Posidonia oceanica represents the key species of the most important ecosystem in subtidal habitats of the Mediterranean Sea. Being sensitive to changes in the environment, it is considered a crucial indicator of the quality of coastal marine waters.

A peculiarity of P. oceanica is the presence of reiterative modules characterizing its growth, which lend themselves to back-dating techniques, allowing for the reconstruction of past history of growth variables (annual rhizome elongation and diameter, primary production, etc.).

Such back-dating techniques provide, for each sampled shoot, a longitudinal series of multivariate data; this is an instance of what Hurlbert (1984) in a seminal paper defined as “pseudo-replications”, for which it becomes crucial to take into account the possible dependence of the data.

A common solution to the “pseudo-replications” in the ecological literature is represented by “pseudo-replications”: given repeated measurements on the same unit, only a random sub-sample of such measurements is analyzed, in order to attenuate correlation and obtain approximately independently distributed observations, to which standard statistical methods can be applied. In its most extreme version, only one measurement is randomly drawn for each unit, i.e. the sub-sampling size is one. If on one hand sub-sampling attenuates correlation, on the other it implies a loss of information (due to the reduction of the total sample size) and then requires a higher number of sampling units to ensure a specified level of efficiency and power.

In the talk, we contrast sub-sampling with the alternative approach of handling dependence directly in the modelling stage, using the class of Generalized Linear Mixed Models. We show that this approach permits remarkable gains of precision in estimation and power in testing, without requiring the increase in sample sizes involved in sub-sampling, and thus avoiding the practice of over-sampling, which has a negative impact on aquatic ecosystems.

Keywords: Generalized linear mixed models; Sub-sampling; Pseudo-replications; Marine ecolology

Biography: Gianfranco Lovison is Professor of Statistics, University of Palermo, Italy, since 1994.

His main research interests are in categorical data analysis; GLM and developments; Statistical methods in environmental monitoring and management.

He has been Head of the Department of Statistical and Mathematical Sciences, University of Palermo, 2004-2010, and Coordinator of the Ph.D. Program in Statistics, University of Palermo, 2002-2008.

He has served as referee of: Biometrika, Statistical Methods & Applications, Journal of Agricultural, Biological and Environmental Statistics; Journal of Environmental and Ecological Statistics, Statistical Papers, and has collaborated to organize several scientific conferences and workshops in the field of environmental statistics. He is a member of The International Environmetrics Society.