Canadian Journal of Fisheries and Aquatic Sciences, Ahead of Print.
Estimation of abundance with wide spatiotemporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a hierarchical Bayesian modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon (Salmo salar) populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates, and fishing effort. Additional data of total or partial abundance collected in four closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied.

中文翻译：

---

充分利用手头的各种数据组合生成丰度时间序列：以布列塔尼大西洋鲑鱼种群为例

加拿大渔业和水生科学杂志，印刷前。
估计具有广泛时空覆盖的丰度对于野生种群的评估和管理至关重要。但是，在许多情况下，可用于估计丰度时间序列的数据具有多种形式，随空间和时间而变化的质量，它们源于多个数据收集程序。我们开发了一种分层贝叶斯建模 (HBM) 方法，该方法充分利用手头的各种数据组合来估计丰度的同质时间序列，而与数据收集程序无关。我们将我们的方法应用于 1987 年至 2017 年布列塔尼（法国）18 条大西洋鲑鱼 (Salmo salar) 种群的成年丰度估计，该方法使用渔获统计、环境协变量和捕捞努力。在四个密切监测的种群中收集的全部或部分丰度的额外数据也被整合到分析中。HBM 框架允许将信息从密切监视的人群转移到其他人群。我们的研究结果表明，在研究期间，布列塔尼的成人回归丰度没有明显的趋势。