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Scaling the effects of ocean acidification on coral growth and coral-coral competition on coral community recovery.
Evensen NR
,
Bozec YM
,
Edmunds PJ
,
Mumby PJ
.
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Ocean acidification (OA) is negatively affecting calcification in a wide variety of marine organisms. These effects are acute for many tropical scleractinian corals under short-term experimental conditions, but it is unclear how these effects interact with ecological processes, such as competition for space, to impact coral communities over multiple years. This study sought to test the use of individual-based models (IBMs) as a tool to scale up the effects of OA recorded in short-term studies to community-scale impacts, combining data from field surveys and mesocosm experiments to parameterize an IBM of coral community recovery on the fore reef of Moorea, French Polynesia. Focusing on the dominant coral genera from the fore reef, Pocillopora, Acropora, Montipora and Porites, model efficacy first was evaluated through the comparison of simulated and empirical dynamics from 2010-2016, when the reef was recovering from sequential acute disturbances (a crown-of-thorns seastar outbreak followed by a cyclone) that reduced coral cover to ~0% by 2010. The model then was used to evaluate how the effects of OA (1,100-1,200 µatm pCO2) on coral growth and competition among corals affected recovery rates (as assessed by changes in % cover y-1) of each coral population between 2010-2016. The model indicated that recovery rates for the fore reef community was halved by OA over 7 years, with cover increasing at 11% y-1 under ambient conditions and 4.8% y-1 under OA conditions. However, when OA was implemented to affect coral growth and not competition among corals, coral community recovery increased to 7.2% y-1, highlighting mechanisms other than growth suppression (i.e., competition), through which OA can impact recovery. Our study reveals the potential for IBMs to assess the impacts of OA on coral communities at temporal and spatial scales beyond the capabilities of experimental studies, but this potential will not be realized unless empirical analyses address a wider variety of response variables representing ecological, physiological and functional domains.
Figure 1. Measuring coral-coral competition in photoquadrats.(A) Measurements of the diameters and circumferences of two Pocillopora colonies (black lines and circles) and their anticipated growth over 6 months (i.e., one model time step) (red circles), based on growth rates measured in experiments in Moorea (Evensen & Edmunds, 2016) . The blue line represents the distance between colony centres, with red dashed lines representing the anticipated radii of each colony after growth and the green lines representing the section (arc) of each colony in contact with the other colony after growth. (B) Using the distance between colony centres (Dist) and anticipated radii of each colony after growth (R1 and R2) to calculate the height of the triangle (H), which was then used to calculate the length of the contact arc between corals (A1 and A2) using Heron’s Formula (Weisstein, 2021). (C) Average relationship between coral cover and the average percent contact among colonies (proportion of the perimeter of corals in contact with one or multiple colonies). Black circles represent the observed relationship between coral cover and contact from photoquadrats recorded at LTER1 from 2010–2015 (n = 65), while green circles and the darker green line represent the simulated relationship between coral cover and contact based on a random distribution of corals within a cell (n = 3,400). Full details of the equations and code used to calculate and simulate coral competition are provided in Supplemental Material.
Figure 2. Determining coral linear growth rates as a function of competition.(A) Photographs and diagram of the experimental set up from Evensen & Edmunds (2016) used to assess linear growth of Pocillopora verrucosa over 28 d as a function of contact with surrounding coral competitors, under ambient (~400 μatm) and elevated pCO2 (~1,030 μatm) in 500-L outdoor flumes (each 5.0 × 0.3 × 0.3 m). Relationships between coral colony planar growth (as a proportion of the mean growth rate under ambient pCO2 conditions in the absence of competition) and percent of the perimeter of a colony in contact with conspecifics (B) and heterospecifics (C) under ambient pCO2 conditions (~400 µatm), and contact with conspecifics (D) and heterospecifics (E) under elevated pCO2. Values are based on mean growth rates (larger black dots), with small grey dots representing individual replicates. Relationships within the measured values are represented by the solid black lines, with the extrapolation to 100% contact represented by the dotted line. Equations used to implement the effects of competition on colony growth are provided in Table 1.
Figure 3. Description of empirical and modeled coral community recovery at 10-m depth, at site LTER1 on the fore reef of Moorea, French Polynesia.(A) Short-term observations and simulations (7 years) of total coral cover. Grey dots show observed coral cover in individual photoquadrats, with black dots depicting annual means. Thin lines show simulations of the recovery under ambient conditions (blue), with the effects of OA on coral growth alone (orange), and with the effects of OA on both coral growth and competition among corals (red), with the mean trajectory represented by the thicker line (n = 100). (B) Observed (black) and simulated cover of each coral genus after the 7-year recovery period. Bars are mean ± S.D. for observed data (n = 65) and 95% confidence intervals (CI) based on percentiles of 100 simulations.
Figure 4. Sensitivity analysis of model simulations to individual changes (±20%) in key parameter values for coral community recovery.Effects of parameter changes are reflected by percent change from 67% coral cover after 7 years under ambient conditions.
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