
Poaching and the illegal wildlife trade (i.e., wildlife crime) are a multibillion-dollar global industry. The commercialization and overexploitation of wildlife caused by wildlife crime threaten biodiversity, particularly many of the species already on the cusp of extinction. Wildlife crime also leads to ecosystem collapse and loss of government revenues and threatens the strength and economic aspiration of developing nations. Efforts from wildlife law enforcement to prevent wildlife crime are a conservation necessity. The purpose of this chapter is to introduce the field of conservation forensics. Conservation forensics is an applied field of conservation crime science that fits within the broader frameworks of green and conservation criminology. This field of study applies hard science techniques used to gather wildlife crime data such as genetics, chemical analysis, geographical analysis, statistics, artificial intelligence, and computational modeling toward techniques that can directly benefit the efforts of law enforcement personnel involved in protecting imperiled wildlife. This chapter identifies and reviews tools and techniques that can help achieve the goals of conservation forensics: the prosecution of wildlife criminals and the prevention of wildlife crime to conserve biodiversity.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
