High natural variability in the condition of fish communities in headwater streams complicates detection of long-term responses to changes in water quality. As a result, little is known about the impacts and recovery of fishes from acid deposition in streams of New York. Twenty-one fish metrics from annual electrofishing surveys at 13 streams sites in the Catskill and Adirondack mountains were assessed to quantify temporal variability and identify effective monitoring strategies for detecting change in headwater stream fish assemblages. Metrics included the density and biomass of Brook Trout populations and entire fish communities using length-, area-, and effort-based standardization techniques. Linear mixed models were used to estimate changes in coefficients of variation (CV) for different classes of metrics and standardization techniques, and a simulation-based power analysis was conducted to assess differences in statistical power for each metric with various sampling designs. Metric CV varied significantly as a result of standardization technique and whether metrics were calculated for the entire community or Brook Trout only. The sampling effort necessary to detect a 30% change with power of 0.80 was strongly correlated with CV (R2 = 0.77). The number of sampling events at the 13 sites needed to detect this change ranged from 1 to >60, suggesting metric selection can strongly affect statistical power and the resources necessary to detect change. Thus, metric selection is a complex decision that must consider study objectives and biological relevance, in addition to natural variability and statistical power. However, adequate statistical power was achieved at relatively small sample sizes using certain metrics, indicating that fish communities in headwater streams can be a valuable component of long-term assessments of acidification impacts and recovery.
Additional publication details
|Publication Subtype||Journal Article|
|Title||Temporal variability in stream fish assemblage metrics and implications for long-term monitoring|
|Series title||Ecological Indicators|
|Contributing office(s)||New York Water Science Center|
|Google Analytic Metrics||Metrics page|