Divergence patterns in Fucus seaweeds (Phaeophyceae) in the northern Baltic Sea and in the Tjongspollen area

To be able to interpret patterns of biodiversity it is important to understand the processes by which new species evolve and how closely related species remain reproductively isolated and ecologically differentiated. Divergence and differentiation can vary during speciation and it can be seen in different stages. Groups of closely related taxa constitute important case studies to understand species and new biodiversity formation. However, it is important to assess the divergence among them at different organismal levels and from an integrative perspective. For this purpose, this study used the brown seaweeds genus Fucus as a model to study speciation, as they constitute a good opportunity to study divergence at different stages. We investigated the divergence patterns in Fucus species from two marginal areas (northern Baltic Sea and the Tjongspollen area), based on phenetic, phylogenetic and biological taxonomical criteria that are respectively characterised by algal morphology, allele frequencies of five microsatellite loci and levels of secondary polyphenolic compounds called phlorotannins. The results from this study showed divergence at morphological and genetic levels to certain extent but complete lack of divergence at biochemical level (i.e. constitutive phlorotannin production) in the Baltic Sea or Norway. Morphological divergence was clearly evident in Tjongspollen (Norway) among putative taxa as they were identified in the field and this divergence corresponds with their neutral genetic divergence. In the Baltic, there are some distinguishable patterns in the morphology of the swedish and finnish individuals according to locality to certain extent but not among putative taxa within localities. Likewise, these morphological patterns have genetic correspondence among localities but not within each locality. At the biochemical level, measured by the phlorotannin contents there were neither evidence of divergence in Norway or the Baltic Sea nor any discernable aggregation pattern among or within localities. Our study have contributed with further understanding of the Baltic Sea Fucus system and its intriguingly rapid and recent divergence as well as of the Tjongspollen area systems where formally undescribed individuals have been observed for the first time; in fact they appear largely differentiated and they may well warrant a new species status. In current times, climate change threatens, peripheral ecosystems, biodiversity, and increased knowledge of processes generating and maintaining biodiversity in those ecosystems seem particularly important and needed.

Effect of multiple sources of disturbance on rocky benthic assemblages in some localities of Salento, Apulia

The first part of my work consisted in samplings conduced in nine different localities of the salento peninsula and Apulia (Italy): Costa Merlata (BR), Punta Penne (BR), Santa Cesarea terme (LE), Santa Caterina (LE), Torre Inserraglio (LE), Torre Guaceto (BR), Porto Cesareo (LE), Otranto (LE), Isole Tremiti (FG). I collected data of species percentage covering from the infralittoral rocky zone, using squares of 50x50 cm. We considered 3 sites for location and 10 replicates for each site, which has been taken randomly. Then I took other data about the same places, collected in some years, and I combined them together, to do a spatial analysis. So I started from a data set of 1896 samples but I decided not to consider time as a factor because I have reason to think that in this period of time anthropogenic stressors and their effects (if present), didn’t change considerably. The response variable I’ve analysed is the covering percentage of an amount of 243 species (subsequently merged into 32 functional groups), including seaweeds, invertebrates, sediment and rock. 2 After the sampling, I have been spent a period of two months at the Hopkins Marine Station of Stanford University, in Monterey (California,USA), at Fiorenza Micheli's laboratory. I've been carried out statistical analysis on my data set, using the software PRIMER 6. My explorative analysis starts with a nMDS in PRIMER 6, considering the original data matrix without, for the moment, the effect of stressors. What comes out is a good separation between localities and it confirms the result of ANOSIM analysis conduced on the original data matrix. What is possible to ensure is that there is not a separation led by a geographic pattern, but there should be something else that leads the differences. Is clear the presence of at least three groups: one composed by Porto cesareo, Torre Guaceto and Isole tremiti (the only marine protected areas considered in this work); another one by Otranto, and the last one by the rest of little, impacted localities. Inside the localities that include MPA(Marine Protected Areas), is also possible to observe a sort of grouping between protected and controlled areas. What comes out from SIMPER analysis is that the most of the species involved in leading differences between populations are not rare species, like: Cystoseira spp., Mytilus sp. and ECR. Moreover I assigned discrete values (0,1,2) of each stressor to all the sites I considered, in relation to the intensity with which the anthropogenic factor affect the localities. 3 Then I tried to estabilish if there were some significant interactions between stressors: by using Spearman rank correlation and Spearman tables of significance, and taking into account 17 grades of freedom, the outcome shows some significant stressors interactions. Then I built a nMDS considering the stressors as response variable. The result was positive: localities are well separeted by stressors. Consequently I related the matrix with 'localities and species' with the 'localities and stressors' one. Stressors combination explains with a good significance level the variability inside my populations. I tried with all the possible data transformations (none, square root, fourth root, log (X+1), P/A), but the fourth root seemed to be the best one, with the highest level of significativity, meaning that also rare species can influence the result. The challenge will be to characterize better which kind of stressors (including also natural ones), act on the ecosystem; and give them a quantitative and more accurate values, trying to understand how they interact (in an additive or non-additive way).