Ecological size-frequency distributions: How to prevent and correct biases in spatial sampling

Assaf Zvuloni*, Yael Artzy-Randrup, Lewi Stone, Robert van Woesik, Yossi Loya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Size-frequency distributions (SFDs) have been used to assess the ecological status of different populations in a variety of ecosystems and recently have become widely used to examine reef corals. SFDs may reflect the response of time-varying influences of the environment, including the intensity and frequency of disturbances and the degree of environmental degradation. Here we elucidate the biases that can arise in the application of popular and traditional sampling methods (e.g. quadrat, belt-transect, and line-intercept). We show that these biases on the estimated SFD arise due to boundary effects of the sampling units. Incorrect evaluations of SFDs may lead to biased estimations of the ecological status of coral populations and may result in, among other things, erroneous nature reserve management policies. Our analysis is based on analytical calculations, simulations, and field observations. We have developed simple mathematical corrections, which provide unbiased estimations for previously collected data acquired by these widely used methods. In addition, we offer alternative sampling methods that do not suffer from these shortcomings. Eliminating these types of sampling errors will not only provide better assessments of the status of a given coral reef, but will also make way for more precise comparisons among coral reefs in different regions. Although we discuss the biases of SFDs in regard to reef coral populations, the work is equally relevant in other ecological contexts.

Original languageEnglish
Pages (from-to)144-153
Number of pages10
JournalLimnology and Oceanography: Methods
Volume6
Issue number3
DOIs
StatePublished - 1 Mar 2008

Fingerprint

Dive into the research topics of 'Ecological size-frequency distributions: How to prevent and correct biases in spatial sampling'. Together they form a unique fingerprint.

Cite this