Biological mediation of carbonate dissolution represents a fundamental component of the destructive forces acting on coral reef ecosystems. Whereas ocean acidification can increase dissolution of carbonate substrates, the combined impact of ocean acidification and warming on the microbioerosion of coral skeletons remains unknown. Here, we exposed skeletons of the reef-building corals, Porites cylindrica and Isopora cuneata, to present-day (Control: 400 μatm - 24 °C) and future pCO2 -temperature scenarios projected for the end of the century (Medium: +230 μatm - +2 °C; High: +610 μatm - +4 °C). Skeletons were also subjected to permanent darkness with initial sodium hypochlorite incubation, and natural light without sodium hypochlorite incubation to isolate the environmental effect of acidic seawater (i.e., Ωaragonite <1) from the biological effect of photosynthetic microborers. Our results indicated that skeletal dissolution is predominantly driven by photosynthetic microborers, as samples held in the dark did not decalcify. In contrast, dissolution of skeletons exposed to light increased under elevated pCO2 -temperature scenarios, with P. cylindrica experiencing higher dissolution rates per month (89%) than I. cuneata (46%) in the high treatment relative to control. The effects of future pCO2 -temperature scenarios on the structure of endolithic communities were only identified in P. cylindrica and were mostly associated with a higher abundance of the green algae Ostreobium spp. Enhanced skeletal dissolution was also associated with increased endolithic biomass and respiration under elevated pCO2 -temperature scenarios. Our results suggest that future projections of ocean acidification and warming will lead to increased rates of microbioerosion. However, the magnitude of bioerosion responses may depend on the structural properties of coral skeletons, with a range of implications for reef carbonate losses under warmer and more acidic oceans.

Human-induced ocean acidification and warming alter seawater carbonate chemistry reducing the calcification of reef-building crustose coralline algae (CCA), which has implications for reef stability. However, due to the presence of multiple carbonate minerals with different solubilities in seawater, the algal mineralogical responses to changes in carbonate chemistry are poorly understood. Here we demonstrate a 200% increase in dolomite concentration in living CCA under greenhouse conditions of high pCO 2 (1,225 matm) and warming (30°C). Aragonite, in contrast, increases with lower pCO 2 (296 matm) and low temperature (28°C). Mineral changes in the surface pigmented skeleton are minor and dolomite and aragonite formation largely occurs in the white crust beneath. Dissolution of high-Mg-calcite and particularly the erosive activities of endolithic algae living inside skeletons play key roles in concentrating dolomite in greenhouse treatments. As oceans acidify and warm in the future, the relative abundance of dolomite in CCA will increase.

Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.