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.

Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea:A case study of Tanaka's snailfish(Liparis tanakae)

Acquiring a comprehensive and accurate understanding of habitat preference is essential for species conservation and fishery management,especially for mobile species that migrate seasonally.Presence and absence data from field surveys are recommended when available due to their high reliability.Using field survey data,we investigated seasonal habitat suitability requirements for Tanaka's snailfish(Liparis tanakae)in the Bohai Sea and Yellow Sea(BSYS)via a machine-learning method,random forests(RFs).Five environmental and biologically relevant variables(bottom temperature,bottom salinity,current velocity,depth and distance to shore)were used to build the ecological niches between the presence/absence data and suitable habitat.In addition,the degree to which false absence data might impact model performance was evaluated.Our results indicated that RFs provided accurate predictions,with seasonal habitat suitability maps of L.tanakae differing substantially.Bottom temperature and salinity were identified as important factors influencing the distribution of L.tanakae.False absence data were found to have negative effects on model performance and the decrease in evaluation metrics was usually significant(P<0.05)after 30%or more errors were added to the absence data.Through identifying highly suitable areas within its geographic range,our study provides a baseline for L.tanakae that can be further applied in ecosystem modelling and fishery management in the BSYS.

Comparison of sampling effort allocation strategies in a stratified random survey with multiple objectives

Stratified random survey is commonly used to estimate abundance indices of fish populations in multispecies survey,providing reliable data for stock assessment and fisheries management.In some cases,however,the sample size is relatively small because of the limitation of survey cost or other factors.The allocation methods of sampling efforts among strata in stratified random surveys with small sample size may need adjustment compared with traditional approaches.In this study,two sampling stations were allocated to each stratum first and then the remaining sampling units were allocated among strata using five traditional allocation methods.In order to distinguish them from traditional methods,we called them adjusted methods in this study.A simulation study was conducted to compare the performances of different allocation strategies of sampling efforts in a stratified random survey for estimating abundance indices of multiple target species.Relative estimation error(REE)and relative bias(RB)were used to measure the precision and accuracy of estimates of abundance indices under different allocation schemes of sampling efforts in the multispecies survey.The performances of different allocation schemes in estimating abundance indices varied greatly for different species over different seasons.The adjusted Neyman allocation scheme could significantly reduce the REE and RB of estimates of abundance index for single species survey.For multiple species surveys,the adjusted average-Neyman allocation method,the adjusted Yate allocation method,the adjusted proportional allocation method and current allocation method had relatively high accuracy and precision of estimates of abundance indices for four species in terms of the total_(REE) and total_(RB).Though the adjusted average-Neyman allocation scheme did not always have the best performance,it was the optimal one considering the accuracy and precision of estimates of abundance indices for all species simultaneously.The allocation of sampling efforts among strata in stratified random surveys targeting for estimating abundance indices of multiple species should comprehensively consider the variance of abundance of different species in stratum and the seasonal changes.