Oysters in Virginia Chesapeake Bay oyster reefs are "age-truncated", possibly due to a combination of historical overfishing, disease epizootics, environmental degradation, and climate change. Research has suggested that oysters exhibit resilience to environmental stressors; however, that evidence is based on the current limited understanding of oyster lifespan. Until this paper, the Virginia Oyster Stock Assessment and Replenishment Archive (VOSARA), a spatially and temporally expansive dataset (222 reefs across 2003-2023) of shell lengths (SL, mm), had yet to be examined comprehensively in the context of resilience. We develop a novel method using Gaussian mixture modeling (GMM) to identify the age groups in each reef using yearly SL data and then link those age groups over time to identify cohorts and estimate their lifespan. Sixty-four reefs (29%) are deemed to have sufficient data (at least 300 oysters sampled for a minimum of 8 consecutive years) for this analysis. We fit univariate GMMs for each year ($t$) and reef ($r$) for each of the seven river strata ($R$) to estimate 1) the mean and standard deviation of SL for each $a_{Rrt}$th age group, and 2) the mixture percentage of each $a_{Rrt}$th age group. We link age groups across time to infer age cohorts by developing a mechanistic algorithm that prevents the shrinking of shell length when an $a_{Rrt}$th group becomes an ($a_{R,r,t+1}$)th group. Our method shows promise in identifying oyster cohorts and estimating lifespan solely using SL data. Our results show signals of resiliency in almost all river systems: oyster cohorts live longer and grow larger in the mid-to-late 2010s compared to the early 2000s.

翻译：弗吉尼亚州切萨皮克湾牡蛎礁中的牡蛎呈现“年龄截断”现象，这可能是历史性过度捕捞、疾病流行、环境退化及气候变化共同作用的结果。研究表明牡蛎对环境压力具有恢复力，但该结论基于当前对牡蛎寿命的有限认知。本文首次对弗吉尼亚牡蛎资源评估与补充档案（VOSARA）——一个涵盖时空维度的壳长数据集（2003-2023年间222个礁体）——在恢复力背景下进行了全面解析。我们开发了一种基于高斯混合模型（GMM）的新方法，利用逐年壳长数据识别各礁体的年龄组，并通过时间关联确定队列并估算其寿命。其中64个礁体（占29%）具备足够数据（至少连续8年采样，每年样本量≥300只牡蛎）用于本分析。我们对七个河流层级（R）中每个年份（t）和礁体（r）分别拟合单变量GMM，以估算：1）每个第a_{Rrt}年龄组的壳长均值与标准差；2）每个第a_{Rrt}年龄组的混合比例。通过设计机制算法关联跨时间年龄组以推断年龄队列，该算法防止第a_{Rrt}组演变为第（a_{R,r,t+1}）组时出现壳长收缩。本方法仅利用壳长数据即可有效识别牡蛎队列并估算寿命。结果显示几乎所有河流系统均存在恢复力迹象：相较于21世纪初，2010年代中后期的牡蛎队列寿命更长、体型更大。