Artificial intelligence: Speed at scale

Rich Rowe explores the growing role of artificial intelligence in conservation and its implications for the protection of wild places.

^ Anti-poaching efforts to protect elephants in Zambia's Kafue National Park are enhanced by a 19km virtual fence. (photograph courtesy of Biggestleaf Travel)

A deeply divisive subject, the mere mention of artificial intelligence (AI) can elicit an extreme response. While some believe that the development of computers and robots that behave in ways that mimic, and even step beyond, human capabilities can help improve lives immeasurably, others have a deep concern that such a development poses an existential threat to humanity itself – a nightmarish scenario depicted in so many ‘rise of the machines’ sci-fi films down the years.

However, those who occupy the middle ground suggest that AI, in and of itself, is neither inherently good nor bad. Instead, it is how humans develop and utilise the technology – and what safeguards are put in place – that really matter.

What is clear is that AI has long left the lab and already touches almost every aspect of our daily lives, from healthcare and finance to manufacturing and agriculture.

Nature conservation is no different. Today, AI and machine learning – a pathway to AI that uses algorithms to recognise patterns from data and inform smarter decisions – is used by thousands of conservation organisations around the world.

It is now considered a vital tool for understanding how best to protect the natural world at a time when the planet faces what is being referred to as the sixth mass extinction event – one driven by human activity rather than natural phenomena.

The use of AI in conservation covers everything from enhanced species and habitat monitoring to pattern recognition and predictive modelling. Offering speed at scale, AI algorithms perform tasks in a fraction of the time it takes humans, analysing vast datasets to identify trends in wildlife populations and highlight the impacts of specific environmental changes.

Varied applications

For organisations such as the UK Centre for Ecology & Hydrology (UKCEH), which works with partners around the world on the use of automated biodiversity monitoring stations, AI has a crucial role to play in tackling the biodiversity crisis.

“The widespread deployment of automated sensors combined with identification of species using AI can transform our understanding of the impacts of environmental change on wildlife and measure progress towards national and international biodiversity targets,” notes Professor David Roy, UKCEH and University of Exeter ecologist.

“This will enable us to identify where biodiversity is under threat, the main drivers of change and to inform solutions to guide local habitat management.”

AI’s applications in conservation are almost limitless. In Alaska, where salmon populations – so crucial to the local economy as well as wider biodiversity – are monitored using sonar technology, AI helps automate the process of detecting, counting and tracking the fish that swim upstream to spawning sites.

In Zambia, AI is being used to enhance conventional anti-poaching efforts by creating a 19km-long virtual fence across Lake Itezhi-Tezhi – an entry point used by poachers to access the nearby Kafue National Park and its population of elephants.

A network of thermal imaging cameras records every boat crossing in and out of the park with automated alerts meaning that only a handful of rangers are now needed to provide round-the-clock surveillance.

Elsewhere, AI is helping track water loss across Brazil’s portion of the Pantanal – the world’s largest tropical wetland – protect koalas in Australia, restore damaged coral reefs, identify the movement of whales through acoustic monitoring and inform policy decisions around wolf management in the US.

Rapid development

The application of AI in conservation is not as new a development as many might think. Its use dates back to the 1990s when machine learning was first applied in remote sensing and data analysis, with researchers using AI algorithms to classify land cover and identify species from satellite imagery.

There have, however, been significant advances, especially over the past decade when AI-powered tools designed specifically for conservation first began to emerge. Increasingly sophisticated, there are now AI-driven predictive models for biodiversity trends, habitat mapping and even for understanding behavioural trait variability of individual species and how those traits are changing over time.

AI modelling can aid habitat restoration, predict suitable habitats for species translocations and analyse genetic data to highlight population viability and extinction risk – levels of detail that can inform highly targeted conservation strategies for species and entire habitats.

The use of AI even touches public services in the UK. Satellite images and machine learning are being used by Natural England to build a detailed map of ‘Living England’ showing the current extent of natural habitats across the country and predicting their change. Far removed from the manual surveys of the past, the aim is to speed up decisions around planning and land use while better protecting nature.

Safeguards

However, while new applications of AI are accelerating our ability to understand the world around us, there are very real challenges, and responsibilities, which accompany them. Scientific inaccuracies, misinformation and misrepresentation all have the potential to undermine public understanding and support for conservation efforts.

There is also AI’s significant environmental footprint to consider. Requiring large datasets to ensure high levels of accuracy, the training and running of AI models consumes huge amounts of energy-intensive computing power, resulting in high carbon emissions.

Writing for the World Economic Forum in 2024, Metolo A. Foyet, Founding Curator at Global Shapers Hubs in Florida, emphasised that the “responsible integration of AI should prioritise inclusive learning, community involvement and AI’s environmental cost, to ensure that [it] supports and enhances conservation efforts while respecting human values and environmental and ethical standards”.

It was perhaps another way of saying that AI must serve conservation and humanity rather than the other way around. The tail must not wag the dog. Get that right and AI has the power to make a very real, positive difference to wild places around the world.

About the author

Rich Rowe is Contributing Editor for the Journal.

• This article first appeared in the Spring/Summer 2025 edition of the John Muir Trust Members' Journal. If you would like to receive our Journal twice a year please consider joining the Trust as a Member.