Artificial Intelligence for Ecological Discovery, Biodiversity, and Ecosystem Dynamics

Ecology and evolution are entering a data-intensive era driven by rapid advances in Earth observation, automated biodiversity monitoring, environmental sensor networks, environmental DNA (eDNA), and ecological informatics. These developments provide unprecedented opportunities to investigate ecological and evolutionary processes across spatial and temporal scales. At the same time, accelerating climate change, biodiversity loss, habitat degradation, and biological invasions have increased the need for more accurate ecological prediction and adaptive management. Artificial intelligence (AI) has emerged as a powerful tool for analysing complex ecological datasets, identifying patterns across multiple scales, and addressing processes that were previously difficult to quantify. Recent studies demonstrate the potential of machine learning and deep learning for biodiversity monitoring, species distribution modelling, ecological forecasting, and the analysis of ecosystem dynamics. However, challenges remain regarding data quality and bias, model transferability, interpretability, reproducibility, uncertainty quantification, and the integration of AI outputs with ecological and evolutionary theory. Further investigation is therefore needed to ensure that AI advances ecological understanding rather than functioning solely as a predictive or computational tool.

This Research Topic aims to advance ecological and evolutionary research by integrating AI with ecological theory, biodiversity science, evolutionary biology, and ecosystem analysis. We seek studies that use AI to reveal ecological and evolutionary mechanisms, improve biodiversity monitoring, understand species interactions and ecosystem dynamics, forecast ecological responses to global change, and support conservation and sustainable ecosystem management. We particularly encourage research that evaluates the ecological meaning, limitations, uncertainty, and transferability of AI-based approaches, and that bridges computational innovation with testable hypotheses and improved understanding of ecological and evolutionary processes.

To gather further insights into the application of AI to ecological and evolutionary processes across terrestrial, freshwater, coastal, and marine ecosystems, this Research Topic will focus on studies with clear ecological or evolutionary relevance and will not be limited to the development of AI methods without demonstrated applications or implications for these fields. We welcome articles addressing, but not limited to, the following themes:

o AI-supported biodiversity monitoring and conservation

o Species distribution, habitat, and occupancy modelling

o Remote sensing and automated ecological observation

o Environmental DNA and other molecular biodiversity applications

o Community ecology and species interaction networks

o Ecosystem functioning, stability, and resilience

o Evolutionary ecology, adaptation, and responses to environmental change

o Ecological forecasting under climate change and other global change drivers

o Biological invasions and the prediction of invasion risk

o Integration of multi-source ecological and evolutionary data

o Machine learning, deep learning, and foundation models for ecology and evolution

o Explainable, interpretable, and uncertainty-aware AI in ecological research

o Ecological digital twins and data-driven ecosystem analysis

o Reproducible, transferable, and ethically responsible AI approaches

o Applications of AI to biodiversity conservation, ecosystem management, and ecological prediction

We welcome Original Research, Review, Mini Review, Methods, Perspective, and Data Report articles that provide methodological innovation, ecological or evolutionary insights, or practical applications for biodiversity conservation, ecosystem management, and ecological prediction.

This Research Topic is part of a series developed in collaboration with the 1st International Conference on Artificial Intelligence for Ecology and Environment. Explore the companion topics in this series:
Artificial Intelligence for Environmental Monitoring, Prediction, and Sustainable Ecosystem Management, Frontiers in Environmental Science.
Artificial Intelligence for Forest Monitoring, Resilience, and Sustainable Management under Global Change, Frontiers in Forests and Global Change.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Ecology, Biodiversity, Ecosystem Dynamics, Ecological Modeling, Remote Sensing, Species Distribution, Evolutionary Ecology, Machine Learning, Global Change

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.