Reconstruction of plant–pollinator networks from observational data

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 statist...

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Bibliographic Details
Published in:Nature communications 2021-06, Vol.12 (1), p.3911-3911, Article 3911
Main Authors: Young, Jean-Gabriel, Valdovinos, Fernanda S., Newman, M. E. J.
Format: Article
Language:English
Subjects:
631/158/2463
639/705/531
639/766/25
Algorithms
Animals
Archipelagoes
Bayesian analysis
Conservation of Natural Resources - methods
Conservation of Natural Resources - statistics & numerical data
Ecology
Ecosystem
Empirical analysis
Estimates
Field study
Flowers & plants
Food chains
Food webs
Host-Parasite Interactions
Humanities and Social Sciences
Insecta - physiology
Insects
Models, Biological
multidisciplinary
National parks
Networks
New South Wales
Parks, Recreational
Plants - parasitology
Pollination - physiology
Pollinators
Rare species
Science
Science (multidisciplinary)
Seychelles
Statistical analysis
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Summary: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 and Kosciusko National Park, calculating estimates of network structure, network nestedness, and other characteristics. Networks describe the intricate patterns of interaction occurring within ecological systems, but they are unfortunately difficult to construct from data. Here, the authors show how Bayesian statistical techniques can separate structure from noise in networks gathered in observational studies of plant-pollinator systems.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-24149-x