Deep-Sea Research II 101 (2014) 4–20

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Deep-Sea Research II journal homepage: www.elsevier.com/locate/dsr2

Understanding harmful algae in stratified systems: Review of progress and future directions E. Berdalet a,n, M.A. McManus b, O.N. Ross a,1, H. Burchard c, F.P. Chavez d, J.S. Jaffe e, I.R. Jenkinson f, R. Kudela g, I. Lips h, U. Lips h, A. Lucas g, D. Rivas i, M.C. Ruiz-de la Torre j, J. Ryan d, J.M. Sullivan k,l, H. Yamazaki m a

Institut de Ciències del Mar (CSIC), Passeig Marítim de la Barceloneta, 37-49, 08003 Barcelona, Catalunya, Spain Department of Oceanography, University of Hawaii at Manoa, 1000 Pope Road, Honolulu, HI 96822, USA c Leibniz Institute for Baltic Sea Research Warnemünde, Department for Physical Oceanography and Instrumentation, Seestrasse 15, D-18119 Rostock-Warnemünde, Germany d Monterey Bay Aquarium Research Institute, 7700 Sandholdt Road, Moss Landing, CA 95039, USA e Marine Physical Laboratory, Scripps Institute of Oceanography, University of California San Diego, CA, USA f Chinese Academy of Sciences, Institute of Oceanology, Nanhai Road 7, Qingdao 266071, PR China g University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA h Marine Systems Institute, Tallinn University of Technology, Akadeemia Road 15a, 12618 Tallinn, Estonia i Biological Oceanographic Department/CICESE, Carretera Ensenada-Tijuana 3918, Ensenada, Baja California, Mexico j Faculty of Marine Science, Autonomus University of Baja California, Carretera Tijuana-Ensenada 3917, Ensenada, Baja California, Mexico k WET Labs Inc., 70 Dean Knauss Drive, Narragansett, RI 02882-1197, USA l University of Rhode Island, Graduate School of Oceanography, South Ferry Road, Narragansett, RI 02882-1197, USA m Faculty of Marine Science, Tokyo University of Marine Science and Technology, 5-7, kjonan 4, Minato-ku, Tokyo 108-8477, Japan b

art ic l e i nf o

a b s t r a c t

Available online 19 October 2013

The Global Ecology and Oceanography of Harmful Algal Blooms (GEOHAB) program of the Scientific Committee on Oceanic Research (SCOR) and the Intergovernmental Oceanographic Commission (IOC) of UNESCO, was created in 1999 to foster research on the ecological and oceanographic mechanisms underlying the population dynamics of harmful algal blooms (HABs). The ultimate goal of this research is to develop observational systems and models that will eventually enable the prediction of HABs and thereby minimize their impact on marine ecosystems, human health and economic activities. In August of 2012, a workshop was held under the umbrella of the GEOHAB program at the Monterey Bay Aquarium Research Institute (MBARI). The over arching goal of this workshop was to review the current understanding of the processes governing the structure and dynamics of HABs in stratified systems, and to identify how best to couple physical/chemical and biological measurements at appropriate spatial and temporal scales to quantify the dynamics of HABs in these systems, paying particular attention to thin layers. This contribution provides a review of recent progress in the field of HAB research in stratified systems including thin layers, and identifies the gaps in knowledge that our scientific community should strive to understand in the next decade. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Harmful algal blooms Thin layers Phytoplankton detection methods Modeling Motility Observational systems

1. Introduction The distribution of phytoplankton in the sea is influenced by a wide range of processes that include ocean circulation, light and nutrient availability, as well as biological interactions. While large scale processes such as climatic forcing may dominate in some situations, the focus of this contribution is on small to mesoscale n

Corresponding author. Tel.: þ 34 932 309 595; fax: þ34 932 309 555. E-mail address: [email protected] (E. Berdalet). 1 Present address: Aix-Marseille Université, Université de Toulon, CNRS/INSU, IRD, MIO UM 110, 13288 Marseille Cedex 09, France. 0967-0645/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.dsr2.2013.09.042

processes mainly in coastal environments where the water column is often stratified and where this stratification can play a crucial role in phytoplankton dynamics (e.g. Berdalet et al., in press; Dekshenieks et al., 2001; Gentien et al., 2005; Ross and Sharples, 2007; Sharples et al., 2001). The adverse effects of high-biomass and/or toxic blooms are particularly pronounced in coastal areas due to their immense economic importance to humans both in terms of aquaculture and tourism. Some areas such as fjords (e.g. Norway, Scotland, US Pacific Northwest), coastal lagoons (Mediterranean), and polar regions (Zemmelink et al., 2008) are characterized by freshwater input from land run-off, which may result in very strong vertical density gradients creating unique

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environmental conditions favorable for phytoplankton to persist. In other parts of the world's oceans (e.g. Gulf of Maine, Monterey Bay CA, North Sea), populations that are incubated in strongly stratified regions may be advected to neighboring areas where they can lead to elevated levels of primary and secondary production. Interestingly, a large number of HAB species have been observed in subsurface ‘thin layers’ where coastal waters are most stratified (Dekshenieks et al., 2001; Sullivan et al., 2003, 2005, 2010b; Partensky and Sournia, 1986). Thin layers are vertically thin and horizontally extensive subsurface patches of plankton (reviewed by Sullivan et al. (2010a)), which have been observed to persist from hours to weeks and can contain 50–75% or more or the total biomass in the water column (Cowles and Desiderio, 1993; Holliday et al., 2003; Sullivan et al., 2010a). Thin layers are likely biological ‘hotspots’ in the water column, important for organism growth rates, reproduction, grazing, and toxin production (Cowles and Desiderio, 1993; Dekshenieks et al., 2001; Hanson and Donaghay, 1998; Lasker, 1978; McManus et al., 2008; Mullin and Brooks, 1976; Rines et al., 2002; Sullivan et al., 2010a; Timmerman et al., 2014). HABs that develop in stratified systems, including those that form into thin layers, are one of the areas of interest pursued by GEOHAB. The Global Ecology and Oceanography of Harmful Algal Blooms (GEOHAB) program was initiated in 1999 with support from the Scientific Committee on Oceanic Research (SCOR) and the Intergovernmental Oceanographic Commission (IOC) of UNESCO. An overall goal of the GEOHAB research program is to allow the development of observational systems and models that facilitate the prediction of HABs, thereby reducing their impact on the marine ecosystem, human health and the local economy (GEOHAB, 2001). A multidisciplinary, international group of twenty-six scientists including engineers, physicists, biologists and modelers from all over the globe, working on various aspects of phytoplankton dynamics in stratified systems, attended a meeting entitled “Advances and challenges for understanding physical–biological interactions in HABs in Stratified Environments” at the Monterey Bay Aquarium Research Institution (MBARI) from 21–23 August 2012. The aim was to provide a review of recent progress in the field of HAB research in stratified systems, including thin layers, and to identify the gaps in knowledge that our scientific community should strive to understand in the next decade (GEOHAB, 2013). The outcomes of the presentations and discussion sessions during the workshop are presented in this document structured into six main subject areas: (A) physical structure, (B) biological structure: rates and interactions, (C) organism behavior, (D) nutrients, (E) temporal evolution of HABs in stratified systems and thin layers and (F) predictive modeling.

2. Theme (A): physical structure 2.1. Vertical physical structure and phytoplankton distribution The existence of physical oceanographic microstructure in the form of small-scale velocity, temperature and salinity variations has been appreciated for several decades (e.g. Osborn and Cox, 1972; Osborn, 1974). More recently, technological advances in in situ biological and chemical instrumentation have allowed progress in the understanding of HAB dynamics and thin layer formation within the context of small-scale physical variability (Gentien et al., 2005). Understanding physical oceanographic structure and processes is critical for the comprehension of phytoplankton abundance, their dynamics and potential to cause harm. The physical modulation of any tracer in the ocean results from the combination of advective and diffusive (mixing) components. In stratified marine systems, strong vertical stratification acts as a barrier against the vertical propagation

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of turbulence from adjacent high mixing layers (wind mixed surface and/or tidally mixed bottom layers). In addition, it provides a sanctuary from the high mixing in those adjacent layers allowing for biological processes to create vertically heterogeneous phytoplankton distributions (Sharples et al., 2001; Stacey et al., 2007; Yamazaki et al., 2010). For example, thin layer formation can be the result of a combination of vertically inhomogeneous mixing and directed swimming (Doubell et al., 2014; Ross and Sharples, 2007; Steinbuck et al., 2009). Recent work has suggested that the ability to swim, even at speeds that are small compared to the turbulent velocity scales in adjacent high turbulence layers, provides a competitive advantage to phytoplankton in stratified systems (Lips et al., 2011; Ross and Sharples, 2008; see also Theme (C). In addition, Durham et al. (2009) suggested that disruption of phytoplankton vertical migration by hydrodynamic shear could also be a mechanism of thin layer formation, the so-called “gyrotactic trapping”. In situ data, which can concurrently quantify physical and biological parameters with high resolution, are necessary to identify the sources of the spatial and temporal variability of phytoplankton layers, HAB formation, and thin layer dynamics. As one example, recent experiments with an autonomous profiling vehicle equipped with instrumentation for turbulence characterization documented the importance of internal wave phenomena to the vertical structure of the subsurface plankton maximum and the ultimate fate of a phytoplankton bloom (Fig. 1). An intense proliferation of Tetraselmis spp. offshore of Scripps Institution of Oceanography (La Jolla, CA) in July 2012 coincided with a pilot deployment of a wave-powered profiling vehicle (instrumented with a Rockland Scientific MicroRider turbulence profiler, Seabird CTD, Turner designs fluorometer, and Nortek current meter). The eight-day pilot deployment in 28 m of water occurred during the termination of the surface signature of the bloom and, ultimately, the disappearance of high chlorophyll concentrations from the water column. This region is subject to a strong internal wave climate, particularly in the semidiurnal tidal band. A snapshot of a portion of the tidal cycle (Fig. 1) demonstrates the influence of the internal tide on the vertical distribution of chlorophyll in the water column during the pilot deployment. As surface water moved onshore, and bottom waters offshore, the pycnocline and associated chlorophyll maximum are pushed downwards (Fig. 1C). High rates of turbulent kinetic energy dissipation (ε, Fig. 1A) and mixing (represented by the vertical eddy diffusivity Kz, Fig. 1B) in the bottom boundary layer acted to erode the base of the chlorophyll layer such that the vertical distribution of chlorophyll was very asymmetric, with a sharper gradient above the maximum than below (Fig. 1A–C, see also Steinbuck et al., 2009). The vertical divergence of the chlorophyll flux indicated that the chlorophyll maximum was decreasing rapidly, and that the net chlorophyll flux was into the bottom boundary layer and presumably to the benthos (Fig. 1D). Although Tetraselmis spp. are motile phytoplankton capable of strong swimming behavior (see Theme (C), Organism Behavior), it seems possible that bloom termination may have been related in part to a decrease in physiological health and subsequent degraded swimming ability. Concurrently, strong turbulence at the base of the chlorophyll maximum contributed to the dilution of the layer, and ultimately the dispersal of the bloom. Future in situ and modeling studies of the competing effects of physical dynamics (advection, turbulence, and mixing) and biological dynamics (growth, swimming, sinking, and aggregation) are needed. A second example illustrates new possibilities to track the fine-scale temporal variability of phytoplankton biomass in shallow coastal waters. The instrument, a new Tow-Yo free-fall YODA Profiler (Yoing Ocean Data Acquisition Profiler, Masunaga and Yamazaki, 2014) is a portable instrument with a 0.04 m and 50 to 200 m, vertical and horizontal resolution respectively, that sinks

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Fig. 1. 75 Profiles of concurrent (A) turbulent kinetic energy dissipation rate (W kg  1), (B) vertical eddy diffusivity (m2 s  1), (C) chlorophyll a concentration (mg m  3), (D) the vertical divergence of the chlorophyll a flux (mg m  3 s  1) collected over 4 h onboard the Wirewalker wave-powered profiling vehicle in August 2012 in the Southern California Bight. Isotherms (1C) are represented by black lines in all panels. This Wirewalker deployment included a Rockland Scientific Microrider turbulence profiler. The area was subject to strongly stratified conditions and an energetic internal tide. Energy from the semidiurnal tide was dissipated in the bottom boundary layer (below thermocline in panels A and B). This energy drove chlorophyll fluxes out of the chlorophyll maximum and towards the benthos (panels C and D). The rate of mixing calculated from the turbulence instrumentation was capable of reducing the chlorophyll concentration at the chlorophyll maximum by a factor of two in 3 h. Such fine-scale, in situ measurements of mixing and chlorophyll distribution are necessary to investigate the impact of turbulent phenomena of HAB distribution and persistence. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. High resolution salinity (A; psu) and fluorescence (B; ppb, arbitrary units) profiles obtained in the transect performed on May 19, 2012 in Tokyo Bay with the YODA Profiler. The corresponding low resolution profiles obtained with the conventional CTD are shown in (C and D). Tick marks and black inverted triangles on the top of panels represent profiling points. Thin white line in (A) indicates halocline of 29.5 psu.

slowly (0.2 m s  1). The YODA Profiler was inspired from the freefall microstructure profilers, such as TurboMap (Wolk et al., 2002), with a brush mounted at the top of the profiler; it contains a small memory-type CTD sensor, a small fishing winch and a strong, thin cable. A comparison of the high resolution profiles obtained with the YODA Profiler and a conventional CTD at the mouth of the Arakawa River in Tokyo Bay, Japan, is shown in Fig. 2. The YODA high-resolution data reveal small wave-like features in salinity under the river plume (Fig. 2A), which are not resolved in the CTD profiles (Fig. 2C). Furthermore, the small-scale internal waves seem to modulate the patchy distribution of the phytoplankton (Fig. 2B) that appears as a typical thin layer structure in Fig. 2D. The study also explored a new statistical method to estimate the turbulent kinetic energy dissipation rate, ε, from the vertical

fluctuation of the conductivity gradient (dC/dz) obtained with the YODA conductivity probe. 2.2. The role of retention processes in the development of particular phytoplankton species and assemblages in stratified environments Retentive environments, where the water residence time is locally increased, create distinct physical conditions which can strongly influence the ecology of HAB species. Inshore areas of coastal upwelling systems, in the lee of headlands, are one such retentive environment. ‘Upwelling shadows’ (retentive oceanic circulation) can develop in such areas and within bays, where surface wind forcing is relatively weak (Graham and Largier, 1997). Increased residence times within these localized areas affect not only the physical

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environment (e.g. stratification caused by cumulative solar heating), but also the planktonic populations, which can develop to relatively great abundance and maturity. Retentive regions can act as incubators for phytoplankton blooms, including HABs, and can favor the establishment of thin layers (Jessup et al., 2009; McManus et al., 2008; Ryan et al., 2010). These locales also provide seed populations for subsequent blooms (Ryan et al., 2008b, 2009) or for neighboring (less productive) areas. Close attention should be paid to retention in some coastal areas. For instance, in Alfacs Bay, a semi-enclosed microtidal embayment in the Northwest Mediterranean Sea, recurrent HAB outbreaks have been associated with high-retention areas in the bay's interior, which are related with the flow regime and the meteorological forcing. Stratification, mainly caused by freshwater runoff from the adjacent rice fields, competes with wind-induced vertical mixing to control the residence time of phytoplankton cells within the bay and eventually the occurrence or absence of HAB events (Berdalet et al., in press; Artigas et al., 2014). In the Bay of Biscay, retentive eddy structures have been detected at mid-depth in the pycnocline, and correspond to a local accumulation of Diarrheic Shellfish Poisoning (DSP) producer Dinophysis acuminata (Gentien, unpublished data). These structures were predicted in the MARS3D simulations of the Bay of Biscay (Lazure et al., 2009). A prediction scheme for the beginning of the D. acuminata bloom season has been established, based on the existence of these eddies and their subsequent advection (Xie et al., 2007). Such patches may act as incubators for one population and their track will determine the delivery of toxins to the coast. In Todos Santos Bay, northwestern Baja California, Mexico, wind-driven upwelling conditions appear to favor the toxic bloom of Pseudo-nitzschia sp. with concurrently high domoic acid (DA) concentrations in 2007 (García-Mendoza et al., 2009), as corroborated by a subsequent numerical-modeling study of the water circulation patterns (Rivas et al., 2010). Indeed, the factors triggering the bloom had their origin in northern regions (Southern California Bight) about two weeks before, driven by the upwelling waters, and likely favored by retention in a recirculation eastern zone of the Bay. Also in the same area of Baja California, the surface bloom of the yessotoxin – producing Lingulodinium polyedrum seems to be modulated or maintained by the combination of a near surface temperature stratification (NSTS) and the sea breeze pattern during the day (Ruiz-de la Torre et al., 2013). The NSTS reduces the frictional coupling with the lower water column, thus facilitating the wind drift transport of the surface layer closer to the coast thereby facilitating bloom maintenance and proliferation of the organism. Acoustic Doppler Current Profiler (ADCP) and drifter data were consistent and showed noticeable current shear within the first meters of the surface where temperature stratification and high cell densities of L. polyedrum during the day were recorded. Chlorophyll profiles showed that cells accumulated in a near surface thin layer between 1 to 3 m depth. The hydrodynamic conditions allowed L. polyedrum to perform diel vertical migrations which also contributed to the maintenance and increase of the high cell numbers in the layer. The temperature stratification in combination with the active breezes, typical for this coastal upwelling area, facilitate the retention of cells and their proliferation in the area. 2.3. Modifications in turbulence by phytoplankton through changes in the viscosity of their physical environment During the last 25 years numerous studies have been devoted to the understanding of how viscosity and other rheological properties are influencing and influenced by several aspects of plankton dynamics, including HABs (as reviewed by Jenkinson and Sun (2011)). Seawater viscosity is comprised of a Newtonian, perfectly

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dispersed component contributed by the water and salts, plus a nonNewtonian, less well dispersed component due to more or less colloidal organic exopolymeric substances (EPS) derived mainly from phytoplankton. Dense phytoplankton patches are often associated with increased viscosity as well as elasticity of seawater or lake water (Jenkinson and Sun, 2011, and references therein; Seuront et al., 2010). Based on knowledge gained, including the few available measurements of rheological properties of algal cultures and phytoplankton blooms (Jenkinson, 1993; Jenkinson and Biddanda, 1995), Jenkinson and Sun (2011) recently modeled how EPS could change pycnocline thickness. Comparison of viscosity at length scales from 0.35 to 1.5 mm in HAB cultures with that of a reference medium has shown that viscosity in cultures can vary with length scale. It can be increased in the presence of plankton, presumably with rheological thickening by EPS. However Jenkinson and Sun (2014) showed that it can also be decreased in the presence of plankton perhaps due to the hydrophobic, rough surfaces of the plankton, the EPS or both. New measurements of EPS of phytoplankton as a function of length scale at natural oceanic concentrations, and future experimental and in situ investigations on the modulation by phytoplankton blooms of pycnocline dynamics are needed.

3. Theme (B): biological structure, rates and interactions In order to understand the population dynamics of specific HAB species we need to be able to estimate changes in cell populations, and in particular, the balance of gains (growth, accumulation) and losses (grazing, cell death, infection, diffusion) that results in the net in situ growth rates (m), all within the context of the variability in the physical environment. These parameters are particularly critical when dealing with HABs in thin layers and should be estimated at high spatio-temporal resolution, i.e. millimeters to a few meters and over hours to a few days. The lack of appropriate technology to resolve these small scales has been one of the main impediments to understanding the formation, evolution and ultimate dispersion of HABs. However, recent technological innovations offer new possibilities for advancement in this field, as will be described in the following sections. 3.1. Challenges and opportunities to estimate cell abundance with high spatial resolution To predict HAB population trajectories, it is important to characterize the spatial heterogeneity in cell abundance (patchiness). These requirements motivate methods that permit synoptic descriptions of the cell numbers of targeted species. In particular, in situ imaging approaches can derive quantitative information about not only HAB phytoplankton populations, but also the zooplankton grazers involved in controlling bloom growth and transferring carbon and toxins into the food web. As one example, over the last sixteen years Jaffe et al. (1998) developed, deployed, and analyzed data from FIDO-Phi, a free descent autonomous profiler that was equipped with a planar laser fluorescent imaging system. Analysis of the fluorescent images of approximate size 10 cm  15 cm resulted in particle size distributions as a function of depth. As a result of this analysis, Prairie et al. (2010) described the observation of “cryptic peaks” where a large increase in the fluorescent signatures from individual fluorophores, assumed to be phytoplankton of sizes greater than 20–100 μm. Such peaks would not be identifiable in the bulk fluorescent measurements, hence the label “cryptic”. At present, new ocean-sensing systems for sampling the euphotic zone are under development (Jaffe et al., 2013) such as an inexpensive miniature vehicle to be deployed in swarms. The instrument has many of the properties of its predecessor, the FIDO-Phi, and it can be localized in

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3-dimensions to track internal waves and mimic larval transport. Additional efforts in the acoustic tracking of zooplankton and the optical imaging of organisms from centimeters to microns are aimed at gaining a better understanding of the statics and dynamics of both plants and animals in the top 100 m of the ocean. One such study (Fig. 3A) shows the result of counting diatoms from images obtained with an in situ long working distance microscope that can be deployed underwater, the Jaffe lab “O” underwater imaging system. The standard fluorometer profile (Fig. 3B) was obtained simultaneously from a CTD – fluorometer package that was co-located with the imaging system. Due to the increased information from the imaging system and the comparatively small volume of interrogation of the fluorometer, the diatom counts from the imaging system are much less noisy compared to standard fluorometer measurements. Making use of recently developed LED/laser fluorescence probes mounted on a microstructure profiler TurboMAP, Doubell et al. (2014) showed that estimates of phytoplankton biomass made at centimeter scales (LED probe) are consistent with millimeter scales (laser probe). Furthermore, a critical scale exists where measures of fluorescence variability transitions from representing an individual to a patch. In this technology line, another promising instrument is the VFA, in situ Video Fluorescence Analysis, which can resolve fluorescent particles ranging from 6 mm to several millimeters in a laser beam which illuminates a shallow region some 3500 mm deep (Lunven et al., 2012). The instrument includes a 473 nm laser and a CCD camera equipped with remotely controlled mobile optical filters. The method has been successfully tested on measurements of particle counts and for size classification using monospecific cultures (Karenia mikimotoi and Pseudo-nitzschia australis). In the field, a size classification of the autofluorescent individual particles was compared to other hydrological properties. In the future, the incorporation of an integrated band pass filter (520–580 nm) could allow the detection of Dinophysis spp. and its prey, both phycoeryhtrin-containing organisms. However, distinguishing predator and prey, which fit into the same size class, would still be a challenge.

Another recently developed imaging instrument is the LISST-HOLO (Sequoia Scientific). This holographic system provides detailed (4.4 mm per pixel) images of plankton across the size range of approximately 25 to 2500 μm. This sensor has been deployed on an autonomous underwater vehicle to acquire densely sampled plankton images that define patchiness, and synoptic maps of the particle size distribution. For example, in a survey in Monterey Bay across an upwelling filament, the adjacent retentive locale in the bay's recess, and the front between them, total particle concentrations were found to be maximal within the front, more than three orders of magnitude higher than in the upwelling filament. Furthermore, it could be seen that the planktonic aggregation was evidently dominated by phytoplankton (Ryan et al., 2008a). Ideally, these synoptic methods should be adapted or combined with traditional tools to provide species-specific estimates of the cell numbers of targeted HAB species. One of the main challenges is to have the sufficient sensitivity to quantify cell numbers. This is not a problem in high-biomass harmful events (such as those caused by Pseudo-nitzschia spp., Noctiluca spp., Phaeocystis spp.). However, some of the most harmful organisms are often present at very low concentrations and constitute a small percentage of the overall community. For instance, toxicity above regulatory levels has been detected in bivalves in the Ría de Vigo (on the Spanish Atlantic coast) concurrently with very low cell abundances of Dinophysis spp. (102 cell L  1) and Alexandrium spp. or Gymnodinium catenatum (103 cell L  1). These cell concentrations may correspond to the beginning of the growth season, and although they can eventually aggregate into higher concentration patches or thin layers, conventional sampling methods (Niskin bottles at fixed depths) often fail to detect the presence of harmful organisms at these low concentrations. Alternatively, integrated sampling, e.g. using dividable hoses (Fig. 4; Lindahl, 1986), has been proven useful in the early warning monitoring programs in aquaculture areas threatened by HAB events (ICES, 1986) for the reliable detection of scarce and patchy toxin-producing dinoflagellates. However, integrated sampling destroys the fine vertical

Fig. 3. (A) A vertical profile that shows diatom counts as a function of depth along with the images from which the counts were derived. (B) The concurrent vertical profile as measured using a standard co-located fluorometer (reprinted from Jaffe et al. (2013), with permission from the editor).

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Fig. 4. Scheme of the information obtained by two sampling methods used for monitoring purposes, the hose sampler and oceanographic bottle, when trying to detect harmful species that may exhibit different spatial distributions (from Escalera et al. (2012), with permission by Elsevier).

structure of thin layers and can underestimate cell abundances when HABs are distributed in discrete layers (Escalera et al., 2012). 3.2. Challenges and opportunities to estimate in situ growth rates with high resolution Every species has a genetically established growth capacity that is modulated by environmental factors (e.g. light, temperature, salinity, small-scale turbulence, etc.). Laboratory experiments under controlled conditions have improved our understanding of the physiological responses of species that can be maintained in culture. However, in some relevant cases such as Dinophysis spp., it has not been possible to culture the target species until recently, when its mixotrophic nature was demonstrated and its preferred prey identified (Park et al., 2006). While laboratory data help to understand nature, the challenge remains to estimate growth rates, m, in the field. At present, there is no universal or recommended method to estimate species-specific in situ m. The mitotic index (McDuff and Chisholm, 1982, modified by Carpenter and Chang (1988)) has been successfully applied on organisms such as Dinophysis spp. in the field (Reguera et al., 2003; Velo-Suárez et al., 2009; Farrell et al., 2014a.), even when they represented a small fraction of the microplankton community. The mitotic index is based on the estimation of the frequency (f) of cells under the so-called “terminal events” within the cell cycle—e.g. nuclear division (s), cytokinesis (c) and regeneration of sulcal list/spines (r)—and their time duration: μ¼

n 1 ∑ ððt s Þi Þln ½1 þ f c ðt i Þ þ f r ðt i Þ nðT c þ T r Þ i ¼ 1

where m is the daily average specific division rate, fc(ti) is the frequency of cells in the cytokinetic (or paired cells) phase (c) and fr(ti) is the half frequency of cells in the recently divided (r) phase in

the ith sample. Tc and Tr are the durations of the c and r phases, n is the number of samples taken in a 24 h cycle and ts is the sampling interval in hours. For some HAB species, e.g. Dinophysis spp., cells undergoing these terminal events can be morphologically recognized by microscopic observation (e.g. Reguera et al., 2003 and references therein, Velo-Suárez et al., 2009) (Fig. 5). However, in most phytoplankton species it is not easy to perform such morphological recognition. In certain scenarios, the mitotic index approach has been applied using the analysis of cellular DNA content stained with specific fluorochromes (in combination with flow cytometry or microfluorimetry), e.g., in quasi-monospecific blooms or whenever the target species could be clearly differentiated from the rest of the assemblage. This approach has been used for growth rate estimates of Karlodinium spp. and Alexandrium spp. (Garcés et al., 1999) in Alfacs Bay (NW Mediterranean). One drawback remains: for accurate estimations of m, the mitotic index based method requires intensive samplings over 24 h at very high temporal resolution with minimum thresholds of cell densities (e.g. Velo-Suárez et al., 2009, 2014). This poses severe limitations on its application using conventional sampling strategies and often requires prior concentration of samples or large sampling volumes (e.g. by continuously pumping water through a series of nets of different mesh size; see e.g. VeloSuárez et al., 2014). Recently, the autonomous FlowCytobot sampler (Campbell et al., 2010), that combines video and flow cytometry technologies, provided a high resolution time series of both Dinophysis spp. cell abundances and frequencies of cells undergoing mitosis during a high density bloom at the Port Aransas shipping channel (Mission-Aransas, Gulf of Mexico). In the past, biochemical approaches were explored to characterize the physiological state and growth rate of different microorganisms. For instance, the estimation of the RNA/DNA ratio was proven to be correlated to m in bacterial cultures, production rate

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LSD

R1

LSI R2 R3

Vegetative cells

C

B

R1

LSD

maLSI

maLSI

LSD R2 mpLSI

mpLSI R3 PMDMB

Recently divided cells

Paired cells

Cell fission in Dinophysis spp.

Fig. 5. Left: simplified diagram of the Dinophysis cellular fission, showing the main morphological features used to estimate in situ m by the mitotic index approach. Microphotographies of D. caudata recently divided cells: still remaining in pairs (top) and already individual (bottom). From Reguera et al. (2003), with permission by InterResearch.

in copepods and also linked to the nutritional state in a variety of organisms (e.g. Berdalet et al., 2005 and references therein). Anderson et al. (1999) investigated the possibility of specifically estimating m for Alexandrium fundyense in natural samples using flow cytometry by combining antibody identification of the organism and a fluorometric staining of RNA. At that time, the available fluorochromes displayed some uncertainties regarding the quantification of their fluorometric response. The recent advances in RNA and DNA specific fluorochromes would encourage reconsideration of this method for adaptions and new tests, possibly in combination with more sophisticated methods such as the Environmental Sample Processor (e.g. Greenfield et al., 2008). In addition, molecular tools that have been developed for use in the study of functional gene expression in microbial ecosystems, should also be adapted to HAB research. Namely, the ratio of 16S rRNA to rDNA (16S rRNA genes) constitutes a proxy of growth rate for specific taxa in natural bacterial communities (e.g. Campbell et al., 2009; Lami et al., 2009; Kerkhof and Kemp, 1999). While the primary goal is often the estimation of growth rates in the targeted harmful species, it may also be of interest to estimate growth rates for some of the other phytoplankton species, groups and/or the whole community, in order to better understand the dynamics of the entire community within the particular ecosystem. Measurements of growth rates are usually based on estimating the bulk increase in chlorophyll and/or primary production (e.g. Bienfang and Takahashi, 1983) or taxon-specific pigments (Latasa et al., 1997). In recent years, these methods have been combined with the dilution technique (Calbet and Landry, 2004; Landry and Hassett, 1982), where the microplankton community is incubated in clear bottles at a single depth or at a range of fixed depths for typically 24 h. The dilution technique can also provide estimates of grazing rates, a critical parameter for the evolution of phytoplankton, including harmful species. This technique can be applied to the community as a whole or to a fraction of it (e.g. a particular functional group), but it would be difficult to estimate growth rates at a species level. In this context, Ross et al. (2011) have shown that in situ point measurements, where the samples are suspended at a fixed depth during the incubation period, can have large errors associated to them. Apart from other sources such as bottle effects, etc.

(see Furnas (2002), for a review), the error due to artificially arresting the cells at a fixed depth, neglecting any vertical displacement due to turbulence and its effect on the light history, can be as large as 100% depending on the amount of mixing in the layer, the optical properties of the water column, and the chosen incubation depth. They showed that these errors could be minimized by choosing an appropriate incubation depth or by incubating at several depths simultaneously. Errors associated to photoacclimation were found to be smaller (of the order of 20–30%, see also Gutiérrez-Rodríguez et al., 2009). More recently, Taniguchi et al. (2012) described a method to estimate size-specific phytoplankton growth and grazing rates based on the two-point dilution method, which also yields the size spectra of the phytoplankton cells present in the samples. This size-dependent dilution method can provide new information on processes driving community dynamics.

4. Theme (C): organism behavior 4.1. The migratory behavior of phytoplankton Many phytoplankton species (including those causing HABs) are motile, others have the capacity to regulate buoyancy, and most exhibit a rather complex migratory behavior. Cell migration can be triggered by nutrient and/or light limitation (Ault, 2000; Eppley et al., 1968; Fauchot et al., 2005; Ross and Sharples, 2007), turbulence avoidance (e.g. Crawford and Purdie, 1992; Sullivan et al., 2003, review by Berdalet and Estrada (2005)), predator avoidance (see Smayda (2010) for a review), and phototaxis/geotaxis related to an endogenous rhythm (Ji and Franks, 2007; Ralston et al., 2007). Some migrations are triggered by forcings with clear periodicities such as solar or tidal cycles. Others depend on physiological processes (Yamazaki and Kamykowski, 2000), trophic interactions (predator– prey) and/or the stage in the cellular life-cycle (encystment, etc.), which complicates the characterization of the migratory strategy. In addition, swimming patterns are both species- and location-specific. Several species of the DSP producer Dinophysis, for instance, have been observed to migrate vertically (e.g. MacKenzie, 1992; Reguera et al., 2003; Villarino et al., 1995). Other species of the same genus, or

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even the same species in different locations, have been reported to remain stationary in the pycnocline or in the surface layer without exhibiting any migratory behavior (e.g. Carpenter et al., 1995; Maestrini, 1998; Pizarro et al., 2008; Velo-Suárez et al., 2008). In the Baltic Sea, recent observations have shown how vertical migrations by the dinoflagellate Heterocapsa triquetra can lead to the formation of subsurface concentration maxima (Lips et al., 2011). The nocturnal downward migration was well documented while the upward migration through the strongest density gradient was difficult to discern from the fluorescence data. It is suggested that the upward migration could be asynchronous and/or take place at certain locations depending on the vertical stratification of the water column (Lips and Lips, 2014). For motile phytoplankton, migration can provide the ability to outcompete non-motile species in environments with vertically opposing resource gradients, namely light and nutrients (e.g. Kamykowski and Zentara, 1977; Klausmeier and Litchman, 2001; Ross and Sharples, 2007). Motility may also reduce the boundary layer limitation for nutrient uptake (e.g. Gavis, 1976; Karp-Boss et al., 1996; Purcell, 1977) in large microalgal cells (450 mm). According to Smayda (1997), the benefits of phytoplankton vertical migration depend on the physiological coupling between the photosynthesis metabolism during the day and the nocturnal nutrient uptake (and storage). In estuaries it has also been suggested that vertical migration in response to tidal forcing may increase the retention of cells within the coastal area (Crawford and Purdie, 1992). Swimming may facilitate sexual encounters through chemical processes (following a chemical plume over a short range) and through simply increasing their encounter rates (Visser and Kiørboe, 2006). Other social behavior may include aggregation around some chemical cue (e.g. Kiørboe et al., 2004). Unveiling swimming strategies is further complicated because the order of magnitude of the turbulent velocity scales is often similar or greater than the swimming velocities (Yamazaki and Squires, 1996). 4.2. Migration and thin layers It is likely that all mentioned processes (a better competition for nutrients, completion of the cell cycle, predatory behavior, etc.) may be facilitated within thin layers and motility can be highly important for their formation, as seems to be confirmed by some field observations and modeling. In a comprehensive study, examining the biology, optics and physics of Monterey Bay (California) over a three-year period, Sullivan et al. (2010b) strove to elucidate the role that speciesspecific properties of phytoplankton play in thin layer dynamics. In 2002, a thin layer of Pseudo-nitzschia spp. formed in the pycnocline and maintained this position for over a week (Fig. 6A). In 2005, the dinoflagellate Akashiwo sanguinea formed intense thin layers near the pycnocline at night, and migrated to near surface waters at dawn (Fig. 6B). During 2006, phytoplankton species assemblages were highly variable in time and space. This was very different from 2002 and 2005, where species assemblages were relatively constant. During the beginning of the 2006 experiment, the water column was intermittently mixed, and became more stably stratified towards the end of the deployment. The distributions of water mass properties (e.g. temperature and salinity) indicated that approximately three different water masses propagated through the location, each residing within the study site for  1/3 of the time. The species variability in 2006 corresponded with the observed advection of these different water masses into the study site. In general, background populations of mixed assemblages of diatom species were usually found throughout the water column with persistent thin layers occurring most often during the day in the near-surface (Fig. 6C). One of the main and often dominant

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Fig. 6. Color contour plots of chlorophyll concentration plotted as a function of density for years 2002 (top panel), 2005 (middle panel) and 2006 (bottom panel). Fig. 3 in Sullivan et al. (2010b). Used with author's permission. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

species found in these daytime thin layers was the motile and PSPproducing dinoflagellate Alexandrium catenella. Modeling has also contributed to understand the role of swimming in thin layer formation. Using a nutrient-light based swimming strategy Ross and Sharples (2007) showed that motility remains effective even in environments where the turbulent velocities may appear to exceed the swimming capabilities of cells. In a later publication, the same authors showed that even in the tidally energetic bottom layer of a stratified shelf sea, where turbulent velocity scales exceed typical swimming velocities, the continued upward motility of cells is crucial for maintaining the subsurface biomass maximum (Fig. 7, Ross and Sharples, 2008). Birch et al. (2009) argued that motility is a key driver in creating the characteristic sharp concentration profiles (i.e. profiles that are continuous but non-differentiable at the peak). Yamazaki et al. (2014) developed a numerical model to simulate thin layer formation due to phototaxis-driven vertical migration. By embedding the model in a one-dimensional turbulence model (GOTM), they showed that thin layer formation takes place when wind speeds are below 5 m s  1. 4.3. Challenges and opportunities to track organism behavior in nature In the past 20 years, the oceanographic community has made tremendous strides in developing instruments to optically and acoustically detect thin layers (Doubell et al., 2009; Franks and Jaffe, 2008; Holliday et al., 2003; Sullivan et al., 2010b; Talapatra et al., 2013) and quantifying the vertical and horizontal extent of thin layers in the coastal environment (reviewed by Sullivan et al. (2010a)). Within this time frame, there have been very few studies that have sent back even still images of thin plankton layers from the field (Alldredge et al., 2002; Timmerman et al., 2014). During the same 20 years, simultaneous to above mentioned progress, new insights were gained into the interactions between entities of the planktonic community by observing the interactions directly (e.g. Bainbridge, 1953; Costello et al., 1990; Doall et al., 1998; Hamner and Carlton, 1979; Kerfoot, 1978; Kiørboe, 2010; Kiørboe et al., 1999; Paffenhöfer et al., 1995; Strickler 1969, 1977, 1984, 1985; Strickler and Bal, 1973; Tiselius, 1992; Yen and Strickler, 1996). These observations led to experiments (e.g. Costello et al., 1990; Fields and Yen, 2002;

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Fig. 7. (A) Example particle track for a motile particle with a swimming velocity, wp ¼0.1 mm s  1 in a stratified environment. The color shows the nutrient status of the cell. While in the light limited and nutrient replete bottom mixing layer (BML), the cell swims toward higher light intensities (upward). Once in the thermocline (marked by the two horizontal dashed lines), the cell continues to swim upward (through the nutricline) until it reaches a critical nutrient level (nutrient limitation) upon which the swimming direction is reversed, exposing the cell again to the risk of being eroded into the BML. (B) Example track for a non-motile, neutrally buoyant particle. (C) And (D) show the daily normalized re-access rates RN d from the BML into the thermocline during the first 20 days of the experiment for all motile and non-motile particles respectively. Clearly, being motile is a great advantage in a stratified environment even if the cells are temporarily caught in one of the adjacent high turbulence layers and their swimming speeds are small. Figure adapted from Ross and Sharples (2008). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Hardy and Bainbridge, 1954; Koehl and Strickler, 1981; Marrasé et al., 1990) and fluid-dynamical simulations designed to understand the underlying principles governing the interactions between phyto- and zooplankton, zoo- and zooplankton, and biology and physics (e.g. Jiang, 2011; Jiang and Osborn, 2004; Jiang and Paffenhöfer, 2004, 2008; Jiang et al., 2002a, 2002b; Koehl, 1983, 1996, 2004; Strickler and Balazsi, 2007). A gap now exists between the results from these direct observations and the results obtained in the previously mentioned investigations on thin layers. For instance, the ability of copepods to react to chemical cues such as phytoplankton exudates, conspecific and predator pheromones, or chemical pollutants has been well illustrated (Woodson et al., 2005, 2007). Despite their likelihood under natural conditions, little is still known, however, about the interactive effects of different chemosensory stimulations on copepods, especially in the context of thin layers that may contain toxic and non-toxic microalgae and exhibit very specific rheological properties. One would expect that zooplankton like to feed in phytoplankton thin layers given that within the layer there is a higher concentration of food than in most parts of the water column. However, the other extreme where

zooplankton are not active in the layer at all, may also be possible because we still do not know how these organisms react physiologically to physical structure and processes in situ. A clear need exists for instruments which allow us to directly observe zooplankton and phytoplankton behavior within and around the edges of thin layers in stratified systems. We are limited by the ability to directly observe and quantify (e.g. swimming velocity and direction) plankton behavior and track physiological changes in response to the ambient physical structure and dynamics. The instrumentation to nonintrusively measure these small-scale behavioral responses to physical structure and processes in situ is needed.

5. Theme (D): nutrients The last decade has provided increasing evidence on the broad range of nutritional strategies among phytoplankton. Although some HAB species are autotrophic, most of them incorporate organic molecules (including Pseudo-nitzschia spp., Loureiro et al., 2009) and also show mixotrophic capacities (e.g., Dinophysis spp. – Jacobson and

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Andersen, 1994; Park et al., 2006; Karlodinum spp. – Adolf et al., 2008; Li et al., 1999). While most studies have been conducted in the laboratory with monospecific cultures, technological advances are clarifying the links between nutrient and phytoplankton dynamics in the field, in particular in stratified systems and thin layers. One of the main challenges is to elucidate the possible relationship between nutrient availability and toxin production, both experimentally and in nature. 5.1. Nutrient gradients and phytoplankton distribution One of the pioneer studies examining the interaction between thin layers and nutrient availability was conducted by Hanson and Donaghay (1998). Continuous vertical profiles obtained with a high-resolution profiler and a reagent-based profiling chemical analyzer in a fjord in the US Pacific Northwest demonstrated that fine-scale chemical gradients and chemically distinct thin layers can exist in stratified coastal environments. From these simultaneous measurements of the physical and chemical environment, rates of nutrient transfer across a thin layer boundary could be calculated. Lunven et al. (2005) carried out a similar study in stratified water columns in the Loire River plume (Bay of Biscay, France) using the ‘Fine Scale Sampler’. The study identified periods of dissolved inorganic nutrient availability and limitation within the seasonal succession. In the Gulf of Finland (Baltic Sea), occurrences of subsurface biomass maxima of the dinoflagellate Heterocapsa triquetra just below the strongest density gradient in the seasonal thermocline have been reported (Kononen et al., 2003; Lips et al., 2010). The biomass maxima often observed as thin layers coincided with the nitracline depth and their distribution pattern was related to the mesoscale features (Lips et al., 2010). Recently it was shown that the biomass of H. triquetra increased in the surface layer concurrently with the appearance of subsurface biomass maxima under conditions of relatively high horizontal variability of vertical stratification at the mesoscale (Lips and Lips, 2014). It supports the suggestion that the mesoscale dynamics can act against dispersal of cells and favor successful vertical migration of this species between the surface layer and deep nitrate reserves. Ryan et al. (2010) using optical methods for nutrient measurements deployed on an Autonomous Underwater Vehicle (AUV), described a nutrient supply process relevant to blooms in stratified environments. Specifically, internal tidal dynamics across a canyonshelf system pumped nitrate-rich waters from the deep canyon into the shallow shelf waters on time scales of 1–2 d (Fig. 8) in concert with a bloom of the vertically migrating HAB dinoflagellate Akashiwo sanguinea (Ryan et al., 2010). Most recently, in the same research area, nutrient ratios and alkaline phosphatase activity (measured from bottle samples taken within and outside the thin layer) suggest that the Pseudo-nitzschia cells (the dominant phytoplankton at that time) were phosphorus stressed (Timmerman et al., 2014). The study suggests that this physiological stress led to increased toxicity of the bloom. In addition, deployment of Solid Phase Adsorption Toxin Tracking (SPATT) bags on a Liquid Robotics Wave Glider in Monterey Bay showed a vertical gradient in domoic acid (DA) concentrations (Fig. 9), suggesting that the enhanced DA production associated with thin layers observed by Timmerman et al. (2014) is a dominant feature in the region. However, it is not clear how nutrient availability modulates DA production in the area. While the link between DA production and nutrient stress is consistent with other reports linking Pseudo-nitzschia toxicity to nutrient availability (Kudela et al., 2008, 2010), other recent laboratory results suggest that nutrient limitation has no important effects on the growth characteristics of Pseudo-nitzschia australis, and no clear influence on the DA production was found (Santiago-

Fig. 8. Nutrient flux caused by internal tidal pumping from Monterey Canyon into the stratified ‘upwelling shadow’ of Monterey Bay, California. Consecutive vertical sections of nitrate were mapped on August 28–29, 2005. The black contour in all panels (13 mM) highlights the northward movement of high nitrate water below the thermocline. The inset in (A) shows bottom pressure from an ADCP on the shelf, illustrating the tidal oscillations (gray line) and the start of the consecutive AUV surveys (black circles). Adapted from Ryan et al. (2010). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Morales and García-Mendoza, 2011). P. australis constitutes the major proportion of cells identified as Pseudo-nitzschia in blooms occurring in the coast of northern Baja California Peninsula (GarcíaMendoza et al., 2009). Clearly, there is a need for a better understanding of how nutrient availability modulates HAB ecophysiology and toxin production, which can be species-specific as it occurs in other biological processes. 5.2. Thin layers as nutritional and microbial loop opportunities Thin layers may provide nutritional advantages for phytoplankton in general, but specially to those microalgae using multiple nutritional modes. If thin layers form in the upper part of the nutricline, concurrence of appropriate nutrient and light conditions support growth (e.g. Cheriton et al., 2009). Breakdown products from senescent cells can accumulate in the density gradient (pycnocline), thus enhancing nutrient recycling and phytoplankton productivity (Le Corre and L'Helguen, 1993). High concentrations of dissolved organic matter (DOM) from phytoplankton excretion may occur in thin layers, thus favoring the microbial loop, with all the associated processes (bacterial growth, phytoplankton-bacteria competition for nutrients, microzooplankton grazing, parasitic infections). Accumulation in layers may also provide high prey concentration for mixotrophic species (e.g., Dinophysis spp., Velo-Suárez et al., 2008). Furthermore, in within the thin layers encounter rates would also be modified compared to the surrounding water. Indeed, reduced

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turbulence in a thin layer would influence encounter rates among the interacting components of the food web (Havskum et al., 2005; Llaveria et al., 2010; Rothschild and Osborn, 1988). Maximum bacterial production rates were closely associated with thin layers of zooplankton and marine snow in a fjord on the US Northwest Pacific coast (McManus et al., 2003). The high bacterial production rates may have been the result of increased DOM production in this layer, where concentrations of marine snow and zooplankton were highest. Similarly, Honner et al. (2012) found a positive relationship between phytoplankton blooms (particularly dinoflagellate “red tides”) and heterotrophic bacteria, including human-pathogenic organisms, suggesting a tight coupling between accumulations of phytoplankton and the microbial loop. While some progress has been made on this

Fig. 9. Domoic acid concentration (ng L  1) at several depths in Monterey Bay, CA. SPATT resin samplers were deployed with HP20 resin on a Liquid Robotics Wave Glider at 1, 5, and 10 m depth for October 11–28, 2010 (red bars). Discrete samples at 0, 10, 20 m depth were collected from nearshore stations and analyzed for particulate domoic acid (pDA) on 25 October (gray) and 8 November (black). Both the SPATT and the discrete samples showed a persistent increase in DA associated with a subsurface thin layer, lasting for several weeks. Both the discrete samples and the Wave Glider track were from the NE corner of Monterey Bay, in the vicinity of the site characterized by Timmerman et al. (2014). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

topic, more investigation into the role of the microbial loop in thin layers is needed.

6. Theme (E): temporal evolution of HABs in stratified systems and thin layers, challenges and opportunities Population dynamics of phytoplankton species depend on the balance of biological processes that cause gains (e.g. cell division, migration) or losses (e.g. senescence, grazing, mortality) of individuals. In all systems, thus including the stratified ones, these processes are strongly affected by the coupling of the life strategies of the species with physical conditions leading to accumulation, dispersion or sedimentation as well as nutrient variability (as described in Theme D). Recent advances in technology have provided insights into the physical and biological co-evolution of thin layers. The temporal evolution of thin layers dominated by the noxious dinoflagellate Akashiwo sanguinea, was closely monitored during a study in Monterey Bay (California, USA) during which an AUV acquired nearly 7000 profiles in a set of sections repeated almost continuously for a week (Ryan et al., 2010). The phytoplankton abundances in the thin layer doubled during a four-day period, in parallel with enhancement of stratification and internal tidally driven nutrient supply. A recent study (Farrell et al., 2014b) also described the dynamics of a haptophyte considering the role of both in situ growth and physical control, in the Bay of Biscay, France. From the observed patterns, it is possible to estimate the overall and coarse in situ net growth rate of the whole phytoplankton community in terms of chlorophyll. However, more specific measurements on the target species or grazing are limited, as explained in Theme (B). It is also of particular interest to characterize the physiological status of harmful organisms, that is, their capacity to thrive under specific environmental conditions. During the bloom period shown in Fig. 10, microplankton cells, including the harmful taxa, likely experienced physiological changes. Tracking variations in phytoplankton health is not straightforward, and further research is required on indicators of physiological status and viability in order to provide consistent methods for application in situ. Examples include staining with vital fluorochromes to visualize cell viability (Fluorescein Diacetate DFA, Velo-Suárez et al., 2014) or apoptosis (rarely applied in natural phytoplankton, although see Veldhuis and Kraay (2000)), membrane permeability (e.g. Agustí and Sánchez, 2002), phosphorus incorporation

Fig. 10. Temporal variability in thin layer attributes relative to stratification. Each point represents the average based on ca. 350 profiles over the northern shelf of Monterey Bay (California, USA). (A) Thin layer intensity (above background) and average chlorophyll concentrations in the depth range 2–20 m; (B) temperature at 3 and 15 m depth, and the density difference between 15 and 3 m depth. Time series were smoothed with a 3-point running mean. Adapted from Ryan et al. (2010).

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Fig. 11. Seabird mortality event linked to a senescent-phase bloom of Akashiwo sanguinea in Monterey Bay in November 2007. (A) data from the the period 11/7 to 11/18; (B) for the 11/19 to 11/23 week (adapted from Jessup et al. (2009)). Much larger mortality events, linked to the same phytoplankton species, were subsequently observed along the greater northeastern Pacific. Maximum Chlorophyll Index (MCI) is an algorithm applied to remote sensing data to quantify the intensity of near-infrared ocean reflectance. High MCI values indicate dense near-surface accumulations of phytoplankton (Gower et al., 2005).

Another challenge that is key to understand the ecology and consequences of toxigenic HABs, is the tracking of toxin pathways through different trophic levels of the food web along the phases of bloom development. In each stage, the different physiological conditions of the toxicogenic cells can influence the nature and degree of harmful effects. For example, the toxicity of Pseudo-nitzschia blooms may be heightened during the late stages of blooms, when certain nutrients (often nitrogen and/or silicate) have been depleted (e.g. Bates et al., 1998; Timmerman et al., 2014; Trainer et al., 2012; however, see also Pan et al. (2001)). The senescent phase of very dense blooms can also accelerate harmful effects due to oxygen depletion (Grindley and Taylor, 1962). A recently discovered mechanism of harm, first documented in the stratification-enhanced upwelling shadow of Monterey Bay, was related to the late phase of dense blooms of A. sanguinea (Jessup et al., 2009; Fig. 11). A surfactant-like protein in the organic matter released by a senescent-phase bloom coated birds that encountered bloom-generated foam. This caused loss of the insulating capacity of the birds' feathers and mortality by hypothermia. Since its discovery, this HAB mechanism has been observed along the coast of Oregon and Washington, associated with the same species (Du et al., 2011; Phillips et al., 2011). The primary difference was the magnitude of the impact, with an estimated mortality of 8,000 birds (Fig. 12).

Fig. 12. The spatial extent of the 2009 Akashiwo sanguinea bird mortality event. The white symbols and shading along the coast indicate areas where A. sanguinea was observed in beach samples and where birds were affected (the majority of birds were from the southern Washington coast). Black shading in the coastal ocean denotes the location of the A. sanguinea bloom on 10 September 2009, based on the Maximum Chlorophyll Index (MCI) obtained from MERIS (European Space Agency). The inset photos show (A) common Grebe feather; (B) feather exposed to clean seawater; (C) feather exposed to seawater with A. sanguinea foam, demonstrating the ability of the foam to matt the feather to the quill.

(e.g. Dyrham et al., 2002), nutritional status (using e.g., RNA/ DNA as proxy, Anderson et al., 1999), and prey ingestion (Sintes and del Giorgio, 2010).

7. Theme (F): predictive modeling, challenges and opportunities In June 2009, the GEOHAB community held a workshop to discuss strategies for using observations and models to both model and predict Harmful Algal Blooms (GEOHAB, 2011). This workshop provided a broad review of challenges and opportunities for modeling HABs in eutrophic, upwelling and stratified systems. A conceptual model balancing convergent and divergent biological and physical processes in stratified systems was discussed. The main needs for modeling HABs in stratified systems included: (1) recognition of the importance of scale, (2)

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identification and ranking of the dominant physical and biological processes driving the system, (3) inclusion of three dimensional processes and (4) resolution at cell scales (growth rates, behavior, etc.). These limitations were revisited in the workshop conducted at the MBARI in August 2012. In order to fully model HABs in stratified systems, a threedimensional (3D) circulation model capable of resolving fine-scale vertical structure would be required. A first limitation of many 3D circulation models, however, is that they do not contain mixed-layer dynamics, and an accurate representation of the vertical structure is needed to simulate HABs in stratified systems. A second limitation is due to the fact that 3D models often employ an Eulerian framework where phytoplankton are represented in terms of concentrations (e.g., g C m  3) rather than individuals. Unless a sufficiently high vertical resolution is enforced, such concentration-based approaches have a systematic weakness in that they cannot represent the strong biological gradients often present in stratified systems, and in particular in thin layers. In order to model these environments, it is necessary to implement Lagrangian approaches where cells are represented by individual particles. In combination with individual-based biological models (e.g. Ross and Geider, 2009; Yamazaki et al., 2014), this allows for a more accurate characterization of both the light history and population dynamics. The need for a Lagrangian approach is further supported by the fact that thin layer generation is often controlled by motility, which (if physiologically driven) needs to be modeled on an individual basis. This Lagrangian approach, however, presents the disadvantage that a very large number of particles need to be considered for an adequate coverage of any three-dimensional model domain (Hense, 2010). At present, implementations of Lagrangian approaches in regional 3D models with a limited number of particles do exist (e.g., Lett et al., 2008; Ross et al., in prep.), often as hybrids (where other parameters are modeled on a Eulerian grid) in order to facilitate the inclusion of complex trophic interactions. For studies that involve a very large model domain, the aim should be to extend existing concentration-based biogeochemical models by processes that allow for thin layer formation. The necessary vertical resolution required by such models could be obtained by the new approach of vertical grid adaptation towards strong vertical gradients of density or shear (Hofmeister et al., 2010, 2011), allowing for vertical resolutions of the scale of centimeters. For situations of relatively thick and continuously stratified thermoclines, the vertical grid adaptation method could be extended to nutriclines or thin plankton layers. With this method, it should be possible to use Eulerian models to simulate thin layer formation and its effects on the biogeochemical dynamics and budget, if the underlying relevant physical and biogeochemical processes are considered (see extended abstract by Burchard and collaborators in GEOHAB (2013). A further limitation is that the biological components of these coupled models need to better incorporate species-specific organism growth and loss rates or behaviors (swimming and sinking) that contribute to distribution patterns. These growth and loss rates or behaviors are often omitted for a variety of reasons. A primary problem is the inherent difficulty in Eulerian based approaches to model behavior as only Lagrangian models can account for individual light and nutrient histories which in turn drive the individual's behavior. As one additional example, many models of basin-scale processes produce daily-averaged output. These models cannot resolve higher-frequency processes like diurnal migration. Another reason that these coupled models do not incorporate species-specific rates and behaviors is that they are simply not known. The scientific community should make an effort to understand and quantify these growth and loss rates, as well as behaviors, whenever possible (McManus and Woodson, 2012). Without these capabilities, the calculated plankton abundance and dispersal patterns may be incorrect.

Coupled models also need to take into account biomodification of water flow, including turbulence and both turbulent and laminar diffusion, particularly in the vertical dimension. Such biomodification is brought about by differential solar heat absorption by phytoplankton in subsurface chlorophyll maxima (Dugdale et al., 1987; Murtugudde et al., 2002; Strutton and Chavez, 2004; Zhang et al., 2009), which often correspond to thin layers; and by changes in turbulence mediated by the rheology and increased viscosity of EPS (Jenkinson and Sun, 2010, 2011, 2014; Seuront et al., 2010). These are all limitations that need to be addressed by our community in order to accurately model and predict HABs in stratified systems.

8. Summary The understanding and quantification of the physical–biological interactions of harmful microalgae in stratified systems can benefit from recent technological advances, the emergence of new sampling methods, and the availability of more advanced computing systems that allow for more complete modeling approaches. While all these components will continue to be improved as new technologies become available, an overarching theme that can be tackled immediately is the need for better coordination between physical, chemical, and biological measurements with fine-scale spatial and temporal resolution. This will lead to an improved understanding of the ecophysiological responses of HAB organisms in their particular environments, ultimately leading to the improved ability to model and eventually predict the occurrence of potentially harmful events. The GEOHAB Core Research Project (CRP) “Harmful Algal Blooms in Stratified Systems” provided a valuable platform for the interaction of scientists from a variety of disciplines who shared the common goal of trying to improve our understanding of HABs in stratified systems. The many meetings and workshops that took place over the 13-year lifetime of GEOHAB provided a forum for evaluating current knowledge, identifying gaps and formulating strategies to close them. The most recent Workshop on “Advances and challenges for understanding physical–biological interactions in HABs in stratified environments” was one of the last activities organized by the CRP Subcommittee. International collaborations across disciplines must continue beyond the lifetime of GEOHAB, because the complexity of the issues involved can only be addressed through multidisciplinarity and would benefit from a multinational approach. International collaboration is also encouraged because it favors the application of the comparative approach from the organisms to the ecosystem level, an approach that has always been at the core of GEOHAB (Anderson et al., 2005). The key questions and gaps in methodology identified during this workshop (see also GEOHAB (2011), for more detailed needs on modeling) can constitute the core for future research programs. Joining efforts among scientists from a variety of disciplines will be an efficient way to achieve scientific progress in a changing world, with a challenging future ahead of us.

Acknowledgments We recognize the contributions from scientists who participated in the Workshop on “Advances and challenges for understanding physical–biological interactions in HABs in stratified environments” (C. Anderson, R. Azanza, G. Basterretxea, T. Cowles, J. Eckman, E. García-Mendoza, J.L. Peña-Manjárrez, J. Piera, H. Maske Rubach, B. Seegers, L. Seuront and L. Velo-Suárez) along with the co-authors. We are also grateful to the organizations that contributed financially to the meeting, specifically the Scientific Committee on Oceanic

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Research (SCOR), the Intergovernmental Oceanographic Commission (IOC) of UNESCO, and the Monterey Bay Aquarium Research Institution (MBARI). EB was funded by the Spanish Government through the ECOALFACS project (CTM2009-09581). ONR wishes to acknowledge financial support from the European Union Seventh Framework Programme (FP7/2007–2013) and Marie Curie IntraEuropean Fellowship for Career Development under Grant agreement no. 255396.

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