The Potential Influence of Short-term Environmental Variability on the Composition of Testate Amoeba Communities in Sphagnum Peatlands

Testate amoebae are a group of moisture-sensitive, shell-producing protozoa that have been widely used as indicators of changes in mean water-table depth within oligotrophic peatlands. However, short-term environmental variability (i.e., sub-annual) also probably influences community composition. The objective of this study was to assess the potential influence of short-term environmental variability on the composition of testate amoeba communities in Sphagnum-dominated peatlands. Testate amoebae and environmental conditions, including hourly measurements of relative humidity within the upper centimeter of the peatland surface, were examined throughout the 2008 growing season at 72 microsites within 11 peatlands of Pennsylvania and Wisconsin, USA. Relationships among testate amoeba communities, vegetation, depth to water table, pH, and an index of short-term environmental variability (EVI), were examined using nonmetric multidimensional scaling and correlation analysis. Results suggest that EVI influences testate amoeba communities, with some taxa more abundant under highly variable conditions (e.g., Arcella discoides, Difflugia pulex, and Hyalosphenia subflava) and others more abundant when environmental conditions at the peatland surface were relatively stable (e.g., Archerella flavum and Bullinularia indica). The magnitude of environmental variability experienced at the peatland surface appears to be primarily controlled by vegetation composition and density. In particular, sites with dense Sphagnum cover had lower EVI values than sites with loose-growing Sphagnum or vegetation dominated by vascular plants and/or non-Sphagnum bryophytes. Our results suggest that more environmental information may be inferred from testate amoebae than previously recognized. Knowledge of relationships between testate amoebae and short-term environmental variability should lead to more detailed and refined environmental inferences.