Optimization of stratification scheme for a fishery-independent survey with multiple objectives

Fishery-independent surveys are often used for collecting high quality biological and ecological data to support fisheries management. A careful optimization of fishery-independent survey design is necessary to improve the precision of survey estimates with cost-effective sampling efforts. We developed a simulation approach to evaluate and optimize the stratification scheme for a fishery-independent survey with multiple goals including estimation of abundance indices of individual species and species diversity indices. We compared the performances of the sampling designs with different stratification schemes for different goals over different months. Gains in precision of survey estimates from the stratification schemes were acquired compared to simple random sampling design for most indices. The stratification scheme with five strata performed the best. This study showed that the loss of precision of survey estimates due to the reduction of sampling efforts could be compensated by improved stratification schemes, which would reduce the cost and negative impacts of survey trawling on those species with low abundance in the fishery-independent survey. This study also suggests that optimization of a survey design differed with different survey objectives. A post-survey analysis can improve the stratification scheme of fishery-independent survey designs.

A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different stratification indicators

Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling.However,these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the uncertainty.To address this lack of error information,this paper introduces a hybrid entropy indicator(HEI).Two conventional indicators,the acreage indicator(AI)and the fragmentation indicator(FI),were also evaluated to compare the results of the three indicators in a homogeneous agricultural area(Pinghu,PH)and a heterogeneous agricultural area(Zhuji,ZJ).The results show that HEI performs the best in heterogeneous areas with the lowest coefficient of variation(CV)(as low as 1.59%)and also has the highest estimation accuracy with the lowest standard deviation of estimation.For both areas,the performances of HEI and AI are very similar,and better than FI.These results highlight that the HEI should be considered as an effective indicator and used in place of AI and FI to help improve sampling efficiency of crop acreage estimation,while FI is not recommended.Furthermore,the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.