Testing for nestedness in the terrestrial isopods and snails of Kyklades islands (Aegean archipelago, Greece)

Most insular communities exhibit nestedness, with the species assemblages o f the more depauperate islands constituting subsets of those of the richer. Several methods for the estimation and evaluation of nestedness have been developed during the last fifteen years. In this paper we use two of the mo re recent and elaborate methods, namely the "temperature" method of Atmar a nd Patterson and the "departures" method of Lomolino. in order to investiga te patterns of nestedness in the distribution of two well studied and speci ose animal groups, terrestrial isopods and land snails, in the Kyklades arc hipelago (Aegean Sea, Greece) that lies between two continental legions. Si gnificant nestedness is present in both species assemblages and, surprising ly, each method gives almost identical levels of nestedness for the two ani mal groups. Isolation has been found to be more important in producing nest edness in both groups than area, which does not seem to be an important exp lanatory factor. However, the role of isolation in this case is better unde rstood under an historical perspective, taking into account the complex pal aeogeography of the region and the differential departmentalisation of dist inct island groups. Additionally, certain metrics of habitat diversity that were included in the analysis were the best explanatory factors of nestedn ess, indicating a more complex causal pattern that also involves extinction . Since the two methods used are based on different assumptions and have di fferent scopes, their results do not converge. The "temperature" method fin ds the maximum possible nestedness in an island sorting which does not nece ssarily lead to plausible biogeographical explanations, while the "departur es" method, although more useful in detecting causality, fails to fully eva luate levels of nestedness. Nevertheless, both methods are valuable tools i n the exploration of interesting distributional patterns, when this effort is accompanied by a good understanding of historical. ecological and idiosy ncratic properties of each particular data set.