Identifying important species for maintaining ecosystem functions is a challenge in ecology. Since species are components of food webs, one way to conceptualize and quantify species importance is from a network perspective. The importance of a species can be quantified by measuring the centrality of its position in a food web, because a central node may have greater influence on others in the network. A species may also be important because it has a unique network position, such that its loss cannot be easily compensated. Therefore, for a food web to be robust, we hypothesize that central species must be functionally redundant in terms of their network position. In this paper, we test our hypothesis by analysing the Prince William Sound ecosystem. We found that species centrality and uniqueness are negatively correlated, and such an observation is also carried over to other food webs.

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Biodiversity is measured from various perspectives. One of them, functional diversity, quantifies the heterogeneity in species traits and roles in an ecosystem. One important aspect of species roles is their interactions with other species, i.e. their network role. We therefore investigate here functional diversity from the network perspective. Species differ in their network positions in a food web, having different interaction patterns. We developed a measure for quantifying the diversity in species interaction patterns in a food web. We examined the relationship between interaction diversity and several global network properties for 92 food webs. Our results showed that high interaction diversity occurs in sparsely connected and less cohesive food webs. High interaction diversity also occurred in food webs with more clusters and high network modularity. We also quantified several conventional functional diversity indices and demonstrate that they show little or no correlation with interaction diversity. Our proposed diversity index therefore provides a measure complementary to current concepts of functional diversity.

A food web is a representation of trophic interactions in an ecosystem. Food webs are often generated by a single dataset aggregated from one or several surveys. Point estimates of food web parameters can be calculated from the data, but it remains an open question as to how their corresponding interval estimates can be quantified. Although conventional methods such as bootstrapping represent potential solutions, they tend to underestimate several network parameters. Here, we propose a simple bootstrap‐based resampling procedure for inferring food web parameters. First, for a particular food web parameter, we obtain its point estimate by calculating the corresponding statistics from the original food web. Second, we generate a resampled food web by sampling with replacement the same number of species from the original food web, and for each resampled species we record how many prey items it consumes in the original food web. Third, a resampled species is allowed to consume its original prey species if such a species is also present; if not present, it instead consumes the resampled species that is most topologically similar to its original prey species. Many resampled food webs can be generated in this manner, and we calculate particular food web statistics for each of them. These form a sampling distribution from which interval estimates of the true food web parameter can be determined. We demonstrate our methodology on two different food web datasets and discuss its application in comparing food webs of various sizes and connectance.