BAYESIAN HIERARCHICAL ANOVA MODEL OF STOCHASTIC SEASONALITY FOR Diodon holocanthus IN NORTHERN TAIWAN

Detecting seasonal variation in ecological phenomena is not always possible by use of traditional methods. The pattern of seasonality fluctuates because of the stochasticity of environmental factors, correlations among organisms, and human activity. Here, we propose a Bayesian hierarchical analysis of variance (ANOVA) model of stochastic seasonality for detecting the annual fluctuation of seasonality. Using 11-year data recorded monthly at 2 nuclear power plants in northern Taiwan, we examined monthly fluctuations in fish species. To illustrate the performance of the proposed model, we conducted a simulation that reflects the varying seasonality of species. The most dominant species, Diodon holocanthus, exhibited a shift in peak abundance from March to July that the traditional ANOVA model failed to detect. Our results suggest that impingement data obtained from the intakes of power plants are useful in studying the long-term temporal variation of fish species. The proposed model provides an in-depth examination of temporal variation for ecological analysis.

[1]  Rainer Froese,et al.  FishBase. World Wide Web electronic publication. , 2014 .

[2]  L. Barnthouse.  Impacts of entrainment and impingement on fish populations: A review of the scientific evidence , 2013 .

[3]  T. Lohner,et al.  The value of long-term environmental monitoring programs: an Ohio River case study , 2013, Environmental Monitoring and Assessment.

[4]  E. Perry,et al.  Longitudinal Variation in the Ohio River Fish Community , 2013 .

[5]  D. Tittensor,et al.  Current and Future Patterns of Global Marine Mammal Biodiversity , 2011, PloS one.

[6]  Chris Field,et al.  Mapping species richness and human impact drivers to inform global pelagic conservation prioritisation , 2011 .

[7]  P. Henderson,et al.  Community level response to climate change: The long-term study of the fish and crustacean community of the Bristol Channel , 2011 .

[8]  V. Chong,et al.  Multispecies impingement in a tropical power plant, Straits of Malacca. , 2010, Marine environmental research.

[9]  Chih-hao Hsieh,et al.  Time series analyses reveal transient relationships between abundance of larval anchovy and environmental variables in the coastal waters southwest of Taiwan , 2009 .

[10]  D. Tittensor,et al.  Effects of temperature on global patterns of tuna and billfish richness , 2008 .

[11]  Kwang-Tsao Shao,et al.  TEMPORAL CHANGES IN FISH ASSEMBLAGE FROM THE IMPINGEMENT DATA AT THE SECOND NUCLEAR POWER PLANT, NORTHERN TAIWAN , 2004 .

[12]  D. R. Lambert,et al.  Field evaluation of a sound system to reduce estuarine fish intake rates at a power plant cooling water inlet , 2004 .

[13]  R. H. Hadderingh,et al.  Comparison of fish impingement by a thermal power station with fish populations in the Ems Estuary , 2002 .

[14]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[15]  F. Ollevier,et al.  Seasonal patterns in the fish and crustacean community of a turbid temperate estuary (Zeeschelde Estuary, Belgium) , 1998 .

[16]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[17]  M. Greenwood.  Trawls and cooling-water intakes as estuarine fish sampling tools: Comparisons of catch composition, trends in relative abundance, and length selectivity , 2008 .

[18]  J. Caselle,et al.  Declines in nearshore rockfish recruitment and populations in the southern California Bight as measured by impingement rates in coastal electrical power generating stations , 1998 .

[19]  K. Shao,et al.  Species composition and seasonal distribution of impinged fishes at the two intakes along the northern coast of Taiwan , 1988 .

[20]  R. L. Wyman,et al.  Factors Influencing Impingement of Fish by Lake Ontario Power Plants , 1984 .