Geo-located attendance data for CITES Conferences of the Parties

Area of Science:

• Environmental Science
• Conservation Biology
• Data Science

Background:

• The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) aims to manage global biodiversity trade.
• Regular CITES meetings involve diverse international stakeholders, but attendee data has been lacking.
• Limited attendee data hinders research on CITES meeting dynamics, causes, and effects.

Purpose of the Study:

• To construct the first comprehensive dataset of CITES meeting attendees.
• To enable detailed analysis of attendance patterns and their implications for conservation.
• To provide a valuable resource for researchers and advocacy organizations.

Main Methods:

• Utilized Natural Language Processing (NLP) techniques to extract information from PDF attendance rosters.
• Compiled attendee-level data, including geo-locations, names, affiliations, and gender.
• Developed a dataset covering 20,987 attendee records from all CITES meetings to date.

Main Results:

• Successfully created the first attendee-level dataset for CITES meetings.
• The dataset includes rich details on attendees and their delegations.
• Data validation confirms its utility and potential for new research.

Conclusions:

• The newly created CITES attendee dataset offers significant potential for advancing research in international wildlife conservation.
• This data resource can illuminate patterns and influences within CITES meetings.
• Further research can now explore the causes and effects of specific attendance trends.