A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay

Honghua Shi; Zongling Wang; Guohong Fang; Wei Zheng; Long Hu

doi:10.1007/s00343-011-0520-x

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A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay

Updated：2023-05-16

- A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay
- Journal of Oceanology and Limnology Vol. 29, Issue 4, Pages: 905-911(2011)
- Affiliations：
  
  1. The First Institute of Oceanography State Oceanic Administration
  2. Institute of Oceanology Chinese Academy of Sciences
  3. School of Mathematical Sciences Fudan University
- Author bio：
- Funds：
- DOI：10.1007/s00343-011-0520-x
  CLC：
- Online First：10 July 2011，
  
  Published：2011
- Accepted：
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Shi, H., Wang, Z., Fang, G. et al. A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay., Chin. J. Ocean. Limnol. 29, 905–911 (2011). https://doi.org/10.1007/s00343-011-0520-x
DOI：

Shi, H., Wang, Z., Fang, G. et al. A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay., Chin. J. Ocean. Limnol. 29, 905–911 (2011). https://doi.org/10.1007/s00343-011-0520-x DOI：

摘要

Abstract

Models of marine ecosystem dynamics play an important role in revealing the evolution mechanisms of marine ecosystems and in forecasting their future changes. Most traditional ecological dynamics models are established based on basic physical and biological laws

and have obvious dynamic characteristics and ecological significance. However

they are not flexible enough for the variability of environment conditions and ecological processes found in offshore marine areas

where it is often difficult to obtain parameters for the model

and the precision of the model is often low. In this paper

a new modeling method is introduced

which aims to establish an evolution model of marine ecosystems by coupling statistics with differential dynamics. Firstly

we outline the basic concept and method of inverse modeling of marine ecosystems. Then we set up a statistical dynamics model of marine ecosystems evolution according to annual ecological observation data from Jiaozhou Bay. This was done under the forcing conditions of sea surface temperature and surface irradiance and considering the state variables of phytoplankton

zooplankton and nutrients. This model is dynamic

makes the best of field observation data

and the average predicted precision can reach 90% or higher. A simpler model can be easily obtained through eliminating the terms with smaller contributions according to the weight coefficients of model differential items. The method proposed in this paper avoids the difficulties of obtaining and optimizing parameters

which exist in traditional research

and it provides a new path for research of marine ecological dynamics.

关键词

Keywords

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