The fitness value of ecological information in a variable world

• Published in: Ecology letters Vol. 26; no. 4; pp. 621 - 639
• Main Authors: Usinowicz, Jacob, O'Connor, Mary I.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.04.2023
• Subjects: Biological Evolution; Changing environments; coexistence; community dynamics; Data processing; Ecology; Ecosystem; Ecosystem dynamics; Environmental changes; Environmental effects; Fitness; Growth rate; Information processing; Information theory; Models, Biological; population; Population Dynamics; Population growth; Reproductive fitness; Social discrimination learning; Social organization; ecology; information theory; coexistence; fitness; community dynamics; population
• ISSN: 1461-023X; 1461-0248
• DOI: 10.1111/ele.14166

Information processing is increasingly recognized as a fundamental component of life in variable environments, including the evolved use of environmental cues, biomolecular networks, and social learning. Despite this, ecology lacks a quantitative framework for understanding how population, community, and ecosystem dynamics depend on information processing. Here, we review the rationale and evidence for ‘fitness value of information’ (FVOI), and synthesize theoretical work in ecology, information theory, and probability behind this general mathematical framework. The FVOI quantifies how species' per capita population growth rates can depend on the use of information in their environment. FVOI is a breakthrough approach to linking information processing and ecological and evolutionary outcomes in a changing environment, addressing longstanding questions about how information mediates the effects of environmental change and species interactions. Information processing is increasingly recognized as a fundamental component of life across biological scales. In ecology, information plays a critical role for life in variable environments because it allows populations to anticipate and better utilize beneficial conditions, while avoiding detrimental ones. We draw on work from diverse ecological sub‐disciplines to synthesize a mathematical framework to analyse information's role in ecological dynamics.