No strong evidence that modularity, specialization or nestedness are linked to seasonal climatic variability in bipartite networks.

Aim: Given the influence of seasonality on most ecological systems, an emerging research area attempts to understand how community network structure is shaped by seasonal climatic variations. To do so, most researchers conduct their analyses using open networks due to the high cost associated with constructing their own community networks. However, unwanted structural differences from the unique sampling and construction methods used to create each open network likely make comparing these networks a difficult task. Here, with the largest set of open bipartite networks collected to date, we test whether seasonal climatic variations explain network structure while additionally accounting for construction/sampling differences between networks. Location: Trying to approach global. Time period: Contemporary. Major taxa studied: Plants and animals. Methods: Using 723 open bipartite networks, we test whether temperature and/or precipitation seasonality explains (un)weighted metrics of nestedness, modularity or specialization across plant–pollinator, seed‐dispersal, plant–ant, host–parasite or plant–herbivore systems. Results: Generally, seasonality only weakly explained network structure: at most 16% of the variation in weighted metrics and 5% of the variation in unweighted metrics. Instead, a control for sampling bias in networks, sampling intensity, often better explained many of the network structural metrics. When limiting our analyses to only intensely sampled networks, however, about 33% of the variation in weighted modularity and specialization was explained by seasonality, but only in plant–pollinator networks. Main conclusions: Altogether, we do not find strong evidence that seasonality explains network structure. Our study also highlights the large amount of structural differences in open networks, likely from the many different sampling and network construction techniques adopted by researchers when constructing networks. Hence, a definitive test for the relationship between network structure and seasonality across large spatial extents will require a dataset free from sampling and other biases, where networks are derived from a consistent sampling protocol that appropriately characterizes communities. [ABSTRACT FROM AUTHOR]