Moving beyond simple descriptive statistics in the analysis of online wildlife trade: an example from clustering and ordination

Collecting data for reports on online wildlife trade is resource-intensive and time-consuming. Learning often focuses on the main item traded by each country only. However, online trade is increasing, providing potential to update the conversation from a national scale to a global scale. We demonstrate how hierarchical clustering can identify wildlife items that follow similar trading patterns.We also ordinate the clusters, and seek correlations between the clusters and global measures, such as Worldwide Governance Indicators. We primarily use a sample dataset from a published report of online traded wildlife, covering 16 countries and 31 taxa or product types. Clustering provided immediate insights, such as rhinos and pangolins were traded similarly to ivory and suspected ivory. Five out of eight clusters represented items predominately traded by one country. An ordination of these clusters, and representation of global measures on the ordination axis, show a strong correlation of the 'Voice and accountability' score with the clusters. Consequently, from the 'Voice and accountability' score of the United States, a country not included in our dataset, we inferred that it traded elephant items (not ivory) and owl items during 2014.