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. 2025 Mar 6;20(3):e0314582.
doi: 10.1371/journal.pone.0314582. eCollection 2025.

Limnological data derived from high frequency monitoring buoys are asynchronous in a large lake

Claire Stevens  1 , Paul C Frost  2 , Nolan J T Pearce  2 , James D Kelley  1 , Arthur Zastepa  3 , Marguerite A Xenopoulos  2
Affiliations

Limnological data derived from high frequency monitoring buoys are asynchronous in a large lake

Claire Stevens et al. PLoS One. .

Abstract

Autonomous data collection is rapidly becoming an integral part of water quality monitoring, particularly for agencies looking to manage and protect aquatic ecosystems. While beneficial, it is unclear how the collection of these data can be applied in spatially complex large lakes (e.g., Laurentian Great Lakes) given the spatial heterogeneity of the ecosystem. To address this potential shortcoming in large lakes, we assessed the synchrony of sensor variables between 10 pairs of static buoys in the western basin of Lake Erie (western basin surface area = 3,282 km2). Within western Lake Erie, water temperature was highly synchronous whereas dissolved oxygen, turbidity, chlorophyll and phycocyanin were asynchronous. The extent of this asynchrony was higher with increasing spatial distance between buoys. We found that between pairs of static buoys, temperature, dissolved oxygen, and turbidity all experienced decreasing correlations with increasing distance. Our results show that if researchers intend to leverage these data to answer important questions and provide real-time applications related to environmental issues like harmful algal/cyanobacterial blooms, monitoring networks need to be designed carefully with spatial complexity in mind. While autonomous data collection has many benefits, the reliance on a single or limited network of anchored monitoring buoys in large lake ecosystems has a high probability of missing important spatial features of these systems.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Map of the study area.
(A) Lake Erie and the western basin in the Laurentian Great Lakes Region of North America, (B) deployed buoy locations in 2021 (red), 2022 (blue) and both years (black). Arrows represent typical prevailing circulation patterns in August in the western basin. Bathymetry data was acquired from NOAA [30].
Fig 2
Fig 2. Boxplots created with all possible pairwise combinations of monitoring buoys in 2021 and 2022 for Pearson correlation coefficient and normalized DTW value.
Fig 3
Fig 3. Linear regressions of the distance between buoys (km) and the Pearson correlation coefficient for each pairwise buoy combination for 2021 (blue) and 2022 (gray).
Only significant (p <  0.05) trends are shown. Note that the scale of the y-axis differ between panels for clarity.
Fig 4
Fig 4. Linear regressions of the distance between buoys (km) and the normalized dynamic time warping value for each pairwise buoy combination for 2021 (blue) and 2022 (gray).
Only significant (p <  0.05) trends are shown.
See this image and copyright information in PMC

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