Dataset: Percent cover of Peyssonnelid Algal Crusts at two sites and depths in Great Lameshur Bay, St. John, USVI from July and August 2019

ValidatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.836071.1Version 1 (2021-01-12)Dataset Type:Other Field Results

Principal Investigator: Peter J. Edmunds (California State University Northridge)

Co-Principal Investigator: Megan K. Williams (California State University Northridge)

BCO-DMO Data Manager: Dana Stuart Gerlach (Woods Hole Oceanographic Institution)


Project: RUI-LTREB Renewal: Three decades of coral reef community dynamics in St. John, USVI: 2014-2019 (RUI-LTREB)

Project: Collaborative Research: Pattern and process in the abundance and recruitment of Caribbean octocorals (Octocoral Community Dynamics)


Abstract

Percent cover of Peyssonnelid Algal Crusts was measured at two sites and depths in Great Lameshur Bay, St. John, USVI in July and August 2019. These sites (Cabritte Horn and Tektite) were selected because the abundance of PAC has been measured in these locations since 2015. Peyssonnelid Algal Crust was surveyed in quadrats placed at random positions along transects positioned haphazardly along the 3 meter and 9 meter isobaths.

Quadrat:  Quadrats with a size of 0.25 square meters were laid along a transect along two different sites and depths in Lameshur Bay, St. John, USVI to determine percent cover of Peyssonnelid Algal Crusts (PAC).  Quadrat number (1 to 80) gives a unique identification to each individual quadrat.

Depth: Depth in meters (either 3 or 9 m) at the site where quadrats were used to determine percent cover of PAC, and whether it varied between depths (3 meters depth vs 9 meters depth)

Site:  The two sites within Lameshur Bay, St. John, USVI where quadrats were used to determine percent cover of PAC, and whether it varied between sites (Cabritte Horn or Tektite).

Percent cover:  Percent cover of Peyssonnelid Algal Crusts (PAC) calculated from the 0.25 m^2 quadrats

Related datasets for Williams and Edmunds (2021) Coral Reefs manuscript:
Figure 2b, https://www.bco-dmo.org/dataset/836097
Figure 3, https://www.bco-dmo.org/dataset/836164
Tables 1 and 2, https://www.bco-dmo.org/dataset/836304

 


Related Datasets

IsRelatedTo

Dataset: Linear growth and competitive ability of PAC, Figure 2b
Relationship Description: Part of the same Coral Reefs publication, Edmunds and Williams (2021)
Williams, M. K., Edmunds, P. J. (2021) Growth rate of Peyssonnelid Algal Crusts at two sites and depths in Great Lameshur Bay, St. John, USVI as recorded in August 2019 and January 2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2021-01-12 doi:10.26008/1912/bco-dmo.836097.1
IsRelatedTo

Dataset: Linear growth and competitive ability of PAC, Figure 3
Relationship Description: Part of the same Coral Reefs publication, Edmunds and Williams (2021)
Williams, M. K., Edmunds, P. J. (2021) Growth rate of Peyssonnelid Algal Crusts on terracotta settlement tiles at five sites across Lameshur Bay, St. John, USVI from 2009 onward. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2021-01-13 doi:10.26008/1912/bco-dmo.836164.1
IsRelatedTo

Dataset: Linear growth and competitive ability of PAC, Tables 1 and 2
Relationship Description: Part of the same Coral Reefs publication, Edmunds and Williams (2021)
Williams, M. K., Edmunds, P. J. (2021) Interactions of scleractinian corals with Peyssonnelid Algal Crusts at two sites and depths in Great Lameshur Bay, St. John, USVI as recorded in August 2019 and January 2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2021-01-13 doi:10.26008/1912/bco-dmo.836304.1

Related Publications

Results

Williams, M. K., & Edmunds, P. J. (2021). Reconciling slow linear growth and equivocal competitive ability with rapid spread of peyssonnelid algae in the Caribbean. Coral Reefs, 40(2), 473–483. https://doi.org/10.1007/s00338-021-02052-7
Software

R Core Team (2019). R: A language and environment for statistical computing. R v3.5.1. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/