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Hierarchical surplus production stock assessment models improve management performance in multi-species, spatially-replicated fisheries
Fisheries Research ( IF 2.4 ) Pub Date : 2021-02-16 , DOI: 10.1016/j.fishres.2021.105885
Samuel D.N. Johnson , Sean P. Cox

Managers of multi-species fisheries aim to balance harvests of target and non-target species that vary in abundance, productivity, and degree of technical interactions. In this paper, we evaluated management performance of five surplus production stock assessment methods used in such a multi-species context. Production models included single-species and hierarchical multi-species models, as well as methods that pooled data across species and spatial strata. Operating models included technical interactions between species intended to produce choke effects often observed in output controlled multi-species fisheries. Average annual yield of each method under three data scenarios were compared to annual yield obtained by a simulated omniscient manager. Yield and conservation performance of hierarchical multi-species models was superior to all other methods under low, moderate, and high data quantity scenarios. Results were robust to a wide range of prior precision in assessment model biomass parameters, hierarchical prior precision for catchability and productivity, and future survey precision; however, results were sensitive to prior precision in assessment model productivity parameters under the low data scenario, where the hierarchical multi-species method had similar performance to the data pooling models and was no longer clearly the best option.



中文翻译:

分层剩余生产量评估模型提高了多物种,空间复制渔业的管理绩效

多物种渔业的管理者旨在平衡目标物种和非目标物种在丰度,生产力和技术互动程度方面的收获。在本文中,我们评估了在这种多物种环境中使用的五种剩余生产库存评估方法的管理绩效。生产模型包括单物种模型和分层多物种模型,以及跨物种和空间层次汇总数据的方法。运作模式包括旨在产生窒息效应的物种之间的技术相互作用,这种相互作用通常在产出受控的多物种渔业中观察到。将三种数据方案下每种方法的年平均收益与模拟的全知经理获得的年收益进行比较。在低,中和高数据量情况下,分层多物种模型的产量和保护性能优于所有其他方法。结果在评估模型生物质参数,分层的可捕获性和生产率的先验精度以及未来的调查精度方面具有广泛的先验精度。但是,在低数据情况下,结果对评估模型生产率参数的先前精度敏感,在这种情况下,分层多物种方法的性能与数据池模型相似,并且不再明显是最佳选择。

更新日期:2021-02-17
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