HELICS pursues research in biodiversity conservation. The lab is developing novel methods, carrying out innovative analyses, and seeks to inform policy-making.
The illegal wildlife trade is considered the largest illegitimate business after narcotics and is threatening the persistence of thousands of species globally. Currently, there is a global spotlight on combatting illegal wildlife trade. A paucity of data on the scale and extent of the problem has thus far limited progress toward assessing the real impact illegal wildlife trade is having on biodiversity. In this project, we will address this limitation by using new data made available by project collaborators and data mined from social media platforms. Specifically, we will use this data to develop innovative analyses to expose the supply chain of the illegal wildlife trade in order to inform global conservation policy.
First, we plan to identify areas globally where pressures on species threatened by illegal wildlife trade are the highest and resources for counteracting illegal activities are the lowest.
Second, we will use new data mined from social media platforms, which have become one of the main venues where to trade illegal wildlife products, to unveil social networks of traders and users of illegal wildlife products and potential trade routes.
Third, we will develop biodiversity indicators that can be used by decision-makers to assess the impact illegal wildlife trade is having on biodiversity and monitor the success of conservation interventions over time.
The impact of the project will be maximized by feeding the results directly into key policy-making processes, such as the Convention of International Trade in Endangered Species of Wild Fauna and Flora, the Convention of Biological Diversity and other decision-making fora.
We were among the first ones to mine and use data from digital platforms in conservation science.
In order to unveil the full potential of such big data, we are applying and developing methods from artificial intelligence, namely machine learning and natural language processing, to investigate human-nature interactions in conservation areas and wildlife trafficking online. Our aim is to combine such methods with other advanced interdisciplinary methods from conservation science, geography, computer sciences, statistics, economics, and social sciences, in order to address data driven, societally relevant, questions.
Conservation biology is about how humans interact with nature.
An important limitation is that socio-ecological systems are still understudied. In order to understand what are the opportunities and threats to biodiversity from human interactions with nature, we use interdisciplinary methods, ranging from social sciences to economics.
Our focus spans from protected areas to privately and communally owned land. Currently, we carry out projects in South America and Africa to develop strategies that may be sustainable for both humans and nature. Particularly, we focus on private land conservation in Uruguay and nature-based tourism (both consumptive and non-consumptive use) in Sub-Saharan Africa.