14 



Abstract— We present a growth analy- 

 sis model that combines large amounts 

 of environmental data with limited 

 amounts of biological data and apply it 

 to Corbicula japonica. The model uses 

 the maximum-likelihood method with 

 the Akaike information criterion, which 

 provides an objective criterion for model 

 selection. An adequate distribution for 

 describing a single cohort is selected 

 from available probability density func- 

 tions, which are expressed by location 

 and scale parameters. Daily relative 

 increase rates of the location parameter 

 are expressed by a multivariate logistic 

 function with environmental factors 

 for each day and categorical variables 

 indicating animal ages as independent 

 variables. Daily relative increase rates 

 of the scale parameter are expressed by 

 an equation describing the relationship 

 with the daily relative increase rate of 

 the location parameter. Corbicula 

 japonica grows to a modal shell length 

 of 0.7 mm during the first year in Lake 

 Abashiri. Compared with the attain- 

 able maximum size of about 30 mm, 

 the growth of juveniles is extremely 

 slow because their growth is less sus- 

 ceptible to environmental factors until 

 the second winter. The extremely slow 

 growth in Lake Abashiri could be a 

 geographical genetic variation within 

 C. japon ica . 



An environmentally based growth model 



that uses finite difference calculus 



with maximum likelihood method: 



its application to the brackish water bivalve 



Corbicula japonica in Lake Abashiri, Japan 



Katsuhisa Baba 



Hokkaido Hakodate Fisheries Experiment Station 



1-2-66, Yunokawa, Hakodate 



Hokkaido 042-0932, Japan 



E-mail address babak@fjshexp pref.hokkaido.jp 



Toshifumi Kawajiri 



Nishiabashin Fisheries Cooperative Association 

 1-7-1, Oomagan, Abashiri 

 Hokkaido 093-0045, Japan 



Yasuhiro Kuwahara 



Hokkaido Abashiri Fisheries Experiment Station 

 31, Masuura, Abashiri 

 Hokkaido 099-3119, Japan. 



Shigeru Nakao 



Graduate School of Fisheries Sciences 

 Hokaido University 

 3-1-1, Minato, Hakodate 

 Hokkaido 041-8611, Japan 



Manuscript approved for publication 

 14 August 2003 by Scientific Editor 



Manuscript received 20 October 2003 

 at NMFS Scientific Publications Office. 



Fish. Bull. 102:14-24 (2004). 



Extreme fluctuations, both short-term 

 and seasonal, in food availability (e.g. 

 phytoplankton density ) make it difficult 

 to determine relationships between 

 the growth of filter-feeding bivalves 

 and environmental factors (Bayne, 

 19931. However, it is becoming easier 

 to acquire large amounts of environ- 

 mental data through the use of data 

 loggers, submersible fluorometers, or 

 remote-sensing satellites, which enable 

 environmental monitoring at daily or 

 subdaily intervals. The development 

 of these devices could solve difficulties 

 in data collection. However, analytical 

 methods that combine large amounts 

 of environmental data with limited 

 amounts of biological data (e.g. shell 

 length) are not yet well developed. 

 We present an environmentally based 

 growth model that combines such 

 unbalanced data sets. This model is 

 useful in elucidating relationships 



between environmental factors and 

 growth of filter feeders from field data. 

 Complex box models, such as eco- 

 physiological models, can derive the 

 relationships between environmental 

 factors and the growth of filter-feeding 

 bivalves (Campbell and Newell, 1998; 

 Grant and Bacher, 1998; Scholten and 

 Smaal, 1998). These models are useful 

 for estimating impacts of cultivated 

 species on an ecosystem or the carrying 

 capacity of a species (or both) (Dame, 

 1993; Heral, 1993; Grant et al„ 1993). 

 They are suitable for animals that have 

 been widely studied, such as Mytilus 

 edulis, because they are derived by 

 integrating a huge amount of ecophysi- 

 ological knowledge acquired mainly 

 from laboratory experiments. However, 

 extrapolation of such knowledge to 

 natural conditions is still controver- 

 sial (Jorgensen, 1996; Bayne, 1998). 

 Our model treats complicated eco- 



