Abstract Species-specific abundance estimates lend insight into fish ecology, inform sustainable fisheries management, and remain the goal of hydroacoustic fisheries sampling. The apportionment process, assigning aggregate hydroacoustic data to individual species, is affected by uncertainties in both hydroacoustic and species composition data. These uncertainties have associated biases that can propagate through the apportionment process and degrade abundance estimates. We developed an apportionment procedure that reduces the influence of sampling, threshold, and misclassification biases leading to more accurate species-specific abundance estimates. We applied our method to Lake Erie walleye, using paired hydroacoustic and gillnet sampling data, and generated distribution and abundance estimates supported by known ecological patterns. Flexibility in this approach performed better than traditional threshold methods that ignore uncertainty in catch composition, threshold choice, and target-strength uncertainty.

中文翻译：

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接受不确定性以减少水声物种分配中的偏差

摘要 特定物种的丰度估计有助于深入了解鱼类生态，为可持续渔业管理提供信息，并且仍然是水声渔业采样的目标。分配过程，将汇总的水声数据分配给单个物种，受水声和物种组成数据的不确定性影响。这些不确定性具有相关的偏差，可以通过分配过程传播并降低丰度估计。我们开发了一种分配程序，可减少采样、阈值和错误分类偏差的影响，从而实现更准确的物种特定丰度估计。我们将我们的方法应用于伊利湖角膜白斑，使用成对的水声和刺网采样数据，并生成由已知生态模式支持的分布和丰度估计。