A tiger skeleton soaks in rice wine in Harbin, China. There is a growing, clamoring demand for tiger bone wine, a tonic made by steeping a tiger carcass in rice wine to produce an extremely expensive elixir. It’s thought to impart the animal’s great strength, a status symbol product bought or gifted by the elite: government officials, military officers, and wealthy businessmen. Photo courtesy Save the Tiger Fund
Ultimately the elimination of cultural folk ideologies on the capitalist demand side should stop the illegal harvesting of wildlife. That such conspicuous consumption still motivates unsustainable practices cannot be minimized in its globally immoral turpitude. Moral suasion seems far too timid in the abuse of complex global economic trade relationships and infrastructure and the demands for serious policy solutions should become paramount, as they perpetuate premodern barbarity and ignorance in the modern name of wretched excess.
But without knowing exactly what’s going on, wildlife agencies and researchers can’t stop these killings. So Nikkita Patel turned to HealthMap, a tool the Boston Children’s Hospital created 10 years ago.The tool searches multilingual news aggregators and forums for media reports, parsing them for relevant keywords. It was already tuned to the wildlife trade, in part because animals can be vectors for disease spread. HealthMap records the key information in each article, such as the location of the reported illegal transaction, and keeps a tally of the number of individuals from each species traded.
Patel’s research, published today in the Proceedings of the National Academy of Sciences, relies more on that data than previous illegal wildlife trade work. “It’s looking at who the key players are,” Patel says, “and how to best break down trade networks.”...
In other words, some data is better than no data. Because the media reports on these animals more frequently, Patel hopes that the data on them was more complete. “We thought that selecting the animals that had the greatest number of reports would perhaps be better reflection of what’s happening in the real world,” she says. With more work, law enforcement might even be able to develop her maps into real-time analysis tools. That’d be a real step toward breaking the trade networks. “As an illicit trade, they’re flexible in changing their routes,” Patel says. But it’s hard to outrun raw data.
For example this news piece provides an information node: (trigger warning)
Police in central Vietnam on Monday seized a 120-kg frozen tiger carcass in a "suspicious" truck heading north on National Route 1A in Nghe An Province.
This kind of analysis would be familiar to law enforcement—researchers have used similar methods to track the drug trade. But whether it would work on wildlife in the real world is still an open question. “The authors are attempting to analyze media-derived information, which will contain some accurate data, but unless it has been officially verified, it should always be regarded as suspect,” writes Richard Thomas, the Global Communications Coordinator for TRAFFIC, an international organization that monitors wildlife trade, in an e-mail. And an outright blockade of illegal animal trade in, say, China—one of the countries Patel identified as a key node—certainly wouldn’t be easy.
The illegal global rhinoceros trade network before (top) and after (bottom) a hypothetical targeted disruption.
The illegal global rhinoceros trade network before (top) and after (bottom) a hypothetical targeted disruption.
Nikkita Gunvant Patel, Chris Rorres, Damien O. Joly, John S. Brownstein, Ray Boston, Michael Z. Levy, and Gary Smith. Quantitative methods of identifying the key nodes in the illegal wildlife trade network PNAS 2015 ; published ahead of print June 15, 2015,
Abstract
Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions.
wildlife trade network analysis key player elephant rhinoceros