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Absence of dissolved oxygen (anoxia) in the hypolimnion of lakes can eliminate habitat for sensitive species and may induce the release of sediment‐bound phosphorus. Lake anoxia generally results from decomposition of organic matter, which is exacerbated by high nutrient loads. Total phosphorus (TP) in lakes is regulated by static aspects of the lake’s watershed, but lake TP can be readily increased by human activities. In some low‐nutrient lakes, basin morphometry may induce naturally occurring anoxia. The occurrence of natural anoxia is especially important to consider in lake water quality assessments that compare observed conditions to expected reference conditions. To investigate the occurrence of natural vs. anthropogenically influenced anoxia, we constructed a logistic regression model to calculate the probability of low‐nutrient lakes (TP < 15 µg/L) developing aerial anoxic extent ≥10% by testing the predictive potential of variables related to basin morphometry, depths of lake thermal strata, epilimnetic TP, and dissolved organic carbon (DOC). Maximum lake depth and the proportion of lake area under the top of the metalimnion were the most important variables to predict the likelihood of hypolimnetic anoxia, which correctly predicted anoxic condition in 84% of lakes (Model 1). Adding TP as a third variable to Model 1 produced a significantly improved model (Model 2) but the prediction success rate was comparable (86%). We also present a model for lakes with limited bathymetric data, which predicts anoxia with 81% accuracy based on maximum lake depth and mean thermocline depth at peak stratification. DOC was relatively low (4.3 ± 1.5 mg/L [mean ± SD]) in the study lakes and its inclusion did not improve model performance. In Model 1, lakes with an anoxic extent ≥10% of lake area had significantly higher epilimnetic TP than lakes with oxic hypolimnia, regardless of prediction category or success. Our results indicate that including TP as a variable helps refine models based on morphometry alone, but lake morphometry and stratification dynamics are the most important factors in the development of anoxic extent in low‐nutrient temperate lakes. Our approach informs studies concerned with identifying key factors that influence regime shifts in a variety of ecosystems.

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.