Stock-wide trends in fish relative abundance are challenging to obtain when a single, comprehensive survey is unavailable, and multiple, spatially, and/or temporally fragmented surveys must be relied upon instead. Indices of abundance from multiple surveys frequently have differing trends, resulting in obscured true abundance patterns of the resource. We use an age-structured simulation model of two coastal shark species in the southeast United States to explore the performance of dynamic factor analysis (DFA) for reconciling multiple indices of abundance that are in conflict. Survey-specific time-variation in catchability was induced to generate conflicting indices of abundance. Key simulation sensitivities included survey variability, abundance pattern in the resource, and missing years of survey data. We caution against using DFA when there is no contrast in the underlying stock abundance or when trends in catchability in all surveys result in no survey that is representative of stock abundance. When multiple representative surveys were available, DFA proved useful across species in estimating stock-wide trends from conflicting survey indices with different selectivities, catchabilities, variances, and, to a lesser extent, with missing data. Our results suggest that resolving contrasting patterns among multiple time-series of relative abundance can improve understanding of the temporal trend in stock abundance.

中文翻译：

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动态因素分析以协调相互矛盾的丰度调查指数

当无法进行单一、全面的调查时，很难获得鱼类相对丰度的全种群趋势，而必须依赖多个、空间和/或时间分散的调查。来自多次调查的丰度指数经常具有不同的趋势，导致资源的真实丰度模式模糊不清。我们使用美国东南部两种沿海鲨鱼物种的年龄结构模拟模型来探索动态因子分析 (DFA) 的性能，以协调多个冲突的丰度指数。可捕获性的调查特定时间变化被诱导产生相互矛盾的丰度指数。关键的模拟敏感性包括调查变异性、资源中的丰度模式以及调查数据的缺失年份。当基础种群丰度没有对比或所有调查中的可捕获性趋势导致没有代表种群丰度的调查时，我们告诫不要使用 DFA。当有多个具有代表性的调查可用时，DFA 被证明可用于从具有不同选择性、可捕获性、差异以及在较小程度上缺少数据的相互冲突的调查指数中估计种群范围的趋势。我们的结果表明，解决多个相对丰度时间序列之间的对比模式可以提高对库丰度时间趋势的理解。事实证明，DFA 在从具有不同选择性、可捕获性、差异以及在较小程度上缺失数据的相互冲突的调查指数中估计种群范围内的趋势时，对跨物种很有用。我们的结果表明，解决多个相对丰度时间序列之间的对比模式可以提高对库丰度时间趋势的理解。事实证明，DFA 在从具有不同选择性、可捕获性、差异以及在较小程度上缺失数据的相互冲突的调查指数中估计种群范围内的趋势时，对跨物种很有用。我们的结果表明，解决多个相对丰度时间序列之间的对比模式可以提高对库丰度时间趋势的理解。