Abstract.— Relationships between 

 mussel shell j^rowth and environmen- 

 tal parameters were investigated in 

 the mussel Mytilus edulis at an in- 

 shore location, Avila Beach, and at 

 an offshore location, oil platform 

 Holly (ARCO). Temporal patterns of 

 mussel growth were similar at both 

 locations. Mussel growth rate was 

 related to chlorophyll a concentra- 

 tion at Holly, but not at Avila. Theo- 

 retical estimates of scope for growth 

 (SFG) were made for mussels at each 

 location using published physiologi- 

 cal data. Good agreement was found, 

 with a time lag, between estimated 

 SFG and shell growth. The SFG anal- 

 ysis independently supported the con- 

 clusion that temporal changes in 

 phytoplankton concentration limits 

 mussel growth at HoUy, but suggested 

 that changes in the composition, 

 rather than the concentration, of 

 suspended particulates limits growth 

 at Avila, as reported for mussels in 

 estuarine environments. 



Food Availability as 



a Limiting Factor to Mussel 



Mytilus edulis Growth 



in California Coastal Waters 



Henry M. Page 

 Yann O. Ricard 



Marine Science Institute, University of California 

 Santa Barbara, California 93106 



Manuscript accepted 16 May 1990. 

 Fishery Bulletin. U.S. 88:677-686. 



Temporal variability in growth rate 

 has been extensively documented for 

 many species of filter-feeding marine 

 invertebrates. Growth rates frequent- 

 ly vary "seasonally," with most rapid 

 growth occurring during the spring 

 and summer months. Season is an 

 ambiguous concept, however, which 

 does not satisfactorily describe fac- 

 tors regulating temporal patterns of 

 growth. Ultimately, environmental 

 factors, which vary over time and 

 with location, contribute to variation 

 in growth rates. The growth rate of 

 mussels Mytilus edulis varies in both 

 time and space. Mussel growth rates 

 near Santa Barbara, California, are 

 highest from May through August 

 (Harger 1970; Page and Hubbard 

 1987), but elevated rates can also oc- 

 cur during the winter months (Page 

 and Hubbard 1987). Physiological 

 and ecological evidence indicates that 

 in many situations worldwide, food 

 availability may be the most impor- 

 tant single factor regulating mussel 

 growth (Seed 1976, Widdows et al. 

 1979, Incze et al. 1980, Rodhouse et 

 al. 1984). Multiple regression and cor- 

 relation analysis indicated that mus- 

 sel growth rate was associated with 

 phytoplankton abundance, but not 

 water temperature, at an offshore 

 location in the Santa Barbara Chan- 

 nel (Page and Hubbard 1987). 



Variation in the concentration and 

 composition of phytoplankton and 

 other suspended particulates which 

 CDuId influence mussel growth exists 



in the open coastal environment. For 

 example, episodic upwelling and high 

 primary productivity characterize the 

 region north of Point Conception, 

 California, relative to the Santa Bar- 

 bara Channel (Owen 1980, Willason 

 et al. 1986). Inshore areas tend to be 

 more productive and to possess high- 

 er total seston concentrations than 

 offshore areas, and phytoplankton 

 concentration varies with depth 

 (Raymont 1980). Little information is 

 available regarding spatial relation- 

 ships between phytoplankton abun- 

 dance and mussel growth in inshore 

 waters. 



In this study, we used correlation 

 analysis and the "scope for growth" 

 concept to evaluate the potential 

 importance of temporal and spatial 

 variation in food availability to mus- 

 sel growth. The concept of scope for 

 growth (SFG, Warren and Davis 

 1967), as applied to mussels, has been 

 reviewed by Bayne et al. (1976a) and 

 Widdows (1985a). SFG analysis uses 

 physiological relationships, together 

 with environmental parameters, to 

 estimate the potential production of 

 soft tissue (soma and gonad) by mus- 

 sels from the general energy equa- 

 tion. SFG = A - (R -h U), where SFG 

 = energy available for growth of soft 

 tissue, A = energy absorbed from 

 food, R = respiratory heat loss, and 

 U = energy lost as excreta. Radford 

 and Bayne (cited in Bayne et al. 

 1976a) and Radford et al. (1981) suc- 

 cessfully used this concept to model 



677 



