Shifting baselines and cross‐scale drivers of lake water clarity: Applications for lake assessment. Issue Volume 67:Issue S1(2022)Special issue (22nd June 2021)

• Record Type: Journal Article
• Authors: Deeds, Jeremy; Amirbahman, Aria; Norton, Stephen A.; Bacon, Linda C.; Hovel, Rachel A.
• Other Names: Seekell David A. guestEditor.; Pace Michael L. guestEditor.; Heffernan James B. guestEditor.; Holbrook Sally J. guestEditor.; David Hambright K. guestEditor.
• Is Part Of: Limnology and oceanography. Volume 67:Issue S1(2022)Special issue
• Page Start: S184
• Page End: S197
• Publication Date: 2021-06-22
• Subjects: Limnology -- Periodicals; Oceanography -- Periodicals; Océanographie; Limnologie; Limnology; Oceanography; Computer network resources; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); Periodicals; 551.4805
• DOI: 10.1002/lno.11873
• Languages: English
• ISSNs: 0024-3590

Abstract

Abstract: 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.