Marine Biology

, Volume 151, Issue 4, pp 1343–1348 | Cite as

The application of adaptive cluster sampling for rare subtidal macroalgae

  • Nisse A. Goldberg
  • John N. Heine
  • Jennifer A. Brown
Research Article

Abstract

Adaptive cluster sampling (ACS) is a targeting sampling method that provides unbiased abundance estimators for populations of rare species that may be inadequately sampled with simple random sampling (SRS). ACS has been used successfully to estimate abundances of rockfish and sardine larvae from shipboard surveys. In this study, we describe the application of ACS for subtidal macroalgae. Using SCUBA, we measured abundances of Codium mamillosum, C. pomoides, and Halimeda cuneata at three islands and two levels of wave exposure. The three species were relatively patchy and could be sampled with ACS at one site per dive. Their distributions differed among islands and with exposure to wave energy, with H. cuneata found at only one island. ACS is a useful tool for understanding the spatial distribution and abundance of populations of rare benthic species, but, as was the case in this study, may not be as efficient as sampling with SRS with comparable replication.

References

  1. Brown JA (2003) Designing an efficient adaptive cluster sample. Environ Ecol Stat 10:95–105CrossRefGoogle Scholar
  2. Cao Y, Williams DD, Williams NE (1998) How important are rare species in aquatic community ecology and bioassessment? Limnol Oceanogr 43:1403–1409CrossRefGoogle Scholar
  3. Chapman MG (1999) Are there adequate data to assess how well theories of rarity apply to marine invertebrates? Biodivers Conserv 8:1295–1318CrossRefGoogle Scholar
  4. Christman MC (1997) Efficiency of some sampling designs for spatially clustered populations. Environmetrics 8:145–166CrossRefGoogle Scholar
  5. Christman MC (2000) A review of quadrat-based sampling of rare, geographically clustered populations. J Agric Biol Environ Stat 5:168–201CrossRefGoogle Scholar
  6. Dias PC (1996) Sources and sinks in population biology. TREE 11:326–330Google Scholar
  7. Drummond SP, Connell SD (2005) Quantifying percentage cover of subtidal organisms on rocky coasts: a comparison of the costs and benefits of standard methods. Mar Freshw Res 56:865–876CrossRefGoogle Scholar
  8. Dryver AL (2003) Performance of adaptive cluster sampling estimators in a multivariate setting. Environ Ecol Stat 10:107–113CrossRefGoogle Scholar
  9. Goldberg NA, Kendrick GA (2004) Effects of island groups, depth, and exposure to ocean waves on subtidal macroalgal assemblages in the Recherche Archipelago, Western Australia. J Phycol 40:631–641CrossRefGoogle Scholar
  10. Hanselman DH, Quinn II TJ (2004) Sampling rockfish populations: adaptive sampling and hydroacoustics. In: Thompson WL (ed) Sampling for rare or elusive species: concepts, designs, and techniques for estimating population parameters. Island Press, Washington, pp 271–296Google Scholar
  11. Magurran AE, Henderson PA (2003) Explaining the excess of rare species in natural species abundance distributions. Nature 422:714–716PubMedCrossRefGoogle Scholar
  12. Noon BR, Ishware NM, Vasudevan K (2006) Efficiency of adaptive cluster and random sampling in detecting terrestrial herpetofauna in a tropical rainforest. Wildl Soc Bull 34:59–68CrossRefGoogle Scholar
  13. O’Hara TD (2002) Endemism, rarity and vulnerability of marine species along a temperate coastline. Invertebr Syst 16:671–684CrossRefGoogle Scholar
  14. O’Hara TD, Poore GCB (2000) Distribution and origin of southern Australian echinoderms and decapods. J Biogeogr 27:1321–1335CrossRefGoogle Scholar
  15. Philippi T (2005) Adaptive cluster sampling for estimation of abundances within local populations of low-abundance plants. Ecology 86:1091–1100Google Scholar
  16. Phillips JA (2001) Marine macroalgal biodiversity hotspots: why is there high species richness and endemism in southern Australian marine benthic flora? Biodivers Conserv 10:1555–1577CrossRefGoogle Scholar
  17. Salehi M.M (2003) Comparison between Hansen–Hurwitz and Horvitz–Thompson estimators for adaptive cluster sampling. Environ Ecol Stat 10:115–128CrossRefGoogle Scholar
  18. Smith DR, Conroy MJ, Brakbage DH (1995) Efficiency of adaptive cluster sampling for estimating density of wintering waterfowl. Biometrics 51:777–788CrossRefGoogle Scholar
  19. Smith DR, Villella RF, Lemarie DP (2003) Application of adaptive cluster sampling to low-density populations of freshwater mussels. Environ Ecol Stat 10:7–15CrossRefGoogle Scholar
  20. Smith DR, Brown JA, Lo NCH (2004) Applications of adaptive sampling to biological populations. In: Thompson WL (ed) Sampling for rare or elusive species: concepts, designs, and techniques for estimating population parameters. Island Press, Washington, pp 77–122Google Scholar
  21. Thompson SK (1990) Adaptive cluster sampling. J Am Stat Assoc 85:1050–1059CrossRefGoogle Scholar
  22. Thompson SK (1992) Sampling. Wiley, New YorkGoogle Scholar
  23. Thompson SK, Seber GAF (1996) Adaptive sampling. Wiley, New YorkGoogle Scholar
  24. Turk P, Brokowski JJ (2005) A review of adaptive cluster sampling: 1990–2003. Environ Ecol Stat 12:55–94CrossRefGoogle Scholar
  25. Womersley HBS (1984) The marine benthic flora of Southern Australia. Part I. South Australian Government Printing Division, AdelaideGoogle Scholar
  26. Womersley HBS (1990) Biogeography of Australasian marine macroalgae. In: Clayton MN, King RJ (eds) Biology of marine plants. Longman Cheshire, Melbourne, pp 368–381Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Nisse A. Goldberg
    • 1
  • John N. Heine
    • 2
  • Jennifer A. Brown
    • 3
  1. 1.School of Plant BiologyUniversity of Western AustraliaCrawleyAustralia
  2. 2.CSIRO Marine and Atmospheric ResearchStrategic Research Fund for the Marine EnvironmentWembleyAustralia
  3. 3.Biomathematics Research CentreUniversity of CanterburyChristchurchNew Zealand

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