Trends and patterns of imports of legal and illegal live corals into the United States

Using Crime Script Analysis to Understand the Illegal Harvesting of Live Corals: Case Studies From Indonesia and Fiji

“…Indonesia and Fiji are among the major exporters of live coral to the United States, coming in at first and fourth, respectively (Petrossian et al, 2019; Rhyne et al, 2012). Indonesia accounted for 91% of the world’s exports in 2005 (Jones, 2008) and for nearly 73% of United States live coral seizures from 2003 to 2012 (Petrossian et al, 2019). Located in the “coral triangle,” Indonesia is surrounded by the most diverse and biologically rich reefs in the world (Roberts et al, 2002).…”

Toward a scientific understanding of the international tiger trade: Response to Kolby and Weissgold (2022)

“…We clearly stated that "unstructured observational data, such as seizure incidents, are biased (Dobson et al, 2020)" (Khanwilkar, Sosnowski & Guynup, 2022, p. 11). We acknowledged the shortcomings of trade analyses using seizure data and were clear in our theoretical approach of LEMIS data, which has been widely used to characterize wildlife trade patterns (Harrington et al, 2020;Hitchens & Blakeslee, 2020;Petrossian et al, 2016;Petrossian et al, 2020;Rhyne et al, 2017;. Our conclusions were presented as a "preliminary overview" (Khanwilkar, Sosnowski & Guynup, 2022, p. 3) to guide future research.…”

Mischaracterizing wildlife trade and its impacts may mislead policy processes

“…Mischaracterizes trade (e.g., conflates source of specimens/purpose of trade)Nellemann et al (2018) Conflation in use of terms/units † For example, misinterpreting blank units in trade records as missing data (and assuming trade involved the recommended unit for specific derivatives e.g., kg) rather than "number of items"Mischaracterizestrade volumes (e.g., inflating the number of individual animals or plants in trade) Andersson and Gibson (2018) Assuming each row of data comprises a single shipment/incident ‡ Miscalculates transaction frequency since rows in a comparative tabulation output, for example, may contain multiple records (see Pavitt et al 2019) Berec et al (2018), Can et al (2019), D'Cruze and Macdonald (2015), Vall-Llosera and Su (2018) Assuming source code I refers to illegal trade Misrepresents illegal trade levels (e.g., number of individual animals or plants involved) D'Cruze and Macdonald (2015, 2016), Ribeiro et al (2019), Ye et al (2020)Misinterpretation of LEMIS data:Treating each row of data as a single seizure eventMistakenly inflates the number of seizures and thereby the extent of illegal tradeGoyenechea and Indenbaum (2015),Petrossian et al (2016),Petrossian et al (2020),Sosnowski and Petrossian (2020) Misinterpretation of seizure data:Failing to acknowledge and/or account for inherent biases in seizure data and describing illegal trade as increasing or similar Misrepresents illegal trade data and the "trends" derived are not meaningful to be threatened by international trade based on the Red List would automatically qualify for inclusion in CITES Overlooks the fact that IUCN and CITES have independent criteria and processes for determining threat statusFrank and Wilcove (2019),Gorobets (2020) Assuming all species included in the CITES Appendices are tradedMisrepresents the CITES Appendices and inflates the number of CITES-listed species considered to be in tradeScheffers et al (2019) …”