Dataset: Growth rate of Peyssonnelid Algal Crusts on terracotta settlement tiles at five sites across Lameshur Bay, St. John, USVI from 2009 onward

ValidatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.836164.1Version 1 (2021-01-13)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

Growth rate of Peyssonnelid Algal Crusts was measured using terracotta settlement tiles at five sites across Lameshur Bay, St. John, USVI. Unglazed terracotta tiles (15 × 15 × 1 cm) originally were deployed to measure coral recruitment, and photographs of the tiles were re-purposed to provide an additional measure of the planar growth of PAC. Years for which photographs of settlement tiles were available to be analyzed for growth rate include 2009, 2011-2012, and 2014-2019.

Replicate:  Replicate number (1 to 566) is the unique identificaton for each individual terracotta settlement tile photograph analyzed.

Site:  The five sites where tiles were deployed within Lameshur Bay, St. John, USVI (Cabritte Horn, Tektite, West Little Lameshur Bay, White Point, Yawzi Point)          

Year:  The years in which a set of photographs of settlement tiles were available to be analyzed for growth rate (2009, 2011, 2012, 2014, 2015, 2016, 2017, 2018, 2019).

Growth Rate:  Growth rate of Peyssonnelid Algal Crusts in centimeters per year (cm^2/year). The area of PAC was calculated using Fiji Software on settlement tile photographs. Because settlement tiles were deployed for one year, growth rate per year was calculated.

Related datasets for Edmunds and Williams (2021) Coral Reefs publication:
Figure 2a, https://www.bco-dmo.org/dataset/836071
Figure 2b, https://www.bco-dmo.org/dataset/836097
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, Tables 1 and 2
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/