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Hypothesis and Theory ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Ecol. Evol. | doi: 10.3389/fevo.2019.00402

Artificial Intelligence for Ecological and Evolutionary Synthesis

 Philippe Desjardins-Proulx1, 2*,  Timothée Poisot2 and Dominique Gravel1
  • 1Université de Sherbrooke, Canada
  • 2Université de Montréal, Canada

The grand ambition of theorists studying ecology and evolution is to discover the logical and
mathematical rules driving the world's biodiversity at every level from genetic diversity within
species to differences between populations, communities, and ecosystems. This ambition has been
difficult to realize in great part because of the complexity of biodiversity. Theoretical work has
led to a complex web of theories, each having non-obvious consequences for other theories. Case in
point, the recent realization that genetic diversity involves a great deal of temporal and spatial
stochasticity forces theoretical population genetics to consider abiotic and biotic factors
generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists
to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved
this problem by using formal languages based on logic to manage theorems. However, theories ecology
and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial
Intelligence in bridging logic and probability theory offers the opportunity to build rich
knowledge bases that combine logic's ability to represent complex mathematical ideas with
probability theory's ability to model uncertainty. We describe these hybrid languages and explore
how they could be used to build a unified knowledge base of theories for ecology and evolution.

Keywords: theoretical ecology, theoretical biology, artificial intelligence, evolution, Theoretical population genetics, machine learning, Knowledge representation

Received: 27 May 2019; Accepted: 08 Oct 2019.

Copyright: © 2019 Desjardins-Proulx, Poisot and Gravel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Philippe Desjardins-Proulx, Université de Sherbrooke, Sherbrooke, Canada,