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Assessing Coral Reef Condition Indicators for Local and Global Stressors Using Bayesian Networks.

ABSTRACT: Coral reefs are highly valued ecosystems currently threatened by both local and global stressors. Given the importance of coral reef ecosystems, a Bayesian network approach can benefit an evaluation of threats to reef condition. To this end, we used data to evaluate the overlap between local stressors (overfishing and destructive fishing, watershed-based pollution, marine-based pollution, and coastal development threats), global stressors (acidification and thermal stress), and management effectiveness with indicators of coral reef health (live coral index, live coral cover, population bleaching, colony bleaching, and recently killed corals). Each of the coral health indicators had Bayesian networks constructed globally and for Pacific, Atlantic, Australia, Middle East, Indian Ocean, and Southeast Asia coral reef locations. Sensitivity analysis helped evaluate the strength of the relationships between different stressors and reef condition indicators. The relationships between indicators and stressors were also evaluated with conditional analyses of linear and nonlinear interactions. In this process, a standardized direct effects analysis was emphasized with a target mean analysis to predict changes in the mean value of the reef indicator from individual changes to the distribution of the predictor variables. The standardized direct effects analysis identified higher risks in the Middle East for watershed-based pollution with population bleaching and in Australia for overfishing and destructive fishing with living coral. For thermal stress, colony bleaching and recently killed coral in the Indian Ocean were found to have the strongest direct associations along with living coral in the Middle East. For acidification threat, Australia had a relatively strong association with colony bleaching, and the Middle East had the strongest overall association with recently killed coral, although extrapolated spatial data were used for the acidification estimates. The Bayesian network approach helped to explore the relationships among existing databases used for policy development in coral reef management by examining the sensitivity of multiple indicators of reef condition to spatially distributed stress. Integr Environ Assess Manag 2021;17:165-187. Published 2020. This article is a US Government work and is in the public domain in the USA.

SUBMITTER: Carriger JF

PROVIDER: S-EPMC8544239 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Coral reefs provide numerous ecosystem goods and services, but are threatened by multiple environmental and anthropogenic stressors. To identify management scenarios that will reverse or mitigate ecosystem degradation, managers can benefit from tools that can quantify projected changes in ecosystem services due to alternative management options. We used a spatially-explicit biophysical ecosystem model to evaluate socio-ecological trade-offs of land-based vs. marine-based management scenarios, and local-scale vs. global-scale stressors and their cumulative impacts. To increase the relevance of understanding ecological change for the public and decision-makers, we used four ecological production functions to translate the model outputs into the ecosystem services: "State of the Reef," "Trophic Integrity," "Fisheries Production," and "Fisheries Landings." For a case study of Maui Nui, Hawai'i, land-based management attenuated coral cover decline whereas fisheries management promoted higher total fish biomass. Placement of no-take marine protected areas (MPAs) across 30% of coral reef areas led to a reversal of the historical decline in predatory fish biomass, although this outcome depended on the spatial arrangement of MPAs. Coral cover declined less severely under strict sediment mitigation scenarios. However, the benefits of these local management scenarios were largely lost when accounting for climate-related impacts. Climate-related stressors indirectly increased herbivore biomass due to the shift from corals to algae and, hence, greater food availability. The two ecosystem services related to fish biomass increased under climate-related stressors but "Trophic Integrity" of the reef declined, indicating a less resilient reef. "State of the Reef" improved most and "Trophic Integrity" declined least under an optimistic global warming scenario and strict local management. This work provides insight into the relative influence of land-based vs. marine-based management and local vs. global stressors as drivers of changes in ecosystem dynamics while quantifying the tradeoffs between conservation- and extraction-oriented ecosystem services.

Project description:Climate change has emerged as a principal threat to coral reefs, and is expected to exacerbate coral reef degradation caused by more localised stressors. Management of local stressors is widely advocated to bolster coral reef resilience, but the extent to which management of local stressors might affect future trajectories of reef state remains unclear. This is in part because of limited understanding of the cumulative impact of multiple stressors. Models are ideal tools to aid understanding of future reef state under alternative management and climatic scenarios, but to date few have been sufficiently developed to be useful as decision support tools for local management of coral reefs subject to multiple stressors. We used a simulation model of coral reefs to investigate the extent to which the management of local stressors (namely poor water quality and fishing) might influence future reef state under varying climatic scenarios relating to coral bleaching. We parameterised the model for Bolinao, the Philippines, and explored how simulation modelling can be used to provide decision support for local management. We found that management of water quality, and to a lesser extent fishing, can have a significant impact on future reef state, including coral recovery following bleaching-induced mortality. The stressors we examined interacted antagonistically to affect reef state, highlighting the importance of considering the combined impact of multiple stressors rather than considering them individually. Further, by providing explicit guidance for management of Bolinao's reef system, such as which course of management action will most likely to be effective over what time scales and at which sites, we demonstrated the utility of simulation models for supporting management. Aside from providing explicit guidance for management of Bolinao's reef system, our study offers insights which could inform reef management more broadly, as well as general understanding of reef systems.

Project description:BackgroundCoral reefs are facing unprecedented pressure on local and global scales. Sensitive and rapid markers for ecosystem stress are urgently needed to underpin effective management and restoration strategies. Although the fundamental contribution of microbes to the stability and functioning of coral reefs is widely recognised, it remains unclear how different reef microbiomes respond to environmental perturbations and whether microbiomes are sensitive enough to predict environmental anomalies that can lead to ecosystem stress. However, the lack of coral reef microbial baselines hinders our ability to study the link between shifts in microbiomes and ecosystem stress. In this study, we established a comprehensive microbial reference database for selected Great Barrier Reef sites to assess the diagnostic value of multiple free-living and host-associated reef microbiomes to infer the environmental state of coral reef ecosystems.ResultsA comprehensive microbial reference database, originating from multiple coral reef microbiomes (i.e. seawater, sediment, corals, sponges and macroalgae), was generated by 16S rRNA gene sequencing for 381 samples collected over the course of 16 months. By coupling this database to environmental parameters, we showed that the seawater microbiome has the greatest diagnostic value to infer shifts in the surrounding reef environment. In fact, 56% of the observed compositional variation in the microbiome was explained by environmental parameters, and temporal successions in the seawater microbiome were characterised by uniform community assembly patterns. Host-associated microbiomes, in contrast, were five-times less responsive to the environment and their community assembly patterns were generally less uniform. By applying a suite of indicator value and machine learning approaches, we further showed that seawater microbial community data provide an accurate prediction of temperature and eutrophication state (i.e. chlorophyll concentration and turbidity).ConclusionOur results reveal that free-living microbial communities have a high potential to infer environmental parameters due to their environmental sensitivity and predictability. This highlights the diagnostic value of microorganisms and illustrates how long-term coral reef monitoring initiatives could be enhanced by incorporating assessments of microbial communities in seawater. We therefore recommend timely integration of microbial sampling into current coral reef monitoring initiatives.

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Assessing Coral Reef Condition Indicators for Local and Global Stressors Using Bayesian Networks.

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