Abstract
Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. Besides providing a compact representation of the interconnections between agents, network analysis allows the identification of especially important nodes, according to various metrics that often rely on the calculation of the shortest paths connecting any two nodes. While the interpretation of a shortest paths is straightforward in binary, unweighted networks, whenever weights are reported, the calculation could yield unexpected results. We analyzed 129 studies of ecological networks published in the last decade that use shortest paths, and discovered a methodological inaccuracy related to the edge weights used to calculate shortest paths (and related centrality measures), particularly in interaction networks. Specifically, 49% of the studies do not report sufficient information on the calculation to allow their replication, and 61% of the studies on weighted networks may contain errors in how shortest paths are calculated. Using toy models and empirical ecological data, we show how to transform the data prior to calculation and illustrate the pitfalls that need to be avoided. We conclude by proposing a five-point check-list to foster best-practices in the calculation and reporting of centrality measures in ecology and evolution studies.
Highlights
Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology
We focus on the effects on Betweenness (BC) and Closeness centralities (CC) as these are the most commonly used centrality measures
The increasing popularity of packages for the analysis of ecological networks will only boost the use of tools and methodologies researchers may be unfamiliar with
Summary
Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. Whenever edge weights are proportional to the strength of the relationship between two nodes (e.g., probability of dispersal, interaction frequency, contact rate, carbon flow, etc.), minimizing the sum of edges along the path makes no sense: the data need to be transformed prior to the analysis, or one has to choose an appropriate method able to deal with this situation This issue has been raised before[23,24] and is widely acknowledged among studies describing community structure, where measures of network structure have been developed to account for weighted edges[25,26,27]. Nine studies (eight of which examine interaction networks) calculated centrality using the binary version of the weighted data, without providing any justification for this methodological choice
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