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Combining fixed-location count data and movement data to estimate abundance of a lake sturgeon spawning run: a framework for riverine migratory species
Canadian Journal of Fisheries and Aquatic Sciences ( IF 2.4 ) Pub Date : 2021-11-18 , DOI: 10.1139/cjfas-2021-0140
Lisa K Izzo 1 , Gayle B. Zydlewski 2 , Donna L Parrish 3
Affiliation  

Canadian Journal of Fisheries and Aquatic Sciences, Ahead of Print.
Estimating abundance of migrating fishes is challenging. While sonars can be deployed continuously, improper assumptions about unidirectional migration and complete spatial coverage can lead to inaccurate estimates. To address these challenges, we present a framework for combining fixed-location count data from a dual-frequency identification sonar (DIDSON) with movement data from acoustic telemetry to estimate spawning run abundance of lake sturgeon (Acipenser fulvescens). Acoustic telemetry data were used to estimate the probability of observing a lake sturgeon on the DIDSON and to determine the probability that a lake sturgeon passing the DIDSON site had passed the site previously during the season. Combining probabilities with DIDSON counts, using a Bayesian integrated model, we estimated the following abundances: 99 (42–215 credible interval, CI) in 2017, 131 (82–248 CI) in 2018, and 92 (47–184 CI) in 2019. Adding movement data generated better inferences on count data by incorporating fish behavior (e.g., multiple migrations in a single season) and its uncertainty into abundance estimates. This framework can be applied to count and movement data to estimate abundance of spawning runs of other migratory fishes in riverine systems.


中文翻译:

结合固定位置计数数据和移动数据来估计湖鲟产卵运行的丰度:河流洄游物种的框架

加拿大渔业和水生科学杂志,印刷前。
估计洄游鱼类的丰度具有挑战性。虽然声纳可以连续部署,但对单向迁移和完整空间覆盖的不正确假设可能会导致估计不准确。为了应对这些挑战,我们提出了一个框架,用于将来自双频识别声纳 (DIDSON) 的固定位置计数数据与来自声学遥测的运动数据相结合,以估计湖鲟 (Acipenser fulvescens) 的产卵运行丰度。声学遥测数据用于估计在 DIDSON 上观察到湖鲟的概率,并确定在本季节之前经过 DIDSON 场地的湖鲟的概率。将概率与 DIDSON 计数相结合,使用贝叶斯集成模型,我们估计了以下丰度:99(42-215 可信区间,CI)在 2017 年,131(82-248 CI)在 2018 年和 92(47-184 CI)在 2019将其不确定性转化为丰度估计。该框架可用于计数和移动数据,以估计河流系统中其他洄游鱼类的产卵量。
更新日期:2021-11-18
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