Dynamic perspectives on biodiversity quantification: beyond conventional metrics

Abstract

Our research addresses the pressing need to assess biodiversity in the face of increasing habitat destruction and species extinctions. Several researchers have modelled conventional measures to assess biodiversity. Every measure evaluates biodiversity by considering different properties. Among them Simpson and Shannon indices are widely used, they primarily focus on species richness and abundance, overlooking the importance of rare or unique species. This limitation makes it challenging to identify which species drive changes in biodiversity and hampers conservation efforts. Moreover, these measures are sensitive to sample size and biased towards dominant species, leading to inaccurate estimations. To overcome these challenges, we propose a novel mathematical model that provides a comprehensive assessment of biodiversity. Our model accounts for species dominance, addresses sample size sensitivity, and highlights the significance of rare species within a community. By applying our measure to real-time scenarios and comparing it with traditional methods using the same dataset, proposed measure demonstrated its efficacy in capturing biodiversity dynamics over time.