, Volume 28, Issue 3, pp 329-348

Testate amoebae as paleoindicators of surface-moisture changes on Michigan peatlands: modern ecology and hydrological calibration

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


Peatland testate amoebae are sensitive indicators of local hydrology and have been used as proxies for surface moisture conditions in fossil studies. However, few regional calibration datasets exist in North America, and knowledge of testate amoeba ecology and distribution patterns are limited. The objectives of this study were to (1) investigate the relationship between testate amoebae, environment, and Sphagnum species in Michigan peatlands; (2) generate transfer functions from this dataset that can be applied to fossil data; and (3) describe vertical variation of testate amoebae inhabiting Sphagnum moss. Testate amoeba assemblages from 139 microsites within 11 peatlands in Michigan were compared to assess variability between and within peatlands. Most peatlands contained similar testate amoeba assemblages, although within individual peatlands the amount of assemblage variability is correlated to the amount of environmental heterogeneity. Of the measured environmental variables, depth to water table showed the strongest relationship with testate amoebae. Depth to water table can be reconstructed from fossil data with a mean error of ±7.5 cm, although predictive ability deteriorates in extremely dry environments (>30 cm water table depth). Vertical variation in testate amoeba assemblages was investigated at 89 Sphagnum-dominated microsites by directly comparing amoeba assemblages and the abundance and frequency of common taxa in upper and lower portions of the Sphagnum stem. Except for extremely dry microsites, considerable vertical variation in assemblage composition exists. Many agglutinate taxa are more abundant on lower portions of the Sphagnum stem, and taxa containing symbiotic zoochlorellae are more abundant on upper portions. Refinements in sampling procedures and analysis may improve the predictive ability of transfer functions.