Understanding the role of different types of conservation area in meeting global biodiversity protection targets

Research background

The most widely used approach for reducing biodiversity loss is establishing conservation areas, which is why scientists and practitioners have called for 30% global coverage by 2030 (Dinerstein et al, 2019). Achieving this will depend on increasing the number of privately- and community-managed conservation areas (Donald et al, 2019), as this is more socially just and politically feasible. In addition, it may help protect species that are under-represented in state conservation areas, which are generally located in ecosystems with low economic value (Joppa & Pfaff, 2009). However, we know little about the locations of privately- and community-managed conservation areas, making it impossible to understand the importance of the different conservation area types or monitor progress towards global targets.

To overcome this, a DICE-led research team developed a sample-based approach. This identified a 10% sample of the terrestrial realm within a minimum number of countries that is representative of spatial patterns of biodiversity and conservation area coverage (Sykes et al, in press). They then worked with in-country partner organisations to collect the best available conservation area data. This work confirmed the value of the approach and its potential to answer important research questions.

Aims and objectives

This studentship will build on this existing sampled conservation area research by:

1) Investigating the robustness of the approach for identifying the subset of countries and global sample by comparing results using different prioritisation techniques (based on the Marxan and Zonation software packages), and using different targets for the percentage of the globe selected. This will involve collecting data on an additional 10% of the terrestrial realm.

2) Based on the 20% sample, investigating the role that different conservation area types play in meeting global conservation targets, such as representing biodiversity, e.g., meeting species and ecosystem representation targets (Venter et al, 2014) and maintaining landscape connectivity (Saura et al, 2019).

3) Determining the effectiveness of different types of conservation area in preventing habitat loss (Andam et al, 2008), using global datasets on the spread of deforestation and agricultural conversion to understand whether and how this varies between biomes and socio-economic conditions.

4) Reviewing the literature to assess how the approach developed in Objective 1 could be applied to the marine realm and piloting the approach using available global datasets on marine biogeography, governance and ecosystem pressures.