Chapter 09 · Sustainability & AISustainability & AI
AI for biodiversity
Definition
The application of AI to understanding and protecting biodiversity: species identification, habitat mapping, population estimation and ecosystem monitoring.
References
This source provides part of the technical or institutional basis for the definition.
This source supports the explanation of how the term is applied, measured or governed in practice.
This source supports the wider sustainability significance and context described in the entry.
Overview
What it means
Biodiversity data arrives as images, sound and observations at scales that exceed expert review. AI classifies camera-trap photos (platforms such as Wildlife Insights), identifies species from photographs in citizen-science tools such as iNaturalist, and maps habitats from satellite imagery — converting raw streams into usable ecological data.
How it is used
Conservation programmes use these tools for monitoring endangered species, detecting illegal activity, prioritising protected areas and reporting against biodiversity frameworks. Effectiveness depends on training-data coverage — which is thinnest exactly where biodiversity is richest.
Why it matters
Biodiversity loss is a measurement problem as much as a protection problem: you cannot manage populations you cannot observe. AI scales observation dramatically, while its blind spots — under-sampled species and regions — need deliberate correction.