Multi‐Omics‐Driven Adaptive Management of Biological Invasions: Toward a Proactive, Predictive, and Integrative Framework
Year: 2025
Type: article
Abstract: ABSTRACT Biological invasion is an escalating driver of global ecological disruption and economic loss. Despite decades of research, effective management remains severely constrained by challenges in detecting early incursions and hidden ecological processes, anticipating rapid adaptive changes, and delivering reliable predictive forecasting. As a result, interventions are often reactive and inefficient. Recent advances in multi‐omics technologies promise to transform invasion science by providing unprecedented resolution from the molecular to the community levels. This review synthesizes recent research progress in multi‐omics applications and evaluates their potential to establish a proactive, predictive, and integrative framework for invasive species management. Specifically, I highlight how multi‐omics data can generate stage‐specific solutions across the invasion process, including transport, introduction, establishment, and spread. Importantly, the successful development of such a framework depends on integrating “micro‐multi‐omics” (cellular and subcellular scales) and “macro‐meta‐omics” (organismal and community levels) with ecological modeling, big data analytics, remote sensing, artificial intelligence, and cross‐disciplinary insights from ecology, evolution, and environmental sciences. Such integration can largely strengthen invasive species management and support more robust, evidence‐based governance.
Source: Biological Diversity
DOI: 10.1002/bod2.70015
Author: Aibin Zhan
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: diamond
Awards: G2002457625, G8419844293
OpenAlex ID: W4416442863