblages associated with the pilings at the surface were compared at each time of sampling using a two factor NP-MANOVA. The analysis consisted of the two types of marinas (with jetties or pontoons), with three marinas nested in each type and four replicate counts at each marina (two marinas with pontoons were excluded from the analysis). Using the Primer software package, non -metric multidimensional scaling (nMDS) plots were used to illustrate spatial patterns of assemblages of fish in different habitats [START_REF] Clarke | Non-parametric multivariate analyses of changes in community structure[END_REF]. Averages were used in all nMDS ordinations because of the large stress values when using individual counts. Between-group similarities (SIMPER; [START_REF] Clarke | Non-parametric multivariate analyses of changes in community structure[END_REF] were used to determine the taxa most responsible for any significant multivariate differences among assemblages.

Analysis of variance (ANOVA) was used to test for differences in the total number and types of fish and the abundance of common species between habitats and marinas. Because the hypotheses were aimed to look for differences among habitats and locations within times, not for differences among times, each time was analysed separately. In addition, because pontoons were not sampled at all marinas, this habitat was excluded from univariate analyses and differences between pontoons and pilings were examined using graphical representations.

Frequencies of occurrence were also examined for each species across the different factors of interest in this study.

Relationships between the physical attributes of marinas and abundance and diversity of fish were tested using linear regressions. Age of the marina (yr), size of marina (number of berths), water depth (m) and distance from mouth of the estuary (m) were regressed against the abundance and number of species of fish. The different physical factors were not correlated (P > 0.05) and therefore relationships between the abundance and diversity of fish and each factor were examined separately.

Results

Fifty species of fish from 29 families were counted in Sydney Harbour. Twenty six species from 20 families were counted in Pittwater. Twenty four species were recorded in both estuaries (Table 2). A few species, including A. australis (bream), G. tricuspidata (luderick), T. taeniatus (hula fish) at both depths, M. strigatus (stripey) at the surface and Trachurus novaezelandiae (yellowtail) at the bottom, were relatively common at some locations at some times. Most other species were relatively uncommon and patchy in their occurrence (Table 2).

Patterns among habitats

Across all locations and times of sampling, 51 species were found around structures; 50 around pilings and 22 around pontoons. These species were equally distributed at the surface and bottom within marinas. Twenty species were counted in the open water adjacent to structures, the majority of which were counted at the bottom. Out of a total of 52 species, 28 were only seen directly adjacent to structures, including T. taeniatus and M. strigatus. Several of these species were only counted around pontoons or pilings and were only sighted on one or two occasions (Table 2).

The nMDS plots based on abundances of species generally suggested that assemblages associated with structures (pontoons and pilings; grey and black symbols) differed from those in open water (empty symbols), at the surface and the bottom within marinas (illustrated for December 2002 in Fig. 2). NP-MANOVA did not, however, detect significant differences at every marina at every time (at P < 0.05). Fish assemblages did not differ between pontoons and pilings (Fig. 2), except at Clontarf Marina and at Holmeport Marina in August (F = 10.84, 3.32; respectively; P < 0.01).

Although over 30 species were found at any one time, relatively few contributed much to differences among habitats. In fact, generally 80 percent of the overall dissimilarity between habitats was accounted for by seven species at the surface and four species at the bottom (Table 3). At the surface, T. taeniatus was most important at distinguishing assemblages around pontoons and pilings from those in open water. A. australis was the main contributor to the dissimilarity between pilings and open water at the bottom (Table 3). Other contributions were largely from other species that were abundant around structures and absent from open water, e.g. Parablennius intermedius (horned blenny) and M. strigatus, or mobile schooling species that occurred in open water, e.g. T. novaezelandiae.

At the surface, a greater number of fish and number of species were associated with both types of structure than with the open water (illustrated for December 2002 and May 2003 in Fig. 3a &b). These differences were not, however, significant at every marina at every time (number of fish, F = 2.70, 4.71, 1.16 for Time 1 -3, respectively; number of species, F = 4.02, 3.70, 3.08 for Time 1 -3, P < 0.05 for Location x Habitat interaction). The average numbers of A. australis and G. tricusidata also tended to be greater around structures at the surface (illustrated for December 2002 in Fig. 4a &b), but due to the large variance associated with these counts these differences were generally not significant (P > 0.05). Their frequency of occurrence was, however, generally much greater around structures than in the open water at the surface (Table 2). Pontoons and pilings, conversely, tended to support similar numbers and types of fish (Fig. 3a &b). The abundances of both T. taeniatus and M. strigatus were very variable among both types of structures, with occasionally very large numbers under pontoons, but neither structure consistently supported a greater abundance of either species (Fig. 4c &d).

At the bottom within marinas, there was consistently a greater number of species around pilings than in the surrounding open water, but this difference between the two types of habitat was only significant in May (Fig. 5b; F = 304.57, P < 0.05). Greater numbers of fish were also generally associated with pilings than with the open water at the bottom within marinas, although differences were not generally significant (Fig. 5i). In contrast, numbers of A. australis, G. tricuspidata and T. novaezelandiae showed variation among marinas and times but were not consistently different between the two habitats (illustrated for A. australis and G. tricuspidata in August 2002 in Fig. 6a andb).

Patterns among marinas

NP-MANOVA indicated that there was no significant difference in fish assemblages associated with marinas between the two estuaries (P > 0.05). Significant differences were detected in fish assemblages among marinas in both estuaries at the surface (F = 2.04, 2.56, 2.24 for Time 1 -3, respectively; P < 0.05) and the bottom (F = 2.00, 2.55, 2.36 for Time 1 -3, respectively; P < 0.05). Pairwise comparisons between all locations were highly significant across all times. The total abundance, number of species and abundances of common species was also very variable between different marinas within each estuary (Fig. 3456). Differences among marinas could not, however, be attributed to the presence or absence of pontoons.

Marinas constructed with pontoons and those built with jetties were compared by analysing differences between surface counts around pilings. The nMDS plots did not reveal a separation between counts done at marinas with pontoons and marinas with jetties (Fig. 7) and no significant difference was found between these two types of marinas (P > 0.05). No consistent pattern was detected between the two types of marinas when the total number of species and abundance of most common species were analysed (illustrated for December 2002 in Fig. 8).

The exception to this was the abundance of G. tricuspidata, which appeared to be greater at marinas with pontoons at all times (Fig. 8c) and was significantly greater at time 1 (F = 8.37, P < 0.05).

Physical attributes of marinas

The number of fish (P < 0.01; r 2 = 0.28) and number of species (P < 0.01; r 2 = 0.33) of fish decreased with increasing distance from the mouth of the estuary. In contrast, age and size of the marina and water-depth were not significantly related to the abundance (P > 0.05; r 2 = 0.06, 0.17, 0.01 respectively), nor diversity of fish (P > 0.05; r 2 = 0.01, 0.14, 0.04 respectively).

Temporal variability of fish assemblages

The number of species present and the total abundance of fish differed among times of sampling (Figs. 3 & 5). Number of species was smallest in August (austral winter; N = 31) and greatest in December (austral summer; N = 47) and the abundance of common species tended to peak in December (Figs. 3 & 5). Spatial patterns in the fish assemblage among habitats and marinas were, however, consistent among times of sampling (Fig. 3456) and differences in the abundance of common species between habitats persisted across sampling times, with the exception of M. strigatus which was only present in December 2002.

Discussion

This study investigated the role of marinas as habitat for fish in an urban estuary by examining the spatial distribution of fish assemblages associated with marinas. This basic knowledge of how natural processes generate variability of species in space and time is essential to predict how the deployment of coastal structures will influence local assemblages and will identify sustainable design options [START_REF] Airoldi | An ecological perspective on the deployment and design of low-crested and other hard coastal defence structures[END_REF]. Extensive descriptive and observational data have been collected on the types of fish associated with artificial structures in many parts of the world. The main body of literature is about the colonisation of fish to artificial reefs, purpose-built or accidental [START_REF] Bohnsack | Artificial reef research: Is there more than the attraction-production issue[END_REF][START_REF] Seaman | Unifying trends and opportunities in global artificial reef research, including evaluation[END_REF]. It is very evident that artificial habitats of all types attract many species of fish, sometimes within hours of their deployment [START_REF] Molles | Fish species diversity on model and natural reef patches: experimental insular biogeography[END_REF][START_REF] Grant | Early development of Pendleton artificial reef[END_REF][START_REF] Cummings | Colonization of a nearshore artificial reef at Boca Raton (Palm Beach County), Florida[END_REF], yet few studies have documented spatial patterns of fish associated with artificial structures not deployed specifically as habitat. Furthermore, there have been few attempts to identify the ecological processes that create and maintain patterns of distribution of fish in artificial habitats [START_REF] Miller | Using ecological processes to advance artificial reef goals[END_REF].

In this study, fish displayed very predictable spatial distributions around marinas. The majority of the dissimilarity between the assemblages associated with both types of structure and the open water at marinas was explained by two types of fish; larger mobile species, such as A. australis (bream) and T. novaezelandiae (yellowtail), that moved between the pilings and pontoons and the open water immediately surrounding the structures, and smaller species, such as T. taeniatus (hula fish), M. strigatus (stripey) and Mecaenichthys immaculatus (immaculate damsel) that were concentrated only around the pilings and pontoons themselves. In fact, more than half the species observed during this study were only counted in the immediate vicinity of structures. This suggests that artificial structures, such as pontoons and pilings, have a strong effect on the distribution of fish.

Such spatial patterns may be determined by the way fish respond to either the physical or biological structure of the habitat. Small-bodied species that were dominant in counts directly around pilings and pontoons are likely to be attracted to these structures because they provide a form of shelter and protection from predation [START_REF] Hixon | Predation, prey refuges and the structure of coral-reef fish assemblages[END_REF], as well as exposure to food [START_REF] Edgar | Experimental analysis of structural versus trophic importance of seagrass beds. II. Effects on fishes, decapods and cephalopods[END_REF][START_REF] Mobley | Diet of Scartella Cristata: an artificial habitat-associated blenny (Pisces: Blenniidae)[END_REF]. In particular, the dominance of planktivores, such as T. taeniatus and M. strigatus may be related to the vertical orientation of pilings. Vertical structures allow access to plankton over a large depth range whilst providing shelter from predators [START_REF] Hamner | Zooplankton, planktivorous fish and water currents on a windward reef face: Great Barrier Reef, Australia[END_REF]. In other cases, small-bodied fish such as Blennidae and Tripterygiidae (Threefins) may find suitable habitat in the biogenic structures, such as mussels and algae, growing on pilings and pontoons. [START_REF] Love | Fish populations around seven oil platforms in the Santa Barbara Channel area[END_REF] observed large abundances of rock fish associated with mussel shells on and underneath offshore platforms in California.

Larger mobile species such as the Sparidae, A. australis and the Carangidae, T. novaezelandiae were common in all counts done at marinas and moved between structures and adjacent open water. Both these families are predatory fish that primarily feed on invertebrates associated with muddy/sandy substratum (Sparidae), or on zooplankton and small fish (Carangidae) in the vicinity of hard structures [START_REF] Kuiter | Guide to sea fishes of Australia[END_REF]. Therefore, these species may obtain much of their food from the sandy bottom or water column around the marina [START_REF] Steimle | The importance of artificial reef epifauna to black sea bass diets in the Middle Atlantic Bight[END_REF].

Although the two depths sampled in this study were not compared directly, it is of interest to compare patterns of distribution of fish at the surface and bottom of marinas. Patterns among pilings persisted at both depths and fish associated with pilings were equally distributed between the two depths. Artificial structures, such as pilings, extend through the water column and therefore have a unique facility of being able to attract fish at all depths from the bottom to the surface. This is in contrast to natural rocky reefs, where depth is often positively correlated with the abundance and diversity of fish as a result of greater structural complexity at depths [START_REF] Roberts | Habitat complexity and coral reef fish diversity and abundance on Red Sea fringing reefs[END_REF].

In open water, very few fish were observed at the surface relative to the number observed at the bottom. Species that moved between pilings and the water surrounding pilings e.g. A. australis and G. tricuspidata did so largely at the bottom of marinas, which was evident from the absence of these species in open water counts done at the surface. This spatial pattern may relate to a behavioural preference, such as a defence against predation in open water [START_REF] Bohnsack | Ecology of artificial reef habitats and fishes[END_REF][START_REF] Rilov | Vertical artificial structures as an alternative habitat for coral reef fishes in disturbed environments[END_REF].

A useful perspective for approaching and managing impacts is to identify changes in the environment from one state to another [START_REF] Knowlton | Thresholds and multiple stable states in coral reef community dynamics[END_REF]. As marinas are generally built over sand, they replace soft sediment with hard substrata. In such instances it is not possible to preserve the natural patterns of organisms, but it may be possible to minimise changes to patterns of distribution of organisms [START_REF] Bulleri | The introduction of artificial structures on marine soft-and hard-bottoms: ecological implications of epibiota[END_REF]. For example, species that were present in bottom counts also included species associated with sandy substrata that have little affinity to hard substrata, such as Gobidae, Upeneus tragula (goatfish) and Urolophidae (stingrees). These species were most likely present prior to the building of marinas and other structures. It is not possible to ascertain whether these species were present in similar numbers to those prior to the marina being built, or if, in fact, they were present at all, as no before-comparisons were done in this study. Previous studies have, however, shown that species generally associated with soft sediment are often present in similar abundance on the soft sediment under marinas as they are in sandy habitats where there is not additional structure [START_REF] Alevizon | Effects of artificial reef deployment on nearby resident fishes[END_REF][START_REF] Barnes | Influences of marinas on assemblages of fish in Sydney Harbour[END_REF].

Interestingly, there appeared to be no difference in the types and numbers of fish associated with pilings from those found under pontoons despite the different nature of these two types of structures. These results differ from those of the majority of research comparing intertidal and/or subtidal assemblages living on different types of artificial structures. Generally, researchers have found each type of structure, e.g. pontoons, pilings, seawalls, is inhabited by a distinct assemblage of organisms (e.g. [START_REF] Glasby | Differences between subtidal epibiota on pier pilings and rocky reefs at marinas in Sydney, Australia[END_REF][START_REF] Glasby | Orientation and position of substrata have large effects on epibiotic assemblages[END_REF][START_REF] People | Mussel beds on different types of artificial structures support different macroinvertebrate assemblages[END_REF].

These studies have, however, only dealt with organisms that live directly on or in biogenic habitat on the substratum.

The response of organisms to habitat is largely dependent on the mobility of the species being investigated [START_REF] Jaenike | Genetic variation for habitat preference: evidence and explanations[END_REF]. In reef-fish systems, the size of an individual's home range or the extent of its daily foraging activities will directly contribute to the scale at which its habitat is perceived [START_REF] Chesson | Spatial scales in the study of reef fishes: A theoretical perspective[END_REF]. The majority of fish associated with pilings and pontoons in this study move over distances at the scale of metres. Therefore, it is probable that fish are not responding to pontoons and pilings as distinct habitats, but rather respond to them at a larger spatial scale, e.g. an area of the marina that has both pontoons and pilings.

When the importance of these separate habitats was examined at the scale of marinas, i.e. marinas that have both pontoons and pilings versus marinas that only have pilings, no difference between the two types of marinas was found. This provided further evidence that the presence of pontoons in marinas does not alter the associated fish assemblage. There was, however, considerable spatial variability in the abundance and composition of fish assemblages among different marinas within the same estuary. These differences could not be attributed to differences in water depth, size nor age of the marinas. In contrast, the abundance and diversity of fish were greater at marinas close to the mouth of the estuary. In general, species diversity tends to decrease with increasing distance from the ocean, which may be a function of larval supply and food availability [START_REF] Jackson | Spatial and temporal variation in nearshore fish and macroinvertebrate assemblages from a temperate Australian estuary over a decade[END_REF]. In this study, however, this result may also be partly attributed to the relative proximity of these outer marinas to natural rocky reefs.

For example, Davis Marina, which had consistently greater numbers and diversity of fish than the other marinas, is located within several hundred metres of an extensive natural rocky reef.

Although the two habitats are separated by bare sand which may limit movement of many species [START_REF] Bell | Variation in seagrass height and density over a wide spatial scale: effects on common fish and decapods[END_REF], other species can leave the reef to forage in open water and, therefore, may find other habitat following excursions from natural reefs [START_REF] Leis | Settlement behaviour of coral-reef fish larvae at subsurface artificial-reef moorings[END_REF].

Temporal variability of fish assemblages at different scales can be quite large and may confound spatial patterns of fish among different habitats [START_REF] Connell | Spatial, temporal and habitat-related variation in the abundance of large predatory fish at One Tree Reef, Australia[END_REF]. In this study, the relative abundances of species fluctuated over the three sampling times. This may have been a result of seasonal recruitment of some numerically dominant species [START_REF] Sale | Ecological structure of assemblages of coral reef fishes on isolated patch reefs[END_REF]. It was evident, however, that patterns across the different spatial scales, i.e. habitats within marinas and marinas, persisted over time. Previous studies have also suggested that relative abundances among sites may not be affected by large changes in fish abundances over time [START_REF] Holbrook | Spatial and temporal patterns in assemblages of temperate reef fish[END_REF][START_REF] Curley | Spatial and habitat-related patterns of temperate reef fish assemblages: implications for the design of Marine Protected Areas[END_REF] Marinas in estuaries surrounding Sydney appear to be functioning as habitat for fish.

The spatial variation in the fish assemblages associated with marinas clearly indicated that fish are responding to the presence of structures within marinas. Different types of structures, in this instance, pontoons and pilings, and different types of marinas, those built with pontoons and those built with jetties, however, did not differ with respect to their associated fish assemblages.

The large variability between marinas also could not be attributed to differences in the size, age and depth of marinas. This variability among marinas makes it difficult to predict the impacts of a marina in a specific area and further research will need to be carefully designed to identify and quantify site-specific impacts. Furthermore, while it is still essential to minimize the effects of marinas on surrounding habitats and biota, it may also be possible to identify design options that maximise their provision of habitat for important species of fish. Information of what changes might be beneficial or cost effective is not yet known. This information will come from field experiments, where managers of marinas make their resources available and collaborate with researchers to investigate different options. Results from such research will also be broadly applicable to all types of hard artificial structures present in urban waterways.

Fig. 1 Fig. 2 Fig. 3

 123 Fig. 1 Position of the four marinas in each waterway; Ferguson's Boatshed ( ), Clontarf Marina ( ), Davis Marina ( ) and Point Piper Marina ( ) in Sydney Harbour (~ 33°51'S, 151°14'E) and Broken Bay Slipway ( ), Quays Marina ( ), Holmeport Marina ( ) and Bayview Marina ( ) in Pittwater (~ 33°36'S, 151°16'E).

Fig. 4

 4 Fig. 4 Mean (+S.E.) abundances of common species in surface counts in Dec. 2002 at the four marinas in Sydney Harbour and four marinas in Pittwater in the three habitats. Pontoons = grey bars; Pilings = black bars; Open water = white bars. n = 4.

Fig. 5 Fig. 6 Fig. 7 Fig. 8

 5678 Fig. 5 Mean (+S.E.) number of (i) fish and (ii) species of fish in bottom counts in (a) Dec. 2002, (b) May 2003 at the four marinas in Sydney Harbour and four marinas in Pittwater in the two habitats. Pilings = black bars; Open water = white bars. n = 4.

Acknowledgements I thank M.G. Chapman and A.J. Underwood for their help and guidance though out this study. I thank D. Blockley, J.J Cruz, S. Cummins and G. Kaplan for help with field work. I am grateful to C. McKindsey and two anonymous referees for useful comments on the manuscript. Funds were provided by an Australian Postgraduate Award and the Australian Research Council Special Research Centre Grant to the Centre for Research on Ecological Impacts of Coastal Cities.

Table 1 The habitats sampled at each depth; S = surface and B = bottom, at each marina. X indicates that habitat was sampled.

Depth

Pontoon Piling Open water

Sydney Harbour

Ferguson