Abstract
BackgroundThe ‘classical’ concept of species diversity was extended in the last decades into other dimensions focusing on the functional and phylogenetic diversity of communities. These measures are often argued to allow a deeper understanding of the mechanisms shaping community assembly along environmental gradients. Because of practical impediments, thus far only very few studies evaluated the performance of these diversity measures on large empirical data sets. Here, data on species-rich riparian moth communities under different flood regimes and from three different rivers has been used to compare the power of various diversity measures to uncover ecological contrasts.ResultsContrary to the expectation, classical metrics of species diversity (Hill numbers N1, N2 and Ninf) and evenness (Buzas-Gibson’s E and Pielous’s J) turned out to be the most powerful measures in unravelling the two gradients investigated in this study (e.g. flood regime and region). Several measures of functional and phylogenetic diversity tended to depict either only one or none of these contrasts. Rao’s Q behaved similarly as species diversity and evenness. NTI and NRI showed a similar pattern among each other but, were different to all the other measures. Functional Divergence also behaved idiosyncratically across the 28 moth communities. The community weighted means of nearly all individual functional traits showed significant ecological patterns, supporting the relevance of the selected traits in shaping assemblage compositions.ConclusionsSpecies diversity and evenness measures turned out to be the most powerful metrics and clearly reflected both investigated environmental contrasts. This poses the question when it is useful to compile the additional data necessary for the calculation of additional diversity measures, since assembling trait bases and community phylogenies often requires a high work load. Apart from these methodological issues, most of the diversity measures related to communities of terrestrial insects like moths increased in forests that still are subject to flooding dynamics. This emphasizes the high conservation value of riparian forests and the importance of keeping and restoring river dynamics as a means of fostering also terrestrial biodiversity in floodplain areas.
Highlights
The ‘classical’ concept of species diversity was extended in the last decades into other dimensions focusing on the functional and phylogenetic diversity of communities
Moth species diversity (SD) was highest in the forests at river Danube, lowest at Morava, and intermediate at river Leitha (Fig. 1). This sequence was strongly reflected by the Hill numbers N1, N2, and N inf, Menhinick’s diversity metric, as well as by evenness
In both the Danube and Leitha regions, moth species diversity and evenness was distinctly higher in flood-prone as opposed to nonflooded forest stands, while no significant variation in local species diversity was observed among the Morava samples (Fig. 1, Table 1)
Summary
The ‘classical’ concept of species diversity was extended in the last decades into other dimensions focusing on the functional and phylogenetic diversity of communities These measures are often argued to allow a deeper understanding of the mechanisms shaping community assembly along environmental gradients. FD concentrates on the frequency distributions of functional traits, rather than mere species identities This approach aims at a more mechanistic understanding of differences in biodiversity along environmental gradients [4]. An inherent assumption in the interpretation of PD is that the more closely related organisms are, the more traits they share by descent and tend to occupy more similar niches than is the case with distantly related organisms (i.e. the principle of phylogenetic conservatism [10]) Along those lines, Faith [11] introduced the term ‘feature diversity’ encompassing morphological and functional diversity [12]. This proposed surrogacy of PD for FD has frequently been invoked as a paradigm in conservation biology [13, 14] despite the lack of empirical evidence [15]
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