Special Issue Article: Conservation CrimeMobile decision-tree tool technology as a means to detect wildlife crimes and build enforcement networks
Introduction
The illegal trade of animals and products made from parts of animals is a major global threat to wildlife. In the last 25 years, populations of many iconic species such as elephant, rhinoceros, tiger, and saiga antelope have decreased dramatically or become locally extinct, and much of this decline is due to illegal hunting for coveted products such as ivory, medicines, and clothing (Emslie et al., 2013, Li et al., 2007, Maisels et al., 2013, Milner-Gulland et al., 2001, Walston et al., 2010). Weak enforcement systems have enabled wildlife to be poached and traded to markets where sales of wildlife and products made from wildlife parts fuel illegal business (Bennett, 2011). Responsibility for enforcement is spread across individuals along supply chains, from rangers to transportation and border guards. Lack of awareness and interest in wildlife conservation are one of the major obstacles, along with lack of training and technical resources, for combating these crimes (Bennett, 2011, Kaaria and Muchiri, 2011). Law enforcement authorities stationed at borders, including customs, immigration, police, military, agricultural and transportation personnel (The World Bank, 2013) are at the front line of the wildlife crime trade-chain battlefield in ensuring illicitly traded products are not crossing borders or being offered for sale in local markets. The rapidly increasing international trade and flow of travelers has put considerable pressure on border authorities, who must screen a broad range of goods and decide whether or not to release a shipment in a matter of minutes or even seconds (Gerson et al., 2008). Building capacity of enforcement personnel can increase the quality of market surveillance for illegally traded wildlife. However, the ability for these law enforcement entities to impact illegal wildlife trade requires having access to easy-to-use tools and resources to aid in the identification of traded wildlife and products.
Knowledge, motivation, and technical resources specific to wildlife are three main factors that can improve the impact of law enforcement agencies on illegal wildlife trade (Bennett, 2011, Gerson et al., 2008, Kaaria and Muchiri, 2011). Ideally, customs agents would have extensive training as well as easy access to the latest forensic techniques and laboratories to facilitate identification of wildlife to the species level (Bell, 2011, Rosen and Smith, 2010). In reality, training is limited; and, while promising, forensic science must occur in properly equipped laboratories (Anderson, 2013), far away from the front lines of trade activity. In the past, organizations have experimented with wild species identification prototypes mimicking field guides (Tallant et al., 2010). This approach revealed significant practical limitations as it required some previous knowledge of wildlife to ultimately be successful in identifying to a species level. Capitalizing on this experience, the “decision-tree style” may be a more viable approach to allow successful operation by officers with relatively little training to make quick and reliable identification of potential crimes in the field (Rosen and Smith, 2010).
Decision-trees are a system to provide classification based on inputs and organization of information. They have wide-spread application, from determining water treatments, to diagnosing medical conditions and classifying astronomical images (Murphy, 2001, Murphy and Olson, 1996, Salzberg et al., 1995). Often, decision-trees facilitate analysis by assigning probabilities to supplied data selections, with additional weights to account for potential errors, to calculate the outcome of system manipulations (Murphy, 2001). Decision-trees can also be simple dichotomous or multichotomous keys employed to identify items of the natural world such as flowering plants, minerals, or insects (Carle, 2010, Alan Plante et al., 2003, Watts, 1998). These keys use a process of elimination to narrow the possibilities at each selection and can be effective as a tool to classify or identify based on different factors. This approach could be equally useful for identification of traded wildlife and wildlife products but until now has yet to be tested.
Emerging technologies such as interactive web pages and mobile applications provide an opportunity to make multichotomous keys available to a variety of audiences at a relatively low cost, as use of mobile technologies is booming around the globe (Asia Digital Marketing Association, 2012, Smith, 2013). For these tools to curb wildlife trade at the scale necessary to prevent decline of wildlife populations, organizations must deploy them in strategic locations and to a network of individuals engaged in using them for specific purposes. The Wildlife Conservation Society (WCS) has been involved in the enforcement of wildlife trade for over a decade supporting networks of trained park rangers and training customs, police and forestry officers in places such as Congo, Thailand, China, Indonesia, Malaysia, and Vietnam (Bennett, 2012, Lee et al., 2005, Roberton, 2013, Walston et al., 2010, Wildlife Conservation Society, 2013a) and training military police to secure on-base markets (Kretser, 2012, Kretser et al., 2012). These networks represent opportunities to test whether emerging technologies using decision-tree approaches will be a viable method for enforcement. We present three cases from WCS field programs in China, Vietnam, and the United States illustrating how to build a network of users employing decision-tree style tools to improve detection of wildlife crimes.
Section snippets
Methods
WCS is developing and pilot-testing decision-tree style technological resources to identify wildlife and products made from wildlife parts to a species level across three diverse contexts and audiences in China, Vietnam, and the United States. All authors on this paper are WCS staff and are intimately involved and leading this development process. To streamline mobile technology production efforts, we are creating a back-end database structure that can be adapted to multiple tools and includes
China
With a population of over 1.3 billion, China is potentially the largest demand market for several species of wildlife globally due to the traditional use of wildlife for medicine, food, and trophies and the increase of that use due to rapid economic development. In recent years, Chinese authorities have progressively acknowledged the impact illegal wildlife trade has on global biodiversity as well as the country’s international reputation (Hu Jintao, 2012). The destruction of 6.15 tons of ivory
Conclusions
Decision-tree style resources for field identification of species in potential wildlife crimes are promising as an emerging enforcement tool. The tools in either app or web-based format can be adapted to particular contexts and are expandable, easy to use, and helpful for individuals with little to no training in wildlife as is the case with many enforcement individuals (Bennett, 2011, Rosen and Smith, 2010). Mobile app technology also has an additional benefit in that a user does not require
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