Variation in Serripes Groenlandicus (Bivalvia) Growth in a Norwegian High-Arctic Fjord: Evidence for Local- and Large-Scale Climatic Forcing

Global Change Biology (2006) 12, 1595–1607, doi: 10.1111/j.1365-2486.2006.01181.x

Variation in Serripes groenlandicus (Bivalvia) growth in a Norwegian high-Arctic fjord: evidence for local- and large-scale climatic forcing

WILLIAM G. AMBROSE Jr.*w , MICHAEL L. CARROLLw , MICHAEL GREENACREz, SIMON R. THORROLD§ andKELTON W. McMAHON*§ *Bates College, Department of Biology, Lewiston, ME 04240, USA, wAkvaplan-niva, Polar Environmental Centre, N9296 Troms, Norway, zPompeu Fabra University, 08005 Barcelona, Spain, §Woods Hole Oceanographic Institution, Biology Department MS 50, Woods Hole, MA 02543, USA

Abstract We examined the growth rate of the circumpolar Greenland Cockle (Serripes groenlandicus) over a period of 20 years (1983–2002) from Rijpfjord, a high-Arctic fjord in northeast Svalbard (801100N, 221150E). This period encompassed different phases of large-scale climatic oscillations with accompanying variations in local physical variables (temperature, atmospheric pressure, precipitation, sea ice cover), allowing us to analyze the linkage between growth rate, climatic oscillations, and their local physical and biological manifestations. Standard growth index (SGI), an ontogenetically adjusted measure of annual growth, ranged from a low of 0.27 in 2002 up to 2.46 in 1996. Interannual variation in growth corresponded to the Arctic climate regime index (ACRI), with high growth rates during the positive ACRI phase characterized by cyclonic ocean circulation and a warmer and wetter climate. Growth rates were inﬂuenced by local manifestations of the ACRI: positively correlated with precipitation and to a lesser extent negatively correlated with atmospheric pressure. A multiple regression model explains 65% of the variability in growth rate by the ACRI and precipitation at the nearest meteorological station. There were, however, complexities in the relationship between growth and physical variables, including an apparent 1 year lag between physical forcing changes and biological response. Also, when the last 4 years of poor growth are excluded, there is a very strong negative correlation with ice cover on a pan-arctic scale. Our results suggest that bivalves, as sentinels of climate change on multi-decadal scales, are sensitive to environmental variations associated with large-scale changes in climate, but that the effects will be determined by changes in environmental parameters regulating marine production and food availability on a local scale. Key words: Arctic, arctic climate regime index, benthic community, benthos, bivalve growth, climate forcing, Serripes groenlandicus, Svalbard

Received 6 July 2005; revised version received 21 December 2005; accepted 3 March 2006

panied by changes in terrestrial and marine ecosystems Introduction (Oechel & Vourlitis, 1997; Morison et al., 2000; Serreze The Arctic climate has changed dramatically in the last et al., 2000). Effects of persistent climate change on several decades (Maxwell, 1997; Overpeck et al., 1997; Arctic marine ecosystems are largely undetermined, Johannessen et al., 2003, 2004; AICA, 2004). The average but changes that occur in response to decadal-scale annual air temperature has increased by 11 to 4 1C in the climate oscillations may provide insight into longer- last half century (AICA, 2004), and this has been accom- term effects of more persistent climate change. Several large-scale climate oscillations have been Correspondence: W. G. Ambrose, Jr., Bates College, Department of shown to influence marine systems (see Allan et al., Biology, Lewiston, ME 04240, USA, tel. 1 1 207 786 6114, 1996; Ottersen et al., 2001; Walther et al., 2002; Stenseth fax 1 1 207 786 8334, e-mail: [email protected] et al., 2003 for reviews). Linkages between the two r 2006 The Authors Journal compilation r 2006 Blackwell Publishing Ltd 1595 1596 W. G. AMBROSE et al. climate oscillations with nodes centered in the Arctic, latitude bivalves have life spans of decades (Tallqvist & the Arctic oscillation (AO) and the Arctic climate regime Sundet,2000;Mu¨ller-Lupp et al., 2003; Sejr & Christensen, index (ACRI), and the marine ecosystem, however, have 2006) to well over 100 years (Turekian et al., 1975; not been demonstrated. Both indices reflect differences Thompson et al., 1980; Zolotarev, 1980; Peck & Bullough, in wind-driven motion in the central Arctic alternating 1993; Witbaard et al., 1999; Sejr et al., 2004). Bivalves can, between two phases, an anticyclonic circulation regime thus, serve as bioproxies by providing uninterrupted (ACCR) and a cyclonic circulation regime (CCR). The records of environmental conditions over decades to climate regimes manifest as physical variables in the centuries, which is critical in the Arctic given the paucity Arctic; ACCR is characterized by a cold and dry high- of long-term data on community structure and dynamics. Arctic atmosphere and a colder and saltier polar ocean We examined interannual variation in growth of the (low AO and negative ACRI), whereas the cyclonic circumpolar Greenland Cockle (Serripes groenlandicus) regime is characterized by a warm and wet atmosphere from 1983 to 2002 in a high-Arctic fjord in northeast and a warm and fresh polar ocean (high AO, positive Svalbard (Norwegian Arctic) to explore the relationship ACRI). Climatic conditions associated with both the AO between benthic communities and environmental var- and ACRI may affect marine ecosystems as has been iations associated with decadal climate oscillations in demonstrated for the North Atlantic oscillation (NAO) the Arctic. Variation in bivalve growth associated with (Ottersen et al., 2001). changes in environmental conditions that occur over the Seafloor communities may be the best location to course of a decadal-scale oscillation cycle provides in- examine the impact of Arctic climate oscillations, and sight into the response of a dominant member of the by extension the potential effects of climate change on Arctic benthos to predicted long-term climate change. the Arctic ecosystem. There is often a close relationship between water column and benthic processes (Grebme- Materials and methods ier et al., 1988; Ambrose & Renaud, 1995; Piepenburg et al., 1997; Wollenburg & Kuhnt, 2000; Dunton et al., Study site 2005), therefore, long lived, sessile benthic organisms, may be more appropriate monitors of climate change Rijpfjord (801100N, 221150E) is located on the north- (e.g. Kro¨ncke et al., 1998, 2001; Dunton et al., 2005) than central shore of Nordaustlandet (Fig. 1), north and east the more transient pelagic system. Additionally, benthic of Spitsbergen in the Svalbard Archipelago. Rijpfjord is communities are key components in the carbon cycle on oriented south–north and opens to a broad shallow Arctic shelves (Grebmeier et al., 1989; Grant et al., 2002; shelf of approximately 200 m depth extending to the Stein & Macdonald, 2004; Clough et al., 2005) and food shelf-break of the Polar Basin at roughly 811N. The for higher trophic levels (e.g. bottom feeding fish, bottom depth averages 200–250 m, but an irregular sill mammals, and birds; Dayton, 1990). Consequently, crosses the width of the fjord midway through its changes to the benthos may have profound effects on length. Shallower depths are dominated by bedrock carbon cycling, trophic structure, and food web dy- and stones covered with a thin layer of mud, while soft namics on Arctic shelves. sediments predominate deeper sections. Bivalves comprise a significant proportion of the Rijpfjord is a true polar fjord. It is predominately ice- benthic biomass of Arctic shelves (Zenkevich, 1963; covered for at least 9 months a year (October–June), McDonald et al., 1981; Gulliksen et al., 1985; Grebmeier with breakup occurring between mid-July and mid- et al., 1988; Dayton, 1990; Feder et al., 1994). The shells of August. Even during the summer period, winds often most bivalves exhibit periodic banding, or growth lines force drifting pack ice into the fjord. The shallow outlet (Rhoads & Pannella, 1970; Clark, 1974; Rhoads & Lutz, of the Rijpfjord shelf to the Polar Ocean results in little 1980), that have proved valuable in developing a history warm Atlantic subsurface water entering the fjord from of environmental change in marine systems (Andrews, the north (A. Sundfjord, unpublished data). The upper 1972; Hudson et al., 1976; Jones, 1981; Jones et al., 1989; 10 m is a mixed layer with surface water of lower Witbaard, 1996; Witbaard et al., 1997, 1999; Tallqvist & salinity and higher temperature associated with sea Sundet, 2000; Scho¨ne et al., 2003; Mu¨ller-Lupp & Bauch, ice melt and in situ radiative warming; below which is 2005). Temperature and food are the two main factors a water mass with a temperature consistently o1 1C influencing bivalve growth (Beukema et al., 1985; Jones and a salinity 433% (Fig. 2). et al., 1989; Beukema & Cadeé, 1991; Lewis & Cerrato, 1997; Witbaard et al., 1997, 1999; Dekker & Beukema, 1999; Bivalve collection Scho¨ne et al., 2005), and both are likely to be influenced by climate change in Arctic marine systems (Carroll & Divers collected four Serripes groenlandicus individuals Carroll, 2003). Furthermore, many deep water and high from 17 m depth on 28 August 2003. Individuals were

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50 Temperature

100 Salinity Depth (m)

150

200 –2–10123 Temperature (°C) 28 30 32 34 36 Salinity (‰)

Fig. 2 Temperature and salinity proﬁle from Inner Rijpfjord, near the location of bivalve collection, on 28 August 2003.

Fig. 1 Map of the study region showing the Spitsbergen Archi- pelago, general current patterns, the locations of the meteorological stations (BI, Bear Island, HO, Hopen, LB, Longyearbyen, a Thermo Finnigan Element2 single collector sector field NA˚ ,NyA˚ lesund), and the collection site of Serripes groenlandicus ICP-MS. The valves were mounted in resin and a 1.0 mm (Rijpfjord, RF). The polar front is the average location of max- cross-section was cut along the axis of maximum imum ice cover in late winter. growth using a low-speed saw equipped with a dia- mond blade. The section was mounted on a petro- graphic slide with cyanoacrylic glue, polished using identified by their pale, white siphon, and were dug 30 and 3 mmAl2O3 lapping film, and then decontami- from the sediment where they were buried to a depth of nated in a clean room. Each valve was scrubbed with a

5 cm. All animals were alive at the time of collection. nylon brush, triple rinsing with 2% HNO3, sonicated for Soft tissues were immediately separated from the shells, 5 min in ultra-pure H2O, triple-rinsed in water, and then and the shells air dried. dried for at least 24 h under a laminar flow clean bench. Instrument set-up was similar to that outlined by Gu¨ nther & Heinrich (1999) as modified by Thorrold Strontium/calcium ratios et al. (2001). Linear spatial resolution was 100 mm along S. groenlandicus deposit distinctive lines during growth the growth axis which corresponded to weekly resolu- which appear as alternating thin dark and thicker light tion during early years of life, declining to monthly bands on the external shell surface. We used changes in resolution as shell growth rates decreased at older ages. the ratio of Strontium (Sr) to Calcium (Ca) in different Quantification of Sr/Ca ratios followed the approach areas of the shell to identify whether external lines were outlined by Rosenthal et al. (1999) for precise element/ deposited annually. The Sr/Ca ratio in carbonate varies Ca ratios using sector field ICP-MS. Average external with the water temperature at the time of deposition precision (RSD) of Sr/Ca ratios in the laboratory stan- (Zacherl et al., 2003; Bath et al., 2004), so systematic dard was 0.2%. changes between presumptive growth lines, reflecting seasonal changes in water temperature, would suggest Growth rates the lines are deposited annually. We measured Sr/Ca ratios along the growth axis within the prismatic layer The external growth lines on S. groenlandicus are annual of two S. groenlandicus shells (N1 and N3) using a New growth checks (Khim et al., 2003; this study – see Sr/Ca Wave Research UP213 laser ablation system coupled to ratio results), thus, can be used to determine growth r 2006 The Authors Journal compilation r 2006 Blackwell Publishing Ltd, Global Change Biology, 12, 1595–1607 1598 W. G. AMBROSE et al. rates. Since we collected all clams live, each growth 1997; Johnson et al., 1999; A. Proshutinsky, personal com- increment can be assigned to a specific calendar year. munication for updated index). The sea surface height The distance between the ventral edges of successive anomaly at the North Pole is indicative of the predo- growth lines along the line of maximum growth (shell minant high-Arctic wind pattern. Data for the NAO, height) was measured with a digital caliper to the using the sea level pressure difference between Gibral- nearest 0.01 cm. We do not include growth beyond the tar and Southwest Iceland (Jones et al., 1997; Osborn last growth line in our analyses because we do not et al., 1999), were obtained from (http://www.cru.uea. know what portion of the present year this growth ac.uk/cru/data/nao.html) and for the AO from (http:// represents. www.cpc.ncep.noaa.gov/products/precip/CWlink/ Shell growth of S. groenlandicus was modeled by daily_ao_index/ao_index.html). fitting the von Bertalanffy growth function to age and The Barents Sea temperature data (PINRO, Mur- shell height data for each clam using prism (Ver. 3.0, mansk) is a time series of integrated ocean temperature 1993). from 0 to 200 m depth along the Kola transect, which Bivalve growth declines with age, so growth incre- runs from the Kola Peninsula northward to the ice ments within an individual and among individuals of edge along the 331300E meridian (Bochkov, 1982; different ages must be standardized before growth Tereshchenko, 1997). We have used yearly means of among years can be compared. We use the methods of Barents Sea temperature in our analysis. Jones et al. (1989), employing the first derivative of the Meteorological data were obtained from the four von Bertalanffy function with respect to time, to derive official weather stations around Svalbard (Longyear- an ontogenetically adjusted measure of annual growth: byen, Ny A˚ lesund, Bear Island, and Hopen; Fig. 1) kt maintained by the Norwegian Meteorological Institute dSHt=dt ¼ kSH1e ; (http://eklima.met.no). Daily data of precipitation, where SHt the shell height at age t, dSHt/dt the mod- pressure, and temperature were used to calculate sea- eled yearly change in shell height, t the age in years, sonal and yearly averages. SH1 the maximum, asymptotic shell height, k the Local ice conditions were estimated from imagery by growth constant. the Nimbus-7 SMMR and DMSP SSM/I passive micro- After determining the average yearly changes in shell wave satellite (Cavalieri et al., 1997). The spatial resolu- height based on growth data from all clams, we calcu- tion of the satellite imagery is 25 km 25 km, and the lated the expected yearly increase in shell height for cell used for the ice analysis is immediately northward each clam for each year. We then divided the measured of Rijpfjord proper. The temporal resolution is daily or observed shell growth for each year by the expected from 1988 to 2003 and every second day from 1978 to growth for that year to generate a standardized growth 1988. We calculated the ice-free days per year as the index (SGI). This removes the ontogenetic changes in number of days with ice cover o25% from 1 July to 30 growth and equalizes the variance for the entire series October of a given year (the estimated period of active (Fritts, 1976). Once annual changes in shell growth were growth of S. groenlandicus). This measure relates closely standardized, we calculated the mean SGI for each (490%) to ice free days in a calendar year and average calendar year (because growth lines are deposited yearly ice concentration. Data on total Arctic-wide in winter, growth year is virtually synonymous with spatial extent (km2) of pack ice were obtained from the calendar year). The result is a record of year-by-year US National Snow and Ice Data Center (http://www. growth for the S. groenlandicus individual, with an SGI nsidc.com/data/seaice_index/) (Fetterer & Knowles, greater than one indicating a better than average year 2002). for growth, while a value less than one reflects a worse than average growth year. Statistical analyses We calculated Pearson’ correlation coefficients in order Climatic and meteorological data to determine basic pairwise relationships between SGI We examined relationships between clam growth and and the environmental and physical variables. We com- climate indices with potential influence on the region: pared annual means and also investigated the time the ACRI, NAO and AO. The AO is the first principal dependence between data in consecutive years, leading component of the sea level pressure field at latitudes us to incorporate two data transformations: 2 year- 4201N (Thompson & Wallace, 1998; Stenseth et al., running means of environmental data were used to 2003), while the ACRI measures variations in Arctic reduce the magnitude of interannual variability of these Ocean and ice circulation based on the sea level height data, and a 1-year lag was used to account for the time anomaly at the North Pole (Proshutinsky & Johnson, for physical processes to be reflected in shell growth.

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We used a general linear model to investigate the data 5 Clam N1 at an individual clam level, ﬁrstly to identify predictors of S. groenlandicus growth, and secondly to test whether there were signiﬁcant differences between the clams, 4 included in the model as a random effect. To further justify pooling the growth rates of the sample of clams, we computed the Cronbach a measure of reliability (e.g. 3 Bland & Altman, 1997), for the available growth data on a common set of years available for all clams (1992– )

1 2

2002) to examine the homogeneity of the four clam − growth rates. Once we established that the clams were a homogenous sample, we combined the clam data into an annual mean value as a more reliable indicator of Clam N3 annual clam growth rate. This mean was then used as a response variable in a multiple linear regression model, Sr/Ca (mmol mol 4 investigating relationships with all available annual predictor variables. In all analyses, we checked for serial correlation in the residuals, which should be 3 absent to satisfy the assumptions of the regression model. Statistical analyses were done using Statistica (ver. 6) or SPSS (ver. 11.5.1). 2

Results 1 The clams collected were 20, 19, 18, and 11 years old. We 0 100 200 300 400 assayed Sr/Ca ratios from 440 samples spanning 16 Sample number (consecutive from umbo growth increments, and 350 samples spanning 12 incre- toward shell margin) ments, in the 20-year-old (N1) and 19-year-old (N3) Fig. 3 Ratio of strontium to calcium in two Serripes groenlandi- shells, respectively. The two Sr/Ca profiles were similar, cus shells (N1 and N3) from Rijpfjord. Samples were taken along with maximum values of between 3.5 and 4.5 mmol each clam’s height and are numbered from the umbo (sample mol1 in aragonite deposited during early life, and a number 0) to the ventral margin. Triangles correspond to the minimum value of approximately 1.5 mmol mol1 re- location of dark growth lines visible on a shell’s exterior. corded as the individuals grew older (Fig. 3). There was clear periodicity in Sr/Ca ratios that corresponded to growth increments visible in the shells. The amplitude appear to follow a cyclic pattern with better growth in of Sr/Ca variation within an increment ranged from 2 the mid-1990s bracketed by two poorer growth periods to 2.5 mmol mol1 in wide increments during the first in the late-1980s and the early part of this century. 2–3 years of life to 1–1.5 mmol mol1 as growth lines Growth has declined steadily for the last 4 years of narrowed with age. Sharp declines in Sr/Ca ratios were the series, with the worst growth in the last 2 years invariably associated with the presence of a dark (2001, 2002). growth line, confirming the growth lines are winter To justify combining the growth rates for the indivi- growth checks. dual clams into an average Cronbach’s reliability coeffi- The von Bertalanffy growth equation (SHt ¼ 112:8 cient was calculated for the years 1992 until 2002, for ð1 e0:032ðtt0ÞÞ had an R2 of 0.956 (P 0), indicating which we had data from all four clams. The value that it was an excellent descriptor of these clams’ a 5 0.663 indicates a reasonably high level of reliability. growth. This allowed us to correct for changes in Unless mentioned otherwise, the following results are growth with age and confidently generate the expected for the average annual SGI values. growth for each calendar year SGI. SGI is most highly correlated with precipitation on SGI varied considerably over the 20 years of the data Hopen and the ACRI (Table 1). These positive correla- set (Fig. 4), with a range for individual clams between tions generally improve when running means are used 0.27 in the poorest growth year (N1 in 2002) to 2.46 in instead of annual means for the environmental para- the best growth year (N2 in 1996). The total population meter and when the environmental data are lagged SGI ranged from 0.45 (2002) to 1.64 (1995). The differ- 1 year with respect to growth, growth corresponding ences in SGIs among years are clearly not random, but better to the previous year’s environmental data than r 2006 The Authors Journal compilation r 2006 Blackwell Publishing Ltd, Global Change Biology, 12, 1595–1607 1600 W. G. AMBROSE et al. the current year’s. The ACRI was the only large-scale Considering all explanatory variables and their lags climate index related to clam growth (Fig. 5) and this as well as their running means as possible predictors, correlation was very significant with a 1-year lag and the best subset of predictor variables for the SGI which using the running mean (r 5 0.671, Po0.001). Atmo- we identified are the ACRI averaged over the 2 years spheric pressure at Hopen and Longyearbyen are also before SGI measurement (running mean with a 1-year significantly correlated, in these cases negatively, with lag) and the running mean (present and previous year) the SGI when a 1-year lag and the running mean of the of precipitation at Ny A˚ lesund. The model is:

Average SGI ¼ 1:715 þ 0:319 ACRI 0:00183 Ny Alesund precipitation ð2 year mean; ð2 year meanÞ 1 -year lagÞ ðP < 0:00001ÞðP < 0:007Þ environmental data are applied. Removal of the varia- This model explains nearly 65% of the variability in bility explained by the ACRI and a correlation analysis the mean SGI (R2 5 0.649). Although the individual between the residuals and the SGI revealed significant variables are serially correlated, the residuals in the negative correlations between the SGI and the Kola Sea model are not autocorrelated (Durbin-Watson test sta- temperature, precipitation at Ny A˚ lesund and air tem- tistic 5 1.67), thus satisfying the assumption of the perature in Longyearbyen. The marginally significant regression model. Figure 6 shows the observed mean correlations in Table 1 should be interpreted with some SGI plotted against the value predicted by our model, caution, because of multiple testing. In order to avoid where the 451 line indicates perfect prediction. This the problem of higher Type I error incurred by testing shows that the model-generated SGI’s closely track several correlations, we should lower the significance the measured values. The only significant residual is level below the customary value of 0.05 (Bland & Alt- for the year 1991, which is over-predicted because of a man, 1995). However, the exact threshold necessary for 1-year drop in average SGI in an otherwise multi-year each individual correlation for an overall significance uptrend in SGI (Fig. 5). In other words, the serial level of 0.05 is difficult to determine because the corre- autocorrelation breaks down in 1991. lations in each row are clearly related. Discussion

2.2 Growth of S. groenlandicus from northeast Svalbard is clearly related to the ACRI, a large-scale arctic climate 2.0 oscillation dependent on high-Arctic atmospheric 1.8 circulation patterns (Proshutinsky & Johnson, 1997; 1.6 Johnson et al., 1999; Polyakov et al., 1999), through 1.4 1.2 2 1.0 1.6

0.8 1 1.4

Standard growth index 0.6 1.2 0.4 n=4 0 1.0 rowth index rowth

0.2 g 1985 1990 1995 2000 0.8 Year –1 0.6 (2 yr running mean) Climate regime index regimeClimate index Fig. 4 Standard Growth Index (SGI) (1 standard error of the Standard mean) of the Serripes groenlandicus from Rijpfjord, Svalbard (Nor- 0.4 –2 way) collected live on 28 August 2003. Growth increments were 1980 1985 1990 1995 2000 calculated using external measurements and the von Bertalanffy Year equation to remove ontogenetic changes in growth. An SGI greater than 1.0 indicates greater than average growth while Fig. 5 Temporal patterns of the Arctic climate regime index one less than 1.0 poorer than average. Growth was measured (ACRI) (gray bars), lagged 1 year, and Serripes groenlandicus SGI along the line indicated on the shell. The dark bands represent- (line). The ACRI data are 2-year running means of raw data, ing winter cessation of growth are visible on the shell. while the SGI data are untransformed.

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Table 1 Pearson Correlations relating Serripes groenlandicus standard growth index (SGI) to various environmental variables, 1983–2002

Correlations with average SGI Correlations with Present Year 1-year lag residuals: 1-year lag

Running Running Running Environmental variable Annual mean Annual mean Annual mean

Kola Transect sea temperature 0.032 0.008 0.045 0.033 0.345 0.462* Summer ice free days in Rijpfjord 0.166 0.107 0.005 0.050 0.132 0.145 Pack ice extent March 0.315 0.232 0.319 0.137 0.416 0.422 Ny A˚ lesund air temperature 0.020 0.141 0.204 0.263 0.409 0.318 Ny A˚ lesund pressure 0.169 0.349 0.381 0.428 0.044 0.202 Ny A˚ lesund precipitation 0.212 0.257 0.158 0.134 0.509* 0.250 Longyearbyen air temperature 0.002 0.112 0.181 0.112 0.434* 0.341 Longyearbyen pressure 0.198 0.383 0.408 0.455* 0.023 0.177 Longyearbyen precipitation 0.047 0.181 0.199 0.389 0.095 0.047 Hopen air temperature 0.015 0.114 0.175 0.199 0.427 0.362 Hopen pressure 0.216 0.417 0.432 0.451* 0.055 0.150 Hopen precipitation 0.476* 0.592** 0.592** 0.515* 0.341 0.244 Bear island air temperature 0.124 0.167 0.152 0.196 0.386 0.354 Bear island pressure 0.221 0.374 0.366 0.364 0.038 0.175 Bear island precipitation 0.323 0.408 0.319 0.213 0.255 0.037 NAO index (Annual) 0.025 0.106 0.140 0.209 0.162 0.148 NAO index (Winter) 0.178 0.297 0.241 0.219 0.081 0.229 AO index (Annual) 0.021 0.129 0.233 0.332 0.175 0.219 AO index (Winter) 0.242 0.358 0.322 0.368 0.124 0.223 Arctic Climate Regime Index 0.297 0.522* 0.492* 0.671*** 0.018 0.000

The first set of two columns of coefficients are from annual data (temperature and pressure data used are annual means, precipitation is annual total sum) and 2-year running means of the annual data. The second set is from the environmental data being set back (lagged) by 1 year with respect to the growth data. The third set contains the correlations (1-year lag only) after the effects of the Arctic Climate Regime Index (2-year running mean, 1-year lag) have been removed. Significant correlations are shown in bold. The levels of the significant correlations are: *Po0.05, **Po0.01, ***Po0.001.

local environmental conditions. Fortunately, the S. groen- 2003), or more rarely in polar environments, mark and landicus growth we examined spanned large changes in recapture studies (Sejr et al., 2002), they have proved to the ACRI (from positive to negative phase and vice be annual and are associated with a winter cessation of versa), offering us the opportunity to examine the growth. In S. groenlandicus from the Chukchi Sea, d18O response of this important member of the benthic com- values vary systematically between growth lines with munity to large changes in climate. The response of S. the highest values, reflecting the coldest temperatures, groenlandicus to the ACRI and local conditions, however, coincident with the dark lines on the shell, strongly was complex, including an apparent 1-year lag between suggesting the lines are deposited annually during physical forcing changes and biological response. winter (Khim et al., 2003). The use of bivalves as bioproxies for climate changes We found that Sr/Ca profiles also varied system- hinges on the ability to recognize annual markers in atically between individual growth lines, with mini- their shell. While it is generally assumed that lines mum values always associated with dark growth visible on the external shell or internally in cross section bands. Recent culturing studies have found that the are deposited annually, particularly at high latitudes, Sr/Ca ratio is positively correlated with temperature in this assumption is not always tested (Andrews, 1972; low-Sr aragonite from mollusk shells and fish otoliths Tallqvist & Sundet, 2000). When the periodicity of (Zacherl et al., 2003; Bath et al., 2004), meaning that increments has been examined using changes in oxygen growth lines in S. groenlandicus from Rijpfjord are likely and carbon isotopes between presumptive annual lines deposited during the winter. The within-year ampli- (Witbaard et al., 1994; Heilmayer et al., 2003; Khim et al., tudes of Sr/Ca variations we found are on the order of r 2006 The Authors Journal compilation r 2006 Blackwell Publishing Ltd, Global Change Biology, 12, 1595–1607 1602 W. G. AMBROSE et al.

2.0 1.8

1.6 95 1.4 1.5 96 92 93 1.2 98 1.0 97 9484 83 1.0 89 90 0.8 85 99 Standard growth index 00 91 y = –0.858x + 14.586 88 0.6 SGI (observed) 86 P < 0.0001, R2=0.53 0.4 0.5 8701 02 15.0 15.2 15.4 15.6 15.8 16.0 16.2 Total Arctic ice pack extent in March (106 km2)

Fig. 7 Relationship between total Arctic-wide winter (March) 0.0 sea ice extent (2-year running mean) and Serripes groenlandicus 0.0 0.5 1.0 1.5 2.0 SGI from 1983 to 1998. (Ice data courtesy of National Snow and SGI (modeled) Ice Data Center, Boulder, CO, USA).

Fig. 6 Scatterplot of observed vs. modeled SGI from 1983 to 2002 with the growth years shown as data points. Line represents a perfect prediction by the model of actual growth rates of with Arctic climate. Once the variability in the SGI the Serripes groenlandicus population. attributed to the ACRI is removed, either in the multiple regression model or in the correlation analysis, precipitation at Ny A˚ lesund is related to the SGI. 50–100%, and are of a similar magnitude to those The polar-scale effect of climate on S. groenlandicus reported by Stecher et al. (1996) for the bivalve Merce- growth, however, is not immediate or direct because naria mercenaria. Thus, the results are clearly consistent variation in S. groenlandicus growth is best explained by with an annual deposition rate of growth lines in the climate index one year earlier than the current S. groenlandicus shells and justify use of external lines years’ growth (Table 1, regression model). Lagged re- on the shells as annual markers. sponse to climate oscillations are common in marine Considerable effort has recently been directed to- systems and can typically span many trophic levels wards understanding the relationships between climate (Post, 2004) from benthic infauna (Tunberg & Nelson, variation and ecosystem structure and function. Cli- 1998), including the bivalve Arctica islandica (Witbaard mate oscillations and the associated changes in physical et al., 2003), to zooplankton (Pershing et al., 2004), fish parameters such as temperature, water circulation, and (Ottersen et al., 2004) and birds (Thompson & Ollason, ice cover have been shown to measurably influence 2001). In the case of S. groenlandicus, the lag is probably marine ecosystems in the North Pacific, North Atlantic, because the local manifestations of the climate oscilla- and Southern Ocean by regulating the abundances of tion (i.e. precipitation) take a period of time to develop. organisms at the base and upper levels of food webs Furthermore, there is likely an additional time lag (Fomentin & Planque, 1996; Stabeno & Overland, 2001; associated with the biophysical coupling via physical Hunt & Stabeno, 2002). S. groenlandicus growth is not constraints on food production, biological processes strongly related to the NAO, the AO, which is itself of consumption and assimilation as tissue and shell related to the NAO (Deser, 2000; Hurrell et al., 2003), or growth, and storage of energy as tissue from previous the Barents Sea temperature (until after the effects of the years. ACRI are removed) (Table 1), which is influenced by Surprisingly, given the importance of ice in mediating incursions of the North Atlantic Current into the environmental conditions in the Arctic, no measure of Barents Sea (Loeng, 1991), and only correlated with ice condition (summer ice free days, total arctic ice pack one of the environmental variables from locations out- extent) was related to S. groenlandicus growth (Table 1). side the polar front (e.g. all locations except Hopen), When the last 4 years of poor growth are removed from atmospheric pressure at Longyearbyen. This pattern of the analysis, however, there is a very strong, significant relationships indicates that we are measuring the negative relationship between SGI and Arctic-wide response of the marine ecosystem to an exclusively extent of the pack ice (Fig. 7). It is remarkable that such Arctic phenomenon, rather than a temperate interaction a large-scale measure of ice conditions as the extent of

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Arctic climatic Regional jord is also food-rather than temperature-limited, pro- regime index atmospheric cesses regulating the quantity and quality of food pressure reaching the bottom of the fjord will have a strong effect on S. groenlandicus growth. In the absence of a + 1 year strong temperature signal, variation in food quantity, Local precipitation and possibly quality, is the most probable explanation Biophysical Bivalve for interannual differences in S. groenlandicus growth coupling growth in Rijpfjord. This is the same conclusion reached by Sea ice concentration Witbaard et al. (2003) for interannual variations in growth of Arctica islandica in the North Sea, and Sejr et al. (2004) for Hiatella arctica in east Greenland. Ecosystem ˚ response Precipitation at Ny Alesund is the only environmental variable that enters the multiple linear regression model and it is negatively related to the SGI. Precipita- Fig. 8 Schematic representation of the Arctic Climate Cascade, tion at Hopen is positively correlated with growth in where bivalve growth, and by inference, other ecosystem pro- current and previous years (Table 1) (precipitation at Ny cesses are linked to climatic forcing factors through variation in ˚ physical variables. Alesund and Hopen are negatively correlated, though not significantly, hence their opposite relationships with the SGI). The effect of precipitation on S. groenlandicus total pack ice across the Arctic explains over 50% of the growth could be due to direct or indirect effects and we interannual variability in the growth of S. groenlandicus. have little data from Rijpfjord to help us interpret these The lack of relationship between growth and Arctic- relationships. The land around Rijpfjord is heavily wide ice conditions when the last 4 years are included glaciated and the un-glaciated areas are sparsely vege- in the analysis suggests a decoupling of the previously tated with nutrient-poor soil, so spring runoff is un- strong relationship in the last 4 years. likely to be laden with nutrients necessary to stimulate a The growth of S. groenlandicus is linked to the ACRI phytoplankton bloom. Precipitation might also stabilize though the impact of the climate oscillation on the local the water column, which has been shown to initiate a physical conditions of precipitation and ice cover (Fig. spring bloom in the Bering Sea (Stabeno & Overland, 8). The integration of water column processes by long- 2001) and west Greenland (Nielsen & Hansen, 1995), lived Arctic benthos means that the effect of climate on but is not necessary for a spring bloom to develop in the S. groenlandicus growth may be direct or indirect, and Rijpfjord system (Hegseth et al., 1995). Precipitation the specific mechanisms of coupling between physical could affect the salinity of surface water and the abun- conditions and bivalve growth in Rijpfjord (Fig. 8) are dance of herbivorous zooplankton, which are capable in not well understood. Variation in bivalve growth is Arctic fjords of consuming over 80% of annual primary typically best explained by variation in temperature production (Nielsen & Hansen, 1995) and indirectly and food (Beukema et al., 1985; Jones et al., 1989; influencing bivalve growth (Witbaard et al., 2003); Beukema & Cadeé, 1991; Lewis & Cerrato, 1997; Wit- a top-down rather than a bottom-up effect. It is also baard et al., 1997, 1999; Dekker & Beukema, 1999). Some possible that precipitation is a reflection of storms, of the best documented effects of climate oscillations on which may cause resuspension of settled phytodetritus, individuals, populations and benthic community struc- and in shallow enough water, benthic microalgae, both ture are mediated through temperature (Kro¨ncke et al., of which could be consumed by S. groenlandicus,a 1998; Ottersen et al., 2001; Hagberg et al., 2004). In late positive relationship, or excessive wind might suspend August when the clams in Rijpfjord were collected, the bottom sediment clogging the gills of S. groenlandicus thermocline roughly coincided with collection depth and resulting in lowered growth, a negative relation- (Fig. 2), indicating that the clams lived deep enough ship. Without more information on environmental to experience relatively small differences in tempera- conditions in Rijpfjord and how they affect primary ture over the year, meaning the temperature effects on productivity and delivery of food to the benthos, we can growth will be limited and likely minor compared to do no more than speculate on the relationships between other factors controlling food availability. growth of S. groenlandicus and precipitation. The growth of many polar benthic organisms appears Perhaps the most intriguing aspect of the relation- to be food limited (Brockington & Clarke, 2001) and ships between S. groenlandicus growth and environmen- differences in growth among sites have been related to tal conditions is the decoupling that occurred during differences in food supply (Brey et al., 1995; Norkko the last 4 years between ice cover and growth. Annual et al., 2005). If the S. groenlandicus population in Rijpf- phytoplankton production in the Arctic is directly pro- r 2006 The Authors Journal compilation r 2006 Blackwell Publishing Ltd, Global Change Biology, 12, 1595–1607 1604 W. G. AMBROSE et al. portional to the length of open water (Rysgaard et al., S. groenlandicus and other bottom dwelling organisms to 1999) and benthic biomass on Arctic shelves is inversely climate fluctuations, we need to better understand related to ice cover (Ambrose & Renaud, 1995). In variation in the quality and quantity of food reaching Rijpfjord, however, 3 weeks after the break-up of ice the sea floor. in 2003 during the period when the ‘spring’ bloom would be expected, there was a very shallow mixed- layer depth (Fig. 2) and little suspended chlorophyll Acknowledgements biomass in the water column (maximum 1 mgL1,E.N. Hegseth, unpublished data). Locally produced phyto- This research was supported in part by the Norwegian Research Council, NORDKLIMA Program (150356-S30 and 151815-S30 to plankton alone would appear to be insufficient to M. L. C.), the US National Science Foundation Offices of Polar sustain the rich benthic communities observed in Rijpf- Programs (OPP-0138596, OPP-0222423 to W. G. A.) and Ocean jord, with infaunal benthic biomass of 130 g WW m2 Sciences (OCE-0215905 to S. R. T.), the BBVA Foundation in (M. L. Carroll, unpublished data) at 200 m depth adja- Madrid (to M. G.), and with funds from the Howard Hughes cent to the bivalve collection site. Thus, we assume a Medical Institute through Bates College. We thank the captain and crew of the R/V Lance. Divers H. Hop and S. Nordang significant amount of food must reach the bottom of collected the bivalve samples and J. Edgerly measured growth Rijpfjord from nonlocal sources, with ice algae from ice lines. Sea ice data were provided by the National Snow and Ice flows episodically driven into the fjord by wind and Data Center (USA), and meteorological data were provided by advected phytoplankton being the most likely explana- the Norwegian Meteorological Institute. O. Pavlova collated the tion. To the extent that the benthos in Rijpfjord is satellite data of ice concentration. Discussions with E. N st- Hegseth clarified our interpretations. J. Carroll, L. Clough, B. dependent on the advection of ice and associated ice Johnson, M. Retelle, P. Renaud, and two anonymous reviewers algae for food, the recent reduction in summer ice north provided valuable comments on earlier versions of the manu- of Svalbard (Serreze et al., 2003; http://nsidc.org/news/ script. press/20050928_trendscontinue.html) or any change in wind or current patterns resulting in less ice entering the fjord would cause a reduction in this food source. 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