Temporal Secchi depth trends are used in lake assessment to evaluate lake condition and possible shifts in trophic state. For accurate lake assessments, it is important to differentiate regional trends from lake-specific trends, but this can be confounded by interacting factors. We present a divergent trend analysis which uses temporal patterns of Secchi depth water clarity from 1999 to 2018 for five different types of reference lakes from minimally disturbed watersheds to create dynamic baselines against which we evaluate Secchi depth trends from nonreference lakes in Maine, USA. We used mixed-effect generalized additive models to generate smoothed curves of the expected baseline Secchi depth for each reference lake type to account for the nonlinear dynamics of lake condition through time. The majority of nonreference lakes (74%) showed no difference between measured trend (actual Secchi depth data) and divergent trend (residual Secchi depth from baseline trends). The most common difference in lakes with inconsistent trend test results showed stability in measured trends but apparent declining trends in divergent Secchi depth clarity. We used a Dynamic Factor Analysis (DFA) model to help interpret the variation and shifts observed in baseline reference lake trends. The best DFA model identified two common trends in water clarity among lake types and precipitation during the primary stratification season as the most informative covariable. Because precipitation amount and intensity are expected to increase according to predictive climate models for the Northeast US, our results suggest that baseline lake clarity in Maine will decrease with climate change.

中文翻译：

---

改变湖泊水清澈度的基线和跨尺度驱动因素：湖泊评估的应用

时间 Secchi 深度趋势用于湖泊评估，以评估湖泊状况和营养状态的可能变化。对于准确的湖泊评估，将区域趋势与特定湖泊趋势区分开来很重要，但这可能会被相互作用的因素混淆。我们提出了一个发散趋势分析，该分析使用 1999 年至 2018 年 Secchi 深度水清晰度的时间模式，用于来自受干扰最小的流域的五种不同类型的参考湖泊，以创建动态基线，我们根据该基线评估来自美国缅因州非参考湖泊的 Secchi 深度趋势。我们使用混合效应广义加法模型为每个参考湖泊类型生成预期基线 Secchi 深度的平滑曲线，以解释湖泊条件随时间的非线性动态。大多数非参考湖泊 (74%) 显示测量趋势（实际 Secchi 深度数据）和发散趋势（基线趋势的残余 Secchi 深度）之间没有差异。趋势测试结果不一致的湖泊中最常见的差异表明测量趋势稳定，但不同的 Secchi 深度清晰度有明显下降趋势。我们使用动态因子分析 (DFA) 模型来帮助解释在基线参考湖泊趋势中观察到的变化和变化。最好的 DFA 模型确定了湖泊类型和主要分层季节降水量的两个常见趋势，作为最具信息性的协变量。因为根据美国东北部的预测气候模型，预计降水量和强度会增加，