Integrating Statistical Models and Principal Component Analysis to Assess Grasshopper Distribution and Diversity in Matiari District, Sindh, Pakistan
DOI:
https://doi.org/10.53555/jaz.v45i6.5011Keywords:
Grasshopper Diversity, Spatial Patterns, Temporal Variability, Principal Component Analysis (PCA), Ecological Niches, Generalized Linear Model (GLM).Abstract
This study offers a comprehensive analysis of grasshopper diversity and distribution across different sites of Matiari district, Sindh, Pakistan during 2021-22 by using advanced statistical and ecological methodologies. This research documented ten grasshopper species, revealing significant spatial and temporal patterns. Acrotylus humertianus and Aiolopus thalassinus were widely distributed, with Acrotylus humertianus showing peak abundance in Mooro Lakho, while Oedaleus rosesense demonstrated the highest overall density, reaching up to 20 individuals per site. Notably, species richness peaked at ten species in Hala during April 2022, indicating substantial temporal variability. Principal Component Analysis (PCA) highlighted distinct ecological niches, with Acrotylus humertianus scoring highly on PC1, suggesting it thrives under specific conditions. The Shannon-Wiener Index revealed Bhit Shah and Mooro Lakho as the most diverse sites, while Simpson's Diversity Index indicated lower diversity in Saedabad. The correlation analysis showed a moderate positive relationship between temperature and grasshopper density (r = 0.452), while humidity and soil type had minimal effects. Generalized Linear Model (GLM) analysis identified Locusta migratoria as the most effective model, with the lowest Deviance, AIC, and BIC values, reflecting its optimal fit. The study emphasizes the complex interplay between environmental factors and grasshopper populations, providing crucial insights for biodiversity conservation and ecological research. These results highlight the dynamic nature of grasshopper ecology and underscore the need for targeted conservation strategies based on environmental and temporal variations.
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Copyright (c) 2024 Muhammad Rafique Pitafi
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