Paul Bürkner, former Junior Research Group Leader for Bayesian Statistics at SimTech and now Professor at TU Dortmund University, has contributed as a co-author to a study recently published in Nature Sustainability. The paper, titled "Regional patterns of wild animal hunting in African tropical forests", was published in January 2025 and led by corresponding author Daniel Ingram from the University of Kent.
The study synthesizes 83 studies across West and Central Africa to analyze the factors influencing wildlife hunting patterns. Using Bayesian statistical modeling, the research identifies that hunters selling a higher proportion of their catch tend to have greater offtake rates. Hunting pressure was found to be higher in areas with better forest conditions, closer to protected areas, and farther from towns, suggesting that more accessible regions with degraded forests may experience significant wildlife depletion. The study also provides evidence that trade-driven hunting and gun use have increased since 1991, with potential long-term consequences for biodiversity and local livelihoods. One of the study's major findings is the rise of trade-driven hunting and gun use, with evidence indicating a significant increase in wildlife exploitation since 1991. The findings highlight the urgent need for context-specific wildlife management strategies to ensure sustainability.
Paul Bürkner's expertise in Bayesian hierarchical modeling played a crucial role in analyzing the large and heterogeneous dataset. Bayesian methods allow researchers to incorporate uncertainty and variability across different study regions, providing more robust insights into hunting behaviors and trends. This approach helped account for differences in study methodologies, regional contexts, and data gaps, making the findings more reliable for policymakers and conservationists.
By using Bayesian models, the study was able to estimate key relationships between hunting rates, economic drivers, and environmental factors while integrating uncertainty measures. This statistical framework is particularly useful in ecological research, where data is often scarce, inconsistent, or highly variable due to the complexity of natural systems.
This publication highlights the interdisciplinary nature of SimTech’s research, demonstrating how advanced computational and statistical techniques can contribute to global environmental challenges. SimTech’s expertise extends beyond engineering and physics into areas such as ecology, sustainability, and socio-economic systems, where data-driven models support decision-making.
The study’s findings can help inform wildlife conservation policies, sustainable hunting regulations, and strategies for balancing biodiversity conservation with local economic needs. By integrating statistical modeling with ecological and social sciences, Paul Bürkner contributes to understanding complex real-world systems with high societal relevance.
Read the full paper here: Nature Sustainability - DOI: 10.1038/s41893-024-01494-5