Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Leray, M., Wilkins, L. G. E., Apprill, A., Bik, H. M., Clever, F., Connolly, S. R., De Leon, M. E., Duffy, J. E., Ezzat, L., Gignoux-Wolfsohn, S., Herre, E. A., Kaye, J. Z., Kline, D. I., Kueneman, J. G., McCormick, M. K., McMillan, W. O., O’Dea, A., Pereira, T. J., Petersen, J. M., Petticord, D. F., Torchin, M. E., Thurber, R. V., Videvall, E., Wcislo, W. T., Yuen, B., Eisen, J. A. . Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution. Plos Biology, 19(8), (2021): e3001322, https://doi.org/10.1371/journal.pbio.3001322.Marine multicellular organisms host a diverse collection of bacteria, archaea, microbial eukaryotes, and viruses that form their microbiome. Such host-associated microbes can significantly influence the host’s physiological capacities; however, the identity and functional role(s) of key members of the microbiome (“core microbiome”) in most marine hosts coexisting in natural settings remain obscure. Also unclear is how dynamic interactions between hosts and the immense standing pool of microbial genetic variation will affect marine ecosystems’ capacity to adjust to environmental changes. Here, we argue that significantly advancing our understanding of how host-associated microbes shape marine hosts’ plastic and adaptive responses to environmental change requires (i) recognizing that individual host–microbe systems do not exist in an ecological or evolutionary vacuum and (ii) expanding the field toward long-term, multidisciplinary research on entire communities of hosts and microbes. Natural experiments, such as time-calibrated geological events associated with well-characterized environmental gradients, provide unique ecological and evolutionary contexts to address this challenge. We focus here particularly on mutualistic interactions between hosts and microbes, but note that many of the same lessons and approaches would apply to other types of interactions.Financial support for the workshop was provided by grant GBMF5603 (https://doi.org/10.37807/GBMF5603) from the Gordon and Betty Moore Foundation (W.T. Wcislo, J.A. Eisen, co-PIs), and additional funding from the Smithsonian Tropical Research Institute and the Office of the Provost of the Smithsonian Institution (W.T. Wcislo, J.P. Meganigal, and R.C. Fleischer, co-PIs). JP was supported by a WWTF VRG Grant and the ERC Starting Grant 'EvoLucin'. LGEW has received funding from the European Union’s Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreement No. 101025649. AO was supported by the Sistema Nacional de Investigadores (SENACYT, Panamá). A. Apprill was supported by NSF award OCE-1938147. D.I. Kline, M. Leray, S.R. Connolly, and M.E. Torchin were supported by a Rohr Family Foundation grant for the Rohr Reef Resilience Project, for which this is contribution #2. This is contribution #85 from the Smithsonian’s MarineGEO and Tennenbaum Marine Observatories Network.

Identification of Candidate Coral Pathogens on White Band Disease-Infected Staghorn Coral.

Bacterial diseases affecting scleractinian corals pose an enormous threat to the health of coral reefs, yet we still have a limited understanding of the bacteria associated with coral diseases. White band disease is a bacterial disease that affects the two Caribbean acroporid corals, the staghorn coral Acropora cervicornis and the elkhorn coral A. palmate. Species of Vibrio and Rickettsia have both been identified as putative WBD pathogens. Here we used Illumina 16S rRNA gene sequencing to profile the bacterial communities associated with healthy and diseased A. cervicornis collected from four field sites during two different years. We also exposed corals in tanks to diseased and healthy (control) homogenates to reduce some of the natural variation of field-collected coral bacterial communities. Using a combination of multivariate analyses, we identified community-level changes between diseased and healthy corals in both the field-collected and tank-exposed datasets. We then identified changes in the abundances of individual operational taxonomic units (OTUs) between diseased and healthy corals. By comparing the diseased and healthy-associated bacteria in field-collected and tank-exposed corals, we were able to identify 16 healthy-associated OTUs and 106 consistently disease-associated OTUs, which are good candidates for putative WBD pathogens. A large percentage of these disease-associated OTUs belonged to the order Flavobacteriales. In addition, two of the putative pathogens identified here belong to orders previously suggested as WBD pathogens: Vibronales and Rickettsiales

Diversity of OTUs.

<p>Bolded text indicates significantly different values as determined by Welch’s two sample t-test.</p

nMDS plots of dissimilarities between samples (a) Field collection samples, showing clustering according to disease state.

<p>“Healthy” “Diseased” and site names denote the centroids of each group and ellipses are 95% confidence ellipses. (<b>b)</b> Tank-exposed samples, showing clustering according to disease state. “Healthy” and “Diseased” labels denote the centroids of each disease state and ellipses are 95% confidence ellipses.</p

Plots of the log2 fold abundance change of each OTU by the mean of normalized counts.

<p>Significantly more or less abundant OTUs are in red (<b>a</b>) Field-collected corals by year + disease state (<b>b</b>) Tank-exposed corals by final disease state.</p

Significantly associated OTUs in tank and field-collected datasets.

<p>Significantly associated OTUs in tank and field-collected datasets.</p

Results of PERMANOVA based on Bray-Curtis dissimilarities of the relative abundance of OTUs on field-collected <i>A</i>. <i>cervicornis</i> in response to disease state, site, and year.

<p>Results of PERMANOVA based on Bray-Curtis dissimilarities of the relative abundance of OTUs on field-collected <i>A</i>. <i>cervicornis</i> in response to disease state, site, and year.</p