AEI Aquaculture Environment Interactions Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections AEI 11:111-127 (2019) - DOI: https://doi.org/10.3354/aei00284 Ecosystem attributes of trophic models before and after construction of artificial oyster reefs using Ecopath Min Xu1,2,3,6,7, Lu Qi4, Li-bing Zhang1,2,3, Tao Zhang1,2,3,*, Hong-sheng Yang1,2,3, Yun-ling Zhang5 1CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, PR China 2Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, PR China 3Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China 4Ocean University of China, Qingdao 266100, PR China 5Hebei Provincial Research Institute for Engineering Technology of Coastal Ecology Rehabilitation, Tangshan 063610, PR China 6Present address: Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai 200090, PR China 7Present address: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, PR China *Corresponding author: tzhang@qdio.ac.cn ABSTRACT: The deployment of artificial reefs (ARs) is currently an essential component of sea ranching practices in China due to extensive financial support from the government and private organizations. Blue Ocean Ltd. created a 30.65 km2 AR area covered by oysters in the eastern part of Laizhou Bay, Bohai Sea. It is important for the government and investors to understand and assess the current status of the AR ecosystem compared to the system status before AR deployment. We provide that assessment, including trophic interactions, energy flows, keystone species, ecosystem properties and fishing impacts, through a steady-state trophic flow model (Ecopath with Ecosim). The model estimated values of 4721.2 and 4697.276 t km-2 yr-1 for total system throughput and 534.74 and -519.9 t km-2 yr-1 for net system production before and after AR deployment, respectively. After AR deployment, sea cucumber and oyster showed the same trophic level (TL = 2.0) while veined whelk Rapana venosa had TL = 3.0. The mean TL of catches was 2.484 after AR deployment and the primary production required to support fisheries (PPR) was 1104 t km-2. Detritus production dominated over primary production and represented 73.82% (2530.82 t km-2) of total primary production required. The sea cucumber showed the lowest PPR/catch value (5.6) among functional groups, indicating that fishing catch biomasses were close to primary production values. The total primary production to total respiration and total primary production to total biomass ratios showed higher system maturity after AR deployment. The trophic flow diagram showed 1 grazing and 2 detritus food chains. Pelagic and bottom fish and different benthic organisms, including large crustaceans and zoobenthos, were the dominant community before AR deployment. Zoobenthos was the key functional group, followed by large crustaceans and Gobiidae, which were the most important prey for top predators after AR deployment. We draw the following conclusions for the management of this area: (1) AR deployment contributes to the maturity of the improved ecosystem; (2) the artificial oyster system is similar to a natural reef system; (3) the enhancement and release of benthic animals in the AR area benefit the ecosystem; and (4) low TL catches do not cause the system to collapse. KEY WORDS: Ecopath model · Artificial oyster reef · Apostichopus japonicus · Rapana venosa · Ecosystem properties · Sea ranching Full text in pdf format Supplementary material PreviousNextCite this article as: Xu M, Qi L, Zhang Lb, Zhang T, Yang Hs, Zhang Yl (2019) Ecosystem attributes of trophic models before and after construction of artificial oyster reefs using Ecopath. Aquacult Environ Interact 11:111-127. https://doi.org/10.3354/aei00284 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in AEI Vol. 11. Online publication date: March 14, 2019 Print ISSN: 1869-215X; Online ISSN: 1869-7534 Copyright © 2019 Inter-Research.
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