Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Biomarker profiling in reef corals of Tonga’s Ha’apai and Vava’u archipelagos

  • Anderson B. Mayfield ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliations National Museum of Marine Biology and Aquarium, Checheng, Pingtung, Taiwan, Khaled bin Sultan Living Oceans Foundation, Annapolis, Maryland, United States of America, Taiwan Coral Research Center, Checheng, Pingtung, Taiwan

  • Chii-Shiarng Chen,

    Roles Funding acquisition, Project administration, Resources, Supervision

    Affiliations National Museum of Marine Biology and Aquarium, Checheng, Pingtung, Taiwan, Taiwan Coral Research Center, Checheng, Pingtung, Taiwan, Graduate Institute of Marine Biotechnology, National Dong-Hwa University, Checheng, Pingtung, Taiwan, Department of Marine Biotechnology and Resources, National Sun Yat-Sen University, Kaohsiung, Taiwan

  • Alexandra C. Dempsey

    Roles Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Visualization, Writing – original draft

    Affiliation Khaled bin Sultan Living Oceans Foundation, Annapolis, Maryland, United States of America


Given the significant threats towards Earth’s coral reefs, there is an urgent need to document the current physiological condition of the resident organisms, particularly the reef-building scleractinians themselves. Unfortunately, most of the planet’s reefs are understudied, and some have yet to be seen. For instance, the Kingdom of Tonga possesses an extensive reef system, with thousands of hectares of unobserved reefs; little is known about their ecology, nor is there any information on the health of the resident corals. Given such knowledge deficiencies, 59 reefs across three Tongan archipelagos were surveyed herein, and pocilloporid corals were sampled from approximately half of these surveyed sites; 10 molecular-scale response variable were assessed in 88 of the sampled colonies, and 12 colonies were found to be outliers based on employment of a multivariate statistics-based aberrancy detection system. These outliers differed from the statistically normally behaving colonies in having not only higher RNA/DNA ratios but also elevated expression levels of three genes: 1) Symbiodinium zinc-induced facilitator-like 1-like, 2) host coral copper-zinc superoxide dismutase, and 3) host green fluorescent protein-like chromoprotein. Outliers were also characterized by significantly higher variation amongst the molecular response variables assessed, and the response variables that contributed most significantly to colonies being delineated as outliers differed between the two predominant reef coral species sampled, Pocillopora damicornis and P. acuta. These closely related species also displayed dissimilar temporal fluctuation patterns in their molecular physiologies, an observation that may have been driven by differences in their feeding strategies. Future works should attempt to determine whether corals displaying statistically aberrant molecular physiology, such as the 12 Tongan outliers identified herein, are indeed characterized by a diminished capacity for acclimating to the rapid changes in their abiotic milieu occurring as a result of global climate change.


Coral reefs are threatened on a global scale, not only by the rapidly changing climate [12], but also by more localized anthropogenic stressors, such as water pollution stemming from poorly regulated coastal development [34]. Unfortunately, most of Earth’s reefs are understudied; for instance, the Kingdom of Tonga (Fig 1) possesses a vast reef tract presumably encompassing thousands of hectares of coral reefs, less than 1% of which (typically tourist dive sites) have ever been observed by humankind (the remainder having been inferred from satellite imagery). Furthermore, there is no information on the physiological condition of the framework-building scleractinians in Tonga, and the first surveys of reef coral health in the entire South Pacific region were not even conducted until 2013; a series of physiological and molecular-scale response variables (MSRV) were measured in pocilloporid corals from both French Polynesia’s Austral Islands and the Cook Islands as part of the Khaled bin Sultan Living Oceans Foundation’s (KSLOF) “Global Reef Expedition” (GRE) [5]. Clearly, if there is an interest in monitoring the effects of global climate change and other anthropogenic insults on coral reef and reef coral health, a more concerted survey effort is warranted, particularly in remote, sparsely populated locations. Such isolated reefs could be hypothesized to be more resilient than those of, for instance, Taiwan, which abut some of the world’s most densely populated cities [6].

Fig 1. Partial map of the Kingdom of Tonga, including a genetic breakdown of the pocilloporids sampled.

Reefs of the island of Tongatapu (the main population center) were not surveyed; therefore, this island, which lies south of Ha’apai, has not been depicted. Pie graphs showing the breakdown of the pocilloporid coral species sampled have been presented for each archipelago in isolation (n = 52 and 56 genotyped colonies for Ha’apai and Vava’u, respectively), as well as pooled across both (n = 108). There was a statistically significant effect of archipelago on host coral genotype proportion (S1 Table; X2 test, p<0.01).

Although diagnosing disease, bleaching, and death is commonplace in the coral biology field, there are currently no validated approaches for estimating the health of normally pigmented corals aside from simply watching colonies grow and reproduce. However, assessing coral growth and reproductive output is infeasible for most scientists working at remote field sites, where only several weeks at most may be spent. Regarding the former parameter, measuring coral growth in situ is time consuming due to the slow rate at which most corals grow; those working off the very research ships that are able to access the most difficult-to-reach reefs are therefore unlikely to have the opportunity to acquire reliable coral growth data. Regarding reproduction, only researchers whose field trips coincide with the target species’ gamete or larval release periods will be able to estimate fecundity. Given these hurdles, researchers have previously attempted to develop a series of molecular biomarkers (sensu [7]) for coral health diagnostics (e.g., [813]); as doing so would simply require the acquisition of a biopsy from a coral colony at a single time point, biomarker profiling could be conducive for those interested in making inferences about coral health/physiology while working at remote field sites far from marine laboratories.

However, despite a concerted effort by numerous individuals, there are currently no well-validated biomarkers for the determination of coral health [14]. Although a discussion as to why this is the case is beyond the scope of this manuscript, it stems from methodological hurdles (e.g., difficulties in working with an organism comprised of two eukaryotes: the coral host and its endosymbiotic dinoflagellates of the genus Symbiodinium [1517]), as well as gaps in our understanding of the basic biology of reef coral-Symbiodinium mutualisms [1821]. Furthermore, biomarkers validated from manipulative experiments performed in one location may not be the most useful for conspecifics sampled from other locations; a gene or protein that is up-regulated in response to elevated temperature in corals from Taiwan, for instance, may not show a similar trend in conspecifics from elsewhere in the Indo-Pacific due to differing environmental histories. In the absence of biomarker expression/concentration/activity data from corals sampled prior to the Industrial Revolution (at which point coral health was likely influenced by humankind to a significantly lesser extent), it is unclear what is actually a normal or “healthy” coral phenotype; indeed, few (if any) of Earth’s reefs could currently be said to be in a pristine state.

Despite issues with employing molecular biomarkers, such as gene mRNAs, to assign a level of health or stress to a sampled reef coral, it is nevertheless possible that a battery of such genes could be used to calculate a baseline molecular phenotype for corals of a particular habitat/region. Not only would the ensuing dataset serve as a comparison/reference for future monitoring endeavors, but the data could also be used to demark corals displaying statistically aberrant cellular behavior at the time of sampling. It should be noted here that “aberrant” simply refers to deviation from a local norm and does not reflect a level of health. Although such aberrantly behaving colonies may ultimately be found to be those that are of compromised resilience, they could, alternatively, simply be characterized by the greatest degree phenotypic plasticity; this property could actually better enable them to cope with future changes in their environment.

Given the needs to 1) survey historically understudied reefs and 2) identify corals displaying statistically aberrant behavior with respect to a number of MSRV (notably gene mRNAs), 59 Tongan coral reefs were surveyed, and the model coral Pocillopora damicornis (Fig 2a-1; [2224]) or, alternatively, its closely related sister species P. acuta (Fig 2b-1), were sampled from half of these reefs in order to attempt to remedy the latter knowledge dearth. Although these two species appear virtually identical in situ, we hypothesized that they might differ physiologically since they were previously found to occupy different niches elsewhere in the South Pacific [5]. It was also hypothesized that 1) a multivariate statistical approach (MSA) could be used to identify outliers and 2) the distribution of such outliers would differ across environmental gradients (e.g., average live coral cover [ALCC; %] levels, depth, etc.). Specifically, outliers were hypothesized to be more abundant in marginalized reefs characterized by low ALCC. The baseline molecular, physiological, and ecological data generated were envisioned to collectively serve as a comparative resource for those looking to track the condition of South Pacific coral reefs in this era of rapidly changing climate.

Fig 2. Maps of the two Tongan archipelagos from which pocilloporid corals were sampled: Ha’apai (a) and Vava’u (b).

The site codes correspond to those found in Tables 1 and 2, respectively, albeit excluding the “TOHA” and “TOVA” prefixes, respectively. Insets in (a) and (b) show representative Pocillopora damicornis (a-1; genotype α) and P. acuta (b-1; genotype β) colonies, respectively, and the length of the white tag (the scaling object) in each is 4.7 cm.

Table 1. Ha’apai site information.

Of the 32 sites surveyed at Ha’apai, the target coral species Pocillopora damicornis, or, alternatively, its closely related sister species P. acuta, was observed at and sampled from 15, and the data in this table are from these 15 sites only. In total 54 pocilloporid coral colonies were sampled across these 15 sites, and neither species was observed at the following 10 sites: TOHA03, 21, 22, 24, 25, 27, and 29–32. For the following seven sites, P. damicornis/P. acuta may have been present, but neither was sampled: TOHA02, 6, 11–12, 16–17, and 20. All reefs were emergent unless noted otherwise. For the environmental data for all 32 surveyed sites, please see S1 Data. The average temperatures (temp.), salinities, and live coral cover (ALCC) percentages (±std. dev.) reflect the means across the 15 sites from which corals were sampled (not all 32 surveyed sites). The 10 samples highlighted in bold were not processed for all molecular-scale response variables (MSRV).

Table 2. Vava’u site information.

Of the 21 sites surveyed at Vava’u, the target coral species Pocillopora damicornis, or, alternatively, its closely related sister species P. acuta, was sampled from 10, and the data in this table are from these sites only (in addition to a “snorkel site” where only several minutes were spent). In total 61 pocilloporid corals were sampled, though neither species was observed at the following eight sites: TOVA36-38, 45–46, 48–50. For the following three sites, P. damicornis/P. acuta may have been present, but no colonies were sampled: TOVA41-43. For the environmental data for all 21 surveyed sites, please see S1 Data. All reefs were emergent unless noted otherwise. The average temperatures (temp.), salinities, and live coral cover (ALCC) percentages (±std. dev.) in the “Vava’u avg.±std. dev.” and “Tonga avg.±std. dev.” rows reflect the means across the 10 Vava’u and 25 Tongan (15 Ha’apai+10 Vava’u) sites from which corals were sampled (excluding the snorkel site at Vava’u, where none of these parameters were measured), respectively, and the temp. was significantly higher at Vava’u than at Ha’apai (student’s t-test, p<0.05). The 17 samples highlighted in bold were not processed for all molecular-scale response variables (MSRV). PM = P. meandrina. PV = P. verrucosa. ND = not determined.

Materials and methods

Field surveys and sample collection

In September 2013, the KSLOF research vessel M.Y. Golden Shadow traversed a significant portion of the Kingdom of Tonga’s territorial waters (Fig 1) upon invitation from the King. Remote sensing, field surveys, and pocilloporid coral collection were carried out as described previously [5], and certain details have been reiterated in the S1 File. Briefly, sites were chosen by analysis of satellite data in conjunction with visual observations from seaplane flyovers. Benthic maps were created (sensu [25]), and shallow-water (5–30 m) coral reef surveys were conducted to document the dominant benthic organisms and substrate types. In total, 59 sites were surveyed over three weeks, and three major regions were visited: the Ha’apai Archipelago (Table 1 and Fig 2a; n = 32 sites surveyed over 11 days), the Vava’u Archipelago (Table 2 and Fig 2b; n = 21 sites surveyed over 7 days), and Niuatoputapu Volcano (n = 6 sites surveyed over 2 days); however, since the target species were not present at the latter region, the site descriptions and ecological data from Niuatoputapu are discussed/presented only in the S1 File and S1 Data, respectively.

As mentioned above, the target coral for biomarker profiling was the α genotype of P. damicornis [26], though when it was not present, the morphologically similar congeneric P. acuta (genotype β) was instead sampled as in a published reference [5]; briefly, corals were sampled across a range of depths (typically 5 to 30 m), habitat types (e.g., forereefs, lagoons, patch reefs, etc.), and environmental gradients (e.g., temperatures) in order to understand the relationship between environment and coral physiology (described in detail below). Coral collection was approved by Tonga’s 1) King, 2) Ministry of Lands, Environment, Climate Change, and Natural Resources (MLECCNR), and 3) Ministry of Agriculture, Forests, and Fisheries (MAFF); ministers from the latter two departments were onboard during the mission and assisted in research efforts.

Of the 115 sampled colonies, 108 were genotyped (discussed below), and all but two were found to be the target species; one P. verrucosa colony (#67 from Vava’u) was inadvertently sampled, as was one P. meandrina colony (#55 from Vava’u). Although these two samples were processed for the 10 MSRV discussed below, they were generally excluded from the data analysis. While in situ, it was noted whether the polyps of the sampled colonies were expanded/ extended (i.e., outside of their corallites), and colonies were scored as either “polyps extended” (considered as such even if only a small portion of the polyps were extended) or “polyps retracted.” The colonies were then photographed with a scaling object; both maximum (max.) colony length (cm) and planar surface area (SA; cm2) were later determined in silico via image analysis with ImageJ (National Institutes of Health, USA). Color was estimated in situ and ranked on a scale of 1 to 4 (1 = normal, 2 = pale, 3 = very pale, and 4 = bleached), though due to subjective nature of this parameter, it was generally excluded from most MSA (with the exception of the multivariate ANOVAs [MANOVA] of the S1 and S2 Tables). Similarly, the two size parameters were excluded from most MSA and the outlier assignment analysis (both described below) since colony size was not predicted to be useful in assigning a level of aberrancy to a colony. The polyp expansion data were categorical and so were not incorporated into the MSA or outlier assignment analysis.

Nucleic acid extractions and MSRV

From the 115 sampled colonies, both RNAs and DNAs were extracted with TRIzol® (Life Technologies) as described previously [5]. Only samples for which both high quality RNA (two distinct bands [28s and 18s rRNA] seen in the ethidium bromide-stained agarose [1%] gel, 260/280 ratio of 1.9–2.1, and 260/230 ratio >2) and DNA (a single high molecular weight band seen in the ethidium bromide-stained agarose [1%] gel, 260/280 ratio of 1.7–1.9, and 260/230 ratio>2) were processed for all 10 MSRV and considered in the statistical analyses described below. First, an RNA/DNA ratio was calculated for each sample to serve as a proxy for total gene expression (MSRV#1). Then, 108 and 88 colonies were genotyped and analyzed for the remaining nine MSRV described below, respectively; all of the latter 88 samples were genotyped. Four Symbiodinium genes (MSRV#2–5) and four host coral genes (MSRV#6–9) were targeted for mRNA expression analysis after conversion of RNA to cDNA as described previously [27]. The former four (Table 3) included the photosynthesis gene ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), the metabolism gene zinc-induced facilitator-like 1-like (zifl1l; known to be down-regulated at high temperature in Symbiodinium populations within P. damicornis [24]), and two genes encoding proteins involved in the cellular stress response: heat shock protein 90 (hsp90) and ubiquitin ligase (ubiq-lig; [30]). The host target genes were the metabolism gene carbonic anhydrase (ca), the cell adhesion gene lectin, the stress gene copper-zinc superoxide dismutase (cu-zn-sod), and the light absorbing gene green fluorescent protein-like chromoprotein (gfp-cp). Please see Table 3 for hypotheses regarding gene expression behavior for each of the eight mRNAs targeted. Real-time PCR (qPCR)-based gene expression quantification of these eight targets was carried out as described previously [31].

Table 3. Molecular-scale response variables (MSRV).

Although 12 quantitative (10 MSRV+2 size parameters) and two categorical (color and polyp expansion) response variables were assessed for each of 88 of the 115 pocilloporid colonies sampled herein, only the 10 MSRV were hypothesized to be useful in assigning a level of aberrancy to the sampled colonies. Statistically significant findings (student’s t-test of outlier vs. non-outlier, p<0.05) have been underlined. One Symbiodinium (zifl1l) and two host coral mRNAs (cu-zn-sod and gfp-cp) were found to be significantly higher in outliers than non-outliers in both the Fiji and Tonga datasets.

From the DNAs co-extracted from the same biopsies from which RNAs were isolated, a genome copy proportion (GCP) was calculated to serve as a proxy for Symbiodinium density (MSRV#10); in addition to the recovery of an exogenous Solaris™ RNA spike (discussed in detail in a prior work [32]), host and Symbiodinium gene expression data were normalized to the host and Symbiodinium GCP, respectively. This parameter controls for differences in the host/Symbiodinium biomass ratio between samples [17], which can vary greatly due to, for instance, bleaching [33], or, alternatively, failure to effectively extract nucleic acids from Symbiodinium cells within certain coral biopsies.

In addition to Symbiodinium density estimates, the co-extracted DNAs were also used for clade-level Symbiodinium genotyping with the primers and qPCR assays of Correa et al. [34]. When a threshold cycle (Ct) value lower than 35 was documented for a particular assay, the sample was deemed positive for the respective clade. Finally, the DNAs were also used to genotype the host corals by PCR amplifying a portion of the mitochondrial genome encompassing the 3’ end of the ATP synthase (subunit 6) gene and the 5’ end of the mitochondrial control region (also known as the mitochondrial open reading frame [mORF]). Details of the genotyping protocol and consequent sequence analysis can be found in Mayfield et al. [5]. Samples were assigned to one of four species (Fig 1).

Overview of statistical analyses

Since both environmental and physiological data were acquired during the mission, there were two major analytical goals. First, a variety of statistical analyses were utilized to attempt to understand the relationship between environment and coral physiology. Then, MSA were used to identify outliers in the dataset to determine whether outlier frequency varied across environmental gradients. Previous studies have employed a combination of univariate and multivariate statistical approaches to analyze coral reef ecological data in isolation, reef coral physiological data in isolation, and both environmental and molecular physiological data in tandem [3132]; readers are referred to these works for a more detailed treatise on the rationale for using the suite of statistical approaches employed herein. Briefly, both univariate and multivariate statistical approaches were exploited because it was hypothesized that statistically significant, biologically meaningful differences in molecular physiology might not be identified using only the former strategy. For instance, MANOVA can identify combinations of response variables that best partition samples from different environments, even when the underlying response variables show no environmental variation when analyzed in isolation with ANOVA [35]. JMP (ver. 12.0.1) was used for all statistical analyses outlined below except for multi-dimensional scaling (MDS) and analysis of similarity (ANOSIM).

First, univariate statistical approaches were used to test for the effects of the environmental factors measured on the response variables quantified in the coral samples within a primarily ANOVA-based framework. MANOVAs employing a canonical correlation analysis (CCA) algorithm were also used to model the effects of environment on coral physiology. Next, both principal components analysis (PCA) and MDS were used to uncover variation in the dataset and similarity amongst samples, respectively.

Outliers were identified using a dual-calculation approach, and both methods are described in greater detail below and in a prior reference [31]. First, the Mahalanobis distance, which is essentially the multivariate equivalent of the standard deviation, must have been greater than the upper control limit (UCL) calculated by JMP were a sampled to be considered an outlier. Although the Mahalanobis distance is the most statistically rigorous means of detecting outliers, it does take the correlation structure into consideration. This means that, in certain cases, samples may have very “typical” expression levels of certain genes, yet be flagged as outliers due instead to their falling outside of the correlation structure of the dataset. For this reason, we instated a second requirement for demarcating a sample as an outlier: the sample had to have at least one response variable that was over 2.5 standard deviations above the mean (z-score>2.5). In certain cases, the sample may have had a “heat map score” of 1, but it would only be considered an outlier were its Mahalanobis distance above the UCL. Finally, we looked to check that these outliers were the most distant and distinct points in the PCA and MDS plots. Although these ordination techniques use completely different algorithms, they have tended to support the strictly quantitative outlier detection assignments described above in prior works [3132]. This conservative system of mathematical “checks and balances” ensured that statistically normal samples were not inadvertently misclassified as outliers. Following the outlier assignment exercise, we used a series of approaches to determine which response variables were most important in partitioning outliers from non-aberrantly behaving coral colonies. All such approaches are discussed in greater detail below.

Univariate statistical analyses

As a preliminary analytical step, we tested the effect of environment (14 parameters) on 1) coral molecular physiology (12 response variables; using ANOVA) and 2) host coral, outlier, and colony polyp expansion frequency (using X2 tests). The 14 environmental parameters included 1) archipelago (n = 2: Ha’apai vs. Vava’u), 2) site (n = 25 reefs from which pocilloporid corals were sampled; Tables 1 and 2), 3) exposure (exposed [typically windward], protected [typically leeward or lagoonal], or intermediate [neither exposed nor protected]), 4) reef zone (forereef, backreef, or lagoon), 5) reef type (barrier, patch, or fringing reef), 6) collection time (n = 3 categorical groupings: <10:00, 10:00–14:00, or >14:00), 7) collection date (n = 15 sampling days), 8) collection depth (n = 7 categorical groupings: <5, 5–10, 10–15, 15–20, 20–25, 25–30, or >30 m), 9) site temperature (n = 4 categorical groupings: 23–24, 24–25, 25–26, or 26–27°C), 10) site salinity (35.2, 35.3, 35.4, 35.5, or 35.6), 11) ALCC (n = 5 categorical groupings: 10–20, 20–30, 30–40, 40–50, or >50%), 12) host species (n = 4; see Fig 1.), 13) colony color (see classifications above.), and 14) Symbiodinium assemblage (clade C only or clades A+C [no samples possessed Symbiodinium of any other clades.]). Although the latter three variables are not environmental parameters, they were nevertheless hypothesized to influence coral physiology.

Host genotype, outlier, and polyp expansion frequency were also tested as response variables, and such frequency data were analyzed with contingency table-based X2 tests. Since host frequency, outlier frequency, and polyp expansion frequency were assessed across 13, 14, and 14 environmental parameters, respectively (see S1 Table.), Bonferroni adjustments of 3.6, 3.7, and 3.7, respectively, were made to the a priori-chosen α level of 0.05, resulting in a modified α level of 0.013. All other response variables, including max. colony length, planar SA, and the 10 MSRV (see Table 3.) were analyzed across the 14 environmental parameters with 1-way ANOVAs after confirming both normality (Shapiro-Wilk W test, p>0.05) and homogeneity of variance (Levene’s test, p>0.05) of the data. Transformations were conducted when the latter two conditions were not met.

As the identification of systematic outliers (discussed in detail below) was amongst the major analytical goals of this work, outliers were not excluded from ANOVA-based analyses; although their inclusion decreased our chances of uncovering significant environmental effects on coral physiology, we justify this inclusion because the associated high variation between coral colonies appears to be common on tropical coral reefs based on prior studies [3132]; in other words, high inter-sample variation did not simply stem from measurement errors, which we attempted to avoid by using only those 88 samples of the 115 total for which both high quality RNA and DNA were extracted (i.e., some samples were excluded from analysis since they were likely to have introduced measurement-derived error into the analysis.). We therefore advocate that other coral ecophysiologists include such outliers in their analyses unless aberrant behavior can be tracked to a measurement error (e.g., the extraction of low-quality RNA that consistently performed poorly in qPCR). By excluding such outliers, one does indeed have a better chance of uncovering environmental effects on coral physiology; however, important information about the population’s phenotypic and physiological variability is lost. Such properties may be important for gauging a coral population’s capacity to acclimate to future environmental change.

Because 168 ANOVAs were performed, a Bonferroni adjustment of 13 was made to the a priori-chosen α level of 0.05, resulting in a modified α of 0.004. Similarly, a 13 environmental parameter (the same as above, though excluding host species) x 12 response variable 1-way ANOVA matrix was also generated individually for each of the two predominant species sampled: P. damicornis and P. acuta. For these species-specific comparisons, the a priori-chosen α level of 0.05 was instead divided by the square root of 156 to generate the Bonferroni-adjusted α level (also 0.004). In general, only results for which 1) there was an ANOVA-derived difference (p<0.004) and 2) Tukey’s post-hoc differences were documented between individual means (p<0.05) were considered to be biologically meaningful. Only the former of these two conditions was generally met for tests of site and date, and, for that reason, we do not discuss such spatio-temporal differences in any detail throughout the manuscript. Finally, for each of the 12 response variables, the coefficient of variation was calculated in addition to the mean and standard deviation to quantify the overall variation between the sampled colonies.


Although a Bonferroni adjustment may suffice in controlling for type I statistical errors, MSA can uncover relationships amongst response variables across environments at a lower false positive error rate. Several MSA were taken herein to understand the relationship between environment and coral physiology; all data were converted to z-scores prior to MSA to account for the response variables having different units and scales. First, MANOVA was performed to determine the effect of each of the 14 environmental parameters on the multivariate mean centroid calculated across the 10 MSRV. The two size-based parameters were excluded from this analysis (and all those henceforth). Briefly, MANOVA tests whether vectors of means (rather than simply individual means) of different samples are from the same distribution. JMP’s CCA algorithm was used in conjunction with MANOVA to model environmental effects for data from both host coral species; however, since P. damicornis and P. acuta showed distinct molecular physiological differences from each other over time, separate MANOVAs were then performed for each species across the 13 environmental parameters (the 14 aforementioned minus “host”).

PCA was next performed with the 10 MSRV to determine the combinations of parameters that best accounted for variation in the dataset (sensu [20]). As an alternate, ordination-based means of visualizing the dataset in multiple dimensions, PRIMER (ver. 5) was used to construct a Bray-Curtis similarity matrix (S1 Data), and an MDS plot based off of this matrix was created using data from the 86 samples analyzed for all MSRV (2 of the 88 samples analyzed for all 10 MSRV were omitted from the MSA due to their being non-target species [colonies #55 and 67].). PRIMER’s ANOSIM function was used to test for the effects of the 14 environmental parameters on separation of samples within the dataspace generated. Upon uncovering a statistically significant ANOSIM finding (p<0.013 [see explanation of the Bonferroni adjustment above.], the MDS plot was consulted to determine whether the difference was obvious to the naked eye; only statistically significant and visually compelling environmental differences in coral physiology have been discussed in the text. For a more detailed explanation of MANOVA+PCA and MDS+ANOSIM, readers are referred to a classic biometry text [36] and Clarke and Warwick [37], respectively. It was hypothesized that both PCA and MDS could be used corroborate the outliers identified (described below).

Outlier analysis

As described above, Mahalanobis distances were calculated using the 10 MSRV only. Samples characterized by distance values above the UCL of ~4 calculated by JMP were considered to be “Mahalanobis distance outliers.” Then, a heat map was constructed with JMP, and samples with z-scores >2.5 for a certain response variable were given a score of 1. For instance, if a sample had a z-score of 3 for 1 of the 10 MSRV, it would be given a heat map score of 1. When a sample was considered a Mahalanobis distance outlier and had a heat map score of 1 or greater, it was considered an outlier. This outlier assignment schematic approximates that of Mayfield et al. [3132] though is somewhat more stringent.

Several measures were taken to understand the response variable(s) that contributed most to a sample being deemed an outlier. First, JMP’s predictor screening function was used to rank the 10 MSRV in order of their contribution to the cumulative difference between the 12 outliers uncovered and all other samples (n = 74). Then, a “variability index” was calculated by taking the standard deviation across the z-scores of all 10 MSRV for each sample. It was hypothesized that those samples with high Mahalanobis distances (i.e., the outliers) would also demonstrate more variability between the individual response variables assessed, particularly gene expression. This variability index was regressed against the Mahalanobis distance, and the significance of the correlation was tested with a linear regression t-test. Differences in certain response variables between outliers and non-outliers were compared across both P. damicornis and P. acuta with 2-way ANOVA (outlier status vs. species), and individual mean differences between the four interaction groups were tested with Tukey’s honestly significant difference (HSD) tests (p<0.05). Next, MANOVA+CCA was performed using JMP’s “discriminant analysis” function to determine the response variables that best led to the separation of samples characterized by different heat map scores (which ranged from 0 [non-outliers] to 3 [extreme outliers]). This CCA was repeated individually for P. damicornis and P. acuta. An α level of 0.05 was established a priori for all such MSA, though a Bonferroni adjustment of 3.6 was made to the α levels of the MANOVAs of the S1 and S2 Tables since 13–14 comparisons were made; this resulted in an adjusted α of 0.013.


Coral reef ecology

As this represents the first in-depth survey of coral reefs of Tonga, detailed site descriptions have been provided for the majority of the 59 surveyed sites in the S1 File. All images of the samples colonies, as well as their surrounding habitats (typically 1-30-m depth), were taken by ABM, and they can be found on the following website: At least one “macro” image of the polyps has been included for each colony (n = 115) on this same website. Additionally, all habitat and sampled coral colony images can be downloaded from Dryad (doi:10.5061/dryad.6vj6n). Interactive maps of the surveyed reefs were generated, in part, by ACD (KSLOF), and they can be accessed and manipulated freely on the following website: The ALCC and other environmental data from sites from which pocilloporid corals were sampled can be found in Tables 1 and 2 for Ha’apai and Vava’u, respectively; for a more detailed treatise of the coral cover data, please see the S1 File. To peruse all environmental data, and not just those from the sites from which corals were collected, please see S1 Data. Briefly, ALCC of Tonga’s reefs was 31±13% (std. dev. for this and all error terms henceforth), and it did not vary significantly between Ha’apai and Vava’u (student’s t-test, p>0.05). Of the corals sampled, host assemblage (i.e., freq.; Fig 1) differed significantly across archipelagos (S1 Table; X2 test, p<0.01), and this was mainly due to a relatively higher proportion of sampled colonies being the α P. damicornis genotype at Ha’apai (38.5% of the 52 genotyped colonies) compared to Vava’u (only 14% of the 56 genotyped colonies). Across the 115 colonies sampled, 108 were genotyped, and less than 1/3 of the sampled colonies were the model coral for research P. damicornis; the vast majority (72%) were instead P. acuta (Fig 1).

Overview of the physiological data and PCA

Pocilloporid corals were sampled from 25 of the 59 surveyed reefs, and 88 and 108 were processed for all MSRV and genotyped, respectively. Heat maps were created to visualize data trends for the two dominant pocilloporid species sampled: P. acuta (Fig 3a) and P. damicornis (Fig 3b). For the actual data, rather than the z-scores depicted in such heat maps, please see S1 Data. Certain samples already stood out as being those demonstrating potentially aberrant behavior with this graphical approach alone; for instance, three red cells (z-score>5) can be seen for colony 75 (i.e., heat map score = 3; Fig 3a), potentially suggesting that this colony was behaving in a significantly different manner from other colonies with respect to the 10 MSRV. More rigorous, quantitative, and multivariate means of determining whether this colony, as well as others, were indeed outliers are discussed below.

Fig 3. Heat maps and ordination analyses.

Heat maps (as z-scores) were created for both the Pocillopora acuta (a; n = 63) and P. damicornis (b; n = 23) datasets. The 12 outliers identified (see Table 4.) are highlighted in blue. Principal components analysis (PCA) was performed to depict variation in the dataset (c), as well as to identify outliers, and the legend for this panel, which is shared with that of the multidimensional scaling (MDS) plot, is found in the bottom right corner of the figure. Two outliers have been labeled. For the loading scores, please see S1 Data. In the MDS plot (d), 11 of the 12 outliers have been labeled (colony 22 being the exception). The underlying Bray-Curtis similarity matrix can be found in S1 Data. For all panels, the species color code is as follows: colony number or icon in black = P. damicornis and colony number or icon in red = P. acuta.

Table 4. The 12 outliers identified in the Tonga dataset.

Gene expression data have been presented as non-normalized (2-Ct*109) in all but the last six rows; this allows for the back-calculation of the raw threshold cycle (Ct) values so that interested individuals can readily estimate the typical range of expression of each gene. Values representing aberrant levels for a particular parameter (z-score>2.5) have been highlighted in bold. When there was a statistically significant difference (student’s t-test, p<0.05) between the outlier and non-outlier averages for a parameter (instead using normalized gene expression data), the lower of the two values has been underlined. All samples hosted Symbiodinium of clade C only unless noted otherwise. The mean Mahalanobis distance did not differ between Pocillopora damicornis and P. acuta (student’s t-test, p>0.05). SA = surface area. GCP = genome copy proportion. Ma Dis = Mahalanobis distance. “.” = missing data.

PCA (Fig 3c) was used to depict the dataset in multiple dimensions in a manner that would also showcase the variability therein. However, it did not appear to be the best means of explaining variation between samples; the first two principal component (PC) axes accounted for less than 55% of the variation in the dataset, and most samples tended to cluster within the core, interior region of the plot. That being said, two colonies, 37 and 75, partitioned away from this central region. The latter showed separation along PC1, in which the Symbiodinium hsp90, rbcL, and ubiq-lig mRNAs possessed the highest positive loading scores (please see S1 Data for PCA loading scores and eigenvector composition.). In contrast to the Symbiodinium mRNA-dominated PC1, PC2 featured a number of host coral genes as those factors contributing the most positive loading scores (namely host lectin and ca), and colony 37 was separated from the core region of the plot along this PC. Both colonies 37 and 75 were found to be outliers based on their Mahalanobis distances and heat map scores (discussed below and in Table 4).


As another means of visualizing similarity (or lack thereof) between the 86 analyzed samples, PRIMER was used to construct a Bray-Curtis similarity matrix (please see S1 Data.), and an MDS plot was created from this matrix (Fig 3d). ANOSIM was used to test the effects of the 14 environmental parameters on the resulting dataspace, and only an effect of time was documented (global R = 0.151, p = 0.001). However, such a temporal effect is not evident from the MDS plot (Fig 3d), and colonies of the three groupings of sampling times (<10:00, 10:00–14:00, and >14:00) appear intermixed. Furthermore, the stress was over 0.2, suggesting that these findings should be interpreted cautiously. Eleven of the twelve outliers discussed below (excluding colony 22, which is masked beneath the densest assemblage of icons in the plot) were labeled, and most appear around the periphery of the plot. Colonies 75, 77, and 83 were the three most different samples from each other (S1 Data), and all three of these were considered outliers (discussed below and in Table 4).

Univariate statistical analyses and MANOVA

At the Bonferroni-adjusted univariate ANOVA α level of 0.004, few environmental factors were found to affect coral physiology (S1 Table). Max. colony length varied significantly across temperature and ALCC (S1 Table). Regarding temperature, colonies sampled between 26 and 27°C tended to be smaller than those sampled at lower temperatures (Tukey’s HSD, p<0.05 against 24–25°C and 25–26°C). Regarding the ALCC effect, corals sampled on reefs with ALCC of 20–30% (the low end of the spectrum) tended to be larger than those sampled from reefs of 40–50% and 50–60% ALCC (Tukey’s HSD, p<0.05 for each comparison). Polyp expansion varied significantly between species (X2 = 12, p<0.001), and P. acuta colonies were over 3-fold more likely than P. damicornis colonies to be characterized by expanded polyps during daylight hours (77 vs. 23%, respectively).

Only one MSRV, Symbiodinium zifl1l mRNA expression (pooled across both species; S1 Table), was significantly affected by any environmental parameter; specifically, its expression varied significantly between the three sampling time periods (1-way ANOVA, p<0.0001). Expression was highest in colonies sampled before 10:00, 2-fold lower in colonies sampled between 10:00 and 14:00 (Tukey’s HSD, p<0.05), and ~3.5-fold lower (relative to <10:00) in colonies sampled after 14:00 (Tukey’s HSD, p<0.05). The species-specific temporal expression patterns of this gene are discussed in more detail below.

When looking at the effects of the 14 environmental parameters on the multivariate response with MANOVA (S1 Table), only one environmental parameter, time, was found to significantly affect the centroid distribution. However, upon looking at the CCA plot of this result (Fig 4a), it is clear that the 95% confidence centroids do not encompass the majority of samples for any of the three categorical time groupings (<10:00, 10:00–14:00, and >14:00). In fact, three 10:00–14:00 samples (triangles) fall closer to the <10:00 centroid (blue). Colony 38 appears especially isolated along canonical axis (CA) 1. When performing individual MANOVAs for each of the two host species, P. damicornis and P. acuta, only the latter’s molecular signatures varied significantly over time (S2 Table); this is depicted graphically in Fig 4b. As when all data were considered, certain samples did not fit the trend; colony 38 again fell closer to the <10:00 sampling centroid despite the colony having been sampled at 11:15 (S1 Data). Colony 22 appears well separated from the other <10:00 samples along CA1, which was dominated by the Symbiodinium zifl1l mRNA (S1 Data; the biplot ray is unlabeled in Fig 4b due to spatial constraints.).

Fig 4. Temporal effects.

Sampling time (three categorical groupings: <10:00, 10:00–14:00, and >14:00) was found to affect the multivariate molecular physiological response (a), though this was mainly due to temporal variation within the Pocillopora acuta dataset only (b). Several notable outliers have been labeled in the first two panels, and only a few biplot rays have been labeled in each due to spatial constraints. The blue, red, and green centroids represent 95% confidence for the <10:00, 10:00–14:00, and >14:00 sampling times, respectively. For the loading scores, please see S1 Data, and the legend for (a-b) is found in (a). The main driver of this multivariate temporal effect for P. acuta was the Symbiodinium zinc-induced facilitator-like 1-like (zifl1l) mRNA (see canonical scores in S1 Data.), and its expression has been plotted for each species at the three sampling time intervals (c). In this panel, the letters above the normal quantile plots reflect Tukey’s honestly significant difference groups (p<0.05) for the interaction of species (P. acuta vs. P. damicornis) and time (2-way ANOVA interaction effect, p<0.001). One-way ANOVA p-values (time effects) for each species analyzed in isolation have been provided in an inset.

Given these results, the zifl1l mRNA expression data were plotted over sampling time for each species (Fig 4c), and Tukey’s post-hoc (HSD) differences were uncovered for P. acuta only; notably, expression of this gene peaked in the morning, gradually decreased at midday, and dropped dramatically at night. Additional univariate ANOVA results for P. damicornis and P. acuta analyzed separately can be found in the S2 Table. In general, findings paralleled those discussed above when both species were considered simultaneously in the data analysis (S1 Table). It is worth noting that the temperature and ALCC effects on the multivariate mean vector for P. damicornis (S2 Table) were due in both cases to the strong influence of two putative outliers: colonies 77 and 83 (discussed in more detail below).

Outlier analysis

Two criteria were used to define outliers: 1) a Mahalanobis distance above the UCL of 4 and 2) a z-score above 2.5 for any MSRV (i.e., heat map score ≥ 1). It bears mentioning that this z-score criterion, and not a comparably low z-score threshold (i.e., <-2.5), was chosen because aberrantly low levels of any MSRV were rare; the lowest z-score in the entire dataset (-1.7) corresponded to a markedly low Symbiodinium density in one very pale P. acuta sample from Ha’apai. In contrast, maximum z-scores for each of the 10 MSRV ranged from 3 to 7; data were therefore highly skewed towards abnormally high values (Fig 3). Thirteen outliers were documented across the 86-sample subset analyzed for all 10 MSRV (Table 4); however, one (colony 67) represented the lone P. verrucosa sample in the dataset and, because it is unclear whether the qPCR assays are equally effective at amplifying gene homologs of this coral species, colony 67 was omitted from this list. The response variables that contributed most significantly to a sample being deemed an outlier are highlighted in bold in Table 4, and only temperature affected outlier frequency (S1 Table); however, this finding was driven by the fact that three outliers were sampled from reefs for which no temperature data existed; therefore, the “no data” category was over-represented in the contingency table (results not presented herein; see S1 Data.). For this reason, this temperature effect is not discussed further.

When calculating Mahalanobis distances for each species individually, one additional P. acuta colony was found to be an outlier: colony 16 had an aberrantly high Symbiodinium density; however, as this sample possessed a mixed assemblage of Symbiodinium clades (A+C), it is possible that differential primer binding to gene homologs of the two clades biased the nucleic acid quantification; therefore, this sample was excluded from the outlier analysis. One additional P. damicornis outlier was also uncovered when considering each species in isolation; colony 15 demonstrated an aberrantly high RNA/DNA ratio. However, as its distance of 4.0 was only slightly higher than the P. damicornis-only UCL of 3.8, and its Mahalanobis distance was below the UCL when all data were analyzed together, this sample was also excluded from the total outlier count.

Outliers vs. non-outliers

To understand the overall differences between the 12 outliers and the 74 non-outliers, a series of student’s t-tests were first performed; several notable differences were unveiled (Table 4). First, the outliers had greater than 2-fold higher RNA/DNA ratios (Table 4). Furthermore, outliers demonstrated higher expression levels of four of the eight target genes, including Symbiodinium zifl1l (~4-fold difference) and host coral ca (~2-fold difference), cu-zn-sod (~2-fold difference), and gfp-cp (~5-fold difference). However, when looking at differences between outliers and non-outliers individually for each of the two species sampled, P. acuta and P. damicornis (Table 4 and Fig 5), it is clear that the RNA/DNA ratio difference mentioned above is mainly attributed to the former species (Fig 5a); this ratio did not differ significantly between outlier and non-outlier P. damicornis colonies. In contrast, the host coral gene expression differences between outliers and non-outliers were mainly driven by P. damicornis; the expression of no host coral gene differed between outliers and non-outliers for P. acuta (Fig 5c and 5d). Only one gene was expressed at significantly different levels between outliers and non-outliers for both host coral species; Symbiodinium zifl11 was expressed at 3- and 4-fold higher levels in outliers of P. acuta and P. damicornis, respectively, relative to non-outliers (Fig 5b).

Fig 5. Species-specific differences between outliers and non-outliers.

Average levels of the four molecular-scale response variables that differed significantly between outliers and non-outliers were plotted separately for each species and included the RNA/DNA ratio (a), the Symbiodinium zinc-induced facilitator-like 1-like (zifl1l) mRNA (b), the host coral copper-zinc superoxide dismutase (cu-zn-sod) mRNA (c), and the host coral green fluorescent protein-like chromoprotein (gfp-cp) mRNA (d). Error bars represent standard deviation. Asterisks (*) denote within-species differences (Tukey’s honestly significant difference, p<0.05). One host coral gene whose expression level differed significantly between outliers and non-outliers when data were pooled across species, carbonic anhydrase (ca; see Table 4.), was not found to differ significantly across outlier status within species and so has not been included in the figure.

Predictor screening and the variability index

As another means of uncovering the response variables that best differentiated outliers from non-outliers, JMP’s predictor screening function was used to rank the response variables in terms of their proportional contribution to the cumulative difference between outliers and non-outliers (Fig 6a). Symbiodinium zifl1l mRNA expression ranked highest, accounting for nearly 20% of the cumulative difference between outliers and non-outliers. This is unsurprising given the statistically significant, ~3.5-fold difference between outlier zifl1l expression and non-outlier expression of this gene mentioned above (Table 4 and Fig 5b). Similarly, the parameters for which there was no statistically significant difference in the outlier vs. non-outlier student’s t-tests of Table 4 ranked lowest: host lectin (~2% of the cumulative difference) and Symbiodinium hsp90 (4%) and ubiq-lig (4%).

Fig 6. Multivariate analysis of the 12 outliers.

JMP’s (ver. 12.0.1) “predictor screening” function was used to rank the molecular-scale response variables in terms of their contribution to the proportional variation between outliers (n = 12) and non-outliers (n = 74; a). There was a statistically significant correlation between the Mahalanobis distance and the variability index (b), and the legend for this panel, which is shared with (c-d), is found at the bottom right corner of the figure. Several notable outliers have been labeled. Canonical correlation analysis (CCA) was used to model the differences between samples characterized by heat map scores of 0, 1, 2, or 3 (c). Those of the latter two groups were typically considered outliers (see Table 4.), of which several have been labeled. The yellow, green, blue, and red centroids in (c) represent 95% confidence for the heat map score groups of 0, 1, 2, and 3, respectively. In (d), CCAs testing the differences between outliers (“yes;” blue 95% confidence centroids) and non-outliers (“no;” red 95% confidence centroids) were performed individually for each species sampled: Pocillopora damicornis (d-1) and P. acuta (d-2). Not all biplot axes have been labeled in (c-d), and icons have been presented in reduced size due to the extensive degree of overlap amongst samples within each group.

There was a positive, linear, and statistically significant correlation (r2 = 0.84, p<0.001) between the Mahalanobis distance and the variability index (the standard deviation calculated across z-scores for all 10 MSRV for each sample; Fig 6b). In other words, those samples whose multivariate means calculated across the response variables assessed deviated most from the global mean centroid (i.e., high Mahalanobis distance) were also those demonstrating the greatest variation between the response variables (i.e., high variability index). The outliers had, on average, nearly 3-fold higher variability amongst the response variables (1.72±0.42) than the non-outliers (0.60±0.31); this difference was statistically significant (student’s t-test p<0.0001). The variability index did not vary across any environmental parameter (results not presented herein; see S1 Data.).

Outlier CCA

MANOVA+CCA featuring the 10 MSRV was also used to determine which parameters (or combinations thereof) best separated samples with high heat map scores (i.e., the outliers) from non-outliers (Fig 6c), and there was good separation along CA1 (52%). The Symbiodinium zifl1l mRNA was the most positively weighted parameter along CA1, whereas the negative relationship between Symbiodinium rbcL and ubiq-lig mRNA expression was most important for separation along CA2 (please see S1 Data for canonical scores.). Four outliers were well separated along CAs 1 and 2 (labeled in Fig 6c). Samples with heat map scores of 2 and 3 (all of which were outliers) were separated from samples with heat map scores of 0 along CA1, whereas the former two groups were separated from each other along CA2 (34%). When looking at multivariate differences between outliers and non-outliers for the two species analyzed in isolation, Symbiodinium zifl1l was strongly positively weighted for both P. damicornis (Fig 6d-1) and P. acuta (Fig 6d-2). However, for the former species, host gfp-cp mRNA expression was more important in the separation of outliers from non-outliers (S1 Data). This is due to the fact that expression of this gene was 7-fold different between outliers and non-outliers for P. damicornis, whereas it did not differ in expression between outliers and non-outliers for P. acuta (Fig 5d).


In addition to serving as the first molecule- to ecosystem-scale dataset for Tonga’s reef corals and coral reefs, the dataset featured herein was characterized by several interesting properties. First, the majority of the data were “negative;” there were, in general, few coral physiological differences across the environmental gradients assessed. Nevertheless, a suite of MSA were able to uncover outlier coral colonies within the dataset, and the cellular properties diagnostic of statistically aberrant behavior differed across the two main pocilloporid species sampled, P. damicornis and P. acuta. Outlier characteristics also differed from those of aberrantly behaving pocilloporid coral colonies of Fiji. The molecular physiology of P. acuta varied more over time than did P. damicornis, and this was due mainly to temporally dynamic Symbiodinium gene expression fluctuations in the former species (but not the latter). This gene mRNA-level difference between these closely related coral species may be related to mode of energy acquisition and have implications for the relative ability of each species to acclimatize to future changes in their environments. All points mentioned in this introductory paragraph are discussed in greater detail below.

As mentioned above, a striking feature of this dataset is the large quantity of negative data. Only 1 of the 10 MSRV, the Symbiodinium zifl1l mRNA, was significantly affected by any environmental parameter upon having Bonferroni-adjusted the ANOVA α levels. This zifl1l finding is discussed in more detail below. We surmise that the preponderance of null findings was largely driven by high variation between samples. The coefficients of variation (located under “sample info” in S1 Data) for the 10 MSRV ranged from ~50 to ~250% and averaged 130%; the standard deviation was higher than the mean for all but three response variables. Although 86 coral colonies were analyzed in detail, representing a much larger sample size than other coral molecular ecophysiology surveys (e.g., 25 colonies [9]), the number of colonies within any one statistical “bin” was relatively small in certain cases. For instance, only one and two colonies were collected shallower than 5 m and deeper than 30 m, respectively; these low sample sizes, and the associated high variation across colonies, made it unlikely that depth effects would be documented in particular. Similarly, only five colonies were sampled from barrier reefs, versus 29 and 52 from fringing and patch reefs, respectively.

Were each of the two target species found at similar abundances at all depths, archipelagos, reef types, reef zones, etc., a more balanced sampling design would have been achieved; such would certainly have increased our statistical power. Nevertheless, as we used the protocol outlined herein to survey reefs and sample pocilloporid corals from a number of nations across the Indo-Pacific as part of the GRE [3132], we might eventually obtain a large enough dataset, with sufficient sample sizes within each statistical bin (i.e., depth, reef type, etc.), to uncover statistically significant, biologically meaningful differences in certain MSRV across the ~10–15 environmental characteristics routinely documented. As mentioned in more detail below, though, certain environmental parameters, such as nutrient levels, were not measured during the GRE, and such seawater properties may instead be important in driving variation in coral cell physiology.

Despite the fact that few MSRV were affected by the environmental properties assessed, a number of outliers were nevertheless detected across the 86 P. damicornis+P. acuta samples analyzed for the 10 MSRV; 12 (14%) were found to be outliers based on an aberrancy detection system featuring both univariate (i.e., z-score [analyzed individually for each parameter] > 2.5) and multivariate (i.e., Mahalanobis distance > 4) statistical approaches. A variety of MSA validated these findings. For instance, 2 of the 12 outliers were also identified by PCA, and the 4 most deviant points in the MDS plot were all flagged as outliers. A MANOVA of the effects of time also led to the separation of certain outliers from the normally behaving colonies (e.g., colony 38). This corroboration between techniques in identifying samples that were exhibiting statistically aberrant behavior at the time of sampling instills greater confidence in the assignment of these samples as outliers and confirms our hypothesis put forth previously [3132] that multiple MSA can be used to collectively determine which corals are statistically abnormal, even when the underlying response variables analyzed in isolation do not vary across environments.

Interestingly, outlier frequency was not influenced by environment; outliers were, for instance, equally likely to be found on reefs with low coral cover as those with high coral cover. This contrasts with what had been hypothesized and suggests that there is not a clear relationship between certain ecosystem-scale measures of reef condition (e.g., algal abundance) and organismal-scale indices of reef coral performance; this was also the case in the Austral Islands of French Polynesia [32], the Cook Islands [32], and Fiji [31]. It is worth noting that for corals of Fiji, light (which was not measured herein) had no affect on gene expression or outlier frequency. Although it is tempting to postulate, then, that outlier distribution is decidedly random, perhaps biotic or abiotic parameters that were not measured herein or in these prior works, such as coral microbiomes or seawater nutrient levels, respectively, are instead more important in eliciting aberrant coral behavior. Therefore, more thorough seawater quality and coral biopsy evaluations may be warranted in future works to conclusively determine the factors that cause certain corals to behave in a statistically different manner from nearby conspecifics or congenerics of presumably similar environmental history, size, and age.

Regarding the properties of the outliers uncovered herein, they were characterized by 2-fold higher RNA/DNA ratios, as well as higher mRNA expression levels of several genes previously found to be implicated in the coral stress/acclimation response: Symbiodinium zifl1l and host coral ca, cu-zn-sod, and gfp-cp. However, only the former Symbiodinium gene, which is involved in zinc transport [38], differed significantly between outliers and non-outliers for both target species: P. acuta and P. damicornis. Zinc concentrations were not measured in seawater or coral tissues, though the aberrant behavior associated with the 12 outliers could have manifested in, or been driven by, metabolic dysfunction within the coral holobiont (sensu [2]). Zinc is necessary for a number of cellular processes, such as gene expression itself, and if the hosts’ ability to uptake zinc from the surrounding seawater or via heterotrophic feeding was compromised, then this could have resulted in an increase in the number of zinc transporters at the host-Symbiodinium interface in the case of the 12 outliers. The potential influence of host coral heterotrophy on Symbiodinium zifl1l mRNA expression is discussed in more detail below.

Differences in light quality/quantity could also have accounted for variation in zifl1l expression, especially given the dramatic drop in expression over the course of the day in Symbiodinium populations within P. acuta, a temporal trend identical to that of Symbiodinium within pocilloporid corals of Fiji’s Lau Archipelago [31]. This temporal variation in zinc transporter expression, which was not observed in Symbiodinium populations housed within Tongan P. damicornis colonies, may indeed attest to the complex, dynamic, dual-compartmental metabolism of coral-dinoflagellate endosymbioses [2]. The fact that Symbiodinium zifl1l mRNA expression, and molecular physiology in general, varied significantly over time for P. acuta, but not P. damicornis, is worth addressing in greater detail. P. acuta and P. damicornis are closely related [26], appear similar in situ (see images on, Dryad, and Fig 2.), and were characterized by similar outlier frequency herein (13 and 17% of all colonies were outliers, respectively). However, they are typically found in different habitats [5], and they demonstrated different behavioral patterns herein; P. acuta was over 3-fold more likely to have its tentacles expanded during the daylight hours than P. damicornis. This may suggest that P. acuta is more adept at, or relies more heavily on, heterotrophy than does P. damicornis; such a feeding strategy difference may have resulted in differential zinc uptake between the two species, thereby accounting for, in part, the varying fluctuation patterns of Symbiodinium zifl1l expression.

Future works should, then, attempt to observe relative differences in autotrophy vs. heterotrophy in these two species, as such will have significant implications for their ability to recover from bleaching. For instance, species that are more adept at switching from autotrophy to heterotrophy have been shown to fair better in response to climate change scenarios than those less adapted to making such a metabolic transition [39]. Furthermore, since there is little congruency between mRNA and protein expression in Symbiodinium [4041], it will be necessary to instead profile ZIFL1l protein concentration and activity over diel cycles in such future studies in order to conclusively determine the role of this molecule in zinc homeostasis and metabolism in the pocilloporid coral-Symbiodinium mutualism.

A similar outlier detection scheme was used to identify corals displaying aberrant behavior in Fiji’s Lau Archipelago [31]. In that work, however, a different suite of response variables best modeled differences between corals behaving normally and those deemed as outliers (11 of the 70 Fijian colonies analyzed in detail [16%] were outliers, a statistically similar percentage to that of the Tonga dataset [14%].). In contrast to the Tonga dataset, though, the Fijian outliers possessed lower densities of Symbiodinium and higher expression levels of several genes encoding proteins involved directly in the cellular stress response, notably ubiq-lig. Therefore, it is more likely that the outliers detected in the Fiji dataset were indeed experiencing stress, whereas it is unclear whether the 12 Tonga outliers were stressed at the time of sampling. For one, neither Symbiodinium density nor Symbiodinium ubiq-lig mRNA expression varied significantly between outliers and non-outliers in the Tonga colonies; instead, as mentioned above, Symbiodinium zifl1l and two host coral genes, cu-zn-sod and gfp-cp, were more important in separating statistically normally behaving corals from those demonstrating aberrant molecular physiology. Amongst these three genes, only cu-zn-sod encodes a protein involved in the cellular stress response as it is classically understood [42], and one completely bleached colony (#37) expressed aberrantly high levels of this gene; given the well documented relationship between reactive oxygen species (ROS) levels and coral bleaching [43], elevated expression of a gene encoding a protein involved in ROS scavenging (i.e., CU-ZN-SOD) by a coral undergoing bleaching is unsurprising.

Although reef corals experiencing stress-inducing temperatures tend to down-regulate expression of gfp-cp [29], which encodes a protein involved in light absorption [44], those exposed to high light levels do the opposite [24]. Therefore, abnormally high or low levels of expression of this gene could be indicative of aberrant behavior stemming from unfavorable environmental conditions. Only two of the four P. damicornis outliers, though (colonies 25 and 77), actually expressed aberrantly high levels of the gfp-cp mRNA. The former colony was located at only 5.8 m, and so the high irradiance associated with such a shallow depth could have explained the high gfp-cp mRNA expression in this colony. However, as neither irradiance nor photosynthetically active radiation was measured at the exact site of coral collection, the role of light in driving the abnormally high expression of gfp-cp in this sample cannot be elucidated. Indeed, an understanding of the effects of abnormally high irradiance on coral molecular biomarker profiling represents a fruitful line of investigation for future study.

Given the prohibitive expense (~$US600,000) of returning to the same reefs during a period of environmental disturbance (e.g., an unusually high temperature spike), it is currently not possible to determine whether the aberrant sub-cellular behavior of the 12 outliers is diagnostic of stress, or, in contrast, highlights a degree of phenotypic plasticity that might actually be associated with an enhanced degree of resilience. If outliers are ultimately less resilient than non-outliers to changes in their environment, then this suite of MSRV, and particularly the stress-targeted genes, may have diagnostic potential (though see additional caveats raised in the Introduction.). It deserves mention that in humans, a high gene expression variability index, which was documented herein for the 12 outliers, is a hallmark of certain cancers and numerous other diseases [45]. If, on the other hand, outliers are demonstrated to be more resilient than normally behaving corals, then it is possible that the “front-loading hypothesis” first uncovered by Mayfield et al. [27] and later corroborated by Barshis et al. [46] is a better explanation for the composite findings of the dataset; high expression levels of stress-targeted genes, in particular, may be a strategy for countering future environmental changes. In this case, the MSRV assays+MSA approach outlined herein could instead be used to identify particularly robust coral colonies; such might be the best candidates for reef restoration projects [47].

Finally, if outliers and non-outliers respond similarly to future environmental changes, then the dataset may simply attest to a broad degree of phenotypic plasticity within colonies of both target pocilloporid species; such a trait may be advantageous given the currently unstable abiotic milieu in which these coral reside [48]. In conclusion, although it is currently premature to forecast which of the colonies sampled herein will be most likely to bleach as sea temperatures rise, we nevertheless believe that these baseline ecological-, physiological-, and molecular-scale data for coral reefs and scleractinian corals of Tonga will be useful to researchers working in the South Pacific, and particularly scientists wishing to monitor and track the health of Tongan corals reefs in this era of changing ocean chemistry. When re-surveying these reefs in the future, and particularly during periods of environmental stress, researchers should attempt to not only uncover whether the outlier assignment exercise presented herein is able to identify corals that are prone to, for instance, bleaching, but they can also directly test the aforementioned hypothesis that P. acuta fairs better during bleaching events due to its hypothetically elevated capacity for heterotrophy.

Supporting information

S1 File. Supporting information.

This document contains a brief introduction to the Tonga mission, which was part of the Khaled bin Sultan Living Oceans Foundation’s “Global Reef Expedition.” In addition to supplementary methods on groundtruthing and coral reef surveys, this document also contains supplemental results, including site descriptions for nearly all sites surveyed.


S1 Data. Online supplemental data file.

This spreadsheet contains all data presented in the manuscript, including environmental data (worksheet 1) and molecular-physiological data from the sampled corals (worksheet 2). It also includes the results of the multivariate statistical analyses (MSA), including principal components analysis (PCA) and multivariate ANOVA (MANOVA; worksheet 3). Finally, a Bray-Curtis similarity matrix has also been included (worksheet 4).


S1 Table. Univariate statistical analysis of the Tonga dataset-I: Host species analyzed together.

The values below the environmental parameters (EP; top row) represent the number of categorical groupings. X2 tests and 1-way ANOVAs were used to analyze the frequency (freq.) and molecular+physiological data, respectively. Comparisons that were statistically significant at the Bonferroni-adjusted α levels of 0.013, 0.013, 0.013, 0.004, and 0.013 for the host freq. X2 tests, outlier freq. X2 tests, polyp expansion freq. X2 tests, molecular-scale response variable (MSRV) ANOVAs, and multivariate ANOVAs (MANOVA; Wilks’ lambda was calculated.), respectively, are highlighted in green, and marginally significant p-values have been highlighted in yellow. The P. verrucosa (n = 1) and P. meandrina (n = 1) samples were excluded from the analysis. ALCC = average live coral cover. Sym = Symbiodinium. NS = not statistically significant. NA = not applicable.


S2 Table. Univariate statistical analysis of the Tonga dataset-II: host species analyzed separately.

The values below the environmental parameters (EP; top row) represent the number of categorical groupings. X2 tests and 1-way ANOVAs were used to analyze the frequency (freq.) and molecular+physiological data, respectively. Comparisons that were statistically significant at the Bonferroni-adjusted α levels of 0.013, 0.013, 0.004, and 0.013 for the outlier freq. X2 tests, polyp expansion (expan.) freq. X2 tests, molecular physiological response variable (MPRV) ANOVAs, and multivariate ANOVAs (MANOVA; Wilks’ lambda was calculated.), respectively, are highlighted in green, and marginally significant p-values have been highlighted in yellow. ALCC = average live coral cover. Sym = Symbiodinium. NS = not statistically significant.



We are deeply indebted to Tonga’s 1) MLECCNR and 2) MAFF, as well as the Vava'u Environmental Protection Association (VEPA). Gratitude is also given to the crew of the M.Y. Golden Shadow for their hospitality during the research mission.


  1. 1. Hoegh-Guldberg O, Mumby PJ, Hooten AJ, Steneck RS, Greenfield P, Gomez E, et al. Coral reefs under rapid climate change and ocean acidification. Science. 2007;318: 1737–1742. pmid:18079392
  2. 2. Mayfield AB, Gates RD. Osmoregulation in anthozoan-dinoflagellate symbiosis. Comp Biochem Physiol. 2007;147A: 1–10.
  3. 3. Fabricius KE. Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis. Mar Pollut Bull. 2005;50: 125–146. pmid:15737355
  4. 4. Huang YCA, Hsieh HJ, Huang SC, Meng PJ, Chen YS, Keshavmurthy S, et al. Nutrient enrichment caused by marine cage culture and its influence on subtropical coral communities in turbid waters. Mar Ecol Prog Ser. 2011;423: 83–93.
  5. 5. Mayfield AB, Bruckner AW, Chen CH, Chen CS. A survey of pocilloporids and their endosymbiotic dinoflagellate communities in the Austral and Cook Islands of the South Pacific. Platax. 2015;12: 1–17.
  6. 6. Liu PJ, Meng PJ, Liu LL, Wang JT, Leu MY. Impacts of human activities on coral reef ecosystems of Southern Taiwan: a long-term study. Mar Pollut Bull. 2012;64: 1129–1135. pmid:22534409
  7. 7. Feder M. Ecological and evolutionary physiology of stress proteins and the stress response: the Drosophila melanogaster model, In: Johnston IA, Bennett AF, editors. Animals and temperature: phenotypic and evolutionary adaptation. Cambridge: Cambridge University Press; 1996. pp. 79–102.
  8. 8. Downs CA, Mueller E, Phillips S, Fauth JE, Woodley CM. A molecular biomarker system for assessing the health of coral (Montastrea faveolata) during heat stress. Mar Biotech. 2000;2: 533–544.
  9. 9. Downs CA, Fauth JE, Robinson CE, Curry R, Lanzendorf B, Halas JC, et al. Cellular diagnostics and coral health: declining coral health in the Florida Keys. Mar Pollut Bull. 2005;51: 558–569. pmid:15992830
  10. 10. Mayfield AB, Hsiao YY, Fan TY, Chen CS, Gates RD. Evaluating the temporal stability of stress-activated protein kinase and cytoskeleton gene expression in the Pacific corals Pocillopora damicornis and Seriatopora hystrix. J Exp Mar Biol Ecol. 2010;395: 215–222.
  11. 11. Mayfield AB, Chen M, Meng PJ, Lin HJ, Chen CS, Liu PJ. The physiological response of the reef coral Pocillopora damicornis to elevated temperature: results from coral reef mesocosm experiments in Southern Taiwan. Mar Environ Res. 2013;86: 1–11 pmid:23453047
  12. 12. Mayfield AB, Fan TY, Chen CS. Real-time PCR-based gene expression analysis in the model reef-building coral Pocillopora damicornis: insight from a salinity stress study. Platax. 2013;10:1–29.
  13. 13. Mayfield AB, Chen YJ, Lu CY, Chen CS (2016) Proteins responsive to variable temperature exposure in the reef-building coral Seriatopora hystrix. In Ortiz S, editor. Coral reefs: ecosystems, environmental impact and current threats. New York: NOVA Publishing; 2016. pp. 13–72
  14. 14. Mayfield AB, Chen YH, Dai CF, Chen CS. The effects of temperature on gene expression in the Indo-Pacific reef-building coral Seriatopora hystrix: insight from aquarium studies in Southern Taiwan. Int J Mar Sci. 2014;4(50): 1–23.
  15. 15. Mayfield AB, Hsiao YY, Fan TY, Chen CS. Temporal variation in RNA/DNA and protein/DNA ratios in four anthozoan-dinoflagellate endosymbioses of the Indo-Pacific: implications for molecular diagnostics. Platax. 2012;9: 1–24.
  16. 16. Mayfield AB, Hsiao YY, Chen HK, Chen CS. Rubisco expression in the dinoflagellate Symbiodinium sp. is influenced by both photoperiod and endosymbiotic lifestyle. Mar Biotech. 2014;16: 371–384.
  17. 17. Mayfield AB, Hirst MD, Gates RD. Gene expression normalization in a dual-compartment system: a real-time PCR protocol for symbiotic anthozoans. Mol Ecol Res. 2009;9: 462–470.
  18. 18. Furla P, Allemand D, Shick JM, Ferrier-Pages C, Richier S, Plantivaux A, et al. The symbiotic anthozoan: a physiological chimera between alga and animal. Integr Comp Biol. 2005;45: 595–604. pmid:21676806
  19. 19. Peng SE, Chen WNU, Chen HK, Lu CY, Mayfield AB, Fang LS, et al. Lipid bodies in coral-dinoflagellate endosymbiosis: ultrastructural and proteomic analyses. Proteomics. 2011;17: 3540–3455.
  20. 20. Chen HK, Song SN, Wang LH, Mayfield AB, Chen YJ, Chen WNU, et al. A compartmental comparison of major lipid species in a coral-Symbiodinium endosymbiosis: evidence that the coral host regulates lipogenesis of its cytosolic lipid bodies. PLoS ONE. 2015;10(7): e0132519. pmid:26218797
  21. 21. Chen WNU, Hsiao YJ, Mayfield AB, Young R, Hsu L, Chen CS, et al. Vertical transmission of heterologous clade C Symbiodinium in a model anemone infection system. Peer J. 2016;4:e2358.
  22. 22. Traylor-Knowles N, Granger B, Lubinski T, Parikh J, Garamszegi S, Xia Y, et al. Production of a reference transcriptome and a transcriptomic database (PocilloporaBase) for the cauliflower coral, Pocillopora damicornis. BMC Genomics. 2011;12(1): 585.
  23. 23. Mayfield AB, Fan TY, Chen CS. Physiological acclimation to elevated temperature in a reef-building coral from an upwelling environment. Coral Reefs. 2013;32: 909–921.
  24. 24. Mayfield AB, Wang YB, Chen CS, Chen SH, Lin CY. Compartment-specific transcriptomics in a reef-building coral exposed to elevated temperatures. Mol Ecol. 2014;23: 5816–5830 pmid:25354956
  25. 25. Saul S, Purkis S. Semi-automated object-based classification of coral reef habitat using discrete choice models. Remote Sensing. 2015;7: 15894–15916.
  26. 26. Schmidt-Roach S, Miller KJ, Lundgren P, Andreakis N. With eyes wide open: a revision of species within and closely related to the Pocillopora damicornis species complex (Scleractinia; Pocilloporidae) using morphology and genetics. Zool J Linnaean Soc. 2014;170: 1–33.
  27. 27. Mayfield AB, Wang LH, Tang PC, Hsiao YY, Fan TY, Tsai CL, et al. Assessing the impacts of experimentally elevated temperature on the biological composition and molecular chaperone gene expression of a reef coral. PLoS ONE. 2011;e26529. pmid:22046302
  28. 28. Mayfield AB, Chan PS, Putnam HM, Chen CS, Fan TY. The effects of a variable temperature regime on the physiology of the reef-building coral Seriatopora hystrix: results from a laboratory-based reciprocal transplant. J Exp Biol. 2012;215: 4183–4195. pmid:22933614
  29. 29. Mayfield AB, Chen CS, Liu PJ. Decreased green fluorescent protein-like chromoprotein gene expression in specimens of the reef-building coral Pocillopora damicornis undergoing high temperature-induced bleaching. Platax. 2014;11: 1–23
  30. 30. Welchman RL, Gordon C, Mayer RJ. Ubiquitin and ubiquitin-like proteins as multifunctional signals. Nature Rev Mol Cell Biol. 2005;6: 599–609.
  31. 31. Mayfield AB, Chen CS, Dempsey AC. Identifying corals displaying aberrant behavior in Fiji’s Lau Archipelago. PLoS ONE. 2017; e0177267. pmid:28542245
  32. 32. Mayfield AB, Chen CS, Bruckner AW, Dempsey AC. The molecular ecophysiology of closely related pocilloporids from the South Pacific: a case study from the Austral and Cook Islands. Platax. 2016;13:1–25.
  33. 33. Gates RD. Seawater temperature and sublethal coral bleaching in Jamaica. Coral Reefs. 1990;8: 193–197.
  34. 34. Correa AMS, McDonald MD, Baker AC. Development of clade-specific Symbiodinium primers for quantitative PCR (qPCR) and their application to detecting clade D symbionts in Caribbean corals. Mar Biol. 2009;156: 2403–2411.
  35. 35. Mayfield AB. Using multivariate statistics approaches to uncover spatio-temporal and treatment-derived differences in the molecular physiology of a model coral-dinoflagellate mutualism. J Mar Sci Eng. 2016;4: 63.
  36. 36. Sokal RR, Rohlf J. Biometry. New York: MacMillan; 1995.
  37. 37. Clarke KR, Warwick RM. Change in marine communities: an approach to statistical analysis and interpretation. Plymouth: Plymouth Marine Laboratory; 1994.
  38. 38. Haydon MJ, Cobbett CS. A novel major facilitator superfamily protein at the tonoplast influences zinc tolerance and accumulation in Arabidopsis. Plant Physiol. 2007;143: 1705–1719. pmid:17277087
  39. 39. Mayfield AB, Wang YB, Chen CS, Chen SH, Lin CY. Dual-compartmental transcriptomic+proteomic analysis of a marine endosymbiosis exposed to environmental change. Mol Ecol. 2016;25: 5944–5958. pmid:27778414
  40. 40. Grottoli AG, Rodrigues LJ, Juarez C. Lipids and stable carbon isotopes in two species of Hawaiian corals, Porites compressa and Montipora verrucosa, following a bleaching event. Mar Biol. 2004;145: 621–631.
  41. 41. Putnam HM, Mayfield AB, Fan TY, Chen CS, Gates RD. The physiological and molecular responses of larvae from the reef-building coral Pocillopora damicornis exposed to near-future increases in temperature and pCO2. Mar Biol. 2013;160: 2157–2173.
  42. 42. Hochachka PW, Somero GN. Biochemical adaptation. Oxford: Oxford University Press; 2002.
  43. 43. Lesser MP. Exposure of symbiotic dinoflagellates to elevated temperatures and ultraviolet radiation causes oxidative stress and inhibits photosynthesis. Limnol Oceanogr. 1996;41: 271–283.
  44. 44. Smith EG, D’Angelo C, Salih A, Wiedenmann J. Screening by coral green fluorescent protein (GFP)-like chromoproteins supports a role in photoprotection of zooxanthellae. Coral Reefs. 2013;32: 463–474.
  45. 45. Ho JWK, Stefani M, Dos Remedios CG, Charleston MA. Differential variability analysis of gene expression and its application to human diseases. Bioinformatics. 2008;24: i390–398. pmid:18586739
  46. 46. Barshis DJ, Ladner JT, Oliver TA, Seneca FO, Traylor-Knowles N, Palumbi SR. Genomic basis for coral resilience to climate change. Proc Natl Acad Sci USA. 2013;110: 1387–1392. pmid:23297204
  47. 47. Rinkevich B. Conservation of coral reefs through active restoration measures: recent approaches and last decade (sp.) progress. Environ Sci Technol. 2005;39(12): 4333–4342. pmid:16047765
  48. 48. Mayfield AB, Chen CS, Dempsey AC. The molecular ecophysiology of closely related pocilloporid corals of New Caledonia. Platax. In press.