ARTICLE IN PRESS
Continental Shelf Research 26 (2006) 2415–2432
www.elsevier.com/locate/csr
Primary production by macroalgae in Kattegat, estimated from
monitoring data, seafloor properties, and model simulations
Jörgen Öberg
Department of Oceanography, Earth Sciences Centre, Göteborg University, P.O. Box 460, S-405 30 Göteborg, Sweden
Received 3 April 2005; received in revised form 29 June 2006; accepted 12 July 2006
Available online 12 September 2006
Abstract
The aim of the study was to estimate yearly macroalgal production in the Kattegat. The estimate was calculated from the
abundance and distribution of nine of the most dominant macroalgal species, and from factors important for abundance,
distribution and growth (e.g. bottom topography and sediment composition, irradiance, nitrogen concentrations and
seawater temperature). The result showed that 6.6% of the Kattegat area is suitable for macroalgal growth. The estimated
production was 4–514 g C m2 year1 depending on depth and sub-area. The total yearly production was estimated to
119 106 kg C y1.
r 2006 Elsevier Ltd. All rights reserved.
Keywords: Biomass; Environmental monitoring; Macroalgal growth model; Phytobenthos; Primary production; Sediment properties;
Solid substrates; Europe; Scandinavia; Kattegat
1. Introduction
The sea of Kattegat between Sweden and Denmark has approximately one third of its seafloor
within the photic zone. This should render the
benthic primary production in Kattegat a relatively
high importance. Still, existing primary production
studies in Kattegat (e.g. Rydberg et al., 2006;
Carstensen et al., 2003; Richardson and Heilmann,
1995; Heilmann et al., 1994; Richardson and
Christoffersen, 1991) are focused on the pelagic
production, whereas a comprehensive study of the
benthic production in Kattegat has hitherto not
Tel.: +46 31 773 2859; fax: +46 31 773 2888.
E-mail address: [email protected].
0278-4343/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.csr.2006.07.005
been made (L. Edler, p. comm.; I. Wallentinus,
p. comm.). A figure of 1 g C m2 y1 for the average
benthic primary production in Kattegat was mentioned by Borum and Sand-Jensen (1996) but the
underlying data, based on microalgal production in
a limited area (Graneli and Sundbäck, 1986), cannot
be considered as being representative for the entire
Kattegat.
Kattegat is a small shallow sea (area 21 600 km2,
mean depth 24 m), situated between Denmark and
Sweden in the transitional zone between the
brackish Baltic Sea and the marine North Sea
(Fig. 1). The scatter diagram (Fig. 2) from the open
sea monitoring station Fladen (Fig. 1, no. 10) shows
the large annual variation in salinity and temperature in the photic zone of Kattegat. The summer
ARTICLE IN PRESS
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
Fig. 1. Map of the Kattegat area, showing the bathymetry as well
as the positions of the sampling locations.
Fig. 2. Salinity (upper panel) and temperature (lower panel)
observations at Fladen in central Kattegat during 1994–1996.
temperature can be even higher in sheltered areas.
The tidal amplitude in the area is generally small,
about 0.2 m in the south-western part and less in the
east. The bottom topography of Kattegat shows
a pronounced shelf in the northwest, with depths
usually less than 20 m. Steep, rocky shores are
found on the northern and southern end of the
eastern coast, but the seafloor is otherwise more
gently sloping. A few islands, and a number of
mid-sea banks, provide substrates within the
photic zone also in the central part of Kattegat.
With a decreased salinity such as in Kattegat,
the number of macroalgal species is lower,
which chiefly affects the non-dominant species
(Middelboe et al., 1997). Further, reduced salinity
often means a reduction in size of the macroalgae
(Lüning, 1990).
The main growth season of macroalgae is in
spring and early summer, extending into early
autumn especially for ephemeral annual macroalgae. Low light and water temperature inhibits
growth in winter. The internal nutrient reserves of
the macroalgae, replenished in winter, enable the
rapid growth in spring to continue into summer in
spite of the reduction of dissolved nutrient concentration caused by the phytoplankton spring bloom
(Dring, 1982). When the internal reserves are
depleted, growth continues at a rate determined by
the external conditions. Blades shed by macroalgae
during growth, and plants torn off by wave action,
are decomposed in the detritus food web. Grazing
may cause a large loss of biomass in some areas, but
has only a small effect in others. In the absence of
limpets, Littorina spp. are the main grazers of
macroalgae in the littoral zone along the Swedish
west coast (Cervin and Åberg, 1997), whereas sea
urchins are the main grazers in the sub-littoral zone
(Lüning, 1990). However, also crustaceans such as
the isopods Idotea spp. may be important (Pavia
et al., 1999).
Mathematical modelling is a useful tool to obtain
quantitative data of objects or phenomena when
actual measurements are unavailable, to investigate
the functioning of an ecosystem, or when a
prediction of the future development is desired.
Regarding macroalgae, recent examples of model
use for the two latter reasons include simulations of
the development of a single opportunistic macroalgal species (Ruesink and Collado-Vides, 2006; de
Guimaraens et al., 2005; Martins and Marques,
2002), and ecosystem models simulating the coexistence of macroalgae of different functional groups
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
(Biber et al., 2004), of macroalgae and benthic
phanerogams (Giusti and Marsili-Libelli, 2005), or
of macroalgae and plankton (Tanaka and Mackenzie, 2005; Trancoso et al., 2005; Baird et al.,
2003). In this study, a single-species model was used
to estimate yearly production for a number of
representative macroalgal species.
The aim of this study was to estimate from
existing data the macroalgal contribution to the
total primary production in Kattegat. This required information on the amount and location of
macroalgal presence, as well as of the species
distribution and productivity. As macroalgae only
grow in the photic zone, mostly attached to a solid
substratum, information on depth distribution and
sediment structure was also needed to obtain a
fair description of the spatial distribution of
locations suitable for brown and red macroalgae. The distribution of green macroalgae was
treated differently. As these macroalgae often
appear aggregated into floating mats, the description was focussed on the availability of shallow and
sheltered areas.
Most of the publicly available Kattegat macroalgal monitoring data from the last decade was used
in this study. Ideally, all of these data would include
biomass determinations. Equally important for the
productivity calculations would be estimates of
macroalgal annual productivity made in the area
or under Kattegat-like conditions. Neither of these
conditions was met for this study. Only a minority
of the macroalgal monitoring data contained
biomass information. Instead, a majority of the
monitoring efforts were concerned with macroalgal
coverage estimations. Through the availability of
simultaneous measurements of both biomass and
coverage at some stations, a relationship was
established to convert the coverage data at the
other stations to biomass figures. The lack of areaspecific annual productivity measurements made
model simulations a suitable alternative to obtain
yearly production to biomass ratios. The simulations were made with an adapted version of the
macroalgal growth model by Öberg (2005) for nine
of the most common species of macroalgae in
Kattegat. The biomass estimations and the yearly
productivity calculations were combined with information on the topography and sediment structure of the Kattegat seafloor to estimate the
production from macroalgae in four depth segments
of the eastern, western, and southern parts of
Kattegat.
2417
2. Material and methods
2.1. Coverage and biomass of various macroalgae
The benthic macrophytes in Kattegat have been
monitored at coastal and offshore stations by the
Danish National Environmental Research Institute
(NERI) and by the Halland and Skåne county
administrations. The monitoring frequency varies;
some sites are visited every year, while others
have been visited only once during the last decade.
Table 1 lists the depths, years, measured variables
and coordinates of the monitoring stations used in
the present study. The positions of the stations are
shown in Fig. 1. All macroalgal monitoring was
made in summer through visual inspection by divers
along transects down to a maximum depth of 20 m.
The assembled coverage, defined as the share of
suitable hard substrate covered with macroalgae
(Krause-Jensen et al., 2001), was recorded at all
sites. The macroalgal coverage was found by
projecting the macroalgal thalli vertically onto the
seafloor, thereby estimating the proportion of
substrate covered. The estimations were made
for three replicate areas in depth segments of
usually 1 m vertical extension. At the S and W
sides of Kattegat, the current method of NERI
(Krause-Jensen et al., 2001) was used. The results
from these stations (Anon., 2005), as well as from
the three most southerly Swedish stations (Anon.,
2001), were given as figures (0–100%) of total
aggregated coverage by all macroalgal species on
suitable substrates. The remaining reports from the
Swedish coast (Carlson, 1996; Lundgren and
Olsson, 2001; Olsson, 2001) all used the previous
NERI method (Krause-Jensen et al., 1995), where
the estimates are given in five categories (0–2%;
2–25%; 25–50%; 50–75%; 75–100%) of the aggregated coverage. At these stations, the biomass
(g dwt m2) of the occurring macroalgae, estimated
from manually collected samples, was also reported
(Fig. 3).
A selection of macroalgal species must include the
most common species in the area. To ascertain
representativity, the choice should also embrace the
major functional groups (Littler, 1980), as otherwise
highly productive annuals might have to stand back
for abundant, but less productive, perennial species
with a high standing stock. For the computations in
the present paper, nine generally abundant species
of macroalgae, two Chlorophyta (green algae), three
Phaeophyta (brown algae), and four Rhodophyta
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
2418
Table 1
The Kattegat monitoring stations used in this study
No
1
2
3
4–6
7
8
9
10
11
12
13
14–16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Station name
Depth (m)
Year interval
NERI, Denmark (Anon., 2005)
Herthas Flak
10–20
1999–2002
Tønneberg Banke
10–15
1999–2002
Læsø Trindel
6–18
1999–2002
Læsø (3 stations)
4–7
1996–1997
Læsø sukkerrev
1–3
1996–1997
Per Nilen
6–11
1999–2002
Læsø rende
0–25
1998–1999
Fladen
0–85
1998–1999
Kims Top
14–19
1999–2002
Aalborg Bugt
0–14
1998–1999
Anholt E
0–25
1998–1999
Hevring Bugt (3 st.)
1–6
1996
Fornæs
0–13
1998–1999
Store Middelgrund
9–18
1999–2002
Lysegrund NE
0–25
1998–1999
Briseis Flak
5–9
1999–2002
Kullen
0–25
1998–1999
Hesselø
0–25
1998–1999
Hesselø
0.5–11
1999–2003
Schultzs Grund
4–18
1999–2002
Gniben
0–25
1998–1999
Gilleleje
0.5–14
1999–2003
Vilingebæk
1–11
1999–2003
Ellekilde Hage
0.5–7
2001–2003
Tisvildeleje
1–12
1999–2003
Liseleje, Torup Flak
1–14
1999–2003
Hallands kustkontrollprogram, Sweden (Carlsson, 1996)
Kalvö
0–3
1996
Lerkil syd
0–6
1996
Bua
0–5
1996
Morups Tånge
0–4
1996
Örnäs Udde
0–3
1996
Nordvästskånes kustvattenkommitté, Sweden (Anon., 2001;
Hovs Hallar
2–4
1996–2000
St. Måseskär
1–4
2000
Ramsjöstrand
0–4
1996–2000
Arild
2–14
1996–2000
Kullaberg nord
0–14
2000
Kullaberg syd
0–16
2000
Variables measured
Position latitude
Position longitude
C
C
C
C
C
C
N,
N,
C
N,
N,
C
N,
C
N,
C
N,
N,
C
C
N,
C
C
C
C
C
571
571
571
571
571
571
571
571
571
561
561
561
561
561
561
561
561
561
561
561
561
561
561
561
561
561
101521E
111164E
111148E
1111E
111130E
111026E
101445E
111400E
111355E
101475E
121070E
1213E
111020E
121042E
121020E
111197E
121222E
111480E
111431E
111114E
111096E
121184E
121229E
121290E
121022E
111547E
T
S, T
T
T
T
T
T
T
T
385N
284N
256N
20N
190N
228N
176N
115N
008N
514N
400N
3N
335N
333N
225N
196N
140N
099N
118N
096N
079N
087N
065N
058N
032N
017N
B, C
571 253N
B, C
571 259N
B, C
571 138N
B, C
561 564N
B, C
561 382N
Lundgren and Olsson 2001; Olsson, 2001)
B, C
561 282N
C
561 272N
B, C
561 231N
B, C
561 165N
C
561 182N
C
561 175N
121040E
111547E
121138E
121217E
121490E
121424E
121329E
121395E
121348E
121277E
121282E
The station numbers are also found in Fig. 1. The measured variables are macroalgal biomass (B), macroalgal coverage (C), nutrients (N),
salinity (S), and temperature (T).
(red algae), were taken to make up 100% of the
macroalgal biomass in the Kattegat.
Macroalgae are found to live at depths down to
25 m in the Kattegat (e.g. Karlsson et al., 1998).
Although macroalgal coverage was measured down
to 20 m or more at some stations, no biomass
measurements were available below 15 m. The depth
of the photic zone, taken as the 1% light penetration level, zq (1%), is 15–18 m in Kattegat
(Richardson and Christoffersen, 1991), and 15.7 m
in Århus Bay, SE Kattegat (Lund-Hansen, 2004).
The depth where 10% of the surface irradiation
remains, zq (10%), roughly corresponds to the
Secchi depth (e.g. Højerslev, 1978), and the ratio
zq (1%)/zq (10%) ¼ 2.18 (Jerlov, 1976). The mean
value of the 3772 Secchi-depth measurements, taken
1960–1999 in the nineteen 0.51 square areas entirely
within Kattegat, of 7.2 m (Aarup, 2002) thus
corresponds to a zq (1%) of 15.7 m. Hence, the
model simulations presented below gave very low
production rates at 16 m, and practically zero at
18 m, for all species. Therefore, the macroalgal
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
Fig. 3. The depth distribution of the mean measured biomass at
the Swedish stations (nos 31–36 and 38, 39 in Table 1). The
dashed lines are 1 m depth intervals, used from 0–3 m in stations
31–35. For each depth segment, the number of measurements
(three replicates each) is given by n. The error bars give the
standard deviations.
production, albeit not the biomass, below 15 m
depth was assumed to be negligible.
In this paper, the macroalgal habitat was divided
into four depth zones; 0–2, 2–4, 4–8, and 8–15 m. In
the 0–2 m depth zone at the eight Swedish stations
that reported biomass, 80% of the total measured
macroalgal biomass consisted of Fucus serratus and
F. vesiculosus. The latter species usually grows in
large amounts in the immediate vicinity of the
shoreline, and may thus be underrepresented in the
biomass measurements (Carlson, 1996). Red algae
dominated in the two intermediate zones, from
2–8 m depth, where 47% of the measured biomass
at the Swedish stations came from the perennial
cartilaginous macroalgae Chondrus crispus and
Furcellaria lumbricalis, whereas the filamentous
macroalgae Ceramium nodulosum and Polysiphonia
fucoides together constituted an additional 16% of
the measured biomass. In the 8–15 m zone, perennial red algae and Laminaria spp. dominated the
measured biomass with about three fifths and one
fifth of the total biomass, respectively. The depth
2419
distribution of the measured macroalgal biomass,
excluding green algae, is shown in Fig. 3. This depth
zonation pattern agrees with depth distribution
studies of macroalgae in the NE Kattegat (Karlsson
et al., 1998; Karlsson, 1999) and a study of two
stone reefs in the SW Kattegat (Dahl et al., 2005).
The topography of the Kattegat seafloor within
the photic zone ranges from the wide, flat, and
mostly sandy Jutland shelf to the steep rocks found
on certain locations on the Swedish coast. Nevertheless, hard substrates such as stones and boulders
suitable for macroalgal establishment are found in
all of Kattegat. Despite the differences in bottom
topography, the majority of the macroalgal species
recorded by Nielsen et al. (1995) have the same
relative importance in the eastern and the western
Kattegat regions. The nine species selected above
are listed as dominant or frequent in all of Kattegat
(Nielsen et al., 1995). These species are characterised
as dominant open coast species on the Danish side
(Middelboe, 2000), and they are also the most
commonly occurring on the Swedish side (Carlson,
1996; Anon., 2001; Lundgren and Olsson, 2001;
Olsson, 2001).
To calculate the macroalgal production, it was
necessary to convert the macroalgal coverage (%) to
biomass density (g dwt m2) of macroalgal standing
stock at stations lacking this information. The
conversion was made assuming that the species
composition and depth zonation pattern was similar
at all stations, that the above chosen species could
represent all macroalgal species in Kattegat, and
that the coverage to biomass relation was linear
(Fig. 4). In each depth zone, a 100% cover of
macroalgae was set to correspond to a different
biomass and species composition. The respective
depth zones were assigned a total biomass that
would be approximately similar to the corresponding total biomasses in Fig. 3, and with a relative
proportion of the chosen macroalgal species reflecting the species composition given above (Table 2).
The chosen green macroalgae were Cladophora
spp. and Ulva spp., opportunistic species commonly
found in shallow locations in the Kattegat (e.g.
Pedersén and Snoeijs, 2001). These annual algae do
not have a standing stock in the sense of the
perennials, but can anyway appear in large quantities at certain locations during part of the year.
They usually appear in locations sheltered from
wave exposure, where they can also detach from the
substrate and form floating mats (Pihl et al., 1999).
Thus, the mat-forming macroalgae are not entirely
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
2420
Fig. 4. Macroalgal biomass versus coverage for: (a) Fucus serratus and F. vesiculosus at 0–2 m depth; (b) Laminaria sp. at 8–14 m depth;
(c), (d) Cartilaginous red macroalgae (Chondrus crispus and Furcellaria lumbricalis) at 0–2 m and 2–4 m depth, respectively; (e), (f)
Filamentous red macroalgae (Ceramium nodulosum and Polysiphonia fucoides) at 0–2 m and 2–4 m depth, respectively. The r2-values for
the linear regressions (dashed lines) are: (a) 0.59; (b) 0.50; (c) 0.25; (d) 0.45; (e) 0.46; (f) 0.93.
Table 2
Standing stock biomass of the chosen species of perennial
macroalgae [g dwt m2] set to correspond to 100% coverage of
macroalgae in the respective depth zones
Depth
0–2 m
2–4 m
4–8 m
8–15 m
Species
F. serratus & F. vesiculosus
C. crispus & F. lumbricalis
C. nodulosum & P. fucoides
Laminaria spp.
Total biomass
1000
150
150
—
1300
—
400
200
—
600
—
100
100
100
300
—
100
—
50
150
The annual species, Cladophora & Ulva spp., were given a
common seasonal biomass of 100 g dwt m2 in the uppermost
depth zone.
dependent on hard substrates for their development.
At 16 monitoring stations in the NE Kattegat,
Karlsson et al. (1998) found an ample coverage of
green macroalgae at seven of eight sheltered
locations, whereas only four of eight exposed
locations reached even a fair coverage. Frequently,
30% or more of the total shallow area from
Tistlarna (N 571310 ) and northwards on the NE
Kattegat coast may be covered with ephemeral
algae (Moksnes and Pihl, 1995; Jenneborg et al.,
2005). On the more exposed coast further south,
only sparse amounts of green algae are reported
(Carlson, 1996; Anon., 2001), although the Laholm
Bay in SE Kattegat experienced mass occurrences of
ephemeral green algae in the 1970s and early 1980s
(Rosenberg et al., 1990). On the Danish side,
ephemeral green macroalgae are also common in
the sheltered bays, but not in the open waters that is
the subject of the current estimation (e.g. Pedersen
and Borum, 1997).
Contrary to the brown and red species, the green
macroalgae in this study were not evaluated in terms
of standing stock. Instead, the estimation was based
on the size of the sheltered area in the uppermost
depth zone (0–2 m), where ephemeral green macroalgae are usually found, and a representative
density. The area within 2 m depth in the Kattegat
is 405 km2. Sheltered areas, suitable for extensive
growth of green ephemeral macroalgae (Pihl et al.,
1999), are found chiefly in NE Kattegat, where
around 90 km2 of the seafloor is inside 2 m depth.
Here, the area above 2 m depth protected from wave
exposure, chosen as the habitat for the green
macroalgae in this study, was determined to be
one third of this (Moksnes and Pihl, 1995;
Jenneborg et al., 2005), or 30 km2. The summer
mean biomass of green macroalgae in shallow bays
in NE Kattegat was 155 g dw m2 in 2003 (Jenneborg, 2004) and 57 g dw m2 in 2004 (Jenneborg
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
et al., 2005). The representative biomass density
of green macroalgae in this study was set to
100 g dwt m2.
2.2. Primary production by macroalgae
The annual production, P, by macroalgae can be
several times higher than the biomass, B (Lüning,
1990). The production in a specific area may
however, due to the local environmental conditions,
deviate substantially from the general picture, or
from observations made elsewhere. For biomass
calculations, reliable figures should, if available,
preferably be derived from studies made in the area
of interest.
The production figures in this paper were
computed with a process based model of macroalgal
growth, originally developed for green macroalgae
in shallow bays (Öberg, 2005), now adapted to the
species included in this study and to Kattegat open
water conditions. The current adaptation of the
model, as described in the Appendix A, included the
use of observed values of temperature and nutrient
concentrations as forcing functions. Further, the
model made use of literature values of the nutrient
uptake, growth, and photosynthesis variables of the
chosen species as shown in Table 3. The values in
this table were selected to, as far as possible, be
representative for Kattegat conditions. Of the 30
papers cited in Table 3, only nine have macroalgae
in Kattegat, Skagerrak, or the Baltic Sea as their
subject. However, 37 of the 64 individual variables
in Table 3 stem from these nine studies.
The nutrient forcing thus consisted solely of open
sea nitrogen concentrations measured about
monthly at the surface and downwards in 5 m depth
intervals during 1998–1999 by NERI (Anon., 2005)
at nine Kattegat stations (Fig. 1). The model
simulations made use of monthly average values
+
of the measured NO
3 and NH4 concentrations
from all nine stations in the 5 m interval centred on
the current depth. The temperature data were taken
from the same stations, depths, and time period,
and used in the model with values interpolated to
the current time and depth. The surface irradiation
was taken from of global radiation observations in
Göteborg on the NE Kattegat coast measured in
five-minute intervals by SMHI during 1998–1999.
The light was reduced to current depth values
according to a photic depth of 15.7 m as explained
above.
2421
As briefly described above, and to more detail in
the Appendix A, the model computes growth of
macroalgae from nutrient, light, and temperature
forcing, and loss from a fixed loss rate. To find
annual production figures, model simulations with
the loss rate set to zero were made for each of the
red and brown macroalgal species (see below for
green algae). The production to biomass (P/B) ratio
for each of these species was taken to be its increase
in biomass during a one year simulation, divided by
the initial biomass of the macroalgae. By using
results from the second year of a 2-year calculation,
model spin-up effects were eliminated. The P/B
ratio of the green algal species, could however not
be computed on an annual basis. The high daily
growth rates together with the difficulty of determining what a reasonable annual biomass should
be, called for a different approach. The production
of the green algae was computed based on the
seasonal biomass values in Table 2, a productive
season of two months duration, and a model
computed daily growth rate according to Table 4.
2.3. Sea bottom topography and sediment
composition
To determine the area of suitable habitats for
macroalgae, a digital sea bottom sediment map
(Hermansen and Jensen, 2000), shown in Fig. 5, was
used. The compilation of this map is based on data
from shallow seismic surveys also including side
scan sonar in combination with information from
grab samplers and other surface sediment samplers
as well as from sediment core data. Most of the
shallow seismic techniques applied have a subbottom resolution normally in excess of 0.5 m,
which implies that the seabed classification presented in the map refers to a series of seabed types
derived from seismic integration of the upper 0.5 m
of sediment (Hermansen and Jensen, 2000). Of the
seven categories in the sediment type map (Fig. 5),
the lag sediments (no 4–6) and the crystalline
bedrock (no 7) can be considered as hard-bottoms
(J.B. Jensen, p. comm.), suitable for macroalgae
according to the criteria of Krause-Jensen et al.
(2001). The sandy areas (no 3), although gravel and
stones occur locally, are not clearly defined as either
erosion or accumulation areas, and were thus
judged as not suitable.
The sediment map was combined with two depth
databases, together covering the whole of Kattegat,
to find the respective size of the depth zones used in
Green, leaf-like
Green,
filamentous
Brown,
leathery
Brown,
leathery
Red,
filamentous
Red,
filamentous
Red,
cartilaginous
Red,
cartilaginous
Ulva spp.
Cladophora
spp.
F. serratus & F.
vesiculosus
Laminaria spp.
24617
10325
2173
80425
86425a
663
15517 71418
13125
683
3725
49725a
2293
3
11904,
5
3461, 2,
14663
391,
873
2, 3
Maximum NO3
uptake rate, Vmax,
(mg g dw1 h1)
Half-saturation
constant (NO3),
km, (mg l1)
7, 8, 9, 10
0.0130
0.0219
0.0228
0.1489
0.01217, 19, 20
0.0199; 0.0359
0.01415, 20
0.316,
0.279
Maximum growth
rate, mmax, (d1)
0.630
331
0.531
0.521
226
321
0.611
0.611,
15
Maximum areal
density
(kg dw m2)
122
1.522
4.222
3.222
1.622
222 423
7.3712,
315
13
Max. photosynthetic rate, Pmax,
(mg C g dw1 h1)
0.1122
0.2922
122
122
0.4922
0.4422 0.523
1.412, 13
0.7815
Dark respiration
rate, R,
(mg C g dw1 h1)
16422
13922
10022
10722
8122
32922 80023
8412, 13
35016
Light satura-tion
point, Ik,
(mE m2 s1)
Yes30
Yes29
No31
No31
Yes27
Yes24
Yes10,
Yes10
Author
1. Fujita (1985)
2. Taylor and Rees (1999)
3. Wallentinus (1984)
4. Hernández et al. (2002)
5. Lotze and Schramm (2000)
6. Fong et al. (1994)
7. Martins et al. (1999)
8. Martins and Marques (2002)
9. Nielsen and Sand-Jensen (1990)
10. Taylor et al. (2001)
11. Pihl et al. (1996)
12. Arnold and Murray (1980)
13. Enrı́quez et al. (1995)
14. Bishoff and Wiencke (1993)
15. Peckol and Rivers (1995)
Location
Falmouth, Massachusetts, USA
NE New Zealand
Baltic Sea, and various locations
Cadiz Bay, SW Spain
W Baltic Sea, Germany
San Francisco Bay, California, USA
Mondego estuary, W Portugal
Mondego estuary, W Portugal
Roskilde fjord, E Denmark
S England, UK
Skagerrak and Kattegat, W Sweden
Laguna Beach, California, USA
Mediterranean Sea, NE Spain
Disko Island, W Greenland
Cape Cod, Massachusetts, USA
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
Ensminger et al. (2000)
Gordillo et al. (2002)
Raven and Taylor (2003)
Bokn et al. (2002)
Creed et al. (1998)
Carlson (1996)
Johansson and Snoeijs (2002)
Brenchley et al. (1997)
Altamirano et al. (2003)
Rees (2003)
A. Samuelsson, p. comm.
Bolton and Lüning (1982)
Aleksandrov et al. (2002)
Tasende and Fraga (1999)
Bird et al. (1991)
Estimation
River Ilm, Thüringia, Germany
N Ireland, UK, low salinity culture
Various locations
Oslofjord, SE Norway
Isle of Man, UK
Kattegat, W Sweden
Skagerrak, W Sweden
NE Scotland, UK
Helgoland, NW Germany
Various locations
Kattegat, W Sweden
Helgoland, NW Germany
Black Sea, Ukraine
Galicia, NW Spain
N Atlantic
14
Temperature
limitation
[0–25 1C]
Carbon contents according to Atkinson and Smith, 1983. Oxygen based photosynthetic rates were converted to carbon fixation assuming a 1.2 photosynthetic quotient.
a
Refers to NH+
4 -uptake.
Ceramium
nodulosum
Polysiphonia
spp.
Chondrus
crispus
Furcellaria
lumbricalis
Type
Species
Table 3
Growth parameters for the chosen macroalgal species used in the model simulations
2422
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2423
Table 4
Model computed annual P/B ratios (for the red and brown species), and daily growth rates (for the green species), compared with the
measured daily growth rates in Nilsson and Oom (1988), and Borum and Pederson (1996)
Depth
0m
1m
2m
3m
4m
5m
6m
7m
8m
10 m
12 m
14 m
16 m
Species
Ulva spp.
Cladophora spp.
F. serr. & F. ves.
C. crispus
F. lumbricalis
C. nodulosum
Polysiphonia spp.
Laminaria spp.
0.05a
0.21a
0.61
0.55
0.28
12.1
2.6
—
0.04a
0.17a
0.59
0.54
0.27
11.4
2.5
—
0.03a
0.12a
0.54
0.51
0.26
10.0
2.3
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
0.50 0.46 0.43 0.41 0.38 0.33 0.23 0.17 0.08 0.02
0.24 0.22 0.21 0.20 0.19 0.16 0.11 0.09 0.05 0.02
9.0
7.6
6.7
6.5
5.6
4.6
2.9
2.1
0.59 0.02
2.1
1.8
1.6
1.3
1.1
0.85 0.48 0.27 0.08 0.006
—
0.75 0.69 0.68 0.63 0.56 0.42 0.32 0.15 0.03
18 m
N&O B&P
—
—
—
2 104
0.005
0
0
0
0.15a
0.49a
0.057a
0,037a
0.025a
0.26a
N. a.
0.027a
0.446a
0.203a
0.039a
N. a.
N. a.
0.294a
N. a.
N. a.
N. a.: Not available.
N & O: From Nilsson and Oom (1988).
B & P: From Borum and Pedersen (1996).
a
Daily growth rates.
in Kattegat with different characteristics that need
to be analysed as separate entities in a monitoring
situation. These regions, shown in Fig. 1, are: (1)
The Jutland shelf, characterised by relatively high
values of salinity, nutrient concentrations, and
chlorophyll, as well as strong mixing: (2) The
Eastern part, is the deepest and has a pronounced
salinity stratification with lower nutrient concentrations in the surface layer: (3) The Southern part, has
the lowest salinity, due to inflow of brackish water
from the Baltic Sea. This shallow area is also
occasionally subject to upwelling of nutrient-rich
bottom water originating from the Jutland current.
Fig. 5. Map showing the sediment types in Kattegat (Hermansen
and Jensen, 2000).
2.4. Computation of total biomass and annual
production
the macroalgal biomass determinations (Fig. 1). The
Danish digital map (Anon., 2000) covers the
western and central parts of Kattegat up to
57.51N with a varying horizontal resolution of
down to 25 m, whereas the IOW Baltic region map
(Seifert et al., 2001), that has 1 NM (1852 m)
resolution north of 561N and 12 NM south of this,
was used for the Swedish coast and the area north of
57.51N. The two depth databases are compiled
using available data from sea surveys made
throughout the 20th century with varying resolution
and accuracy (Anon., 2000; Seifert et al., 2001). The
coarse resolution of the IOW map was improved by
manual addition of depth information from Swedish coastal charts.
This study adopts the division suggested by
Danielsson et al. (2004), who identify three regions
The coverage figures for the stations that lacked
information on biomass were converted to biomass
according to the assumptions above, using the mean
coverage value of the stations in each sub-area and
depth segment. For each depth segment, the model
calculated P/B ratios of each of the chosen species
were multiplied with their given proportion of the
biomass to get annual production figures per unit
area, and with the size of the suitable areas to find
the total yearly production. The carbon content of
the macroalgae was assumed to be 31% of the dry
weight (Atkinson and Smith, 1983).
3. Results
The model simulations of the growth of the nine
macroalgal species in this study were made to obtain
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
2424
yearly production to biomass ratios for the red and
brown species, and daily growth rates for the green
macroalgae. The result of the model runs is shown
in Table 4. For comparison, literature values of
locally measured daily growth rates (Nilsson and
Oom, 1988; Borum and Pedersen, 1996) are shown
in the two rightmost columns of Table 4.
The sediment and depth maps were combined to
find hard-bottom areas above 15 m depth (Fig. 6).
Table 5 shows the areas judged to be suitable for
macroalgae, a total of 1438 km2 or 6.6% of the full
Kattegat area. The Jutland shelf has 62% of the
suitable area, almost all of which is found below 4 m
depth. This shallow western and central part of the
Kattegat seafloor is covered by lag sediment on
glacial till, sand, and sandy mud, where only the
first was judged as a suitable substratum for
macroalgae. These sediment types also dominate
in the southern part (11% of the suitable area) with
the addition of lag sediments on quaternary clay
and peat. The seafloor on the steeper Swedish side
(27% of the suitable area) does have some solid
rock, but 94% of the suitable sediments are also
here of the lag types, except now mainly on
quaternary clay and peat.
The observed mean coverage at the stations where
biomass figures were unavailable is shown in Table 6,
and the macroalgal standing stock (calculated from
coverage figures, or directly measured) for all areas is
shown in Table 7. With the exception of the
Chlorophyta species, the macroalgal annual production was calculated from the size of the standing
stock, multiplied with the P/B ratios derived from
model simulations. The influence of the nutrient,
light, and temperature limitation functions on the
macroalgal growth rate is seen in Fig. 7.
The estimated macroalgal yearly production is
shown as production per m2 in Table 8 and as total
figures for the respective sub-areas in Table 9. The
total macroalgal yearly production in the Kattegat
was found to be 119 106 kg C y1, and to reach
0.5 kg C m2 y1 in the most productive, shallow
areas. In the 0–2 m depth segment of NE Kattegat,
green macroalgae contributed with 315 g C m2 y1
on 30 km2 of sheltered areas, or 9.5 106 kg C y1 in
all.
The sensitivity of the P/B ratios and the annual
production values to a 10% change in the values
of six of the model variables is shown in Tables 10
and 11, respectively. The included variables were the
Table 6
Macroalgal cover on suitable substrates, mean values (%)
Depth
interval (m)
Jutland shelf
Southern
Kattegat
Stations 18,
37 and 40, 41
0–2
2–4
4–8
8–15
100
98
94
87
78
96
99
82
95
90
100
72
All stations in E Kattegat except nos. 13, 30, 31 and 32 (Table 1)
report biomass.
Fig. 6. Map showing the areas in Kattegat that are suitable
habitats for macroalgal growth.
Table 5
Hard surface areas [km2] in Kattegat
Depth
interval (m)
Jutland
shelf
Southern
Kattegat
Eastern
Kattegat
Total area
0–2
2–4
4–8
8–15
Total
8
13
305
574
900
2
6
49
97
154
43
37
62
242
384
53
56
416
913
1438
Table 7
Macroalgal standing stock, from calculated or measured values,
in total figures for each area (1000 ton C)
Depth
interval (m)
Jutland
shelf
Southern
Kattegat
Eastern
Kattegat
Total
0–2
2–4
4–8
8–15
Total
3.2
2.4
27
15
48
0.7
1.2
4.5
2.4
9
12.1
8.0
6.3
13
39
16
12
38
30
96
Calculated figures from stations 18, 37, 40 and 41 (Table 1) are
included in the measured figures from E Kattegat.
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2425
Fig. 7. Modelled nutrient (thick solid line), light (thin solid line), and temperature (thin dotted line) limitation (shown as 5 days running
mean) as expressed by the functions f1(N), f2(L), and f3(T), respectively, for: (a) Fucus spp. at the sea surface; (b) Polysiphonia spp. at the
sea surface; (c) Laminaria spp. at 6 m depth; (d) Polysiphonia spp. at 6 m depth.
Table 8
Annual production per unit area (g C m2 y1)
Table 9
Annual production (1000 ton C y1)
Depth
interval (m)
Jutland shelf
Southern
Kattegat
Eastern
Kattegat
Depth
interval (m)
Jutland
shelf
Southern
Kattegat
Eastern
Kattegat
Total
production
0–2
2–4
4–8
8–15
514
376
138
5
401
369
145
4
320
432
163
11
0–2
2–4
4–8
8–15
Total
4
5
42
3
54
1
2
7
0.4
10
26
16
10
3
55
31
23
59
6
119
half saturation constant, km, the maximum nutrient
uptake rate, Vmax, the maximum growth rate, mmax,
the water NO3 concentration, the irradiation, and
the water temperature. The resulting shifts in the
results were moderate, with a positive or negative
response of usually about 5% for the tested
variables.
4. Discussion
This study is based on publicly available macroalgal monitoring data, combined with hydrographic
and sediment structure data, and model computed
macroalgal productivity. The accuracy of the
estimations rely on that the monitoring stations
are representative, that the choice of species is wellfounded, that the conversion of coverage to biomass
is justified, that the hard bottom areas are correctly
represented, and that the model parameters and
estimations are realistic.
The macroalgal monitoring programmes differ in
the various parts of Kattegat. The NERI visits each
station on an annual or bi-annual basis, whereas
most of the Swedish efforts are less intense. The
monitoring methods are however similar in all
studies used for this study. The more than 200
species of macroalgae reported found in Kattegat
since 1970 (Nielsen et al., 1995) makes it difficult to
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
2426
Table 10
Relative sensitivity of the modelled P/B ratios to a 10% increase of selected variables
Species
ku
Vmax
mmax
NO3 conc.
Irradiation
Temperature
Ulva spp.
Cladophora spp.
F. serr. & F. ves.
C. crispus
F. lumbricalis
C. nodulosum
Polysiphonia spp.
Laminaria spp.
0.96
0.99
0.94
0.94
0.94
0.93
0.98
0.95
1.05
1.00
1.06
1.07
1.06
1.05
1.02
1.05
1.06
1.11
1.07
1.07
1.08
1.06
1.14
1.07
1.04
1.02
1.06
1.07a
1.06
1.05
1.02
1.05
0.99
0.94
1.02
1.02
1.02
1.04
1.04
1.02
0.97
0.92
0.98
0.98
0.93
1.01
0.99
0.95
The simulations were made at a water depth of 4 m, except for Fucus, Ulva, and Cladophora (0 m), and Laminaria (8 m). The figures in the
table were calculated as the ratio of a simulation with a 10% increase of a single variable and a simulation with standard values. Annual P/
B ratios were used for all species, except Ulva and Cladophora where daily growth rates were compared. For temperature, the 10% increase
was made based on values in 1C.
a
Value refers to NH4 concentration.
Table 11
Relative sensitivity of the annual production to a 10% increase of
selected variables
Depth ku
(m)
Vmax mmax NO3 conc. Irradiation Temperature
1
3
6
10
1.05
1.05
1.05
1.06
0.94
0.94
0.94
0.95
1.07
1.08
1.07
1.07
1.05
1.05
1.05
1.06
1.03
1.04
1.04
1.02
0.99
1.00
0.99
0.96
The individual entries in the table represent a situation with
100% coverage and species composition in conformity
with Table 2. The calculation was made according to Rel:sens: ¼
9
P
½ðT2z T4z T10var Þ=ðT2z T4z Þ where sp(1–9) is the macro-
sp¼1
algal species from Ulva to Laminaria, T2z and T4z are entries
from Table 2 and Table 4 at depth z, respectively, and T10var is
entries from Table 10 for each variable var.
represent all aspects of such a vast diversity through
the only nine species chosen for this study. To
include all occurring species in this calculation
would however hardly be feasible. As the species
composition is similar on both sides of the Kattegat
(Nielsen et al., 1995), the general species distribution
was assumed to be the same in all of Kattegat. The
chosen nine species represent about three quarters
of the total biomass measured at the monitoring
stations. As both perennial and ephemeral species
are included, reflecting the different production
rates of these functional groups, the chosen species
can be considered representative for the total
macroalgal biomass in the Kattegat. The remaining
species mostly belong to the same functional groups
as the chosen species, and thus have similar growth
and production characteristics.
An unfavourable circumstance for the calculations is the lack of biomass figures at the Danish
stations. The biomass versus coverage relation used
is based on the simultaneous coverage and biomass
figures given for the Swedish stations, but this does
not necessarily imply that the relationship is correct
for macroalgae in the western half of Kattegat.
Different degrees of exposure could cause the
macroalgae to vary in shape, and thus also in
weight. Dahl et al. (2004) have shown that, on a
central Kattegat stone reef, a fair relationship of
biomass and coverage exists for Phycodrys rubens
but not for Polysiphonia fucoides and Rhodomela
confervoides. Although the biomass versus coverage
plots shown in Fig. 4 also indicates that an elevated
biomass corresponds to a higher coverage, they do
not give a distinct support that a linear relation can
be applied to all species. The linear relation was still
deemed to be consistent with Fig. 4, and was used
for all species in this study. Also, the biomasses set
to match the 100% coverage (Table 2) are generally
lower than what is indicated in Fig. 4. As the total
measured biomass in each depth segment (Fig. 3)
was not to be exceeded even for 100% coverage, the
individual biomass figures in Table 2 were set to
mirror the relative proportions only. Also, the mean
values of the measured macroalgal cover on suitable
substrates being between 72% and 100%, and
indeed above 90% in seven of the 12 measurements, indicate that the determination of the total
biomass is more important for the accuracy of
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
the result than the linearity of the biomass versus
coverage relations.
Either of the environmental variables nutrients,
light, or temperature, may limit the algal growth
during various parts of the year. In contrast to
production figures taken from field studies made in
summer, the model calculations employed in this
study expressively considers the influence of these
growth limiting factors during all seasons. When
interpreting production measurements made in high
light conditions, i.e. in summer, the light saturation
status of the macroalgae must be considered to
ensure that correct results are reached (Wallentinus,
1978). Provided accurate and appropriate growth
variables are at hand, model simulations may even
be preferred before using production figures from
short-term measurements. By using model calculations with local environmental forcing and growth
parameters, an adequate interpretation of the limiting factors influence on the macroalgal growth
throughout the year is distinctly possible.
The primary responses of the nutrient, light, and
temperature limitation functions (Fig. 7) are rather
self-evident. The nutrient limitation is strongest in
the main growth season, light is more limiting at
depth than at the surface, and both low and high
temperatures will adversely affect macroalgal
growth. Less obvious is that although the limitation
functions are normalised, each function may still
limit the growth of an individual species throughout
the year. Macroalgae growing at depth will be light
limited even in the height of summer. At usual
nutrient concentrations, species with high halfsaturation constants, km, will not be able to reach
the maximum growth rate predicted by the model
equations. Another interpretation to the latter
observation is that species with high km will not be
as adversely affected from lowered nutrient concentrations as the opportunistic species with low km
values.
The actual growth rate used in the model imposes
simultaneous nutrient, light, and temperature limitation on the macroalgae. The growth rate (Appendix A, Eq. (4)) is given by the product of the
maximum growth rate and three independent limiting functions, thus suggesting the response of the
macroalgae to a change in, e.g. temperature to be
independent to the status of the other limiting
factors. An alternative option would be to only
consider the most limiting factor at any given time,
in which case the resulting growth rate would be
higher. Another possibility is that the growth
2427
limiting factors interact to produce a greater
reduction in the growth rate than suggested by their
individual contributions. To reliably reproduce such
potential interactions in the model equations would
however stretch beyond the intent of the simple
model used here. The outcome of the model runs
thus originates from the adoption of simultaneous
limitation functions, and from the quality of the
forcing data and model parameters.
The P/B ratios, derived from model simulations
using literature values of growth parameters taken
from several studies in various locations, are not
exact figures as if taken from local in situ growth
studies, but can anyway be seen as representative for
a Kattegat location. Despite the seemingly heterogeneous selection of growth parameters (Table 3), a
majority of these stem from Kattegat or adjoining
waters, with most of the remainder taken from other
temperate areas. There is thus reason to believe that
the model simulations gave a fair representation of
the annual production rates in Kattegat. The
current formulation of the model is based on
nitrogen alone. An expansion of the model equations to include phosphorus should increase the
reliability of the model results, as local phosphorus
limitation could act to decrease growth. The
nutrient data used in the model simulations stem
from open sea monitoring stations. Local runoff
may give higher nutrient concentrations to certain
coastal locations, thereby potentially increasing the
macroalgal production.
In addition to the model computed results, Table
4 also lists for comparison locally measured daily
production figures from Nilsson and Oom (1988)
regarding eight of the chosen species, and from
Borum and Pedersen (1996) regarding four of these
species. Nilsson and Oom (1988) measured the daily
production by 25 species of macroalgae during the
first half of July 1985 at Tjärnö Marine Biology
Station on the Swedish Skagerrak coast. The report
by Nilsson and Oom (1988) contains temperature
and light data for the studied period, but unfortunately not any information about the nutrient levels
in the water where the study was made. The nutrient
situation at the time of observation can however be
assumed to be at least satisfactory, because data
from a nearby monitoring station in the Kosterfjord
1
show very high NO
3 values (4100 mg l ) on June
1
18, and 5 mg l remaining on July 11, 1985 (B. Rex,
p. comm.). Borum and Pedersen (1996) made
laboratory culture experiments on six macroalgal
species collected in southern Kattegat, measuring
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
the daily growth rates along with other variables.
The modelled P/B ratios in this paper are not very
high compared to the summer growth rates published in the two papers, but their relative order
agree. Whereas the annual P/B ratios in this study
all decrease with increasing depth, the summer
situation, markedly influenced by photoinhibition,
is clearly reflected in the July 1985 depth gradient
experiments on four species by Nilsson and Oom
(1988) as the production hardly decreased from the
surface down to 5 m depth. Only further down to
10 m a slight reduction in production was seen.
The sediment survey methods used and the few
sediment classes defined imply that there is a certain
variation within the sediment classes. With more
detailed sediment information, some of the lag
sediment areas (1419 km2 above 15 m depth) might
be classified as unsuitable sediments, and some of
the sand areas (5003 km2 above 15 m depth) as
suitable. The lag sediments are mainly erosion areas
with relatively hard bottoms, whereas the available
data is insufficient to further divide the sand areas
into erosion or accumulation bottom types (J.B.
Jensen, p. comm.). Hence, the current estimate of
the suitable area may be somewhat exaggerated, but
there is also a distinct possibility that the area is
understated. About two thirds of the suitable areas
are in the deepest (8–15 m) interval, where light is
often the main limiting factor. Less than 10% of the
areas are located in the most productive zone above
4 m depth.
The rocky shores found in the NE and SE parts of
Kattegat offer ideal substrates for macroalgae, but
the highest standing stock of the three areas was
recorded on the Jutland shelf. Although the total
amount of macroalgae in the eastern part of
Kattegat was four fifths of that in the western part,
the suitable area in the eastern part was less than
half of that on the Jutland shelf. The depth
distribution of the standing stock biomass also
differs considerably between the two areas, reflecting the diverse sediment structures. Whereas the
macroalgae on the Jutland shelf were almost
exclusively found below 4 m depth, the eastern
counterpart had a much more even distribution. On
depths shallower than 4 m, nearly three quarters was
found in the eastern part, while the Jutland shelf
only harboured one fifth of the standing stock.
Combined with the higher productivity on the
shallower depths, this acted to even out the
differences in production in the respective depth
zones so that, except for the low production in the
deepest zone, the total Kattegat macroalgal production was rather similar in the other three zones.
The relative error in the above estimations is
probably larger for the suitable areas and biomass
measurements than for the macroalgal nutrient
uptake as well as for the calculation of the P/B
ratios and production. Should a more detailed
sediment classification and a higher density of
macroalgal biomass measurements be available, a
more exact estimation of the macroalgal production
in the Kattegat could be made.
Kautsky and Kautsky (1995) have made measurements of the macroalgal biomass along the
Swedish coast of the Baltic Sea, and have calculated
the total annual macroalgal production in that area.
The dominating species in the Baltic Sea are, as
reported by Kautsky and Kautsky (1995), F.
vesiculosus, F. lumbricalis, and in addition F.
serratus in the south, whereas annual algae have
30% of the biomass. The total standing stock of
macroalgae, according to Kautsky and Kautsky
(1995), is 109 106 kg C on the Swedish side of the
Baltic, which can be compared to the 34 106 kg C
on the Swedish Kattegat coast or 95 106 kg C
standing stock of macroalgae in all of Kattegat
(Table 7). As for production, the figures to be
compared are 565 106 kg C y1 on the Swedish
Baltic coast, and 119 106 kg C y1 in the Kattegat.
While the size of the standing stocks in the two seas
are fairly similar, the production estimates are not.
The macroalgal production estimates in Kautsky
and Kautsky (1995) are based on literature values,
corrected for irradiation but not for nutrient
availability or temperature, whereas the production
figures in this study come from full-year model
simulations where both nutrient availability, and
ambient light and temperature, have influenced the
results. The three growth limitation functions used
in the model simulations, each reduces the growth at
a different time of the year. Thus, instead of a
seasonal decrease in growth resulting from the
application of a single restraint, the macroalgae in
this study are subject to a persistent limitation
throughout the year; nutrient availability limits
growth in summer, access to light in winter, and
low temperature in spring.
The total production by macroalgae in Kattegat
was above estimated to 0.12 109 kg C y1. For
comparison, the current estimate of the pelagial
production in Kattegat (Rydberg et al., 2006)
of about 200 g C m2 y1 gives a total of 4 109 kg C y1. As the area available for the macro-
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J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
algae is much smaller than the total surface area of
Kattegat, the productivity per unit area is considerably
higher than for plankton. Although the macroalgal
share of the primary production could perhaps be
disregarded in the context of the total marine
primary production in Kattegat, it should be
considered when discussing the production in the
shallow areas where macroalgae grow. The benthic
production also includes production by microalgae
and rooted plants, further adding to its importance.
This should be included in later studies.
Acknowledgements
Thanks are due to Anders Stigebrandt for
constructive help throughout the work, to Inger
Wallentinus for helpful comments on the manuscript, and to Karsten Dahl for preliminary data.
My thanks also extend to three anonymous
reviewers whose comments greatly aided to improve
the manuscript. This work was in part financially
supported by the Swedish Foundation for Strategic
Research (MISTRA) through MARE—Marine
research on eutrophication, and by the Faculty of
Science at Göteborg University.
Appendix A. A brief description of the macroalgal
model
This model describes the development of macroalgae through their nutrient uptake and growth
capacity, limited by access to nutrients, light, and
ambient temperature. The model (Öberg, 2005) was
originally developed to simulate the growth of green
algal mats in a shallow bay, but is here generalised
to accommodate all the macroalgae in the current
study, one at a time. This was made by including
model variable values for all species in this study
(Table 3), and by using values of ambient nutrient
concentrations, light, and temperature measured
locally in Kattegat.
The model is set up as a box, with no horizontal
resolution. A run with the current version of the
model simulates, in time steps of one day, the
development of one macroalgal species for an
arbitrary time period. The development of the live
algae is described by
dB
¼ ðm OÞB,
(1)
dt
where B (kg m2) is the amount of live macroalgae,
m (day1) is the growth rate, and O (day1) is the
2429
loss rate. The assimilation of nutrients by the
macroalgae is modelled in two steps, with nutrients
first taken up into an internal nutrient pool before
being used in the growth process. The internal
storage of nutrients is modelled according to Droop
(1968). Here, the rate of change of the internal pool
is governed by
dQ
¼ V mQ
dt
(2)
with Q (mg N/g dw) the internal nitrogen quota of
the algae, t time, and V (mmol h1 g1) the nitrogen
uptake rate. The effective uptake rate V of nitrogen
into the internal nutrient pool, Q (mg N/g dw) was
formulated as
V ¼ V max
N
Qmax Q
,
km þ N Qmax Qmin
(3)
where Vmax (mmol h1 g1) is the maximum nutrient
uptake rate, km (mg l1) is the half-saturation
constant for nitrogen uptake, and Qmax and Qmin
the upper and lower limits of the pool size,
respectively. The subsequent growth of the macroalgae is modified by the access to nutrients, light,
and temperature so that the actual growth rate, m
(day1), can be described by
m ¼ mmax f 1 ðNÞf 2 ðLÞf 3 ðTÞ,
(4)
1
where mmax (day ) is the maximum growth rate, N
nutrients (NO3), L incident light, and T temperature, while f1–3 are the normalised functions of the
limiting variables.
The nutrient limitation function, f1(N), controls
the transfer of nitrogen from the internal nutrient
pool to algal growth through
Q Qmin
f 1 ðNÞ ¼
.
(5)
Qmax Qmin
The uptake of nitrogen into the internal pool (Eq.
(3)) is thus guided by Michaelis–Menten kinetics
and by the nutrient status of the pool, while the
growth is limited by the amount of nitrogen
available in the pool. The light limitation function,
f2(L), accounts for reduction of photosynthesis from
low irradiation and from photoinhibition. This
relationship was described by the following equation (Platt et al., 1980)
P ¼ Ps ð1 ea Þeb R,
(6)
where P (mg C g dw1 h1) is the photosynthetic
rate and Ps the maximum rate if there were no
photoinhibition. Further, a ¼ aI=Ps and b ¼ bI=Ps
ARTICLE IN PRESS
2430
J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
where a (mg C g dw1 h1 mE1) is the photosynthetic efficiency, I (mE) the light intensity, b
(mg C g dw1 h1 mE1) the photoinhibition parameter, and R (mg C g dw1 h1) is dark respiration.
For each species, Eq. (6) was fitted to the respective
values of R, the saturation point, Ik, and the
maximum photosynthesis rate, Pmax. The measured
surface irradiation, I0, was reduced to current depth
values according to
I z ¼ I 0 eK d z ,
(7)
where Iz is the irradiation at depth z, and Kd is the
attenuation coefficient (e.g. Kirk, 1994). In the
model, zq (1%) ¼ 15.7 m as above, which corresponds to K d ¼ 0:293. The original version of the
model (Öberg, 2005) assumes the photosynthetic
yield to be converted to growth without any further
losses, and this is also the case with the current
model adaptation. The temperature limitation
function, f3(T), was constructed by linear interpolation of literature values of growth rates at various
temperatures for the different species, measured at
6–9 points in the interval 0–28 1C. The limiting
functions, f13, were normalised with their respective maximum values, so that a value of 1 would
mean an unrestricted growth (m ¼ mmax ). A zero
value to any of the three functions would mean that
no growth could take place. The model results were
presented as daily macroalgal biomass densities for
the duration of the simulation period. Values of
other model variables, e.g. NO3 concentration,
irradiation, and water temperature could also be
extracted.
References
Aarup, T., 2002. Transparency of the North Sea and Baltic Sea—
a Secchi depth data mining study. Oceanologia 44 (3),
323–337.
Aleksandrov, B.G., Minicheva, G.G., Strikalenko, T.V., 2002.
Ecological aspects of artificial reef construction using scrap
tires. Russian Journal of Marine Biology 28, 120–126.
Altamirano, M., Flores-Moya, A., Figueroa, F.L., 2003. Effects
of UV radiation and temperature on growth of germlings of
three species of Fucus (Phaeophyceae). Aquatic Botany 75,
9–20.
Anonymous, 2000. Arealinformationssystemet AIS—Dybdemodel for indre danske farvande. Danish National Environmental Research Institute. WWW Page, http://www.dmu.dk/
Udgivelser/Kort_og_Geodata/AIS/ (in Danish).
Anonymous, 2001. Undersökningar i Skälderviken och södra
Laholmsbukten. Toxicon AB, Årsrapport 2000. County
Administration of Skåne, Landskrona, 86pp (in Swedish).
Anonymous, 2005. Den nationale database for marine data,
MADS. WWW Page, http://www.dmu.dk/Vand/Havmilj%
C3%B8/MADS/, The Danish National Environmental
Research Institute (in Danish).
Arnold, K.E., Murray, S.N., 1980. Relationships between
irradiance and photosynthesis for marine bentic green algae
(Chlorophyta) of differing morphologies. Journal of Experimental Marine Biology and Ecology 43, 183–192.
Atkinson, M.J., Smith, S.V., 1983. C:N:P ratios of benthic
marine plants. Limnology and Oceanography 28, 568–574.
Baird, M.E., Walker, S.J., Wallace, B.B., Webster, I.T., Parslow,
J.S., 2003. The use of mechanistic descriptions of algal growth
and zooplankton grazing in an estuarine eutrophication
model. Estuarine, Coastal and Shelf Science 56, 685–695.
Biber, P.D., Harwell, M.A., Cropper, W.P., 2004. Modelling the
dynamics of three functional groups of macroalgae in tropical
seagrass habitats. Ecological Modelling 175, 25–54.
Bird, C.J., Saunders, G.W., McLachlan, J., 1991. Biology of
Furcellaria lumbricalis (Hudson) Lamoroux (Rhodophyta:
Gigartinales), a commercial carragenophyte. Journal of
Applied Phycology 3, 61–82.
Bischoff, B., Wiencke, C., 1993. Temperature requirements for
growth and survival of macroalgae from Disko Island (Greenland). Helgoländer Meeresuntersuchungen 47, 167–191.
Bokn, T.L., Moy, F.E., Christie, H., Engelbert, S., Karez, R.,
Kersting, K., Kraufvelin, P., Lindblad, C., Marba, N., 2002.
Are rocky shore ecosystems affected by nutrient enriched
seawater? Some preliminary results from a mesocosm experiment. Hydrobiologia 484, 167–175.
Bolton, J.J., Lüning, K., 1982. Optimal growth and maximal
survival temperature of Atlantic Laminaria species (Phaeophyta) in culture. Marine Biology 66, 89–94.
Borum, J., Pedersen, M.F., 1996. Nutrient control of algal
growth in estuarine waters. Nutrient limitation and the
importance of nitrogen requirements and nitrogen storage
among phytoplankton and species of macroalgae. Marine
Ecology Progress Series 142, 261–272.
Borum, J., Sand-Jensen, K., 1996. Is total primary production in
shallow coastal marine waters stimulated by nitrogen loading?
Oikos 76 (2), 406–410.
Brenchley, J.L., Raven, J.A., Johnston, A.M., 1997. Resource
acquisition in two intertidal fucoid seaweeds, Fucus serratus
and Himanthalia elongata: seasonal variation and effects of
reproductive development. Marine Biology 129, 367–375.
Carlson, L., 1996. Hallands kustkontrollprogram. Årsrapport
1996, makroalger. County Administration of Halland, Landskrona, 31pp (in Swedish).
Carstensen, J., Daniel Conley, D., Müller-Karulis, B., 2003.
Spatial and temporal resolution of carbon fluxes in a shallow
coastal ecosystem, the Kattegat. Marine Ecology Progress
Series 252, 35–50.
Cervin, G., Åberg, P., 1997. Do littorinids affect the survival of
Ascophyllum nodosum germlings? Journal of Experimental
Marine Biology and Ecology 218, 35–47.
Creed, J.C., Kain (Jones), J.M., Norton, T.A., 1998. An
experimental evaluation of density and plant size in two large
seaweeds. Journal of Phycology 34, 39–52.
Dahl, K., Nicolaisen, J., Nielsen, R., Tendal, O.S., 2004. Udvikling
og afprøvning af metoder til indsamling af flora og fauna på
småstenede hårdbundshabitater. Report no. 521, Danish
National Environmental Research Institute, 85pp (in Danish).
ARTICLE IN PRESS
J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
Dahl, K., Lundsteen, S., Tendal, O.S., 2005. Mejlgrund og
Lillegrund. En undersøgelse af biologisk diversitet på et
lavvandet område med stenrev i Samsø Bælt. Report no. 529,
Danish National Environmental Research Institute, 87pp
(in Danish).
Danielsson, Å., Rahm, L., Conley, D.J., Carstensen, J., 2004.
Identification of characteristic regions and representative
stations: a study of water quality variables in the Kattegat.
Environmental Monitoring and Assessment 90, 203–224.
Dring, M.J., 1982. The Biology of Marine Plants. Edward
Arnold, London, 199pp.
Droop, M.R., 1968. Vitamin B12 and marine ecology. IV: The
kinetics of uptake, growth and inhibition in Monochrysis
lutheri. Journal of the Marine Biological Association of the
United Kingdom 48, 689–733.
Enrı́quez, S., Duarte, C.M., Sand-Jensen, K., 1995. Patterns in
the photosynthetic metabolism of Mediterranean macrophytes. Marine Ecology Progress Series 119, 243–252.
Ensminger, I., Hagen, C., Braune, W., 2000. Strategies providing
success in a variable habitat: II. Ecophysiology of photosynthesis of Cladophora glomerata. Plant, Cell and Environment 23, 1129–1136.
Fong, P., Foin, T.C., Zedler, J.B., 1994. A simulation model of
lagoon algae based on nitrogen competition and internal
storage. Ecological Monographs 64, 225–247.
Fujita, R.M., 1985. The role of nitrogen status in regulating
transient ammonium uptake and nitrogen storage by macroalgae. Journal of Experimental Marine Biology and Ecology
92, 283–301.
Giusti, E., Marsili-Libelli, S., 2005. Modelling the interactions
between nutrients and the submersed vegetation in the
Orbetello Lagoon. Ecological Modelling 184, 141–161.
Gordillo, F.J.L., Dring, M.J., Savidge, G., 2002. Nitrate and
phosphate uptake characteristics of three species of brown
algae cultured at low salinity. Marine Ecology Progress Series
234, 111–118.
Graneli, W., Sundbäck, K., 1986. Can microbenthic photosynthesis influence below-halocline oxygen conditions in the
Kattegat? Ophelia 26, 195–206.
de Guimaraens, M.A., Paiva, A.D., Coutinho, R., 2005.
Modeling Ulva spp. dynamics in a tropical upwelling region.
Ecological Modelling 188, 448–460.
Heilmann, J.P., Richardson, K., Ærtebjerg, G., 1994. Annual
distribution and activity of phytoplankton in the Skagerrak/
Kattegat frontal region. Marine Ecology Progress Series 112,
213–223.
Hermansen, B., Jensen, J.B., 2000. Digital sea bottom sediment
map around Denmark. Report 2000/68, Geological Survey of
Denmark and Greenland, CD-ROM, 14 MB.
Hernández, I., Martı́nez-Aragón, J.F., Tovar, A., Pérez-Lloréns,
J.L., Vergara, J.J., 2002. Biofiltering efficiency in removal
of dissolved nutrients by three species of estuarine macroalgae cultivated with sea bass (Dicentrarchus labrax) waste
waters. 2. Ammonium. Journal of Applied Phycology 14,
375–384.
Højerslev, N.K., 1978. Daylight measurements appropriate for
photosynthetic studies in natural sea waters. Journal du
Conseil International pour l’Exploration du Mer 38, 131–146.
Jenneborg, L.-H., 2004. Utbredning och biomassa av fintrådiga
grönalger i grunda bottnar utmed Bohuskusten år 2003.
Bohuskustens Vattenvårdsförbund, HydroGIS AB, rapport
345, 18pp (in Swedish).
2431
Jenneborg, L.-H., Jenneborg, M.-L., Thorsell, J., 2005. Utbredning och biomassa av fintrådiga grönalger i grunda vikar
utmed Bohuskusten år 2004. Bohuskustens Vattenvårdsförbund, HydroGIS AB, rapport 380, 20pp (in Swedish).
Jerlov, N.G., 1976. Marine Optics. Elsevier, Amsterdam, 231pp.
Johansson, G., Snoeijs, P., 2002. Macroalgal photosynthetic
responses to light in relation to thallus morphology and depth
zonation. Marine Ecology Progress Series 244, 63–72.
Karlsson, J., 1999. Kungsbackafjordens marina flora: Djuputbredning av makroalger samt utbredning av ålgräs (Zostera
marina) och nating (Ruppia maritima) sommaren 1999. Tjärnö
Marine Biological Laboratory, Strömstad, 15pp (in Swedish).
Karlsson, J., Loo, L.-O., Loo-Luttervall, P.-L., 1998. Inventering
av marin fauna och flora i Halland 1997: NidingenHållsundsudde-Fjärehals. Länsstyrelsen i Hallands län, Livsmiljö, meddelande 2000:1. County Administration of Halland, 47pp (in Swedish).
Kirk, J.T.O., 1994. Light and Photosynthesis in Aquatic
Ecosystems. Cambridge University Press, Cambridge,
509pp.
Kautsky, U., Kautsky, H., 1995. Coastal productivity in the
Baltic Sea. In: Eleftheriou, A., Ansell, A.D., Smith, C.J.
(Eds.), Biology and Ecology of Coastal Shallow Waters:
Proceedings of the 28th European Marine Biology Symposium, Iraklio, Crete, 1993. Olsen & Olsen, Fredensborg, pp.
31–38.
Krause-Jensen, D., Christensen, P.B., Sandbeck, P., 1995.
Retningslinier for marin overvågning—bundvegetation. Teknisk anvisning fra DMU 9. Danish National Environmental
Research Institute, 49pp (In Danish).
Krause-Jensen, D., Sund Laursen, J., Middelboe, A.-L., Stjernholm, M., Manscher, O., 2001. Teknisk anvisning for marin
overvågning. 12. Bundvegetation. Danish National Environmental Research Institute, 50pp (in Danish).
Littler, M.M., 1980. Morphological form and photosynthesic
performances of marine macroalgae: tests of a functional/
form hypothesis. Botanica Marina 23, 161–165.
Lotze, H.K., Schramm, W., 2000. Can ecophysiological traits
explain species dominance patterns in macroalgal blooms.
Journal of Phycology 36, 287–295.
Lund-Hansen, L.C., 2004. Diffuse attenuation coefficients
Kd(PAR) at the estuarine North Sea-Baltic Sea transition:
time-series, partitioning, absorption, and scattering. Estuarine, Coastal and Shelf Science 61, 251–259.
Lundgren, F., Olsson, P., 2001. Reservat Hallands Väderö.
Marina undersökningar 2000. Rapport 2001:20, County
Administration of Skåne, 24pp (in Swedish).
Lüning, K., 1990. Seaweeds. Their Environment, Biogeography,
and Ecophysiology. Wiley, New York, 527pp.
Martins, I., Marques, J.C., 2002. A model for the growth of
opportunistic macroalgae (Enteromorpha sp.) in tidal estuaries. Estuarine, Coastal and Shelf Science 55, 247–257.
Martins, I., Oliveira, J.M., Flindt, M.R., Marques, J.C., 1999.
The effect of salinity on the growth rate of the macroalgae
Enteromorpha intestinalis (Chlorophyta) in the Mondego
estuary (west Portugal). Acta Oecologica 20, 259–265.
Middelboe, A.L., 2000. Species diversity, distribution and
abundance of marine macrophytes. Ph.D. Thesis, University
of Copenhagen, Denmark, 137pp.
Middelboe, A.L., Sand-Jensen, K., Brodersen, K., 1997. Patterns
of macroalgal distribution in the Kattegat-Baltic region.
Phycologia 36 (3), 208–219.
ARTICLE IN PRESS
2432
J. Öberg / Continental Shelf Research 26 (2006) 2415–2432
Moksnes, P.-O., Pihl, L., 1995. Utbredning och produktion av
fintrådiga alger i grunda mjukbottensområden i Göteborgs
och Bohus län. Länsstyrelsen i Göteborgs och Bohus län,
miljöavdelningen, publikation 1995: 10. County administration of Västra Götaland, 14pp (in Swedish).
Nielsen, R., Kristiansen, A., Mathiesen, L., Mathiesen, H., 1995.
Distributional index of the benthic macroalgae of the Baltic
Sea area. Acta Botanica Fennica 155, 1–51.
Nielsen, S.L., Sand-Jensen, K., 1990. Allometric scaling of
maximal photosynthetic growth rate to surface/volume ratio.
Limnology and Oceanography 35, 177–181.
Nilsson, T., Oom, I., 1988. Produktion hos marina makroalger—
en jämförelse mellan alger inom olika funktionella formgrupper och av olika taxonomisk tillhörighet. Master thesis,
Marine Botany Department, Göteborg University, 39pp
(in Swedish).
Öberg, J., 2005. Model simulations of conditions suitable for the
establishment of Enteromorpha sp. (Chlorophyta) macroalgal
mats. Marine Biology Research 1 (2), 97–106.
Olsson, P., 2001. Länsstyrelsen i Skåne län. Kullabergs marina
reservat. Undersökningar 2000. Rapport 2001:19. County
Administration of Skåne, 9pp (in Swedish).
Pavia, H., Carr, H., Åberg, P., 1999. Habitat and feeding
preferences of crustacean mesoherbivores inhabitation the
brown seaweed Ascophyllum nodosum (L.) Le Jol. and its
epiphytic macroalgae. Journal of Experimental Marine
Biology and Ecology 236, 15–32.
Peckol, P., Rivers, J.S., 1995. Physiological responses of the
opportunistic macroalgae Cladophora vagabunda (L.) van den
Hoek and Gracilaria tikvahiae (McLachlan) to environmental
disturbances associated with eutrophication. Journal of
Experimental Marine Biology and Ecology 190, 1–16.
Pedersén, M., Snoeijs, P., 2001. Patterns of macroalgal diversity,
community composition and long-term changes along the
Swedish west coast. Hydrobiologia 459, 83–102.
Pedersen, M.F., Borum, J., 1997. Nutrient control of estuarine
macroalgae: growth strategy and the balance between
nitrogen requirements and uptake. Marine Ecology Progress
Series 161, 155–163.
Pihl, L., Magnusson, G., Isaksson, I., Wallentinus, I., 1996.
Distribution and growth dynamics of ephemeral macroalgae
in shallow bays on the Swedish west coast. Journal of Sea
Research 35, 169–180.
Pihl, L., Svensson, A., Moksnes, P.-O., Wennhage, H., 1999.
Distribution of green algal mats throughout shallow soft
bottoms of the Swedish Skagerrak archipelago in relation to
nutrient sources and wave exposure. Journal of Sea Research
41, 281–294.
Platt, T., Gallegos, C.L., Harrison, W.G., 1980. Photoinhibition
of photosynthesis in natural assemblages of marine phytoplankton. Journal of Marine Research 38, 687–701.
Raven, J.A., Taylor, R., 2003. Macroalgal growth in nutrientenriched estuaries: a biogeochemical and evolutionary perspective. Water, Air, and Soil Pollution 3, 7–26.
Rees, T.A.V., 2003. Safety factors and nutrient uptake by
seaweeds. Marine Ecology Progress Series 263, 29–42.
Richardson, K., Christoffersen, A., 1991. Seasonal distribution
and production of phytoplankton in the southern Kattegat.
Marine Ecology Progress Series 78, 217–227.
Richardson, K., Heilmann, J.P., 1995. Primary production in the
Kattegat: past and present. Ophelia 41, 317–328.
Rosenberg, R., Elmgren, R., Fleischer, S., Jonsson, P., Persson,
G., Dahlin, H., 1990. Marine eutrophication case studies in
Sweden. Ambio 19 (3), 102–108.
Ruesink, J.L., Collado-Vides, L., 2006. Modeling the increase
and control of Caulerpa taxifolia, an invasive marine
macroalga. Biological Invasions 8, 309–325.
Rydberg, L., Ærtebjerg, G., Edler, L., 2006. Fifty years of
primary production measurements in the Baltic entrance
region, trends and variability in relation to land-based input
of nutrients. Journal of Sea Research 56, 1–16.
Seifert, T., Tauber F., Kayser, B., 2001. A high resolution
spherical grid topography of the Baltic Sea—revised edition’’,
Proceedings of the Baltic Sea Science Congress, Stockholm.
Tanaka, K., Mackenzie, F.T., 2005. Ecosystem behavior of
southern Kanehoe Bay, Hawaii: a statistical and modelling
approach. Ecological Modelling 188, 296–326.
Tasende, M.G., Fraga, M.I., 1999. Growth of Chondrus crispus
Stackhouse (Rhodophyta, Gigartinaceae) in laboratory culture. Ophelia 51, 203–213.
Taylor, M.W., Rees, T.A.V., 1999. Kinetics of ammonium
assimilation in two seaweeds, Enteromorpha sp. (Chlorophyceae) and Osmundaria colensoi (Rhodophyceae). Journal of
Phycology 35, 740–746.
Taylor, R., Fletcher, R.L., Raven, J.A., 2001. Preliminary studies
on the growth of selected ‘green tide’ algae in laboratory
culture: effects of irradiance, temperature, salinity and
nutrients on growth rate. Botanica Marina 44, 327–336.
Trancoso, A.R., Saraiva, S., Fernandes, L., Pina, P., Leitão, P.,
Neves, R., 2005. Modelling macroalgae using a 3D hydrodynamic-ecological model in a shallow, temperate estuary.
Ecological Modelling 187, 232–246.
Wallentinus, I., 1978. Productivity studies on Baltic macroalgae.
Botanica Marina 21, 365–380.
Wallentinus, I., 1984. Comparison of nutrient uptake rates for
Baltic macroalgae with different thallus morphologies.
Marine Biology 80, 215–225.