Testing for nestedness in the terrestrial isopods and snails of Kyklades islands (Aegean archipelago, Greece)
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Most insular communities exhibit nestedness, with the species assemblages of 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 more recent and elaborate methods, namely the 'temperature' method of Atmar and Patterson and the 'departures' method of Lomolino, in order to investigate patterns of nestedness in the distribution of two well studied and speciose animal groups, terrestrial isopods and land snails, in the Kyklades archipelago (Aegean Sea, Greece) that lies between two continental regions. Significant nestedness is present in both species assemblages and, surprisingly, each method gives almost identical levels of nestedness for the two animal groups. Isolation has been found to be more important in producing nestedness in both groups than area, which does not seem to be an important explanatory factor. However, the role of isolation in this case is better understood under an historical perspective, taking into account the complex palaeogeography of the region and the differential departmentalisation of distinct island groups. Additionally, certain metrics of habitat diversity that were included in the analysis were the best explanatory factors of nestedness, indicating a more complex causal pattern that also involves extinction. Since the two methods used are based on different assumptions and have different scopes, their results do not converge. The 'temperature' method finds the maximum possible nestedness in an island sorting which does not necessarily lead to plausible biogeographical explanations, while the 'departures' method, although more useful in detecting causality, fails to fully evaluate levels of nestedness. Nevertheless, both methods are valuable tools in the exploration of interesting distributional patterns, when this effort is accompanied by a good understanding of historical, ecological and idiosyncratic properties of each particular data set.