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Nestedness describes patterns of species composition within continental biotas and among isolated habitats such as islands and landscape fragments. In a nested pattern, the species composition of small assemblages is an ordered subset (a true sample) of the species composition of large assemblages. Nested subsets of species are generated by environmental and ecological gradients, such as habitat quality, carrying capacities of sites, isolation, or fragmentation, that cause ordered sequences of species extinctions and colonization. Therefore nestedness analysis can be used to identify gradients that influence species composition and richness among sites and to identify species that run counter to these gradients (idiosyncratic species). Here I review the use of nestedness analysis to identify such gradients. I also describe how to perform the analysis and which metrics and null models to use for statistical inference.
Sixty-one specimens of the piranha Serrasalmus marginatus Valenciennes, 1837 were analyzed, aiming at assessing the community structure of their gill parasites. The samples were collected in lagoons of the Paraná, Ivinheima and Baia Subsystems within the Upper Paraná River Floodplain (Brazil). Host size and sex had little or no influence on the abundance and prevalence of parasites. The organization of the gill parasite infracommunities of S. marginatus was significantly non-random according to null models and ordination analyses. In general, parasite infrapopulations were not affected by interspecific associations or host characteristics (e.g. size, sex), what highlights the importance of local habitat characteristics to community organization of gill parasites of S. marginatus in the Upper Paraná River Floodplain.
Interaction networks are a tool to visualize and to study the relationships between interacting species across and within trophic levels. Recent research uncovered many properties of such networks that remained undetected in previous food web studies. These patterns could be related to evolutionary and ecological processes. The study of interaction networks promises therefore progress in the study of constraints that act on the coevolution of interacting species and on food webs. However, there are still many pitfalls associated with the statistical analysis, the properties of the metrics involved and the appropriate null model choice. Here I review the mechanisms that shape interaction matrices, the possible internal structures and their ecological interpretation, and the analytical tools to identify matrix structure. Progress in the field needs critical meta-analytical and comparative studies that indentify the best suited null models (low type I and II error probabilities and high power to disentangle statistical from ecological processes) and clarify the interdependence of different concepts and metrics associated with network approaches. It is not improbable that many patterns recently associated with ecological and evolutionary processes might turn out to be simple side effects of the sampling from the underlying metacommunity distributions.
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