The rapid changes observed in many marine ecosystems that support fisheries pose a challenge to stock assessment and management predicated on time-invariant productivity and considering species in isolation. In single-species assessments, two main approaches have been used to account for productivity changes: allowing biological parameters to vary stochastically over time (empirical), or explicitly linking population processes such as recruitment (R) or natural mortality (M) to environmental covariates (mechanistic). Here, we describe the Woods Hole Assessment Model (WHAM) framework and software package, which combines these two approaches. WHAM can estimate time- and age-varying random effects on annual transitions in numbers at age (NAA), M, and selectivity, as well as fit environmental time-series with process and observation errors, missing data, and nonlinear links to R and M. WHAM can also be configured as a traditional statistical catch-at-age (SCAA) model in order to easily bridge from status quo models and test them against models with state-space and environmental effects, all within a single framework.

We fit models with and without (independent or autocorrelated) random effects on NAA, M, and selectivity to data from five stocks with a broad range of life history, fishing pressure, number of ages, and time-series length. Models that included random effects performed well across stocks and processes, especially random effects models with a two dimensional (2D) first-order autoregressive, AR(1), covariance structure over age and year. We conducted simulation tests and found negligible or no bias in estimation of important assessment outputs (SSB, F, stock status, and catch) when the operating and estimation models matched. However, bias in SSB and F was often non-trivial when the estimation model was less complex than the operating model, especially when models without random effects were fit to data simulated from models with random effects. Bias of the variance and correlation parameters controlling random effects was also negligible or slightly negative as expected. Our results suggest that WHAM can be a useful tool for stock assessment when environmental effects on R or M, or stochastic variation in NAA transitions, M, or selectivity are of interest. In the U.S. Northeast, where the productivity of several groundfish stocks has declined, conducting assessments in WHAM with time-varying processes via random effects or environment-productivity links may account for these trends and potentially reduce retrospective bias.

在支持渔业的许多海洋生态系统中观察到的快速变化给以时不变生产力为基础并单独考虑物种的种群评估和管理提出了挑战。在单物种评估中，使用了两种主要方法来解释生产力的变化：允许生物学参数随时间随机变化（经验），或将诸如招聘（R）或自然死亡率（M）之类的种群过程与环境协变量明确联系起来。（机械的）。在这里，我们描述了结合了这两种方法的伍兹孔评估模型（WHAM）框架和软件包。WHAM可以估计随年龄变化的数字对年均变化的随机影响（NAA），M，选择性，以及适合环境时间序列的过程和观测误差，数据丢失以及与R和M的非线性链接。WHAM还可以配置为传统的统计适龄（SCAA）模型，以便轻松地与现状模型建立桥梁，并在单一框架内针对具有状态空间和环境影响的模型进行测试。

我们拟合对NAA，M具有和不具有（独立或自相关）随机影响的模型，以及对具有广泛生命历史，捕鱼压力，年龄数和时间序列长度的五种种群数据的选择性的模型。包含随机效应的模型在股票和流程中表现良好，尤其是具有二维（2D）一阶自回归，AR（1），年龄和年份的协方差结构的随机效应模型。我们进行了模拟测试，发现当操作模型和估计模型匹配时，重要评估输出（SSB，F，库存状态和捕获量）的估计偏差可忽略不计或没有偏差。但是，SSB和F中存在偏差当估计模型比操作模型复杂时，这通常是不平凡的，特别是当没有随机效应的模型适合具有随机效应的模型模拟的数据时。如预期的那样，控制随机效应的方差和相关参数的偏差也可以忽略不计或略为负。我们的结果表明，当环境对R或M或NAA过渡M的随机变化有影响时，WHAM可以用作库存评估的有用工具。或选择性很重要。在美国东北地区，一些底栖鱼类种群的生产力下降了，在WHAM中通过随时间变化的过程通过随机效应或环境-生产力联系进行评估，可以解释这些趋势，并有可能减少追溯偏差。