Navigating the integration of biotic interactions in biogeography

• Published in: Journal of biogeography Vol. 51; no. 4; pp. 550 - 559
• Main Authors: Thuiller, Wilfried, Calderón‐Sanou, Irene, Chalmandrier, Loïc, Gaüzère, Pierre, O'Connor, Louise M. J., Ohlmann, Marc, Poggiato, Giovanni, Münkemüller, Tamara
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01.04.2024; Wiley
• Subjects: Anthropocene; Anthropocene epoch; biodiversity models; biodiversity patterns; Biogeography; biotic interactions; Demography; Ecology, environment; ecosystems; Empirical analysis; geographical distribution; Integration; Life Sciences; networks; space and time; species distributions; Symbiosis
• DOI: 10.1111/jbi.14734
• ISSN: 0305-0270

Biotic interactions are widely recognised as the backbone of ecological communities, but how best to study them is a subject of intense debate, especially at macro‐ecological scales. While some researchers claim that biotic interactions need to be observed directly, others use proxies and statistical approaches to infer them. Despite this ambiguity, studying and predicting the influence of biotic interactions on biogeographic patterns is a thriving area of research with crucial implications for conservation. Three distinct approaches are currently being explored. The first approach involves empirical observation and measurement of biotic interactions' effects on species demography in laboratory or field settings. While these findings contribute to theory and to understanding species' demographies, they can be challenging to generalise on a larger scale. The second approach centers on inferring biotic associations from observed co‐occurrences in space and time. The goal is to distinguish the environmental and biotic effects on species distributions. The third approach constructs extensive potential interaction networks, known as metanetworks, by leveraging existing knowledge about species ecology and interactions. This approach analyses local realisations of these networks using occurrence data and allows understanding large distributions of multi‐taxa assemblages. In this piece, we appraise these three approaches, highlighting their respective strengths and limitations. Instead of seeing them as conflicting, we advocate for their integration to enhance our understanding and expand applications in the emerging field of interaction biogeography. This integration shows promise for ecosystem understanding and management in the Anthropocene era.