Using Regression-Based Effect Size Meta-Analysis to Investigate Coral Responses to Climate Change

• Main Author: Kornder, Niklas Alexander
• Format: Thesis
• Language: unknown
• Published: NSUWorks 2016
• Subjects: Multiple stressors; rising sea surface temperatures; elevated pCO2; calcium carbonate; substrate inhibitor ratio; proton-flux hypothesis; reproduction; taxonomic variability; fail-safe analysis; R computing; Marine Biology; Oceanography and Atmospheric Sciences and Meteorology; Ocean acidification
• Online Access: https://nsuworks.nova.edu/occ_stuetd/415; https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1420&context=occ_stuetd

Attempts to quantify the effects of ocean acidification and warming (OAW) on scleractinian corals provide a growing body of response measurements. However, placing empirical results into an ecological context is challenging, owing to variations that reflect both natural heterogeneity and scientific bias. This study addresses the heterogeneity of climate change induced changes in coral recruitment and calcification. To discern scientific bias and identify drivers of the remaining heterogeneity, 100 publications were analyzed using a combination of weighted mixed effects meta-regression and factorial effect size meta‑analysis. A linear model was applied to quantify the variation caused by differing stress levels across studies. The least squares predictions were then used to standardize individual study outcomes and effect size meta-analysis was performed on original and standardized outcomes separately. On average, increased temperature significantly reduces larval survival, while ocean acidification impedes settlement and calcification. Coral resistance to OAW is likely governed by biological traits (genera and life cycle stage), environmental factors (abiotic variability) and experimental design (feeding regime, stressor magnitude, and exposure duration). Linear models suggest that calcification rates are driven by carbonate and bicarbonate concentrations, which act additively with warming. Standardizing outcomes to linear model predictions proved useful in discerning strong sources of scientific bias. The approach used in this study can improve modelling projections and inform policy and management on changes in coral community structure associated with the expected future intensification of OAW.