Conclusion
The approach we take for network reconstruction highlights the utility of non-traditional methods (e.g., species co-occurrence data) to infer interactions and thus community structure. Moreover, through our indirect methods (i.e. abundance measurements over time), we captured potential interactions other than those that are amenable to direct observation. Although our network reconstruction is not without its uncertainties, we demonstrate how EMtree methods can be used to elucidate multiplex network structure. Overall, we find strong evidence that differences in our seasonal networks appear to be driven mainly by rewiring as compared to species turnover. Additionally, while there is recognition that traits are important factors of community assembly (e.g., Kneitel & Chase 2004), our findings that maximum length and piscivore status contributes to a species’ number of rewirings provide evidence that traits may influence how temporal interaction networks change. Finally, our study highlights the need to consider communities as evolving through time. Since seasonal change is capable of dramatically altering network topology, failing to capture temporal heterogeneities may cause us to mischaracterize true community structure.