AI in conservation: Where we came from — and where we are heading

Mar 5, 2024

Technology has long been a vital tool for conservationists — and Artificial Intelligence is no different. Image: REUTERS/Stephanie Keith

Metolo A. Foyet

Founding Curator and Curator, Global Shapers Hubs Gainesville, Florida

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Climate and Nature

Nature conservationists have long used technology to aid their vital work.
Today, AI is becoming a powerful force in nature conservation, with applications ranging from monitoring wildlife to collecting environmental DNA.
As the technology develops, new ways of processing data and engaging the public on conservation issues are set to continue to emerge.

AI is emerging as a powerful ally for communities engaged in conservation efforts — and it is coming at a time in which we face increasingly complex ecological challenges and the urgent need to protect our natural world and respective biocultural heritage.

The synergy between AI and conservation has the potential to enhance our ability to monitor and safeguard ecosystems, mitigate human-wildlife conflicts, optimize resource management and foster sustainable coexistence between people and wildlife.

In this context, AI is not just a technological advancement but a catalyst for empowering conservation stakeholders (including local communities) and strengthening their capacity to protect the planet's biodiversity and the livelihoods of the people who depend on it — everyone on the planet.

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A timeline of AI in conservation

The integration of AI in conservation efforts has been a gradual process, with notable advancements in recent years. While the exact date of its initial integration is challenging to pinpoint, here are some key milestones and developments:

1990s: AI techniques like machine learning began to be applied in remote sensing and data analysis for ecological and conservation purposes. Researchers started using AI algorithms to classify land cover and identify species from remotely sensed data like satellite imagery.

Early 2000s: Conservationists started to explore AI for tasks like species identification using computer vision and the analysis of acoustic data to monitor wildlife populations, especially birds and marine mammals.

Mid-2010s: The application of AI in conservation gained momentum. Machine learning algorithms were increasingly used for camera trap image analysis to identify and track wildlife. AI-driven data analysis tools started aiding in the monitoring of endangered species and the prevention of poaching.

Late 2010s to Early 2020s: AI-powered tools and platforms specifically designed for conservation began to emerge. These tools helped automate data collection, species identification, and the analysis of environmental data. AI-driven predictive models for biodiversity trends and habitat mapping started gaining attention.

AI in conservation today

In 2024, AI's role in conservation is growing, with applications in habitat monitoring, wildlife protection, data analysis and pattern recognition. AI-equipped drones and remote sensing tech are enhancing cost-effective conservation. Also, AI is increasingly used in conservation decision-making and policy formulation to speed up responses to emerging threats like disease surveillance, for example.

Predictive modeling and species distribution: AI algorithms use existing data to develop predictive models that estimate species distribution and habitat suitability. This information is valuable for identifying areas of high conservation priority and planning conservation interventions. AI also helps forecast the impacts of climate change on species and ecosystems, aiding in adaptation planning.
Wildlife monitoring and anti-poaching efforts: AI-powered technologies like sensors are used for wildlife monitoring and anti-poaching efforts. Algorithms analyze real-time video and image feeds to support ecosystem-based disaster risk reduction (Eco-DRR), detect and identify wildlife, including endangered species, and trigger alerts for potential poaching activities. This helps law enforcement agencies respond quickly and effectively.

Studies frequently combine data from multiple sensors at the same geographic location, or data from multiple locations to achieve deeper ecological insights. Image: U.S. Geological Survey

Environmental DNA (eDNA): eDNA sampling involves collecting and analyzing DNA traces present in environmental samples, such as water or soil, to detect the presence of species. Advances in eDNA technologies and AI analysis techniques can improve species detection, especially for elusive or cryptic species. This approach can provide valuable information for conservation planning and monitoring biodiversity.
Ecosystem restoration and conservation interventions: AI supports ecosystem restoration efforts by analyzing ecological data and recommending appropriate restoration techniques. AI algorithms can optimize interventions like reforestation, invasive species management, increasing the efficiency and effectiveness of conservation efforts.
Climate change modelling and resilience planning: AI algorithms can analyze climate data, ecological variables and species distributions to model the impacts of climate change on ecosystems. This information can aid in developing climate change adaptation strategies, identifying climate refugia and enhancing ecosystem resilience to changing environmental conditions.
Smart resource management: AI optimizes resource management in conservation, such as efficient deployment of park rangers, predictive maintenance of conservation equipment and intelligent monitoring of conservation projects. AI-powered systems automate routine tasks, improve data collection efficiency and enhance the overall effectiveness of conservation operations.

The future of AI in conservation

Despite these myriad uses of AI in conservation, its integration in conservation citizen science and engagement remains relatively new. There is a pressing need for more professionals in the fields of conservation culturomics and computational sustainability who possess local knowledge to establish the linkages between semantics, social behaviour and conservation patterns. Additionally, several urgent areas within conservation are expected to undergo significant developments, including technology-driven advancements:

Conservation planning and decision support: AI will assist in optimizing conservation planning by considering various factors, such as ecological connectivity, habitat fragmentation and the effectiveness of protected areas. AI algorithms already suggest optimal locations for protected areas, wildlife corridors and habitat restoration efforts, considering multiple ecological and socio-economic variables.
AI-powered Natural Language Processing and Communication: AI will enhance the analysis and understanding of large volumes of various data types such as research papers, reports and conservation-based social media discussions. Further allowing conservationists to extract valuable insights, detect emerging issues and understand public perceptions and sentiments regarding conservation topics.
Public engagement and education: AI-powered chatbots and virtual assistants will be used to engage the public and answer conservation-related questions. Leveraging AI tools to facilitate interactive learning experiences and disseminate accurate information about conservation issues will lead to a more informed and engaged public, freeing up resources for addressing other pressing human-centered concerns.

While AI has immense potential in conservation, it is crucial to consider ethical considerations, such as data privacy, bias and human-AI collaboration.

The responsible integration of AI should prioritize inclusive learning, community involvement and AI’s environmental cost, to ensure that AI supports and enhances conservation efforts while respecting human values and environmental and ethical standards.

If done right, AI’s journey in conservation is just beginning — and its impact could be historic.

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