PT  - JOURNAL ARTICLE
AU  - Jean-Gabriel Young
AU  - Fernanda S. Valdovinos
AU  - M. E. J. Newman
TI  - Reconstruction of plant–pollinator networks from observational data
AID  - 10.1101/754077
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 754077
4099  - http://biorxiv.org/content/early/2019/09/04/754077.short
4100  - http://biorxiv.org/content/early/2019/09/04/754077.full
AB  - Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but also estimates of their statistical errors, paving the way for principled statistical analyses of ecological variables and outcomes. We demonstrate the use of the method with an application to previously published data on plant–pollinator networks in the Seychelles archipelago, calculating estimates of network structure, network nestedness, and other characteristics.