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

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Environmetrics Pub Date : 2024-10-30 DOI:10.1002/env.2885
L. Altieri, D. Cocchi, M. Ventrucci
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引用次数: 0

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.

Abstract Image

基于熵的生物多样性评价及其在蚁巢数据中的应用
目前的工作采取了一个创新的观点,在两个蚂蚁物种的标记点模式数据集的研究,在一个不规则的区域与空间协变量。该方法基于熵测度,为蚁群筑巢习性的解释提供了新的视角,可用于生物多样性评价的一般领域。我们适当地利用描述性熵测度和推理方法,对它们在生物多样性背景下的不确定性和可解释性进行了比较研究。在对这些蚁巢数据的研究中,首次充分利用了所有可用信息,并在同时考虑空间结构、协变量和点间相互作用的情况下,为评估系统的观察和潜在生物多样性提供了解释指南。新版本的R包SpatEntropy支持计算。
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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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