Study of variability and genetic structure of European populations of Myotis emarginatus and Myotis capaccinii (Chiroptera, Vespertilionidae)

The Geoffroy’s bat Myotis emarginatus is mainly present in southern, south-eastern and central Europe (Červerný, 1999) and is often recorded from northern Spain (Quetglas, 2002; Flaquer et al., 2004). It has demonstrated the species’ preference for forest. Myotis capaccinii, confined to the Mediterranean (Guille´n, 1999), is classified as ‘vulnerable’ on a global scale (Hutson, Mickleburgh & Racey, 2001). In general, the species preferred calm waters bordered by well-developed riparian vegetation and large (> 5 m) inter-bank distances (Biscardi et al. 2007). In this study we present the first results about population genetic structure of these two species of genus Myotis. We used two methods of sampling: invasive and non-invasive techniques. A total of 323 invasive samples and a total of 107 non-invasive samples were collected and analyzed. For Myotis emarginatus we have individuated for the first time a set of 7 microsatellites, which can work on this species, started from a set developed on Myotis myotis (Castella et al. 2000). We developed also a method for analysis of non-invasive samples, that given a good percentage of positive analyzed samples. The results have highlighted for the species Myotis emarginatus the presence on the European territory of two big groups, discovered by using the microsatellites tracers. On this species, 33 haplotypes of Dloop have been identified, some of them are presented only in some colonies. We identified respectively 33 haplotypes of Dloop and 10 of cytB for Myotis emarginatus and 25 of dloop and 15 of cytB for Myotis capaccinii. Myotis emarginatus’ results, both microsatellites and mtDNA, show that there is a strong genetic flow between different colonies across Europe. The results achieved on Myotis capaccinii are very interesting, in this case either for the microsatellites or the mitochondrial DNA sequences, and it has been highlighted a big difference between different colonies.

Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach

Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.