The Influence of Dissolved Copper on the Production Of

Home , Domoic acid

THE INFLUENCE OF DISSOLVED COPPER ON THE PRODUCTION OF

DOMOIC ACID BY PSEUDO-NITZSCHIA SPECIES

IN MONTEREY BAY, CALIFORNIA:

LABORATORY EXPERIMENTS AND FIELD OBSERVATIONS.

A Thesis

Presented To

The Faculty of Moss Landing Marine Laboratories

In Partial Fulfillment

of the Requirements for the Degree

Masters of Science

Marine Sciences

By:

Nicolas Christopher Ladizinsky

ALLRlGHTSRESERVED ABSTRACT

Tiffi INFLUENCE OF DISSOLVED COPPER ON Tiffi PRODUCTION OF DOMOIC ACID BY PSEUDO-NITZSCHIA SPECIES IN MONfEREY BAY, CALIFORNIA: LABORATORY EXPERIMENTS AND FIELD OBSERVATIONS.

Nicolas Christopher Ladizinsky

Domoic acid (DA) is a neurotoxic amino acid produced by several members of the diatom genus Pseudo-nitzschia. Trophic transfer of DA has been implicated in the deaths of 100' s of marine birds and mammals along the central California Coast.

Although the physiological role of DA has not been well established, evidence herein strongly suggests that DA functions to buffer dissolved inorganic copper (Cu').

Evaluating the homeostatic function ofDA with respect to copper metabolism gave rise to three major findings: 1) DA binds cu' with an avidity comparable to the L21igand

12 class KoondCuDA = 1.0 x 10 (pH 8.2, I= 0.02), 2) P. multiseries is tolerant to wide fluctuations [Cu']loadings (pCu101n1 10.8 - 3 .26) because of strain-specific modulation of free amino acids pools (FAA), with up to a 10-fold increase in DA accumulation (31fg

DA/cell) in response to elevated [Cu'], 3) Cu' and total DA concentrations were highly correlated during a field survey spanning 97 days along a 3 kilometer transect in

Monterey Bay, CA (r 2:: 0.79, n = 3, a= 0.01). Cumulatively, these results establish DA's significant influence on Cu homeostasis in Pseudo-nitzschia species. Acknowledgments

I am deeply appreciative of everyone at Moss Landing Marine Labs who has either directly or indirectly fostered my intellectual, social and spiritual evolution over the days, months and years as my tenure as graduate student. I am deeply grateful to

Kenneth Hamilton Coale for accepting me as his student and guiding me into the lustrous world of trace metals. To G. Jason Smith, who has functioned as mentor, financier, hacky buddy and most importantly true friend I cannot thank enough. Thanks also to Richard

Zimmerman (el Jefe) who always managed to find me a job or dwelling and to Nicholas

Welshmeyer for his open ears and constant encouragement. Thanks also to everyone at the Environmental Biotechnology lab and in particular my three adopted sisters Sally

Witlinger, Sherry Palacios and Clare Dominick whose intellectual prodding's and companionship always made coming to the lab a joy; the donuts were great too. Thank you to the Chemical Oceanography and Trace Metal Labs and in particular John Haskins,

Jocelyn Nowicki-Douglas, Wes Heim, Sara Tanner, Michael R. Gordon and Craig

Hunter. I'd also like to thank William C.ochlan and Julian Herndon for adopting me into their lab and providing me the continued harassment needed to complete this work.

Without the financial aid from Sea Grant of California (Traineeship ), Dr. Earl H. Myers and Ethel M. Myers (Grant), Sigma Xi (Grant in Aid of Research) much of the work herein would not have been possible. I extend all my love and gratitude to Tobi

Langford, my life partner and the mother of our beautiful baby Olivia, for tolemting and encouraging me during my most challenging moments, to Dennis Kisler who always believed in me and to my friends and family whose nurturing made all this possible. TABLE OF CONTENTS

Table of contents vi

List of tables . vii

List of figures . VIU

Introduction . I

Materials and methods 12

Results. 20

Discussion 30

Literature cited 41

Tables . 51

Figures legends. 56

Figures 58

vi LIST OF TABLES

Table Page

I. Free Fe and Cu concentrations derived from K.:onds 52

2. Time-series, correlation analysis of [Cu] and [DA] for COM! 53

3. Time-series, correlation analysis of [Cu] and [DA] for COM2 54

4. Amino acid stability constants 55

5. Data review: Toxic blooms, nutrients and trace metals 56

vii LIST OF FIGURES

Figure Page

1. Ligand diagrams 58

2. Domoic acid titration curve 59

3. Competitive coordination between CuDA and CuDAFe 60

4. Growth histograms: Incubation# 1 61

5. Free amino acid response to increasing [Cu]: Incubation #1 62

6. Growth histograms: Incubation #2 63

7. Free amino acid response to increasing [Cu]: Incubation #2 64

8. Sampling site map 65

9. Shore buoy composite analysis 66

10. Wharf buoy composite analysis 67

11. Mile buoy composite analysis 68

12. Scanning electron micrographs of diatom assemblages 69

viii INTRODUCTION

In the last twenty years a preponderance of research has supported the viewpoint that the world's oceans are experiencing escalations in the incidence of harmful algal blooms (HABs), which have had a pronounced affect on human health, fisheries, and marine ecosystems (Smayda 1990, Hallegraeff 1993, Smayda 1997, Sierra Beltran et al.

1997, Anderson et al. 2002, Parsons and Dortch 2002). Although there is debate as to whether HABs are actually increasing or that their apparent rise in frequency is the result of increased scientific scrutiny, there is no question that emergent species are contributing to the subset of algae considered harmful (Smayda 1997, Lundholm et al.

2002).

In 1987 three people died and 150 others were poisoned from consuming cultured blue mussels from Prince Edward Island, Canada It was subsequently determined that the neurotoxin domoic acid (DA), produced by a pennate diatom, Pseudo-nitzschia pungens, had accumulated in the mussels tissues and caused this intoxication event (Bird et al. 1988, Bates et al. 1989, Smith et al. 1990). Never before had a diatom been implicated in a toxic HAB event. In 1989, a different species, Pseudo-nitzschia australis, was implicated in a toxic event that culminated in the deaths of !OO's of marine birds and marine mammals, but this time it occurred on the west coast of the United States in

Monterey Bay, CA. (Garrrison et al. 1992, Bucket al. 1992, Work et al. 1993, Walz et al.

1994, Horner et al. 1997). Since these events, several additional species of Pseudo nitzschia have been confirmed to produce DA (Lundholm et al. 2002, Trainer et al.

2000). Shellfish and fish that feed on potentially toxic strains of Pseudo-nitzschia spp. function as vectors facilitating the movement of this water soluble toxin throughout food webs (Todd 1993, Lefebvre et al. 2001, Goldberg 2003). Toxic blooms of Pseudo nitzschia spp. have subsequently resulted in closures of fisheries along the West Coast of

North America and spurred intensive state mandated DA monitoring programs that measure tissue burdens ofDA in shellfish (Van Dolah et al. 1995, Trainer et al. 2000). lncreased monitoring of HABs has necessitated the development and application of sensitive molecular probes for identifYing the presence of potentially toxin-producing species in the water column (Scholin et al. 1996). Although, these programs monitor the distribution and abundance of potentially toxic species, presence alone does not indicate whether these species are producing toxin or not. The only reliable approach to predicting when toxic events will occur is by characterizing the environmental factors that induce DA biosynthesis in potentially toxic diatom species.

An increase in the frequency of HAB has largely been attributed to recent changes

(the last I 00 years) in the biogeochemistry of nutrients, in particular the marked increase of nitrogen loading into coastal waters (Hung and Chan 1998, Horton and Dewar 2000,

Anderson 2002, Parsons and Dortch 2002). Coastal waters, including upwelling regions and Eastern boundary waters, where the majority of primary production occurs, are subject to wide variations in the type and concentrations of natural and anthropogenic inputs. A constant or periodic influx of anthropogenically derived chemicals, such as

N03', P04, NH/, and trace metals, may act as a selective pressure forcing phytoplanl:ton communities to either adapt or perish (Gustavson et al. 1999). One adaptive survival

2 strategy may include the production of novel metabolites to buffer abiotic and biotic challenges. Phycotoxins, which comprise a broad group of metabolites, are produced by only a Hmited number of species withln the diverse taxonomy of algae whlch occur throughout the world's oceans (Turner and Tester 1997, Hallegraeff 1993, Horner et al.

1997). Most algal phycotoxins, including brevetoxins, okadaic acid and ciguatoxin are high molecular weight polyether compounds that exhibit no obvious physiological benefit to the cell (Australian Research Network for Algal Toxins 2003, http://www.airns.gov.aularnat). Domoic acid, by contrast, is an amino add derivative and as such, may likely have analogous physiological functions in the alga. Domoic acid's neurotoxicity derives from its amino add structure enabling it to function as a potent glutamate receptor agonist (Laycock eta!. 1989, Hampson et al. 1992, Todd

1993). DA binding-induced hyperstirnulation of glutamate receptors can lead to neuronal necrosis, hemorrhagic seizures, and in extreme intoxication, can cause death of vertebrates (Hampson eta!. 1992, Work eta!. 1993, Scholin et al. 2000); conditions diagnosed as Domoic Acid Poisoning (DAP) in animals or Amnesic Shellfish Poisoning

(ASP) in humans. The physiological regulation of DA synthesis and its accumulation in

Pseudo-nitzschia spp., like all other phycotoxins, has been poorly characterized, although environmental stress may stimulate cellular accumulation and exudation of DA by these diatoms (Bates eta!. 1991, Bates eta!. 1993, Lewis eta!. 1993, Smith eta!. 1993,

Ladizinsky and Smith 2000, Moldonado et al. 2002).

Coastal environments, where Pseudo-nitzschia spp. based HAB events are observed, are subjected to significant hydrologic and chemical variability (Garrison eta!.

3 1993, Trainer eta!. 2000). Several environmental parameters have been assessed for their influence on growth and toxin production in multiple Pseudo-nitzschia spp. including temperature, irradiance, salinity, nitrogen (N03, NOi, NH3, Urea), phosphate and silicate availability (Pan eta!. 1996, Bates eta!. 1991, Bates eta!. 1993, Jackson et al.

1992, Pan et al. 1993, Smith eta!. 1993). As modulators of growth and DA production by Pseudo-nitzschia spp., a host of variable environmental parameters have been studied.

Unfortunately many of these efforts have provided equivocal results.

Several studies revealed a temperature-dependent growth rate in Nitzschia pungensf multiseries (Pseudo-nitzschia multiseries) over 0-25 °C, however no specific correlation between domoic acid accumulation and temperature was identified (Pan et al.

1993, Lewis et al.1993, Smith 1993). Silicate and phosphate limitation have both been shown to increase DA production (Pan et al. 1996). These findings are consistent with the general view that DA production is highest during periods of growth limitation and therefore do not demonstrate that either Si or P04 availability directly control the production ofDA. Studies on the effect ofN sources (including N03, Nf)4, Urea) on DA production/accumulation in both Pseudo-nitzschia multiseries and P. australis have shown that production of DA is not directly linked to the N source for P. multi series

(Bates eta!. 1992) and P. australis (Cochlan and Ladizinsky, unpublished data), but cellular N status may limit DA production due to concornitaut limitations on amino acid biosynthesis (Smith and Kudela, unpublished data). Although these investigations have indirectly sought to decipher DA's potential physiological function(s) no hypothesized function has been as compelling as the recent investigations targeting DA' s potential role

4 in trace metal homeostasis (Subba Rao et al. 1998, Ladizinsky and Smith 2000, Rue and

Bruland 2001, Maldonado et al. 2002). This physiological function is immediately suggested by a consideration ofDA's molecular structure.

Domoic acid features two distinct structural domains: a prolyl ring with two carbonyl moieties at the '1 and '2 position and an isoprenoid side chain attached to C3

(Takemoto 1966, Laycock et al. 1989, Wright et al. 1989, Wright et al. 1990, Fig. lA).

While DA functions as a glutamate analog in terms of its neurotoxic mode of action, viewing the phycotoxin as a derivative of the amino acid proline suggests homologous physiological functions for DA in the algal cell (Smith et al. 2000). Intracellular proline pools are involved in a variety of homeostatic functions including mitigation of trace metal toxicity and oxidative stress (Wu et al. 1998, Siripomadulsil et al. 2002). The affinity and specificity of ligand-metal coordination complexes are dependent primarily on the total number of functional groups, or availability oflone pair electrons to participate in metal coordination. In the case of proline, the strength of the proline-metal complex depends on the interaction with the single carbonyl group. Domoic acid containing three negatively charged functional groups at seawater pH, possesses more potential sites for complexation than proline and other non-toxic amino acids that are frequently involved in trace metal complexation, i.e. proline, glutamate, lysine, cysteine and histidine (Fattorusso and Piattelli 1980, Wu et al. 1998). It stands to reason then, that

DA which possesses 2 more carbonyl groups than proline maintains a significantly greater metal binding potential than proline. As a result production of DA may facilitate a

5 physiological advantage for algae inhabiting marine environments where excessive concentrations of trace metals occur.

The influence of fluctuations in trace metal availability on the production ofDA has received increased attention. One of the fust experiments attempting to draw a correlation between metal toxicity and DA found that elevated [Li] resulted in significant increases in cellular DA content in P. multiseries (Subba Rao et al. 1998). Maldonado and coworkers (2002) found that Fe limitation resulted in greater DA production by P. multiseries in semi-continuous batch cultures. In a similar study, Bates reported that batch cultures of P. multiseries with no added Fe produced less DA than Fe-replete batch cultures during exponential phase (Bates et al. 2001 ). Acute exposure to elevated [Cutolal}

specifically induced DA accumulation relative to other free amino acids in P. multiseries

(Ladizinsky and Smith 2000). Findings by Moldonado et al. (2002), independently confirmed that acute exposure to elevated [Cu] resulted in enhanced DA accumulation

and release in P. multiseries, again associated with groVI'th-limiting conditions. In eacb

of the above cases, evaluating DA's potential role as a metal ligand has been at the heart

of these investigations.

Molecules that bind dissolved trace metals typically contain negative functional

groups that donate a pair of electrons to the metallic atoms, thns forming a semi-covalent

bond or coordination complex (Libes 1992). Ligands with the highest trace metal

affmities often contain carboxylic functional groups associated with nitrogen substituted

ring structures (Sunda 1989, Stuum and Morgan 1996); such as DA (Fig. 1).

Furthermore, the strength of a metal-ligand complex increases according to the number of

6 functional groups, i.e. carboxylic acids, amines and sulfates that participate in the binding of cations (Zamzow et al. 1998). Once a potentially toxic metal, either the free ion or inorganic species, is complexed to an organic chelator its bioavailability to phytoplankton is reduced (Sunda 1975, Sunda and Guillard 1976, Bruland et al. 1991, Sunda and

Huntsman 1991, Croot, 2003).

Copper functions as a micronutrient at ambient marine concentrations (Cu..,tlll =

0.5-2 nM) being an essential enzyme cofactor in cytochrome oxidase, plastocyanin in chloroplasts, Cu-Zn superoxide dismutase, ceruloplasmin and 1ysyl oxidase (Sunda 1976,

Guillard and Sunda 1989, Sunda 1996, Crichton and Pierre 2001). However, at even moderately elevated concentrations, Cu can limit cell growth and viability (Schenck

1983, Hutchins and Bruland 1998). Unlike the micronutrient Fe, which is often found to be in limiting concentrations, both rapid regeneration of scavenged continental she1f-Cu and anthropogenic loading of Cu into coastal waters contribute to frequent Cu-excesses rather than Cu-limitation (Klinkhammer et al. 1982, Widerlund 1996, Hung and Chan

1998). Hence, Cu is considered one of the most toxic trace metals to marine algae

(Anderson and Morel1978, Bruland 1991, Sunda 1996, Gledhill et al. 1999).

In order for phytoplankton to survive in environments subject to fluctuations in

[Cu],many produce a variety of molecules to reduce the bioavailability of potentially toxic concentrations of metals (McKnight and Morel1979, Moffet et al.l990, Brown et al. 1998, Leal et al. 1999, Gledhill et al. 1999, Gordon et al. 2000, Pistocchi et al. 2000,

Brown et al. 2001, Brown et al. 2001 ). The vast majority ofligands that control Cu speciation are derived as algal and bacterial organic exudates. In fuct, > 80 % of

1 dissolved copper in coastal regions is bound to organic ligands (Coale et a!. 1992, Beck

2002, Croot 2003), while> 99% is bound in oceanic regions (Coale and Bruland 1990).

Algal derived ligands can be classified according to their relative strength in complexing metals measured as stability constants (equilibrium constants that measure the effectiveness of a ligand in coordinating metal ions in aqueous media,

(Eqn.l) where M' is the free metal concentration and L is the free ligand concentration). Ligand

12 classes have been divided into two groups, L 1 the stronger ligand possesses a Koond::: I 0

12 and 12 the weaker ligand possesses a Kcond < I 0 (Coale and Bruland 1990, Moffett et a!. 1990). The complexation of Cu by organic ligands in euphotic zones is essential to productivity in these regions (Barber and Ryther 1969, Bruland eta!. 1991, Coale 1991,

Sunda and Huntsman 1991 ). As a means of mitigating potential trace metal toxicity from exposure to elevated concentrations of copper, algae have two common responses: 1) exclusion of toxic metals from the cell or 2) sequestration of toxic metals within the cell

(Silverberg eta!. 1976, Pistocchi eta!. 2000). Both of these responses initially require the metal to be complexed to some type of ligand in order to reduce the metals bioavailability. Although little definitive work has identified structural or functional chemistry in regards to these ligands several types of molecules have been reported to be produced in response to copper toxicity (Gordon eta!. 2000).

Mucilage, composed of acidic polysacharides, is produced by Amphora coffeaeformis (a diatom) and has been reported to facilitate extracellular copper binding

(Brown et a!. 1988, Gledhill et a!. 1999). Amino acids incorporated into proteins,

8 polypeptides or as free amino acids (e,g. proline), having a high complexation capacity for copper (Log Koonds 8-14) are often produced in response to elevated copper (Wu and

Tanoue 2001, Wu et al. 1998). Conversely, another category of metal binding molecules known as siderophores largely derived from photosynthetic prokaryotes, facilitate the uptake of iron, which is often limiting in pelagic waters. These ligands often are planar porphyrin type molecules that make Fe more utilizable to the cell (Rue and Bruland 1995,

Witter et al. 2000). Metallothionines and phytochelatins in general, and more specifically, cuprothionieneins, are small cysteine rich polypeptides {1-7 kDa MW), which bind reactive metals through covalent sufhydrl bonds, are often produced in response to metal toxicity (Stumm and Morgan 1996). Although the need for algae to bind iron in coastal waters is seldom required due to sufficient concentrations of the micronutrient, enormous pressure exists for these same species to control [Cu] through complexation, because of potentially large fluxes in Cu bioavailability in coastal waters.

From 1977 to 1990 copper concentrations within coastal marine waters of

California increased by as much as 25% (Stephenson and Leonard, 1994). Most of the increase in these waters is thought to derive from expanding harbor development, where copper-based antifouling chemicals are applied extensively to both boat hulls and pier pilings (WQPP MBNMS 1996, 1999, Gustavson et al, 1999, Brown et al. 2001). In

California the introduction of Cu is exacerbated by the maintenance of water distribution channels, abundant and productive agricultural regions where again Cu is used as both major component of insecticides and antifouling chemicals used to maintain irrigation and drainage lines,

9 Blooms of Pseudo-nitzschia spp. generally occur in upwelling regions such as

Monterey Bay where they can dominate the plan1.1:on communities (Buck et al. 1992,

Walz et al. 1994, Trainer et al. 2000, Scholin et al. 2000). Beyond the injection of nutrient rich waters that support the production of algal blooms, upwelling in these zones also increases the surface concentrations of many trace metals including copper (Barber and Ryther 1969, Sunda 1989, Coale and Bruland 1990, Bruland et al. 2001). Most copper, originally derived from anthropogenic sources, reaches marine sediments along coastal margins and returns to the water column during upwelling episodes

(Klinkhammer et al. 1982, Widerlund et al. 1996).

The function of domoic acid in metal metabolism, whether as a facilitator of metal uptake or as a sequesterant needs to be considered within the context of environmental conditions that are associated with increased cellular toxicity. Pseudo-nitzschia spp. blooms initiate in upwelling regions where nutrient concentrations are generally high, under such nutrient-enriched conditions, accumulation ofDA can vary by orders of magnitude between isolates of the same species (Lange et al. !994, Trainer et al. 2000b,

Adams et al. 2000, Smith et al. 2001 ). Current autecology suggest that blooms of

Pseudo-nitzschia spp. become increasingly toxic when cells entrained within newly upwelled water are advected inshore (Walz et al. 1994, Bucket al. 1992, Scholin et al.

2000, Trainer et al. 2000a, Table 4.). While ambient nutrient and trace metal concentrations become depleted as the bloom progresses, inshore Cu concentrations may increase to toxic levels.

10 The general hypothesis that has driven this research is that toxic species of

Pseudo-nitzschia produce DA in order to bind toxic levels of copper, thereby enabling these cells to survive and even flourish within coastal waters and sediments where elevated concentrations of copper may otherwise inhibit their growth and viability, To evaluate the potential homeostatic function of DA with respect to copper metabolism in

Pseudo-nitzschia spp. the following experiments were conducted: 1) stability constants were measured for Cu:DA coordination complexes to identifY whether DA has a significant binding affinity for copper relative to other free amino acids, 2) competitive binding experiments were designed to determine complexation interactions among DA,

2 3 Cu + and Fe + at environmentally realistic concentrations in order to assess the relative role ofDA in metal sequestration versus trace nutrient acquisition, 3) comparative growth bioassays and amino acid analyses were performed to distingnish differences in physiology and biochemistry between different isolates of P. multiseries when grown in media ranging in [pCu10tat] from limiting to toxic. Finally, intensive field sampling of a near-shore transect proximal to Monterey Wharf, Monterey Bay CA, was undertaken to identifY causal relationships between elevated dissolved [Cu"] and presence intracellular and extracellular DA.

II MATERIALS AND MEmODS

Analysis ofCopper Speciation

Chemiluminescent Assay ofCu'

The method used in this study quantified Cu', where Cu' = free copper (Cu2l

plus all inorganic species of Cu, with the most domiuant species in seawater being

CuOtr + Cu(OH)2 + CuHC03 + + CuCt + CuC03 + Cu(C03)£2 + CuS04. The

measurement of Cu' is based on the light emitting reaction of copper (11)-1, l 0

pheuanthroline chelates with lh02 at pH> 11.0. This reaction mechanism involves the

oxidation ofH2(h to the superoxide radical by bound copper (II) (Fedorova eta!. 1982,

Sunda and Huntsman 1991, Coale et al. 1992). The superoxide radical causes the

oxidative destruction of I, 10 phenanthroline, which forms the unstable 1,2 dioxeane end

product, resulting in photon production proportional to the amount of Cu' in the sample

(Yamada and Suzuki, 1984). Modifications of the chemiluminescent flow injection

analysis method used by Coale and Zamow (Coale eta!. 1992, Zamzow eta!. 1998) were

employed to measure Cu' for both complexation experiments and field surveys. Rather

than using the complex flow injection system first developed by Yamada and Suzuki and

subsequently modified by Johnson (Yamada and Suzuki 1984, Coale eta!. 1992), a

simple, portable and inexpensive batch design was developed to reduce inherent technical

complications posed by the FIA system.

Chemiluminescence reagents were made up in trace metal clean plasticware

(Coale eta!. 1992) with 18Mn Milli-Q water. Sample handling, reagent, copper stock

12 preparation and all chemiluminescent analysis were performed within a Class I 00 trace

metal clean room using established trace metal clean techniques.

Reagent I (Rl) was comprised of a I 0% hydrogen peroxide solution made by

diluting 16.7 mL of 30% reagent grade H201 with MQ water in a 50-mL polypropylene

volumetric flask. Reagent 2 (R2) was comprised of a solution consisting of 10 mL of a

60 J.!M stock solution of I, I 0-phenanthroline, 800 mg of cetyldimethylethylammonium

bromide (CEDAB), 20 J.!L of 0.4 J.!M tetraethylenepentamine (TEPA) and 300 mg of

sodium hydroxide (NaOH) and was prepared daily in a 100 mL polypropylene volumetric

flask.

Reagents 1 and 2 were made fresh daily from prepared stocks, which remained

stable for up to 7 days as evidenced by no significant variability in Cu-dependent voltage

signal between daily calibration curves (mean luminescent regression; m = 0.0116 (±

0.002) volts/sec,~ > 0.99 volts/sec).

To measure Cu' in samples, 500 J.!L of reagent Rl and 250 J.!L of reagent R2 were

mixed in a 2.5 mL trace metal clean methacrylic cuvette (Fisher Scientific) within the

light insulated reaction chamber. The reagent mixture was allowed to equilibrate for 45

seconds to eliminate any background photon production due to potential environmental

contamination. After baseline signal monitoring, 200 J.!L of sample was injected into the

light tight reaction chamber. Emitted photons were detected by a Hamamatsu H57-84 photomultiplier with an integrated amplifier circuit. The analogue luminescence voltage

signal was digitized by aDATAQ ND converter and recorded on a DATAQ Dl-190 2-

13 Channel Serial VO module using a 250Hz sampling frequency. Peak area was used to quantify [Cu'] in samples.

Complexatio11 Asstzys

To calculate the strength of CuD A complexes required measurement of Cu' in

CuD A mixtures at pH 8.2 and in acidified CuDA mixtures at pH 2.0 to force dissociation of the metal-ligand complex. These measurements enabled estimation of the conditional stability constant for CuDA complexation, K,ondsCu'DA· Employing the same technique with the addition of Fe to the solution provided insight into coordination site competition that may occur between Cu and Fe for DA (Fig 3A and 3B).

The conditional stability constants for CuD A were measured by titration of a known concentration ofDA (5.41 nM) with cupric ion following the techniques previously described for characterization ofLl and 12 in natural waters (Coale eta!.

1992, Sunda and Huntsman, 1991, Zamzow eta!. 1998). As the chemilurninescent signal is proportional to the concentration of Cu' species, then if CuD A complexes form,

2 titration of fixed concentrations of DA with known additions of Cu + will result in a

2 diminution of the predicted Cu-chemilurninsent signal until the added Cu + exceeds the

DA ligand capacity in the test solution. Once the Cu-DA chelate becomes saturated, the

2 chemiluminescent response will increase in direct proportion to further additions of Cu +, resulting in an inflection point (increase in slope) in the titration curve (Zamzow et a!.

1998). Continued titration beyond the inflection point results in a linear chemilurninescent response with a slope equal to the Cu standard curve, provided no organic contamination occurs or that no higher order CuDA complexes are formed.

14 Thennodynamic conditional stability constants were determined by fitting experimental data to a single ligand model using nonlinear least-square minimization algorithms provided in StatMost 3.5, based on the equations by Zamzow (1997):

(Eqn. 2)

where Curotal =all copper species including CuDA and Cu', L1 is free [DA], the concentration of unbound domoic acid chelates and K1 = the KcondCuoA,. Equation 1 was used to estimate the ~ondCuDA for single ligand model in a defined solution. Titration data was incorporated into non-linear regression algorithms in StatMost 3.5 (2001).

Conditional stability constants are defmed relative to the experimental conditions namely, temperature, ionic strength and pH. In these experiments temperamres were maintainted at l7°C ± 1°C, pH was 8.2 with an ionic strength of 0.02. Ionic strength was calculated using the equation in Stumm and Morgan (1996):

(Eqn. 3)

where m1 =molarity of each ion and z1 = the ions charge.

Bioassays For Cupric Ion Sensitivity

Cell Cltltllre

Unialgal cultures of Pseudo-nitzschia multiseries were established as single chain isolates from samples collected from Monterey Bay sample waters by P. Hughes

(MU411) and P. Miller (MU6) of the University of California at Santa Cruz (UCSC).

Upon receipt from UCSC, local stock cultures were maintained in 500 mL borosilicate

Erlenmeyer flasks containing autoclaved, filtered Monterey Bay seawater (0.22!-i

15 Gelman, FMBSW, 15°C, ca. 33 %o) amended with f/2 media ("standard media conditions"). Stock cultures received 12 h illumination at 75 ~mole photon m·2 s· 1 PAR and were transferred weekly for 3 months prior to the start of the experiment. During log phase growth under standard media conditions the DA content of isolate MU6 was at the limit for detection (S 3 ± 1.8 pmoles/1 06 cells or < I ± 0.6 fg DA!cell) while

MU411exhibited a sizeable DA burden (ca. 25.0 ± 9.1 pmoles DAII06cells or 7.8 ± 2.8 fglcell).

Cell abundance was enumerated at 2 day intervals from haemocytometer counts of 3. 7% (w/v) formaldehyde preserved samples. Subsequently, replicate counts were made using a Palmer-Maloney nanoplankton counter, which resulted in< 10% variation from the original haemocytometer counts. A minimum of 100 fields were counted per sample. Exponential growth phases were identified from linear regions of plots of log cells mL-1 numbers vs. time for each sample (i :<: 0.95) and used to calculate the

1 maximum growth rate during exponential growth (~ daf ):

(Eqn. 4) where N =the cells mL-1 and t =time in days.

Copper Exposure Bioassays

Prior to conducting bioassay experiments stock cultures were acclimated to growth in trace metal clean polycarbonate bottles containing FMBSW, amended with f/2 media receiving 24 h illumination at 75 ~for 2 transfers(> 10 doublings). Once cultures reached exponential growth phase(~= 0.5-0.7 daf'), 2mL aliquots (cell

16 1 density"" 200 cells mL" ) were transferred into one of seven incubation bottles amended to [pCUto1al] ranging from 10.8 to 3.26 . Each treatment was replicated three times (n=3).

DA Analysis

Intracellular and extracellular DA pools along with total free amino acid (FAA) profiles were quantified at both inception and termination of the experiment using

Reverse Phase High Performance Liquid Chromatography (RP-HPLC) seperation coupled with UV detection of either native DA or phenyl isothiocyanate (PITC) derivatized-DA and FAAs as described in (Cohen and Strydom 1988, Smith et al, 2001).

Elution of phenyl thio-carbamyl FAA (PTC-FAAs) in a 5 - 30% acetonitrile:water (v/v)

O.lM ammonium acetate (NJ:40Ac) gradient through a Metachem 0083 2 x 150 mm column at 31 oc permitted resolution at 254 urn of over 23 low mole;;:ular weight amines including DA, thereby enabling determination of spe;;:ific responses in DA pools relative to other amines. !socratic elution of native DA in 11% (v/v) acetonitrile, 0.1% trifluoroacetic acid at 42°C with peak detection at 242 J.llii provided independent corroboration of PTC-DA peaks and was also used for detection of extracellular DA in media and seawater samples. Certified DA calibration solutions (DACS-10 Lots 1052-

1055, NRC) were used for detector calibration and confirmation of peak ID's by co migration with standard additions.

Cultures (125 mL) were filtered onto 25 mm GF/F filters (Whatman) using :S

5mm Hg vacuum and stored at -75°C. Blank filters were prepared in an identical manner to sample filters with the exception that only filtered sea water (125 mL) was passed through a GF/F to provide reagent blanks. Frozen filters were subsequently extracted

17 with 1 mL of 80% (v/v) HPLC grade methanoVwater. The methanol extracts were concentrated and processed for analysis as previously descn"bed (Smith eta!. 2001)

Field Studies

Sample collection

A "straight" polyethylene kayak (Cobra Tandem, Gardena, CA) was used as the sampling platform from which seawater samples were retrieved using 250-mL trace metal clean, polycarbonate (Nalgene) bottles along a 3 kilometer transect adjacent to

Wharf# 2 in Monterey Bay, California (N 36° 35' 20", W 121 o 53' 10" toN 36° 37', W

121 o 53' 50''). Concurrently, surface (1m) net plankton samples were drawn at each location using 20-J.L mesh size plank-ton net. Once in the lab, 30 mL of seawater sample were filtered through a trace metal clean 0.22 polycarbonate syringe filter to collect filtrate for Cu analysis. Net plankton sample volumes ranging from 100-125 mL were filtered onto GFIF filters and stored -75°C for subsequent analysis of particulate DA and

FAAs as described above. The filtrate was also collected and frozen for later analysis of the dissolved fraction ofDA. One mL of net tow sample was added to 1.5 mL centrifuge vials with 0.1 mL formalin (3.7% w/v formaldehyde final concentration) for cell counts and 5 mL of sample, preserved with 0.5 mL formalin were archived for future identification by scanning electron microscopy (P. Miller, UCSC).

Statistics and Modeling

Analysis of variance (ANOVA) and Student Newman-Keuls test (SNK) were used to test for differences between treatments in CuDAFe competitive ligand experiments, growth rates and amino acids concentrations for Cu bioassays. Linear

18 regression analysis was used to assess the strength of response in relationships between the bioassay parameters (growth rate and amino acid composition) with [Cu']. Cross correlation analysis was used to identify relationships between environmental conditions and [DA] in the field time series. All statistical analyses were conducted using

STATMOST 3.5 (Dataxiorn Software, Los Angeles, CA 2001).

Metal speciation determinations derived from chemiluminescent assays including, stability constant, ligand competition and field studies were modeled using MINEQL version 4.0 (Schecher and McAvoy 1998) incorporating knov

19 RESULTS

DA can function as a copper ligand

At normal seawater pH, DA's three carboxyl groups are deprotonated (pKa 2.1,

3.7 and 5.0) availing 3 lone pair electrons for complexation with trace metals.

Chemiluminescent titration analysis revealed a concentration and pH dependent interaction between DA and Cu' in solution (Fig. 2). At pH (8.2) equirnolar solutions of

DACS_1D reduced the cupric ion chemiluminescent response by 84-100%. Incomplete quenching of Cu' chemiluminescent signal resulted from complex dissociation that occurs when titrations of a ligand approach their complexation capacity (Sunda et al. 1991,

Zamzow et al1998). Thus as incremental additions Cu exceeded ca. 80% of the [DAtotaJ] of 5.41 nM, chemiluminescent signals corresponding to [Cu'] between 0 nM and 1 nM were measured. Acidified solutions of CuDA complexes (pH 2.0), which functioned as the CutoiBI standard, provided a linear response (y = 1.00x + 2.151 Cu'/Cutotal, r > 0.99

Cu'/Cllto!BI, Fig. 2). The suppression of the Cu' signal, up to 5.41nM CutotaJ, relative to the

2 Cu standard signal is indicative of a 1:1 complex formation between Cu + and DA at pH

8.2. In the titration series when total added [Cu] exceeded equirnolar [DA] (5.41 nM) the

2 chemiluminescent response paralleled the standard response slope for uncomplexed Cu +, indicating the saturation of the Cu-DA complex. Comparable responses were obtained with titrations of different DA standard concentrations.

The Cu' signal response exhibited during the titrations ofDA with cupric ion

(CuDA) is consistent with classic metal-ligand interactions exemplified by strong and

20 weak ligand classes L1 and L2 respectively (Zamzow et al. 1998, Coale and Bruland

1990, Moffett 1990). The non-linear regression analysis used to fit the titration data to a single ligand model (Eqn. 2) supported these observations and enabled estimation of the conditional stability constant for the CuDA complex at pH 8.2 and ionic strength I =

0.02 ; Log Ko.ndCuDA = 11.995 :l: 0.5 M (1?; 0.95). At titrant additions above 40 nM Cu', a secondary inflection occurred, resulting in a deviation from the Cu' standard response

(Cu\11ensured/Cu'added = 0.85 ± 0.07 versus 1.00 ± 0.05) indicating that weakly binding organic ligands may have contributed to additional complex fonnation.

Chernilurninescent analysis was also used to test for competitive binding

2 interactions between Fe3+ and Cu + for forming coordination bonds with DA (Fig. 3A and

2 B). Two standard concentrations ofCu + were used to challenge pre-equilibrated Fe-DA

3 solutions with molar Fe:DA ratios ranging from 0-4 at pH 82. Pre-equilibration ofFe + with DA for 18 hours insured maximal theoretical complex formation between DA and

3 2 Fe + before Cu + stock additions proceeded. MINEQL (Schecher and MeAvoy 1998) was used to model concentrations of FeDA complexes and inorganic species in the test solutions. By incorporating the Log K.:ondFe{lll)DA = 18.7 ± 0.2 derived from Rue and

Bruland, 2001, modeling of potential CuDAFe interactions was possible. At Fe:DA

ratios= 4, 9.5% (1.9nM) of added Fe10w was complexed as FeDA with the remainder of

3 dissolved Fe + as oxyhydroxide species: 45.6% Fe(OH)3. 35.8% Fe(OH)t and 9.1%

Fe(OH)4. Titration ofFeDA complex solutions with Cu, however, resulted in no statistical difference in the fonnation of CuDA complexes in the absence (Cu:DA ratios at 6.5:5 or 11.6:8) or presence (Cu:DA:Fe ratios at 6.5:5:5, 6.5:5:10, 6.5:5:20 and

21 11.6:8:5, 11.6:8.3:10, 11.6:8.3:20 of Fe over a range of [Ferolnl] 5-20 nM. Statistical resolution derived from ANOV A, using a Student Newman-Kuels post hoc with a= 0.05, was sufficient to detect differences between Cu' signals derived from CuDA and CuDAFe. Precision of these titration assays for Cu' was 0.400 nM ± .033 for

CuDAFe treatments titrated with Fe at 6.5:5:5, 6.5:5:10, 6.5:5:20 and 0.649 ± .065 nM for CuDAFe treatments titrated with Fe at 11.6:8:5, 11.6:8.3:10, 11.6:8.3:20, values well below the 1.9 nM reduction in CuD A complexation modeled by MINEQL attributable to the presence of FeD A complexes in the test solutions. Hence, in spite of a higher

3 apparent stability constant for FeDA, Fe + did not significantly interfere with CuD A complex formation at environmentally realistic concentrations. Prior experimentation ruled out possible interference in the chemiluminescent reaction of Fe with Cu (yamada and Suzuki 1984, Coale et al. 1991, Zamzow 1998 and personal observations). This data reveals no [Fe]-dependent trend in CuD A formation even at Fe:Cu ratios in excess of those observed in natural waters.

Bioassays of Cupric Ion Sensitivity in Pseudo-nitzschia multiseries

In order to determine whether DA production is a tracsient biochemical response to Cu2+ variability, acute Cu exposure assays were conducted on cells acclimated to f/2 media under standard growth conditions. Cupric ion-dependent responses in cell growth rates and dissolved free amino acid pools were compared across isolates, MU6 and

MU411 to identify strain-dependent variability in [Cu]-response.

22 Growth Response, Strain MU6

A biphasic response in exponential growth rate to cupric ion exposure was exhibited by P. multisereis, MU6 (Fig. 4). Cupric ion toxicity was evident at pCurollll

3.26, with significantly lower growth rates compared to unenriched f/2 (0.08 day ·I vs.

5 0.29 day ·I, F = 12.30, p 6.98 x 10" ). A significant enhancement in growth rate relative to f/2 was observed at intermediate [Cu} enhancements, (pCu1oroJ6.13 and pCUrollll 5.83), suggesting that f/2 media may be Cu limiting for optimum growth of P. multiserie.s

(MU6) a trait not frequently observed, yet consistent with prior observations on dinoflagellates (Schenck 1983 ). Excluding the highest Cu treatment growth rates for

1 MU6 under these experimental conditions averaged (0.36 ± 0.03 fJ. day" )

Amino Acid Pools, Strain MU6

Following the gro\'ith assays, cultures were harvested in early stationary phase for characterization of the FAA pools. Here attention was focused on the responses ofDA as well as Taurine (TAU) and Proline (PRO), a nonproteinogenic and proteinogenic amino acid pair known to function in intracellular homeostasis, along with glutamate (GLU) which in addition provides a proxy for cellular nitrogen (N) status (G. J. Smith, pers.

Cornm.).

Significant differences in intracellular DA concentrations were detected in cells grown at the highest [Cu], pCUtollll 3.26, compared to all other treatments (F = 3.96, p =

0.12, Fig. 5). Furthermore, graphical trends indicated that perturbations in pCu from standard media conditions, either Cu-lirniting or Cu-excess media, resulted in greater intracellular accumulation of DA On average, cells grown at pCutotnl 3.26 maintained an

23 intracellular DA burden an order of magnitude greater than all other treatments (936.3 ±

742.8 pmoles DA/1 06cells; 291.6 ± 231.2 fg DA/cell). While no significant trends between extracellular DA pools and [Cu] were detected, due to large standard deiviations, mean extracellular [DA] (1,597 ± 1,600 pmoles DA/106cells) were 8-fold greater on average than mean intracellular burdens for the same treatments (182.9 ± 150.1 pmoles

DA/106 cells).

Intracellular concentrations of GLU, TAU and PRO, unlike DA, did not exhibit similar variation with pCUtotlll treatment (Fig. 5). Taurine, which has been measured in high concentrations as a dissolved free amino acid in all species and strains Pseudo nitzschia spp. (~ 10% ofFAAs; Smith et al. 2001) maintained mean intracellular concentrations almost double that ofGLU (2.0 x103 pmoles/106 cells and 1.2 x!03 pmoles/1 06 cells, respectively). Proline content, which was higher than DA in standard

£'2 media concentrations (I I pmoles/1 06 cells vs. 5 pmole DA/1 06 cells respectively), became undetectable above pCutotnJ5.83 (< 4 pmole PR0/106 cells); as DA content increased by 100-fold.

An unknown PTC-amine (UA_I3.8) is included in these comparisons because sample chromatograms consistently displayed a secondary peak eluting with a mean retention time of 13.8 minutes, 0.3 ± 0.1 minutes after the PTC-DA_CS peak, which ran at 13.5 ± 0.2 minutes in our protocol (PTC-FAA). Unknown amine 13.8 was

undetectable in cells derived from low [Cu10tai] treatments (pCu1otlll10.08- 7.52), while a

10-fold increase in UA 13.8 content occurred and was positively correlated with

increases in copper loadings (pCuwtlll 6.13- 3.26, r = 0.80, p = 0.05). Unknown amine

24 13.8 was not observed in the HPLC_UV assays of extracellular pools, nor in non derivatized samples.

Acute Exposure Assays: Growth, Strain MU411

Maximum growth rates, of P. multiseries MU411 were not significantly different below the pCUtotnl"" 3.26 exposure treatment (F = 3.40, p = 0.03, Fig. 6). At the highest

1 pCu.ow exposure, only I of3 samples exhibited detectable growth (0.2911 day- ). On

1 average, growth rates ofMU4llat pCUtotal 3.26 equaled 0.12 ± 0.15 11 day" , which were significantly lower than cumulative mean growth rates of 0.43 ± .02 11 day-1 for

2 treatments grown in lower [CUtotnl] (F = 4. 78, p = 1.8 X 10- ). Average growth rates of

MU411 were 26 % greater than observed for MU6 under similar cnltnre conditions.

Acute Exposure Assays: Amino Acid Pools, Strain MU411

The MU411 isolate when cnltnred in standard media maintained significantly higher intracellular DA pools (25 ± 16 pmoles/106 cells or 7.8 ± 3.4 fglcell; F = 5.38, p =

3 4.5 xt o· ), when compared to all other Cu treatments, save the most heavily chelated

(pCu10tn~ 10.08, 17.5 ± 9.4 prnoles/1 06 cells or 5.4 ± 2.9 fglcell, Fig. 7). Intracellular DA pools declined in treatments where CUmtul varied from standard media conditions, with intracellular [DA] falling below detection limits at pCUtotnl greater than 6.52. Mean extracellular DA concentrations exceeded mean intracellular levels by more than an order of rnagnitnde (243.1 ± 74.6 pmolesl106 cells) and were detectable up to pCiltotnJ of 4.21.

Acute Exposure Assays: Amino Acid ComparisoiUl, MU6 vs. MU411

Comparisons of amino acid contents in Pseudo-nitzschia multiseries isolates MU6 and MU411, were performed to evaluate 1) if DA production was inducible and a specific response rather than a global response in amino acid metabolism, 2) whether elevated ofDA content represents a generalized species-level response to Cu stress and

3) if increased concentrations of other FAAs can function in lieu ofDA response, mitigating Cu stress in strains of P. nitzschia spp. that produce little to no DA.

ln standard growth media (l'l2, pCutotnJ 7 .52) isolate MlJ411 maintained significantly higher intracellular [DA] compared to MlJ6 (25.44 ± 15.77 pmoles/106 cells vs. 2.9& ± 3.00 prnoles/1 06 cells). However in media with altered [pCu 10ro~], MlJ6 exhibited a 20-fold higher average value for intracellular [DA] than MlJ411.

Extracellular [DA] accumulation was variable with respect to Cu treatments, therefore, confounding direct comparisons between the MlJ6 and MlJ411 experimental series.

Hence, comparisons of total DA (DA1otni = DAp.rueu!oiO + DA.!issolved) between MlJ6 and

MlJ411 were used to determine differences between the strains. Average DAtotu! for MlJ6 was 1,7&0.5 ± 900.6 pmoles/106 cells and 250.6 ± 33.5 pmoles/106 cells for MlJ411.

Thus overall, MlJ6 produced over 7 times more DA/cell than MlJ411 in response to perturbations in [Cu]. The release ofDA into the media by MlJ6 shows a positive correlation with intracellular DA (r = 0.8&7, p = 0.05). Intracellular and extracellular DA accumulation by MlJ411 cultures did not exhibit a similar level of correlation (r = 0.627, p = 0.05). Of the average total DA accumulated in stationary phase cultures, MlJ6 released2: 84 (± 8)% to the media, while MlJ411 released 2:97 (± 5)% to the media.

Intracellular GLU pools for MlJ411 increased linearly with media pCu1oml and averaged 30-times more GLU than isolate MlJ6 (52,749 ± 47,961 pmol/106 vs. 1,665 ±

1,016 pmole/106cell). The highest values for intracellular GLU pools were observed at

26 the extreme Cu exposure level (9.7xl05 ± 4.7x104 pmoles!l06cells, 30 ± 1.4 pg/cell, F =

3 4.95, p = 6.4x I 0" ). The taurine pool responses tracked that of GLU in MU411, and compared to the MU6 isolate TAU:GLU ratios were an order of magnitude higher (37.1

± 27.8 vs. 3.50 ± 0.98 respectively) in non-growth inhibiting pCurollll exposures(< pCu10tn~

3.26).

Field Studies

Evaluation of total Pseudo-nitzschia spp. abundance patterns along the sampling transect (Fig. 8) indicated that the 97 day sampling program overlapped both non-bloom and bloom events (Fig 9, 10, 11 ). Furthermore, variability in cellular and water column

DA burdens indicated the presence of Pseudo-nitzschia spp. populations with different toxicity and hence physiological status. Scanning electron micrographs (SEMs) of representative samples from peak toxic events (April24, 2001) and peak bloom events

(June 04, 2001) revealed that the sampling period encompassed two distinct Pseudo nitzschia communities (Fig. 12A, B, C and D, Peter Miller direct observations). A toxic population, consisting primarily of Pseudo-nitzschia australis and a few Pseudo-nitzschia pungens, occurred during the period from Mar 28- May 22 (COM I, Fig. 12A and B) and a non-toxic community dominated by Pseudo-nitzschiafraudulenta and Pseudo-nitzschia heimii with few Pseudo-nitzschia pungens, occurred from May 25- July 03, 2001

(COM2, Fig. 12C and D).

Net plankton community compositions were also inferred from scanning electron micrographs of water samples. Scanning electron micrographs from all three sites showed that < 25% of the total phytoplankton assemblage consisted of pennates for

27 COM!, based on numerical abundance. However, all pennates detected during COM1 were identified asP. australis, a species implicated as the causative species involved in many DA poisonings in the Monterey Bay region (Fig. 12A and B). Conversely, SEMs ofCOM2 depicted an almost monogeneric bloom of pennate diatoms composed of>

80% Pseudo-nitzschia hemeii and Pseudo-nitzschia.fraudulenta and < 10% Pseudo nitzschia pungens (Fig. 12C and D). Neither of the dominant Pseudo-nitzschia spp. comprising COM2 phytoplankton assemblages had ever been associated with toxic events in Monterey Bay (P. Miller, pers. Comm.). Only a single report has observed DA production by the minor constituent of the COM2 assemblage, Pseudo-nitzschia pungens

(Trainer et al. 1998)

Abundance ofDA in both particulate and dissolved fractions during COM1 were highly correlated (r > 0.90, p = O.Dl, n = 14), while undetectable levels of particulate DA

(< 0.01 nM DA total) resulted in no correlation ofDA content in these fractions during the COM2 period (r < 0.40, p > 0.05, n = 17 Table 2), while no such correlation was detected during the COM2 interval (Table 3). These trends suggest that intracellular DA accumulates in response to [Cu'].

Near shore Cu' Gradients and Cu complexiog capacity:

To accurately evaluate total Cu bioavailability in near shore environs, both [Cu']

and [Cu1ow] were measured (Figs. 9D, E, through liD, E), from which [CuL] was

estimated (CuL = Cu10w - Cu'). Extreme variability in [Cu'] and [Cutow] occurred

throughout the survey period. The concentration of bioavailable copper, Cu', was variable

throughout the survey period and across sampling locations, ranging from ca. 0.5 nM to

28 25 nM. The proportion of Cu1ntal in apparent organic complexes was estimated as

1- x I 00, ranged from 50% to 97% of all copper speciation. The lowest ( Cu' J Cutot111 proportion of Cu complexation was measured at the offshore MB site with the complexation capacity of the water column increasing along a gradient towards shore with [CuL] ranking as MB

Cu' and daily upwelling indices derived for the Monterey Bay Region (PFEL Coastal

Upwelling Indices, http:/lwww/pfeg.noaa.gov), suggesting that copper availability along this transect is more strongly influenced by near-shore anthropogenic and watershed processes.

29 DISCUSSION

Harmful algal bloom data compiled over the last decade indicate that blooms of potentially toxigenic species of Pseudo-nitzschia occur periodically within waters along the west coast ofNorlh America (Bucket al. 1992, Scholin et al. 2000, Adams et al.

2000, Trainer et al. 2002). Although Monterey Bay, California has experienced many significant toxic blooms of Pseudo-nitzschia spp. since 1989, the majority of seasonal

Pseudo-nitzschia spp. blooms have been benign in either production of domoic acid or transfer of the toxin to higher trophic levels. Thus, it is important to note that not all blooms of these ubiquitous species are equally toxic due to intra- and interspecific variability in toxin production. Furlher, because the observable endpoint, DAP or ASP, depends on the timing of food chain interactions, defining a toxic DA bloom event is often confounded. At a minimum, the development of Pseudo-nitzschia spp. associated toxic events requires oceanographic conditions that promote bloom formation. However, the identification of the precise triggers for stimulating DA production during these blooms has remained elusive. The approach taken in this study has been to assess potential physiological functions ofDA in the diatom in order to identify significant environmental triggers fur DA production by Pseudo-nitzschia spp.. Specifically, this study has sought evidence to support the working hypothesis that domoic acid plays a role in copper homeostasis.

Complexation Experiments

The conditional stability constant for formation of CuDA complexes, measured in this study (Log KcondCu(li)DA = 11.995 ± 0.5), falls within a ligand strength just

30 intermediate in magnitude to the strong ligand class L1, Log Koonds?. 12 and the weaker ligand class L2, Log Kconds < 12, both which have been shown to be produced by algae in response to elevated elevated [Cu] (Gledhill et al1999, Brown and Gordon 2001). It should be noted however, that the Log K.:ondCu(ll)DA from this study is significantly higher than the Log KcondCu(II)DA reported by another research group using differential pulse anodic stripping voltametry (DPASV, Rue and Bruland, 2001, Log KcondCu(ll)DA = 10.30 ±

0.2). Differences in the apparent l

I= 0, measurements were made at a low ionic strength (I= 0.02, Stuum and Morgan

1996, Libes 1998, Schecher personal cormnunication). This ionic strength was chosen as a balance between the idealized I = 0 and the need to measure the l

The conditional stability constant measured during this study places DA within the following ranking for other amino acids with measured conditional stability constants for copper (Table 4):

ORN

This hierarchal scheme places DA above all other natural occurring amino acids in its capacity to complex Cu. Glutamate, which can account for ca. 50% of the total intracellular free amino acids over a range of physiological states (Smith et a!. 2001 ),

31 and has the potential to mitigate significant Cu stress (Log Koc..dcu(II)GLU =' 8.8), is almost

l 000 times weaker than DA in its ability to complex Cuper mole ofN invested in its

biosynthesis.

Domoic acid's affinity for complexing copper has been firmly established, yet

3 much recent research has focused on the iron (Fe ) acquisition properties ofDA rather than its potential role in binding excess Cu (Rue and Bruland 200 l, Maldonado et al.

8 2002). Although, the reported Koc..dFe(IU)!lA is l 0 times greater than K.ondCu(II)Dru at a

natural [Fe3j of l o-18 M and [Cu2j of l 0-9 M, equilibrium calculations alone indicate that complexes with DA will be dominated by Cu when experimental conditions isolate

these two trace metals (Table 1). Equilibrium modeling of [Ferotlll] and [CUtotni] in

MINEQL estimated that over 90% of Fe is unavailable for complexation, whereas 90% of

Cu is free for complexation interactions. Even though DA exhibits a higher theoretical

3 3 complexation capacity for Fe + than CuZ+, [Fe j would have to exist at concentrations at

leru.i: an order of maguitude greater than normally found in natural marine waters for this

metal to inhibit CuDA complex formation. Therefore, ifDA's primary role was to

complex Fe3+, when Fe is at limiting concentrations (0.2 nM total dissolved Fe, Gordon

et. al. 1982, Wells personal communication), then titration of equilibrated FeDA

complexes with Cu would result in impaired complexation of Cu relative to Fe free

solutions. This effect was not detected in this study. Rather, the results are consistent

with the dissociation of pre-equilibrated FeDA complexes by Cu'.

While the competitive complexation experiments reported here clearly

demonstrate that the ~ority ofDA will be bound to Cu even at Fe:Cu molar ratios as

32 great as 4:1 it is unlikely that DA functions exclusively as a copper complex.ing ligand.

Instead, it is more plausible that DA functions as a generic trace metal buffer, indiscriminant in regards to which trace metal it complexes. The potential Cu buffering capacity of DA was clearly exhibited in Cu-exposure bioassays with P. multiseries MU6 where under extremes of both Co-limitation and Cu-toxicity, intracellular DA content increased by 1-2 orders of magnitude. What drives the specificity ofDA-metel complexation, determining whether a metal is acquired for metabolism or sequestered to become metabolically inert, is dependent on a complex interplay of chemical parameters including trace metal composition, concentration, speciation, pH, redox potential, ligand composition and ionic strength. However, since Cu is seldom if ever limiting in coastal waters, where toxigenic species of Pseudo-nitzschia abound, it is likely that DAmore often benefits Pseudo-nitzschia spp. by mitigating excesses of Cu rather than facilitating its storage. In this way DA may provide an analogous function to the low molecular weight metallothionines, with DA's simple structure placing lower demands on cellular resources for its biosynthesis.

Incubation Experiments

Growth responses of Pseudo-nitzschia multiseries isolates lv!U6 and MU411 indicate that this species exhibits significant tolerance to total cupric ion loadings up to pCu101w 3 .26. Only at the highest pCUtotnl of 3.26 were growth rates and cell yields significantly reduced from standard media conditions. No consistent evidence for Cu· limitation was obtained even though application of bathocuproinedisulfonate (BCS), a

Cu(I) specific chelator (Cu(I), is the biochemically required fonn, Jones et al. 1987,

33 Twiss 1996) was used to deplete the media ofCu(I) up to a factor of80. Earlier studies indicated narrow tolerance ranges for dinoflagellates in response to variability in [Cu] with Cu-limitation occurring at pCu = 14 while Cu-toxicity occurred at pCu = 9 (Schenk

1983).

Consistent with observed variation in DA accumulation in natural blooms

(Garrison eta!. 1992, Lange 1994, Scholin eta!. 2000, personal observations this study) and laboratory isolates of P. multiseries and P. australis from Monterey Bay (Smith et a!.

2001) significant differences among treatments was apparent in intracellular and extracellular DA accumulation in the experimental isolates used in the present studies.

DA analysis of semi-continuous batch cultures of P. multiseries MU6 and MU411 stocks revealed intraspecific differences in the accumulation of intracellular DA pools when grown in standard f/2 -enriched seawater. Physiological differences exhibited between strains acclimated to the standard media conditions, suggested that MU6 was limited in its capacity to produce DA, while MU411 produced DA in significant quantities.

However, once [Cu] were perturbed from f/2 conditions, these apparent strain-specific physiological characters were reversed, with P. multiseries MU6 exhibiting a specific pCu-buffering induction ofDA accumulation. While MU6 maintained almost

undetectable DA, contents in standard f/2 media conditions (pCu1otni = 7.52) an 80-fold increase in intracellular DA:GLU molar ratios was observed as media [Cu] increased up to pCUtotnl 3.26 and a 40-fold increase in intracellular DA:GLU was associated with a

reduction in media [Cu] down to pCu1otni 10.08. No significant or parallel response in either proline or taurine pools were observed, suggesting that DA is providing specific Cu

34 ion buffering activity in P. multiseries MU6. In contrast DA pools in P. multiseries

MU411 were non-responsive to analogous [Cu J exposure treatments and in fact both

DA:GLU and PRO:GLU molar ratios declined in response to perturbations in [Cu] from standard media conditions. However, MU411 was readily distinguishable from MU6 by maintaining 10-fold higher glutamate pools than MU6 and lower TAU:GLU molar ratios at sublethal pCUto!l!! exposures.

Presumably, isolates of Pseudo-nitzschia multiseries that can accumulate large reservoirs of free GLU, may be able to utilize a portion of this 'luxury pool' of amino acid (suggestive ofN-enriched growth conditions) as the principle source for direct mitigation of trace metal homeostasis. Conversely those strains with reduced GLU pools, possibly strains experiencing N-limitation, may mobilize alternate metabolites like DA which provide greater metal-ligand capacity per mole ofN. In P. multiseries MU411, where elevated [Cu] did not induce DA accumulation, 10· to 100- fold larger pools of the potential homeostatic amino acids proline and glutamate occurred may have provided inherent metal-buffering capacity

Analysis ofintracellular UA 13.8 further differentiates strains MU6 and MU411 in regards to their biochemical response to Cu variability. In strain MU411 no statistical differences existed in [UA 13.8] across all Cu treatment ranges, and furthermore remained at concentrations just near the limit of detection (:5 1.0 pmoles PTC amine/1 06 cells). Conversely, strain MU6, which produced undetectable levels ofDA at f12 [Cu], exhibited an increasing trend between treatment [Cu] and intracellular levels ofUA 13.8.

These results indicate that UA 13.8 is highly regulated by elevated [Cu], and begs further

35 investigation into its ability to complex Cu and other trace metals. This amine compound may provide yet another weapon in the cellular arsenal to buffer metal stress.

Many studies bave shown that phytoplankton modifY intracellular pools and extracellular release of amino acids in response to excess Cu. The ability of Pseudo nitzschia spp. to form nearly unialgal blooms provides circumstantial evidence that they exhibit a "selective advantage" over other taxa during unique oceanographic conditions.

Domoic acid production, accumulation and eventoal release may be a novel method for mitigating near shore regions where extremes in dissolved Cu are commonplace.

Glutamate, PRO and DA all contain one nitrogen per molecule, therefore based on observed ligand strengths, the utilization ofDA to regulate Cu homeostasis may conserve as much as I 000 nmoles of nitrogen to achieve the same buffering of bioavailable Cu.

That DA is a biomarker for Pseudo-nitzschia spp., and tbat these species can dominate plankton communities during specific oceanographic conditions suggests tbat DA may be the mechanism by which Pseudo-nitzschia spp. take advantage of a niche inhospitable to phytoplankton lacking similar trace metal homeostatic mechanisms. However, the bioassay studies summarized here also suggest that DA may provide only one of several avenues for mitigation of Cu stress and tbat other free amino acid pools such as GLU,

PRO and TAU may also be called into play a compensatory function in the mitigation of excess levels of environmental Cu.

36 Field Survey

Intensive sampling of a 3 k transect, proximal to the municipal wharf in Southern

Monterey Bay revealed a significant association between water column DA burdens and copper bioavailability. Variation in the strength of CuD A complexation was attributable to variation in the Pseudo-nitzschia species composition in the local water column.

Overall, when P. australis were present at [cell] :S 105 cells!L, total DA accumulation was positively correlated with local increases in Cu' and Cutotnl·

This is the first study where correlations between Cu availability and DA accumulation have been rigorously established in field samples splllllling non-bloom and bloom periods. Copper can become toxic to algae at even slightly elevated concentrations. Harbor environments such as Monterey Bay Harbor and urbanized coastlines are sources of significant anthropogenic inputs of Cu (Stephenson and Leonard

1994, WQPP MBNMS 1996, 1999, Zamzowet al. 1998). Establishing a correlation between cellular and dissolved levels of DA and dissolved Cu species provides insight into the mechanisms that phytoplankton in general may employ in order to survive potentially hostile chemical environments. Monitoring of Cu' and Cu10w during this period revealed an apparent increase in the CuL fraction comprising COM2. That this period corresponded to non-toxic species blooms, while fluctuations in [Cu] continued indicates that ligands other than DA, like GLU or PRO were being produced to mitigate elevated [Cu] in a fushion similar to MU41l. The challenge to dissecting these metabolic signals in mixed communities remains an ongoing effort.

37 In this field study relationships among Pseudo-nitzschia spp. abnndance,

particulate and dissolved domoic acid concentrations and concentrations of Cu' and Cu1otnl

were analyzed. While several papers (Rue and Bruland 2001, Maldonado et a!. 2002)

have cited that iron acquisition rather than Cu buffering is the primary physiological role for DA, no studies to date have demonstrated a positive correlation between the co occurrence ofDA and Fe in nature. Furthermore, in upwelling regions where P. multiseries are fonnd to be consistently toxic, sufficient [silicate] and [Fe] have also been measured (Hutchins and Bruland 1998, Trainer et al. 2000). These same environments, are often characterized by supra-nanomolar concentrations ofCu (Table 5.). What is notable is that regions often characterized as toxic Pseudo-nitzschia spp. "hot spots" exhibit macro- and micro-nutrient concentrations sufficient for phytoplankton growth.

Time series data from this study measured short-term (3 day) fluctuations in [Cu'] ranging from 0.5 to 25 nM within a spatially constrained marine environment, far in excess of variations reported for [Fe'] in this region (Bruland eta!. 1991, Hutchins and

Bruland 1998). These observations illuminate just how variable and rapid the physiological responses of phytoplankton must be in order to survive sporadic influxes of potentially toxic levels of Cu into their coastal domain.

Conclusions

This study has demonstrated that DA has the capacity to buffer Cu, responding through enhanced production/accumulation to both limiting and toxic levels of Cu. ln nature however, it is likely that DA functions strictly to mitigate excess [Cu] where total

DA production is enhanced in response to elevated [Cu]. Incubation experiments support

38 a dual functionality for DA in Cu metabolism. This study determined that DA has a

significant effect on dissolved copper speciation, thereby impacting coastal [Cu']/[Cu101uJ] that result in reduced concentrations ofbioavailable Cu; [CuJ that may otherwise be toxic to phytoplankton.

The observed accumulation ofDA in both intracellular and extracellular pools is consistent with the literature and suggests two potential models in regards to DA's function with respect to Cu homeostasis. First, acting as a Cu buffer with a competitive complexation capacity just intermediate to Ll and L2 ligands, DA can theoretically compete for Cu when, if ever in the natural environment, Cu is limiting, at the same time, extracellular DA may reduce the bioavailability of excess Cu in the environment.

Secondly, because Cu-Iimitation, particularly in coastal waters, is seldom observed, it is more likely that DAis produced within the cell in response to elevated [Cu), and subsequently transported as CuD A complexes outside the cell thereby reducing intracellular levels of Cu.

Since the industrial revolution at the tum of century, anthropogenic activities such as fossil fuel combustion, agriculture and mining have greatly altered the biogeochemical cycles of many chemicals including trace metals. In coastal environments, which experience the highest degree of anthropogenic input, the greatest selective forces are put upon biota (Guvstavson et al. 1999). Consequently, organisms must adapt or perish. One of many possible adaptive responses of phytoplankton may be to produce novel molecules, through altered biochemical pathways, that counteract the deleterious potential imported via natural and anthropogenic inputs. The extraordinarily diverse

39 approaches to biochemical buffering of Cu' by algal cells suggests that Cu perhaps more than any metal, due to its potential toxicity and environmental ubiquity, trumps other

metals in regards to the necessity for biological control achieved through development of novel biochemistry (Lippard 1999, Rae et al. 1999, Gustavson et al. 1999). As industry, agriculture and coastal development encroach further into coastal zones, escalation in the

frequency ofHABs may be symptomatic of phytoplankton's only defense to an

increasingly hostile environment.

40 LI1ERA TURE CI1ED

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49 2 Table 1. Free concentrations of Fe3+ and Cu + calculated from derived Log K.onds of trace metals with DA in MlNEQL at pH 8.2, I = 0.02 Added Dissolved Log Species Concentration Concentration Kr:ond

WithoutCu 3 Fe • 2.00E-OB 1.22E-19 Fe3.DA 2.00E-OB 1.90E-09 18.7 With Cu 3 Fe • 2.00E-08 1.98E-19 Fe3.DA 2.00E-08 4.85E-10 18.7 WithoutDA Cu2+ 6.00E-09 5.12E-11 WithDA 2 Cu • 6.00E-09 8.32E-12 Cu2.DA 6.00E-09 4.03E-09 12

50 Table 2. Results from time-series correlation analysis, which measured the strength of concomitance between [Cu] and [DA] in samples taken from three regions, Shore Buoy (SB), Wharf Buoy (WB) and Mile Buoy (MB), when Pseudo-nitzschia assemblages were dominated by toxic P. austl"alis, March 28- May 25, 2001, COM I.

Correlation variables Region Correlation n t significance COM 1 coefficient - r !o.o,(2) Cu':DApwt'""""' (nmoles/10" cells) SB -0.1415 11 -0.4288 no " WB -0.1685 14 -0.5922 no " MB 0.1671 11 0.5360 no

Cu':DA,o,., (nmoles/10° cells) SB 0.8650 11 5.1717 yes " WB 0.8234 14 5.0265 yes " MB 0.7863 11 3.8179 yes

5 Cu 101111:DAoota, (nmoles/10 cells) SB 0.9206 11 7.0723 yes " WB 0.6337 14 2.8377 yes " MB 0.4350 11 1.4493 no

Cu':DA.,.,.,,.. (nM) SB 0.8300 11 4.4643 yes WB 0.2727 14 0.9819 no " MB 0.8239 11 4.5972 yes

cu,... ,:oAp.-sl. (nmoles/105cells) SB -0.0527 11 -0.1583 no " WB -0.2619 14 -0.9401 no " MB -0.3428 11 -1.1539 no

Cu101s,:DA,n""""oo (nM) SB 0.8725 11 5.3570 yes WB 0.2102 14 0.7448 no " MB -0.1493 11 -0.4775 no

51 Table 3. Results from time-series correlation analysis, which measured the strength of concomitance between [Cu] and [DA] in samples taken from three regions, Shore Buoy (SB), Wharf Buoy (WB) and Mile Buoy (MB), when Pseudo-nitzschia assemblages were dominated by non-toxic P. hemii, P. fraudulenta and P. pungens May 27-July 03, 2001, COM2.

Correlation variables Region Correlation n t significance COM2 coefficient - r !o.o,(2) Cu':DAp.,....,.,. (nmoles/106 cells) SB ..0.0925 28 -0.2787 no • WB -0.1164 28 -0.4060 no • MB 0.0000 28 0.0000 no

6 Cu':DA,o,.1 (nmoles/10 cells) SB 0.2580 28 0.8011 no .. WB 0.6404 28 2.8864 yes .. MB 0.2818 28 0.8811 no

Cutotal:DA.., (nmoles/1 d' cells) SB 0.2580 28 0.8011 no .. WB 0.0124 28 0.0430 no • MB ..0.1533 28 -0.4654 no

Cu':DA.,...,Ived (nM) SB 0.2697 28 0.6402 no .. WB 0.2403 28 0.8576 no .. MB 0.1463 28 0.4677 no

8 Cu.,..,:OA,...""1.,. (nmoles/10 cells) SB ..0.0925 28 -0.2787 no .. WB -0.1982 28 -0.7005 no " MB 0.2818 28 0.9288 no

cu.,.1:DA....o:..., (nM) SB 0.8587 28 5.0267 yes WB ..0.0183 28 ..0.0634 no MB 0.0872 28 0.2768 no

52 Table 4. Literature review of stability constants (Log K.:onds) for fonnation of 1 complexes between copper and amino acids .

Bathocuproine Glycine Glutamate Proline Do mole Disulfonic Acid Acid2

? 8.6 8.8 8.8 12.0

'Table adapted from Morel and Hering 1993. Stability constants are expressed as logarithms of overall formation constants; I = 0, neutral pH. "The stability constant presented for domoic acid was measured by the author using a modified chemiluminescent method; I= 0, 6.2 pH.

53 Table 5. Co-occurrence of toxic Pseudo-nitzschia spp. and the concentrations of macro- nutrients (Sio/· and N<)J") and trace metals (Fe and Cu) in coastal upwelling regions along the west coast USA.

4 Location Date DApa-lalo DAcllssolved Si04 - NO.! Fe Cu Reference pg/cell 1!9/L 11M !J.M nM nM Monterey Bay, Mar-01 2-120 this thesis CA Monterey Bay, Sep-93 9.8. 8.1- Kudela et al. 1997 CA 18.1 17.3 Ano Nuevo, Jun-98 0.3-6.3 0.1-0.7 Trainer et al. 2000 CA Monterey Bay, Jun-98 0.8-1.2 0.1-0.4 Trainer et al. 2000 CA La Push,WA Aug-98 0.7 10.3 4.2 Trainer et al. 2002 Kalalocl! Sep-98 0.6 33 21.4 Trainer et al. 2002 Beach, WA Monterey Bay, May-92 9.55 2.69 Wilkerson et al. 2000 CA Monterey Bay, Sep-92 6.37 5.02 Wilkerson et al. 2000 CA Monterey Bay, Mar-93 11.16 6.51 Wilkerson et a!. 2000 CA Monterey Bay, May-93 27.85 18.29 Wilkerson et al. 2000 CA Monterey Bay, Sep-93 9.45 8.41 Wilkerson et al. 2000 CA Monterey Bay, Mar-00 0.2 Gordon et al. 1982 CA Monterey Bay, 1 Bruland, 1980 CA

54 FIGURE LEGENDS

Figure l. Structural diagrams of domoic acid, proline, glutamine and bathocuproine disulfonate.

Figure 2. Titration analysis of copper complexation using chemiluminescent analysis for Cu'. Plot 1 (o) illustrates the effect of titrating a fixed concentration ofDA (6.53nM; DA_CS certified reference standard) with increasing concentrations of Cu (pH= 8.2). Further, values from Plot 1 ( o) have been incorporated into a least-squares linear regression model (STATMOST) routine to calculate the K.:ondCuDA· Acidification of CuD A complexes from Plot Jresult in the dissociation of CuD A complexes from which a standard curve Plot 2 (•) is derived (~ > 0.99). Quenching of the Cu voltage signal observed in Plot I ( o) indicates that Cu forms coordination complexes with DA at a 1: I stoichiometry, because an inflection point, indicative of DA coordination site saturation, occurs when [Cu] exceeds [DA]: [Cu) > 6.53 nM.

Figure 3A Plots of Cu standard at 6.5nM, 0) Cu standard at 6.5nM equilibrated with DA of 5nM, 1) Cu standard at 6.5nM added to equilibrated FeD A complex at 5:5nM Fe (1 :1), 2) Cu standard at 6.5nM added to equilibrated FeD A of 10:5nM (2:1) and, 4) Cu standard at 6.5nM added to equilibrated FeD A 20:5nM (4:1) at pH= 8.2. Vertical error bars report ± 1 standard deviation.

Figure 3B. Plots of Cu standard at !l.OnM, 0) Cu standard at ll.OnM equilibrated with DA of8.0nM, 2) Cu standard at ll.OnM added to equilibrated FeD A complex at 10:8nM Fe and 4) Cu standard at ll.OnM added to equilibrated FeDA of20:8nM. Vertical error bars report ± 1 standard deviation . . 1 Figure 4. Maximum growth rate (1-1 day' ) of Pseudo-nitzschia multiseries isolate MU6 from I.E. #1 in modified f/2 media ranging in pCU;ota~Concentrations from 10.08 to 3.26 M. Vertical error bars report± 1 standard deviation.

Figure 5. Glutamate, taurine, proline, domoic acid and an unknown amine (UA 13.8) are plotted as a function of [Cu] from growth bioassay experiment with Pseudo-nitzschia multiseries isolate MU6. Connection of points is used as a visual device and does not suggest a continuum of values. Vertical error bars report± 1 standard deviation from a mean of three replicates.

1 Figure 6. Maximum growth rate (J.l day" ) of Pseudo-nitzschia multiseries isolate MU411 from I.E. #2 in modified f/2 media ranging in pCu101ru concentrations from 10.08 to 3.26 M. Vertical error bars report± 1 standard deviation.

55 Figure 7. Glutamate, taurine, proline, domoic acid and UA 13.8 are plotted as a function of [Cu] from growth bioassay experiment with Pseudo-nitzschia multiseries isolate MU411. Connection of points is used as a visual device and does not suggest a continmun of values. Vertical error bars report± 1 standard deviation from a mean of three replicates.

Figure 8. Map of study site in Monterey Bay, CA where kayak field survey was conducted. Copper and net phytoplankton samples were taken from March 28, 2001 - July 3' 200 l.

Figure 9. Biological and chemical conditions in Monterey Bay during the period March 28 ·July 3, 2001 at Shore Buoy Site. A) Cell abundance of Pseudo-nitzschia; B) [DAtow]; dissolved DA +particulate DA per 106 cells; C) dissolved [DA]; D) [Cu']; E) [CUtotrul as a function of time in Julian days.

Figure 10. Biological and chemical conditions in Monterey Bay during the period March 28 -July 3, 2001 at Wharf Buoy Site. A) Cell abundance of Pseudo-nitzschia; B) [DAww]; dissolved DA +particulate DA per 106 cells; C) dissolved [DA]; D) [Cu']; E) [CIItotol] as a function of time.

Figure 11. Biological and chemical conditions in Monterey Bay during the period March 28 -July 3, 2001 at Mile Buoy Site. A) Cell abundance of Pseudo-nitzschia; B) [DAww]; dissolved DA +particulate DA per 106 cells; C) dissolved [DA]; D) [Cu']; E) [CUtotru] as a function of time in Julian days.

Figure 12. Scanning electron micrographs of representative samples of phytoplankton assemblages during maximal [DA] occurring on April19, image 12A and 12B, and maximal cell abundance occurring on June 21,2001 image 12C and& 12D, indicated the presence of two distinct Pseudo-nitzschia communities; COM1 and COM2 respectively. COMl, image 12A, contained a mixed assemblage of phytoplankton with only a minority of the total comprised of Pseudo-nitzschia spp.. Image 12B) at I 0,000 x positively identifies the diatom as the toxigenic species P. australis (Miller, UCSC). COM2, image 12C, shows a large monospeci:fic bloom of Pseudo-nitzschia cells. Image 12d identified that COM2 was comprised of P. hemii, P. fraudu/enta and to a lesser extent P. pungens, species of Pseudo-nitzschia never implicated in a toxic DA event.

56 Figure 1.

'·········u·.·•·.· ···... ··.·.·· ···: .. ·..·· .. ·.·•··.·II ..· .. •.··· ··.. 0 ·....•.. ·. ··.·., •. ·.. ·...··. · · .. N •. , c...,.OH .···.H'

Proline

0 ... 0 ·.·.·.··illc, ...· .• ,·:• ... ··'I.J :'+·.·· ··•·• •07C-CH • CH:l CH-c.,.;.o-· :.·· · . . ,...... > 2• . . J . > ,. ···•·· . . NH 2 · Glutarrlihe .· ·.··cr.

''' ·· .. · ·.... ·. tl··.··.·... ··.·· .. Hi2NCH1CH2~S-OI;t > ···.·.· .. ··.·.. ·· H.·····.

: - ':', · .•.. Taurine .. f.· ' •·::...s.-,.oNaII . .. {j ...... ~· ·' :.:_S-'-'0Na .IF . '"'0' .· · Bathocupfoine dis~

57 Figure 2.

58 Figure 3. Figure 4.

0.50 r

~ ~ '-c 0.40 ~ .Slm ~ .c ~ 0.30 0 ~ 0> E :::l 0.20 ·;;:E m E 0.10

0.00 - - 10 9 8 7 6 5 4 3

pC utotal (M)

60 Figure 5.

10' ... DA -l>- PRO -o-- UA 13.8

102

Ul 101 Qi (J

~ 0 ._~ If) Ql 0 E .e, 10° If) "0·c:; ro 0 5 .Ec 10 ro Ql Ql u..~ 104

103

-a- GLU .....zv- TAU 102

101 ~,------,------,------,-----,------,------,------~ 10 9 8 7 6 5 4 3

pCutotal (M)

61 Figure 6.

- 0.60 ·r

~ ~ 0.50 "1:1 ~ $ rn ~ 0.40 ..c: ~ 0 ~ Cl 0.30 E ::1 E ·x 0.20 rn E

0.10

0.00 l...__ 10 9 8 7 6 5 4 3

pCutotal (nM)

62 Figure 7.

103

2 10 DA -0-• UA 13.8 -.: -A- PRO "'0 .--0 - 101 Ill 0"' E a. ~ Qi

-y- TAU -111-GLU

10 9 8 7 6 5 4 3 pCutotal (M)

63 Figure ~L Figure 9.

Shore Buoy Composite

107 COM1 A ~ 10' '...J ="' 105 ~ 10' COM2 10' 1600 ~ 1400 1200 B "'0 1000 800 }~ 600 0 2 0 400 E 200 ~"' 0 • •• • Gl •• Ill 8 :E s e c1 i 4 £3" 2 0

14 12 0 :E 10 .s 8 -:J 6 () 4 2 0

100 ao :E <:: eo ~ El :J~ 40 () 20 0

60 100 120 140 160 180 200 Julian days

65 Figure 10. Wharf Buoy Composite

107 COM1 A ~:._, 10'

:!!J 10' "'0 10'

10' ~ 3500 :!!J 3000 B "'0 2500 lil"'o 2000 .t~ 1500 Cl 31 0 1000 E 500 5 0

3.0 :;~ 2.5 c c: 2.0 -u 1.5 !m 1.0 4:.."6 0.5 Cl 0.0

30 25 D :;~ 20 5 15 -:::J 10 () 5 0

160 140 E :.? 120 5 100 BO ~ ::J 60 () 40 20 0

60 100 120 140 160 160 200 Julian days

66 Figure 11. Mile Buoy Composite

10'~------.------, COM1 108 A ~ .!!!. 105 1lw 10' COM2

1~~------~------~4-----~~------~------~------~ 6000~------~------, i 5000 B lil'"o 4000 m ~ 3000 i]! 2000 ~ 1000 s 0

8,------~------, ~ e c s !l 4 I 2 (3 0

3.5 ,------+------,..------, u D 2.5 5? 2.0 .s 1.5 ":o 0 1.0 0.5 0.0

~,------4------, 50 E ~ 40 s 30 J 20 0 10 0

80 100 120 140 1~ 180 200 Julian Days

67 Figure 12.

20~ 1 OOOX Z~m i OOOOX

20J.Jm 1 OOOX 2~ 1 COO OX

C) D)