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

Abstract

Assessing the progress of rare species is good for effective conservation, in potential areas for ecology like the Ngorongoro Conservation Area in Tanzania. The study uses the Negative Binomial Regression Model (NBRM) to examine the reasons influencing the abundance of rare species. It employs species abundance as the outcome variable, which indicates the number of individuals in a certain rare species population, and two main predictors are the Habitat Quality Index and the Area of Habitat Fragmentation. It talks about the common overdispersion in ecological count data, where variance is greater than the mean, hence NBRM would be the correct model. The results show higher habitat quality positively correlates with species abundance, showing the importance of maintaining ecological integrity. Contrariwise, increased habitat fragmentation is related with reduced species abundance, underscoring the negative effect of habitat disruption on rare species. The study findings provide actionable insights into the ecological dynamics of the Ngorongoro Conservation Area, providing guidance for conservation approaches aimed at mitigating habitat fragmentation and strengthen the habitat quality to support rare species survival.