To rescue species from the brink of extinction, first you have to know what you have, and how many \u2013 which is often easier said than done, especially in fields with a lot to count. Scientists estimate less than 20 percent of insect species<\/a> on Earth have been identified. After AI reviewed just a week\u2019s worth of camera trap footage in Panama, researchers say they found over 300 species<\/a> previously unknown to science. <\/p>\n Pelicans in Senegal. Image: Alexandre Delplanque<\/p>\n The premise of AI in scientific research is not without critics<\/a>. Proponents of high-tech in conservation cite the ability of AI to analyze large datasets in seconds that would otherwise take months, for AI to decipher patterns in species\u2019 interactions and distributions undetectable to humans, and to unravel a dizzying array of genomes. Critics point to its environmental impact, potential for bias, and insufficient ethical standards.<\/p>\n Much of AI work in conservation is focused on analyzing thousands of hours of footage taken from remote cameras or aerial surveys, but it\u2019s unlikely to end there. For now, researchers are focused on processing footage with object detection models, a type of AI that can identify and locate objects within an image or video. These models are often built with Convolutional Neural Networks (CNNs) and are trained to identify species or detect their presence or absence. <\/p>\n Projects employing AI to \u201csave species\u201d often generate a media frenzy. Researchers in South Africa generated a flurry of headlines asking if AI can save \u201cthe world\u2019s loneliest plant.\u201d<\/a> Scientists deployed drones over inaccessible swathes of the dense Ngoye Forest in search of a female partner for a male cycad at London\u2019s Kew Botanical Gardens. AI scanned the footage for signs of a species considered extinct in the wild, which researchers hope really isn\u2019t extinct \u2013 just obscured under the canopy. But some say these headlines are overblown without considering the consequences. <\/p>\n Counting pelicans using a drone equipped with cameras and AI in Senegal. Image: Alexandre Delplanque<\/p>\n \u201cThere is a tidal wave of enthusiastic research about the applications of AI and much less critical research that looks at the costs, environmentally and socially,\u201d said Hamish van der Ven, head of the Business, Sustainability, and Technology Lab at the University of British Columbia. <\/p>\n The training process for an AI model, such as a large language model (LLM), can consume over a thousand megawatt hours<\/a> of electricity. The less obvious problem, says Shaolei Ren, whose research focuses on minimizing the health impacts of AI, is the water consumption of data centers. <\/p>\n Data centers house the infrastructure needed to provide the processing power for AI, and all the technology must be cooled down, usually via freshwater sourced from the local water supply. Due to its cooling needs, AI is projected to withdraw between 4.2 billion and 6.6 billion<\/a> cubic meters of water annually by 2027, much of which is lost to evaporation. And the environmental impact is not equally felt, as tech giants export their data centers overseas. Google\u2019s plan to construct new data centers in Latin America sparked massive protests in Chile and Uruguay<\/a>, biodiverse regions already suffering from severe drought. <\/p>\n \u201cData centers also create a public health crisis due to the air pollutants emitted, including fine particulate matter (PM2.5) and nitrous oxide (NOx),\u201d said Ren. The public health burden triggered by data centers in the U.S. \u2013 primarily situated in low-income areas \u2013 is projected to cost twenty billion by 2030<\/a>.<\/p>\n \u201cThe models we\u2019re running aren\u2019t huge \u2013 they\u2019re big for us, but it\u2019s not like Social Network Big Data.\u201d<\/p>\n Yet the footprint of most biologists\u2019 AI work, for the moment, is negligible. For his part, Delplanque has one local computer processing the images, and his HerdNet model \u2013 which aids in population counts of densely packed animals, such as elephants and antelopes on the savannah \u2013 took around twelve hours to train, compared to LLMs operating on massive servers that run for weeks during the training process.<\/p>\n \u201cWe have this concern as scientists all the time: are we actually harming the environment that we\u2019re trying to help? At least for the cases we\u2019re talking about, I don\u2019t think so, because the models we\u2019re running aren\u2019t huge \u2013 they\u2019re big for us, but it\u2019s not like Social Network Big Data,\u201d says Laura Pollock, Assistant Professor in quantitative ecology at McGill University, who aims to deploy AI to extrapolate species interactions.<\/p>\n But computational ecologist Tanya Berger-Wolf argues current low-power applications aren\u2019t harnessing the full potential of the technology, referring to image recognition as \u201cold-school AI.\u201d Berger-Wolf and Pollock co-authored a paper exploring the \u201cunrealized potential<\/a> of AI\u201d to expand biodiversity knowledge.<\/p>\n \u201cWe want to go beyond scaling and speeding up what people already do to something new, like generating testable hypotheses or extracting unseen patterns and combinations,\u201d says Berger-Wolf. <\/p>\n \u201cWhat we\u2019ve been doing with AI so far is obvious, which is all of this rapid image detection and acoustic monitoring, but we should be doing much more than that: using AI to ask the right ecological questions,\u201d says Pollock.<\/p>\n One potential application that generates attention, to both applause and denunciation, is the concept of using AI to decode animal communication. The Earth Species Project<\/a> is using generative AI and LLMs in hopes of building a translator to communicate with non-human life. There is also Project CETI<\/a>, which focuses on using a similar approach to understand sperm whales, which communicate via morse-code-like clicks that, theoretically, can be deciphered. Already, scientists have managed to employ machine learning to suggest elephants address individuals<\/a> in their family by unique names. But the larger premise of decoding animal communication raises ethical questions and concerns over success. In other words: Will it work? Is it a waste of resources to try? Should we talk to animals at all?<\/p>\n Counting elephants using on the Ivory Coast with cameras attached to light-weight aircraft and AI. Image: Alexandre Delplanque<\/p>\n \u201cWe have to choose where these models will make a difference, not just use them because you have a shiny new toy,\u201d Berger-Wolf cautioned. Applications like LLMs foster a large environmental footprint, so it\u2019s \u201cirresponsible to spend resources if the research outcome does not change. And data is a resource.\u201d <\/p>\n Models are only as good as the data they\u2019re trained on, which can potentially lead to bias and a misprioritization of conservation actions. One of the most common issues include spatial bias, where species are overrepresented in certain regions in data sets, and taxonomic bias, where charismatic species like pandas receive more funding and thus more data is readily available on them than, say, an obscure beetle. But AI can also bias our perceptions and even subtly shape the questions we\u2019re asking, argued van der Ven, who authored a paper<\/a> on how LLMs downplay environmental challenges. <\/p>\n \u201cThere are far more options for AI to offer bias, extract resources, and drive overconsumption than there are conservation applications. If I could wave a wand and uninvent AI, I would,\u201d he said. \u201cIf we weigh the benefits for conservation against how effective Amazon is using AI to get consumers to buy more things, it\u2019s a vastly uneven scale.\u201d<\/p>\n In 2024, for its part, Google announced the deployment of an AI model to listen to coral reefs: SurfPerch. Bioacoustics play a key role in assessing reef stability \u2013 healthier reefs sound different \u2013 and SurfPerch analyzes audio signatures to measure the success of coral restoration efforts or identify impending threats. Around the time of the tool\u2019s deployment, Google also announced it was falling short of pledged climate targets due to the environmental demands of AI. <\/a><\/p>\n \u201cIt\u2019s not hypocritical to use AI in conservation \u2013 it just needs to be used responsibly,\u201d said Berger-Wolf. But when it comes to regulation, neither biodiversity nor AI neatly conform to geopolitical boundaries, she mused. <\/p>\n![\"Pelicans](\"https:\/\/www.europesays.com\/uk\/wp-content\/uploads\/2025\/04\/Pelicans_ground_cAlexandre_Delplanque.jpg\"\/)
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