Uncovering Spatio-Temporal and Treatment-Derived Differences in the Molecular Physiology of a Model Coral-Dinoflagellate Mutualism with Multivariate Statistical Approaches

Mayfield, Anderson B.

doi:10.20944/preprints201608.0118.v1

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Although a Bonferroni adjustment may suffice in controlling for type I statistical errors, MSA can uncover relationships amongst response variables that are not evident from univariate-based statistics alone, while doing so at a lower false positive error rate [32]. Several MSA were taken herein to understand the relationship between environment and coral physiology.…”

Section: Methodsmentioning

confidence: 99%

Identifying corals displaying aberrant behavior in Fiji’s Lau Archipelago

2017

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Given the numerous threats against Earth’s coral reefs, there is an urgent need to develop means of assessing reef coral health on a proactive timescale. Molecular biomarkers may prove useful in this endeavor because their expression should theoretically undergo changes prior to visible signs of health decline, such as the breakdown of the coral-dinoflagellate (genus Symbiodinium) endosymbiosis. Herein 13 molecular- and physiological-scale biomarkers spanning both eukaryotic compartments of the anthozoan-Symbiodinium mutualism were assessed across 70 pocilloporid coral colonies sampled from reefs of Fiji’s easternmost province, Lau. Eleven colonies were identified as outliers upon employment of a detection method based partially on the Mahalanobis distance; these corals were hypothesized to have been displaying aberrant sub-cellular behavior with respect to their gene expression signatures, as they were characterized not only by lower Symbiodinium densities, but also by higher levels of expression of several stress-targeted genes. Although these findings could suggest that the sampled colonies were physiologically compromised at the time of sampling, further studies are warranted to state conclusively whether these 11 scleractinian coral colonies are more stress-prone than nearby conspecifics that demonstrated statistically normal phenotypes.

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Section: Methodsmentioning

confidence: 99%

Identifying corals displaying aberrant behavior in Fiji’s Lau Archipelago

2017

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Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence

et al. 2022

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Numerous physical, chemical, and biological factors influence coral resilience in situ, yet current models aimed at forecasting coral health in response to climate change and other stressors tend to focus on temperature and coral abundance alone. To develop more robust predictions of reef coral resilience to environmental change, we trained an artificial intelligence (AI) with seawater quality, benthic survey, and molecular biomarker data from the model coral Pocillopora acuta obtained during a research expedition to the Solomon Islands. This machine-learning (ML) approach resulted in neural network models with the capacity to robustly predict (R2 = ~0.85) a benchmark for coral stress susceptibility, the “coral health index,” from significantly cheaper, easier-to-measure environmental and ecological features alone. A GUI derived from an ML desirability analysis was established to expedite the search for other climate-resilient pocilloporids within this Coral Triangle nation, and the AI specifically predicts that resilient pocilloporids are likely to be found on deeper fringing fore reefs in the eastern, more sparsely populated region of this under-studied nation. Although small in geographic expanse, we nevertheless hope to promote this first attempt at building AI-driven predictive models of coral health that accommodate not only temperature and coral abundance, but also physiological data from the corals themselves.

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