The use of pseudo-multivariate standard error to improve the sampling design of coral monitoring programs

Abstract

The characteristics of coral reef sampling and monitoring are highly variable, with numbers of units and sampling effort varying from one study to another. Numerous works have been carried out to determine an appropriate effect size through statistical power; however, these were always from a univariate perspective. In this work, we used the pseudo multivariate dissimilarity-based standard error (MultSE) approach to assess the precision of sampling scleractinian coral assemblages in reefs of Venezuela between 2017 and 2018 when using different combinations of number of transects, quadrats and points. For this, the MultSE of 36 sites previously sampled was estimated, using four 30m-transects with 15 photo-quadrats each and 25 random points per quadrat. We obtained that the MultSE was highly variable between sites and is not correlated with the univariate standard error nor with the richness of species. Then, a subset of sites was re-annotated using 100 uniformly distributed points, which allowed the simulation of different numbers of transects per site, quadrats per transect and points per quadrat using resampling techniques. The magnitude of the MultSE stabilized by adding more transects, however, adding more quadrats or points does not improve the estimate. For this case study, the error was reduced by half when using 10 transects, 10 quadrats per transect and 25 points per quadrat. We recommend the use of MultSE in reef monitoring programs, in particular when conducting pilot surveys to optimize the estimation of the community structure.

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Citation

BibTeX citation:

@online{m._montilla2020,
  author = {M. Montilla, Luis and Miyazawa, Emy and Ascanio, Alfredo and
    López-Hernandez, María and Mariño-Briceño, Gloria and
    Rebolledo-Sánchez, Zlatka and Rivera, Andreína and S. Mancilla,
    Daniela and Verde, Alejandra and Cróquer, Aldo},
  title = {The Use of Pseudo-Multivariate Standard Error to Improve the
    Sampling Design of Coral Monitoring Programs},
  date = {2020-04-22},
  url = {https://www.luismmontilla.com/papers/montilla2020/},
  doi = {10.7717/peerj.8429},
  langid = {en},
  abstract = {The characteristics of coral reef sampling and monitoring
    are highly variable, with numbers of units and sampling effort
    varying from one study to another. Numerous works have been carried
    out to determine an appropriate effect size through statistical
    power; however, these were always from a univariate perspective. In
    this work, we used the pseudo multivariate dissimilarity-based
    standard error (MultSE) approach to assess the precision of sampling
    scleractinian coral assemblages in reefs of Venezuela between 2017
    and 2018 when using different combinations of number of transects,
    quadrats and points. For this, the MultSE of 36 sites previously
    sampled was estimated, using four 30m-transects with 15
    photo-quadrats each and 25 random points per quadrat. We obtained
    that the MultSE was highly variable between sites and is not
    correlated with the univariate standard error nor with the richness
    of species. Then, a subset of sites was re-annotated using 100
    uniformly distributed points, which allowed the simulation of
    different numbers of transects per site, quadrats per transect and
    points per quadrat using resampling techniques. The magnitude of the
    MultSE stabilized by adding more transects, however, adding more
    quadrats or points does not improve the estimate. For this case
    study, the error was reduced by half when using 10 transects, 10
    quadrats per transect and 25 points per quadrat. We recommend the
    use of MultSE in reef monitoring programs, in particular when
    conducting pilot surveys to optimize the estimation of the community
    structure.}
}

For attribution, please cite this work as:

M. Montilla, Luis, Emy Miyazawa, Alfredo Ascanio, et al. 2020. “The Use of Pseudo-Multivariate Standard Error to Improve the Sampling Design of Coral Monitoring Programs.” PeerJ, April 22. https://doi.org/10.7717/peerj.8429.