Primary Production by Macroalgae in Kattegat, Estimated from Monitoring Data, Seaﬂoor Properties, and Model Simulations

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

Jo¨ rgen O¨ bergÃ

Department of Oceanography, Earth Sciences Centre, Go¨teborg University, P.O. Box 460, S-405 30 Go¨teborg, 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 been made (L. Edler, p. comm.; I. Wallentinus, p. comm.). A ﬁgure of 1 g C m2 y1 for the average The sea of Kattegat between Sweden and Den- benthic primary production in Kattegat was men- mark has approximately one third of its seaﬂoor tioned by Borum and Sand-Jensen (1996) but the within the photic zone. This should render the underlying data, based on microalgal production in benthic primary production in Kattegat a relatively a limited area (Graneli and Sundba¨ ck, 1986), cannot high importance. Still, existing primary production be considered as being representative for the entire studies in Kattegat (e.g. Rydberg et al., 2006; Kattegat. Carstensen et al., 2003; Richardson and Heilmann, Kattegat is a small shallow sea (area 21 600 km2, 1995; Heilmann et al., 1994; Richardson and mean depth 24 m), situated between Denmark and Christoffersen, 1991) are focused on the pelagic Sweden in the transitional zone between the production, whereas a comprehensive study of the brackish Baltic Sea and the marine North Sea benthic production in Kattegat has hitherto not (Fig. 1). The scatter diagram (Fig. 2) from the open sea monitoring station Fladen (Fig. 1, no. 10) shows ÃTel.: +46 31 773 2859; fax: +46 31 773 2888. the large annual variation in salinity and tempera- E-mail address: [email protected]. ture in the photic zone of Kattegat. The summer

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 Fig. 1. Map of the Kattegat area, showing the bathymetry as well (Middelboe et al., 1997). Further, reduced salinity as the positions of the sampling locations. often means a reduction in size of the macroalgae (Lu¨ ning, 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 A˚berg, 1997), whereas sea urchins are the main grazers in the sub-littoral zone (Lu¨ ning, 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, Fig. 2. Salinity (upper panel) and temperature (lower panel) 2002), and ecosystem models simulating the coex- observations at Fladen in central Kattegat during 1994–1996. istence of macroalgae of different functional groups ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 2417

(Biber et al., 2004), of macroalgae and benthic 2. Material and methods phanerogams (Giusti and Marsili-Libelli, 2005), or of macroalgae and plankton (Tanaka and Mack- 2.1. Coverage and biomass of various macroalgae enzie, 2005; Trancoso et al., 2005; Baird et al., 2003). In this study, a single-species model was used The benthic macrophytes in Kattegat have been to estimate yearly production for a number of monitored at coastal and offshore stations by the representative macroalgal species. Danish National Environmental Research Institute The aim of this study was to estimate from (NERI) and by the Halland and Ska˚ne county existing data the macroalgal contribution to the administrations. The monitoring frequency varies; total primary production in Kattegat. This re- some sites are visited every year, while others quired information on the amount and location of have been visited only once during the last decade. macroalgal presence, as well as of the species Table 1 lists the depths, years, measured variables distribution and productivity. As macroalgae only and coordinates of the monitoring stations used in grow in the photic zone, mostly attached to a solid the present study. The positions of the stations are substratum, information on depth distribution and shown in Fig. 1. All macroalgal monitoring was sediment structure was also needed to obtain a made in summer through visual inspection by divers fair description of the spatial distribution of along transects down to a maximum depth of 20 m. locations suitable for brown and red macro- The assembled coverage, defined as the share of algae. The distribution of green macroalgae was suitable hard substrate covered with macroalgae treated differently. As these macroalgae often (Krause-Jensen et al., 2001), was recorded at all appear aggregated into floating mats, the descrip- sites. The macroalgal coverage was found by tion was focussed on the availability of shallow and projecting the macroalgal thalli vertically onto the sheltered areas. seafloor, thereby estimating the proportion of Most of the publicly available Kattegat macro- substrate covered. The estimations were made algal monitoring data from the last decade was used for three replicate areas in depth segments of in this study. Ideally, all of these data would include usually 1 m vertical extension. At the S and W biomass determinations. Equally important for the sides of Kattegat, the current method of NERI productivity calculations would be estimates of (Krause-Jensen et al., 2001) was used. The results macroalgal annual productivity made in the area from these stations (Anon., 2005), as well as from or under Kattegat-like conditions. Neither of these the three most southerly Swedish stations (Anon., conditions was met for this study. Only a minority 2001), were given as figures (0–100%) of total of the macroalgal monitoring data contained aggregated coverage by all macroalgal species on biomass information. Instead, a majority of the suitable substrates. The remaining reports from the monitoring efforts were concerned with macroalgal Swedish coast (Carlson, 1996; Lundgren and coverage estimations. Through the availability of Olsson, 2001; Olsson, 2001) all used the previous simultaneous measurements of both biomass and NERI method (Krause-Jensen et al., 1995), where coverage at some stations, a relationship was the estimates are given in five categories (0–2%; established to convert the coverage data at the 2–25%; 25–50%; 50–75%; 75–100%) of the aggre- other stations to biomass figures. The lack of area- gated coverage. At these stations, the biomass specific annual productivity measurements made (g dwt m2) of the occurring macroalgae, estimated model simulations a suitable alternative to obtain from manually collected samples, was also reported yearly production to biomass ratios. The simula- (Fig. 3). tions were made with an adapted version of the A selection of macroalgal species must include the macroalgal growth model by O¨ berg (2005) for nine most common species in the area. To ascertain of the most common species of macroalgae in representativity, the choice should also embrace the Kattegat. The biomass estimations and the yearly major functional groups (Littler, 1980), as otherwise productivity calculations were combined with in- highly productive annuals might have to stand back formation on the topography and sediment struc- for abundant, but less productive, perennial species ture of the Kattegat seafloor to estimate the with a high standing stock. For the computations in production from macroalgae in four depth segments the present paper, nine generally abundant species of the eastern, western, and southern parts of of macroalgae, two Chlorophyta (green algae), three Kattegat. Phaeophyta (brown algae), and four Rhodophyta ARTICLE IN PRESS 2418 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432

Table 1 The Kattegat monitoring stations used in this study

No Station name Depth (m) Year interval Variables measured Position latitude Position longitude

NERI, Denmark (Anon., 2005) 1 Herthas Flak 10–20 1999–2002 C 571 385N 101521E 2 Tønneberg Banke 10–15 1999–2002 C 571 284N 111164E 3 Læsø Trindel 6–18 1999–2002 C 571 256N 111148E 4–6 Læsø (3 stations) 4–7 1996–1997 C 571 20N 1111E 7 Læsø sukkerrev 1–3 1996–1997 C 571 190N 111130E 8 Per Nilen 6–11 1999–2002 C 571 228N 111026E 9 Læsø rende 0–25 1998–1999 N, T 571 176N 101445E 10 Fladen 0–85 1998–1999 N, S, T 571 115N 111400E 11 Kims Top 14–19 1999–2002 C 571 008N 111355E 12 Aalborg Bugt 0–14 1998–1999 N, T 561 514N 101475E 13 Anholt E 0–25 1998–1999 N, T 561 400N 121070E 14–16 Hevring Bugt (3 st.) 1–6 1996 C 561 3N 1213E 17 Fornæs 0–13 1998–1999 N, T 561 335N 111020E 18 Store Middelgrund 9–18 1999–2002 C 561 333N 121042E 19 Lysegrund NE 0–25 1998–1999 N, T 561 225N 121020E 20 Briseis Flak 5–9 1999–2002 C 561 196N 111197E 21 Kullen 0–25 1998–1999 N, T 561 140N 121222E 22 Hesselø 0–25 1998–1999 N, T 561 099N 111480E 23 Hesselø 0.5–11 1999–2003 C 561 118N 111431E 24 Schultzs Grund 4–18 1999–2002 C 561 096N 111114E 25 Gniben 0–25 1998–1999 N, T 561 079N 111096E 26 Gilleleje 0.5–14 1999–2003 C 561 087N 121184E 27 Vilingebæk 1–11 1999–2003 C 561 065N 121229E 28 Ellekilde Hage 0.5–7 2001–2003 C 561 058N 121290E 29 Tisvildeleje 1–12 1999–2003 C 561 032N 121022E 30 Liseleje, Torup Flak 1–14 1999–2003 C 561 017N 111547E Hallands kustkontrollprogram, Sweden (Carlsson, 1996) 31 Kalvo¨ 0–3 1996 B, C 571 253N 121040E 32 Lerkil syd 0–6 1996 B, C 571 259N 111547E 33 Bua 0–5 1996 B, C 571 138N 121138E 34 Morups Ta˚nge 0–4 1996 B, C 561 564N 121217E 35 O¨ rna¨ s Udde 0–3 1996 B, C 561 382N 121490E Nordva¨stska˚nes kustvattenkommitte´, Sweden (Anon., 2001; Lundgren and Olsson 2001; Olsson, 2001) 36 Hovs Hallar 2–4 1996–2000 B, C 561 282N 121424E 37 St. Ma˚seska¨ r 1–4 2000 C 561 272N 121329E 38 Ramsjo¨ strand 0–4 1996–2000 B, C 561 231N 121395E 39 Arild 2–14 1996–2000 B, C 561 165N 121348E 40 Kullaberg nord 0–14 2000 C 561 182N 121277E 41 Kullaberg syd 0–16 2000 C 561 175N 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 The depth where 10% of the surface irradiation macroalgal biomass in the Kattegat. remains, zq (10%), roughly corresponds to the Macroalgae are found to live at depths down to Secchi depth (e.g. Højerslev, 1978), and the ratio 25 m in the Kattegat (e.g. Karlsson et al., 1998). zq (1%)/zq (10%) ¼ 2.18 (Jerlov, 1976). The mean Although macroalgal coverage was measured down value of the 3772 Secchi-depth measurements, taken to 20 m or more at some stations, no biomass 1960–1999 in the nineteen 0.51 square areas entirely measurements were available below 15 m. The depth within Kattegat, of 7.2 m (Aarup, 2002) thus of the photic zone, taken as the 1% light penetra- corresponds to a zq (1%) of 15.7 m. Hence, the tion level, zq (1%), is 15–18 m in Kattegat model simulations presented below gave very low (Richardson and Christoffersen, 1991), and 15.7 m production rates at 16 m, and practically zero at in A˚rhus Bay, SE Kattegat (Lund-Hansen, 2004). 18 m, for all species. Therefore, the macroalgal ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 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. Never- theless, 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; Fig. 3. The depth distribution of the mean measured biomass at Olsson, 2001). the Swedish stations (nos 31–36 and 38, 39 in Table 1). The To calculate the macroalgal production, it was dashed lines are 1 m depth intervals, used from 0–3 m in stations necessary to convert the macroalgal coverage (%) to 31–35. For each depth segment, the number of measurements biomass density (g dwt m2) of macroalgal standing (three replicates each) is given by n. The error bars give the standard deviations. stock at stations lacking this information. The conversion was made assuming that the species composition and depth zonation pattern was similar production, albeit not the biomass, below 15 m at all stations, that the above chosen species could depth was assumed to be negligible. represent all macroalgal species in Kattegat, and In this paper, the macroalgal habitat was divided that the coverage to biomass relation was linear into four depth zones; 0–2, 2–4, 4–8, and 8–15 m. In (Fig. 4). In each depth zone, a 100% cover of the 0–2 m depth zone at the eight Swedish stations macroalgae was set to correspond to a different that reported biomass, 80% of the total measured biomass and species composition. The respective macroalgal biomass consisted of Fucus serratus and depth zones were assigned a total biomass that F. vesiculosus. The latter species usually grows in would be approximately similar to the correspond- large amounts in the immediate vicinity of the ing total biomasses in Fig. 3, and with a relative shoreline, and may thus be underrepresented in the proportion of the chosen macroalgal species reflect- biomass measurements (Carlson, 1996). Red algae ing the species composition given above (Table 2). dominated in the two intermediate zones, from The chosen green macroalgae were Cladophora 2–8 m depth, where 47% of the measured biomass spp. and Ulva spp., opportunistic species commonly at the Swedish stations came from the perennial found in shallow locations in the Kattegat (e.g. cartilaginous macroalgae Chondrus crispus and Pederse´ n and Snoeijs, 2001). These annual algae do Furcellaria lumbricalis, whereas the filamentous not have a standing stock in the sense of the macroalgae Ceramium nodulosum and Polysiphonia perennials, but can anyway appear in large quan- fucoides together constituted an additional 16% of tities at certain locations during part of the year. the measured biomass. In the 8–15 m zone, per- They usually appear in locations sheltered from ennial red algae and Laminaria spp. dominated the wave exposure, where they can also detach from the measured biomass with about three fifths and one substrate and form floating mats (Pihl et al., 1999). fifth of the total biomass, respectively. The depth Thus, the mat-forming macroalgae are not entirely ARTICLE IN PRESS 2420 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432

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 (Carlson, 1996; Anon., 2001), although the Laholm Standing stock biomass of the chosen species of perennial Bay in SE Kattegat experienced mass occurrences of 2 macroalgae [g dwt m ] set to correspond to 100% coverage of ephemeral green algae in the 1970s and early 1980s macroalgae in the respective depth zones (Rosenberg et al., 1990). On the Danish side, Depth 0–2 m 2–4 m 4–8 m 8–15 m ephemeral green macroalgae are also common in the sheltered bays, but not in the open waters that is Species the subject of the current estimation (e.g. Pedersen F. serratus & F. vesiculosus 1000 — — — C. crispus & F. lumbricalis 150 400 100 100 and Borum, 1997). C. nodulosum & P. fucoides 150 200 100 — Contrary to the brown and red species, the green Laminaria spp. — — 100 50 macroalgae in this study were not evaluated in terms Total biomass 1300 600 300 150 of standing stock. Instead, the estimation was based The annual species, Cladophora & Ulva spp., were given a on the size of the sheltered area in the uppermost common seasonal biomass of 100 g dwt m2 in the uppermost depth zone (0–2 m), where ephemeral green macro- depth zone. algae are usually found, and a representative density. The area within 2 m depth in the Kattegat dependent on hard substrates for their development. is 405 km2. Sheltered areas, suitable for extensive At 16 monitoring stations in the NE Kattegat, growth of green ephemeral macroalgae (Pihl et al., Karlsson et al. (1998) found an ample coverage of 1999), are found chiefly in NE Kattegat, where green macroalgae at seven of eight sheltered around 90 km2 of the seafloor is inside 2 m depth. locations, whereas only four of eight exposed Here, the area above 2 m depth protected from wave locations reached even a fair coverage. Frequently, exposure, chosen as the habitat for the green 30% or more of the total shallow area from macroalgae in this study, was determined to be Tistlarna (N 571310) and northwards on the NE one third of this (Moksnes and Pihl, 1995; Kattegat coast may be covered with ephemeral Jenneborg et al., 2005), or 30 km2. The summer algae (Moksnes and Pihl, 1995; Jenneborg et al., mean biomass of green macroalgae in shallow bays 2005). On the more exposed coast further south, in NE Kattegat was 155 g dw m2 in 2003 (Jenne- only sparse amounts of green algae are reported borg, 2004) and 57 g dw m2 in 2004 (Jenneborg ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 2421 et al., 2005). The representative biomass density As briefly described above, and to more detail in of green macroalgae in this study was set to the Appendix A, the model computes growth of 100 g dwt m2. macroalgae from nutrient, light, and temperature forcing, and loss from a fixed loss rate. To find annual production figures, model simulations with 2.2. Primary production by macroalgae the loss rate set to zero were made for each of the red and brown macroalgal species (see below for The annual production, P, by macroalgae can be green algae). The production to biomass (P/B) ratio several times higher than the biomass, B (Lu¨ ning, for each of these species was taken to be its increase 1990). The production in a specific area may in biomass during a one year simulation, divided by however, due to the local environmental conditions, the initial biomass of the macroalgae. By using deviate substantially from the general picture, or results from the second year of a 2-year calculation, from observations made elsewhere. For biomass model spin-up effects were eliminated. The P/B calculations, reliable figures should, if available, ratio of the green algal species, could however not preferably be derived from studies made in the area be computed on an annual basis. The high daily of interest. growth rates together with the difficulty of deter- The production figures in this paper were mining what a reasonable annual biomass should computed with a process based model of macroalgal be, called for a different approach. The production growth, originally developed for green macroalgae of the green algae was computed based on the in shallow bays (O¨ berg, 2005), now adapted to the seasonal biomass values in Table 2, a productive species included in this study and to Kattegat open season of two months duration, and a model water conditions. The current adaptation of the computed daily growth rate according to Table 4. model, as described in the Appendix A, included the use of observed values of temperature and nutrient 2.3. Sea bottom topography and sediment concentrations as forcing functions. Further, the composition model made use of literature values of the nutrient uptake, growth, and photosynthesis variables of the To determine the area of suitable habitats for chosen species as shown in Table 3. The values in macroalgae, a digital sea bottom sediment map this table were selected to, as far as possible, be (Hermansen and Jensen, 2000), shown in Fig. 5, was representative for Kattegat conditions. Of the 30 used. The compilation of this map is based on data papers cited in Table 3, only nine have macroalgae from shallow seismic surveys also including side in Kattegat, Skagerrak, or the Baltic Sea as their scan sonar in combination with information from subject. However, 37 of the 64 individual variables grab samplers and other surface sediment samplers in Table 3 stem from these nine studies. as well as from sediment core data. Most of the The nutrient forcing thus consisted solely of open shallow seismic techniques applied have a sub- sea nitrogen concentrations measured about bottom resolution normally in excess of 0.5 m, monthly at the surface and downwards in 5 m depth which implies that the seabed classification pre- intervals during 1998–1999 by NERI (Anon., 2005) sented in the map refers to a series of seabed types at nine Kattegat stations (Fig. 1). The model derived from seismic integration of the upper 0.5 m simulations made use of monthly average values of sediment (Hermansen and Jensen, 2000). Of the + of the measured NO3 and NH4 concentrations seven categories in the sediment type map (Fig. 5), from all nine stations in the 5 m interval centred on the lag sediments (no 4–6) and the crystalline the current depth. The temperature data were taken bedrock (no 7) can be considered as hard-bottoms from the same stations, depths, and time period, (J.B. Jensen, p. comm.), suitable for macroalgae and used in the model with values interpolated to according to the criteria of Krause-Jensen et al. the current time and depth. The surface irradiation (2001). The sandy areas (no 3), although gravel and was taken from of global radiation observations in stones occur locally, are not clearly defined as either Go¨ teborg on the NE Kattegat coast measured in erosion or accumulation areas, and were thus five-minute intervals by SMHI during 1998–1999. judged as not suitable. The light was reduced to current depth values The sediment map was combined with two depth according to a photic depth of 15.7 m as explained databases, together covering the whole of Kattegat, above. to find the respective size of the depth zones used in ARTICLE IN PRESS 2422 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 C] 1 10, 14 10 24 27 29 30 31 31 Yes Yes Yes Yes No No Yes Yes Temperature limitation [0–25 ringia, Germany ¨ ) 1 23 s , k 2 I 800 16 22 22 22 22 22 12, 13 22 Em m ( 84 350 329 81 107 100 139 164 Light satura-tion point, River Ilm, Thu 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 Helgoland, NW Germany Black Sea, Ukraine Galicia, NW Spain N Atlantic ) 1 h 1 23 , 0.5 R 15 22 22 22 22 12, 13 22 22 Dark respiration rate, (mg C g dw 1.4 0.78 0.44 0.49 1 1 0.29 0.11 ) , 1 h max 1 P ning (1982) ¨ 23 12, 13 4 22 22 22 22 15 22 22 (mg C g dw 7.37 3 2 1.6 3.2 4.2 1.5 1 Max. photosynthetic rate, 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) Bolton and Lu Aleksandrov et al. (2002) Tasende and Fraga (1999) Bird et al. (1991) ) 2 31. Estimation 11 11, 15 21 31 30 21 26 31 Maximum areal density (kg dw m 0.6 0.6 3 2 0.5 0.5 3 0.6 ) 1 9 ,(d max ; 0.035 17, 19, 20 9 15, 20 9 9 m 6, 7, 8, 9,9 10 28 30 Maximum growth rate, 0.31 0.27 0.012 0.019 0.014 0.148 0.02 0.021 0.01 , 3 max ) 4, 5 1 V h 1 1190 . Oxygen based photosynthetic rates were converted to carbon fixation assuming a 1.2 photosynthetic quotient. 3 1, 2, 3 17 25 3 25 25a 3 ggdw m uptake rate, 346 1466 246 103 217 804 864 66 Maximum NO ( Falmouth, Massachusetts, USA 16. NE New Zealand 17. Baltic Sea, and various locations 18. Cadiz Bay, SW Spain 19. W Baltic Sea, Germany 20. San Francisco Bay, California, USA 21. Mondego estuary, W Portugal 22. Mondego estuary, W Portugal 23. Roskilde fjord, E Denmark 24. S England, UKSkagerrak and Kattegat, WLaguna Sweden Beach, California, USA 26. A. Samuelsson, p. comm. 27. 25. Kattegat, W Sweden Mediterranean Sea, NE Spain 28. Disko Island, W GreenlandCape Cod, Massachusetts, USA 30. 29. ), 3 ) 1 18 gl 714 m 17 25 25a 3 ,( 1, 2, 3 3 3 25 m constant (NO k 131 87 155 68 37 497 229 Atkinson and Smith, 1983 -uptake. + 4 leathery Green, filamentous Brown, leathery Red, filamentous Red, filamentous Red, cartilaginous Red, cartilaginous F. & spp. Brown, ndez et al. (2002) ´ quez et al. (1995) ´ spp. Green, leaf-like 39 Taylor et al. (2001) Pihl et al. (1996) Arnold and Murray (1980) Enrı Bishoff and Wiencke (1993) Peckol and Rivers (1995) Refers to NH Fujita (1985) Taylor and Rees (1999) Wallentinus (1984) Herna Lotze and Schramm (2000) Fong et al. (1994) Martins et al. (1999) Martins and Marques (2002) Nielsen and Sand-Jensen (1990) a Author1. Location Table 3 Growth parameters for the chosen macroalgal speciesSpecies used in the model simulations TypeCladophora spp. F. serratus vesiculosus Half-saturation Laminaria Ceramium nodulosum Polysiphonia spp. Chondrus crispus Furcellaria lumbricalis Carbon contents according to Ulva 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 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 0 m 1 m 2 m 3 m 4 m 5 m 6 m 7 m 8 m 10 m 12 m 14 m 16 m 18 m N & O B & P

Species Ulva spp. 0.05a 0.04a 0.03a —————————— — 0.15a 0.446a Cladophora spp. 0.21a 0.17a 0.12a —————————— — 0.49a 0.203a F. serr.&F. ves. 0.61 0.59 0.54 —————————— — 0.057a 0.039a C. crispus 0.55 0.54 0.51 0.50 0.46 0.43 0.41 0.38 0.33 0.23 0.17 0.08 0.02 2 104 0,037a N. a. F. lumbricalis 0.28 0.27 0.26 0.24 0.22 0.21 0.20 0.19 0.16 0.11 0.09 0.05 0.02 0.005 0.025a N. a. C. nodulosum 12.1 11.4 10.0 9.0 7.6 6.7 6.5 5.6 4.6 2.9 2.1 0.59 0.02 0 0.26a 0.294a Polysiphonia spp. 2.6 2.5 2.3 2.1 1.8 1.6 1.3 1.1 0.85 0.48 0.27 0.08 0.006 0 N. a. N. a. Laminaria spp. — — — — 0.75 0.69 0.68 0.63 0.56 0.42 0.32 0.15 0.03 0 0.027a N. a.

N. a.: Not available. N & O: From Nilsson and Oom (1988). B & P: From Borum and Pedersen (1996). aDaily 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 2.4. Computation of total biomass and annual and Jensen, 2000). production the macroalgal biomass determinations (Fig. 1). The The coverage figures for the stations that lacked Danish digital map (Anon., 2000) covers the information on biomass were converted to biomass western and central parts of Kattegat up to according to the assumptions above, using the mean 57.51N with a varying horizontal resolution of coverage value of the stations in each sub-area and down to 25 m, whereas the IOW Baltic region map depth segment. For each depth segment, the model (Seifert et al., 2001), that has 1 NM (1852 m) calculated P/B ratios of each of the chosen species 1 1 resolution north of 56 N and 2 NM south of this, were multiplied with their given proportion of the was used for the Swedish coast and the area north of biomass to get annual production figures per unit 57.51N. The two depth databases are compiled area, and with the size of the suitable areas to find using available data from sea surveys made the total yearly production. The carbon content of throughout the 20th century with varying resolution the macroalgae was assumed to be 31% of the dry and accuracy (Anon., 2000; Seifert et al., 2001). The weight (Atkinson and Smith, 1983). coarse resolution of the IOW map was improved by manual addition of depth information from Swed- 3. Results ish coastal charts. This study adopts the division suggested by The model simulations of the growth of the nine Danielsson et al. (2004), who identify three regions macroalgal species in this study were made to obtain ARTICLE IN PRESS 2424 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 yearly production to biomass ratios for the red and here of the lag types, except now mainly on brown species, and daily growth rates for the green quaternary clay and peat. macroalgae. The result of the model runs is shown The observed mean coverage at the stations where in Table 4. For comparison, literature values of biomass figures were unavailable is shown in Table 6, locally measured daily growth rates (Nilsson and and the macroalgal standing stock (calculated from Oom, 1988; Borum and Pedersen, 1996) are shown coverage figures, or directly measured) for all areas is in the two rightmost columns of Table 4. shown in Table 7. With the exception of the The sediment and depth maps were combined to Chlorophyta species, the macroalgal annual produc- find hard-bottom areas above 15 m depth (Fig. 6). tion was calculated from the size of the standing Table 5 shows the areas judged to be suitable for stock, multiplied with the P/B ratios derived from macroalgae, a total of 1438 km2 or 6.6% of the full model simulations. The influence of the nutrient, Kattegat area. The Jutland shelf has 62% of the light, and temperature limitation functions on the suitable area, almost all of which is found below 4 m macroalgal growth rate is seen in Fig. 7. depth. This shallow western and central part of the The estimated macroalgal yearly production is Kattegat seafloor is covered by lag sediment on shown as production per m2 in Table 8 and as total glacial till, sand, and sandy mud, where only the figures for the respective sub-areas in Table 9. The first was judged as a suitable substratum for total macroalgal yearly production in the Kattegat macroalgae. These sediment types also dominate was found to be 119 106 kg C y1, and to reach in the southern part (11% of the suitable area) with 0.5 kg C m2 y1 in the most productive, shallow the addition of lag sediments on quaternary clay areas. In the 0–2 m depth segment of NE Kattegat, and peat. The seafloor on the steeper Swedish side green macroalgae contributed with 315 g C m2 y1 (27% of the suitable area) does have some solid on 30 km2 of sheltered areas, or 9.5 106 kg C y1 in rock, but 94% of the suitable sediments are also 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 Jutland shelf Southern Stations 18, interval (m) Kattegat 37 and 40, 41

0–2 100 78 95 2–4 98 96 90 4–8 94 99 100 8–15 87 82 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 7 Macroalgal standing stock, from calculated or measured values, in total figures for each area (1000 ton C) Table 5 Hard surface areas [km2] in Kattegat Depth Jutland Southern Eastern Total interval (m) shelf Kattegat Kattegat Depth Jutland Southern Eastern Total area interval (m) shelf Kattegat Kattegat 0–2 3.2 0.7 12.1 16 2–4 2.4 1.2 8.0 12 0–2 8 2 43 53 4–8 27 4.5 6.3 38 2–4 13 6 37 56 8–15 15 2.4 13 30 4–8 305 49 62 416 Total 48 9 39 96 8–15 574 97 242 913 Total 900 154 384 1438 Calculated figures from stations 18, 37, 40 and 41 (Table 1) are included in the measured figures from E Kattegat. ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 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 Table 9 Annual production per unit area (g C m2 y1) Annual production (1000 ton C y1)

Depth Jutland shelf Southern Eastern Depth Jutland Southern Eastern Total interval (m) Kattegat Kattegat interval (m) shelf Kattegat Kattegat production

0–2 514 401 320 0–2 4 1 26 31 2–4 376 369 432 2–4 5 2 16 23 4–8 138 145 163 4–8 42 7 10 59 8–15 5 4 11 8–15 3 0.4 3 6 Total 54 10 55 119 half saturation constant, km, the maximum nutrient uptake rate, Vmax, the maximum growth rate, mmax, the water NO3 concentration, the irradiation, and are representative, that the choice of species is well- the water temperature. The resulting shifts in the founded, that the conversion of coverage to biomass results were moderate, with a positive or negative is justiﬁed, that the hard bottom areas are correctly response of usually about 5% for the tested represented, and that the model parameters and variables. estimations are realistic. The macroalgal monitoring programmes differ in 4. Discussion the various parts of Kattegat. The NERI visits each station on an annual or bi-annual basis, whereas This study is based on publicly available macro- most of the Swedish efforts are less intense. The algal monitoring data, combined with hydrographic monitoring methods are however similar in all and sediment structure data, and model computed studies used for this study. The more than 200 macroalgal productivity. The accuracy of the species of macroalgae reported found in Kattegat estimations rely on that the monitoring stations since 1970 (Nielsen et al., 1995) makes it difﬁcult to ARTICLE IN PRESS 2426 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432

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. 0.96 1.05 1.06 1.04 0.99 0.97 Cladophora spp. 0.99 1.00 1.11 1.02 0.94 0.92 F. serr.&F. ves. 0.94 1.06 1.07 1.06 1.02 0.98 C. crispus 0.94 1.07 1.07 1.07a 1.02 0.98 F. lumbricalis 0.94 1.06 1.08 1.06 1.02 0.93 C. nodulosum 0.93 1.05 1.06 1.05 1.04 1.01 Polysiphonia spp. 0.98 1.02 1.14 1.02 1.04 0.99 Laminaria spp. 0.95 1.05 1.07 1.05 1.02 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 species mostly belong to the same functional groups Relative sensitivity of the annual production to a 10% increase of as the chosen species, and thus have similar growth selected variables and production characteristics. An unfavourable circumstance for the calcula- Depth ku Vmax mmax NO3 conc. Irradiation Temperature (m) tions is the lack of biomass ﬁgures at the Danish stations. The biomass versus coverage relation used 1 0.94 1.05 1.07 1.05 1.03 0.99 is based on the simultaneous coverage and biomass 3 0.94 1.05 1.08 1.05 1.04 1.00 6 0.94 1.05 1.07 1.05 1.04 0.99 ﬁgures given for the Swedish stations, but this does 10 0.95 1.06 1.07 1.06 1.02 0.96 not necessarily imply that the relationship is correct for macroalgae in the western half of Kattegat. The individual entries in the table represent a situation with Different degrees of exposure could cause the 100% coverage and species composition in conformity macroalgae to vary in shape, and thus also in with Table 2. The calculation was made according to Rel:sens: ¼ weight. Dahl et al. (2004) have shown that, on a

P9 central Kattegat stone reef, a fair relationship of ½ðT2z T4z T10varÞ=ðT2z T4zÞ where sp(1–9) is the macro- biomass and coverage exists for Phycodrys rubens sp¼1 algal species from Ulva to Laminaria, T2z and T4z are entries but not for Polysiphonia fucoides and Rhodomela from Table 2 and Table 4 at depth z, respectively, and T10var is confervoides. Although the biomass versus coverage entries from Table 10 for each variable var. 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 represent all aspects of such a vast diversity through be applied to all species. The linear relation was still the only nine species chosen for this study. To deemed to be consistent with Fig. 4, and was used include all occurring species in this calculation for all species in this study. Also, the biomasses set would however hardly be feasible. As the species to match the 100% coverage (Table 2) are generally composition is similar on both sides of the Kattegat lower than what is indicated in Fig. 4. As the total (Nielsen et al., 1995), the general species distribution measured biomass in each depth segment (Fig. 3) was assumed to be the same in all of Kattegat. The was not to be exceeded even for 100% coverage, the chosen nine species represent about three quarters individual biomass figures in Table 2 were set to of the total biomass measured at the monitoring mirror the relative proportions only. Also, the mean stations. As both perennial and ephemeral species values of the measured macroalgal cover on suitable are included, reflecting the different production substrates being between 72% and 100%, and rates of these functional groups, the chosen species indeed above 90% in seven of the 12 measure- can be considered representative for the total ments, indicate that the determination of the total macroalgal biomass in the Kattegat. The remaining biomass is more important for the accuracy of ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 2427 the result than the linearity of the biomass versus limiting factors interact to produce a greater coverage relations. reduction in the growth rate than suggested by their Either of the environmental variables nutrients, individual contributions. To reliably reproduce such light, or temperature, may limit the algal growth potential interactions in the model equations would during various parts of the year. In contrast to however stretch beyond the intent of the simple production figures taken from field studies made in model used here. The outcome of the model runs summer, the model calculations employed in this thus originates from the adoption of simultaneous study expressively considers the influence of these limitation functions, and from the quality of the growth limiting factors during all seasons. When forcing data and model parameters. interpreting production measurements made in high The P/B ratios, derived from model simulations light conditions, i.e. in summer, the light saturation using literature values of growth parameters taken status of the macroalgae must be considered to from several studies in various locations, are not ensure that correct results are reached (Wallentinus, exact figures as if taken from local in situ growth 1978). Provided accurate and appropriate growth studies, but can anyway be seen as representative for variables are at hand, model simulations may even a Kattegat location. Despite the seemingly hetero- be preferred before using production figures from geneous selection of growth parameters (Table 3), a short-term measurements. By using model calcula- majority of these stem from Kattegat or adjoining tions with local environmental forcing and growth waters, with most of the remainder taken from other parameters, an adequate interpretation of the limit- temperate areas. There is thus reason to believe that ing factors influence on the macroalgal growth the model simulations gave a fair representation of throughout the year is distinctly possible. the annual production rates in Kattegat. The The primary responses of the nutrient, light, and current formulation of the model is based on temperature limitation functions (Fig. 7) are rather nitrogen alone. An expansion of the model equa- self-evident. The nutrient limitation is strongest in tions to include phosphorus should increase the the main growth season, light is more limiting at reliability of the model results, as local phosphorus depth than at the surface, and both low and high limitation could act to decrease growth. The temperatures will adversely affect macroalgal nutrient data used in the model simulations stem growth. Less obvious is that although the limitation from open sea monitoring stations. Local runoff functions are normalised, each function may still may give higher nutrient concentrations to certain limit the growth of an individual species throughout coastal locations, thereby potentially increasing the the year. Macroalgae growing at depth will be light macroalgal production. limited even in the height of summer. At usual In addition to the model computed results, Table nutrient concentrations, species with high half- 4 also lists for comparison locally measured daily saturation constants, km, will not be able to reach production figures from Nilsson and Oom (1988) the maximum growth rate predicted by the model regarding eight of the chosen species, and from equations. Another interpretation to the latter Borum and Pedersen (1996) regarding four of these observation is that species with high km will not be species. Nilsson and Oom (1988) measured the daily as adversely affected from lowered nutrient con- production by 25 species of macroalgae during the centrations as the opportunistic species with low km first half of July 1985 at Tja¨ rno¨ Marine Biology values. Station on the Swedish Skagerrak coast. The report The actual growth rate used in the model imposes by Nilsson and Oom (1988) contains temperature simultaneous nutrient, light, and temperature lim- and light data for the studied period, but unfortu- itation on the macroalgae. The growth rate (Ap- nately not any information about the nutrient levels pendix A, Eq. (4)) is given by the product of the in the water where the study was made. The nutrient maximum growth rate and three independent limit- situation at the time of observation can however be ing functions, thus suggesting the response of the assumed to be at least satisfactory, because data macroalgae to a change in, e.g. temperature to be from a nearby monitoring station in the Kosterfjord 1 independent to the status of the other limiting show very high NO3 values (4100 mgl ) on June factors. An alternative option would be to only 18, and 5 mgl1 remaining on July 11, 1985 (B. Rex, consider the most limiting factor at any given time, p. comm.). Borum and Pedersen (1996) made in which case the resulting growth rate would be laboratory culture experiments on six macroalgal higher. Another possibility is that the growth species collected in southern Kattegat, measuring ARTICLE IN PRESS 2428 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 the daily growth rates along with other variables. deepest zone, the total Kattegat macroalgal produc- The modelled P/B ratios in this paper are not very tion was rather similar in the other three zones. high compared to the summer growth rates pub- The relative error in the above estimations is lished in the two papers, but their relative order probably larger for the suitable areas and biomass agree. Whereas the annual P/B ratios in this study measurements than for the macroalgal nutrient all decrease with increasing depth, the summer uptake as well as for the calculation of the P/B situation, markedly influenced by photoinhibition, ratios and production. Should a more detailed is clearly reflected in the July 1985 depth gradient sediment classification and a higher density of experiments on four species by Nilsson and Oom macroalgal biomass measurements be available, a (1988) as the production hardly decreased from the more exact estimation of the macroalgal production surface down to 5 m depth. Only further down to in the Kattegat could be made. 10 m a slight reduction in production was seen. Kautsky and Kautsky (1995) have made mea- The sediment survey methods used and the few surements of the macroalgal biomass along the sediment classes defined imply that there is a certain Swedish coast of the Baltic Sea, and have calculated variation within the sediment classes. With more the total annual macroalgal production in that area. detailed sediment information, some of the lag The dominating species in the Baltic Sea are, as sediment areas (1419 km2 above 15 m depth) might reported by Kautsky and Kautsky (1995), F. be classified as unsuitable sediments, and some of vesiculosus, F. lumbricalis, and in addition F. the sand areas (5003 km2 above 15 m depth) as serratus in the south, whereas annual algae have suitable. The lag sediments are mainly erosion areas 30% of the biomass. The total standing stock of with relatively hard bottoms, whereas the available macroalgae, according to Kautsky and Kautsky data is insufficient to further divide the sand areas (1995), is 109 106 kg C on the Swedish side of the into erosion or accumulation bottom types (J.B. Baltic, which can be compared to the 34 106 kg C Jensen, p. comm.). Hence, the current estimate of on the Swedish Kattegat coast or 95 106 kg C the suitable area may be somewhat exaggerated, but standing stock of macroalgae in all of Kattegat there is also a distinct possibility that the area is (Table 7). As for production, the figures to be understated. About two thirds of the suitable areas compared are 565 106 kg C y1 on the Swedish are in the deepest (8–15 m) interval, where light is Baltic coast, and 119 106 kg C y1 in the Kattegat. often the main limiting factor. Less than 10% of the While the size of the standing stocks in the two seas areas are located in the most productive zone above are fairly similar, the production estimates are not. 4 m depth. The macroalgal production estimates in Kautsky The rocky shores found in the NE and SE parts of and Kautsky (1995) are based on literature values, Kattegat offer ideal substrates for macroalgae, but corrected for irradiation but not for nutrient the highest standing stock of the three areas was availability or temperature, whereas the production recorded on the Jutland shelf. Although the total figures in this study come from full-year model amount of macroalgae in the eastern part of simulations where both nutrient availability, and Kattegat was four fifths of that in the western part, ambient light and temperature, have influenced the the suitable area in the eastern part was less than results. The three growth limitation functions used half of that on the Jutland shelf. The depth in the model simulations, each reduces the growth at distribution of the standing stock biomass also a different time of the year. Thus, instead of a differs considerably between the two areas, reflect- seasonal decrease in growth resulting from the ing the diverse sediment structures. Whereas the application of a single restraint, the macroalgae in macroalgae on the Jutland shelf were almost this study are subject to a persistent limitation exclusively found below 4 m depth, the eastern throughout the year; nutrient availability limits counterpart had a much more even distribution. On growth in summer, access to light in winter, and depths shallower than 4 m, nearly three quarters was low temperature in spring. found in the eastern part, while the Jutland shelf The total production by macroalgae in Kattegat only harboured one fifth of the standing stock. was above estimated to 0.12 109 kg C y1. For Combined with the higher productivity on the comparison, the current estimate of the pelagial shallower depths, this acted to even out the production in Kattegat (Rydberg et al., 2006) differences in production in the respective depth of about 200 g C m2 y1 gives a total of 4 zones so that, except for the low production in the 109 kg C y1. As the area available for the macro- ARTICLE IN PRESS J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432 2429 algae is much smaller than the total surface area of loss rate. The assimilation of nutrients by the Kattegat, the productivity per unit area is considerably macroalgae is modelled in two steps, with nutrients higher than for plankton. Although the macroalgal first taken up into an internal nutrient pool before share of the primary production could perhaps be being used in the growth process. The internal disregarded in the context of the total marine storage of nutrients is modelled according to Droop primary production in Kattegat, it should be (1968). Here, the rate of change of the internal pool considered when discussing the production in the is governed by shallow areas where macroalgae grow. The benthic dQ production also includes production by microalgae ¼ V mQ (2) dt and rooted plants, further adding to its importance. This should be included in later studies. with Q (mg N/g dw) the internal nitrogen quota of the algae, t time, and V (mmol h1 g1) the nitrogen Acknowledgements uptake rate. The effective uptake rate V of nitrogen into the internal nutrient pool, Q (mg N/g dw) was Thanks are due to Anders Stigebrandt for formulated as constructive help throughout the work, to Inger N Qmax Q Wallentinus for helpful comments on the manu- V ¼ V max , (3) km þ N Qmax Qmin script, and to Karsten Dahl for preliminary data. 1 1 My thanks also extend to three anonymous where Vmax (mmol h g ) is the maximum nutrient 1 reviewers whose comments greatly aided to improve uptake rate, km (mgl ) is the half-saturation the manuscript. This work was in part financially constant for nitrogen uptake, and Qmax and Qmin supported by the Swedish Foundation for Strategic the upper and lower limits of the pool size, Research (MISTRA) through MARE—Marine respectively. The subsequent growth of the macro- research on eutrophication, and by the Faculty of algae is modified by the access to nutrients, light, Science at Go¨ teborg University. and temperature so that the actual growth rate, m (day1), can be described by

Appendix A. A brief description of the macroalgal m ¼ mmaxf 1ðNÞf 2ðLÞf 3ðTÞ, (4) model 1 where mmax (day ) is the maximum growth rate, N nutrients (NO ), L incident light, and T tempera- This model describes the development of macro- 3 ture, while f are the normalised functions of the algae through their nutrient uptake and growth 1–3 limiting variables. capacity, limited by access to nutrients, light, and The nutrient limitation function, f (N), controls ambient temperature. The model (O¨ berg, 2005) was 1 the transfer of nitrogen from the internal nutrient originally developed to simulate the growth of green pool to algal growth through algal mats in a shallow bay, but is here generalised to accommodate all the macroalgae in the current Q Q ð Þ¼ min study, one at a time. This was made by including f 1 N . (5) Qmax Qmin model variable values for all species in this study (Table 3), and by using values of ambient nutrient The uptake of nitrogen into the internal pool (Eq. concentrations, light, and temperature measured (3)) is thus guided by Michaelis–Menten kinetics locally in Kattegat. and by the nutrient status of the pool, while the The model is set up as a box, with no horizontal growth is limited by the amount of nitrogen resolution. A run with the current version of the available in the pool. The light limitation function, model simulates, in time steps of one day, the f2(L), accounts for reduction of photosynthesis from development of one macroalgal species for an low irradiation and from photoinhibition. This arbitrary time period. The development of the live relationship was described by the following equa- algae is described by tion (Platt et al., 1980) a b dB P ¼ Psð1 e Þe R, (6) ¼ðm OÞB, (1) dt where P (mg C g dw1 h1) is the photosynthetic 2 where B (kg m ) is the amount of live macroalgae, rate and Ps the maximum rate if there were no 1 1 m (day ) is the growth rate, and O (day ) is the photoinhibition. Further, a ¼ aI=Ps and b ¼ bI=Ps ARTICLE IN PRESS 2430 J. O¨berg / Continental Shelf Research 26 (2006) 2415–2432

1 1 1 where a (mg C g dw h mE ) is the photosyn- Anonymous, 2005. Den nationale database for marine data, thetic efficiency, I (mE) the light intensity, b MADS. WWW Page, http://www.dmu.dk/Vand/Havmilj% (mg C g dw1 h1 mE1) the photoinhibition para- C3%B8/MADS/, The Danish National Environmental meter, and R (mg C g dw1 h1) is dark respiration. Research Institute (in Danish). Arnold, K.E., Murray, S.N., 1980. Relationships between For each species, Eq. (6) was fitted to the respective irradiance and photosynthesis for marine bentic green algae values of R, the saturation point, Ik, and the (Chlorophyta) of differing morphologies. Journal of Experi- maximum photosynthesis rate, Pmax. The measured mental Marine Biology and Ecology 43, 183–192. Atkinson, M.J., Smith, S.V., 1983. C:N:P ratios of benthic surface irradiation, I0, was reduced to current depth values according to marine plants. Limnology and Oceanography 28, 568–574. Baird, M.E., Walker, S.J., Wallace, B.B., Webster, I.T., Parslow, I ¼ I eKdz, (7) J.S., 2003. The use of mechanistic descriptions of algal growth z 0 and zooplankton grazing in an estuarine eutrophication model. Estuarine, Coastal and Shelf Science 56, 685–695. where Iz is the irradiation at depth z, and Kd is the attenuation coefficient (e.g. Kirk, 1994). In the Biber, P.D., Harwell, M.A., Cropper, W.P., 2004. Modelling the dynamics of three functional groups of macroalgae in tropical model, zq (1%) ¼ 15.7 m as above, which corre- seagrass habitats. Ecological Modelling 175, 25–54. sponds to Kd ¼ 0:293. The original version of the Bird, C.J., Saunders, G.W., McLachlan, J., 1991. Biology of model (O¨ berg, 2005) assumes the photosynthetic Furcellaria lumbricalis (Hudson) Lamoroux (Rhodophyta: yield to be converted to growth without any further Gigartinales), a commercial carragenophyte. Journal of losses, and this is also the case with the current Applied Phycology 3, 61–82. 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