ABSTRACT
Littoral habitat is critical for lake biota but is adversely affected by residential shoreland development through the loss and reduced structural complexity of lakeshore vegetation. There currently exists no assessment methodology for evaluating littoral habitat condition of individual lakes in northeastern US. We addressed this assessment need by creating multi-metric indices of littoral habitat condition that focus on lakeshore residential development as the primary stressor. We did this by using habitat metrics derived primarily from National Lake Assessment (NLA) Physical Habitat (PHAB) survey field observations to create Linear Discriminant Analysis (LDA) models that assign lakeshore stations into littoral habitat condition categories. Lake PHAB survey data were used from New England NLA surveys as well as state-level surveys completed in Maine, New Hampshire, and Vermont. Prediction success rates in New England models averaged 83%. The Maine LDA models, which used finer scale survey methods, had an average prediction success rate of 89%. We used 95% bootstrapped confidence intervals to make assessment designations of natural (meeting reference quality), diminished (not meeting reference quality), or intermediate (existing between natural and diminished) littoral habitat condition for each lake. Our results show that efficacious single-lake littoral habitat assessments may be completed within the framework of NLA PHAB methodology, but confidence in assessment results, and therefore better-informed management decisions, can be improved with finer-scale observation data.
ABSTRACT
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.
