Environmental Management

, Volume 54, Issue 3, pp 641–655 | Cite as

Evaluation of a Regional Monitoring Program’s Statistical Power to Detect Temporal Trends in Forest Health Indicators

  • Stephanie J. PerlesEmail author
  • Tyler Wagner
  • Brian J. Irwin
  • Douglas R. Manning
  • Kristina K. Callahan
  • Matthew R. Marshall


Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service’s Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of the Vital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year−1 for all indicators and is appropriate for detecting a 1 % trend·year−1 in most indicators.


Monitoring Trend detection Sampling design Forest health indicators Variance components 



We would like to thank the park superintendents, resource managers, and staff who have provided valuable logistic support to the field crews and enabled this monitoring program to be implemented. We also thank the forest monitoring field crew members and botanists whose hard work collecting the data has made this research possible. We extend our thanks to Jim Comiskey, John Paul Schmit, Paul Roth, Leigh Ann Starcevich, and Iyob Tsehaye for providing constructive feedback on this manuscript. This research was funded by the National Park Service and the US Geological Survey. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Supplementary material

267_2014_313_MOESM1_ESM.docx (1.2 mb)
Supplementary material 1 (DOCX 1277 kb)


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Copyright information

© Springer Science+Business Media New York (outside the USA) 2014

Authors and Affiliations

  • Stephanie J. Perles
    • 1
    Email author
  • Tyler Wagner
    • 2
  • Brian J. Irwin
    • 3
  • Douglas R. Manning
    • 4
  • Kristina K. Callahan
    • 4
  • Matthew R. Marshall
    • 4
  1. 1.National Park Service, Eastern Rivers and Mountains Network, Department of Ecosystem Science and ManagementPennsylvania State UniversityUniversity ParkUSA
  2. 2.U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Department of Ecosystem Science and ManagementPennsylvania State UniversityUniversity ParkUSA
  3. 3.U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensUSA
  4. 4.National Park Service, Eastern Rivers and Mountains Network, Department of Ecosystem Science and ManagementPennsylvania State UniversityUniversity ParkUSA

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