Tandem studies of paleoclimatic and paleoecological proxies can be used to directly assess past ecological responses to climate change.  Success of this approach is dependent on reliable climate proxies that capture aspects of climate variability relevant to ecological dynamics.  Most proxy development has focused on estimating mean climatic conditions, although, changes in seasonality influence ecological dynamics by modulating recruitment success and altering disturbance regimes. Recent work using testate amoebae has demonstrated their utility in reconstructing mean annual water-table depth in peatlands. However, testate amoebae are probably also sensitive to hydrologic fluctuations during the growing season.  We examined testate amoebae and species-environment relationships along a gradient of hydrologic variability in Tannersville Bog, a peatland in northeastern Pennsylvania, in an effort to assess their potential as proxies for hydrologic variability. Four shallows wells equipped with water level loggers were established across this gradient to provide a continuous record of water level changes during the growing season. Testate amoeba communities were analyzed at 12 microsites near each well. Mean water-table depth, conductivity, and pH were measured at all microsites, and the range of hydrologic variability experienced during the growing season was calculated from the well data. 

Results/Conclusions

Results suggest that, in addition to mean water-table depth, the range of hydrologic variability was important in structuring testate amoebae communities.  Some species, such as Hyalosphenia subflava and Difflugia pulex, appear to be particularly good indicators of high annual variability in water-table depth.  Our results suggest that reconstructions of past hydrological variability using testate amoebae are possible given appropriate calibration datasets. Tandem application of this technique with pollen and macrofossil records may dramatically improve our understanding of past ecological responses to hydroclimate variability.