Effects of sample standardization on mean species detectabilities and estimates of relative differences in species richness among assemblages

Yong Cao, Charles P. Hawkins, David P. Larsen, John Van Sickle

Research output: Contribution to journalArticle


Ecological surveys provide the basic information needed to estimate differences in species richness among assemblages. Comparable estimates of the differences in richness between assemblages require equal mean species detectabilities across assemblages. However, mean species detectabilities are often unknown, typically low, and potentially different from one assemblage to another. As a result, inferences regarding differences in species richness among assemblages can be biased. We evaluated how well three methods used to produce comparable estimates of species richness achieved equal mean species detectabilities across diverse assemblages: rarefaction, statistical estimators, and standardization of sampling effort on mean taxonomic similarity among replicate samples (MRS). We used simulated assemblages to mimic a wide range of species-occurrence distributions and species richness to compare the performance of these three methods. Inferences regarding differences in species richness based on rarefaction were highly biased when richness estimates were compared among assemblages with distinctly different species-occurrence distributions. Statistical estimators only marginally reduced this bias. Standardization on MRS yielded the most comparable estimates of differences in species richness. These findings have important implications for our understanding of species-richness patterns, inferences drawn from biological monitoring data, and planning for biodiversity conservation.

Original languageEnglish (US)
Pages (from-to)381-395
Number of pages15
JournalAmerican Naturalist
Issue number3
StatePublished - Sep 1 2007
Externally publishedYes



  • Lincoln-Petersen model
  • Mean species detectability
  • Sample representativeness
  • Species richness
  • Species-occurrence distributions
  • Statistical estimators

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics

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