How Many Samples?! Assessing the Mean of Parameters Important for Denitrification in High and Low Disturbance Headwater Wetlands of Central Pennsylvania

  • Aliana BritsonEmail author
  • Denice H. Wardrop


Due to large time and monetary costs involved with sampling for denitrification and other biogeochemical processes, many studies are limited to only a few samples per site. However, denitrification is a highly spatially and temporally variable process due to high variability of the biogeochemical parameters underpinning denitrification such as nitrate (NO3), dissolved organic carbon (DOC), dissolved oxygen (DO), temperature, and pH. Accurately assessing wetlands for the means of parameters underpinning denitrification is vital, as inaccurate means can result in incorrect estimates of wetland-scale denitrification as well as inaccurate parameterization and validation of denitrification models. To examine adequate sample size for these parameters, shallow groundwater samples were collected from twenty wells each at two high disturbance (surrounded by agriculture) and two low disturbance (surrounded by forest) headwater riparian wetlands in central Pennsylvania on May 21st, 2013. A Monte Carlo analysis was used along with a cutoff of 10 % coefficient of variation to assess the appropriate number of samples required to accurately assess mean temperature, pH, DO, DOC, and NO3 within headwater wetlands. Temperature and pH required only one sample to accurately assess the site mean. DO required seven or more samples, while DOC and NO3 required ten or more samples to accurately assess the site mean. No differences were noted in required sample size between high and low disturbance sites. These results highlight the importance of taking numerous samples for wetland denitrification studies.


Denitrification Sample size Headwater wetlands Dissolved organic carbon Nitrate 



Funding for this work was provided by the United States Environmental Protection Agency (Grant #R834262), the National Aeronautics and Space Administration, and The Pennsylvania State University. We would like to thank the Pennsylvania Department of Conservation and Natural Resources, The Pennsylvania State Experimental Forest, and Larry Suwak for wetland access. We thank Karol Confer with and Michael Brown for assistance with water sample analysis. We wish to acknowledge Dr. Jessica Moon for assistance with the Monte Carlo simulation R code. Last but not least, we also appreciate field assistance from Bret Dietz, Marla Korpar, Kyle Martin, Danny Molinaro, and Jason Britson.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Geography DepartmentPenn State UniversityUniversity ParkUSA

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