Poachers VS Computers: How Technology Can End Animal Trade
In an increasingly tech-driven world, authorities are developing technology to help catch poachers.
125 tigers. 470 leopards. 85 elephants. 117 rhinos.
These grim figures show just a tiny fraction of the countless plants and animals that poachers in India have killed since 2013. With a joint UN and Interpol report estimating environmental crimes between $91 and $258 billion in value in 2015, it’s clear that these numbers are just the tip of the iceberg.
India is far from being the only country to confront well-organised global poaching syndicates. In developing countries, financial constraints often result in under-equipped wildlife authorities being stretched thin while trying to monitor huge areas. However, far from giving up, scientists, conservationists and officials are teaming up and fighting back in surprising ways.
Today, “green security games”, Artificial Intelligence (AI) and DNA technologies are changing our approach to wildlife management. These technologies aid in the detection of poachers and traffickers by predicting the ways in which they act and move – if you think that sounds too science fiction-y, read on!
DNA barcoding is proving to be a next-gen tool in detecting and curbing poaching. First developed to compare the DNA in ivory with elephant populations that were the likely source of the ivory, this method is considered a game-changer.
In India, this method is being used in cases of rhino poaching in states like Assam. The state is using the Rhino DNA indexing system (RhoDIS) developed in South Africa to back up their rhino poaching investigations with scientific evidence. So how does this work?
- Like humans, each individual animal has a unique DNA “fingerprint”
- If we can develop databases of animals within a particular area (for example, of the rhinos in Kaziranga), we’ll be able to match poached rhino horns to the animal and region they were poached from
- This will help the authorities in building stronger cases against poachers and wildlife traffickers
- The information will also be used to manage rhino populations
Protection Assistance for Wildlife Security (PAWS) tries to predict the movement of poachers using a combination of AI and game theory. According to one of the PAWS team members, “In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual.” This allows the poachers to learn how to avoid the patrols and get away with their crimes.
PAWS was developed in 2013 and it was field-tested in Uganda and Malaysia in 2014. Building on this experience, the programme has improved with time and has been in use in Malaysia since 2015. Recently, PAWS was combined with a new algorithm called Comprehensive Anti-Poaching Tool with Temporal and Observation Uncertainty Reasoning (CAPTURE) which goes one step further in detecting poachers and their likely attacks.
PAWS and CAPTURE work by:
- Finding the best patrol routes given the terrain, previous poaching attempts and animal movement data
- Randomising the patrol data to constantly change the route and thus prevent poachers from being able to predict the movements of forest guards
- Predicting poacher movement using probability and animal movement data to find poaching “hotspots”
- Constantly improving its prediction using new updated information;
- Detecting and stopping wildlife trafficking
Tackling the menace of poaching is a problem that requires a vast array of tools since the complex causes for it make any one solution ineffective. Under-resourced officials, high poverty, inadequate enforcement and low conviction rates are just a few of the massive issues at play here. However, such cutting edge technologies, when used in combination with other measures, can be an invaluable resource in ensuring that the authorities stay one step ahead of the poachers.
Shalini Iyengar is an environmental lawyer and Faculty at Srishti Institute of Art, Design and Technology
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