Download Complex seasonal patterns of primary producers at the land–sea

Ecology Letters, (2008) 11: xxx–xxx
doi: 10.1111/j.1461-0248.2008.01244.x
LETTER
Complex seasonal patterns of primary producers at
the land–sea interface
James E. Cloern1* and Alan D.
Jassby2
1
U.S. Geological Survey MS496,
345 Middlefield Rd., Menlo
Park, CA 94025, USA
2
Department of Environmental
Science and Policy, University of
California, Davis, CA 95616, USA
*Correspondence: E-mail
[email protected]
Abstract
Seasonal fluctuations of plant biomass and photosynthesis are key features of the Earth
system because they drive variability of atmospheric CO2, water and nutrient cycling, and
food supply to consumers. There is no inventory of phytoplankton seasonal cycles in
nearshore coastal ecosystems where forcings from ocean, land and atmosphere intersect.
We compiled time series of phytoplankton biomass (chlorophyll a) from 114 estuaries,
lagoons, inland seas, bays and shallow coastal waters around the world, and searched for
seasonal patterns as common timing and amplitude of monthly variability. The data
revealed a broad continuum of seasonal patterns, with large variability across and within
ecosystems. This contrasts with annual cycles of terrestrial and oceanic primary
producers for which seasonal fluctuations are recurrent and synchronous over large
geographic regions. This finding bears on two fundamental ecological questions: (1) how
do estuarine and coastal consumers adapt to an irregular and unpredictable food supply,
and (2) how can we extract signals of climate change from phytoplankton observations
in coastal ecosystems where local-scale processes can mask responses to changing
climate?
Keywords
Coastal ecosystems, estuaries, global change, phenology, phytoplankton, primary
producers, seasonal patterns.
Ecology Letters (2008) 11: 1–10
INTRODUCTION
A key feature of the biosphere is its regular seasonal pattern
of changing plant biomass and primary production. Land
plants have life histories that are Ôfinely tunedÕ to the climate
system (Cleland et al. 2007) such that the annual cycles of
temperature, photoperiod and precipitation induce transitions across phenophases of spring budburst, flowering,
greenup, senescence and winter dormancy. The timing of
these transitions varies among species, but overall biomass
of vegetation on land follows a regular cycle of growth and
senescence having a period of 1 year. Satellite-sensed indices
such as the Normalized Difference Vegetation Index reveal
strong recurrence in the annual vegetation cycles averaged
across broad latitudinal bands (Myneni et al. 1998; Fig. 5b),
and the canonical seasonal pattern at mid and high latitudes
is spring growth leading to peak biomass and photosynthesis
in summer. The annual vegetation cycle has global
significance because it drives seasonal fluctuations in net
primary production and atmospheric CO2 concentration
(Randerson et al. 1999), nutrient cycling (Eviner et al. 2006)
and land–atmosphere fluxes of water and energy as key
processes of the climate system (Arora & Boer 2005).
Consumers on land have life histories adapted to the annual
periodicity of primary producers such that the timing of
insect emergence (Visser & Holleman 2001) and the
reproduction, hibernation and migrations of birds and
mammals are synchronized with the seasonal fluctuations of
their food resources (Inouye et al. 2000; Post & Forchhammer 2008).
Nearly half of global primary production occurs in the
oceans where the phytoplankton primary producers have
very different seasonal patterns; phytoplankton biomass
responds quickly to environmental fluctuations because algal
cells have the capacity to divide daily under optimal
conditions. Fast growth is quickly balanced by fast
consumption as pelagic grazers reproduce and grow as
blooms develop; phytoplankton biomass across the worldÕs
oceans is consumed every 2–6 days (Behrenfeld et al. 2006).
Therefore, phytoplankton cycles of biomass growth and
senescence have periods much shorter than a year,
characteristically on the order of a month. So, in contrast
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
2 J. E. Cloern and A. D. Jassby
to terrestrial producers, variability of the oceanÕs primary
producers is characterized by relatively short-period events.
The seasonal occurrence of these events varies across
oceanic domains (Longhurst 1995; Yoder & Kennelly 2006),
but seasonality is recurrent within domains because it is
tightly linked to the climate system through its influence on
vertical mixing and solar radiation. For example, the
northern temperate oceanÕs canonical spring bloom is
triggered by increasing daily irradiance and atmospheric
heat input that stratifies the water column after winter
mixing brings nutrients to the surface (Cushing 1959).
Biomass then falls as the nutrient stock becomes depleted
and grazers catch up. The seasonal cycles of phytoplankton
are also important globally because they too drive variability
of ocean–atmosphere CO2 exchange, nutrient cycling, and
pelagic and benthic metabolism (Falkowski et al. 1998).
Oceanic food webs supporting fisheries are built of species
having life histories adapted to the regular seasonal blooms
of phytoplankton as their source of energy and essential
biochemicals (Platt et al. 2003).
Whereas life cycles of vascular plants have a characteristic
period of 1 year, the short-period phytoplankton growth
cycles can, in principle, occur any time within a year.
However, the timing of phytoplankton blooms is constrained across large regions of the open ocean by thermal
stratification that blocks upward entrainment of nutrients
from deep water. This strong nutrient constraint on the
seasonal occurrence of phytoplankton production in the
open ocean weakens in coastal marine waters that receive
nutrient inputs from land. Therefore, we might expect
different seasonal patterns of primary producers at the land–
sea interface where the nutrient constraint is relaxed.
However, there has been no global inventory available of
phytoplankton seasonal patterns in nearshore coastal ecosystems such as estuaries, bays, lagoons, inland seas and
river plumes. The ecological significance of phytoplankton
seasonality in these habitats is best illustrated by the
biological and biogeochemical transformations of Narragansett Bay (USA) since the 1970s after the winter–spring
bloom largely disappeared, leading to reduced inputs of
organic matter to sediments. Subsequent transformations
included marked declines in the abundance of benthic fauna
and demersal fish that may be related to decreasing food
supply; reduced rates of benthic metabolism and nutrient
regeneration, leading to smaller phytoplankton blooms
during summer; and a shift in the sediments from being a
net sink to a net source of fixed nitrogen (Nixon et al. 2008).
We compiled annual measurements of phytoplankton
biomass from 114 such coastal ecosystems representing the
global diversity of marine habitats influenced by connectivity to land. We probed this data compilation to search for
canonical seasonal patterns and then to compare our
findings with the recurrent patterns observed on land and
Letter
in the open ocean. Our results reveal a surprisingly broad
spectrum of seasonal patterns, even within small geographic
regions, providing strong evidence that site-specific, localscale processes can be dominant drivers of seasonality at the
land–sea interface where influences from watersheds, the
atmosphere and coastal ocean intersect (Cloern 1996).
Ecosystems at the land–sea interface are therefore unique in
this respect, exhibiting a complexity of primary-producer
seasonal patterns at a much finer scale than their terrestrial
and oceanic counterparts.
METHODS
Approach and variables
Our basic approach was to use simple indices of the
seasonal chlorophyll a (Chl-a) pattern to depict the range of
patterns among years at any given site, as well as the range
among sites. A comparison among years indicates how
recurrent the patterns are, i.e. how stable they are from year
to year. A comparison among sites indicates how synchronous the patterns are, i.e. how much sites resemble each
other in their behaviour. There are many dimensions or
aspects of a seasonal pattern, and one cannot expect a single
index to suffice. We focused on two different Chl-a-based
indices that contain important ecological information about
the seasonal pattern, but acknowledge that other choices are
possible. The first index is simply the timing of the annual
maximum. The second index is the difference between the
largest and smallest annual values. These indicators are
analogous to the phase and amplitude of a wave, but they
make no prior assumptions about the shape of the seasonal
pattern. These two indicators have been used to describe
seasonal patterns of terrestrial vegetation (Myneni et al.
1998) and oceanic phytoplankton (Yoder et al. 1993).
Data sets
We analysed records of phytoplankton biomass measured as
surface (0–5 m) Chl-a concentration in tidal rivers, estuaries,
bays, lagoons, fjords, enclosed seas and nearshore coastal
waters. Data sets were provided by individuals or obtained
from published reports or online databases (see Table S1 in
Supporting information). We analysed only surface values
because many programmes do not sample below the
surface. We note, however, that seasonal patterns of
depth-averaged Chl-a can differ from patterns of nearsurface Chl-a (Ribera dÕAlcalà et al. 2004). Some large
ecosystems (e.g. Baltic Sea, Chesapeake Bay) were sampled
at multiple sites. We included data from up to four sites per
ecosystem where sites were chosen to capture distinctive
patterns of within-ecosystem Chl-a. Our compilation
includes 946 complete years (i.e. sampled every month) of
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
Letter
Phytoplankton seasonal patterns 3
Chl-a from 154 sites in 114 separate coastal ecosystems plus
two ocean sites for comparison. The longest series began in
the late 1960s (e.g. Baltic-Kattegat, Rhode River Estuary,
Bedford Basin), and ecosystems spanned latitude 38.8 S
(Bahia Blanca Estuary, Argentina) to 63.5 N (Ore Estuary,
Sweden). Our data compilation is strongly weighted by
coastal sites in the North Atlantic, reflecting the global
distribution of phytoplankton observational programmes.
collected here suggest an almost direct proportionality on
average between the range and mean (see Results). In order
to account for this effect, we normalized the range by
dividing by the annual mean.
All statistical and other calculations were carried out using
R version 2.7.0 (R Development Core Team 2007).
RESULTS
Analyses
Magnitude of phytoplankton biomass
The data from each site were processed individually and
identically. The daily mean biomass was used in the few cases
where there were multiple surface Chl-a measurements on a
given day. A monthly mean was then determined by averaging
all available daily means for the month. An annual mean was
determined, in turn, by averaging all the monthly means, but
only when data were available for every month of the calendar
year or, when full calendar years were not available, only when
data were available for 12 consecutive months. These
procedures guard against biases that might enter with data
for less than 12 consecutive months or with sampling
intensity not evenly distributed throughout the year.
The first index, the ÔphaseÕ or timing of the seasonal
pattern, was defined as the month number (i.e. 1–12) of the
largest monthly mean. An average frequency distribution for
this peak month was determined for the northern temperate
zone, defined to lie between the Tropic of Cancer at
23.4 N and the Arctic Circle at 66.6 N. Most sites (75%)
lie within this zone. The month numbers of the largest
monthly means at each site were first converted to a
probability distribution. For example, a site with three years
of data and annual peaks in March, April and March
respectively would have a distribution with March set to
0.67, April to 0.33 and other months to 0. All 116 sites
within the zone were then averaged to create an overall
distribution for the zone. This overall probability distribution was converted to a frequency distribution by multiplying by the number of sites. Each available year thus
contributes equally to the distribution for an individual site,
and each individual site contributes equally to the overall
distribution.
The second index, the ÔamplitudeÕ or size of the seasonal
pattern, was defined as the range of monthly mean Chl-a, i.e.
the difference between the maximum and minimum
monthly means. The relationship between this annual range
r and the annual mean m of the monthly series was examined
with the nonlinear model r ¼ c1 m c2 , where the ci are
constant. The parameters were estimated using nonlinear,
weighted least squares; exploratory analysis suggested
weighting squared residuals by 1 ⁄ m2. The simple range
turned out to be highly dependent on the overall biomass
level as expressed by the annual mean. In fact, the data
The data compilation analysed here includes over 58 000
individual measurements of Chl-a, the most comprehensive
inventory of phytoplankton biomass attempted across the
worldÕs nearshore coastal marine ecosystems. The distribution of mean Chl-a for the 154 individual coastal sites
reveals several key features. Annual mean biomass varies
over three orders of magnitude, but most values (73%) fall
within the range 1–10 lg L)1 (Fig. 1). For comparison, we
included annual mean Chl-a from two oligotrophic regions
of the ocean, the Hawaii Ocean Time series (HOT) and
Bermuda Atlantic Time Series (BATS), where the median of
annual mean Chl-a was only 0.09 and 0.11 lg L)1 respectively. The longest records revealed a characteristic 10-fold
interannual variability, so phytoplankton patterns include
high variability of annual mean biomass between and within
sites. We also show (Fig. 1, inset) the frequency distribution
of all individual nearshore Chl-a measurements, for which
the overall mean was 6.0 lg L)1 and most values (84%)
were less than 10 lg L)1.
Variability of seasonal patterns
A large variety of seasonal patterns are found in this data
compilation, a few of which are illustrated in Fig. 2 along
with the parameters chosen to characterize these patterns.
High-frequency samples collected in the Gulf of Aqaba
adjacent to Eilat during 1994 exhibited a late winter–early
spring bloom, low mean Chl-a and small normalized range.
Sampling in the Westerschelde during 1995 showed much
higher Chl-a, with peaks occurring in early summer and a
larger range. Northern Adriatic waters (1990) were similar to
Eilat in annual mean Chl-a, but the peak occurred in autumn
and the range was even higher than in the Westerschelde.
Normalizing the annual range by the annual mean (Fig. 2) is
motivated by the relationship between them. The fitted
values of the parameters and their standard errors for the
full data set (n = 946) show that there is nearly direct
proportionality between the annual range and mean of Chl-a
concentration: r = (2.3 ± 0.06)m(1.06±0.01).
The 946 time series of annual Chl-a exhibit a wide
diversity of seasonal patterns, as characterized by timing of
the annual maximum (Fig. 3a). Depending on the ecosys-
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
4 J. E. Cloern and A. D. Jassby
Letter
(a)
160
140
Count
0 4000
150
Cienaga Grande de
Santa Marta
Patuxent R.
Ret1.1
Chesapeake 4.1C
Neuse R. 70
0.1
130
10
Seine Bay
120
Patos Lagoon
Westerschelde Doel
110
(b)
100
Bedford Basin
Rank
90
Oosterschelde OS5
80
70
N. San Francisco Bay 13
60
Moreton Bay 2
50
(c)
40
Florida Bay 12
Seto Inland Sea 6
30
20
N. Adriatic SJ107
10
Gulf of Aqaba
French Mediterranean
HOT
0.1
BATS
0.5 1
5
10
50
Chlorophyll a (µg L–1)
Figure 1 Median (filled dots) and range of the annual mean
phytoplankton biomass (Chl-a) at 154 nearshore coastal sites and 2
ocean sites (HOT, BATS). Sites are ordered by the medians and
cross-referenced to site details in Table S1. Inset: frequency
distribution of individual Chl-a values from the coastal sites.
tem, annual peaks can occur at any time of year; this
generalization appears to hold for both northern and
southern hemisphere sites. The annual range or amplitude
of seasonal patterns is also striking (Fig. 3b), from 0.5 to
10.2 with a median of 2.2, even after normalizing to account
for the effect of the overall biomass level. This contrasts
with monthly amplitudes of satellite-derived Chl-a across
nine regions of the world oceans, where r ⁄ m ranged from
0.3 to 2.3 with median of 0.8 (Yoder et al. 1993; Table 2).
Therefore, the timing of biomass peaks is more diverse and
the amplitude of biomass variability is larger in nearshore
coastal waters than the open ocean. The distributions of
seasonal timing and amplitude (Fig. 3a,b) also highlight the
Figure 2 Examples of seasonal Chl-a patterns in coastal waters
from: (a) Gulf of Aqaba (Eilat, Israel), (b) Westerschelde
(Netherlands) and (c) Northern Adriatic Sea (Croatia). Dots,
original data; bars, monthly means; arrows, peak months; vertical
lines, annual range of monthly means; squares, annual mean of
monthly means; number, normalized range (range divided by
mean). Data provided by Amatzia Genin (Steinitz Marine Biology
Laboratory. The Hebrew University), Jacco Kromkamp (Centre for
Estuarine and Coastal Research, Netherlands Institute of Ecology)
and Robert Precali (Rudjer Boskovic Institute).
continuity of patterns in this data set, i.e. the lack of distinct
subgroups of patterns. Moreover, the large year-to-year
range in timing and normalized range at many individual
sites indicates that seasonal patterns are not necessarily
consistent even at a single location.
The frequency distribution of peak month provides an
alternate way of examining pattern variability (Fig. 4). In the
northern temperate zone, where there is sufficient number
of sites, the colder months have a lower frequency of annual
peak biomass. But the distribution is unexpectedly even
from March through September, and peaks occur in all the
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
Letter
Phytoplankton seasonal patterns 5
(a)
(b)
Figure 3 Two scalar indices of the annual
Chl-a pattern based on monthly mean
values, calculated for each full year at each
site: (a) month of the annual maximum, and
(b) amplitude as annual range divided by the
annual mean. Solid circles, median value for
each site (red, northern hemisphere sites;
green, southern hemisphere sites).
remaining months. Clearly, there are many exceptions to the
canonical spring-bloom pattern of phytoplankton seasonal
variability along continental margins where there is no
characteristic single seasonal pattern, nor can we find
evidence for even a small number of consistent patterns.
DISCUSSION
Phytoplankton biomass in nearshore coastal waters
Our compilation shows a continuous distribution of
phytoplankton biomass with the majority of coastal sites
having annual mean Chl-a in the range of 1–10 lg L)1
but high interannual variability around means at each site
(Fig. 1). Proximity to land has a strong enrichment effect
evidenced by the 70-fold amplification of mean Chl-a in
nearshore coastal sites compared to two oligotrophic
oceanic sites. Elevated biomass reflects nutrient enrichment
from multiple processes including inputs of nitrogen and
phosphorus from coastal upwelling, atmospheric deposition,
land runoff and waste discharge. The broad biomass range
indicates variability among sites in the magnitude of their
enrichment and efficiency of assimilating nutrients into
phytoplankton biomass (Cloern 2001). For example, the
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
6 J. E. Cloern and A. D. Jassby
Figure 4 Frequency distribution of the peak Chl-a month for 116
northern temperate zone sites.
lowest biomass (mean Chl-a < 1 lg L)1) occurs at sites
within oligotrophic marine domains connected to landscapes producing low nutrient runoff, such as the coastal
Mediterranean and Gulf of Aqaba (Fig. 1). Highest biomass
develops in estuaries and other retentive ecosystems that
receive large inputs of nutrients from agricultural and
municipal sources such as Neuse River Estuary, Seine
Estuary and Chesapeake Bay.
Whereas mean Chl-a is an indicator of landscape setting,
individual values of Chl-a are indicators of the food resource
available to consumers, such as copepods and clams, that
rely on phytoplankton as their source of energy and essential
biochemicals. Growth and reproduction rates of cladocerans
(Müller-Solger et al. 2002) and estuarine copepods (Kiørboe
et al. 1985) increase in direct proportion to phytoplankton
biomass up to about 10 lg L)1 Chl-a (or c. 300–500 mg
C m)3). The frequency distribution compiled here (Fig. 1,
inset) shows that 84% of Chl-a measurements are below this
threshold, so food limitation is apparently the norm for
primary consumers in these habitats (Durbin et al. 1983;
Rheault & Rice 1996). Blooms are periods when food
limitation is suppressed, so phytoplankton seasonal patterns
have important implications for the timing and efficiency of
secondary production and, therefore, production at higher
trophic levels (see below).
A continuum of seasonal patterns at the land–sea
interface
The seasonal patterns of primary producers in the open
ocean (Yoder & Kennelly 2006) and on land (Myneni et al.
1998) follow characteristic climatologies – regular cycles of
biomass growth and senescence that are recurrent from year
to year and synchronous across large geographic regions.
This temporal and spatial structure of seasonal variability
arises because the annual climate cycle is the primary driver
of biomass variability. Models can reproduce this structure
with inputs from only a few climatic factors. Seasonal
Letter
patterns of vegetation greenness can be described across the
planetÕs terrestrial biomes from daylength, temperature and
a precipitation proxy ( Jolly et al. 2005). Bloom timing in the
North Atlantic can be explained with a simple model forced
only by seasonal changes in solar radiation and mixing
(Cushing 1959); regularity in the timing of oceanic spring
blooms also results in part from the germination of diatom
resting stages cued to increasing photoperiod (Edwards &
Richardson 2004).
In contrast, our synthesis reveals no comparable structure in the seasonal variability of phytoplankton at the land–
sea interface where biomass can peak during any season
(Figs 2 and 4). Distinct monthly climatologies are not
evident but, instead, the data reveal continuous distributions
in the timing and amplitude of biomass cycles and high
variability of seasonal patterns within geographic regions
and even within ecosystems. This result is strong evidence
that phytoplankton seasonality at the land–sea interface is
driven by more than a few climatic factors. This is a
fundamental ecological distinction from the open marine
and terrestrial biomes. It confirms LonghurstÕs (1995)
insightful conclusion about the Ôunpredictability of oceanographic processes along the margins of the oceans, where it
is exceedingly difficult to generalize the processes which
determine seasonality of plankton productionÕ.
Why does proximity to land create irregularity in the
timing and amplitude of phytoplankton seasonal variability?
Place-based studies have identified local- and regional-scale
processes associated with the unique habitat attributes of
nearshore coastal systems: shallowness and connectivity to
both land and sea. Most of the ecosystems considered here
have water depths on the order of tens of meters compared
to the 1000-m depth scale of the open ocean. Oscillating
tidal currents are primary sources of turbulent kinetic energy
that mix shallow (but not deep) waters; wind waves
penetrate to suspend bottom sediments and increase
turbidity; fluxes across the sediment–water interface are
key processes of nutrient cycling; and grazing by benthic
suspension feeders can be the dominant process of
phytoplankton consumption in shallow (but not deep)
ecosystems (Cloern 1996). Shallowness implies that coastal
habitats function as tightly linked benthic–pelagic systems, a
habitat type distinct from the pelagic open ocean.
Connectivity to land usually implies nutrient-stimulated
biomass levels, evident from the contrasting magnitudes of
Chl-a in the oligotrophic ocean (HOT, BATS) compared to
nearshore sites that receive nutrient inputs from land
(Fig. 1). Enrichment weakens the tight constraint of
phytoplankton growth by nutrient limitation that occurs in
the open ocean, such that other processes come into play.
Connectivity to land further implies time-varying inputs of
fresh water and sediments and strong influence from the
myriad human disturbances that are focused in the coastal
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
Letter
Phytoplankton seasonal patterns 7
zone (Harley et al. 2006; Worm et al. 2006). Ocean connectivity means that shelf waters can be a source of nutrients,
phytoplankton biomass, or marine predators or herbivores
that immigrate into estuaries and influence phytoplankton
population dynamics through trophic cascades (e.g. Cloern
et al. 2007). Exchanges across ocean, land and sediment
interfaces can be the dominant processes of estuarine
phytoplankton variability, such as the following:
(1) Import of marine phytoplankton produced during
upwelling events (Willapa Bay: Banas et al. 2007;
Spanish Rı́as: Cermeño et al. 2006) or delivered by
shifts in coastal currents (Gulf of Thailand: Tang et al.
2006).
(2) Riverine inputs of nutrients that fuel seasonal blooms
(Kasouga Estuary: Froneman 2004; Chesapeake Bay:
Miller & Harding 2007).
(3) Shifts in phasing of the diel solar cycle and semidiurnal
tide cycles that control the balance between light-limited
growth and benthic grazing (Lucas & Cloern 2002).
(4) Weather events such as heat waves (San Francisco Bay:
Cloern et al. 2005) or tropical storms (Neuse River
Estuary: Hall et al. 2008) that trigger dinoflagellate
blooms in estuaries through intensification of thermal or
salinity stratification.
(5) Changes in hydraulic residence time by floods that dilute
phytoplankton biomass (Brunswick Estuary: Eyre &
Ferguson 2006) or seasonal closure of bar-built estuaries
that retains biomass and allows it to grow (Mhlanga and
Mdloti Estuaries: Thomas et al. 2005).
Upwelling, river flow, wind-driven resuspension, tidal
mixing, hydraulic manipulations, nutrient inputs and species
introductions are examples of regional- and local-scale
processes of phytoplankton variability in the coastal zone
(Longhurst 1995; Cloern 1996; Cebrián & Valiela 1999). The
strengths of these processes vary between ecosystems and
change over time within ecosystems, so the diversity of
phytoplankton seasonal patterns revealed here is understandable. This diversity implies that phytoplankton seasonal patterns in nearshore marine ecosystems are more
fluid and less predictable than the seasonal patterns of
primary producers on land and in the open ocean.
Chlorophyll a is a valuable proxy because it is a good
predictor of primary production and the quantity of food
available to consumers. However, the efficiency of production in food webs varies strongly with the species
contributing to the Chl-a stock (Cloern 1996). Phytoplankton species records are short and few in number compared
to the hundreds of thousands of species-based phenological
records on land (Menzel et al. 2006). As a result, we still do
not know which processes determine when and where
individual species occur (Smetacek & Cloern 2008). Sustained records of phytoplankton species fluctuations across
the diversity of coastal ecosystem types will be essential for
identifying the site-specific processes of seasonal Chl-a
variability and for measuring seasonal fluctuations in the
quality of the phytoplankton food resource.
Implications of irregular seasonal patterns
The absence of canonical phytoplankton patterns might be
expected, given the multiplicity of anthropogenic, atmospheric, terrestrial and oceanic forces that drive physical and
community dynamics of nearshore marine ecosystems.
Nonetheless, this first systematic assessment reveals an
unexpected continuum of seasonal patterns (Fig. 3). What are
the implications of this discovery that primary producers at
the land–sea interface do not follow a few common rules of
seasonal variability? We suggest two, posed as questions that
are central to developing a fuller understanding of the
causes, implications and future states of coastal phytoplankton variability.
First, how do consumers adapt and evolve in ecosystems
where their phytoplankton food supply can peak during any
month and the seasonal patterns shift from year to year?
The oceanÕs pelagic fish and their zooplankton prey have life
histories adapted to the recurrence of seasonal blooms. For
example, annual survival of larval haddock is highly
correlated with timing of the spring phytoplankton bloom
in Nova Scotia shelf waters (Platt et al. 2003). Advancement
of bloom timing by only a few weeks has a large influence
on larval fish survival, providing compelling evidence for the
match–mismatch hypothesis that bloom timing strongly
influences annual fish stock recruitment in the ocean by
determining food supply to fish during their critical larval
stage. Freshwater zooplankters also have life histories
adapted to exploit the spring bloom; advancement of spring
by 20 days over four decades of warming has caused
Daphnia population declines in Lake Washington (Winder &
Schindler 2004). So, how do consumers adapt to the varied
and unpredictable fluctuations of phytoplankton biomass in
coastal habitats?
Many estuarine consumers have adaptations that provide
flexibility to survive periods of low phytoplankton biomass
and exploit events of high biomass, regardless of seasonal
timing. Copepods feed on heterotrophic protistans and
detritus, and bivalve mollusks exploit a broad range of food
resources including dissolved organic matter, bacteria and
even copepod nauplii. Spionid polychaetes, copepods,
tintinnid ciliates and bivalve mollusks are opportunists
poised to exploit blooms through reproduction or development of resting stages (Cloern 1996). For example, egg
production by the copepod Acartia tonsa responds within
1 day to changes in phytoplankton biomass (Kiørboe et al.
1985). Bivalves are highly adapted to a variable food supply
through physiological compensations that stabilize food
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
8 J. E. Cloern and A. D. Jassby
assimilation in varying environments (Bayne et al. 1993) and
their capacity to shift between deposit and suspension
feeding (Levinton et al. 1996). The oceanÕs pelagic consumers depend on phytoplankton production as their primary
food resource, but consumers utilize other food resources in
estuarine habitats including organic matter delivered by land
runoff (Hoffman et al. 2008). Therefore, survival mechanisms in habitats of unpredictable phytoplankton food
supply are established. But has the irregularity of phytoplankton seasonal patterns selected for different life
histories and feeding modes of consumers in the coastal
zone compared to those occupying habitats with consistent
seasonal patterns in their food supply? What are the
implications of this flexibility for fisheries production,
carrying capacity of aquaculture systems that are expanding
rapidly and adaptability to global change?
Second, is it possible to extract signals of climate change
from the confounding effects of local processes so that
phytoplankton variability in the coastal zone can be used as
an indicator of global change to complement responses
detected in phenological networks on land (Menzel et al.
2006), Continuous Plankton Recorder surveys of the North
Sea (Edwards & Richardson 2004) and satellite measures of
Chl-a across the global ocean (Behrenfeld et al. 2006)?
Multidecadal observations show that shifts in the largescale climate system can alter phytoplankton communities
and biomass in some coastal ecosystems. For example, a
shift to the North Atlantic Oscillation warm phase in the
late 1980s caused advancement of the spring bloom and
altered phytoplankton communities in the Baltic Sea (Alheit
et al. 2005) and declines of phytoplankton biomass and
suppression of the winter–spring bloom in Narragansett
Bay (Oviatt 2004). Unprecedented autumn blooms
appeared in San Francisco Bay after the unusually large
El Niño-La Niña transition of 1998–1999 (Cloern et al.
2007), and Narrangansett BayÕs winter–spring bloom
disappeared completely during the 1998 El Niño (Oviatt
et al. 2002).
However, local environmental changes bring about
equally abrupt changes in phytoplankton seasonality, biomass and community composition. The recurrent summer
bloom in brackish regions of San Francisco Bay disappeared
immediately after colonization by the alien clam Potamocorbula amurensis (Alpine & Cloern 1992). The same responses
occurred in DenmarkÕs Ringkøbing Fjord after hydraulic
manipulations altered salinity and allowed colonization by
the clam Mya arenaria (Petersen et al. 2008), and phytoplankton biomass increased in the nutrient-enriched southern
Caspian Sea after introduction of the comb jellyfish
Mnemiopsis leidyi (Kideys et al. 2008). Each of these
responses, induced by a local human disturbance, operated
through trophic cascades that led to either increased or
decreased herbivory and top–down control of phytoplank-
Letter
ton growth. The influence of local-scale processes on
bottom–up regulation of phytoplankton is also evident from
coherent trends of increasing Chl-a and anthropogenic
nutrient inputs, such as the 36% increase in Skidaway River
Estuary over a decade of population growth in its watershed
(Verity 2002) and 5- to 10-fold increases of Chl-a in
Chesapeake Bay since the 1950s (Harding & Perry 1997).
Phenological observations on land (Menzel et al. 2006)
and in the North Sea (Edwards & Richardson 2004) are
measuring strong signals of biological response to climate
change because the recurrent seasonal timing and spatial
synchrony of transitions across phenophases in these
systems are cued to the annual climate cycle. In contrast,
our synthesis shows that the timing and amplitude of
phytoplankton variability usually do not follow recurrent
and spatially synchronous seasonal patterns in nearshore
coastal waters where variability tied to large-scale climate
can be overwhelmed by that driven by local processes.
Coastal marine ecosystems, and the vital ecological and
socio-economic services they provide, are at risk from
anthropogenic climate change (Harley et al. 2006; Worm
et al. 2006), and plankton monitoring programmes have
been proposed Ôas sentinels to identify future changes in
marine ecosystemsÕ (Hays et al. 2005). But the strongly
recurrent patterns of terrestrial and oceanic primary
producers are much weaker at the land–sea interface,
and the global ÔfingerprintÕ of climate change (Parmesan
& Yohe 2003) is more difficult to discern where it is
confounded by the effects of local-scale processes,
including those associated with human disturbances from
fishing, aquaculture, landscape transformations, nutrient
enrichment, hydraulic manipulations and species introductions. Plankton monitoring can detect the integrated
responses to global- and local-scale environmental
changes at the land–sea interface, but more detailed
understanding of diverse variability mechanisms is
required to isolate the components of response attributable to climate change.
ACKNOWLEDGEMENTS
We express here our deep appreciation to the researchers
and programme managers (Table S1) who generously shared
data sets, each produced from unwavering commitments to
biological observation programmes in the coastal ocean. We
thank our colleagues Rochelle Labiosa, Anke Müller-Solger,
Monika Winder and Adriana Zingone for their valuable
comments on earlier versions of this paper. This research
was supported by the U. S. Geological Survey Toxic
Substances Hydrology Program and National Research
Program for Hydrologic Research. ADJ is also grateful for
partial support of this research by the California Department of Water Resources (contract 4600004660).
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works
Letter
Phytoplankton seasonal patterns 9
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SUPPORTING INFORMATION
Additional supporting information may be found in the
online version of this article:
Table S1 Listing of marine ecosystems and sites where
Chl-a concentration has been measured monthly, ranked by
overall mean Chl-a.
Please note: Wiley-Blackwell are not responsible for the
content or functionality of any supporting materials supplied
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should be directed to the corresponding author for the
article.
Editor, Emmett Duffy
Manuscript received 29 July 2008
Manuscript accepted 19 August 2008
2008 Blackwell Publishing Ltd/CNRS. No claim to original US government works