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

Abstract

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.

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References

  1. Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–684

    Article  Google Scholar 

  2. Anderson RC (1994) Height of white-flowered trillium (Trillium grandiflorum) as an index of deer browsing intensity. Ecol Appl 4(1):104–109

    Article  Google Scholar 

  3. Augustine DJ, Frelich LE (1998) Effects of white-tailed deer on populations of an understory forb in fragmented deciduous forests. Conserv Biol 12(5):995–1004

    Article  Google Scholar 

  4. Burkett VR, Wilcox DA, Stottlemyer R, Barrow W, Fagre D, Baron J, Price J, Nielsen JL, Allen CD, Peterson DL, Ruggerone G, Thomas D (2005) Nonlinear dynamics in ecosystem response to climate change: case studies and policy implication. Ecol Complex 2:357–394

    Article  Google Scholar 

  5. Chamberlain SJ, Ingram HM (2012) Developing coefficients of conservatism to advance floristic quality assessment in the Mid-Atlantic region. J Torrey Bot Soc 139(4):416–427

    Article  Google Scholar 

  6. Comiskey JA, Schmit JP, Tierney G (2009) Mid-Atlantic Network forest vegetation monitoring protocol. Natural Resource Report NPS/MIDN/NRR—2009/119. National Park Service, Fort Collins

  7. Fairweather PG (1991) Statistical power and design requirements for environmental monitoring. Aust J Mar Freshw Res 42:555–567

    Article  Google Scholar 

  8. Fancy SG, Gross JE, Carter SL (2009) Monitoring the condition of natural resources in US National Parks. Environ Monit Assess 151:161–174

    CAS  Article  Google Scholar 

  9. Fournier DA, Skaug HJ, Ancheta J, Ianelli J, Magnusson A, Maunder MN, Nielsen A, Sibert J (2012) AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw 27:233–249

    Article  Google Scholar 

  10. Gibbs JP, Droege S, Eagle P (1998) Monitoring populations of plants and animals. Bioscience 48(11):935–940

    Article  Google Scholar 

  11. Hatch SA (2003) Statistical power for detecting trends with applications to seabird monitoring. Biol Conserv 111:317–329

    Article  Google Scholar 

  12. Horsley SB, Stout SL, DeCalesta DS (2003) White-tailed deer impact on the vegetation dynamics of a northern hardwood forest. Ecol Appl 13(1):98–118

    Article  Google Scholar 

  13. Irwin BJ, Wagner T, Bence JR, Kepler MV, Liu W, Hayes DB (2013) Estimating spatial and temporal components of variation for fisheries count data using negative binomial mixed models. Trans Am Fish Soc 142:171–183

    Article  Google Scholar 

  14. Kincaid TM, Larsen DP, Urquhart NS (2004) The structure of variation and its influences on the estimation of status: indicators of condition of lakes in the northeast USA. Environ Monit Assess 98:1–21

    Article  Google Scholar 

  15. Kirschbaum CD, Anacker BL (2005) The utility of Trillium and Maianthemum as phyto-indicators of deer impact in northwestern Pennsylvania, USA. For Ecol Manag 217:54–66

    Article  Google Scholar 

  16. Larsen DP, Kincaid TM, Jacobs SE, Urquhart NS (2001) Designs for evaluating local and regional scale trends. Bioscience 51:1069–1078

    Article  Google Scholar 

  17. Latham RE, Beyea J, Brenner M, Dunn CA, Fajvan MA, Freed RR, Grund M, Horsley SB, Rhoads AF, Shissler BP (2005) Managing white-tailed deer in forest habitat from an ecosystem perspective: Pennsylvania case study. Report by the Deer Management Forum for Audubon Pennsylvania and Pennsylvania Habitat Alliance, Harrisburg

  18. Logan JA, Régnière J, Powell JA (2003) Assessing the impacts of global warming on forest pest dynamics. Front Ecol Environ 1(3):130–137

    Article  Google Scholar 

  19. Mack R (2003) Global plant dispersal, naturalization, and invasion: Pathways, modes, and circumstances. In: Ruiz GM, Carlton JT (eds) Invasive species: vectors and management strategies. Island Press, Chicago, pp 3–30

    Google Scholar 

  20. Marshall MR, Piekielek NB (2007) Eastern Rivers and Mountains Network ecological monitoring plan. Natural Resource Report NPS/ERMN/NRR—2007/017. National Park Service. Fort Collins, Colorado

  21. Marshall PL, Davis G, LeMay VM (2000) Using line intersect sampling for coarse woody debris. Forest Research Technical Report TR-003, Vancouver Forest Region, British Columbia Ministry of Forests, Nanaimo, p 34

  22. McDonald TL (2004) GRTS for the average Joe: a GRTS sampler for Windows. http://www.west-inc.com/statisticsandbiometrics_reports.html. Accessed 31 Aug 2007

  23. McWilliams WH, Bowersox TW, Brose PH, Devlin DA, Finley JC, Gottschalk KW, Horsley S, King SL, LaPoint BM, Lister TW, McCormick LH, Miller GW, Scott CT, Steele H, Steiner KC, Stout SL, Westfall JA, White RL (2005) Measuring tree seedlings and associated understory vegetation in Pennsylvania’s forests. In: McRoberts, RE, GA Reams, PC Van Deusen, WH McWilliams. CJ Cieszewski, (eds) Proceedings of the fourth annual forest inventory and analysis symposium, 2002, November 19–21. New Orleans. General technical report NC-252. US Department of Agriculture, Forest Service, North Central Research Station, St. Paul

  24. Norby RJ, Hanson PJ, O’Neill EG, Tschaplinski TJ, Weltzin JF, Hansen RA, Cheng W, Wullschleger SD, Gunderson CA, Edwards NR, Johnson DW (2002) Net primary productivity of a CO2-enriched deciduous forest and the implications for carbon storage. Ecol Appl 12(5):1261–1266

    Google Scholar 

  25. Perles SJ, Podniesinski GS, Zimmerman EA, Millinor WA, Sneddon LA (2006a) Vegetation classification and mapping at Fort Necessity National Battlefield. Technical Report NPS/NER/NRTR—2006/038. National Park Service, Philadelphia

  26. Perles SJ, Podniesinski GS, Zimmerman EA, Millinor WA, Sneddon LA (2006b) Vegetation classification and mapping at Friendship Hill National Historic Site. Technical Report NPS/NER/NRTR—2006/041. National Park Service, Philadelphia

  27. Perles SJ, Podniesinski GS, Zimmerman EA, Millinor WA, Sneddon LA (2006c) Vegetation classification and mapping at Johnstown Flood National Memorial. Technical Report NPS/NER/NRTR—2006/034. National Park Service, Philadelphia

  28. Perles SJ, Podniesinski GS, Zimmerman EA, Eastman E, Sneddon LA (2007a) Vegetation classification and mapping at Allegheny Portage Railroad National Historic Site. Technical Report NPS/NER/NRTR—2007/079. National Park Service. Philadelphia

  29. Perles SJ, Podniesinski GS, Eastman E, Sneddon LA, Gawler SC (2007b) Classification and mapping of vegetation and fire fuel models at Delaware Water Gap National Recreation Area: volume 1 of 2. Technical Report NPS/NER/NRTR—2007/076. National Park Service, Philadelphia

  30. Perles S, Finley J, Manning D, Marshall M (2014). Vegetation and soil monitoring protocol for the Eastern Rivers and Mountains Network, Version 3. Natural resource report NPS/ERMN/NRR—2014/758. National Park Service. Fort Collins

  31. Peterman RM (1990) Statistical power analysis can improve fisheries research and management. Can J Fish Aquat Sci 47:2–15

    Article  Google Scholar 

  32. Peters DPC, Pielke RA, Bestelmeyer BT, Allen CD, Munson-McGee S, Havstad KM (2004) Cross-scale interactions, nonlinearities, and forecasting catastrophic events. Proc Natl Acad Sci USA 42:15130–15135

    Article  Google Scholar 

  33. Piepho H-P, Ogutu JO (2002) A simple mixed model for trend analysis in wildlife populations. J Agric Biol Environ Stat 7:350–360

    Article  Google Scholar 

  34. Pimentel D, Lach L, Zuniga R, Morrison D (2000) Environmental and economic costs of nonindigenous species in the United States. Bioscience 50:53–65

    Article  Google Scholar 

  35. Pimentel D, Zuniga R, Morrison D (2006) Update on the environmental and economic costs associated with alien invasive species in the United States. Ecol Econ 52:273–288

    Article  Google Scholar 

  36. Rooney TP, Wiegmann SM, Rogers DA, Waller DM (2004) Biotic impoverishment and homogenization in unfragmented forest understory communities. Conserv Biol 18(3):787–798

    Article  Google Scholar 

  37. Ruhren S, Handel SN (2003) Considering herbivory, reproduction, and gender when monitoring plants: a case study of jack-in-the-pulpit (Arisaema triphyllum [L.] Schott). Nat Areas J 20(3):261–266

    Google Scholar 

  38. Russell FL, Zippin DB, Fowler NL (2001) Effects of white-tailed deer (Odocoileus virginianus) on plants, plant populations, and communities: a review. Am Midl Nat 146(1):1–26

    Article  Google Scholar 

  39. Sanders S, Johnson SE, Waller DM (2008) Vegetation monitoring protocol: Great Lakes inventory & monitoring network. Natural resource report NPS/GLKN/NRR—2008/056. National Park Service, Fort Collins

  40. Schmit JP, Sanders G, Lehman M, Paradis T (2009) National capital region network long-term forest monitoring protocol, version 2.0. Natural resource report NPS/NCRN/NRR—2009/113. National Park Service, Fort Collins

  41. Steinman J (2004) Forest health monitoring in the Northeastern United States: disturbances and conditions during 1993–2002. USDA Forest Service, Newtown Square, Pennsylvania. NA-TP-01-04. 46

  42. Stevens DL, Olsen AN (2004) Spatially balanced sampling of natural resources. J Am Stat Assoc 99(465):262–278

    Article  Google Scholar 

  43. Stow CA, Carpenter SR, Weber KE, Frost TM (1998) Long-term environmental monitoring: some perspective from lakes. Ecol Appl 8:269–276

    Article  Google Scholar 

  44. Tierney G, Mitchell B, Miller K, Comiskey J, Kozlowski A, Faber-Langendoen D (2009) Long-term forest monitoring protocol: northeast temperate network. Natural Resource Report NPS/NETN/NRR—2009/117. National Park Service, Fort Collins

  45. United States Department of Agriculture, Forest Service (2007) Forest inventory and analysis national core field guide: field data collection procedures for phase 2 plots. http://www.fia.fs.fed.us/library/field-guides-methods-proc/docs/core_ver_4-0_10_2007_p2.pdf. Accessed 30 Nov 2007

  46. Urquhart NS, Kincaid TM (1999) Designs for detecting trend from repeated surveys of ecological resources. J Agric, Biol Environ Stat 4(4):404–414

    Article  Google Scholar 

  47. Urquhart NS, Overton WS, Birkes DS (1993) Comparing sampling designs for monitoring ecological status and trends: impact of temporal patterns. In: Barnett V, Turkman KF (eds) Statistics for the environment. Wiley, Hoboken

    Google Scholar 

  48. Urquhart NS, Paulsen SG, Larsen DP (1998) Monitoring for policy-relevant regional trends over time. Ecol Appl 8:246–257

    Google Scholar 

  49. Vanderhorst JP, Jeuck J, Gawler SC (2007) Vegetation classification and mapping of new River Gorge National River, West Virginia. Technical Report NPS/NER/NRTR—2007/092. National Park Service, Philadelphia

  50. Vanderhorst JP, Streets BP, Jeuk J, Gawler SC (2008) Vegetation Classification and Mapping of Bluestone National Scenic River, West Virginia. Technical Report NPS/NER/NRTR—2008/106. National Park Service, Philadelphia

  51. Vanderhorst JP, Streets BP, Arcaro Z, Gawler SC (2010) Vegetation classification and mapping of Gauley River National Recreation Area, West Virginia. Technical Report NPS/NER/NRTR—2010/148. National Park Service, Philadelphia

  52. Vitousek PM (1990) Biological invasions and ecosystem processes: towards an integration of population biology and ecosystem studies. Oikos 57(1):7–13

    Article  Google Scholar 

  53. Wagner T, Bence JR, Gremigan MT, Hayes DB, Wilberg MJ (2007) Regional trends in fish mean length at age: components of variance and the statistical power to detect trends. Can J Fish Aquat Sci 64:968–978

    Article  Google Scholar 

  54. Wagner T, Vandergoot CS, Tyson J (2009) Evaluating the power to detect temporal trends in fishery-independent surveys: a case study based on gillnets set in the Ohio waters of Lake Erie for walleye. North Am J Fish Manag 29:805–816

    Article  Google Scholar 

  55. Wagner T, Irwin BJ, Bence JR, Hayes DB (2013) Detecting temporal trends in freshwater fisheries surveys: statistical power and the important linkages between management questions and monitoring objectives. Fisheries 38:309–319

    Article  Google Scholar 

  56. Willis CG, Ruhfel BR, Primack RB, Miller-Rushing AJ, Losos JB (2010) Favorable climate change response explains non-native species’ success in Thoreau’s Woods. PLoS One 5(1):e8878. doi:10.1371/e8878

    Article  Google Scholar 

  57. Woodall CW, Oswalt CM, Westfall JA, Perry CH, Nelson MD, Finley AO (2010) Selecting tree species for testing climate change migration hypotheses using forest inventory data. For Ecol Manag 259:778–785

    Article  Google Scholar 

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Acknowledgments

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.

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Correspondence to Stephanie J. Perles.

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Perles, S.J., Wagner, T., Irwin, B.J. et al. Evaluation of a Regional Monitoring Program’s Statistical Power to Detect Temporal Trends in Forest Health Indicators. Environmental Management 54, 641–655 (2014). https://doi.org/10.1007/s00267-014-0313-z

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Keywords

  • Monitoring
  • Trend detection
  • Sampling design
  • Forest health indicators
  • Variance components