Machine Learning and Artificial Intelligence in Ecological Synthesis and Discovery

The fields of ecology and evolution are undergoing a profound transformation, driven by an unprecedented influx of large-scale ecological data and the computational tools to analyse it. Modern ecological research is characterised by the constant need to explain if and how species and communities adapt to changes such as climate warming, habitat fragmentation, or pollution. Despite providing a robust framework for testing hypotheses, the classical statistical approach in ecology often struggles with the immense complexity, non-linearity, and high dimensionality inherent in ecological systems. The contemporary era is therefore increasingly defined by the adoption of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in ecological studies. These methods excel at processing vast and heterogeneous datasets, unlocking new capabilities for revising foundational ecological theories, extensive modelling, and forecasting the dynamics of life in a rapidly changing world.

By analysing vast and complex ecological datasets, AI methods also reveal evolutionary patterns and hidden dynamics that traditional methods might overlook. The synergy between AI, ecology, and evolutionary biology, especially in eco-evolutionary and conservation contexts, is a relatively new and rapidly evolving frontier. Moreover, ecology and evolution are not merely predictive fields; they also strive for mechanistic and causal understanding. For instance, a model that can predict where, and more importantly why, a species will be in 2100 contributes to fundamental scientific knowledge, potential conservation efforts, and policymaking. In this application of modern methods to ecological research, explainable AI (XAI) is an invaluable tool dedicated to demystifying these approaches, aiming to make their predictions transparent, trustworthy, and scientifically insightful.

We aim to bring together authors who can contribute to advancing novel modelling techniques, theories, and their applications.

This Research Topic aims to compile Original Research, Perspectives, and Review articles on the use of AI to address challenges in multi-scale ecological data integration, focusing on the following sub-themes, among others:

- Exploring gene-environment interactions using machine learning techniques
- Machine learning, deep learning, and AI in modelling species distribution
- AI-driven modelling of rapid species adaptation to environmental change
- Predictive modelling of biodiversity responses to climate warming and pollution
- AI applications in assessing ecological resilience and ecosystem stability
- Machine learning for hypothesis generation in complex ecological networks
- Detection of evolutionary patterns from large-scale ecological datasets
- Uncovering patterns of biodiversity through ML and AI
- Synergistic development of AI and ecological theory for improved conservation strategies

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
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... 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
• FAIR² DATA Direct Submission
• 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: Ecology, Evolution, Artificial Intelligence (AI), Machine Learning (ML), Data, Modelling, Conservation, Biodiversity, Complexity, Explainable AI (XAI)

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.