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Spatial population dynamics of eastern oyster in the Chesapeake Bay, Maryland
Fisheries Research ( IF 2.4 ) Pub Date : 2021-01-27 , DOI: 10.1016/j.fishres.2020.105854
Marvin M. Mace , Kathryn L. Doering , Michael J. Wilberg , Amy Larimer , Frank Marenghi , Alexei Sharov , Mitchell Tarnowski

Incorporating spatial information can improve estimates from stock assessment models when there are differences in population processes (e.g., natural mortality) among areas. Population dynamics of the eastern oyster Crassostrea virginica vary spatially within the Chesapeake Bay, Maryland, and our objective was to better characterize oyster population dynamics by estimating changes in natural mortality, fishing mortality, abundance, and recruitment over time and space. We developed statistical stage-structured models for 36 regions and fit the models to fishery dependent and independent data sources. Regional patterns in population dynamics emerged that would have been lost in a spatially aggregated approach. Regions that were closer together tended to have similar patterns in natural mortality, exploitation rates, abundance, and recruitment over time. We were able to estimate time-varying natural mortality because ancillary data on the number of dead individuals were incorporated into the population dynamics model. This approach to estimating time-varying natural mortality may be more widely applicable to species where dead individuals are observed in routine surveys.



中文翻译:

马里兰切萨皮克湾东部牡蛎的空间种群动态

当区域之间的人口过程(例如自然死亡率)存在差异时,合并空间信息可以改善来自种群评估模型的估计。东部牡蛎Crassostrea virginica的种群动态马里兰州切萨皮克湾的空间变化很大,我们的目标是通过估算自然死亡率,捕捞死亡率,丰富度和随时间和空间的吸收变化来更好地描述牡蛎种群动态。我们为36个地区开发了具有统计阶段结构的模型,并使模型适合于依赖渔业和独立的数据源。出现了人口动态的区域性模式,而这些模式本可以通过空间聚集的方法来弥补。距离较近的地区,随着时间的推移,自然死亡率,剥削率,丰度和征募趋势往往相似。我们能够估计随时间变化的自然死亡率,因为有关死亡个体数量的辅助数据已纳入人口动态模型。

更新日期:2021-01-28
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