Background

The evolutionary history of European biodiversity has been greatly shaped by the highly variable Pleistocene climatic variations, during which glacial/interglacial periods caused multiple range shifts. These have left their fingerprints on the current genetic diversity of species and their past influence can help to predict future consequences of further changes in climatic variables. 

 

One of the consequences of such climatic variability was the separation of refugial populations that with time and isolation might have speciated. In the Adriatic Sea, the species Fucus virsoides was one such case (e.g., Canovas et al. 2011). Currently endemic to the Adriatic Sea, this species now has a very restricted distribution and several populations have been reported to have gone extinct. The causes of such regression are unknown but presumed to be related to climatic or habitat variables that have become unsuitable. It is also unknown whether the species retains differentiated populations and hotspots of unique genetic diversity along its range, presumed to occur along long-term persistence zones. Species distribution models, developed for other intertidal Fucus (Assis et al. 2014), suggest indeed, the occurrence of favourable persistence regions in the Adriatic, for such intertidal canopy species. This thesis plan aims to understand the past evolutionary history of this species, how has it influenced its present genetic background and fitness, also predicting its future under new climatic scenarios. In addition, comparing transcriptome and fitness responses to climatic stress between populations and with its sister species will allow to infer evolutionary selective processes involved in the adaptation of this species to the very distinct and unique habitat of the Adriatic sea.

 

Objectives

The proposed MARES project will cover three objectives:

 

Work programme

- Distribution models will be produced by an ensemble of a set of transferable models, trained with distribution records in relation to current climate conditions, and projected onto multiple proxies of past climate extremes derived from ocean-atmosphere general circulation models (e.g., Neiva et al. 2014). Regions of long-term persistence will be inferred as those where suitable habitat occurred over the most extreme events of the recent past (Last Glacial Maximum, Mid-Holocene, present). The area of potential habitat will be determined for every region of persistence (Assis et al. 2014). 

 

- Samples will be collected across the distributional ranges of F. virsoides to cover the potential expansion, contraction and persistence zones. Sampling will include sister taxa as reference points of comparison. These will be genotyped for multiple loci using high resolution nuclear markers already available, to recover the ancient biogeographic/demographic history of the species and to compare with sister species (Cánovas et al. 2011; Zardi et al. 2011). 

 

- For comparison of fitness and gene expression (transcriptome) responses to climatic stress between populations and with its sister species, common garden experiments and comparative transcriptome analyses of gene expression will be conducted. Acclimated samples will be exposed to controlled thermal and heat shock (HS) treatments and physiological indicators of resilience relative to appropriate controls will be assayed after exposure . Physiological response (photosynthetic parameters, growth) will be determined during stress exposure and recovery in control conditions. For assessment of gene expression responses in RNA-seq experiments experimental tissues will be lyophilized and total RNA extracted. Generation and sequencing of cDNA libraries will be outsourced (Illumina). Gene expression from biological replicates will be analyzed by mapping reads onto de novo assembled reference transcriptomes (Velvet-Oases, Trinity; [Pearson et al., unpublished]); statistical analysis of differential expression between treatments will be performed with the aid of open-source software (e.g., RSEM). These results will be validated by real-time PCR (qPCR), following the methods of Pearson et al. (2009) and Martins et al. (2013). Functional annotation and statistical analysis will use public protein databases and expression and annotation data will be integrated in a relational database to facilitate analyses.

 

References