Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations

This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint


Tanya Strydom , Salomé Bouskila, Francis Banville, Ceres Barros, Dominique Caron , Maxwell Jenner Farrell, Marie-Josée Fortin, Victoria Hemming, Benjamin Mercier, Laura J. Pollock


Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large-scale species interaction networks are structured.
Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs.
One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems.
Here, we outline how the challenges associated with inferring metawebs line-up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human-made (or arbitrarily assigned) boundaries and how these my influence ecological hypotheses.



Biodiversity, Life Sciences


ecological networks, network embedding, network macroecology, transfer learning


Published: 2022-02-18 19:57

Last Updated: 2023-01-31 18:30

Older Versions

CC-BY Attribution-No Derivatives 4.0 International