General framework of the internship on protected areas

Faced with the decline in biodiversity, the capacity of protected areas to maintain their ecological integrity is regularly questioned. African savannas are unique environments, in particular because of the diversity and socio-ecological functions of their ungulate community. In order to propose appropriate public policies, it is essential to take into account all the levels of complexity of the causes of the observed declines, especially in West and Central Africa where these trends are the most pronounced (Craigie et al. 2010). Many factors are mentioned (land use, overexploitation of resources, climate change, governance, armed conflicts...). However, methodologies that question the different levels of complexity of human-biodiversity relationships in order to identify families of indicators that can guide conservation policy changes remain rare (Massarella et al., 2021). In West and Central Africa, protected areas have had very variable trajectories of anthropogenic pressures over the past few decades. These pressures have been exerted at different scales (local, regional and sub-regional) with very different effects on ungulates. The subregional dimension is a structuring element, in particular through:

1. links to wildlife and space,
2. the effects of climate oscillations;
3. the impacts of socio-political changes;
4. and changes in the governance of protected areas.

Understanding the trends observed requires the transdisciplinary use of several sets of indicators that reflect the various possible causes and of an analytical methodology adapted to take into account these different contexts.

Objectives of the biodiversity internship

1. Model ungulate trends using existing and very heterogeneous datasets specific to wildlife inventories in 13 protected areas in West and Central Africa (Bayesian inference). The analysis scripts are finalized. It will therefore be a question of implementing them on the database and formatting the results for analysis.
2. Collect existing data to build a set of indicators of the socio-ecological integrity of protected areas. It will be necessary to collect qualitative and quantitative data (demography, change in land use, socio-economic indicators, governance indicators, security indicators...). This data collection phase will be preceded by a conceptual reflection on the indicators and the spatial and temporal scales at which they affect the performance of the protected area.
3. Identify the most relevant indicators to explain trends in the numbers of the main ungulates in protected areas. This step will require mobilizing wildlife trends (response variable) and collected indicators (explanatory variables) through qualitative and/or quantitative analyses. This prospective approach will be supported by “field-driven” expertise on the part of the internship supervisors.

Scientific framework

The working hypothesis is based on the importance of the scalar dimension where changes in land use in the savannah may be more influenced by regional trends in changes in average income or governance than by local trends (Espirito-Santo et al. 2016). Taking this dimension into account should make it possible to work on families of indicators adapted to the socio-ecological challenges specific to each protected area. The intern will have at his disposal 126 time series already collected. It will use the Bayesian approach to model, quantify, and statistically qualify families of population trends for the main ungulates. This approach will make it possible, for heterogeneous time series, to calculate an apparent annual growth rate (r) and then, by comparison with the average growth rate over the entire chronosequence, to statistically identify the inflection points in the conservation status of each species. The intern will then have to extract, for each protected area, the sets of socio-ecological indicators and propose models identifying and prioritizing the key determinants (climatic, political, social, ecological), the effect of combinations between scales and their ability to explain biodiversity trends from the Bayesian model. A “field-driven” approach will pre-identify the indicators that potentially have the most impact on ungulates in the 13 protected areas.

Organization of the internship

The internship can start from February 2022, for a period of 6 months. Initially, a visit to Montpellier will allow us to focus on modeling and identifying families of trends in ungulate numbers (objective 1, for 1.5 months). The rest of the internship (objectives 2 and 3) will take place in South Africa (George, Nelson Mandela University) to extract the indicators, define the explanatory model and write the report and other valuation deliverables.

Profile wanted

M2 research in ecology/Biodiversity management/Environmental geography/Agronomy Good skills in statistical analysis. Knowledge of Bayesian inference desirable. Skills and previous experiences in geomatics and management of spatial databases (QGIS, ArcGIS...). Skills in searching in spatial databases, extracting, formatting data and processing attribute tables will be required. Strong taste for environmental issues centered on the management of protected areas. Strong capacity for conceptual synthesis. Knowledge of tropical environments would be a plus. Ability to research, read and write scientific articles in English. Team work in a multi-cultural and mobile environment.

Reception conditions

Internship paid according to in accordance with the texts in force. Several reception structures will be mobilized: University of Angers, University of Nelson Mandela (George South Africa), CEFE (Montpellier). Travel will be covered by the project. Application deadline: 30/11/2021 Reference of this internship offer for biodiversity in protected areas: OE-111021-1