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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
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
5
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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E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
7
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
8
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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).
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
9
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
10
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
11
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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E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
13
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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E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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).
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
15
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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E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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
E. Berdalet et al. / Deep-Sea Research II 101 (2014) 4–20
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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