Navigating the integration of biotic interactions in biogeography

Abstract

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.

Vulnerability of terrestrial vertebrate food webs to anthropogenic threats in Europe

“…Furthermore, interactions themselves can vary across space and time, due to interaction plasticity and behavioral changes, and species may adapt to the loss of interactions through interaction rewiring (Kamaru et al., 2024). It is challenging to include interaction plasticity with our metaweb approach, which is inherently static (Thuiller et al., 2023). However, the metaweb includes information on whether interactions are obligate (i.e., typical) or occasional.…”

Land‐use intensity influences European tetrapod food webs

“…Unlike sampled interaction networks, our local meta food webs are neither snapshots frozen in time nor limited by the imperfect detection of interactions. Instead, they represent a ‘maximum’ depiction of all the interactions that likely occurred locally over several years, which makes sense in the context of our study (Thuiller et al., 2023). However, these potential trophic interactions may not necessarily manifest locally due to factors like phenological mismatches or low abundances of one or both interacting partners.…”

Advancing ecological networks: moving beyond binary classification to probabilistic interactions

“…As trophic interactions gain prominence in biogeography and macroecology, understanding and modeling these relationships become crucial for elucidating fundamental patterns and addressing conservation challenges (O’Connor et al, 2020; Strona & Bradshaw, 2022; Thuiller et al, 2023). Transitioning from binary to probabilistic interaction models represents a significant opportunity to refine our analyses and explore novel outcomes in ecological research.…”