This paper gives a detailed introduction to the biotic resources in Panxi Area and lists the most typical biotic resources in this area. The authors of this paper adopt the biotic resource abundance evaluation index m...This paper gives a detailed introduction to the biotic resources in Panxi Area and lists the most typical biotic resources in this area. The authors of this paper adopt the biotic resource abundance evaluation index model R1= （S0t -- S1t ） × S1i-1 （i= 1,2,3, …n） to make a quantitative calculation of the biotic resource abundance in this area, and the calculation results show that this area abounds in biotic resources. Through the analysis of the causes of abundant biotic resources in this area, the luxuriant biotic resources in Panxi Area are largely attributed to the complex and varied environment, atrocious climate in history and the introduction of alien species. The purpose of this paper is to point out that biotic resource exploitation is one of the driving forces of economic development in this area, and to emphasize the necessity of biotic resource preservation and its harmonious development with the environment.展开更多
We developed an approach that integrates generalized additive model（GAM） and neural network model（NNM）for projecting the distribution of Argentine shortfin squid（Illex argentinus）. The data for this paper was ba...We developed an approach that integrates generalized additive model（GAM） and neural network model（NNM）for projecting the distribution of Argentine shortfin squid（Illex argentinus）. The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature（SST）, sea surface height（SSH） and chlorophyll a（Chl a） from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort（CPUE）. The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables（longitude and latitude） and environmental variables（SST, SSH and Chl a） were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors（MSE） and average relative variances（ARV）.The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean.展开更多
文摘This paper gives a detailed introduction to the biotic resources in Panxi Area and lists the most typical biotic resources in this area. The authors of this paper adopt the biotic resource abundance evaluation index model R1= （S0t -- S1t ） × S1i-1 （i= 1,2,3, …n） to make a quantitative calculation of the biotic resource abundance in this area, and the calculation results show that this area abounds in biotic resources. Through the analysis of the causes of abundant biotic resources in this area, the luxuriant biotic resources in Panxi Area are largely attributed to the complex and varied environment, atrocious climate in history and the introduction of alien species. The purpose of this paper is to point out that biotic resource exploitation is one of the driving forces of economic development in this area, and to emphasize the necessity of biotic resource preservation and its harmonious development with the environment.
基金The Public Science and Technology Research Funds Projects of Ocean under contract No.20155014the National Natural Science Fundation of China under contract No.NSFC31702343
文摘We developed an approach that integrates generalized additive model（GAM） and neural network model（NNM）for projecting the distribution of Argentine shortfin squid（Illex argentinus）. The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature（SST）, sea surface height（SSH） and chlorophyll a（Chl a） from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort（CPUE）. The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables（longitude and latitude） and environmental variables（SST, SSH and Chl a） were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors（MSE） and average relative variances（ARV）.The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean.
