Problem/Objective 

Biodiversity provides us with water, building materials, clothing, food and medicine, among many other physical and cultural contributions that species make to ecosystem services and people’s lives. Unfortunately, our endeavors to maximize the short-term benefits have become unsustainable. Such actions resulted in the depletion of biodiversity and threatened the life-sustaining foundations of humanity. Due to this, we live in the age of mass extinction. 

A theoretical and practical framework for biological conservation has existed since the 1960s. The field initially focused on the preservation of nature without human intervention. But gradually recognizing our ubiquitous influence on nature and the multifaceted contributions derived from it, the sustainable use of products was introduced.  

Several tools and algorithms were designed to facilitate systematic conservation planning. They often enable the optimization of trade-offs variables, leading to substantial social, economic, and environmental gains.  

Solution/Approach 

In a new paper published in the journal Nature Sustainability, scientists from Royal Botanic Gardens highlight how can artificial intelligence be used to improve conservation policies and detail a novel framework that could be used to prioritize areas for protection. The model was developed within the constraints of a limited financial budget and is used to explore previously identified trade-offs in real-world conservation through simulations and empirical analyses. It is also used to evaluate the impact of data gathering on specific outcomes. They have named their framework ‘CAPTAIN', denoting Conservation Area Prioritization through Artificial Intelligence. The international team of scientists from RGB Kew, the University of Fribourg in Switzerland, the University of Gothenburg in Sweden and London-based company Thymia Ltd drew evidence from biology, environmental economics, and computer science to develop the model. 

Impact/Implementation 

An analysis of hundreds of simulations witnessed CAPTAIN outperform popular conservation planning software Marxan in 64 to 77 per cent of cases, depending on its settings. The researchers believe that CAPTAIN is the first of its kind to implement RL. Models trained in RL can highlight areas of interest for policymakers based on biodiversity simulations through time and in response to external pressures such as climate change and human activity.  

CAPTAIN, tested on a real-world database of more than 1500 trees native to Madagascar, and outperformed Marxan even in the budget constraints. Moreover, CAPTAIN increased the average fraction of protected range per species by 50 per cent and even exceeded the set conservation targets. 

Nature Sustainability