Introduction
Coral reef growth is mostly limited to warm, aragonite-saturated, and
oligotrophic tropical oceans and is pivotal for reef ecosystem functioning
(Buddemeier, 1997; Kleypas et al., 1999). The coral reef framework not only
maintains a remarkable biodiversity, but also provides highly valuable
ecosystem services that include food supply and coastal protection, among
others (Moberg and Folke, 1999; Reaka-Kudla, 1997). Biogenic calcification,
erosion, and dissolution contribute to the formation of the reef framework
constructed of calcium carbonate (CaCO3, mainly aragonite). The
balance of carbonate loss and accretion is influenced by biotic and abiotic
factors. On a reef scale, the main antagonists are calcifying benthic
communities on the one hand, such as scleractinian corals and coralline algal
crusts, and grazing and endolithic bioeroders on the other hand, such as
parrotfish, sea urchins, microbioeroding chlorophytes, boring sponges, and
other macroborers (Glynn, 1997; Hutchings, 1986; Perry et al., 2008;
Tribollet and Golubic, 2011). The export or loss of carbonate as sediments is
considered an essential part, in particular in the wider geomorphic
perspective of reef carbonate production states (Cyronak et al., 2013; Perry
et al., 2008, 2017). Temperature and carbonate chemistry parameters (e.g.,
pH, total alkalinity: TA, aragonite saturation state:
Ωa, and pCO2) have been identified as important
players in regulating these carbonate accretion and erosion processes
(Albright et al., 2018; Schönberg et al., 2017). Furthermore, different
light regimes across depths, water flow, and wave exposure can alter the
rates of reef-formation processes (Dullo et al., 1995; Glynn and Manzello,
2015; Kleypas et al., 2001).
Reef growth is maintained when reef calcification produces more
CaCO3 than is being removed, and it depends largely on the ability of
benthic calcifiers to precipitate calcium carbonate from seawater (e.g.,
Langdon et al., 2000; Tambutté et al., 2011). TA and Ωa
positively correlate with calcification rates (Marubini et al., 2008;
Schneider and Erez, 2006), and while calcification rates of corals and
coralline algae increase with higher temperature, they have upper thermal
limits (Jokiel and Coles, 1990; Marshall and Clode, 2004;
Vásquez-Elizondo and Enríquez, 2016). Today's oceans are warming and
high temperatures are beginning to exceed the thermal optima of calcifying
organisms, thereby slowing down or interrupting calcification (e.g.,
Carricart-Ganivet et al., 2012; Death et al., 2009). At the same time, ocean
acidification decreases the oceans' pH and Ωa (Orr et al.,
2005). Arguably, calcification under these conditions becomes energetically
costlier (Cai et al., 2016; Cohen and Holcomb, 2009; Strahl et al., 2015;
Waldbusser et al., 2016). In addition, ocean acidification stimulates
destructive processes, for instance the proliferation of bioeroding
endolithic organisms (e.g., Enochs, 2015; Fang et al., 2013; Tribollet et
al., 2009). Apart from that, locally impaired reef growth due to an increased
intensity or frequency of extreme climate events (Eakin, 2001; Schuhmacher et
al., 2005), human impacts including pollution and eutrophication (Chazottes
et al., 2002; Edinger et al., 2000), and other ecological events such as
population outbreaks of grazing sea urchins or crown-of-thorn starfish that
feed on coral can induce reef framework degradation (Bak, 1994; Pisapia et
al., 2016; Uthicke et al., 2015).
A number of studies have employed experimental limestone blocks cut from
coral skeletons to study reef growth processes (Chazottes et al., 1995; Kiene
and Hutchings, 1994; Silbiger et al., 2014; Tribollet and Golubic, 2005).
Deployment of such blocks in a reef captures the endolithic and epilithic
accretion and erosion agents and forces, simultaneously allowing for the
measurement of net-accretion and net-erosion rates. In particular, these
studies have provided insight into the colonization progression and activity
of endolithic micro- and macro-organisms. To comparatively assess the
persistence of the reef framework on the ecosystem scale, a census-based reef
carbonate budget (ReefBudget) approach that integrates reef
site-specific ecological data into the calculation of the erosion–accretion
balance was introduced recently (Kennedy et al., 2013; Perry et al., 2012,
2015). Using the ReefBudget approach, a study determined that
37 % of all current reefs that were investigated are in a net-erosive
state (Perry et al., 2013). For the Caribbean, it revealed a 50 %
decrease in reef growth compared to historical mid- to late-Holocene reef
growth (Perry et al., 2013). Furthermore, the use of carbonate budgets provided
valuable insight into the reef growth trajectories in the Seychelles, where
surveys conducted since the 1990s provide important ecological baseline data
that were employed in reef growth calculations (Januchowski-Hartley et al.,
2017). Most recently, carbonate budget data were used to explore the relation
of vertical reef growth potential and trends in sea level rise, suggesting
that reef submergence poses a threat as long as climate-driven and human-made
perturbations persist (Perry et al., 2018). Other studies highlight the
susceptibility of marginal coral reefs to ocean warming and acidification
(Couce et al., 2012). Such marginal reefs are found in the eastern Pacific or
in the Middle East in the Persian/Arabian Gulf, where reefs exist at their
environmental limits, e.g., at low pH or high temperatures, respectively
(Bates et al., 2010; Manzello, 2010; Riegl, 2003; Sheppard and Loughland,
2002).
Design of studies and reef sites in the central Red Sea. Maps
(a) and (b) indicate geographic location and the study
sites along a cross-shelf gradient. Schemes in (c)– (e)
summarize the study designs for the assessment of the two reef growth
metrics, Gnet and Gbudget, and the characterization
of the abiotic environments in the central Red Sea. Maps have been adapted
from Roik et al. (2015).
![]()
Although the Red Sea features high sea surface temperatures that exceed
thermal thresholds of tropical corals elsewhere (Kleypas et al., 1999; Osman
et al., 2018), it supports a remarkable coral reef framework along its entire
coastline (Riegl et al., 2012). However, coral skeleton core samples indicate
that calcification rates have been declining over the past decades, which has
been widely attributed to ocean warming (Cantin et al., 2010). In this regard
Red Sea coral reefs are on a similar trajectory as other coral reefs under
global ocean warming (Bak et al., 2009; Cooper et al., 2008). In the central
and southern Red Sea, present-day data show reduced calcification rates of
corals and calcifying crusts when temperatures peak during summer (Roik et
al., 2015; Sawall et al., 2015). While increasing temperatures are seemingly
stressful and energetically demanding for reef calcifiers, high TA values, as
found in the Red Sea (∼2400 µmol kg-1, Metzl et al.,
1989), are indicative of a putatively beneficial environment for
calcification (Albright et al., 2016; Langdon et al., 2000; Tambutté et
al., 2011). At present, little is known about the reef-scale carbonate
budgets of Red Sea coral reefs (Jones et al., 2015). Apart from one early
assessment of reef growth capacity for a high-latitude reef in the Gulf of
Aqaba (GoA, northern Red Sea) that considered both calcification and
bioerosion/dissolution rates (Dullo et al., 1996), studies only report
calcification rates (e.g., Cantin et al., 2010; Heiss, 1995; Roik et al.,
2015; Sawall and Al-Sofyani, 2015) or focus on bioerosion generally caused by
one group of bioeroders (Alwany et al., 2009; Kleemann, 2001; Mokady et al.,
1996). Therefore, we set out to determine reef growth in central Red Sea
coral reefs and evaluate the biotic and abiotic drivers. We show and compare
two reef growth metrics: Gnet and Gbudget. We present
net-accretion/-erosion rates (Gnet) measured in situ using
limestone blocks deployed in the reefs, which simultaneously capture the
rates of epilithic accretion and epilithic and endolithic bioerosion. We also
apply a census-based approach adapted from the ReefBudget protocol
(Perry et al., 2012) to estimate reef growth on an ecosystem scale, as the
net carbonate production state or carbonate budget (Gbudget). Our
study provides a broad and first insight into reef growth dynamics and a
comparative baseline to further assess the effects of environmental change on
reef growth in the central Red Sea.
Material and methods
Study sites and environmental monitoring
Study sites are located in the Saudi Arabian central Red Sea along an
environmental cross-shelf gradient, described in detail in Roik et al. (2015)
and Roik et al. (2016). Data for this study were collected at three sites: an
offshore forereef at ∼25 km distance from the coastline
(22∘20.456 N, 38∘51.127 E; “Shi'b Nazar”), a midshore
forereef at ∼10 km distance (22∘15.100 N,
38∘57.386 E; “Al Fahal”), and a nearshore forereef
(22∘13.974 N, 39∘01.760 E; “Inner Fsar”) at ∼3 km
distance to the shore (Fig. 1a, b). All sampling stations were located between 7.5 and
9 m depth. In the following, reef sites are referred to as “offshore”,
“midshore”, and “nearshore”, respectively. Abiotic variables were
measured during “winter” and “summer” 2014. CTD data were collected
continuously during “winter” (9 February–7 April 2014) and “summer”
(19 June–23 October 2014). At each station, seawater samples were collected
on SCUBA for 5–6 consecutive weeks during each of the seasons to determine
inorganic nutrients, i.e., nitrate and nitrite (NO3- and
NO2-), ammonia (NH4+), phosphate
(PO43-), and total alkalinity (TA) (Fig. 1c and Table S1 in the Supplement).
Net-accretion/-erosion rates of limestone blocks
Net-accretion/-erosion rates (Gnet, Table 1) were assessed using a
“limestone block assay”. Blocks cut from “coral stone” limestone were
purchased from a local building material supplier in Jeddah, KSA. Each block
was fixed with one stainless steel bolt to aluminum racks permanently
deployed at the monitoring station of each reef site (a total of 36 blocks,
n = 4, Fig. S1 in the Supplement). The blocks were oriented in parallel
to the reef slope with one side facing up while the other side was facing
down towards the reef. Block dimensions were
100 × 100 × 21 mm with an average density of ρ=2.3 kg L-1. Blocks were dry-weighed before and after deployment on the
reefs (Mettler Toledo XS2002S, readability = 10 mg). Before weighing, the
blocks were autoclaved and dried in a climate chamber (BINDER, Tuttlingen,
Germany) at 40 ∘C for a week. Four replicate blocks were deployed at
the reef sites for three different exposure periods each (Fig. 1d) to measure
natural processes of calcification and erosion. Exposure periods were
6 months (September 2012–March 2013), 12 months (June 2013–June 2014), and
30 months each (January 2013–June 2015). We measured a total of 12 blocks
and all blocks were measured only once. Upon recovery, the blocks were
treated with 10 % bleach for 24–36 h and rinsed with deionized water to
remove organic material and any residual salts. Gnet were
expressed as normalized differences of pre-deployment and post-deployment
weights (kg CaCO3 m-2 yr-1) (Table 1).
Glossary of reef growth metrics.
Metric
Description
Input data for calculation of the metric
Gnet
Site-specific net-accretion/-erosion rates (internal andepilithic) measured in situ using limestone blocks
–
Gbudget*
Ecosystem-scale census-based carbonate budget ofa reef site
Gbenthos, Gnetbenthos, Eechino, Eparrot
Gbenthos
Census-based calcification rate of benthic calcifiercommunity (corals and coralline algae) per reef site
Site-specific benthic calcification rates (collated from this study and from Roik et al. 2015)
Gnetbenthos
Census-based net-accretion/-erosion rates ofreef “rock” surface area per reef site
Site-specific net-accretion/-erosion rates measured inthis study using limestone blocks (Gnet)
Eechino
Census-based echinoid (sea urchin) erosion ratesper reef site
Genus- and size-specific erosion rates for sea urchinsfrom the literature
Eparrot
Census-based parrotfish erosion rate per reef site
Genus- and size-specific erosion rates for parrotfishfrom the literature
* The method of Gbudget calculation is described in the Supplement
(please refer to Text S1).
Biotic parameters
To assess coral reef benthic calcifier and epilithic bioeroder communities
(as input data for the reef carbonate budgets), we conducted in situ
surveys on SCUBA along the cross-shelf gradient at each of our study sites.
Benthic community composition
Community composition and coverage of coral reef calcifying groups were
assessed in six replicate transects per site using the rugosity transect (Perry et al., 2012) as detailed in Roik et al. (2015). From these
surveys we extracted data on benthic calcifiers (% cover total hard coral,
% hard coral morphs (branching, encrusting, massive, and platy/foliose),
% major reef-building coral families (Acroporidae, Pocilloporidae, and
Poritidae), % cover calcareous crusts, % recently dead coral, and
% rock surface area for carbonate budget calculations (Table S2). In
addition, benthic rugosity was assessed in the same transects according to the
chain-and-tape method (n=6, Perry et al., 2012).
Epilithic bioeroder/grazer populations along the cross-shelf
gradient
For each reef site, we surveyed abundances and size classes of the two main
groups of coral reef framework epilithic bioerorders, parrotfish (Scaridae)
(Bellwood, 1995; Bruggemann et al., 1996) and sea urchins (Echinoidea) (Bak,
1994). Surveys were conducted on SCUBA using stationary plots (adapted from
Bannerot and Bohnsack, 1986, Text S1 in the Supplement) and line transects
(n=6 per site), respectively. Briefly, abundances of parrotfish and sea
urchins were assessed for different size classes. Abundances for all
prevalent parrotfish species were assessed in six size classes, based on
estimated fork length (FL; FL size classes: 1=5–14, 2=15–24, 3=25–34, 4=35–44, 5=45–70, and 6>70 cm). We focused
on the most abundant bioeroding parrotfish species in the Red Sea,
which encompassed two herbivorous functional groups: excavators and scrapers
(Green and Bellwood, 2009). Most abundant across study sites were the
excavators Chlorurus gibbus, Scarus ghobban, and
Cetoscarus bicolor, and the scrapers Scarus frenatus,
Chlorurus sordidus, Scarus niger, and Scarus ferrugenius, following
Alwany et al. (2009). Additionally, we counted Hipposcarus harid,
which occurred frequently at the study sites, along with members of the genus
Scarus that could not be identified to the species level and were
therefore pooled in the category “Other Scarus”. Both H. harid and Scarus spp. were broadly categorized as scrapers (Green
and Bellwood, 2009). The sea urchin census targeted five size classes of the
four most common bioerosive genera Diadema, Echinometra,
Echinostrephus, and Eucidaris, based on urchin diameter
(size classes 1=0–20, 2=21–40, 3=41–60, 4=61–80, and 5=81–100 mm, Table S7). For details on the field surveys and data treatment
for biomass conversion, refer to the Supplement (Text S1 and references
therein).
Reef carbonate budgets
Ecosystem-scale reef carbonate budgets, Gbudget
(kg CaCO3 m-2 yr-1), were determined following the
census-based ReefBudget approach by Perry et al. (2012) (Fig. 1e and Table 1).
Gbudget incorporates local census data, site-specific
net-accretion/-erosion data (Gnet over 30 months), and
calcification data (buoyant weight measurements) collected for this and a
previous study (Roik et al., 2015). Importantly, the approach incorporates
epilithic bioerosion, which is based on abundance rather than bite or erosion
rates; therefore, parrotfish and sea urchin census data collected in this
study were employed in the ReefBudget calculations using bite and
erosion rates from the literature (Alwany et al., 2009; Perry et al., 2012).
In summary, site-specific benthic calcification rates (Gbenthos,
kg CaCO3 m-2 yr-1), net-accretion/-erosion rates of
reef “rock” surface area (Gnetbenthos,
kg CaCO3 m-2 yr-1), and epilithic erosion rates by sea
urchins (Eechino, kg CaCO3 m-2 yr-1) and
parrotfish (Eparrot, kg CaCO3 m-2 yr-1)
were determined for the Gbudget calculations (Figs. 1e and 3a). A
detailed account of Red Sea specific calculations and modifications of the
ReefBudget approach employed in this study are outlined in the
Supplement (Text S1, Equation box S1–S3, and Tables S2–S8).
Abiotic parameters
Continuous data: temperature, salinity, and diurnal pH
variation
Factory-calibrated conductivity–temperature–depth loggers (CTDs, SBE
16plusV2 SEACAT, RS-232, Sea-Bird Electronics, Bellevue, WA, USA) were
deployed at the sampling stations on tripods at ∼0.5 m above the
reef to collect time series data of temperature, salinity, and
pHNBS at hourly intervals. The pH probes (SBE 18/27, Sea-Bird
Electronics) were factory-calibrated before the winter deployment
(9 February–7 April 2014). Calibrations were verified using NBS-scale
standard buffers (pH 7 and 10, Fixanal, Fluka Analytics, Sigma Aldrich,
Germany) before the winter and the summer deployment
(19 June–23 October 2014).
Seawater samples: inorganic nutrients and total alkalinity
Seawater samples were collected on SCUBA at each of the stations using 4 L
collection containers (Table S1). Simultaneously, 60 mL seawater samples
were taken through a 0.45 µm syringe filter for TA measurements.
Seawater samples for inorganic nutrient analyses and TA measurements were
transported on ice in the dark and were processed on the same day. Samples
were filtered over GF/F filters (0.7 µm, Whatman, UK) and filtrates
were frozen at -20 ∘C until analysis. The inorganic nutrient
content (NO3- and NO2-, NH4+, and
PO43-) was determined using standard colorimetric tests and a
Quick-Chem 8000 AutoAnalyzer (Zellweger Analysis, Inc.). TA samples were
analyzed within 2–4 h after collection using an automated acidimetric
titration system (Titrando 888, Metrohm AG, Switzerland). Gran-type
titrations were performed with a 0.01 M HCl (prepared from 0.1 HCl standard,
Fluka Analytics) at an average accuracy of ±9 µmol kg-1
(standard deviation of triplicate measurements).
Net-accretion/-erosion rates Gnet (kg
CaCO3 m-2 yr-1) in coral reefs along a
cross-shelf gradient in the central Red Sea. Gnet was calculated
using weight gain/loss of limestone blocks that were deployed in the reefs.
For each deployment duration, 6, 12, and 30 months, a set of four replicate
blocks was used. Each block was measured once. Provided are means per reef
site and standard deviations (in brackets).
Gnet
Deployment time (months)
Reef site
6
12
30
Offshore
0.14 (0.11)
0.08 (0.09)
0.37 (0.08)
Midshore
0.11 (0.16)
0.01 (0.07)
0.06 (0.12)
Nearshore
0.11 (0.07)
-0.61 (0.49)
-0.96 (0.75)
Statistical analyses
Net-accretion/-erosion rates and carbonate budgets
Gnet data (Table 2) were tested for effects of the factors
“reef” (fixed factor: nearshore, midshore, and offshore) and “deployment
time” (random factor: 6, 12, and 30 months). A univariate 2-factorial
PERMANOVA was performed on logn(x) transformed data (i.e.,
logn (x+1-min (x1-n)) as data contained negative and near-zero
values). A Euclidian distance matrix and 9999 permutations of residuals under
a reduced model and type III partial sum of squares were employed. Pair-wise
tests followed where applicable (PRIMER-E V6, Table S9).
Reef carbonate budgets and contributing biotic variables (kg
CaCO3 m-2 yr-1) along a cross-shelf gradient in the central Red Sea. Calcification
rates of benthic calcifiers (Gbenthos), net-accretion/-erosion
rates of the reef “rock” surface area (Gnetbenthos), and the
erosion rates of echinoids and parrotfish (Eechino,
Eparrot) contribute to the total carbonate budget
(Gbudget) at a reef site. Shown are means per site and standard
deviations (in brackets).
Reef
Gbudget
Gbenthos
Gnetbenthos
Eechino
Eparrot
Offshore
2.44 (1.03)
2.81 (0.65)
0.09 (0.02)
-0.02 (0)
-0.44 (0.7)
Midshore
1.02 (0.35)
1.76 (0.24)
0.01 (0)
-0.02 (0.04)
-0.73 (0.31)
Nearshore
-1.48 (1.75)
0.43 (0.15)
-0.31 (0.13)
-0.23 (0.19)
-1.36 (1.89)
Gbudget data (Table 3) were tested for statistical differences
between the reef sites (fixed factor: nearshore, midshore, and offshore)
using a 1-factorial ANOVA. In parallel, Gbenthos was tested using
a 1-factorial ANOVA with log10 transformed data, while non-parametric
Kruskal–Wallis tests were employed for non-transformed
Gnetbenthos, Eechino, and Eparrot data.
Tukey's HSD post hoc tests or Dunn's multiple comparisons followed where
applicable (Table S10). Assumptions about parametric distribution of data
were evaluated using the Shapiro–Wilk normality test. Statistical tests were
performed as implemented in R (R Core Team, 2013).
Abiotic parameters relevant for reef growth at the study sites along
a cross-shelf gradient in the central Red Sea. Temperature (Temp), salinity
(Sal), and diurnal pH variation (diurnal SDs of pHNBS
measurements) were continuously measured using in situ probes (CTDs).
Weekly collected seawater samples were used for the determination of
inorganic nutrient concentrations, i.e., nitrate and nitrite
(NO3- and NO2-), ammonia (NH4+),
phosphate (PO43-), and total alkalinity (TA). Provided are means
and standard deviations (in brackets).
Site/
Temp
Sal
Diurnal pH
NO3- and
NH4+
PO43-
TA
season
(∘C)
variation
NO2-
(µmol kg-1)
(µmol kg-1)
(µmol kg-1)
(µmol kg-1)
Avg. winter
26.07 (0.54)
39.18 (0.18)
0.11 (0.12)
0.32 (0.19)
0.38 (0.29)
0.08 (0.02)
2487 (20)
Avg. summer
30.85 (0.69)
39.44 (0.18)
0.05 (0.05)
0.61 (0.25)
0.54 (0.34)
0.04 (0.05)
2417 (27)
Offshore/winter
25.97 (0.36)
39.18 (0.16)
0.04 (0.02)
0.4 (0.23)
0.38 (0.41)
0.09 (0.02)
2492 (21)
Offshore/summer
30.68 (0.63)
39.38 (0.17)
0.04 (0.04)
0.59 (0.24)
0.51 (0.17)
0.04 (0.03)
2439 (15)
Midshore/winter
26.1 (0.49)
39.17 (0.2)
0.07 (0.04)
0.28 (0.22)
0.35 (0.19)
0.07 (0.02)
2494 (16)
Midshore/summer
30.56 (0.61)
39.39 (0.14)
0.05 (0.05)
0.63 (0.26)
0.7 (0.53)
0.06 (0.08)
2422 (26)
Nearshore/winter
26.13 (0.69)
39.2 (0.17)
0.23 (0.14)
0.29 (0.12)
0.4 (0.29)
0.07 (0.01)
2476 (19)
Nearshore/summer
31.32 (0.59)
39.56 (0.15)
0.09 (0.06)
0.6 (0.28)
0.42 (0.16)
0.02 (0.01)
2391 (15)
Abiotic parameters
All abiotic data were summarized as means and standard deviations per reef
and season and over each season and boxplots were generated. Diurnal pH variation was extracted from the continuous data as the
pHNBS standard deviation per day. Outliers were detected and
removed from the TA data. All outliers (data points beyond the upper boxplot
1.5 IQR) clustered to 1 sampling day (23 June 2014), which we considered an
artifact of the chemical analysis, and the outliers from this day were
removed. All continuous abiotic variables and inorganic nutrients
(PO43- after square-root transformation) fulfilled parametric
assumptions and were evaluated using univariate 2-factorial ANOVAs testing
the factors “reef” (nearshore, midshore, and offshore) and “season”
(winter and summer). TA data were square-root transformed, which improved
symmetry of data (Anderson et al., 2008), and tested under the same
2-factorial design, as outlined above, using a PERMANOVA (Euclidian
resemblance matrix and 9999 permutations of residuals under a reduced model
and type II partial sums of squares). Within each significant factor, Tukey's
HSD post hoc tests or PERMANOVA integrated pair-wise tests followed
(Tables S11 and S12). Assumptions were evaluated by histograms and the
Shapiro–Wilk normality test. Statistical tests and outlier detection were
performed in R or PRIMER-E V6.
Coefficients from Spearman rank order correlations for abiotic and
biotic predictor variables vs. Gnet and Gbudget.
The means of abiotic and biotic variables per reef site were correlated with
Gnet (= net-accretion/-erosion rates of limestone blocks) and
Gbudget (= census-based carbonate budgets). Strong and
significant correlations (ρ values >|0.75|) are marked
in bold. P-values were adjusted by the Benjamini–Hochberg method. CCA:
crustose coralline algae; CC: calcifying crusts
Gnet
Gbudget
Abiotic variables
ρ
p (adj.)
ρ
p (adj.)
Temperature
-0.47
n.s.
-0.52
n.s.
Salinity
-0.82
<0.01
-0.82
0.001
Diurnal pH variation
-0.95
<0.001
-0.89
<0.001
NO3- and NO2-
0.95
<0.001
0.89
<0.001
NH4+
0.47
n.s.
0.52
n.s.
PO43-
0.82
<0.01
0.82
0.001
TA
0.95
<0.001
0.89
<0.001
Biotic variables
ρ
p (adj.)
ρ
p (adj.)
% cover CCA/CC
0.95
<0.001
0.78
<0.01
% cover algae/soft coral/sponge
0.47
n.s.
0.26
n.s.
Parrotfish abundance
-0.95
<0.001
-0.49
n.s.
Echinoid abundance
0.47
n.s.
-0.54
n.s.
% cover branching hard corals
-0.25
n.s.
% cover encrusting hard corals
0.26
n.s.
% cover massive hard corals
0.34
n.s.
% cover foliose hard corals
0.50
n.s.
% cover Acroporidae
0.27
n.s.
% cover Pocilloporidae
0.51
n.s.
% cover Poritidae
0.45
n.s.
% cover hard coral
0.63
n.s.
Rugosity
0.75
<0.01
n.s.: not significant.
Abiotic–biotic correlations
To evaluate the relationship of abiotic and biotic predictors of
Gnet and Gbudget, Spearman rank correlation
coefficients were obtained for the predictor variables (at a confidence level
of 95 %) using cor.test in R (R Core Team, 2013; Wickham and
Chang, 2015). P-values were adjusted using p.adjust in R employing
the Benjamini–Hochberg method.
Correlations were performed using Gnet data obtained in the
30-month measurements from the reef sites (nearshore, midshore, and offshore)
(Tables 5 and S13). Predictor variables were the site-specific means of CTD
measured variables (temperature, salinity, and diurnal pH variation), means
of inorganic nutrients (NO3- and NO2-,
NH4+, and PO43-), and TA. Biotic
predictors were variables that likely impacted the limestone blocks, i.e.,
parrotfish abundances, sea urchin abundances, calcareous crusts cover, and
algal and sponge cover. Since we did not observe any coral recruits of
substantial size on the blocks, we did not include % coral cover and
related variables in the correlations.
Gbudget correlations included all the above-mentioned abiotic
variables and 13 biotic transect variables (i.e., parrotfish abundances, sea
urchin abundances, % branching coral, % encrusting coral % massive
coral, % platy/foliose coral, % Acroporidae, % Pocilloporidae,
% Poritidae, % total hard coral cover, calcareous crusts cover, algal
and sponge cover, and rugosity). Prior to analysis, some of the predictors
(i.e., % platy/foliose corals and % Poritidae) were log10 (x+1)
transformed to improve the symmetry in their distributions (Tables 5
and S14).
Net-accretion/-erosion rates (Gnet) in the central Red
Sea. Gnet were measured in situ using limestone blocks
(100 × 100 mm) that were deployed along the cross-shelf gradient;
three sets of four blocks were deployed for 6, 12, or 30 months in each reef site, respectively.
Photos (a), (b), and (c) show freshly collected
limestone blocks that were recovered after 30 months of deployment. The
photos (d), (e), and (f) show the same blocks
after bleaching and drying. Boring holes of endolithic sponges are clearly
visible in blocks from the nearshore and midshore reef sites. Blocks from the
midshore and offshore reefs are covered with crusts of biogenic carbonate
mostly accreted by coralline algae assemblages (scales in the photos show
centimeters). Gnet data obtained from the limestone block assay
are plotted in (g). All data are presented as means ± standard
deviations.
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Results
Net-accretion/-erosion rates of limestone blocks
Net-accretion/-erosion rates Gnet were measured in limestone block assays over
periods of 6, 12, and 30 months in the reef sites along the cross-shelf
gradient. These measurements represent the result of calcification and
bioerosion processes impacting the deployed limestone blocks. Visible traces
of boring endolithic fauna were only found on the surface of blocks recovered
after 12 and 30 months as presented in Fig. 2a–f. A brief visual inspection
of the block surfaces after retrieval showed colonization by coralline algae,
bryozoans, boring sponges, small-size boring worms and clams, as well as
parrotfish bite marks. No coral recruits were noticed by the unaided eye.
Further analyses of the established presence of calcifying and bioeroding
communities were not within the scope of this study. Gnet based
on the 30-month deployment of blocks ranged between -0.96 and 0.37 kg
CaCO3 m-2 yr-1 (Table 2). Gnet for 12- and
30-month blocks were negative on the nearshore reef (between -0.61 and
-0.96 kg CaCO3 m-2 yr-1; i.e., net erosion is apparent),
slightly positive on the midshore reef (0.01–0.06 kg
CaCO3 m-2 yr-1, i.e., an almost neutral carbonate
production state), and positive on the offshore reef (up to 0.37 kg
CaCO3 m-2 yr-1, i.e., net accretion of the reef
framework). Deployment times had a significant effect on the variability of
Gnet (Pseudo-F=5.9, pPERMANOVA<0.01,
Table S9). As expected, accretion/erosion was overall higher when measured
over the longer deployment period (Fig. 2g) in comparison to the shorter
deployment times, reflecting the continuous and exponential nature of
bioerosion due to the colonization progress of fouling organisms over time.
The significant interaction of reef site and deployment time (Pseudo-F=7.3, pPERMANOVA<0.001) shows that only blocks deployed
over 12 and 30 months revealed significant site variability; specifically,
the differences between nearshore vs. offshore and midshore vs. offshore
sites became evident (ppair-wise<0.05, Table S9). The
within-group variability was highest for the nearshore reef, where standard
deviations were up to 7 times higher compared to the midshore and offshore
reefs.
Census-based carbonate budgets in the central Red Sea. A schematic
overview of the census-based carbonate budget approach that was adapted from
the ReefBudget methodology by Perry et al. (2012) is displayed
in (a). Details on input data and equations, employed in the
calculations, are available as the Supplement (Text S1 and the respective
Supplement Tables). In (b) reef carbonate budgets are plotted in
dark grey (Gbudget) and related biotic variables in white. The
biotic variables, i.e., site-specific calcification rates of benthic
communities (Gbenthos), net-accretion/-erosion rates of reef
“rock” surface area (Gnetbenthos), and the epilithic erosion
rates of echinoids and parrotfish (Eechino, Eparrot)
contribute to the total reef carbonate budget (Gbudget) at each
reef site. All data are presented as means ± standard deviations. Images
from http://www.ian.umces.edu; last access: 1 April2016; photos by Anna Roik.
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Biotic parameters
Benthic community composition
A detailed account of the benthic community structure of the study sites is
provided in Roik et al. (2015). In brief, a low percentage of live substrate
(20 %) and calcifier community cover (hard corals = 11 % and
calcifying crusts = 1 %) was characteristic of the nearshore site,
while rock (23 %) and rubble (4 %) were more abundant compared to the
other sites. The midshore and offshore reefs provided live benthos cover of
around 70 % and a large proportion of calcifiers (48 and 59 %). The
proportion of coral and calcifying crusts, which were dominated by coralline
algae, were 38 % and 10 % in the midshore reef compared to 35 %
and 23 % in the offshore reef, respectively. Major reef-building coral
families were Acroporidae, Pocilloporidae, and Poritidae, forming
32 %–56 % of the total hard coral cover. A soft coral community (of
around 25 %) occupied large areas in the midshore reef. This community
was minor in the nearshore and offshore reefs, with 4 % and 8.5 %,
respectively. Specific benthic accretion rates Gbenthos
(kg CaCO3 m-2 yr-1), which were used as input data for
the Gbudget calculation, were determined using these benthic data
in addition to site- and calcifier-specific calcification rates (Tables S2
and S3).
Epilithic bioeroder/grazer populations along the cross-shelf
gradient
A total of 718 parrotfish and 110 sea urchins were observed and included in
subsequent ReefBudget analyses. Parrotfish mean abundances and
biomass estimates ranged between 0.08±0.01 and 0.17±0.60
individuals m-2, and 24.69±6.04 and 82.18±46.67 g m-2, respectively (Table S4). The largest parrotfish
(category 5 parrotfish, i.e., >45–70 cm fork length) were
observed at the midshore site. With the exception of the midshore reef,
category 1 (5–14 cm) parrotfish were commonly observed at all sites. Large
parrotfish (category 6 with >70 cm fork length) were not
observed during the surveys. For sea urchins, mean abundances of 0.002±0.004 to 0.014±0.006 individuals m-2 per site were observed and
mean biomasses of 0.05±0.04 to 1.43±0.98 g m-2 estimated per
site, respectively (Table S7). The midshore site exhibited the largest range
of sea urchin size classes (from categories 1 and 2 to the largest size
class 5), while at the other two exposed sites, only the two smallest size
classes of sea urchins were recorded.
Abiotic conditions in the reef sites. Temperature, salinity, and
diurnal pHNBS variation (= diurnal standard deviations) were
measured continuously over the respective seasons by CTDs
(conductivity–temperature–depth loggers including an auxiliary pH probe).
Inorganic nutrients and total alkalinity (TA) were measured in
discrete samples across reef sites and seasons. Boxplots illustrate the
differences of seawater parameters between the reefs within each season (box:
1st and 3rd quartiles; whiskers: 1.5-fold inter-quartile range; points: raw
data scatter).
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Reef carbonate budgets
The carbonate budget, Gbudget, averaged over all sites was 0.66±2.01 kg CaCO3 m-2 yr-1, encompassing values
ranging from a negative nearshore budget (-1.48±1.75 kg
CaCO3 m-2 yr-1) to a positive offshore budget (2.44±1.03 kg CaCO3 m-2 yr-1) (Fig. 3 and Table 3).
Gbudget significantly differed between reef sites (F=16.7,
pANOVA<0.001, Table S10), where nearshore vs. offshore
sites and midshore vs. offshore sites showed significant differences
(pTukeyHSD<0.01). Further, biotic variables that
contribute to the final Gbudget were diverse:
Gbenthos significantly varied between midshore vs. nearshore
sites and offshore vs. nearshore sites (pTukeyHSD<0.01), Gnetbenthos varied between all site combinations
(pTukeyHSD<0.001), Eechino significantly
differed between midshore and nearshore, and Eparrot variability
was similar at all sites. Notably, the within-group variation for the nearshore reef
was 5 times higher compared to the midshore reef and the offshore reef.
Overall, 15 % of accreted carbonate was lost to bioerosion
in the offshore reef and 42 % in the midshore reef. The loss
even exceeded the accretion by 4-fold in the nearshore reef, i.e., ∼440 % proportional loss of accreted carbonate to
bioerosion.
Abiotic parameters
Temperature, salinity, and diurnal pH variation
We used abiotic data to characterize environmental conditions at
each reef site throughout the year (Tables 4, S11, and S12). Temperature and
salinity comprised ∼4400 data points per reef site in the nearshore and
offshore reefs, and ∼2700 in the midshore reef; diurnal pH standard
deviations comprised 185 data points for the midshore and offshore sites, and
87 for the nearshore site (Fig. 4). The seasonal mean temperature varied between 26.1±0.5 ∘C in winter and 30.9±0.7 ∘C in summer
across all reefs. The cross-shelf difference was largest in summer (∼0.6 ∘C) and significant during both seasons (F=1042.6,
pANOVA<0.001). From all sites, the nearshore site
experienced the lowest mean temperature (26.1 ∘C) in winter and the
highest (31.3 ∘C) in summer. In comparison, the midshore and
offshore reefs were slightly cooler, with means around 30.6 ∘C
during summer. Overall salinity was high, ranging between 39.18 and 39.44
over the year. In summer nearshore salinity was significantly increased by
0.36 compared to winter and by 0.18 compared to the other reefs (F=945.3,
pANOVA<0.001). Salinity in the midshore and offshore
reefs was not significantly different between the two sites. Mean diurnal
standard deviations of pH ranged between 0.04 and 0.07 of pH units in the
midshore and offshore reefs. The nearshore reef experienced the largest
diurnal variations, as indicated by mean diurnal standard deviations of
0.29 pH units during winter and 0.6 pH units during summer. The diurnal pH
fluctuation differed significantly between all reef sites (F=1241,
pANOVA<0.001).
Seawater samples: inorganic nutrients and total alkalinity
Concentrations of all measured inorganic nutrients were below
1 µmol kg-1 (Table 4). NO3- and
NO2- were on average between 0.63±0.26 and 0.28±0.22 µmol kg-1, NH4+ between 0.51±0.17
and 0.35±0.19 µmol kg-1, and PO43- as low
as 0.02±0.01 and 0.09±0.02 µmol kg-1 (the highest
and lowest site-season averages are reported here). By trend, mean
NO3- and NO2- and NH4+ levels were
higher in winter compared to summer, with a difference of 0.29 and
0.16 µmol kg-1, respectively (Fig. 4, Tables S11 and S12). In
contrast, PO43- was significantly higher in winter than in
summer, with means differing on average by 0.04 µmol kg-1 (F=16, pANOVA<0.001, Table S11). Mean differences
across the shelf were 0.1 µmol kg-1 in NO3- and
NO2- during winter, 0.1 µmol kg-1 in
NH4+ during summer, and 0.02 µmol kg-1 in
PO43- throughout both seasons. TA ranged between 2391±15
and 2494±16 µmol kg-1. TA was significantly different
between seasons and reef sites (Pseudo-Fseason=297.6,
Pseudo-Freefsite=22.5, pPERMANOVA<0.001,
Tables S11 and S12). During both seasons, TA decreased from the offshore to
nearshore reefs. During winter, TA was slightly higher, with 2487±20 µmol kg-1 compared to 2417±27 µmol kg-1 during summer. The increase from nearshore to
offshore was on average between 20 and 50 µmol kg-1 (Fig. 4).
Abiotic–biotic correlations
To explore the relationship between environmental variables and reef growth,
we performed correlation analyses. For Gnet, strong, positive,
and significant correlates were calcareous crust cover, NO3- and
NO2-, PO43-, and TA. Negative correlates were
salinity, diurnal pH variation, and parrotfish abundance (strong correlates:
ρ>|0.75|, p<0.001). For Gbudget,
abiotic correlates were NO3- and
NO2-, PO43-, and TA, the same correlates as for
Gnet. Looking at significant biotic correlates of
Gbudget, we only found positive relationships, including
calcareous crusts, hard corals, and rugosity. Conversely, parrotfish and sea
urchin abundances had a negative effect on Gbudget, but the
correlation was weak and not significant (ρ∼-0.5). The
non-calcifying benthos, which represents the coverage by algae, soft corals,
and sponges, was not correlated with the dynamics of Gbudget and
was correlated only weakly and not significantly with Gnet (ρ∼0.5) (Tables 5, S13, and S14).
Discussion
Central Red Sea reefs are characterized by unique environmental conditions of
high temperature, salinity, TA, and oligotrophy (Fahmy, 2003; Kleypas et al.,
1999; Steiner et al., 2014). On a global scale they support remarkable reef
growth, sustaining well-established fringing reefs along most of the
coastline. To date, processes affecting reef growth in various regions of the
Red Sea have mostly been investigated individually. For instance, some
studies focused on bioerosion by one specific group of bioeroders only
(Alwany et al., 2009; Kleemann, 2001; Mokady et al., 1996), while other
studies assessed calcification of reef-building corals (e.g., Cantin et al.,
2010; Heiss, 1995; Roik et al., 2015; Sawall et al., 2015). To provide a more
comprehensive picture, the present study integrated assessment of the
antagonistic processes of calcification and bioerosion. We achieved this in a
two-step approach assessing two central metrics of reef growth along a
cross-shelf gradient. First, we assessed net-accretion/-erosion rates
(Gnet) from three reef sites along the cross-shelf gradient in
situ using a limestone block assay. Second, we constructed ecosystem-scale
estimates of reef carbonate budgets for Red Sea reef sites
(Gbudget) adapting the census-based ReefBudget approach
by Perry et al. (2012). In the following, we highlight the complex dynamics
and interactions of reef growth processes and discuss the importance of
carbonate budgets as a powerful tool to explore the trajectories of reef
growth in a global and historical context.
Net-accretion/-erosion rates
(<inline-formula><mml:math id="M403" display="inline"><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi mathvariant="normal">net</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) in the central Red Sea
Cross-shelf dynamics in a global context
The limestone block assay revealed three reef production states in the
central Red Sea: (1) net erosion (nearshore), (2) near-neutrality (midshore),
and (3) net accretion (offshore). This is in contrast to the pattern observed
on the Great Barrier Reef (GBR), where total bioerosion rates were higher in
offshore reefs than inshore reefs as assessed from limestone blocks
(Tribollet et al., 2002; Tribollet and Golubic, 2005). Generally, most block
assay studies conducted in various reef habitats and regions found
net-erosive rates. For instance, studies from reefs in the Thai Andaman Sea
and Indonesian Java Sea found that the accretion by calcifying crusts, such as
coralline algae, were negligible compared to the high degree of bioerosion
measured in the limestone blocks (Edinger et al., 2000; Schmidt and Richter,
2013). In contrast, our limestone block assays captured a substantial net
accretion rate, in particular for the offshore reef site in the central Red
Sea (0.37 kg CaCO3 m-2 yr-1 net accretion), indicating
that accretion was substantial, while erosion was negligible. The midshore
reef was characterized by a near-neutral or minor net accretion (0.06 kg
CaCO3 m-2 yr-1), on the order of net accretion rates
recorded in French Polynesia in reef sites of uninhabited, oceanic atolls
(0.08 and 0.62 kg CaCO3 m-2 yr-1; Pari et al., 1998).
Notably, our study recorded a net-erosive state only in the Red Sea nearshore
site (-0.96 kg CaCO3 m-2 yr-1, 30 months
deployment). This is a moderate rate compared to the larger net erosion
observed in the GBR, French Polynesia, and Thailand (-4 or -8 kg
CaCO3 m-2 yr-1) (Osorno et al., 2005; Pari et al.,
1998; Schmidt and Richter, 2013; Tribollet and Golubic, 2005).
Limestone block deployment duration and biotic drivers
Our data show that Gnet values were overall higher with longer
deployment times, reflecting the succession and early establishment of
calcifying crusts and bioeroding communities on the limestone blocks. Due to
our sampling design (weight-based block assay), accretion and erosion
processes however are simultaneously captured and cannot be disentangled.
Overall, the block assay data are indicative of a calcifier-beneficial
offshore environment and a nearshore reef habitat that is supporting
endolithic bioeroders.
Following other work, carbonate loss in the 12-month blocks from the
nearshore site was supposedly due to a young microbioeroder community, which
is typically most active during this early phase. For instance, during the
early stages of colonization by endolithic microorganisms, the chlorophyte
Ostreobium sp. predominantly contributes to microbioerosion, while
the erosion rate steadily increases with deployment time (Grange et al.,
2015; Tribollet and Golubic, 2011). Microbioerosion rates have been reported
to be -0.93 kg CaCO3 m-2 yr-1 after 12 months of
block exposure, which represents the average rate at the early colonization
stage when the steadily increasing microbioerosion rate has leveled off
(Grange et al., 2015). This rate is slightly higher compared to our
measurements of net erosion in the nearshore site after the same deployment
time (i.e., -0.61 kg CaCO3 m-2 yr-1), and the
difference may reflect measurements encompassing both bioerosion and
accretion.
Studies have shown that site differences in total bioerosion typically become
visible after 1 year of deployment and are significantly enhanced after
3 years (Tribollet and Golubic, 2005). In line with this, the deployment time
of 12 months in our study was sufficient to reveal differences between the
nearshore and offshore reef sites. Further, calcifying crusts, specifically
coralline algae, observed on all blocks from the offshore reef contributed to
the respective net accretion. This is corroborated by the positive
correlation of their abundances with Gnet across all reef sites.
Given that we could not identify coral recruits on any limestone block, we
assume that contribution of corals to the measured accretion was minor.
However, we acknowledge that we might have missed some that could be detected
by more sophisticated methods (e.g., microscopic examination).
Significant differences in accretion/erosion between all three sites of the
cross-shelf gradient became apparent after 30 months of deployment, and
macroborer traces were observed in blocks for the first time (Fig. 2). Over
the course of 2–3 years, macrobioeroders such as polychaetes, sipunculids,
bivalves, and boring sponges can establish communities in limestone blocks
(Hutchings, 1986). Between the first 2 years, macrobioeroder contribution to
the total bioerosion can quadruple (0.02–0.09 kg
CaCO3 m-2 yr-1), before levelling off around 3–4 years
of post-deployment (Chazottes et al., 1995).
In our study, the increase in Gnet between the 12- and 30-month
deployment (∼0.30 kg CaCO3 m-2 yr-1 on average
in the nearshore and offshore sites) indicates that calcifying and eroding
communities were still in a state of succession. As such, we cannot
unequivocally rule out that the blocks deployed for 30 months still
represented an immature community, and hence underestimated maximal
calcification and erosion rates.
Correlation analyses indicate a significant contribution of parrotfish to the
net erosion rates in the nearshore reef. This observation is in line with
previous work demonstrating a significant contribution of parrotfish activity
to bioerosion (Alwany et al., 2009; Bellwood, 1995; Bellwood et al., 2003).
By comparison, sea urchin size and abundance do not appear to be significant
for bioerosion in the central Red Sea reefs. On other reefs, sea urchin
bioerosion can be substantial, equaling or even exceeding reef carbonate
production (e.g., Bak, 1994). The low contribution of sea urchins to bioerosion on
central Red Sea reefs may be a result of potentially low abundances of highly
erosive sea urchins (McClanahan and Shafir, 1990). This is in line with the
observed parrotfish bite marks and a lack of sea urchins on and in the direct
vicinity of the recovered blocks. Taken together, our data confirm that
endolithic micro- and macro-bioerosion, as well as parrotfish feeding, likely
provide a substantial contribution to calcium carbonate loss.
Carbonate budgets (<inline-formula><mml:math id="M440" display="inline"><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi mathvariant="normal">budget</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) in the central Red Sea
Cross-shelf dynamics, regional and global context
On an ecosystem scale, the Gbudget data suggest that the offshore
reef site in the central Red Sea loses about 15 % accreted carbonates to
bioerosion per year. On the midshore and nearshore reef this loss increases
to 42 % and to well over 100 %, respectively. By comparison, on the scale
of a single coral colony, the boring clam Lithophaga lessepsiana
alone can erode up to 40 % of the carbonate deposited by the coral
Stylophora pistillata (Lazar and Loya, 1991). In our study sites,
the spatial dynamics of the two metrics Gnet and the census-based
Gbudget were consistent and suggest net erosion in nearshore reef
sites and net accretion in offshore reef sites in the central Red Sea. Reef
growth along the central Red Sea cross-shelf gradient averaged 0.66±2.01 kg CaCO3 m-2 yr-1, which was driven by the
substantial positive budget of the offshore reef, reflecting the location and habitat
dependence for reef growth potential. That the offshore reef budget is
essential to maintain the entire shelf budget has also has been observed on a
reef platform in the Maldives. In the respective study, reef accretion was
minor and highly heterogeneous at most sites and only a few reef sites at the
platform margin promoted substantial net accretion and thereby greatly
contributed to the positive average budget of the entire platform (Perry et
al., 2017).
The central Red Sea Gbudget data presented here are within the
range of contemporary reef carbonate budgets from the Atlantic (2.55±3.83 kg CaCO3 m-2 yr-1) and Indian (1.41±3.02 kg CaCO3 m-2 yr-1) oceans (Perry et al., 2018).
Notably, these data are below the suggested “optimal reef budget” of
5–10 kg CaCO3 m-2 yr-1 observed in “healthy”, high
coral cover fore reefs (see data in Perry et al., 2018, and comparisons
therein; Vecsei, 2001, 2004). The overall decline in coral cover is likely central to
the reduced carbonate budgets in contemporary reefs. For instance, the reefs
investigated in the present study do not exceed a coral cover of 40 % (as
observed in the offshore study site). In comparison, the dataset compiled by
Vecsei (2001) encompasses reef sites with hard coral cover of up to 80 % for the
Indo-Pacific and up to 95 % on various Pacific islands. Further, the reduced
contemporary carbonate budgets coincide with the observed decrease in
calcification rates of Red Sea corals at large (Cantin et al., 2010; Steiner
et al., 2018). As such, the effect of climate change and the corresponding
increase in seawater temperature may have severe consequences via overall
decrease in coral reef cover as well as via reduced calcification of the
resident corals. Hence, although the present Gbudget data still
suggest effective barrier reef formation in the central Red Sea (substantial
accretion on the offshore reef), carbonate accretion rates and therefore reef
formation in the central Red Sea may be hampered in the long run by the
ongoing warming trend.
Biotic drivers
Regional differences
Cross-shelf patterns of Gbudget drivers from the central Red Sea
are distinct from other reef systems. The central Red Sea system is
characterized by a nearshore site with a negative Gbudget,
impacted by high parrotfish abundances and erosion rates, low coral cover,
and putatively considerable endolithic bioerosion rates (see the discussion
of Gnet data). Conversely, the offshore reef is characterized by
high calcification rates, driven by high coral and coralline algae
abundances. In the GBR an opposing trend with high net accretion in the
nearshore reefs (Browne et al., 2013) coincided with high coral cover, low
bioerosion rates, and the lowest rates of parrotfish bioerosion (Hoey and
Bellwood, 2007; Tribollet et al., 2002). On Caribbean reefs, parrotfish
erosion rates were higher on leeward reefs (which may be similar to protected
nearshore habitats), but in contrast to the central Red Sea, these sites were
typically characterized by overall high coral cover driving a positive
Gbudget (Perry et al., 2012, 2014). This inter-regional
comparison strongly suggests that reef accretion/erosion dynamics encountered
in any given reef system cannot be readily extrapolated to other reef
systems. Hence, in situ assessments of individual reef systems are required
to unravel local dynamics and responses to environmental change, and are
therefore imperative for the development of effective management measures.
The role of coral and coralline crusts
Benthic calcifiers, in particular reef-building corals, are major
contributors to carbonate production and are considered the most influential
drivers of Gbudgets globally (Franco et al., 2016). Corals can contribute as much as 90 % to the gross carbonate
production across different reef zones, which also includes low coral cover
lagoonal and rubble habitats (Perry et al., 2017). Hence, loss of coral cover
rapidly gives way to increased bioerosion and thereby critically contributes
to reef framework degradation (Perry and Morgan, 2017). Indeed, on Caribbean
reefs, Gbudget data were reported to shift into erosional states
once live hard coral cover was below 10 % (Perry et al., 2013). A live
coral cover threshold remains to be determined for the central Red Sea and
will require evaluation of a larger dataset. However, we find that the
nearshore reef featuring a negative Gbudget is characterized by a
coral cover of 11 %, while the midshore and offshore reefs, characterized
by near-neutral vs. positive carbonate budgets, both feature similar average
coral covers (at 35 % and 40 %, respectively). In this respect, our
data show that a 2-fold higher abundance of coralline algae and other
encrusting calcifiers in the offshore reef (compared to the midshore reef)
significantly added to a higher Gbudget. The positive
contribution of coralline algae for central Red Sea reef accretion is
corroborated by their strong and significant correlation to
Gbudget. Coralline algae in particular are considered an
important contributor to reef growth, as they stabilize the reef framework
through “cementation” (Perry et al., 2008) and by habitat priming for
successful coral recruitment (Heyward and Negri, 1999).
Epilithic grazers
Epilithic grazers such as parrotfish and sea urchin are considered important
drivers of bioerosion on many reefs (Hoey and Bellwood, 2007; Mokady et al.,
1996; Pari et al., 1998; Reaka-Kudla et al., 1996). Sea urchins were
identified as significant bioeroders in some reefs of Réunion Island,
French Polynesia, and in the GoA, northern Red Sea (Chazottes et al., 1995,
2002; Mokady et al., 1996). For the northern Red Sea, sea urchins were
abundant, and their removal of reef carbonates was estimated to range around
13 %–22 % of total reef slope calcification (Mokady et al., 1996).
In contrast, sea urchins were rare in our study sites, contributing only
2 %–3 % of the total bioerosion, resulting in low contributions to
Gbudget. Only in the net-erosive nearshore reef were sea urchins
more abundant, causing 12 % of total bioerosion.
Compared to sea urchins, parrotfish played a more important role for
Gbudgets throughout the entire reef system, contributing
70 %–96 % of the total bioerosion. In the correlation analyses, both
grazers, i.e., sea urchins and parrotfish, negatively correlated with
Gbudget; however, these correlations were not very strong (ρ∼-0.5) and non-significant. The weak correlation may be influenced
by a considerable variability in the reef census dataset, specifically
regarding parrotfish abundances. Observer bias (parrotfish keep minimum
distance from surveyors during dives and may therefore not enter survey
plots; Claudia Pogoreutz, personal observation, 2014), natural (e.g., species distribution, habitat
preferences, reef rugosity, and mobility or large roving excavating species,
such as Bolbometopon muricatum), and/or anthropogenically driven
factors (e.g., differential fishing pressure) may also contribute to the
observed data heterogeneity (McClanahan, 1994; McClanahan et al., 1994).
Indeed, the Saudi Arabian central Red Sea has been subject to decade long
fishing pressure, which has significantly altered reef fish community
structures and reduced overall fish biomass compared to less impacted Red Sea
regions (Kattan et al., 2017). Unregulated fishing could at least in part
explain the differences of fish abundance dynamics between the present study
and reefs on the GBR and the Caribbean. The heterogeneity of grazer
populations further propagates into Gbudget estimates, resulting
in a considerable within-site variability that reduces the power of
statistical tests and correlations.
Abiotic factors and reef growth dynamics
Reef habitats in the central Red Sea are characterized by abiotic factors
that differ from the majority of tropical reef environments (Couce et al.,
2012; Kleypas et al., 1999). Our sites were exposed to high summer
temperatures (30–33 ∘C) and a high salinity throughout the year
(39–40). Inorganic nutrients were
mostly far below 1 µmol kg-1, whereas TA was comparably high,
2400–2500 µmol kg-1, values typical for much of the Red Sea
basin (Acker et al., 2008; Steiner et al., 2014). As such, the Red Sea is
considered a natural model system or “laboratory”, which can advance our
understanding of ecosystem functioning under extreme or marginal conditions,
of which some are projected under ocean change scenarios (Camp et al., 2018).
The study of such natural systems is a challenge and the documentation of
governing factors both abiotic and biotic will contribute to a better
understanding of the dynamics and interactions, which can significantly
improve ecosystem-scale predictions (Boyd and Hutchins, 2012; Boyd and Brown,
2015; Camp et al., 2018). In the present study, reef framework decline (i.e.,
net erosion) was associated with reef habitats of slightly increased
salinity and stronger diel pH fluctuations, which are characteristic of
shallow water, limited flow systems, and semi-enclosed reefs (Camp et al.,
2017; Shamberger et al., 2017), such as the nearshore study site investigated
here (Roik et al., 2016). On the other hand, positive reef growth was
associated with reef habitats characterized by higher TA levels, but also
with slightly increased inorganic nutrient species, namely NO3- and
NO2- and PO43-.
The nearshore site
The nearshore reef is located on the shelf, surrounded by shallow waters of
extended residency time, and has a lower water exchange rate compared to the
other two reef sites (Roik et al., 2016). Evaporation and limited flow,
particularly during summer, may increase salinity, which was overall higher
at this reef site. However, the difference to the other sites was minuscule
and unlikely to have affected calcifying (Röthig et al., 2016) and
bioeroding biota. The variability of diurnal pH on the other hand presumably
has stronger impacts on the performance of calcifiers and bioeroders.
Previously, pH variability across a reef flat and slope was demonstrated to
correlate with net accretion dynamics by showing higher net accretion
prevailing in sites of less variable pH conditions (Price et al., 2012;
Silbiger et al., 2014), which reflects the pattern observed here.
The fluctuation in pH may (in part) represent a biotic feedback signature in
reef habitats, which entails changes in seawater chemistry caused by dominant
biotic processes, i.e., calcification, carbonate dissolution, and
respiration/photosynthesis (Bates et al., 2010; Silverman et al., 2007a;
Zundelevich et al., 2007). Commonly, such pH fluctuations are influenced by
changes in carbonate system variables, e.g., DIC and TA (Shaw et al., 2012;
Silbiger et al., 2014), which can modify the antagonistic processes of
calcification and bioerosion/dissolution (e.g., Andersson, 2015; Langdon et
al., 2000; Tribollet et al., 2009). In particular, in our nearshore study
site, where benthic macro-community abundance was low, biological activity in
the sandy bottom (e.g., permeable carbonate sands) might be a crucial factor
contributing to the biotic feedback (Andersson, 2015; Cyronak et al., 2013;
Eyre et al., 2018).
Total alkalinity and nutrients
An increase in TA is often associated with increased carbonate ion
concentration and Ωa, which facilitate the precipitation of
carbonates supporting the performance of reef builders (Albright et al.,
2016, 2018; Langdon et al., 2000; Schneider and Erez, 2006; Silbiger et al.,
2014). We identified a positive correlation of TA with reef growth in our
dataset. The difference in TA across our study sites was small, but in the
range of natural cross-shelf differences reported from other reefs (e.g.,
reefs in Bermuda, 20–40 µmol kg-1, Bates et al., 2010), and
as high as 50 µmol kg-1, which was shown to enhance
community net calcification in a reef-enclosed lagoon (Albright et al.,
2016). On the other hand, high calcification rates can deplete TA, whereas
dissolution of carbonates can enrich TA measurably, specifically in
(semi-)enclosed systems (Bates et al., 2010), which we did not observe along
the cross-shelf gradient. It remains to be further investigated how TA
dynamics across the shelf relate to reef growth processes.
Although increased nutrients are commonly linked to reef degradation
initiated through phase shifts, increased bioerosion rates, and/or the
decline of calcifiers (Fabricius, 2011; Grand and Fabricius, 2010; Holmes,
2000), our dataset suggests that a highly oligotrophic system such as the
central Red Sea reefs may benefit from slight increases in certain nutrient
species. Specifically, natural minor increases in N and P might have a
positive effect on ecosystem productivity and functioning, including
carbonate budgets. A moderate natural source of nutrients, e.g., from sea
bird populations, can indeed have a positive effect on ecosystem functioning
(Graham et al., 2018). Interestingly,
our study also identified PO43- concentration as an abiotic
correlate of reef growth. In the Red Sea, high N : P ratios indicate that P
is a limiting micronutrient, e.g., for phytoplankton (Fahmy, 2003).
PO43- is not only essential for pelagic primary producers, but
also for reef calcifiers and their photosymbionts, such as the stony corals
and their micro-algal Symbiodiniaceae endosymbionts (Ferrier-Pagès et
al., 2016; LaJeunesse et al., 2018). Experimental studies have demonstrated
that PO43- provision can maintain the coral–algae symbiosis in
reef-building corals under heat stress (Ezzat et al., 2016). Conversely, P
limitation can increase the stress susceptibility of this symbiosis
(Pogoreutz et al., 2017; Rädecker et al., 2015; Wiedenmann et al., 2013).
In light of our results, it will be of interest to link spatio-temporal
variation of inorganic nutrient ratios with patterns of reef resilience in
the central Red Sea to understand their effects on long-term trends of reef
growth.
Reef growth trajectories in the Red Sea
Carbonate budgets provide insight into ecosystem functioning and can be
used as a powerful tool to track reef trajectories through time. This
includes the exploration of past and current reef trends, which may be
critical for prediction of future reef development (Januchowski-Hartley et
al., 2017). Indeed, the absence of comparative baseline data limits a
historical perspective on the central Red Sea Gbudget presented
here. Previously reported Red Sea data include pelagic and reefal carbonate
accretion rates from 1998, estimated using basin-scale historical
measurements of TA (Steiner et al., 2014). Another dataset employed the
census-based budget approach for a seasonal high-latitude fringing
reef in the GoA from 1994 to 1996 (Dullo et al., 1996), which is
methodologically similar to the ReefBudget approach. Both reef
growth estimates provide similar rates: the TA-based reef accretion estimate
from 1998 was 0.9 kg CaCO3 m-2 yr-1 and the GoA
fringing reef budget from 1994 to 1996 ranged between 0.7 and 0.9 kg
CaCO3 m-2 yr-1. Additionally, our gross calcification
rate of the offshore benthic communities (Gbenthos) compares well
with the maxima measured in the GoA reefs in 1994 (i.e., 2.7 kg
CaCO3 m-2 yr-1) (Heiss, 1995). The Gbudgets
assessed in the present study are in accordance with these data, indicating
stable reef growth rates in the Red Sea basin in the last 20 years, despite
the ongoing warming trend and observed impairment of calcification in a
Red Sea coral species (Cantin et al., 2010; Raitsos et al., 2011). However, data are
limited and comparisons between the central Red Sea and the GoA should be
interpreted with caution. Due to the strong latitudinal gradient of
temperature and salinity in addition to differences in seasonality between the
central Red Sea and the GoA, reef growth dynamics from the two regions may
fundamentally differ. Hence, far larger (and ideally cross-latitude) datasets
will be needed to determine more accurately whether a declining calcification
capacity of Red Sea corals has already become a basin-scale phenomenon and
whether there are coral species-specific differences. In this study we have
demonstrated that offshore reefs in the central Red Sea still maintain a
positive carbonate budget yet can be considered “underperforming” and below
“optimal reefal production” (Vecsei, 2004). In the context of reef growth
trajectories, the data presented in this study should serve as a valuable
contemporary baseline for comparative future studies in the central Red Sea.
Importantly, these data were collected before the Third Global Bleaching
Event, which impacted the region during the summers of 2015 and 2016 (Monroe et al.,
2018). The present effort therefore will be of great value when assessing
potential (long-term) changes in Red Sea Gbudgets following this
substantial disturbance.