Nestedness has been widely reported for both metacommunities and networks of interacting species. Even though the concept of this ecological pattern has been well-defined, there are several metrics by which it can be quantified. We noted that current metrics do not correctly quantify two major properties of nestedness: (1) whether marginal totals (i.e. fills) differ among columns and/or among rows, and (2) whether the presences (1's) in less-filled columns and rows coincide, respectively, with those found in the more-filled columns and rows. We propose a new metric directly based on these properties and compare its behavior with that of the most used metrics, using a set of model matrices ranging from highly-nested to alternative structures in which no nestedness should be detected. We also used an empirical dataset to explore possible biases generated by the metrics as well as to evaluate correlations between metrics. We found that nestedness has been quantified by metrics that inappropriately detect this pattern, even for matrices in which there is no nestedness. In addition, the most used metrics are prone to type I statistical errors while our new metric has better statistical properties and consistently rejects a nested pattern for different types of random matrices. The analysis of the empirical data showed that two nestedness metrics, matrix temperature and the discrepancy measure, tend to overestimate the degrees of nestedness in metacommunities. We emphasize and discuss some implications of these biases for the theoretical understanding of the processes shaping species interaction networks and metacommunity structure.

Nestedness analysis has become increasingly popular in the study of biogeographic patterns of species occurrence. Nested patterns are those in which the species composition of small assemblages is a nested subset of larger assemblages. For species interaction networks such as plant–pollinator webs, nestedness analysis has also proven a valuable tool for revealing ecological and evolutionary constraints. Despite this popularity, there has been substantial controversy in the literature over the best methods to define and quantify nestedness, and how to test for patterns of nestedness against an appropriate statistical null hypothesis. Here we review this rapidly developing literature and provide suggestions and guidelines for proper analyses. We focus on the logic and the performance of different metrics and the proper choice of null models for statistical inference. We observe that traditional ‘gap‐counting’ metrics are biased towards species loss among columns (occupied sites) and that many metrics are not invariant to basic matrix properties. The study of nestedness should be combined with an appropriate gradient analysis to infer possible causes of the observed presence–absence sequence. In our view, statistical inference should be based on a null model in which row and columns sums are fixed. Under this model, only a relatively small number of published empirical matrices are significantly nested. We call for a critical reassessment of previous studies that have used biased metrics and unconstrained null models for statistical inference.

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Understanding and predicting species extinctions and coextinctions is a major goal of ecological research in the face of a biodiversity crisis. Typically, models based on network topology are used to simulate coextinctions in mutualistic networks. However, such topological models neglect two key biological features of species interactions: variation in the intrinsic dependence of species on the mutualism, and variation in the relative importance of each interacting partner. By incorporating both types of variation, we developed a stochastic coextinction model capable of simulating extinction cascades far more complex than those observed in previous topological models. Using a set of empirical mutualistic networks, we show that the traditional topological model may either underestimate or overestimate the number and likelihood of coextinctions, depending on the intrinsic dependence of species on the mutualism. More importantly, contrary to topological models, our stochastic model predicts extinction cascades to be more likely in highly connected mutualistic communities.

Aim To assess the geographical variation in the relative importance of vertebrates, and more specifically of birds and mammals, as seed dispersal agents in forest communities, and to evaluate the influence of geographical and climatic factors on the observed trends.Location One hundred and thirty-five forest communities in the Brazilian Atlantic forest. MethodsWe collected data on dispersal modes for 2292 woody species. By combining species × site with species × trait matrices, we obtained the percentages of endozoochory, ornithochory, mastozoochory and the mean fruit diameter for the local forest communities. We used Spearman's correlation to assess bivariate relationships between variables. Subsequently, we performed paired t-tests to verify if variations in frequency of dispersal modes and mean fruit diameter were influenced by altitude or temperature. Then, we applied multiple linear regressions to evaluate the effect of geographical and climatic variables on variation in the relative frequency of dispersal modes and mean fruit diameter across communities. ResultsWe found no consistent latitudinal or longitudinal trend in the percentage of vertebrate-dispersed species, neither bird-nor mammal-dispersed species along the Atlantic forest. Endozoochory was affected chiefly by annual mean rainfall, increasing towards moister sites. Forest communities located at higher altitudes had a higher percentage of bird-dispersed species. Even when sites with identical values of annual mean temperature were compared, altitude had a positive effect on ornithochory. Conversely, we found a higher percentage of mammal-dispersed species in warmer forests, even when locations at the same altitudinal belts were contrasted. Fruit diameter was clearly related to altitude, decreasing towards higher elevations.Main conclusions This is the first analysis of a large data set on dispersal syndromes in tropical forest communities. Our findings support the hypotheses that: (1) geographical variation in the relative number of fleshy fruit species is mainly driven by moisture conditions and is relatively independent of geographical location, and (2) broad-scale trends in fruit size correspond to geographical variation in the relative importance of mammals and birds as seed dispersal agents at the community level.