Harnessing technology and nature's principles for sustainable conservation solutions

About this Research Topic

Background

As ecosystems face unprecedented threats from climate change, biodiversity loss, pollution, and habitat degradation, there is a growing need for innovative, interdisciplinary approaches to conservation. Nature-inspired solutions rooted in the principles of ecology, evolution, and biomimicry, offer powerful tools to address these complex environmental challenges. Recent advances in biotechnology, artificial intelligence (AI), and molecular biology have opened new frontiers in conservation science, enabling precise monitoring, predictive modeling, ecosystem restoration, and the development of sustainable, biologically-informed interventions. From gene editing and synthetic biology to AI-assisted species tracking and environmental DNA (eDNA) analysis, these technologies hold immense promise for enhancing biodiversity protection and ecosystem resilience. This Research Topic aims to highlight cutting-edge

Conservation efforts today face mounting pressure from rapidly accelerating environmental threats, including climate change, habitat destruction, and biodiversity loss. Conventional strategies, while valuable, often fall short in the face of complex, large-scale ecological disruptions. There is an urgent need to integrate nature-inspired, technology-driven solutions that can enhance precision, scalability, and sustainability in conservation practices. This Research Topic aims to address this gap by exploring how advances in biotechnology, artificial intelligence, and molecular tools can be harnessed to support ecosystem monitoring, species conservation, habitat restoration, and climate resilience. By uniting disciplines such as algal biotechnology, synthetic biology, molecular ecology, and AI-based predictive modeling, we hope to catalyze innovative approaches that are both ecologically informed and technologically empowered. We invite research that demonstrates practical applications, theoretical models, and cross-sectoral collaborations that bridge biology with machine learning, molecular tools with field conservation, and nature’s strategies with engineered solutions. Together, these contributions can pave the way toward smarter, more effective conservation in the Anthropocene.

This Research Topic invites interdisciplinary contributions that explore nature-inspired, biotechnology-driven, and AI-integrated solutions for conservation in the Anthropocene. We welcome original research, reviews, and perspectives focused on themes including, but not limited to, biotechnological tools for biodiversity conservation, molecular ecology, AI and machine learning applications in species monitoring, algal and microbial biotechnology for habitat restoration, synthetic biology in conservation, and eDNA-based biodiversity assessment. Contributions highlighting novel biomimetic strategies, multi-omics approaches, and data-driven conservation modeling are especially encouraged. We are also interested in frameworks and field applications that demonstrate how modern innovations can support ecosystem resilience, climate adaptation, or restoration efforts. This collection aims to bridge scientific innovation with practical conservation needs, offering a platform for researchers across biology, ecology, biotechnology, and computational sciences (AI-ML) to propose transformative solutions for a rapidly changing planet.

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
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

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Article types

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

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

Keywords: Synthetic Biology, Algal molecular tools, Biological remediation, Biosensors, Nature-based Carbon capture, Artificial Intelligence in Conservation, Biodiversity Monitoring, Bioinformatics, Machine learning for Wildlife protection, Conservation genetics, B

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.

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