Abstract
AbstractState variables in lake ecosystems are subject to processes that act on different time scales. The relative importance of each of these processes changes over time, e.g., due to varying constraints of physical, biological, and biogeochemical processes. Correspondingly, continuous automatic measurements at high temporal resolution often reveal intriguing patterns that can rarely be directly ascribed to single processes. In light of the rather complex interplay of such processes, disentangling them requires more powerful methods than researchers have applied up to this point. For this reason, we tested the potential of wavelet coherence, based on the assumption that different processes result in correlations between different variables, on different time scales and during different time windows across the seasons. The approach was tested on a set of multivariate hourly data measured between the onset of an ice cover and a cyanobacterial summer bloom in the year 2009 in the Müggelsee, a polymictic eutrophic lake. We found that processes such as photosynthesis and respiration, the growth and decay of phytoplankton biomass, dynamics in the CO2‐carbonate system, wind‐induced resuspension of particles, and vertical mixing all occasionally served as dominant drivers of the variability in our data. We therefore conclude that high‐resolution data and a method capable of analyzing time series in both the time and the frequency domain can help to enhance our understanding of the time scales and processes responsible for the high variability in driver variables and response variables, which in turn can lay the ground for mechanistic analyses.
Highlights
These examples illustrate that the type of prevailing constraint of a limnological process may change over time, and can happen on different time scales ranging from fractions of seconds ([bio-]chemical reaction rates), from seconds to days, from minutes to days or weeks, from hours to weeks, and even up to several months (Reynolds 1990; Behrendt et al 1993; Hanson et al 2006)
The way the variation of a variable, or the covariation of two variables, can pinpoint a process and indicate the state of a lake ecosystem is not always straightforward: Many processes may affect more than one single variable; a given process may affect each of these variables in a different way, on a different time scale, and during a different time window of the year
The time domain is shown on the x-axis with the same scaling as for the time series given above, while the respective period length is given on the y-axis
Summary
Speeds (Lampert and Sommer 2007; Read et al 2011) In this regard, research is increasingly focusing on episodic events and how, for instance, storms affect a lake ecosystem or certain processes in it (Jennings et al 2012; Klug et al 2012; Kasprzak et al 2017). Understanding and identifying the huge intra-annual variability of various state variables, their interactions, and their time scales remains challenging Analyzing limnological processes, their time scales, and their constraints requires a high amount of multivariate data, which are increasingly being collected in lakes worldwide (Marcé et al 2016; Meinson et al 2016). Analysis of the synchronicity of temporal patterns of different variables on different time scales is a necessary first step in disentangling different processes that occur in parallel
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