The chronology of Lake Baikal plankton: have warming waters had an impact on the timing of annual population dynamics?

Climate change has had a significant impact on the Sacred Sea. Shimaraev and colleagues have documented an increase in the ice-free season by 18 days between 1869-2000, and an analysis using the amazing Izmest’eva plankton and temperature data set suggests that surface waters in Lake Baikal are warming fast (Hampton and colleagues 2008). These environmental changes could have a significant impact on Baikal plankton communities, as both Pislegina and Silow and Hampton and Colleagues have documented a relationship between the abundance of zooplankton species in the lake and temperature.

One of the potential impacts of warming waters is a shift in seasonal timing for organisms living in the lake. For example, past studies in marine environments have found that zooplankton population dynamics have shifted forward in time with rising temperatures (Edwards and Richardson 2004). A recent study by Izmest’eva and colleagues found no evidence for a change in seasonal timing for three out of the four common Baikal phytoplankton genera they assessed, but they did document a weak change for the green alga Ankistrodesmus.

How can we tell if the timing of annual events is changing in Lake Baikal? One way is to use an approach called spectral analysis, a method that allows for the decomposition of a complex time series into a series of simple sinusoidal functions. For example, the time series in the figure below can be broken up into components that fluctuate once per year, twice per year, and once every two years. By adding up these three components you can get the original time series in the upper left panel of the figure. This method relies on the fast fourier transform to identify the underlying sinusoidal functions.

Using the 60-year Izmest’eva plankton data set collected by our Russian colleagues I am breaking the time series into a series of time windows (e.g. 5 year windows), performing the fast fourier transform on each of the windows, and then extract something called the phase for each window. The phase represents the relative placement of these functions along the time axis. For example, in the figure below the green sinusoid has a more advanced phase then the black sinusoid, while the red lags behind the black. By comparing the phase in all of the windows in the time series I am able to determine whether the phase has changed positively or negatively through time. For more information on this fascinating technique see the paper by Katz and colleagues that describes the same analysis using Baikal temperature data. 

This is only one of the many neat analyses our Russian-American team will undertake together in this project. Please check back here for updates on the results!

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