Modeling somatic growth of animals whose growth rates are seasonally variable is a challenge. Seasonal variation in growth reduces model fit and precision if not accounted for, and ad hoc adjustments to growth models may be biased or biologically unrealistic. We developed a growth phenology model (GPM) that uses a logistic function to model the cumulative proportion of total annual growth. We applied this model using two different approaches to datasets from temperate-climate populations of two freshwater turtle species that experience extended winter dormancy during which no growth occurs. The first dataset consisted of repeated intra-annual observations of sub-adult snapping turtles (Chelydra serpentina) tracked by radio telemetry, which we analyzed in a Bayesian context, focusing on growth over a single season. We then demonstrated a post hoc combination of the fitted GPM with a separate overall growth model. For the second application, we fully integrated the GPM into a hierarchical von Bertalanffy growth model, which we applied to a dataset of primarily inter-annual observations of juvenile midland painted turtles (Chrysemys picta marginata). Specifying informative priors allowed us to fit the model despite the sparseness of intra-annual information in the data. We also demonstrate using the beta cumulative distribution function as an alternative to the logistic function in the GPM. We discuss incorporating prior knowledge about seasonal foraging and activity periods into growth models via a GPM as a transparent alternative to deterministic, implicit, a priori constructs.

对动物生长速度随季节变化而变化的动物进行建模是一个挑战。如果不考虑增长的季节性变化，则会降低模型的拟合度和精度，并且对增长模型的临时调整可能存在偏差或生物学上不现实。我们开发了一种增长物候模型（GPM），该模型使用逻辑函数来对年度总增长的累积比例进行建模。我们使用两种不同的方法将此模型应用于来自两个淡水龟物种的温带气候种群的数据集，这些种群经历了延长的冬季休眠，在此期间没有任何生长。第一个数据集包括对亚成年鳄龟（Chelydra serpentina）的年度重复观测）由无线电遥测跟踪，我们在贝叶斯背景下进行了分析，重点是单个季节的增长。然后，我们展示了拟合的GPM与单独的整体增长模型的事后组合。对于第二个应用程序，我们将GPM完全集成到了一个分层的von Bertalanffy生长模型中，该模型用于主要对幼年中陆彩绘海龟（Chrysemys picta marginata）的年度观测数据集。）。尽管数据中年内信息稀疏，但指定信息先验使我们能够拟合模型。我们还演示了使用beta累积分布函数作为GPM中逻辑函数的替代方法。我们讨论通过GPM将有关季节性觅食和活动期的先验知识纳入增长模型，以作为确定性，隐含先验构造的透明替代方案。