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Implementing two-dimensional autocorrelation in either survival or natural mortality improves a state-space assessment model for Southern New England-Mid Atlantic yellowtail flounder
Fisheries Research ( IF 2.4 ) Pub Date : 2021-01-20 , DOI: 10.1016/j.fishres.2021.105873
Brian C. Stock , Haikun Xu , Timothy J. Miller , James T. Thorson , Janet A. Nye

Survival is an important population process in fisheries stock assessment models and is typically treated as deterministic. Recently developed state-space assessment models can estimate stochastic deviations in survival, which represent variability in some ambiguous combination of natural mortality (M), fishing mortality (F), and migration. These survival deviations are generally treated as independent by age and year, despite our understanding that many population processes can be autocorrelated and that not accounting for autocorrelation can result in notable bias. We address these concerns, as well as the strong retrospective pattern found in the last assessment of Southern New England yellowtail flounder (Limanda ferruginea), by incorporating two-dimensional (2D, age and year) first-order autocorrelation in survival and M. We found that deviations were autocorrelated among both years (0.53 ± 0.09, 0.63 ± 0.16) and ages (0.33 ± 0.12, 0.40 ± 0.16) when estimated for survival or M, respectively. Models with 2D autocorrelation on survival or M fit the data better and had reduced retrospective pattern than models without autocorrelation. The best fit model included 2D autocorrelated deviations in survival as well as independent deviations in M and altered estimates of spawning stock biomass by 18 % and F by 21 % in model years. In short-term projections with F = 0, including 2D autocorrelation in survival or M reduced spawning stock biomass by 48 %. We conclude that incorporating 2D autocorrelated variation in survival or M could improve the assessment of Southern New England yellowtail flounder in terms of model fit and consistency of biomass projections.



中文翻译:

在生存或自然死亡率中实施二维自相关改善了新英格兰南部-大西洋中部yellow鱼比目鱼的状态空间评估模型

生存是渔业种群评估模型中重要的种群过程,通常被视为确定性的。最近开发的状态空间评估模型可以估计生存的随机偏差,其代表自然死亡率(M),捕鱼死亡率(F)和迁徙的某些模棱两可组合。尽管我们了解许多人口过程可能是自相关的,并且不考虑自相关会导致明显的偏倚,但通常将这些生存偏差按年龄和年份视为独立的。我们解决了这些问题,以及在对新英格兰南部yellow鱼比目鱼(Limanda ferruginea)的最新评估中发现的强回顾性模式),通过在生存率和M中合并二维(2D,年龄和年份)一阶自相关。我们发现两年之间的偏差是自相关的(0.53± 0.09、0.63 ± 0.16)和年龄(0.33 ± 0.12、0.40 ±估计生存时间或M分别为0.16)。具有2D自相关生存率或M的模型比没有自相关模型的模型更适合数据,并且减少了追溯模式。最佳拟合模型包括生存期中的二维自相关偏差以及M中的独立偏差,并且在模型年中产卵生物量的估计值更改了18%,F更改了21%。在F  = 0的短期预测中,包括生存或M的2D自相关将产卵生物量降低48%。我们得出的结论是,将2D自相关变异纳入生存率或M 可以在模型拟合和生物量预测的一致性方面改善新英格兰南部Southern鱼比目鱼的评估。

更新日期:2021-01-21
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