A roadmap towards predicting species interaction networks (across space and time)

Autor: Andrew Gonzalez, Timothée Poisot, Benjamin Mercier, Philippe Desjardins-Proulx, Dominique Caron, Laura J. Pollock, Norma R Forero-Muñoz, Michael D Catchen, Tanya Strydom, Francis Banville, Gabriel Dansereau, Gracielle Teixeira Higino, Dominique Gravel
Rok vydání: 2021
Předmět:
Zdroj: Philos Trans R Soc Lond B Biol Sci
ISSN: 1471-2970
0962-8436
DOI: 10.1098/rstb.2021.0063
Popis: Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species—and to describe the structure, variation, and change of the ecological networks they form—we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward.This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
Databáze: OpenAIRE