Work

Cracking the black box: a technology–management–policy nexus for invasive species control

Year: 2026

Type: article

Abstract: Biological invasions are a primary driver of global biodiversity loss and impose socio-economic costs. While the application of Artificial Intelligence and Machine Learning in invasion science has grown in recent years, a significant disconnect remains between advanced computational capabilities and on-the-ground management and policy implementation in biological invasions. This mini-review synthesises current trends in AI applications in invasion science and ecology. The results reveal a landscape dominated by fundamental research and species distribution modelling, with fewer studies focusing on tools for rapid response and control. Critical barriers to integration are identified, including data heterogeneity, the “black box” nature of deep learning, and the lack of standardised training data for management outcomes. Finally, a “Nexus” framework is proposed to align AI development with the invasion policy cycle, emphasising explainable AI, multimodal data integration, and the inclusion of Traditional Ecological Knowledge to transform predictive algorithms into robotic treatment or physical action and actionable Decision Support Systems.

Source: Frontiers in Ecology and Evolution

DOI: 10.3389/fevo.2026.1783145

Authors: Grace Al-Khawand, Mohsen B. Mesgaran, Mir Matin, Kaveh Madani, Philipp Robeck

Institutions: United Nations University Institute for Water, Environment, and Health, University of California, Davis, The University of Melbourne

Language: English

Topic: Ecology and Vegetation Dynamics Studies

Subfield: Nature and Landscape Conservation

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,077