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Aly Pavitt

Programme Officer | Species

Aly’s work in the Species programme supports international wildlife trade Conventions and Agreements, often at the species level, including the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), the EU Wildlife Trade Regulations and the EU Timber Regulations. Her work focusses on large-scale data analysis and analytical automation, particularly in species- or commodity-related trade data, including the CITES trade data and commodity datasets such as EU Comext. She is responsible for the development of novel methodologies, tools and analyses to support decisionmakers by increasing the accessibility of the trade data, as well as identifying trade patterns and potential priority taxa for conservation attention.

Previous experience & expertise

Aly has a background in scientific research, particularly ecological modeling and conservation science. She has a PhD in quantitative ecology from the University of Edinburgh, where she worked on the Isle of Rum red deer project. Her research focussed on modeling individual hormone profiles and life-history traits, including through the use of quantitative genetic approaches. She also holds an MSc in conservation science from Imperial College London, where she collaborated with CEH to model species morphometric traits and Spatio-temporal range patterns in British dragonflies.

Recent work by Aly Pavitt


Aly is responsible for the development of tools, analyses and scientific outputs aimed at better understanding and communicating patterns of international wildlife trade, particularly in the context of CITES, and supporting effective decision-making around sustainable wildlife trade.

CITES and EU Wildlife Trade Support |

Aly is responsible for the analysis of large-scale wildlife trade and life history data to support CITES and the EU Wildlife Trade Regulations, including developing methodologies and analyses to identify noteworthy trade patterns and potential priority species. She is also responsible for automating analytical and data management processes to ensure they are streamlined and repeatable.