Temporal variations of food web in a marine bay ecosystem based on LIM-MCMC model

Climate change has led to significant fluctuations in marine ecosystems,including alterations in the structure and function of food webs and ecosystem status.Coastal ecosystems are critical to the functioning of the earth’s lifesupporting systems.However,temporal variations in most of these ecosystems have remained unclear so far.In this study,we employed a linear inverse model with Markov Chain Monte Carlo(LIM-MCMC)combined with ecological network analysis to reveal the temporal variations of the food web in Haizhou Bay of China.Food webs were constructed based on diet composition data in this ecosystem during the year of 2011 and 2018.Results indicated that there were obvious temporal variations in the composition of food webs in autumn of 2011 and 2018.The number of prey and predators for most species in food web decreased in 2018 compared with 2011,especially for Trichiurus lepturus,zooplankton,Amblychaeturichthys hexanema,and Loligo sp.Ecological network analysis showed that the complexity of food web structure could be reflected by comprehensive analysis of compartmentalized indicators.Haizhou Bay ecosystem was more mature and stable in 2011,while the ecosystem’s self-sustainability and recovery from disturbances were accelerated from 2011 to 2018.These findings contribute to our understanding of the dynamics of marine ecosystems and highlight the importance of comprehensive analysis of marine food webs.This work provides a framework for assessing and comparing temporal variations in marine ecosystems,which provides essential information and scientific guidance for the Ecosystem-based Fisheries Management.

Estimating biological reference points for Largehead hairtail(Trichiurus lepturus) fishery in the Yellow Sea and Bohai Sea

It is important to find a reliable method to estimate maximum sustainable yield(MSY)or total allowable catch(TAC)for fishery management,especially when the data availability is limited which is a case in China.A recently developed method(CMSY)is a data-poor method,which requires only catch data,resilience and exploitation history at the first and final years of the catch data.CMSY was used in this study to estimate the biological reference points for Largehead hairtail(Trichiurus lepturus,Temminck and Schlegel)in the Yellow Sea and Bohai Sea,based on the fishery data from China Fishery Statistical Year Books during 1986 to 2012.Additionally,Bayesian state-space Schaefer surplus production model(BSM)and the classical surplus production models(Schaefer and Fox)performed by software CEDA and ASPIC,were also projected in this study to compare with the performance of CMSY.The estimated MSYs from all models are about 19.7×104–27.0×104 t,while CMSY and BSM yielded more reasonable population parameter estimates(the intrinsic population growth rate and the carrying capacity).The biological reference points of B/BMSY smaller than 1.0,while F/FMSY higher than 1.0 revealed an over-exploitation of the fishery,indicating that more conservative management strategies are required for Largehead hairtail fishery.