COMPARATIVE EVALUATION OF SIMPSON’S AND SHANNON-WIENER’S INDICES FOR ASSESSING TREE SPECIES DIVERSITY IN ILORIN METROPOLIS, NORTH-CENTRAL NIGERIA

Abstract

Assessing tree species diversity is paramount for understanding ecosystem dynamics and guiding conservation efforts. In urban landscapes like Ilorin metropolis, where human activities exert significant pressure on ecosystems, accurate assessments of tree diversity are crucial for sustainable urban planning and conservation strategies. This study conducted a comparative analysis of Simpson’s and Shannon-Wiener’s indices to evaluate tree species diversity within Ilorin Metropolis, Nigeria. Data on tree species were gathered from the central areas of Ilorin. Data collection was from road networks and houses with trees using a systematic method. Data was analysed with descriptive and inferential statistics (t-test and correlation analysis). Tree species diversity was assessed using Shannon-Wiener’s and Simpson’s diversity index. The study identified 3225 individual trees belonging to 86 species and 23 taxonomic families. The most common species was Polyalthia longifolia (8.40%), while Ceiba pentadra, Ficus carica, and Strychnos spinosa (0.06% each) were the least common. The Simpson’s and Shannon-Wiener indices for the study area were 0.97 and 3.88, respectively. The results further revealed that Simpson’s had a mean, a standard error of the mean and a standard deviation of 0.00035, 0.00011 and 0.00098, respectively. In contrast, Shannon-Wiener’s had a mean, a standard error of the mean and a standard deviation of 0.045, 0.0045 and 0.041, respectively. The correlation coefficient between the indices was 0.83, while the t-test showed a significant relationship (t =10.189; p-value = 0.000). The study concludes that both diversity indices were highly suitable for assessing tree species diversity in the study area, but Simpson’s diversity is more suitable.

Key Words:  Comparative evaluation, Shannon-Wiener’s diversity index, Simpson’s diversity index, Ilorin Metropolis

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