Detect coastal disturbances and climate change effects in coralligenous community through sentinel stations

(RM-PERMANOVA). Univariate analyses were performed using SPSS.13© according to the guidelines of Pardo and Ruiz [96]. For the multivariate analyses, square root transformations were applied to the data, and the analyses were performed using the Bray-Curtis similarity. The Bray-Curtis similarity matrix was used to generate a non-metric multidimensional scaling (nMDS) analysis, and the Kruskal stress coefficient was calculated to test the ordination [97]. The multivariate analyses were performed using the PRIMER6 software (complete with the PERMANOVA+ package) [98–101]. Results The average covers for the five most abundant species (including the two pre-selected sensitive target species A. calycularis and P. clavata) over the period of 2005 to 2014 at the two locations (horizontal and vertical) are presented in Figs 6 and 7. Fig 8 illustrates the evolution of the covers of the sampled indicator species A. calycularis and P. clavata at both locations (horizontal and vertical) together with the time series of the Fig 5. Digital manipulation of the captured images prior to analysis. https://doi.org/10.1371/journal.pone.0231641.g005 PLOS ONE Detect coastal disturbances in coralligenous PLOS ONE | https://doi.org/10.1371/journal.pone.0231641 May 5, 2020 8 / 23