Environmental Monitoring and Assessment

, Volume 185, Issue 8, pp 6963–6987 | Cite as

Sampling design for long-term regional trends in marine rocky intertidal communities

  • Gail V. Irvine
  • Alice Shelly


Probability-based designs reduce bias and allow inference of results to the pool of sites from which they were chosen. We developed and tested probability-based designs for monitoring marine rocky intertidal assemblages at Glacier Bay National Park and Preserve (GLBA), Alaska. A multilevel design was used that varied in scale and inference. The levels included aerial surveys, extensive sampling of 25 sites, and more intensive sampling of 6 sites. Aerial surveys of a subset of intertidal habitat indicated that the original target habitat of bedrock-dominated sites with slope ≤30° was rare. This unexpected finding illustrated one value of probability-based surveys and led to a shift in the target habitat type to include steeper, more mixed rocky habitat. Subsequently, we evaluated the statistical power of different sampling methods and sampling strategies to detect changes in the abundances of the predominant sessile intertidal taxa: barnacles Balanomorpha, the mussel Mytilus trossulus, and the rockweed Fucus distichus subsp. evanescens. There was greatest power to detect trends in Mytilus and lesser power for barnacles and Fucus. Because of its greater power, the extensive, coarse-grained sampling scheme was adopted in subsequent years over the intensive, fine-grained scheme. The sampling attributes that had the largest effects on power included sampling of “vertical” line transects (vs. horizontal line transects or quadrats) and increasing the number of sites. We also evaluated the power of several management-set parameters. Given equal sampling effort, sampling more sites fewer times had greater power. The information gained through intertidal monitoring is likely to be useful in assessing changes due to climate, including ocean acidification; invasive species; trampling effects; and oil spills.


Probability design Monitoring Marine Rocky intertidal Statistical power Extensive Intensive Sampling methods Alaska Glacier Bay 



This project has benefited from the statistical and design advice of L. McDonald (WEST, Inc.) and M. Udevitz. Project and field support was provided by J. Bodkin, K. Kloecker, T. Gage, M. Ferguson, K. Vandersall, G. Esslinger, D. Monson, Jennifer Mondragon, Jeffrey Mondragon, A. Delorenzo, T. Stoltey, E. Madison, J. de la Bruere, D. Douglas, M. Whalen, and K. Oakley of the US Geological Survey (USGS); M. Lindeberg of the National Oceanic and Atmospheric Administration; J. Williams of Sitka National Historical Park; and L. Sharman, W. Eichenlaub, M.B. Moss, and L. Basch of Glacier Bay National Park and Preserve (NP&P). We thank boat captain Jim Luthy and the crew of the Nunatak (NPS) and boat captain Jim de la Bruere and the crew of the Alaska Gyre (USGS) for great field support. Initial funding was from USGS Natural Resource Preservation Program; additional support was provided by Glacier Bay NP&P and the USGS. We thank P. Geissler, S. Fradkin, and an anonymous review for their constructive comments on drafts of this manuscript. Any mention of trade names is for descriptive purposes only and does not represent endorsement by the US government. With kind remembrance of C.A. Toft (died 2011) and her contributions to ecology.

Supplementary material

10661_2013_3078_MOESM1_ESM.pdf (240 kb)
Online resource 1 (PDF 240 kb)


  1. Ambrose, R.F., Engle, J.M. Raimondi, P.T., Wilson, M., & Alstatt, J.M. (1995). Rocky intertidal and subtidal resources: Santa Barbara County mainland. Report to the Minerals Management Service, Pacific OCS Region, OCS Study MMS 95-0067.Google Scholar
  2. Benedetti-Cecchi, L. (2001a). Beyond BACI: Optimization of environmental sampling designs through monitoring and simulation. Ecological Applications, 11, 783–799.CrossRefGoogle Scholar
  3. Benedetti-Cecchi, L. (2001b). Variability in abundance of algae and invertebrates at different spatial scales on rocky sea shores. Marine Ecology Progress Series, 215, 79–92.CrossRefGoogle Scholar
  4. Bodkin, J. L., Ballachey, B. B., Esslinger, G. G., Kloecker, K. A., Monson, D. H., & Coletti, H. A. (2007). Perspectives on an invading predator—Sea otters in Glacier Bay. In J. F. Piatt & S. M. Gende (Eds.), Proceedings of the Fourth Glacier Bay Science Symposium. US Geological Survey scientific investigations report 2007-5047 (pp. 133–137). Reston: US Geological Survey.Google Scholar
  5. Bodkin, J. L., Dean, T. A., Coletti, H. A., & Kloecker, K. A. (2008). Nearshore marine vital signs monitoring in the Southwest Alaska network of National Parks. Annual report. Anchorage: US National Park Service.Google Scholar
  6. Brandt, J., Bunce, R. G. H., Howard, D. C., & Petit, S. (2002). General principles of monitoring land cover change based on two case studies in Britain and Denmark. Landscape and Urban Planning, 62, 37–51.CrossRefGoogle Scholar
  7. Bunce, R. G. H., Metzger, M. J., Jongman, R. H. G., Brandt, J., de Blust, G., Elena-Rossello, R., et al. (2008). A standardized procedure for surveillance and monitoring European habitats and provision of spatial data. Landscape Ecology, 23, 11–25.CrossRefGoogle Scholar
  8. Castilla, J. C., & Duran, L. R. (1985). Human exclusion from the rocky intertidal zone of central Chile: The effects on Concholepas concholepas (Gastropoda). Oikos, 45, 391–399.CrossRefGoogle Scholar
  9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  10. Connell, J. H. (1961a). The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology, 42, 710–723.CrossRefGoogle Scholar
  11. Connell, J. H. (1961b). Effects of competition, predation by Thais lapillus, and other factors on natural populations of the barnacle Balanus balanoides. Ecological Monographs, 31(1), 61–104.CrossRefGoogle Scholar
  12. Cubit, J. D., & Connor, J. L. (1993). Effects of the 1986 Bahía Las Minas oil spill on reef flat communities. In Proceedings of the 1993 International Oil Spill Conference (pp. 329–334). Washington, DC: American Petroleum Institute.Google Scholar
  13. Dayton, P. K. (1971). Competition, disturbance and community organization: The provision and subsequent utilization of space in a rocky intertidal community. Ecological Monographs, 41, 351–389.CrossRefGoogle Scholar
  14. Eager, C., Miller-Weeks, M., Gillespie, A. J. R., & Burkman, W. (1991). Summary report: Forest health monitoring—New England/Mid-Atlantic. Technical report NE-INF-115-92. Radnor: US Department of Agriculture, Forest Service.Google Scholar
  15. Eckert, G. L. (2009). A synthesis of variability in nearshore Alaskan marine populations. Environmental Monitoring and Assessment, 155, 593–606.CrossRefGoogle Scholar
  16. Elzinga, C. L., Salzer, D. W., & Willoughby, J. W. (1998). Measuring and monitoring plant populations. BLM/RS/ST-98/004 + 1730 (BLM technical reference 1730-1). Denver: US Department of the Interior, Bureau of Land Management.Google Scholar
  17. Ernst, T. L., Leibowitz, N. C., Roose, D., Stehman, S., & Urquhart, N. S. (1995). Evaluation of USEPA Environmental Monitoring and Assessment Program’s (EMAP)—Wetlands sampling and design classification. Environmental Management, 19, 99–113.CrossRefGoogle Scholar
  18. Etherington, L. L., Hooge, P. N., Hooge, E. R., & Hill, D. F. (2007). Oceanography of Glacier Bay, Alaska: Implications for biological patterns in a glacial fjord estuary. Estuaries and Coasts, 30(6), 927–944.Google Scholar
  19. Fairweather, P. G. (1991). Statistical power and design requirements for environmental monitoring. Australian Journal of Marine and Freshwater Resources, 1991(42), 555–567.CrossRefGoogle Scholar
  20. Fradkin, S. C., & Boetsch, J. R. (2012). Intertidal monitoring protocol for the North Coast and Cascades Network. Natural Resources Report NPS/NCCN/NRR-2012/511. Fort Collins: National Park Service, Natural Resource Program Center.Google Scholar
  21. Gabrielson, P. W., Widdowson, T. B., & Lindstrom, S. C. (2006). Keys to the seaweeds and seagrasses of Southeast Alaska, British Columbia, Washington and Oregon. Phycological contribution number 7. Vancouver: Department of Botany, University of British Columbia.Google Scholar
  22. Geiselman, J., Dunlap, J., Hooge, P., & Albert, D. (1997). Glacier Bay ecosystem geographic information system, CD-ROM. Anchorage: USGS-Interrain Pacific.Google Scholar
  23. Grabherr, G., Gottfried, M., & Pauli, H. (2000). GLORIA: A global observation research initiative in alpine environments. Mountain Research and Development, 20(2), 190–191.CrossRefGoogle Scholar
  24. Highsmith, R. C., Rucker, T. L., Stekoll, M. S., Saupe, S. M., Lindeberg, M. R., Jenne, R. N., et al. (1996). Impact of the Exxon Valdez oil spill on intertidal biota. In S. D. Rice, R. B. Spies, D. A. Wolfe, & B. A. Wright (Eds.), Proceedings of the Exxon Valdez Oil Spill Symposium: American Fisheries Society Symposium 18 (pp. 212–237). Bethesda: American Fisheries Society.Google Scholar
  25. Holland-Bartels, L. E., Dewey, M. R., & Zigler, S. J. (1995). Ichthyoplankton abundance and variance in a large river system concerns for long-term monitoring. Regulated Rivers: Research & Management, 10, 1–13.CrossRefGoogle Scholar
  26. Irvine, G.V. (1998). Development of coastal monitoring protocols and process-based studies to address landscape-scale variation in coastal communities of Glacier Bay National Park and Preserve, Katmai National Park and Preserve, and Wrangell-St. Elias National Park and Preserve; Phase II: Development and testing of monitoring protocols for selected intertidal habitats and assemblages. Natural Resources Preservation Program, Annual Report. Gustavus: Glacier Bay National Park and Preserve.Google Scholar
  27. Irvine, G. V. (2000). Persistence of spilled oil on shores and its effects on biota. In C. Sheppard (Ed.), Seas at the millennium: An environmental evaluation (Global issues and processes, Vol. 3, pp. 267–281). Oxford: Elsevier Science.Google Scholar
  28. Irvine, G.V. (2010). Development of monitoring protocols to detect change in rocky intertidal communities of Glacier Bay National Park and Preserve. US Geological Survey Open-File Report 2010-1283.Google Scholar
  29. Irvine, G.V., & Madison, E.N. (2008). Development of a monitoring protocol to detect ecological change in the intertidal zone of Sitka National Historical Park, Alaska. US Geological Survey Scientific Investigations Report 2008-5139.Google Scholar
  30. Kinnetic Laboratories, Inc. (1992). Study of the rocky intertidal communities of Central and Northern California (final report) vol. I. Prepared in association with the University of California, Santa Cruz, Moss Landing Marine Laboratories, and TENERA Corporation for the Pacific OCS Region, Minerals Management Service, US Department of the Interior, Contract No. 14-12-0001-30057, OCS Study, MMS 91-0089.Google Scholar
  31. McDonald, T. L. (2003). Review of environmental monitoring methods: Survey designs. Environmental Monitoring and Assessment, 85, 227–292.CrossRefGoogle Scholar
  32. McDonald, T.L. (2004). GRTS for the average Joe: A GRTS sampler for Windows. Cheyenne: WEST, Inc. Retrieved from
  33. McRoberts, R. E., & Hansen, M. (1999). Annual forest inventories for the North Central Region of the United States. Journal of Agricultural, Biological, and Environmental Statistics, 4(4), 361–371.CrossRefGoogle Scholar
  34. Menge, B. A., & Branch, G. M. (2001). Rocky intertidal communities. In M. D. Bertness, S. D. Gaines, & M. E. Hay (Eds.), Marine community ecology (pp. 221–251). Sunderland: Sinauer Associates, Inc.Google Scholar
  35. Miller, W. A., & Ambrose, R. F. (2000). Sampling patchy distributions: Comparisons of sampling designs in rocky intertidal habitats. Marine Ecology Progress Series, 196, 1–14.CrossRefGoogle Scholar
  36. Minchinton, T. E., & Raimondi, P. T. (2001). Long-term monitoring of rocky intertidal communities at the Channel Islands National Park: Summary of spatial and temporal trends and statistical power analyses. Report for the scientific and management review of monitoring protocols. Ventura: Channel Islands National Park.Google Scholar
  37. Minchinton, T.E., & Raimondi, P.T. (2005). Effect of temporal and spatial separation of samples on estimation of impacts. MMS OCS Study 2005-002. Coastal Research Center, Marine Science Institute, University of California, Santa Barbara, California. MMS Cooperative Agreement Number 14-35-0001-30758. Retrieved from
  38. Murray, S. N., Ambrose, R. N., & Dethier, M. N. (2006). Monitoring rocky shores. Berkeley: University of California Press.CrossRefGoogle Scholar
  39. Nautical Software, Inc. (1993–1997). Tides & currents for Windows, version 2.5a.Google Scholar
  40. Nielson, R. & McDonald, L. (2005). Power analyses of Olympic National Park intertidal monitoring data. Report by WEST, Inc. to Olympic National Park and the US Geological Survey. Port Angeles: Olympic National Park.Google Scholar
  41. Nusser, S. M., & Goebel, J. J. (1997). The national resources inventory: A multi-resource monitoring program. Ecological and Environmental Statistics, 4, 181–204.CrossRefGoogle Scholar
  42. Nusser, S. M., Breidt, F. J., & Fuller, W. A. (1998). Design and estimation for investigating the dynamics of natural resources. Ecological Applications, 8(2), 234–245.CrossRefGoogle Scholar
  43. Olsen, A. R., Sedransk, J., Edwards, D., Gotway, C. A., Liggett, W., Rathbun, S., et al. (1999). Statistical issues for monitoring ecological and natural resources in the United States. Environmental Monitoring and Assessment, 54, 1–45.CrossRefGoogle Scholar
  44. Overton, W. S., White, D., & Stevens, D. L. (1990). Design report for EMAP, environmental monitoring and assessment program. Technical report EPA/600/3-91/053. Washington, DC: US Environmental Protection Agency.Google Scholar
  45. Paine, R. T. (1966). Food web complexity and species diversity. American Naturalist, 103, 91–93.Google Scholar
  46. Paine, R. T. (1969). A note on trophic complexity and community stability. American Naturalist, 103, 91–93.CrossRefGoogle Scholar
  47. Paine, R. T. (1977). Controlled manipulations in the marine intertidal zone and their contributions to ecological theory. In Academy of Natural Sciences, 12, 245–270.Google Scholar
  48. Peterman, R. M. (1990). Statistical power analysis can improve fisheries research and management. Canadian Journal of Fisheries and Aquatic Sciences, 47, 2–15.CrossRefGoogle Scholar
  49. Peterson, C. H., & Estes, J. A. (2001). Conservation and management of marine communities. In M. D. Bertness, S. D. Gaines, & M. E. Hay (Eds.), Marine community ecology (pp. 469–507). Sunderland: Sinauer Associates, Inc.Google Scholar
  50. R Foundation for Statistical Computing. (2009). R statistical software, version 2.10.0.Google Scholar
  51. Raimondi, P. T., Ambrose, R. F., Engle, J. M., Murray, S. N., & Wilson, M. (1999). Monitoring of rocky intertidal resources along the Central and Southern California mainland. 3-year report for San Luis Obispo, Santa Barbara, and Orange Counties (fall 1995–spring 1998). Technical report MMS 99-0032. Camarillo: US Department of the Interior, Minerals Management Service, Pacific OCS Region.Google Scholar
  52. Richards, D. V., & Davis, G. E. (1988). Rocky intertidal communities monitoring handbook. Ventura: National Park Service, Channel Islands National Park, NTIS.Google Scholar
  53. Roughgarden, J., Gaines, S., & Possingham, H. (1988). Recruitment dynamics in complex life cycles. Science, 241, 1460–1466.CrossRefGoogle Scholar
  54. Schoch, G. C., & Dethier, M. N. (1996). Scaling up: The statistical linkage between organismal abundance and geomorphology on rocky intertidal shorelines. Journal of Experimental Marine Biology and Ecology, 201(1), 37–72.CrossRefGoogle Scholar
  55. Schoch, G. C., Menge, B. A., Allison, G., Kavanaugh, M., Thompson, S. A., & Wood, S. A. (2006). Fifteen degrees of separation: Latitudinal gradients of rocky intertidal biota along the California current. Limnology and Oceanography, 5(16), 2564–2585.CrossRefGoogle Scholar
  56. Sousa, W. P. (1979). Disturbance in marine intertidal boulder fields: The non-equilibrium maintenance of species diversity. Ecology, 60, 1125–1239.CrossRefGoogle Scholar
  57. Southward, A. J. (1958). The zonation of plants and animals on rocky sea shores. Biological Reviews, 33, 137–177.Google Scholar
  58. Stahl, G., Allard, A., Esseen, P.-A., Glimskär, A., Ringvall, A., Svensson, J., et al. (2011). National Inventory of Landscapes in Sweden (NILS)—Scope, design and experiences from establishing a multi-scale biodiversity monitoring system. Environmental Monitoring and Assessment, 173, 579–595.CrossRefGoogle Scholar
  59. Steneck, R. S., & Carlton, J. T. (2001). Human alterations of marine communities: Students beware! In M. D. Bertness, S. D. Gaines, & M. E. Hay (Eds.), Marine community ecology (pp. 445–468). Sunderland: Sinauer Associates, Inc.Google Scholar
  60. Stephenson, T. A., & Stephenson, A. (1949). The universal features of zonation between tidemarks on rocky coasts. Journal of Ecology, 37, 289–305.CrossRefGoogle Scholar
  61. Stephenson, T. A., & Stephenson, A. (1972). Life between tidemarks on rocky shores. San Francisco: Freeman.Google Scholar
  62. Stevens, D. L., Jr., & Olsen, A. R. (2004). Spatially balanced sampling of natural resources. Journal of the American Statistical Association, 99(465), 262–278 (Retrieved from Scholar
  63. Summers, J. K., Paul, J. F., & Robertson, A. (1995). Monitoring the ecological condition of estuaries in the United States. Toxicological and Environmental Chemistry, 49(1–2), 93–108.CrossRefGoogle Scholar
  64. Toft, C. A., & Shea, P. J. (1983). Detecting community-wide patterns: Estimating power strengthens statistical inference. American Naturalist, 122, 618–625.CrossRefGoogle Scholar
  65. Underwood, A. J., & Denley, E. J. (1984). Paradigms, explanations and generalizations in models for the structure of intertidal communities on rocky shores. In D. R. Strong, D. Simberloff, L. G. Abele, & A. Thistle (Eds.), Ecological communities: Conceptual issues and the evidence (pp. 151–180). Princeton: Princeton University Press.Google Scholar
  66. Wooten, J. T. (1993). Indirect effects and habitat use in an intertidal community: Interaction chains and interaction modifications. American Naturalist, 141, 71–89.CrossRefGoogle Scholar
  67. Zar, J. H. (1984). Biostatistical analysis (2nd ed.). Englewood Cliffs: Prentice-Hall, Inc.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2013

Authors and Affiliations

  1. 1.Alaska Science CenterUS Geological SurveyAnchorageUSA
  2. 2.TerraStat Consulting GroupWoodinvilleUSA

Personalised recommendations