In 2020, a Concept Note for the preparation of a second volume of the FAO Fisheries and Aquaculture Technical Paper “The Nansen Programme: Investigations of Fishery Resources in Developing Regions, History of the Programme and Review of Results”, prepared by the EAF-Nansen Programme Coordinating Unit, was approved at the Norad/FAO/IMR Annual meeting. The report is intended to cover results of the activities in the third and fourth phases of the Nansen Programme, including surveys with the second R/V Dr Fridtjof Nansen which operated between 1994 and 2016.

Understanding The Drivers of Urban Demand for Bushmeat

As in many parts of west and central Africa, the bushmeat trade in Ghana plays an important role in peoples’ livelihoods and provides a valuable source of animal protein and micronutrients to numerous consumers. It also represents one of the most pressing conservation challenges in the region (Ripple et al. 2016).

Dynamic Drivers of Disease in Africa

The Dynamic Drivers of Disease in Africa was an ESPA (Ecosystem Services for Poverty Alleviation) funded research programme designed to deliver much-needed, cutting-edge science on the relationships between ecosystems, zoonoses, health and wellbeing, with the objective of helping people move out of poverty and promoting social justice. The project investigated whether disease regulation as an ecosystem service is affected by changes in biodiversity, climate and land use, with differential impacts on people’s health and well-being.

Unblocking the flow of biodiversity data for multi-stakeholder environmental sustainability management

Governments, businesses and civil society require biodiversity data to facilitate informed decision making on environmental management and conservation. However, biodiversity data are fragmented, challenging to collect or access, difficult to use, and rarely available to decision makers in appropriate formats. Challenges include lack of capacity and the absence of appropriate tools for identifying indicators and for collecting, analysing and interpreting data. Causal factors include taxonomic and geographic data biases, differences in spatial scales, and governance issues such as willingness to share information, especially in risk-averse governments and businesses.