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

  • Binduo Xu
  • , Yiping Ren
  • , Yong Chen
  • , Ying Xue
  • , Chongliang Zhang
  • , Rong Wan
  • Ocean University of China
  • University of Maine
  • Pilot National Laboratory for Marine Science and Technology

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)154-169
Number of pages16
JournalActa Oceanologica Sinica
Volume34
Issue number12
DOIs
StatePublished - Dec 1 2015

Keywords

  • computer simulation
  • fishery-independent survey
  • optimization
  • stratification scheme
  • stratified random sampling

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