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

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 di...

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Autores principales: Akbar, Sana, Saritha, Sri Khetwat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275618/
https://www.ncbi.nlm.nih.gov/pubmed/34253748
http://dx.doi.org/10.1038/s41598-021-93122-x
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author Akbar, Sana
Saritha, Sri Khetwat
author_facet Akbar, Sana
Saritha, Sri Khetwat
author_sort Akbar, Sana
collection PubMed
description 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.
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spelling pubmed-82756182021-07-13 Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change Akbar, Sana Saritha, Sri Khetwat Sci Rep Article 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. Nature Publishing Group UK 2021-07-12 /pmc/articles/PMC8275618/ /pubmed/34253748 http://dx.doi.org/10.1038/s41598-021-93122-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Akbar, Sana
Saritha, Sri Khetwat
Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275618/
https://www.ncbi.nlm.nih.gov/pubmed/34253748
http://dx.doi.org/10.1038/s41598-021-93122-x
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