Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change

Abstract Community detection remains little explored in the analysis of biodiversity change. The challenges linked with global biodiversity change have also multiplied manifold in the past few decades. Moreover, most studies concerning biodiversity change lack the quantitative treatment central to s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sana Akbar, Sri Khetwat Saritha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ffa8f58e3ab54ba79b9e2afc4dc7b99d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ffa8f58e3ab54ba79b9e2afc4dc7b99d
record_format	dspace
spelling	oai:doaj.org-article:ffa8f58e3ab54ba79b9e2afc4dc7b99d2021-12-02T16:08:06ZQuantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change10.1038/s41598-021-93122-x2045-2322https://doaj.org/article/ffa8f58e3ab54ba79b9e2afc4dc7b99d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93122-xhttps://doaj.org/toc/2045-2322Abstract Community detection remains little explored in the analysis of biodiversity change. The challenges linked with global biodiversity change have also multiplied manifold in the past few decades. Moreover, most studies concerning biodiversity change lack the quantitative treatment central to species distribution modeling. Empirical analysis of species distribution and abundance is thus integral to the study of biodiversity loss and biodiversity alterations. Community detection is therefore expected to efficiently model the topological aspect of biodiversity change driven by land-use conversion and climate change; given that it has already proven superior for diverse problems in the domain of social network analysis and subgroup discovery in complex systems. Thus, quantum inspired community detection is proposed as a novel technique to predict biodiversity change considering tiger population in eighteen states of India; leading to benchmarking of two novel datasets. Elements of land-use conversion and climate change are explored to design these datasets viz.—Landscape based distribution and Number of tiger reserves based distribution respectively; for predicting regions expected to maximize Tiger population growth. Furthermore, validation of the proposed framework on the said datasets is performed using standard community detection metrics like—Modularity, Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), Degree distribution, Degree centrality and Edge-betweenness centrality. Quantum inspired community detection has also been successful in demonstrating an association between biodiversity change, land-use conversion and climate change; validated statistically by Pearson’s correlation coefficient and p value test. Finally, modularity distribution based on parameter tuning establishes the superiority of the second dataset based on the number of Tiger reserves—in predicting regions maximizing Tiger population growth fostering species distribution and abundance; apart from scripting a stronger correlation of biodiversity change with land-use conversion.Sana AkbarSri Khetwat SarithaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sana Akbar Sri Khetwat Saritha Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
description	Abstract Community detection remains little explored in the analysis of biodiversity change. The challenges linked with global biodiversity change have also multiplied manifold in the past few decades. Moreover, most studies concerning biodiversity change lack the quantitative treatment central to species distribution modeling. Empirical analysis of species distribution and abundance is thus integral to the study of biodiversity loss and biodiversity alterations. Community detection is therefore expected to efficiently model the topological aspect of biodiversity change driven by land-use conversion and climate change; given that it has already proven superior for diverse problems in the domain of social network analysis and subgroup discovery in complex systems. Thus, quantum inspired community detection is proposed as a novel technique to predict biodiversity change considering tiger population in eighteen states of India; leading to benchmarking of two novel datasets. Elements of land-use conversion and climate change are explored to design these datasets viz.—Landscape based distribution and Number of tiger reserves based distribution respectively; for predicting regions expected to maximize Tiger population growth. Furthermore, validation of the proposed framework on the said datasets is performed using standard community detection metrics like—Modularity, Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), Degree distribution, Degree centrality and Edge-betweenness centrality. Quantum inspired community detection has also been successful in demonstrating an association between biodiversity change, land-use conversion and climate change; validated statistically by Pearson’s correlation coefficient and p value test. Finally, modularity distribution based on parameter tuning establishes the superiority of the second dataset based on the number of Tiger reserves—in predicting regions maximizing Tiger population growth fostering species distribution and abundance; apart from scripting a stronger correlation of biodiversity change with land-use conversion.
format	article
author	Sana Akbar Sri Khetwat Saritha
author_facet	Sana Akbar Sri Khetwat Saritha
author_sort	Sana Akbar
title	Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
title_short	Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
title_full	Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
title_fullStr	Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
title_full_unstemmed	Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
title_sort	quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ffa8f58e3ab54ba79b9e2afc4dc7b99d
work_keys_str_mv	AT sanaakbar quantuminspiredcommunitydetectionforanalysisofbiodiversitychangedrivenbylanduseconversionandclimatechange AT srikhetwatsaritha quantuminspiredcommunitydetectionforanalysisofbiodiversitychangedrivenbylanduseconversionandclimatechange
_version_	1718384634999341056

Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change

Ejemplares similares