The Swampy Black Iguana is the oldest specimen living at the Iguana Station scientific station, where they have a breeding and conservation project for black spiny-tailed iguanas. This species, endemic to Utila, is in danger of extinction. The Utila Iguana Conservation Project seeks to ensure the survival of this species. Breakthroughs, discoveries, and DIY tips sent every weekday. With the turning of the calendar comes a new year and new vulnerable endangered plant and animal species to keep a watchful eye on.

This story was originally published by Slate's Future Tense partnership and is reproduced here as part of the Climate Desk collaboration. In August 2021, forest range officer Remya Raghavan caught three people carrying wild boar meat in the Wayanad forest of Kerala, a state in southern India. Possessing wild animal meat is a crime under the country's 1972 Wildlife Protection Act, so Raghavan entered all the details of the crime--location, witnesses, names of the accused, items seized, and section of the forest--in a mobile application. Just like that, the case was officially registered in the app-based system, which signaled that it needed to be taken to court. The app Raghavan used is called HAWK, or Hostile Activity Watch Kernel, and it appears to be the first such digital intelligence gathering system for wildlife crime in India.

In August 2021, forest range officer Remya Raghavan caught three people carrying wild boar meat in the Wayanad forest of Kerala, a state in southern India. Possessing wild animal meat is a crime under the country's 1972 Wildlife Protection Act, so Raghavan entered all the details of the crime--location, witnesses, names of the accused, items seized, and section of the forest--in a mobile application. Just like that, the case was officially registered in the app-based system, which signaled that it needed to be taken to court. The app Raghavan used is called HAWK, or Hostile Activity Watch Kernel, and it appears to be the first such digital intelligence gathering system for wildlife crime in India. It helps officers like Raghavan centralize and share information on forest and wildlife crimes in real time.

Scientists applied machine vision models and were able to deduce from the context of an image if it pertained to the sale of a live animal. These methods make it possible to flag the posts which may be selling animals illegally. Illegal wildlife trade is estimated to be a multi-billion dollar industry where hundreds of species are traded globally. A considerable proportion of the illegal wildlife trade now uses online marketplaces to advertise and sell live animals or animal products as it can reach more buyers than previously possible. With the trade happening across the internet it is extremely challenging to manually search through thousands of posts and methods for automated filtering are needed.

Singapore, 8 June 2022 – The Singapore National Parks Board (NParks), Microsoft and Conservation International announced the launch of Fin Finder, Asia's first mobile application that employs artificial intelligence (AI) to visually identify illegally traded shark and ray species. Through the tripartite collaboration, the mobile app was created by a Singapore-led team from Conservation International in consultation with NParks with support from the Microsoft AI for Earth program. The app will be used by officers from the Singapore National Parks Board to combat illegal wildlife trade. According to the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix II [1], there are approximately 1,000 species of sharks and rays in the world, of which over 30 species are listed under CITES Appendix II for regulated trade. In Singapore, more than 160,000 kilograms of fins from CITES-listed sharks and rays have entered the borders between 2012 and 2020 [2].

Pangolins, also known as scaly anteaters, are currently the most trafficked mammal species. Criminals can be resourceful and unrelenting in their efforts to find a way around obstacles. Wildlife traffickers are no exception. Today's trade in wildlife and wildlife products has shifted from physical markets to online marketplaces where traffickers apply e-commerce business models and use encrypted messages in an attempt to evade detection by law enforcement. While the move towards online platforms started several years before the Covid-19 pandemic, the restrictions imposed to contain the virus accelerated this digital transformation.

In the wildlife reserves of East Africa, elephants, rhinos, gorillas, and other large mammals are hunted by poachers. All that stands between these animals and harm's way are small teams of park rangers and conservationists. The danger is very real for these species on the brink: A staggering 35,000 African elephants are killed each year, putting them just a decade away from extinction, according to the non-profit RESOLVE. Technology is an increasingly critical tool for protecting elephants and other large animals, given their necessarily expansive habitats: A group of just 50 rangers in Kenya, for example, covers a reserve of 3,000 square miles. Park rangers and conservationists have used motion-activated camera traps to catch poachers in action, but the animals are tragically already lost by the time rangers can respond.

In the fight against poachers and illegal trade, animals can use all the help they can get. Thanks to researchers at the University of Helsinki's Digital Geography Lab, wildlife may find that aid through a popular tool traffickers use to deal their illegal wares -- social media. "With an estimated two and a half billion users, easy access has turned social media into an important venue for illegal wildlife trade," Enrico Di Minin, a conservation scientist working on the project, told Digital Trends. "Wildlife dealers active on social media release photos and information about wildlife products to attract and interact with potential customers, while also informing their existing network of contacts about available products. Currently, the lack of tools for efficient monitoring of high volume social media data limits the capability of law enforcement agencies to curb illegal wildlife trade. We plan to develop and use methods from artificial intelligence to efficiently monitor illegal wildlife trade on social media."

Despite their potential, approaches from artificial intelligence are still rarely used in addressing the biodiversity crisis," he says. Many social media platforms provide an application programming interface that allows researchers to access user-generated text, images and videos, as well as the accompanying metadata, such as where and when the content was uploaded, and connections between the users. Assistant professor Tuomo Hiippala highlights how machine learning methods can be used to process the language of social media posts. "Natural language processing can be used to infer the meaning of a sentence and to classify the sentiment of social media users towards illegal wildlife trade. Most importantly, machine learning algorithms can process combinations of verbal, visual and audio-visual content," Hiippala says.

In a new article published in the journal Conservation Biology, scientists from the University of Helsinki, Digital Geography Lab, argue that methods from artificial intelligence can be used to help monitor the illegal wildlife trade on social media. Dr. Enrico Di Minin, a conservation scientist at the University of Helsinki, who leads an interdisciplinary research group where methods from artificial intelligence are being developed and used to investigate the supply chain of the illegal wildlife trade in an innovative and novel way, stresses the importance of such novel methods to identify relevant data on the illegal wildlife trade from social media platforms. "Currently, the lack of tools for efficient monitoring of high-volume social media data limits the capability of law enforcement agencies to curb illegal wildlife trade," says Dr. Di Minin "Processing such data manually is inefficient and time consuming, but methods from artificial intelligence, such as machine-learning algorithms, can be used to automatically identify relevant information. Despite their potential, approaches from artificial intelligence are still rarely used in addressing the biodiversity crisis," he says. Many social media platforms provide an application programming interface that allows researchers to access user-generated text, images and videos, as well as the accompanying metadata, such as where and when the content was uploaded, and connections between the users.