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Entropy‐Based Assessment of Biodiversity, With Application to Ants' Nests Data

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  • L. Altieri
  • D. Cocchi
  • M. Ventrucci

Abstract

The present work takes an innovative point of view in the study of a marked point pattern dataset of two ants' species, over an irregular region with a spatial covariate. The approach, based on entropy measures, brings new insights to the interpretation of the behavior of such ants' nesting habits, which can be exploited in the general area of biodiversity evaluation. We make proper use of descriptive entropy measures and inferential approaches, performing a comparative study of their uncertainty and interpretability in the context of biodiversity. For the first time in the study of these ants' nests data, all the available information is fully exploited, and interpretation guidelines are given for assessing both the observed and the latent biodiversity of the system, with a simultaneous consideration of spatial structures, covariate and interpoint interaction effects. Computations are supported by the new release of our R package SpatEntropy.

Suggested Citation

  • L. Altieri & D. Cocchi & M. Ventrucci, 2025. "Entropy‐Based Assessment of Biodiversity, With Application to Ants' Nests Data," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.
  • Handle: RePEc:wly:envmet:v:36:y:2025:i:1:n:e2885
    DOI: 10.1002/env.2885
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    References listed on IDEAS

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    1. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    2. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
    3. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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