Coral disease research encompasses five decades of undeniable progress. Since the first descriptions of anomalous signs, we have come to understand multiple processes and environmental drivers that interact with coral pathologies. In order to gain a better insight into the knowledge we already have, we explored how key topics in coral disease research have been related to each other using network analysis. We reviewed 719 papers and conference proceedings published from 1965 to 2017. From each study, four elements determined our network nodes: (1) studied disease(s); (2) host genus; (3) marine ecoregion(s) associated with the study site; and (4) research objectives. Basic properties of this network confirmed that there is a set of specific topics comprising the majority of research. The top five diseases, genera, and ecoregions studied accounted for over 48% of the research effort in all cases. The community structure analysis identified 15 clusters of topics with different degrees of overlap among them. These clusters represent the typical sets of elements that appear together for a given study. Our results show that while some coral diseases have been studied considering multiple aspects, the overall trend is for most diseases to be understood under a limited range of approaches, e.g., bacterial assemblages have been considerably studied in Yellow and Black band diseases while immune response has been better examined for the aspergillosis-Gorgonia system. Thus, our challenge in the near future is to identify and resolve potential gaps in order to achieve a more comprehensive progress on coral disease research.

Estimating variability across spatial scales has been a major issue in ecology because the description of patterns in space is extremely valuable to propose specific hypotheses to unveil key processes behind these patterns. This paper aims to estimate the variability of the coral assemblage structure at different spatial scales in order to determine which scales explain the largest variability on β-diversity. For this, a fully-nested design including a series of hierarchical-random factors encompassing three spatial scales: (1) regions, (2) localities and (3) reefs sites across the Venezuelan territory. The variability among spatial scales was tested with a permutation-based analysis of variance (Permanova) based on Bray-Curtis index. Dispersion in species presence/absence across scales (i.e., β-diversity) was tested with a PermDisp analysis based on Jaccard’s index. We found the highest variability in the coral assemblage structure between sites within localities (Pseudo-F = 5.34; p-value = 0.001, CV = 35.10%). We also found that longitude (Canonical corr = 0.867, p = 0.001) is a better predictor of the coral assemblage structure in Venezuela, than latitude (Canonical corr = 0.552, p = 0.021). Largest changes in β-diversity of corals occurred within sites (F = 2.764, df1= 35, df2 = 107, p = 0.045) and within localities (F = 4.438, df1= 6, df2 = 29, p = 0.026). Our results suggest that processes operating at spatial scales of hundreds of meters and hundreds of kilometers might both be critical to shape coral assemblage structure in Venezuela, whereas smaller scales (i.e., hundreds of meters) showed to be highly- important for the species turnover component of β-diversity. This result highlights the importance of creating scale-adapted management actions in Venezuela and likely across the Caribbean region.

Healthy and diseased corals are threatened by different anthropogenic sources, such as pollution, a problem expected to become more severe in the near future. Despite the fact that coastal pollution and coral diseases might represent a serious threat to coral reef health, there is a paucity of controlled experiments showing whether the response of diseased and healthy corals to xenobiotics differs. In this study, we exposed healthy and Caribbean yellow band disease (CYBD)-affected Orbicella faveolata colonies to 3 sublethal concentrations of anthracene to test if enzymatic responses to this hydrocarbon were compromised in CYBD-affected tissues. For this, a 2-factorial fully orthogonal design was used in a controlled laboratory bioassay, using tissue condition (2 levels: apparently healthy and diseased) and pollutant concentration (4 levels: experimental control, 10, 30 and 100 ppb concentration) as fixed factors. A permutation-based ANOVA (PERMANOVA) was used to test the effects of condition and concentration on the specific activity of 3 enzymatic biomarkers: catalase, glutathione S-transferase, and glutathione peroxidase. We found a significant interaction between the concentration of anthracene and the colony condition for catalase (Pseudo-F = 3.84, df = 3, p < 0.05) and glutathione S-transferase (Pseudo-F = 3.29, df = 3, p < 0.05). Moreover, our results indicated that the enzymatic response to anthracene in CYBD-affected tissues was compromised, as the activity of these enzymes decreased 3- to 4-fold compared to healthy tissues. These results suggest that under a potential scenario of increasing hydrocarbon coastal pollution, colonies of O. faveolata affected with CYBD might become more vulnerable to the deleterious effects of chemical pollution.

The characteristics of coral reef sampling and monitoring are highly variable, with numbers of units and sampling effort varying from one study to another. Numerous works have been carried out to determine an appropriate effect size through statistical power; however, these were always from a univariate perspective. In this work, we used the pseudo multivariate dissimilarity-based standard error (MultSE) approach to assess the precision of sampling scleractinian coral assemblages in reefs of Venezuela between 2017 and 2018 when using different combinations of number of transects, quadrats and points. For this, the MultSE of 36 sites previously sampled was estimated, using four 30m-transects with 15 photo-quadrats each and 25 random points per quadrat. We obtained that the MultSE was highly variable between sites and is not correlated with the univariate standard error nor with the richness of species. Then, a subset of sites was re-annotated using 100 uniformly distributed points, which allowed the simulation of different numbers of transects per site, quadrats per transect and points per quadrat using resampling techniques. The magnitude of the MultSE stabilized by adding more transects, however, adding more quadrats or points does not improve the estimate. For this case study, the error was reduced by half when using 10 transects, 10 quadrats per transect and 25 points per quadrat. We recommend the use of MultSE in reef monitoring programs, in particular when conducting pilot surveys to optimize the estimation of the community structure. Subjects Ecology, Marine BiologyKeywords Coral reefs, Coral assemblage, Venezuela, Pseudo multivariate dissimilarity-based standard error, Precision How to cite this article Montilla LM, Miyazawa E, Ascanio A, López-Hernández M, Mariño-Briceño G, Rebolledo-Sánchez Z, Rivera A, Mancilla DS, Verde A, Cróquer A. 2020. The use of pseudo-multivariate standard error to improve the sampling design of coral monitoring programs. PeerJ 8:e8429 http://doi.org/10.7717/peerj.8429