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Assessing biodiversity is crucial in ecology, requiring robust measures of species distribution and similarity between communities. Traditional similarity indices do not account for asymmetry and are limited in detecting deviations from expected biodiversity patterns. In this study we present a modified version of the Kullback-Leibler divergence that can be effectively used as a similarity index in biodiversity assessment. Results form a simulation study and a real data application show that our proposal outperforms consistently the reference standard, the Bray-Curtis similarity.

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Assessing Similarity in Biodiversity Count Data Using a Kullback-Leibler Divergence Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Carola Canella,
  • Giovanna Jona Lasinio,
  • Salme Kärkkäinen,
  • Fabio Divino

摘要

Assessing biodiversity is crucial in ecology, requiring robust measures of species distribution and similarity between communities. Traditional similarity indices do not account for asymmetry and are limited in detecting deviations from expected biodiversity patterns. In this study we present a modified version of the Kullback-Leibler divergence that can be effectively used as a similarity index in biodiversity assessment. Results form a simulation study and a real data application show that our proposal outperforms consistently the reference standard, the Bray-Curtis similarity.