Mathematical modeling of Gorilla Gorilla Delhi population in cross river state, Nigeria

This study investigates various mathematical models to ascertain their suitability for predicting gorilla population dynamics over three decades in Cross River State, Nigeria. Models including linear, quadratic, exponential, logarithmic, power law, logistic growth, polynomial, sinusoidal, and piecewise linear were evaluated based on their R2 (coefficient of determination) and SSE (sum of squared errors) metrics. The analysis, conducted using historical gorilla population data from 1990 to 2020, aimed to identify the model that best captures the observed growth patterns and fluctuations in the gorilla population. Among the models tested, the piecewise linear model emerged as the most effective, achieving the highest R2 of 0.966 and the lowest SSE of 71,866.8. This model's segmented approach accommodates shifts in growth rates over distinct time intervals, reflecting real-world ecological dynamics influenced by environmental factors and conservation efforts. In contrast, models such as the power law exhibited poor performance due to a significant overestimation of gorilla populations, highlighting their limited applicability in ecological studies. Understanding these dynamics through effective modeling not only enhances our ability to predict future population trends but also informs strategic conservation initiatives aimed at ensuring the long-term viability of gorilla populations in their natural habitats. This research underscores the importance of robust mathematical modeling in wildlife management and conservation decision-making processes.