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The molecular ecophysiology of closely related
pocilloporid corals of New Caledonia
Anderson B. Mayfield1,2,3*, Chii-Shiarng Chen1,3,4,5, Alexandra C. Dempsey2
1

National Museum of Marine Biology and Aquarium, Checheng, Pingtung, Taiwan

2

Khaled bin Sultan Living Oceans Foundation, Annapolis, MD, USA

3

Taiwan Coral Research Center, Checheng, Pingtung, Taiwan

4

Graduate Institute of Marine Biotechnology, National Dong-Hwa University, Checheng, Pingtung, Taiwan

5Department of Marine Biotechnology and Resources, National Sun Yat-Sen University, Kaohsiung, Taiwan*Corresponding author. E-mail: andersonblairmayfield@gmail.com

Abstract
Given the severe threats against Earth’s coral reef ecosystems, there isan urgent need to collect baseline data such that the condition of these
endangered habitats can be tracked over the coming decades. Many of themost spectacular coral reefs are found in remote locations that are only
accessible by research ships; therefore, little is known about the vast majorityof the planet’s reefs. In order to fill in a critical knowledge dearth in an
ecologically important section of Melanesia, the Khaled bin Sultan’s Living
Oceans Foundation carried out coral reef surveys in both the far southern andfar northern regions of New Caledonia as part of their “Global Reef
Expedition.” There was a particular emphasis on monitoring the health of theresident pocilloporid corals, which, like all reef-building scleractinians, areengaged in a mutualistic relationship with intracellular dinoflagellates of thegenus Symbiodinium. Of the 120 Pocillopora spp. colonies analyzed in detail,10 were found to be outliers based on a series of multivariate statistical
approaches. These aberrantly behaving colonies differed from statisticallynormal ones in demonstrating higher expression levels of two host coral andfour Symbiodinium mRNAs. Furthermore, all 10 colonies were found in therelatively more densely populated and mined south; no outliers were
uncovered in the northern atolls, which encompass some of the most pristine
and aesthetically appealing reef ecosystems in the world. That being said,1Platax 14: 1-45 2017

mRNA expression of nearly all stress-sensitive biomarkers was higher inthese New Caledonian corals than in closely related pocilloporids from otherregions of the South Pacific. This suggests that even the remote reefs andatolls of the country’s far northern reaches have not been completely sparedfrom anthropogenic harm.
Key words: coral reefs, dinoflagellate, ecology, molecular biology, New
Caledonia, stress

Introduction

the resident corals with an added degree

Earth’s coral reefs are threatened bya barrage of anthropogenic insults,
including global climate change (GCC;
Hoegh-Guldberg et al., 2007; Mayfield &Gates, 2007) and water pollution(Kabricius, 2005; Huang et al., 2011).
Unfortunately, little is known about thevast majority of the planet’s coral reefs.
For instance, reefs of the southernmost
regions of French Polynesia were notsurveyed for the first time until early 2013
(Mayfield et al., 2015). Similarly, themost remote regions of Fiji (Lau Province)
and Tonga (the Ha’apai and Vava’uarchipelagos) also went un-surveyed until
well into 2013 (Mayfield et al., 2017b and2017a, respectively). Coral reefs of New
Caledonia (Figure 1) have also received
little scientific focus (but see Andrefouetet al., 2009), yet the remoteness andgovernmental protection of the atollsin the northernmost part of thisMelanesian nation might have conferredof resilience to GCC given the absence of
local-scale anthropogenic pressures (e.g.,
overfishing).

In

other

words,

wehypothesize that reefs far from humanpopulation centers are healthier than thoseabutting cities (e.g., reefs of Southern
Taiwan, which are within 50 km of one of
the biggest cities in Asia, Kaohsiung [Liu
et

al.,

2012]).

Even

withinNew

Caledonia, a gradient of anthropogenic
impact could be hypothesized to exist. In
the southern reefs (SR), 1) nickel mining
around Prony Bay and 2) tourism andrelatively higher population densities in
the Ile des Pins Archipelago could haveresulted in decreases in water quality that
may be detrimentally impacting theresident corals. No such industry or
tourism exists in the northern reefs andatolls (NRA); only scientists holding
government permits may visit thisregion.

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Fig. 1. Map of New Caledonia showing genetic breakdown of the sampled
Pocillopora spp. colonies. Due to the proximity of the northern reefs andatolls, they have been additionally displayed in a 5-fold magnified inset.
The six genotyped colonies from Neulka have been grouped with the 48
genotyped colonies from Ile des Pins into the same pie graph. In this and all
remaining figures, Pocillopora acuta and P. damicornis are presented as red
and black icons/wedges, respectively.

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Molecular biology-driven approaches
have dramatically aided in our
understanding of both the basic biology(Mayfield et al., 2010; Peng et al., 2011;
Chen et al., 2012; Mayfield et al., 2012b;
Wang et al., 2013; Mayfield et al., 2014c;
Chen et al., 2015, 2016, 2017) andenvironmental physiology (Mayfield et al.,2011, 2012a, 2013a-d, 2014a-b, d; Putnam
et al., 2013; Mayfield, 2016; Mayfield etal., 2016b) of reef-building corals and sea
anemones engaged in endosymbiotic
relationships with dinoflagellates of thegenus Symbiodinium. For nearly 20 years,
scientists have attempted to use molecularbiomarkers to confer a level of health toboth experimental (Downs et al., 2000)
and field (Downs et al., 2005) coralcolonies. However, these endeavors havebeen fraught with methodological hurdles;
for instance, these works by Downs andcolleagues failed to consider that theirantibodies likely bound protein antigensof both host corals and their intracellulardinoflagellates. The data generated are
therefore inherently biased. Such a failure
to consider both compartments of thecoral-Symbiodinium mutualism has also
led to the production of unreliabletranscriptome-scale datasets (e.g., Barshis
et al., 2013). Therefore, in 2009 wegenerated the first protocol for
quantifying gene expression in an
endosymbiotic organism (Mayfield et al.,2009). Since then, we have used these
real-time PCR (qPCR)-based protocols toassess expression of gene mRNAbiomarkers in samples of the model reef
coral
Pocillopora
damicornis(Traylor-Knowles et al., 2011) fromacross the Indo-Pacific (e.g., Mayfield etal., 2016a). Specifically, we have focusedon expression of genes encoding proteins
involved in certain cellular processes
known to be temperature-sensitive incorals,suchasSymbiodinium
photoinhibition and metabolism (Jones etal., 1998; Table 1) and host coral
detoxification of the reactive oxygen
species (ROS) produced by photoinhibited
Symbiodinium (Lesser, 1996, 1997, 2006),
includingcopper-zincsuperoxide
dismutase (cu-zn-sod).
Although gene expression levels
cannot be used to make inferences about
reef-building coral physiology and cellbiology due to the lack of congruency
between gene and protein expression incoral anthozoans and Symbiodinium(Mayfield et al., 2016c), they cannevertheless be used to identify corals
behaving in a statistically aberrant manner(Mayfield et al., 2017a). For instance,molecular biomarker data from Fijiancorals were analyzed within a multivariate
statistical framework, and a number ofoutliers were uncovered (Mayfield et al.,2017b). These corals were characterized
not only by lower Symbiodinium densities,
but also by elevated expression levels ofgenes known to encode proteins involvedin the cellular stress response (Table 1),

4

Table 1. Response variables. Although 14 response variables were assessed for each of 120 of the 140 total pocilloporid colonies
sampled in New Caledonia, only the 10 molecular-scale response variables (MSRV) were hypothesized to be useful inassigning a level of aberrancy to the sampled colonies (i.e., the color [scored as described in the text though excluding
green, purple, and blue colonies], polyp expansion, and size [two parameters] data were excluded from the outlier
assignment exercise.). The Fiji and Tonga results were presented in Mayfield et al. (2017b and 2017a, respectively).
Statistically significant intra-country outlier vs. non-outlier differences (student’s t-test, p<0.05) have been underlined,
and only the Symbiodinium zinc-induced facilitator-like 1-like and host coral copper-zinc superoxide dismutase mRNAs
were expressed at higher levels by outliers than non-outliers in all three countries (both highlighted in green). In contrast,
lectin was not expressed at significantly different levels between outliers and non-outliers for any of the three countries(highlighted in yellow). For gene expression data, the ranges and means have been expressed as non-normalized
threshold cycle (Ct) values. When 1-way ANOVA detected a significant country effect (p<0.01), Tukey’s post-hoc tests
were performed, and letters adjacent to standard deviations (std. dev.) in these columns only reflect Tukey’s honestly
significant differences (p<0.05). Lower-case letters in the left-most column instead correspond to the references (asfootnotes) that first described the response variables. For the normalized MSRV data, please see Figure S3.
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such as heat shock protein 90 (hsp90) andubiquitin ligase (ubiq-lig; Welchman et al.,2005). Curiously, outliers were not more
frequently sampled from marginalenvironments, such as reefs with low
average live coral cover (ALCC); theirdistribution was effectively random,though it is possible that environmental
parameters (EP) which were not measured,
such as nutrient levels, were instead
responsible for eliciting aberrant behavior
in these coral colonies.
As mentioned above, New Caledonia
is hypothetically characterized by agradient of anthropogenic influence, fromthe relatively more densely populated andintensively mined south, to the atolls of
the north that are dozens of kilometersfrom human habitations and are protected
within a series of federally governedmarine parks. We therefore sought hereinto employ the same molecular biomarker
profiling+multivariate statistical analysis(MSA) approach used to assess coral
health elsewhere in the South Pacific todetermine whether corals displaying
aberrant behavior were more common in
the southern, potentially more impacted
reefs. We hypothesized that not only
would this be the case but also that, moregenerally, expression of stress biomarker
mRNAs would be lower in P. damicorniscolonies sampled from the more remotereefs of the NRA relative to the SR.
Finally, we hypothesized that stressbiomarker levels would be lower in coralsof the NRA than at reefs surveyed in Fiji
(Mayfield et al., 2017b) and Tonga(Mayfield et al., 2017a) given their remotenature and protected status.

Materials and Methods
Sample and environmental datacollection. In November 2013, the Khaled
bin Sultan Living Oceans Foundation’s(LOF) research vessel M. Y. Golden
Shadow traversed two major regions of
New Caledonia (Figure 1): the SR (Ile desPins [Figure 2], Neulka [Figure 2], andProny Bay [Figure 3a]; 26 sites) and the
NRA (Figure 3b-f; 53 sites). For details of
the coral reef surveys, readers are referredto our prior works given that the identicalsurvey methods were used herein(Mayfield et al., 2015, 2017b). Briefly,
habitat maps were made as in Saul andPurkis (2015), and point-intercept surveys
were conducted by multiple divers at
depths typically spanning 5-30 m (in 5-m
increments) to characterize the benthos.
ALCC was calculated at each site, as it
was hypothesized that a greater number ofcorals displaying aberrant behavior(described in more detail below) would besampledfrommarginalized
reefscharacterized by low ALCC or, moregenerally, a diminished level of health (asinferred from ecosystem-scale parameterssuch as algal cover and diseaseprevalence).
Unlike in our prior surveys in theAustral Islands (French Polynesia), Cook

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Islands, Fiji, and Tonga, a series of night
dives were conducted (see Tables 2-3.).
Night dive sites were chosen earlier in theday, and colonies were tagged with neonglow sticks so that they could be easily
re-located at night. In general, 10 colonies
were selected at 10 m±1 within a ~100 m2
area at each site (n=4 night dive sites).
The same characteristics as described
below for all sampled colonies were
measured, except that for night divecolonies, biopsies (50 mg) were removed
at around 12:00 (±1 hr) and again aftersunset (18:30±30 min). By sampling thesame colony during daylight and darkness,
we hoped to gain not only greater insight
into the molecular pathways exploited bycorals to recover from high ultraviolet(UV) radiation exposure and high pO2 invivo, but also to better gauge whichcolonies were of comprised resilience;
those colonies with wider fluctuations inmolecular physiology between day andnight could be hypothesized to be lessphysiologically robust since such behavior
may coincide with significant deviations
from homeostasis. In the context of thismanuscript, we seek to mainly present
these light-dark data, without focusing on
the diel differences and their physiologicalimplications, which will be presented ina future work (Mayfield et al., in
prep.).
The model reef coral P. damicornis,
or, alternatively, its closely related sister
species P. acuta (Schmidt-Roach et al.,2014), was sampled from 11 (Table 2) and18 (Table 3) of the survey sites in the SR
and NRA, respectively. Colonies were
sampled at a range of depths, fromtypically 5 to 30 m (the same depths at
which surveys were conducted), and there
was always at least a 10-m distance
between colonies except for those of thenight dive sites. Prior to removing a small
biopsy with bone-cutting pliers as
described in Mayfield et al. (2015),
colony polyp expansion behavior wasnoted. If even a small fraction of acolony’s polyps were outside of theircoralites, colonies were scored as “polyps
expanded.” Then, colonies were
photographed. Maximum (max.) colony
length (i.e., the distance spanning the twomost distant points of the colony; cm) andplanar surface area (SA; cm2) were
measured from images with ImageJ(National Institutes of Health, USA) by
scaling to an object of known size
included next to each colony when it wasphotographed. Color was determinedsubjectively for each of the sampledcolonies, and each was scored as normal(1), pale (2), very pale (3), bleached (4),green, purple, or blue (the latter threewere rare and not given numeric scores.).
Max. length, planar SA, polyp expansion,and color were deemed “physiologicalresponse variables” (PRV; Table 1).
A number of EP were assessed at thesurvey sites, including 1) region (SR vs.
NRA), 2) island (n=8; see Figures 1-3 and8Platax 14: 1-45 2017

Fig. 2. Southern New Caledonia dive sites: Ile des Pins and Neulka.

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Table 2. Southern New Caledonia site information. The target coral species Pocillopora damicornis, or, alternatively, itsclosely related sister species P. acuta, was observed at and sampled from 11 of the 26 sites surveyed across Ile des Pins
(NCPI; n=19 survey sites + 2 night dive [ND] sites; Figure 2), Neulka (NCNE; n=2 sites; Figure 2), and Prony Bay(NCPB; n=3 sites; Figure 3a), and the data in this table are from these 11 sites only. In total 91 samples were obtained
from 62 pocilloporid coral colonies (ND colonies were sampled twice.), and all but one of the uniquely sampledcolonies were genotyped (excluding colony #33 from Neulka). Of these 91 samples, 80 (67, 6, and 7 from NCPI,
NCNE, and NCPB, respectively) were analyzed in detail and are the focus of this work. For the following 15 sites, P.
damicornis may have been present, but it was not sampled: NCPI03, NCPI06-09, NCPI12-14, NCPI16-21, and
NCPB24. For the environmental data for all 26 surveyed sites, please see the online supplemental data file. The
average temperatures (temp.), salinities, and live coral cover (ALCC) percentages reflect the means across the 11 sites
from which corals were sampled (±std. dev.), not all 26 surveyed sites. “.” = missing data.
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Table 3. Northern New Caledonia site information. Of the 53 sites surveyed in the northern reefs and atolls (NRA), the target
coral species Pocillopora damicornis, or, alternatively, its closely related sister species P. acuta, was sampled from 18,
and the data in this table are from these sites only. In total 49 samples were taken from 42 unique pocilloporid coralcolonies from Pelotas Atoll (NCPE; n=3 colonies; Figure 3b), Cook Reef (NCCR; n=14; Figure 3c), Portail Atoll (NCPO;
n=4; Figure 3d), Huon Atoll (NCHU; n=9; Figure 3e), and Surprise Atoll (NCSU; n=12; Figure 3f); 40 of these 49
samples are the focus of this work. For the following 35 sites, P. damicornis/P. acuta may have been present, but nocolonies were sampled: NCPE25, NCCR28-29, NCCR32, NCCR35, NCCR38, NCPO41, NC42 (a remote reef in the
Coral Sea near the town of Puom; Figure 1), NCHU48-54, NCGU55-56 (Guibert Atoll; Figure 1), NCGU58-59,
NCHU60, NCSU62-63, NCME64-65 (Merite Atoll; Figure 1), NCSU66-71, NCPO72-73, NCSU74, and NCPE75-76.
For the environmental data for all 53 surveyed sites, please see the online supplemental data file. The averagetemperatures (temp.), salinities, and live coral cover (ALCC) percentages reflect the means across the 18 sites from whichcorals were sampled (±std. dev.), and the average seawater temperature (temp.) at the NRA (25.8ºC) was significantly
higher than in the southern reefs (SR; 24.4ºC; student’s t-test, p<0.05). ALCC did not vary between regions (p>0.05). The
New Caledonia averages presented in the final row reflect the means across all 79 survey sites (±std. dev.), not just thosefrom which corals were sampled. “.” = missing data.
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Tables 2-3.), 3) site (n=29; see Figures 2-3
and Tables 2-3.), 4) reef exposure (n=3classifications: exposed, intermediate, or
protected [see Mayfield et al., 2017b for
details].), 5) reef zone (n=5: forereef,
backreef, lagoon, channel, or bay), 6) reeftype (n=4: fringing reef, patch reef,
barrier reef, or atoll), 7) sampling date
(n=18 sampling days), 8) samplingtime (n=4 categorical groupings:<10:00, 10:00-14:00, 14:00-18:00, or
18:00-22:00), 9) colony depth (n=6categorical groupings: 5-10, 10-15, 15-20,
20-25, 25-30, or >30 m), 10) site
temperature (n=4 categorical groupings:
23-24, 24-25, 25-26, or >26°C), 11) site
salinity (n=4 categorical groupings: <35,
35.1, 35.2, or 35.3), 12) colony
photosynthetically active radiation (PAR)level (n=2 categorical groupings: 0 or >0μmol m-2 s-1), 13) site ALCC (n=4categorical groupings: 15-30, 30-45,
45-60, or >60%), 14) host (n=2: P.
damicornis or P. acuta), 15) color(n=7; see classifications above.), and
16) Symbiodinium assemblage (n=2categorical groupings: clade C only orclades A+C). Although the latter
three parameters are not strictly“environmental,” they were neverthelesshypothesized to influence coral
physiology.
Molecular-scale response variables(MSRV). RNAs and DNAs were extracted
from the 140 pocilloporid samples, which
had been preserved in RNALater® (LifeTechnologies) and transported under a
CITES permit from New Caledonia to
Taiwan’s National Museum of Marine
Biology and Aquarium (NMMBA). Upon
extracting the nucleic acids as in Mayfield
et al. (2015), an RNA/DNA ratio was
calculated (MSRV#1) to serve as a proxy
for total gene transcription. The
Symbiodinium genome copy proportion(GCP; Mayfield et al., 2009; MSRV#2)
was calculated from 20 ng DNA as
described previously (Putnam et al., 2013)
in order to estimate the density ofSymbiodinium within the sampled tissues.
It was hypothesized that abnormally highand low RNA/DNA and Symbiodinium
GCP, respectively, would be indicative orstress; regarding the latter parameter,corals require high Symbiodinium
densities (106 cells cm-2) in order tocalcify and reproduce (Gates, 1990;
Brown, 1997; Furla et al., 2005). In
addition to these two biologicalcomposition
response
variables,
expression of four and four gene mRNAs
was measured in the host coral andSymbiodinium compartments, respectively(Table 1), as described in Mayfield et al.
(2017a-b). Briefly, the eight genes encode
proteins involved in a number of cellular
processes previously hypothesized to beaffected by environmental change, such as
the stress response (hsp90, ubig-lig, andcu-zn-sod) and metabolism (zinc-induced
facilitator-like 1-like [zifl1l] and carbonic
anhydrase [ca]). Specific justification for

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the incorporation of these biomarkers into
this “coral aberrancy test” can be found in
Mayfield et al. (2017b).
In addition to these 14 response
variables (the 10 MSRV+colony color,size [2 parameters], and polyp expansion
behavior), two additional parameters were
measured from the same DNAs fromwhich the Symbiodinium GCP and
RNA/DNA ratio values were calculated.
First, the host corals were genotyped with
PCR followed by sequencing (twodirections) as in Mayfield et al. (2015).
Secondly, the Symbiodinium assemblage
was determined to clade level using theqPCR assays of Correa et al. (2009).
Although host coral genotype andSymbiodinium assemblage are technicallyproperties of the sampled organisms, they
were generally considered to be EP for thesuite of statistical analyses employed(discussed in greater detail below).
Overview of statistical analyses.
Data analysis was near-identical to that ofour prior works (Mayfield et al., 2017a-b).
Briefly, a suite of statistical approaches
was used to model the relationshipbetweencoral
physiology
andenvironment. MSA were then exploited to
identify outliers in the dataset. Univariate
and MSA were next utilized to determine
whether outlier frequency varied acrossenvironmental gradients and whether the
response variables that delineated outliers
from normally behaving colonies were thesame for the two predominant coral

species sampled: P. damicornis and P.
acuta. Unlike in our prior works, theeffect of country (Fiji, Tonga, and New
Caledonia) was also tested herein for eachof the 14 response variables using 1-way
ANOVA. The Austral and Cooks Islands
were excluded from this analysis since, in
general, different MSRV were assessed inthosepocilloporid
coral
samples(Mayfield et al., 2016a). JMP (ver. 12.0.1)
was used for all statistical analyses
outlined
below
exceptfor
multi-dimensional scaling (MDS) andanalysis of similarity (ANOSIM), whichwere carried out with PRIMER (ver. 5).
Although 140 coral samples weretaken from 104 unique Pocillopora spp.
colonies (the night dive colonies were
sampled twice.), 119 such samples are thefocus of the remaining statistical analyses(the 120 listed in Tables 2-3 minus colony
94, which was not genotyped and so wastherefore excluded from most statisticalanalyses). Please see the online
supplemental data file for reasons why theadditional 20 colonies were excluded
from the analysis. In general, thisstemmed from poor extraction efficiency(low RNA or DNA yield), though in othercases, the biopsies were never extracted at
all since it was envisioned that they mightserve as backups were something tohappen to the extracted RNAs, DNAs, andproteins.
Univariate statistical approaches,primarily ANOVA, were first used to test

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Fig. 3. Prony Bay and Northern New Caledonia dive sites. In (b), Pelotas Atoll has
additionally been plotted as a red dot in an inset of the entire nation of New
Caledonia to emphasize the far northerly position of the northern reefs andatolls (b-f). The fourth night dive site of the mission, which took place at
Surprise Atoll (f), was not plotted, as GPS coordinates were not taken.
However, the site was a sheltered, lagoonal patch reef. Maps of Guibert andMerite Atolls have not been included since pocilloporid corals were notsampled from sites surveyed within these.

15Platax 14: 1-45 2017

for the effects of the EP listed above on
the response variables quantified in thecoral biopsies. Multivariate ANOVAs(MANOVAs), which featured a canonicalcorrelation analysis (CCA) algorithm,
were also used to model the effects ofenvironment
oncoral
molecular
physiology. Then, principal components
analysis (PCA) and MDS were used touncover variation in the dataset andsimilarity between samples, respectively.
Outliers were identified using thedual-calculation approach described in ourprior works (Mayfield et al., 2017a-b).
First, the Mahalanobis distance wascalculated for each sample across the 10
MSRV, and, if it was greater than theupper control limit (UCL) calculated by
JMP (4.2), a sampled was considered to
be a potential outlier. Then, for thosesamples whose Mahalanobis distanceswere above 4.2, we required that at least
one MSRV was over three standard
deviations above the mean (z-score>3).
For each response variable whose z-score
was >3, the sample was given a “heat map
score” of 1. In other words, a sample wasonly considered an outlier when its
Mahalanobis distance was >4.2 and its
heat map score was ≥1. Finally, we looked
to check that these outliers were the most
distant and distinct points in the PCA andMDS plots. Although these ordination
techniques use completely different
algorithms, they tended to support thestrictly quantitative outlier detection

assignments described above in ourprior works (Mayfield et al., 2016a,
2017a-b). This system of statistical“checks and balances” ideally ensuresthat statistically normal samples are
not inadvertently misclassified as
outliers.
Followingtheoutlier
assignment exercise, we used a seriesof approaches to determine which
response
variables
were
mostimportant in partitioning outliers from
non-aberrantly behaving coral colonies.
All such statistical methods are
discussed in greater detail below.
Univariate statistical analyses. As apreliminary step, we tested the effect ofenvironment (the 16 EP described above)on 1) coral molecular physiology (12response variables; using ANOVA) and 2)
host coral, outlier, and colony polypextension frequency (using X2 tests).
Since host frequency, outlier frequency,
and polyp expansion frequency wereassessed across 15, 16, and 16 EP,
respectively (Table 4), Bonferroni
adjustments of 3.9, 4.0, and 4.0,
respectively,
were
made
to
a
priori-chosen α level of 0.05, resulting ina modified α level of 0.013. All other
response variables, including max. colony
length, planar SA, and the 10 MSRV
(Table 1; Symbiodinium GCP, theRNA/DNA ratio, and expression of theeight genes; n=12 response variables intotal [color was considered as an EP inthis analysis.]) were analyzed across the16Platax 14: 1-45 201716 EP with 1-way ANOVAs after
confirming both normality (Shapiro-Wilk
W test p>0.05) and homogeneity ofvariance (Levene’s test p>0.05) of thedata. Transformations were conductedwhen the latter two conditions were notmet. Because 192 ANOVAs were
performed, a Bonferroni adjustment of
13.9 was made to the a priori-chosen αlevel of 0.05, resulting in a modified α of
0.004. Similarly, a 15 EP (the same asabove, though excluding host) x 12
response variable 1-way ANOVA matrix
was also generated individually for eachof the two species sampled: P. damicornis
and P. acuta. For these species-specificcomparisons, the a priori-chosen α levelof 0.05 was instead divided by the square
root
of
180
to
generate
the
Bonferroni-adjusted α level (also 0.004).
For each of the 12 response variables, thecoefficient of variation was calculated inaddition to the mean and standard
deviation to quantify the overall variation
between the sampled colonies.
MSA. MSA can 1) uncover
relationships amongst response variables
at a lower false positive error rate thanunivariate ANOVA and 2) identify
combinations of response variables thatbest partition samples within amulti-dimensional dataspace. Several
MSA were taken herein to understand therelationship between environment andcoral 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 eachof the 16 EP on the multivariate meancentroid calculated across the 10 MSRV.
The two size-based parameters were
excluded from this analysis (and all thosehenceforth). Briefly, MANOVA tests
whether vectors of means (rather thansimply individual means) of different
samples are from the same distribution.
JMP’s CCA algorithm was used inconjunction with MANOVA to model
environmental
effects.
Separate
MANOVAs were then performed for eachspecies across the 15 EP (the 16
aforementioned minus “host”) since it was
hypothesized that these closely related
speciesmaydemonstrate
subtledifferences in their molecular physiology.
PCA was next performed with the 10
MSRV to determine the combinations ofparameters that best accounted forvariation in the dataset (sensu Chen et al.,2015). As an alternate, ordination-basedmeans of visualizing the dataset in
multiple dimensions, PRIMER was used
to construct a Bray-Curtis similarity
matrix (online supplemental data file), andan MDS plot based off of this matrix was
created using data from the 119 samples
analyzed for all MSRV. PRIMER’s
ANOSIM function was used to test for theeffects of the 16 EP on separation ofsamples within the dataspace generated.
For a more detailed explanation of17

Platax 14: 1-45 2017

MANOVA+PCA and MDS+ANOSIM,
readers are referred to Sokal & Rohlf(1995) and Clarke & Warwick (1994),
respectively. It was hypothesized that both
PCA and MDS could be used corroborate
the outliers identified (described below).
Outlieranalysis.
Mahalanobis
distances were calculated using the 10
MSRV only. Samples characterized bydistance values above the UCL of 4.2
were considered to be “Mahalanobis
distance outliers.” Then, a heat map wasconstructed, and samples with z-scores >3
for a certain response variable were givena score of 1. When a sample wasconsidered a Mahalanobis distance outlier
and had a heat map score of 1 or greater,
it was considered an outlier. Several
measures were taken to understand the
response variable(s) that contributed mostto a sample being deemed an outlier. First,
JMP’s predictor screening function wasused to rank the 10 MSRV in order of
their contribution to the cumulative
difference between the 10 outliers
uncovered and all other samples (n=109).
Then, a “variability index” was calculated
by taking the standard deviation across thez-scores of all 10 MSRV for each sample.
It was hypothesized that those samples
with high Mahalanobis distances (i.e., theoutliers) would also demonstrate morevariability between the individual
response variables assessed, particularly
gene expression. This variability index
was regressed against the Mahalanobis

distance, and the significance of thecorrelation was tested with a linear
regression t-test.
Differences in certain response
variables
betweenoutliers
andnon-outliers were compared across both P.
damicornis and P. acuta with 2-way
ANOVA (outlier status vs. species), andindividual mean differences between thefour 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 todetermine the response variables that best
led to the separation of samples
characterized by different heat map scores(which ranged from 0 [non-outliers] to 4
[extreme outliers]). An α level of 0.05 wasestablished a priori for all such MSA,
though a Bonferroni adjustment of 3.9
was made to the α levels of the
MANOVAs of Tables 4-5 since 15-16
comparisons were made; this resulted in
an adjusted MANOVA α of 0.013.

Results and Discussion
Data accessibility. For highresolution habitat maps of the survey sites,
including drop-cam video footage, please
consult the following, open-accesswebsite: http://maps.lof.org/lof. Ideally,maps will be available in 2018. For
images of the 1) surveyed reefs, 2)colonies sampled (in situ), and 3) “macro”
photographs of these colonies’ polyps,

18Platax 14: 1-45 2017

please see the “New Caledonia”
sub-heading of coralreefdiagnostics.com.
This website also includes all data
presented herein. Additionally, the data
have been consolidated into an online
supplemental data (Excel) file (“NewCaledonia manuscript data file”), which
can be downloaded from the Platax
website. This spreadsheet includes allenvironmental data from the 79 sites, allmolecular+physiological data from119-120 of the 140 sampled colonies,
z-score-transformed data, results of the
PCA (eigenvectors and eigenvalues) andcertain CCA (e.g., effect of the heat map
score), a Bray-Curtis similarity matrix(used to build the MDS plot), and aseparate worksheet on the light vs. dark
data (which will be the focus of aforthcoming, sister work [Mayfield et al.,in prep.]).
Ecological data. Reefs of Ile desPins (Figure 2) were largely dominated byalgae and were therefore characterized bylow coral cover (online supplemental datafile). Asparagopsis sp. was the mostcommon red algae species found at depths
ranging from 15 to 20 m. The reefs had
generally been overgrown by both macroand turf algae, and there was a highdensity of damselfish and their algal
lawns. Leptoria sp. was the most abundantcoral, possibly because this species isknown to prefer cooler waters (theaverage temperature of the 19 sites was
only 24.4±0.5ºC std. dev.).

Prony Bay’s reefs (Figure 3a) werecharacterized by a very high diversity ofcorals (the highest of all 79 surveyedsites), and communities ranged fromplating Leptoseris spp. areas to dense
thickets of bottlebrush acroporids. There
were also medium-sized to large table
Acropora spp. colonies. Several hard coral
species that were not observed at Ile desPins were observed, such as Cantharellus
jebbi, Madracis kirbyi, Cyphastrea
japonica, and Leptoseris gardineri. In theshallows (3-5 m), extensive thickets orbushes of Anacropora spp. were present,
and these patches shifted to staghorn
thickets and branching Porites spp.
meadows at greater depths. The sites werecharacterized by relatively poor visibility,with substantial particulate organic matterin the water column. This could be due tothe intensive mining efforts nearby (seeFigure 3a.).
At Cook Reef (Figure 3c), the reefframework appeared to be from Porites
lobata, though large, burrowing bivalves
had eroded extensive sections of the reef;
this resulted in brittle and incongruent
substrate in many areas. Several uniquespecies of corals were observed, including
Favites chinensis, Zoopilus sp.,
Hydnophora grandis, and Pocillopora
kelleri. Thick stands of Acropora robusta
had 30-60% partial mortality due togardening by damselfish. Indeed, as at Ile
des Pins, there were large numbers ofdamselfish present amongst the dead19

Platax 14: 1-45 2017

staghorn coral matrix. Furthermore,
numerous crown of thorns seastars (COTS)
were collected, and an extensive amountof coral had been consumed at the time ofsurveying. Storm damage was alsoprevalent, as evidenced by large rubble
fields. Soft corals were colonizing a large
proportion of the open substrate, whichmight account for the low number of hardcoral recruits observed. Finally, large,
calcified Halimeda sp. flakes wereconcentrated in small pockets or mounds.
This could potentially be an indication ofa high carbonate budget on the forereef
slope of the reef system.
The following observations weremade elsewhere in the NRA (inc. Merite
Atoll, Surprise Atoll, Huon Atoll, Guibert
Atoll, Portail Atoll, and Pelotas Atoll).
First,
many
surveysites
werecharacterized by relatively high coral
cover, with mixed communities of plating
Echinophyllia, bottlebrush acroporids, andPavona maldivensis. The lagoon divesconsisted mainly of Porites spp.
framework, with smaller coral recruits
present. Large stands of spindle
acroporids and foliaceous Turbinaria spp.
were also common. Monospecific standsof Porites cylindrica were also noted.
Large mats of encrusting leather corals
covered substantial portions of the tops ofthe reef pinnacles. Extensive COTS
damage was documented at Guibert andHuon Atolls. This is unusual as theseatolls are remote (far from emergent

landmasses) and unaffected by nutrient
runoff. Finally, disease was seen on thelarge table acroporids of Surprise Atoll.
Coral cover (Tables 2-3 and online
supplemental data file). ALCC averaged
30% across the 79 surveyed sites (Table 3),
and it was similar in the NRA (31±8.5%[std. dev. for this and all error terms fromhence forth]) and the potentially moreheavily impacted SR (29±16%; student’s
t-test, p>0.05). It did vary significantly
across the 11 islands surveyed (1-way
ANOVA effect of island, p<0.001); thiswas mainly driven by the cover at Prony
Bay (60±2.3%) being significantly higher
than that of all other islands except for
Pelotas Atoll (45±6.9%) and Puom (31%;n=1 site only). Although coral cover did
not differ significantly across exposure
categories or reef types (1-way ANOVA,p>0.05), it did vary across reef zones(p<0.001); reefs within the sheltered bay(Prony Bay; the only bay reef systemsurveyed; 60±2.3%) were characterizedby higher coral cover than were forereefs(30±9.5%), backreefs (28±5.8%), andlagoons (27±12%).
Genotype breakdown (Figure 1 andTables 2-3). The genetic breakdown of
101 of the sampled colonies can be seenin Figure 1. All but 3 of the 104 uniquely
sampled colonies were genotyped(excluding Neulka colony 33, Cook Reef
colony 71, and Surprise Atoll colony 94).
Briefly, all sampled colonies from Ile desPins were genotyped as P. damicornis

20Platax 14: 1-45 2017

(Table 2), as were the majority of the
genotyped colonies across the country as awhole; of the 101 unique colonies
genotyped, 80 (79%) and 21 (21%) were P.
damicornis and P. acuta, respectively(Table 3). The first P. acuta colonies of
the research mission, which began at Ilesde Pins (Figure 2), were observed at
Prony Bay, an extremely shelteredembayment lined with nickel mines (seesite description above for details of thestrange marine habitats abutting thesemines.). No P. damicornis colonies wereobserved at, or sampled from, Prony Bay(Figure 1).
The sampled colonies of the NRA(Figure 3), in contrast, featured a mix of P.
damicornis (67%) and P. acuta (33%;Figure 1 and Table 3). The host genotype
frequency did not differ significantly
between the SR and NRA (Table 4),though it did vary significantly across alleight islands/reefs/atolls from whichpocilloporid corals were sampled (Table4). Host genotype frequency also varied
across reef exposure, type, and zone.
Regarding reef type, P. acuta was notfound on patch reefs, whereas P.
damicornis was found on all reef types(atolls, barrier reefs, fringing reefs, andpatch reefs). Similarly, P. acuta was rarely
sampled from the interior regions of thelagoons (though they were found in thebackreef region). P. acuta was only foundat intermediate or protected sites; only P.
damicornis was sampled from exposed

reefs. Indeed, P. acuta preferred sites ofintermediate exposure (65% of thecolonies at sites characterized byintermediate exposure were genotyped as
P. acuta.). Physiological differences
between P. acuta colonies of the protected
vs. the intermediately exposed sites are
discussed in greater detail below.
Univariate environmental differencesin coral molecular physiology (Figure
S1 and Tables 4-5). When controlling
for the large number of ANOVAs
performed by Bonferroni-adjusting the αlevel, there were relatively few significant
effects of environment on coral physiology
when the data were analyzed across both
host species sampled (Table 4). The hostgenotype frequency, which did varyacross environments, is discussed above.
Coral size (max. length and planar SA)
varied across reef zones and reef types.
Corals were larger in the lagoon (meanmax. length=23 cm) than on the forereef(18 cm; Tukey’s HSD, p<0.05), andcorals were larger on fringing reefs (24cm) than all other reef types (~17-20 cm;
Tukey’s HSD, p<0.05). These findings
were driven mainly by P. damicornis(Table 5); colony size did not varyacross environments for P. acuta (Table5). This may mean that the former
species is more phenotypically andmorphologically plastic than the latter,as is also evidenced by numerous otherfindings outlined below (e.g., diversityof colony color).

21

Platax 14: 1-45 2017

There were several interesting,species-specific differences in coral
physiology. First, there was a significant
effect of host on colony color (Table 4);
only P. damicornis colonies were found to
be fluorescent blue, purple, or green. In
contrast, P. acuta tended to be the normal
brown-yellow color, or variations thereof(see images on coralreefdiagnostics.com.).
This may be due to color-relateddifferences in host coral gfp-cp expression;
expression of this gene mRNA wassignificantly higher in purple fluorescentcolonies than those of normal coloration(Figure S1). P. acuta colonies possessedover 2-fold higher Symbiodinium densities
than P. damicornis (i.e., Sym GCP in
Table 4), and this could also havecontributed to color differences between
colonies of the two pocilloporid species
sampled herein.
The MSRV that varied most acrossenvironments was the Symbiodinium
hsp90 mRNA (Table 4). Expression of thisgene, which encodes a molecularchaperone involved in the cellular stressresponse (Feder 1996), varied over both
space and time. The region effect is
discussed in more detail below. With
respect to islands, it was expressed atsignificantly higher (~30-fold) levels bycolonies from Ile des Pins than those of
Pelotas Atoll (Tukey’s HSD, p<0.05).
This was due to the behavior of
Symbiodinium populations within P.
damicornis (Table 5), as P. acuta was notfound at Ile des Pins (Figure 1 and Table
2). As another statistically significant
spatial effect, Symbiodinium within coralcolonies found on fringing reefs expressedsignificantly higher levels (~4-fold) of
hsp90 than those within corals found on
atolls (Table 4). This difference was also
driven by P. damicornis (Table 5) and not
P. acuta. Indeed, virtually noenvironmental effects were documented
across the response variables for P. acuta,
though this may be due to the 4-fold lower
sample size of this species relative to P.
damicornis (Figure 1).
Regardingtemporaleffects,
Symbiodinium hsp90 mRNA expression
was 4-fold higher at night than it was in
the morning (Tukey’s HSD p<0.05; Table4), a difference detected only for
Symbiodinium within P. damicornis (Table
5). Unsurprisingly, then, its expression
was ~50% higher at PAR levels of 0 μmol
m-2 s-1 than at positive PAR values (Table4). Host coral lectin mRNA expression
was also significantly, 2-fold higher in thedark than in the light (Table 4). Such lightvs. dark differences in molecularbiomarker expression levels are thesubject of a sister work (Mayfield et al., in
prep.) and will not be discussed at length
herein.
Although there were multiple
environmental effects on the multivariate
means for both species analyzed together(Table 4) and for P. damicornis analyzed
separately (Table 5), in most cases, there

22

Table 4. Univariate statistical analysis of the New Caledonia dataset-I: host species analyzed together. The values below theenvironmental parameters (EP; top row) represent the number of categorical groupings, and when analysis of similarity
found an EP to significantly affect coral physiology (p<0.013), the EP name has been underlined. X2 tests and 1-way
ANOVAs were used to analyze the frequency (freq.) and molecular+physiological data, respectively. Comparisons thatwere 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 (expan.) freq. X2 tests, molecular-scale response variable (MSRV)ANOVAs, and multivariate ANOVAs (MANOVA; Wilks’ lambda was calculated.), respectively, are highlighted in green.
Colony 94, which was not genotyped, was excluded from the analysis. Underlined p-values are discussed in the Results
section, and, in general, the time and photosynthetically active radiation (PAR) results will be discussed in another work
(Mayfield et al., in prep.). Symbiodinium (Sym) assemblage was tested as an EP, though since it did not significantly
affect any of the response variables, it has been excluded from the table. ALCC=average live coral cover. NS=not
statistically significant. NA=not applicable.
Platax 14: 1-45 201723

Platax 14: 1-45 201724

Table 5. Univariate statistical analysis of the New Caledonia dataset-II: host species analyzed separately. The values below
the environmental parameters (EP; top row) represent the number of categorical groupings, and when analysis ofsimilarity found an EP to significantly affect coral physiology (p<0.013), the EP name has been underlined. X2 tests and1-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 forthe outlier freq. X2 tests, polyp expansion (expan.) X2 tests, molecular-scale response variable (MSRV) ANOVAs, andmultivariate ANOVAs (MANOVA; Wilks’ lambda was calculated.), respectively, are highlighted in green. Underlined
p-values are discussed in the Results section, and, in general, the time and photosynthetically active radiation (PAR)results are discussed in another work (Mayfield et al., in prep.). ALCC=average live coral cover. Sym=Symbiodinium.
NS=not statistically significant. NA=not applicable.

Platax 14: 1-45 201725

Platax 14: 1-45 201726

Platax 14: 1-45 2017

was no clear distinction between centroids
in the CCA plots (data not shown);
furthermore, samples did not tend tocluster near the centers of their respective
centroids. This likely has to do with theextensive spread in the individual
response variables (Table 1). For instance,
the average coefficient of variation for the
10 P. damicornis MSRV was nearly 130%,
meaning that the standard deviation was
typically higher than the mean. Such was
also the case for P. acuta (meancoefficient of variation=105%). Extensive
variation between samples, even those of
the same species from the same sampling
site, also characterized the Austral-Cooks(Mayfield et al., 2016a), Fiji (Mayfield etal., 2017b), and Tonga (Mayfield et al.,2017a) datasets. In fact, the averagecoefficient of variation across all 10
MSRV was also 130% in the lattercountry’s dataset. Given such variation,
the statistically significant MANOVA
findings are not discussed at great length
herein except for the MANOVA+CCA of
the heat map scores, which is discussed in
the context of the outlier analysis below.
Regional differences in molecularphysiology (Figure 4 and Tables 4-5).
Upon looking at the MSRV data in theform of a heat map (Figure 4), it wasevident that P. damicornis colonies of the
SR tended to have higher expression ofstressbiomarker
mRNAs
than
conspecifics in the NRA (Figure 4a); thisis in alignment with our hypothesis.

Specifically, a larger number of green
cells (low z-scores) can be seen in Figure
4b, which corresponds to P. damicorniscolonies of the NRA. In fact, thefollowing two mRNAs were expressed atsignificantly higher levels in the southern,
presumably
moreanthropogenically
impacted part of the country, incomparison to the relatively more pristine
NRA (Table 4): Symbiodinium hsp90(5-fold difference) and host coralcu-zn-sod (2-fold difference). Both suchdifferences were driven by P. damicornis(Table 5); neither gene differed in
expression across regions for P. acuta.
Furthermore, a large number of green
cells (low z-scores) was not seen in the P.
acuta samples from the NRA (Figure 4c),
meaning that P. acuta behaved moresimilarly across the two regions than did P.
damicornis.
Outlier analysis (Figures 5-6 andS2 and Tables 4-6). Only 10 of the 119
samples analyzed in detail for all 10
MSRV fulfilled the two outlier criteria
outlined above (8.5% of all samples;
Table 6), and all 10 outliers were found in
the SR (10/80=13%); this represents asignificantly higher percentage than in the
NRA (0/39=0%; X2 test, p<0.01). Outlier
frequency was affected by only one otherenvironmental parameter (Table 4): depth.
This is due to the fact that outliers were
only sampled across three of the seven
depth categories: 5-10 m (n=4 outliers outof 11 total colonies sampled from these27Platax 14: 1-45 2017

Fig. 4. Heat maps depicting standardized (z-score) molecular physiological data
for the 99 Pocillopora damicornis and 20 Pocillopora acuta colonies
sampled across both Southern and Northern New Caledonia. Only P.
damicornis was found at Ile des Pins (a), and outliers are highlighted in blue.
Both P. damicornis (b) and P. acuta (c) were found in the northern reefs andatolls, and outliers are highlighted in blue and purple, respectively. The legend
for all three panels is found below (a). A z-score >3 for any response variable
was associated with a heat map score of 1 (i.e., if two response variables had
z-scores >3 for one sample, that sample’s heat map score would be 2.).

28

Platax 14: 1-45 2017

Fig. 5. Principal components analysis (PCA) and multi-dimensional scaling(MDS). PCA was performed for the Pocillopora damicornis dataset inisolation (a), the P. acuta dataset in isolation (b), and both datasets analyzed
in tandem (c). As another ordination approach, PRIMER was then used tocreate an MDS plot based off of a Bray-Curtis similarity matrix for both
species (d). The legends for all panels appear in the lower left-hand corner of(a) and the lower right-hand corner of (b), and the P. damicornis (n=8) and P.
acuta (n=2) outliers are highlighted in blue and purple, respectively.
Twenty-five data points in the MDS plot overlapped extensively and havebeen instead depicted by a hand-drawn ellipse outlined in red.

29Platax 14: 1-45 2017depths; 36%), 10-15 m (n=5 outliers outof 64 total; 8%), and 20-25 m (n=1 outlier
out of 17 total; 6%). Outlier frequency did
not vary across environment when data
were analyzed separately for each species(Table 5). In contrast to our hypothesis,
outliers were not more frequent on reefs
characterized by low ALCC levels (Table
4).
Seven of the eight P. damicornis
outliers were found to be the most distantand distinct points in the PCA plot (Figure5a). The four blue triangles to theright-most region of the plot (Figure 5a)
represent colonies 24, 37, 44, and 48; allfour colonies were from Ile des Pins, andall four were outliers (Table 6). These fourcolonies were differentiated from the“core” region of the plot along principal
component (PC) axis 1, which was
dominated by the four Symbiodinium gene
mRNAs in terms of loading scores (seethe online supplemental data file for
eigenvalues and eigenvectors.). In contrast,
three other outliers were differentiatedfrom the core sample region along PC2:
colonies 17, 34, and 46. The MSRV
contributing most to PC2 were instead
host coral genes: ca and gfp-cp. Only oneof the eight P. damicornis outliers, colony
53-D (dark), was not well differentiatedfrom the non-outlier samples with PCA.
Only one of the two P. acuta outliers,
in contrast, was well differentiated by
PCA when analyzing the P. acuta dataset
in isolation (Figure 5b); colony 59

diverged markedly along PC1, in which
the four Symbiodinium genes contributed
the highest positive loading scores (as was
also the case for PC1 of the P.
damicornis-only plot [Figure 5a]). The
second P. acuta outlier, colony 58, which
was also from Prony Bay, appears near thecenter of the PCA biplot (Figure 5b).
When analyzing the data from both
species together (Figure 5c), the biplot
appears much like that of the P.
damicornis-only biplot; this is due to thefact that nearly 80% of the sampledcolonies were genotyped as P. damicornis.
In this case, the seven P. damicornis
outliers that were distinguished by PCA in
Figure 5a again partitioned away from thecentral region of the plot. In contrast,
neither of the two P. acuta outliers
appeared to be distinct. This is likely due
to the fact that the P. damicornis dataset
was characterized by more variation thanthat of P. acuta (see the discussion on
coefficientsofvariation
above.).
Furthermore, the PCA biplots of P.
damicornis and P. acuta encompassed~55% and ~68% of the variation,
respectively, and this disparity may have
led to the differential ability to distinguishoutliers for each species.
When using a completely different
calculation known as Bray-Curtis
similarity, it was clear that the majority of
the 10 outliers were the most dissimilar
samples in the dataset (Figure 5d; thedistance between two points is inversely

30Platax 14: 1-45 201731

Platax 14: 1-45 201732

Platax 14: 1-45 2017

proportional to their similarity or directly
proportion to their dissimilarity.). With the
exception of one of the two P. acuta
outliers (see purple triangle in Figure 5dcorresponding to colony 59 from Prony
Bay.), all other outliers essentially formed
the perimeter of the MDS plot. Please see
the online supplemental data file for exact
similarity values between all pairs ofsamples. It should be noted that the stressof the MDS plot was greater than 0.2,
meaning that these findings should beinterpreted with caution.
When looking at the similarity data
(Figure 5d) in more detail, several EP
wererevealed
by ANOSIM
tosignificantly influence coral molecularphysiology (see underlined EP in Table 4.).
In our experience, though, ANOSIM is
more likely to detect a significant
difference when there are a large numberof categorical groupings (>10; e.g., dateand site), regardless of whether or not
such findings are 1) visually compellingin the MDS plot and 2) biologicallymeaningful. Therefore, the date and site
effects documented by ANOSIM are notdiscussed further herein. However,another EP with only four categoricalgroupings, reef type, was found by
ANOSIM to significantly affect coralmolecular physiology (Table 4), so aseparate MDS plot featuring data points
labeled by reef type was created todetermine whether such a reef type
difference was visually compelling (datanot shown); such was not found to be thecase, and samples from all four reef types
appeared intermixed and not well
distinguished from each other in the MDS
plot. It is worth noting that, when
MANOVA determined an EP tosignificantly affect coral molecularphysiology, ANOSIM typically did, as
well (one exception being site, which was
found to significantly affect coralmolecular physiology by ANOSIM but
not by MANOVA [Table 4].).
When looking at ANOSIM findings
for the P. acuta dataset (Table 5), asignificant effect of reef exposure on coralmolecular physiology was documented.
Upon creating an MDS plot (Figure S2),
this finding appeared visually convincing;
the protected coral colonies generally
clustered towards the right side of the plot,
with the corals sampled from reefs ofintermediate exposure generally clusteringon the left. It is also clear that one of thetwo outliers, colony 59 (a P. acuta colony
from Prony Bay) is the most dissimilar
point in the plot, though, curiously, thesecond outlier (also a P. acuta colony
from Prony Bay) is near the MDS plot’s
center. This indicates that its behavior ismore similar to that of colonies fromintermediately exposed reefs than it is tothose of protected reefs. That being said, anumber of protected reef colonies
partitioned with those of the intermediate
reef colonies. This is evidence of the far
greater spread/diversity in physiology of33

Platax 14: 1-45 2017

protected reef corals compared tointermediate reef corals, which tended tobe more similar to each other in theirmolecular physiology.
An exploration of what drives thegreater variability in the molecularphysiology of corals from protectedenvironments relative to those inhabiting
intermediately exposed reefs is aworthwhile topic for future research andmay be related to the fact sheltered waters
may be more stagnant; this would mean
the corals would have to rely morestrongly on autotrophy as food may not bereadily swept past their extended polyps.
In other words, variability in theirnutritional
strategies
could
drive
sub-cellular differences in expression ofgenes involved in, for instance,
metabolism (e.g., Symbiodinium zifl1l). In
contrast, murky waters with low visibility(e.g., Prony Bay) may actually necessitatethat coral hosts rely more on heterotrophy
unless they have adaptations for low lightlevels, such as high chlorophyll a content.
Although Symbiodinium densities were
not abnormally high in corals of Prony
Bay, it is possible that their pigment
concentrations were higher than those
Symbiodinium from corals of high lightareas (e.g., most of the high-visibility,
clear NRA), and that such highchlorophyll a levels could aid them inliving in low light environments.
As it stands, the reasons underlying
the physiological differences between

corals of protected and intermediately
exposed reefs remain to be elucidated.
Perhaps upon increasing the size of thecollective LOF GRE pocilloporid coral
health dataset, only half of which has been
analyzed to date, we will begin to be morethoroughly able to tease apart drivers ofphysiological variation in corals sampledacross an array of different environmental
states. For instance, a larger dataset may
allow us to further explore not only thedifferences in the molecular physiology ofP. acuta across reefs of differing exposure
mentioned above but also across ALCC
levels (Table 5); although these data havenot been depicted, the molecularphysiology of corals of the lower ALCC
grouping (15-30%) was well separated(i.e., highly different) from that of coralsof the highest ALCC grouping (>60%).
This lack of congruency between coral
health and reef health (i.e., coral cover) is
discussed in more detail below.
When looking at the differences
between the 10 outliers and the 109
non-outlier samples (Table 6), theexpression of all four Symbiodinium genes
and two of the four host coral genes (caand cu-zn-sod) were significantly higher
in outliers. Specifically, Symbiodinium
rbcL, zifl1l, hsp90, and ubiq-lig wereexpressed at 3-, 5-, 5-, and 3-fold higher
levels, respectively, by outliers over
non-outliers, and host coral ca andcu-zn-sod were expressed at 3- and 2-foldhigher levels, respectively, by outliers

34Platax 14: 1-45 2017

JMP’s website, predictor screening uses a“bootstrap forest partitioning model” toevaluate the “contribution of predictors”(i.e., the MSRV) on the response (i.e.,
difference
betweenoutliers
andnon-outliers). Therefore, the relative
proportional contribution calculated by
the predictor screening model does notnecessarily scale with the relative
magnitude of the difference of a particularresponse variable between outliers andnon-outliers, though it has tended to in ourprior works (e.g., Mayfield et al., 2017a-b).
As a further means of gaining insight
into the differences between outliers andnon-outliers, the variability index was
calculated for each sample (i.e., thestandard deviation calculated across all 10
MSRV z-scores). The mean variability
index for the 10 outliers of 1.79 was
nearly 3-fold higher than that of the
non-outliers (0.65; student’s t-test
p<0.001), though it did not vary acrossenvironments (data not shown). As
hypothesized, the variability index didcorrelate significantly, positively, andlinearly with the Mahalanobis distance(Figure 6b); those samples whose
multivariate mean deviated most from theglobal mean (i.e., high Mahalanobis
distances) tended to be those with thewidest variation between response
variables (i.e., high variability indices).
This is unsurprising given that theMahalanobis distance flags samples thatfall outside of the correlation structure of
the dataset.

over non-outliers. However, when looking
at outlier vs. non-outlier differencesindividually for the two coral species
sampled, host coral lectin differedsignificantly for P. damicornis, thoughSymbiodinium rbcL did not. Furthermore,
expression of no gene mRNAs differed
between the two P. acuta outliers and the
18 non-outlier P. acuta samples (Table 6),though this is likely due to the small
number of P. acuta outliers and theassociated high variation (and therefore
inability to detect a significant differencein the student’s t-tests).
JMP’s predictor screening function
was used to determine which MSRV
contributed most significantly to theglobal difference between outliers andnon-outliers (Figure 6a), and results
tended to corroborate those of thestudent’s t-tests of Table 6; Symbiodinium
zifl1 and host coral cu-zn-sod mRNA
expression contributed most significantly
to the overall difference. Similarly, those
MSRV that did not differ significantly
between outliers and non-outliers in thestudent’s t-tests of Table 6, such as hostcoral
gfp-cpmRNA
expression,
contributed small portions to the overallvariance. Curiously, some MSRV that did
differ significantly between outliers andnon-outliers, such as Symbiodinium rbcLmRNA expression, did not contributesignificantly to the cumulative difference
between outliers and non-outliers in thepredictor screening analysis, despite being
highly expressed genes. According to35

Platax 14: 1-45 2017

Fig. 6. Outlier analysis. JMP’s predictor screening algorithm was used to rank the 10
molecular-scale response variables (MSRV) in terms of their contribution to theoverall difference between the 10 outliers and the 109 non-outliers (a). Linear
regression analysis was used to assess the degree of correlation between the
Mahalanobis distance and the variability index (b), and several outliers (blueand purple for Pocillopora damicornis and P. acuta, respectively) have beenlabeled. The legend for (b-c) is found within (b). Canonical correlation analysis(CCA) was used to model differences between samples with heat map scores of
0-1 (non-outliers) and 2-4 (outliers). The 95% confidence centroids for the heatmap score groups of 0 and 1 are masked beneath the densest assemblage of data
points to the left of the plot. The standardized scoring coefficients have been
included in an inset, as have the images of two prominent outliers (scale bars=5
cm).

36

Platax 14: 1-45 2017

As a final means of modelingdifferences
betweenoutliers
andnon-outliers, a CCA plot was generated
after testing for the effect of heat map
score on the multivariate mean. In general,
samples with heat map scores of 0 and 1
were not outliers, whereas all samples
with heat map scores of 2 or more were.
In some cases, samples with heat map
scores of 1 were outliers. Using thisapproach (Figure 6c), five outliers
identified by other means appear distinct
from the core region of the plot along
canonical axis (CA) 1: colonies 17 and 48(heat map score=2), colony 24 (heat map
score=3; pictured in bottom-most inset),and colonies 37 (pictured in top-mostinset) and 44 (heat map score=4). Thesesamples were distinguished from the coreregion of the plot by virtue of their
Symbiodinium zifl1l and hsp90 mRNA
expression levels (see standardized
scoring coefficients in inset of Figure 6c.).
Interestingly, one sample not found to be
an outlier by other approaches, colony
104-D (dark), appears relatively distinct
from the other points along CA2.
Although the variation encompassed bythis axis is only half that of CA1, thisresult is unsurprising given the relatively
high Mahalanobis distance (5.6) of thissample. This represents the highest
distance for a sample that was not
ultimately considered to be an outlier (dueto the fact that no MSRV possessed az-score>3).

Country-wide differences in the
MSRV (Table 1 and Figure S3). As theidentical protocols were used to measure
the same 10 MSRV in pocilloporid corals
sampled from Fiji and Tonga, we
compared data across the three countries
with 1-way ANOVA. The non-normalized
threshold cycle (Ct) values are shown in
Table 1, and the normalized data havebeen plotted in Figure S3; it is evident that,in most cases, the normalized andnon-normalized gene expression data
show very different trends. For instance,
Symbiodinium hsp90 mRNA expression
was highest for samples of New
Caledonia when the data were left
un-normalized (Table 1). However, whennormalizing to recovery of the exogenous
Solaris®
RNA
spike
and
the
Symbiodinium GCP (Figure S3c),
expression of this gene was actuallyhighest in samples of Fiji. This was the
only gene, in fact, whose expression was
not highest in corals of New Caledonia;
all other genes, including the stress genes
ubiq-lig (Symbiodinium; Figure S3f) andcu-zn-sod (coral host; Figure S3i), wereexpressed at significantly higher levels by
samples of New Caledonia relative to bothFijiandTonga.
Furthermore,
Symbiodinium densities were 5-6-foldlower in New Caledonian corals than inthose of Fiji and Tonga (Figure S3b).
Regardingoutliervs.
non-outlier
differences for each country analyzed
individually, only two genes, Symbiodinium

37

Platax 14: 1-45 2017zifl1l and host coral cu-zn-sod, differedsignificantly between outliers andnon-outliers in the datasets of all three
countries (Table 1). In contrast, host coral
lectin did not differ between aberrantly
behaving corals and statistically normal
ones in any country (Table 1). In future
projects, then, this gene may be excluded
from the biomarker panel.
The observation that expression of
all stress-targeted mRNA biomarkers,
excluding Symbiodinium hsp90, washigher in pocilloporid corals of New
Caledonia than in those of the two othersurveyed nations is a curious finding,especially given that the NRA of New
Caledonia, in particular, were amongst thehealthiest surveyed during the GRE, atleast from an ecological perspective (highfish biomass, high coral cover anddiversity, low disease prevalence, etc.).
When comparing the MSRV data fromthese NRA only against the Fiji and Tonga
datasets, all such differences weremaintained (data not shown). This
demonstrates that those reef ecosystems
that appeared the healthiest to the nakedeye (i.e., the NRA) were not necessarily
characterized by the healthiest corals.
Such a lack of congruency between coral
health and coral reef health was actually
proposed several years ago by Wooldridge(2014) and is hypothetically related to thefact that the corals on reefs characterizedby high coral cover tend to be growing
quickly; this is associated with higher

respiration rates, which, in turn, reduce
future capacity for autotrophy. If corals
become malnourished through suchdiminished autotrophy, they may become
more susceptible to environmental
changes since, for instance, they may haveconsumed their cellular energy storesprematurely. This could also explain why
the molecular physiology of P. acuta
differed significantly between low(15-30%) and high (>60%) coral cover
reefs (Table 5 [the MDS plot has not beenshown.]); whereas no outliers were
uncovered from reefs of coral cover lower
than 45%, both of the P. acuta outliers(colonies 58 and 59) were from reefs ofProny Bay with incredibly high coral
cover (59 and 63%, respectively). In short,such high ALCC reefs may notnecessarily be associated with healthiercorals than those reefs characterized byrelatively lower hard coral cover. As afurther, more extreme example of asituation in which coral reef and reef coral
health may be incongruent, the few
surviving colonies on reefs that haveundergone a high-temperature-induced,
mass coral bleaching event are likely to be
more robust than conspecifics on a highcoral cover reef that have not yet been
exposed to such bleaching-inducing
temperatures.
Although we incorrectly hypothesizedthat corals of New Caledonia would
demonstrate lower stress gene expression
levels than pocilloporids from elsewhere38Platax 14: 1-45 2017

in the South Pacific, our hypothesis that anorth-south gradient in molecularbiomarker expression would exist was
confirmed; as mentioned above, corals of
the SR tended to express higher levels ofstress biomarker mRNAs, such as hostcoral cu-zn-sod, than conspecifics fromthe more remote NRA. Furthermore, all
10 outliers in the 119-sample New
Caledonia dataset were from the SR (8and 2 at Ile des Pins and Prony Bay,
respectively). Despite the existence ofsuch a gradient, and in alignment with theprior treatise on inter-country differences
in molecular biomarker expression, theoverall gene expression levels were highfor all 119 samples analyzed in detail.
This includes genes such as hsp90, whichshould only be synthesized by individuals
undergoing a cellular stress response(Hochachka & Somero, 2002); such was
also documented in the highly remote
Austral and Cook Islands (Mayfield et al.,2016a). These data seem to suggest that,in contrast to our hypothesis, even corals
far from large human population centers
have not been spared from the effects ofglobal-scale stressors, such as GCC.
Although some have hypothesized that it
may benefit corals to continually engage
their cellular stress responses, as, forinstance, doing so could better preparethem for future environmental changes(Barshis et al., 2013), the significant
energetic expense required for sustaining
such a stress response over long-termtimescales may eventually detract fromprocesses
suchas
growthandreproduction. In closing, whether or not
such constitutively high stress biomarker
mRNA expression levels documented incorals of New Caledonia and elsewhere in
the South Pacific are diagnostic of future
decreases in physiological performanceremains to be determined. This topic
should be addressed urgently, though
since, if the molecular biomarker
panel+MSA unveiled herein does prove tohave diagnostic capacity, it could be used
for proactive monitoring of coral reef
ecosystems around the Indo-Pacific.

Acknowledgements
The Khaled bin Sultan Living
Oceans Foundation is graciously thanked
for 1) the exclusive funding of this work,2) support of all field work aboard theM.Y Golden Shadow, and 3) scientificdiscourse and collaboration both during
and after the New Caledonia research
mission. The crew of the M.Y. Golden
Shadow are also acknowledged for their
dutiful and dedicated service during the2013 New Caledonia cruise.

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Fig. S1. Host coral green fluorescent protein-like chromoprotein (gfp-cp)mRNA expression across coral colonies of different colors. The global
mean across all samples (~100,000) has been plotted as a gray, horizontal
line, and data points have been plotted within both mean diamonds(green; the width is proportional to the sample size.) and normal quantile
box plots (red). Tukey’s honestly significant difference (HSD) post-hoc
tests were used to determine individual mean differences (p<0.05), andresults can be found in an inset in the upper right of the figure. The
Pocillopora acuta outliers (purple; n=2) are generally not visible.

43Platax 14: 1-45 2017

Fig. S2. Multi-dimensional scaling (MDS) plot demonstrating the statisticallysignificant (ANOSIM Global R, p<0.01) effect of exposure (intermediatevs. protected) on the molecular physiology of Pocillopora acuta (n=20).
Both P. acuta outliers from Prony Bay (protected) have been labeled (purple
icons). Only 19 of the 20 colonies are visible since colonies 64 and 65
overlapped.

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Platax 14: 1-45 2017

Fig. S3. Country-wide differences in the 10 molecular-scale response variables(MSRV). All coral sample data, as well as normal quantile box plots, havebeen plotted for the Fiji (June, 2013), Tonga (September, 2013), and New
Caledonia (November, 2013) research missions (the x-axis labels for all
panels are found at the bottom of the figure.). In certain cases, the box plots
are so compressed that they appear are horizontal lines. Significant effects ofcountry were detected in the 1-way ANOVA (p<0.05) for all MSRV. Tukey’s
honestly significant difference tests were then performed, and lowercase
letters signify differences between individual country means (p<0.05). For
the non-normalized threshold cycle (Ct) values, please see Table 1.
45