Summary1. Ecological networks are often composed of different subcommunities (often referred to as modules). Identifying such modules has the potential to develop a better understanding of the assembly of ecological communities and to investigate functional overlap or specialization. 2. The most informative form of networks are quantitative or weighted networks. Here, we introduce an algorithm to identify modules in quantitative bipartite (or two-mode) networks. It is based on the hierarchical random graphs concept of Clauset et al. (2008 Nature 453: 98-101) and is extended to include quantitative information and adapted to work with bipartite graphs. We define the algorithm, which we call QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study. 3. Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within-or betweenmodules. Recursively applying the algorithm results in additional information of within-module organization of the network. 4. The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks.

Modularity is a recurrent and important property of bipartite ecological networks. Although well-resolved ecological networks describe interaction frequencies between species pairs, modularity of bipartite networks has been analysed only on the basis of binary presence-absence data. We employ a new algorithm to detect modularity in weighted bipartite networks in a global analysis of avian seed-dispersal networks. We define roles of species, such as connector values, for weighted and binary networks and associate them with avian species traits and phylogeny. The weighted, but not binary, analysis identified a positive relationship between climatic seasonality and modularity, whereas past climate stability and phylogenetic signal were only weakly related to modularity. Connector values were associated with foraging behaviour and were phylogenetically conserved. The weighted modularity analysis demonstrates the dominating impact of ecological factors on the structure of seed-dispersal networks, but also underscores the relevance of evolutionary history in shaping species roles in ecological communities.

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