Dataset: 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

ValidatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.836097.1Version 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)

BCO-DMO Data Manager: Taylor Heyl (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

The growth rate of Peyssonnelid Algal Crusts was measured at two sites (Cabritte Horn and Tektite) and two depths (3 and 9 meters) in Great Lameshur Bay, St. John, USVI. The growth rate of the PAC margin (micrometer/day) was calculated using the number of days between when the tag was initially deployed to mark the margin of PAC in August 2019 to when the margin was re-measured again in January 2020.

Replicate: Replicate number (1 to 80) is the unique identification for each individual margin of PAC that was tagged between August 2019 and January 2020 in order to measure the growth rate of PAC.

Site:  The two sites within Lameshur Bay, St. John, USVI where margins of PAC were tagged to determine growth rate of PAC, and whether it varied between sites (Cabritte Horn, Tektite).

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

Growth Rate:  Growth rate of PAC margin (micrometer/day) calculated using the number of days between when the tag was initially deployed to mark the margin of PAC to when the margin was re-measured again in January 2020.

Related datasets for Williams and Edmunds (2021) Coral Reefs manuscript:
Figure 2a, https://www.bco-dmo.org/dataset/836071
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 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
IsRelatedTo

Dataset: Linear growth and competitive ability of PAC, Figure 2a
Relationship Description: Part of the same Coral Reefs publication, Edmunds and Williams (2021)
Williams, M. K., Edmunds, P. J. (2021) Percent cover of Peyssonnelid Algal Crusts at two sites and depths in Great Lameshur Bay, St. John, USVI from July and August 2019. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2021-01-12 doi:10.26008/1912/bco-dmo.836071.1

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/