Google Study: How AI Advances Wildlife Conservation

The global biodiversity crisis has left governments and conservation organisations scrambling for better data and monitoring tools to track species decline and habitat loss. 

It’s a race against time – and increasingly, AI is being deployed as part of the solution.

A study published by Google and the World Resources Institute, a research organisation focused on environmental issues, examines how AI technologies can support conservation goals established under international frameworks. 

The research involved interviews with 22 people working at the intersection of AI and conservation and the findings reveal a striking gap: fewer than 25% of countries currently have goals aligned with the Kunming-Montreal Global Biodiversity Framework, an international agreement adopted in December 2022, that aims to halt and reverse global biodiversity loss by 2030.

Kate Brandt, Chief Sustainability Officer (CSO) at Google, says the technology sector must accelerate efforts to address environmental challenges: “From the air we breathe to the food we eat, a healthy planet matters to every single one of us,” she says. 

“For over 10 years, Google and the World Resources Institute have used the latest technology to protect our planet. But we need to do more, faster.”

How Wildlife Insights and Global Fishing Watch prove practical impact

The report identifies several initiatives demonstrating how AI technologies are already making a difference on the ground. 

Wildlife Insights, developed by Google and conservation organisations, has created a publicly accessible database containing 253 million camera trap images of 4,292 species across 112 countries.

The database documents candid behaviour of flora and fauna in natural habitats, enabling researchers to study species with far greater accuracy than traditional methods allowed.

Global Fishing Watch takes a different approach, using AI to analyse ship movements and identify potential illegal activity such as fishing in protected areas. 

In 2024, authorities in Chile used the platform to enforce the closure of toothfish fisheries after detecting illegal activity. 

The action resulted in fines for 21 vessels and more importantly, improved compliance levels that suggest the monitoring is having a deterrent effect.

The citizen science platform iNaturalist also allows individuals to document biodiversity using their mobile phones and share observations. 

The platform has built a community of more than 400,000 people who have contributed over 100 million research-grade observations to the Global Biodiversity Information Facility – a testament to how technology can democratise conservation efforts.

Kate says current applications demonstrate tangible results beyond pilot projects: “The report highlights real-world examples of people using this technology as we speak to protect and restore nature around the globe.

“Governments using satellites to monitor the seas and prevent illegal fishing. Researchers using AI to help identify, map and protect endangered species. Indigenous communities equipped with real-time alerts to stop illicit logging on their land.”

Why data quality matters more than flashy models

The report identifies three areas requiring investment – and the first might surprise those focused on cutting-edge algorithms. 

It calls for expansion of primary biodiversity data collection globally, alongside infrastructure for open access sharing. 

Second, it emphasises developing open AI systems that can address information gaps in species monitoring. 

Third, it stresses capacity building to ensure conservation practitioners can actually benefit from existing AI capabilities.

Sara Beery, Assistant Professor of AI and Decision-Making at MIT, says: “People spend a lot of time trying to sell models [but] models are only as good as the data.

“Data is never a bad investment, and data that can be open-sourced and have mutual and diverse downstream uses; that is the no-regret investment.”

The report acknowledges risks that can’t be ignored, including the concentration of AI expertise in a handful of countries, which could deepen inequalities rather than solve them. 

Data centres currently account for approximately 1.5% of global electricity use, projected to double by 2030 according to the International Energy Agency (IEA). 

The report also highlights concerns about bias in training data, which could limit AI systems’ ability to identify species in regions outside North America and Europe where most observations originate.

Stephanie O’Donnell, Senior Technology Specialist at the World Bank’s Global Wildlife Program, says human collaboration matters more than technology alone: “Finding the right people and helping them collaborate, build capacity to problem solve and work together is way more important than the technology applications,” she says.

The report estimates that global financing for nature needs to increase by US$500bn annually to achieve goals outlined in the Sustainable Development Goals and the Kunming-Montreal Global Biodiversity Framework – a sobering reminder that technology alone won’t solve the crisis.

Kate says: “Partnership is key to meeting this opportunity.”