Negative binomial regression model for monitoring rare species data: A case study at Ngorongoro conservation Area-Tanzania

Higher habitat quality significantly boosts the abundance of rare species, while increased habitat fragmentation reduces it—key insights from a study applying negative binomial regression modeling to data from Tanzania’s Ngorongoro Conservation Area. The research addresses common overdispersion in ecological count data by using this model to analyze how two predictors—habitat quality and fragmentation—affect rare species populations. Findings underscore the critical role of maintaining ecological integrity and mitigating habitat disruption for conservation success, offering actionable guidance for preserving biodiversity in the region.

Ally, S. J., & Mrutu, H. A. (2024). Negative binomial regression model for monitoring rare species data: A case study at Ngorongoro conservation Area-Tanzania. International Journal of Statistics and Applied Mathematics, 9(6), 109–114. https://doi.org/10.22271/maths.2024.v9.i6b.1904