How does artificial intelligence play an important role amid human-wildlife conflict

 

Human-wildlife conflict has remained a critical challenge for years, posing a significant threat to both people and animals. These conflicts not only result in loss of life but also have broader implications for biodiversity and ecosystems. India, with the world’s second-largest human population, also harbors a significant wild population of tigers, Asian elephants, one-horned rhinos, Asiatic lions, and other globally threatened species. Consequently, the country faces pressing challenges related to human-wildlife interactions.

The elephant and the tiger are just two of the many creatures that frequently appear in the news these days due to unpleasant interactions with humans. Here are some statistics related to human-wildlife conflicts in India:

Between 2014-2015 and 2018-2019, over 500 elephants were killed due to conflicts with humans. During the same period, 2,361 people lost their lives as a result of encounters with elephants.

And, from 2014-2015 to 2019-2020, a total of 276 people were killed due to conflicts with tigers. On average, about one person per day has been killed in India over the past three years due to encounters with roaming tigers or rampaging elephants.

India has the largest number of wild Asian Elephants, estimated to be around 30,000. Due to loss of their traditional foraging environments, wild elephants are increasingly coming into contact with human habitations, leading to conflicts. Annually, over 500 humans are killed in encounters with elephants. Conversely, more than 100 elephants die due to human-related activities, including poaching for ivory, poisoning, electrocution, and collisions with trains.

India is proud to be home to more than 75% of the world’s wild tiger population. Human-tiger conflicts have existed since ancient times. These conflicts can escalate tension among locals, often resulting in demands for the killing of tigers labeled as “man-eaters.” Factors contributing to conflicts include shrinking animal habitats due to mining, quarrying, developmental activities, encroachments, and corridor disruptions.

In such a scenario, the question of whether artificial intelligence (AI) can contribute to animal conservation arises, considering its current benefits across various industries, including healthcare, manufacturing, education, agriculture, and law. 

Indeed, the integration of AI in this field has opened up new possibilities and instilled hope for preserving our planet’s biodiversity. Digital innovation is revolutionizing wildlife conservation and this Artificial intelligence (AI) has the potential to play a significant role in mitigating human-wildlife conflict in India.

The role of AI in wildlife conservation in India,

Researchers and organizations in India are leveraging AI for wildlife conservation. Recently, the National Tiger Conservation Authority (NTCA) and the Wildlife Institute of India (WII) collaborated with IIIT-Delhi to conduct a nationwide assessment of India’s tiger population and habitat using AI-based wildlife monitoring. AI helps identify animals in camera trap images, a task that would be laborious for humans. The NTCA aims to develop a system that provides rangers with optimal patrol routes across vast areas.

Additionally, the Wildlife Conservation Trust (WCT) collaborates with Google Research India’s AI Lab to design AI models that predict human-wildlife interactions in the state of Maharashtra, India. These models analyze data to anticipate conflict situations and enable proactive conservation efforts.

Artificial Intelligence (AI) plays a crucial role in wildlife conservation in India. Let’s explore how AI contributes to safeguarding our planet’s biodiversity:

Wildbook Platform - Researchers are using an open-source platform called Wildbook to collect and analyze wildlife images. Wildbook enables tracking of individual animals using natural markings, genetic identifiers, or vocalizations. It also engages citizen scientists and social media to collect sighting information. By scanning millions of crowdsourced wildlife photographs using computer vision and deep learning algorithms, Wildbook identifies species and individual animals. Wildbook is used by almost 900 active wildlife researchers in tracking over 188,000 individual animals for multiple species across the globe as well as for new AI research in academia. Scientists utilize this aggregated data to make informed conservation decisions.

Surveillance and Monitoring - AI illuminates methods of protecting life forms by delivering vast amounts of data on what is actually happening to wildlife. Wildlife monitoring using AI provides a comprehensive view of the world’s biodiversity and aids in the development of conservation policy frameworks.

Achieving Sustainable Development Goals (SDGs) - AI and Machine Learning (ML) assist in achieving specific SDGs related to wildlife conservation:

SDG 14 (Life Below Water): AI helps in surveillance of illicit catches, improving transparency in fishing supply chains, and analyzing fishing fleet motions.

SDG 15 (Life on Land): AI addresses deforestation, ecological degradation, desertification, and the poaching and trafficking of threatened species. It contributes to targets like checking biodiversity loss and preventing poaching.

Automating Species Classification - Citizen scientists and communities use AI to classify species.

Monitoring Land Use Changes - AI tracks land use changes, including those affecting wildlife habitats.

Combating Poaching - ML algorithms developed jointly by institutions like IIT Madras and Harvard University help combat poaching.

Identifying Rare Species with Camera Traps - AI algorithms analyze thousands of camera trap images to identify rare species. By learning patterns, AI can quickly pinpoint photos containing elusive animals, reducing the manual effort required for data collection. This technology aids in monitoring species like humpback whales, koalas, and snow leopards.

Monitoring Wildlife with Audio Recordings - AI can analyze hours of field recordings to identify specific animal calls. Instead of manually sifting through audio data, researchers can focus on critical conservation tasks. For instance, identifying the vocalizations of endangered species or detecting signs of distress in wildlife populations.

Demographic Management Using Machine Learning - Machine learning (ML), a subset of AI, assists in demographic management. Robotics and drone image datasets enable organizations to monitor species more effectively. ML algorithms can track population dynamics, migration patterns, and habitat changes, aiding conservation efforts.

Enhancing Biodiversity Research and Protection - AI helps protect endangered species by analyzing large datasets. It can predict habitat suitability, assess climate change impacts, and identify areas requiring conservation attention. By automating data analysis, AI accelerates research and informs targeted conservation strategies.

Monitoring Wildlife with Audio Recordings - AI can analyze hours of field recordings to identify specific animal calls. Instead of manually sifting through audio data, researchers can focus on critical conservation tasks. For instance, AI can assist in identifying the vocalizations of endangered species or detecting signs of distress in wildlife populations.

Demographic Management Using Machine Learning - Machine learning (ML), a subset of AI, plays a crucial role in demographic management. Robotics and drone image datasets enable organizations to monitor species more effectively. ML algorithms can track population dynamics, migration patterns, and habitat changes, thereby aiding conservation efforts.

Enhancing Biodiversity Research and Protection - AI helps protect endangered species by analyzing large datasets. It can predict habitat suitability, assess climate change impacts, and identify areas requiring conservation attention. By automating data analysis, AI accelerates research and informs targeted conservation strategies.

State governments are implementing various measures to mitigate conflicts, such as digging trenches, using different types of fences, community involvement, radio collars, and village relocation. Emerging technologies like trip alarms, sensory-based alarms, drones, and AI-based warning systems are also being used to track and alert about animal movements.

 

Challenges of using AI in conservation efforts,                           

While artificial intelligence (AI) holds great promise for wildlife conservation, it can also create several challenges in India's ecosystem. Let’s explore some of these:

Cost of Implementation -Implementing AI systems can be expensive. Many conservation organizations may lack the necessary resources to invest in advanced technology.

Human Involvement and Ethical Considerations -There’s a delicate balance between using AI effectively and ensuring human involvement in conservation efforts. Over Reliance on AI could potentially replace essential human roles in monitoring and decision-making.

Data Privacy and Surveillance - As AI collects and processes large datasets, concerns about data privacy arise. In protected areas, surveillance using AI could inadvertently invade privacy or compromise sensitive information.

Bias in Data and Algorithms - AI systems learn from historical data, which may contain biases. If not addressed, these biases can perpetuate inequalities and affect conservation decisions.

Technological Limitations in Remote Areas - Some wildlife habitats are in remote or challenging locations with limited connectivity. Deploying and maintaining AI systems in such areas can be difficult.

Context Understanding and Ethical Decision-Making - AI lacks contextual understanding and ethical reasoning. Collaborating with human experts ensures responsible decision-making in conservation efforts