Dataset: Micromorphological analyses of Pocillopora damicornis overall skeleton collected from reef of Heron Island, southern Great Barrier Reef from Jan 2021 to Feb 2021

This dataset has not been validatedPreliminary and in progressVersion 1 (2025-03-28)Dataset Type:Other Field Results

Principal Investigator: Katie Barott (University of Pennsylvania)

Co-Principal Investigator: Kristen Brown (University of Queensland)

Co-Principal Investigator: Hollie Putnam (University of Rhode Island)

Student: Zoe Dellaert (University of Rhode Island)

BCO-DMO Data Manager: Audrey Mickle (Woods Hole Oceanographic Institution)


Project: Influence of environmental pH variability and thermal sensitivity on the resilience of reef-building corals to acidification stress (Coral Resilience)


Abstract

Corals residing in habitats that experience frequent seawater pCO2 variability may possess an enhanced capacity to cope with ocean acidification. Yet, we lack a clear understanding of the molecular toolkit enabling acclimatization to environmental extremes, and how life-long exposure to pCO2 variability influences biomineralization. We examined the gene expression responses and micro-skeletal characteristics of Pocillopora damicornis originating from the reef flat and reef slope of Heron Island,...

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Sample Collection

The experiment was performed during the austral summer from mid-January to late March 2021 at Heron Island Research Station (HIRS), southern Great Barrier Reef (23 27°S, 151 55°E). Heron Reef is composed of five distinct geomorphological habitats characterized by diverse benthic communities and biogeochemical conditions. Fragments of the coral P. damicornis were collected from the reef flat and slope locations within the same depth range (1–3 m) on 14 and 15 January 2021. Four fragments were collected from each individual colony (genetic clones), totalling 96 fragments from 24 colonies (n = 12 per habitat). For more information about sample collection methods and treatment, see Brown et al., 2022. 

Skeletal micromorphological analysis 

The limited amount of new CaCO3 deposition observed during the 8-week exposure (~15–30% for each fragment; (Brown et al., 2022)) precluded our resolution to detect changes in net calcification or CaCO3 density of newly formed skeleton that were attributable to experimental pCO2 treatment conditions. To better resolve changes in biomineralization resulting from the seawater pCO2 variability treatments, a total of 16 coral fragments (n=4 per origin per treatment) were selected for skeletal micromorphological analyses. All tissue was removed from the skeletons by soaking the fragments in 10% sodium hypochlorite for 24 hr, rinsing with deionized (DI) water, and drying. Areas of CaCO3 deposition that occurred during the experiment were identified by comparing images at the start and end of the 8-week experiment (Figure S1). These deposits of new CaCO3 were carefully chipped off of the experimental fragments using a razor blade and imaged using a scanning electron microscope (SEM; Quanta 600 FEG Mark II Environmental Scanning Electron Microscope, Field Electron and Ion Company). Using the SEM, fragments were imaged across scales with magnification maintained between samples: an overall view of the skeleton (56x), individual whole calyxes (124x), spine structures between (141x) and inside (164x) the calyxes, and the rapid accretion deposits (RADs) on the spines (1013x) (Figure 2). Several features of interest previously used to investigate coral biomineralization (Scucchia et al., 2023; Scucchia, Malik, Zaslansky, et al., 2021) were quantified using ImageJ (v1.53c) (Schneider et al., 2012), including: number of corallites, distance between corallites (i.e., coenosteum width), corallite diameter, circularity of the corallite, number of spines within calyx, spine length and maximum spine width (on spines both between and inside the calyx), number of RADs, and size of RADs.

The significant interaction between treatment and origin was explored on all micromorphological features using linear mixed effects models, with colony as a random effect. The significance of fixed effects and their interactions was determined using an analysis of variance with a type III error structure using the Anova function in car package (Fox et al., 2012). Significant interactive effects were followed by pairwise comparison of estimate marginal means using the emmeans package with Tukey HSD adjusted p values (Lenth et al., 2018). Data were tested for homogeneity of variance and normality of distribution through graphical analyses of residual plots for all models. All statistical analyses were done using R version 4.0.3 software (R Core Team, 2020), and graphical representations were produced using the package ggplot2 (Wickham, 2016).


Related Datasets

IsRelatedTo

Dataset: Gene expression of Pocillopora damicornis
Relationship Description: Datasets from the same study published in Brown et al. (2024) and utilized the same code package (doi:10.5281/zenodo.14041606).
Barott, K., Brown, K., Putnam, H. (2025) Gene expression of Pocillopora damicornis collected from reef of Heron Island, southern Great Barrier Reef from Jan 2021 to Feb 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-11-21 doi:10.26008/1912/bco-dmo.942938.1
IsRelatedTo

Dataset: Pocillopora damicornis skeletal micromorphological analysis: Spine RADs
Relationship Description: Datasets from the same Pocillopora damicornis skeletal micromorphological analysis.
Brown, K., Barott, K., Dellaert, Z., Putnam, H. (2025) Micromorphological analyses of Pocillopora damicornis Spine RADs on samples collected from reef of Heron Island, southern Great Barrier Reef from Jan 2021 to Feb 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-03-28 http://lod.bco-dmo.org/id/dataset/942955
IsRelatedTo

Dataset: Pocillopora damicornis skeletal micromorphological analysis: Calyxes
Relationship Description: Datasets from the same Pocillopora damicornis skeletal micromorphological analysis.
Brown, K., Barott, K., Dellaert, Z., Putnam, H. (2025) Micromorphological analyses of Pocillopora damicornis calyxes collected from reef of Heron Island, southern Great Barrier Reef from Jan 2021 to Feb 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-03-28 http://lod.bco-dmo.org/id/dataset/942962
IsRelatedTo

Dataset: Pocillopora damicornis skeletal micromorphological analysis: Spine structures
Relationship Description: Datasets from the same Pocillopora damicornis skeletal micromorphological analysis.
Brown, K., Barott, K., Dellaert, Z., Putnam, H. (2025) Micromorphological analyses of Pocillopora damicornis spine structures collected from reef of Heron Island, southern Great Barrier Reef from Jan 2021 to Feb 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-03-28 http://lod.bco-dmo.org/id/dataset/942948
Software

Dataset: https://doi.org/10.5281/zenodo.14041606
Zoe Dellaert, Kristen Brown, &amp; Hollie Putnam. (2024). <i>imkristenbrown/Heron-Pdam-gene-expression: pCO2 variability and biomineralization</i> (Version v1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.14041606

Related Publications

Methods

Brown, K. T., Mello-Athayde, M. A., Sampayo, E. M., Chai, A., Dove, S., & Barott, K. L. (2022). Environmental memory gained from exposure to extreme pCO2 variability promotes coral cellular acid–base homeostasis. Proceedings of the Royal Society B: Biological Sciences, 289(1982). https://doi.org/10.1098/rspb.2022.0941
Methods

Scucchia, F., Malik, A., Zaslansky, P., Putnam, H. M., & Mass, T. (2021). Combined responses of primary coral polyps and their algal endosymbionts to decreasing seawater pH. Proceedings of the Royal Society B: Biological Sciences, 288(1953). https://doi.org/10.1098/rspb.2021.0328
Methods

Scucchia, F., Zaslansky, P., Boote, C., Doheny, A., Mass, T., & Camp, E. F. (2023). The role and risks of selective adaptation in extreme coral habitats. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-39651-7
Software

Fox J et al. (2012) Package 'car': Companion to Applied Regression. R package version 2.0. Vienna: R Foundation for Statistical Computing. Available from https://cran.r-project.org/package=car
Software

Lenth, R. et al. (2018). emmeans: Estimated Marginal Means, aka Least-Squares Means. Estimated Marginal Means, aka Least-Squares Means.R package version 1.3. Vienna: R Foundation for Statistical Computing. Available from https://cran.r-project.org/package=emmeans