Impacts of multiple environmental stressors on coral reef erosion - PeerJ

Impacts of multiple environmental stressors on coral reef erosion and secondary accretion Nyssa J Silbiger, Òscar Guadayol, Florence I.M. Thomas, Megan J Donahue

Ocean acidification threatens to shift coral reefs from net accreting to net eroding. While corals build reefs through accretion of calcium carbonate (CaCO3) skeletons, net reef

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growth depends on bioerosion by grazers and borers and on secondary calcification by crustose coralline algae and other calcifying invertebrates. Primary calcification, secondary calcification, and erosion processes respond differently to climate change stressors; therefore, the combined accretion-erosion response is uncertain. Using a new microcomputed tomography (µCT) method, we measured the simultaneous response of secondary accretion and bioerosion along an environmental gradient: bioerosion rates ranged from 0.02 to 0.91 kg m-2 yr-1 and secondary accretion rates ranged from 0.01 to 0.4 mm yr-1 across a 32m transect. Co-located measures of secondary accretion and bioerosion had different environmental drivers: bioerosion rates were highly sensitive to ocean acidity while secondary accretion rates were most sensitive to physical drivers. These results suggest that bioerosion plays a significant role in the shift from net accretion to net erosion on coral reefs.

PeerJ PrePrints | https://dx.doi.org/10.7287/peerj.preprints.1190v1 | CC-BY 4.0 Open Access | rec: 19 Jun 2015, publ: 19 Jun 2015

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Title: Impacts of multiple environmental stressors on coral reef erosion and secondary accretion

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Authors: Nyssa J Silbiger1 *, Òscar Guadayol2 , Florence I.M. Thomas1 and Megan J. Donahue1

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Author affiliations: 1

Hawai‘i Institute of Marine Biology, University of Hawai‘i at M¯anoa, K¯ane‘ohe, Hawai‘i, USA

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School of Life Sciences, Joseph Banks Laboratories, University of Lincoln, Green Lane, Lincoln, UK

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Corresponding author:

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Nyssa J Silbiger

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Hawai‘i Institute of Marine Biology

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University of Hawai‘i at M¯anoa

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PO Box 1346

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K¯ane‘ohe, Hawai‘i, USA 96744

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Ph: (808) 236-7424

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email: [email protected]

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KEYWORDS: Ocean acidification, accretion, bioerosion, natural gradients, coral reefs

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ABSTRACT

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Ocean acidification threatens to shift coral reefs from net accreting to net eroding. While corals build reefs

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through accretion of calcium carbonate (CaCO3 ) skeletons, net reef growth depends on bioerosion by graz-

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ers and borers and on secondary calcification by crustose coralline algae and other calcifying invertebrates.

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Primary calcification, secondary calcification, and erosion processes respond differently to climate change

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stressors; therefore, the combined accretion-erosion response is uncertain. Using a new micro-computed

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tomography (µCT) method, we measured the simultaneous response of secondary accretion and bioerosion

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along an environmental gradient: bioerosion rates ranged from 0.02 to 0.91 kg m−2 yr−1 and secondary

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accretion rates ranged from 0.01 to 0.4 mm yr−1 across a 32m transect. Co-located measures of secondary

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accretion and bioerosion had different environmental drivers: bioerosion rates were highly sensitive to ocean

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acidity while secondary accretion rates were most sensitive to physical drivers. These results suggest that

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bioerosion plays a significant role in the shift from net accretion to net erosion on coral reefs.


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INTRODUCTION

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Human-induced changes in ocean chemistry (Hoegh-Guldberg et al., 2007; Andersson and Gledhill, 2013; DeCarlo

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et al., 2014; Silbiger et al., 2014; Silbiger and Donahue, 2015; Wisshak et al., 2012; Tribollet et al., 2009; Reyes-

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Nivia et al., 2013; Fang et al., 2013), temperature (Hoegh-Guldberg et al., 2007; Silbiger and Donahue, 2015;

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Davidson et al., 2013; Fang et al., 2013), and water quality (Fabricius, 2005; DeCarlo et al., 2014; Risk et al.,

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1995b; Holmes et al., 2000; Tribollet and Golubic, 2005; Edinger et al., 2000; Le Grand and Fabricius, 2011) are

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threatening coral reefs (Pandolfi et al., 2011; Hoegh-Guldberg et al., 2007; Fabricius, 2005). Predictions of reef

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response to changing ocean conditions are often based on the response of reef building corals alone (Silverman

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et al., 2009; Pandolfi et al., 2011); however, coral reef bioerosion from borers and grazers and secondary accretion

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from crustose coralline algae and other encrusting invertebrates are also critical processes for reef sustainability

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(Przeslawski et al., 2008). There are several gaps in our knowledge about the response of coral reefs to future ocean

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conditions and whether reef accretion will continue to exceed reef erosion. 1) Will primary accretion, secondary

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accretion, and reef erosion respond similarly to environmental drivers and will their responses combine to accel-

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erate reef loss? Studies that examine accretion or erosion processes individually have found different responses

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to environmental stress. In a field experiment in Indonesia, coral calcification and bioerosion had different func-

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tional relationships with land-based pollution (Edinger et al., 2000). Laboratory experiments focusing on climate

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stressors (i.e. temperature and ocean acidity) have found that bioerosion is linearly related to ocean acidity and

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temperature (Silbiger and Donahue, 2015; Wisshak et al., 2012; Tribollet et al., 2009; Reyes-Nivia et al., 2013;

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Fang et al., 2013), but that calcification exhibits both linear (Pandolfi et al., 2011; Comeau et al., 2013) and non-

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linear (Castillo et al., 2014; Silbiger and Donahue, 2015; Comeau et al., 2013; Pandolfi et al., 2011) responses.

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These differential responses of primary and secondary accretion and bioerosion challenge our ability to predict

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the net response of coral reefs to environmental change. 2) How will multiple environmental stressors impact

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individual reef processes? Many environmental parameters interact to drive patterns in accretion and erosion, in-

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cluding ocean acidity (Hoegh-Guldberg et al., 2007; Andersson and Gledhill, 2013; DeCarlo et al., 2014; Silbiger

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et al., 2014; Silbiger and Donahue, 2015; Wisshak et al., 2012; Tribollet et al., 2009; Reyes-Nivia et al., 2013;

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Fang et al., 2013), temperature (Hoegh-Guldberg et al., 2007; Silbiger and Donahue, 2015; Davidson et al., 2013;

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Fang et al., 2013), nutrients (Fabricius, 2005; DeCarlo et al., 2014; Risk et al., 1995b; Holmes et al., 2000; Tri-

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bollet and Golubic, 2005), and gradients of human influence (e.g., chlorophyll, turbidity, sedimentation) (Edinger

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et al., 2000; Le Grand and Fabricius, 2011). This myriad of drivers complicates the predictions of reef response

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to climate change. 3) How does local, natural variability contextualize global changes in the accretion-erosion

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balance? Shallow coral reef ecosystems persist in highly variable physicochemical environments (Guadayol et al.,

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2014; Hofmann et al., 2011; Rivest and Gouhier, 2015) driven by tidal flushing, photosynthesis and respiration,

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ground-water inputs, and other benthic biological processes (Shamberger et al., 2014; Yates et al., 2007; Drupp

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et al., 2011; Massaro et al., 2012; Duarte et al., 2013; Smith et al., 2013). Little is known about how climate change


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interacts with this naturally variable environment to drive patterns accretion and erosion. Here, we address each

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of these knowledge gaps by leveraging a natural environmental gradient in K¯ane‘ohe Bay to rank the impact of

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multiple environmental stressors on simultaneous measures of secondary accretion and reef bioerosion.

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The physicochemical variability of reefs in K¯ane‘ohe Bay, Hawai‘i has been extensively investigated (Guadayol

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et al., 2014; Drupp et al., 2011; Lowe et al., 2009). Like many reefs around the globe (Hofmann et al., 2011;

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Rivest and Gouhier, 2015; Shamberger et al., 2014; Manzello et al., 2008; Crook et al., 2013), K¯ane‘ohe Bay has

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persistent areas of natural acidification that reach the low open-ocean pH values expected by the end of the 21st

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century (Silbiger et al., 2014; Guadayol et al., 2014). Using a space-for-time framework, we can leverage this

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natural variability to understand how reefs will respond to ocean acidification in the context of a naturally variable

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environment. Our prior work in K¯ane‘ohe Bay demonstrates that net reef erosion (calculated as the percent change

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in volume of experimental blocks) is driven by natural variation in pH and that reefs could shift from net accretion

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to net erosion with increasing ocean acidity (Silbiger et al., 2014). Here, we separate and simultaneously measure

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secondary accretion and bioerosion along the same environmental gradient (Figure 1) and then compare the relative

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importance of a suite of environmental drivers to each of these processes.

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In prior studies using experimental substrates, pre- and post-deployment buoyant weights have been used to

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calculate rates of change in density, mass, or volume (reviewed in Table 1), but this method cannot distinguish

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between secondary accretion and bioerosion processes. In prior studies using natural substrates, imaging method-

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ologies in 2-dimensions and, more recently, 3-dimensions (CT and µCT) can be used to separate accretion and

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bioerosion on slabs or cores of reef (Table 1), but rates are difficult to estimate because the time the substrate

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became available to bioeroders and secondary calcifiers is unknown. Here, we describe a new analysis using µCT

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to separate secondary accretion and bioerosion from the same experimental substrate exposed to the same envi-

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ronmental variation over the same time-scale (Figure 2). Our µCT method also allows for a 3D visualization of

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the experimental blocks that highlights specific areas of secondary accretion and bioerosion (See Movies S1 and

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S2 in Supplement). Using µCT, we calculate secondary accretion and bioerosion rates from experimental blocks

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deployed along a 32 m reef transect (Figure S1). Patterns in carbonate chemistry, nutrients, chlorophyll a, tempera-

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ture, and depth were characterized along this same transect (Figure 1; described in Silbiger et al. (2014)), and used

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to compare five specific hypotheses about drivers of the accretion-erosion balance: carbonate chemistry, resource

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availability, temperature, depth, and hydrodynamics (distance from shore and depth). We use a model selection

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approach to test which of these drivers has the strongest relationship to secondary accretion and bioerosion.

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Materials and Methods

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(a) Experimental Design: Our study site is located in K¯ane‘ohe Bay, O‘ahu on the windward (eastern) side of

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Moku o Lo‘e. We used the same environmental gradient as Silbiger et al. (2014), which we briefly describe here


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(see supplement for full description of methods). Twenty-one experimental blocks were deployed along a 32 m

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transect, stratified between reef flat and reef slope (Figure S1). Experimental blocks were cut from dead pieces of

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massive Porites sp. skeleton into 5cm x 5cm x 2cm blocks, soaked in freshwater, and then autoclaved to remove

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any living organisms. Collections were made under special activity permit # SAP2011-1 to the Hawai‘i Institute

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of Marine Biology at the University of Hawai‘i at M¯anoa. The average skeletal density of each experimental

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block was 1.57 ±0.07(sd) g cm−3 . Blocks were deployed from March 31, 2011 to April 10, 2012. We collected

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both discrete water samples (pH, TA, nitrate, nitrite, ammonium, phosphate, and chlorophyll a) and data from

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continuous sensors (temperature and depth) along the transect. Water samples were collected directly above each

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block four times within 24 hours in September, December, and April in order to capture both diel and seasonal

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variability in the environment. Continuous sensors were stationed over each block for a minimum of two weeks to

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calculate high frequency (0.1 min−1 ) variation in temperature and depth. These short time series were normalized

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to a continuous time series from a permanent station positioned adjacent to the transect, allowing comparison of

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the micro-environments at each block (Guadayol et al., 2014).

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(b) µCT: Secondary accretion and bioerosion rates were calculated using µCT (Figure 2). µCT is an X-ray

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technology that non-destructively images the external and internal structures of solid objects, resulting in a three-

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dimensional array of object densities. We used an eXplore CT120 µCT (GE Healthcare Xradia, Inc) at the Cornell

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University Imaging Multiscale CT Facility (Figure 2.1-2) to scan blocks before and after deployment (voltage =

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100kV, current = 50mA). A three-dimensional array of isotropic voxels at 50 µm3 resolution was generated using

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the GE Console Software and were averaged to 100 µm3 for data analysis. We used a threshold of 200 Hounsfield

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Units to separate coral from air (Silbiger et al., 2014) (Figure 2.3). The pre and post-deployment scans were aligned

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using an intensity-based registration technique (Figure 2.4), converted to binary (Figure 2.5), and subtracted from

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one another resulting in a matrix of 0’s, 1’s, and -1’s (Figure 2.5). All positive values were new pixels added to the

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post-deployment scan which indicate secondary accretion, negative values were pixels that were lost and indicate

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bioerosion, and zeros meant there was no change at that pixel between the two scans. All values were summed and

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multiplied by the resolution of the scan to obtain the volume lost or gained per block (Figure 2.6).

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(c) Rates: Bioerosion and secondary accretion rates were calculated using the following equations: Bioerosion

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Rate (kg m−2 yr−1 ) = (V oli × ρi )/(SAi × T ime) and Secondary Accretion Rate (mm yr−1 ) = V oli /(SAi ×

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T ime) , where i represents an individual block, V ol is the volume lost (bioerosion) or gained (secondary accretion)

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in m3 , SA is the surface area of the pre-deployment blocks (m2 ), ρ is the skeletal density of the pre-deployment

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block (kg m−3 ), and T ime is the deployment time (years). Secondary accretion rates were converted from m to

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mm per year to compare with literature values.

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(d) Model Selection: Our goal was to compare pH with other known drivers and correlates of the accretion-

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erosion balance. Many of the environmental variables were collinear along the transect; thus, we removed collinear-

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ity by using the residuals of a regression of each environmental variable against log(depth) and distance from shore


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(Silbiger et al., 2014). Correlation coefficients for raw environmental data and the residual environmental data are

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available in Silbiger et al. (2014).

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We used a model selection framework to compare models for five specific hypotheses about the accretion-

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erosion balance and test which of these drivers had the strongest relationship to secondary accretion and bioerosion

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(Table 2). In a model selection framework, Akaike Information Criterion (AIC) values are used to rank candidate

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models, accounting for both fit and complexity. Carefully constructed model selection avoids problems associated

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with multiple hypothesis testing that are common in stepwise regression, such as arbitrary α levels and uninter-

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pretable functional relationships (Johnson and Omland, 2004; Anderson, 2008). Here, we used the corrected AIC

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(AICc), which is recommended for sample sizes