Literature DB >> 34253748

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

Sana Akbar1, Sri Khetwat Saritha2.   

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.
© 2021. The Author(s).

Entities:  

Year:  2021        PMID: 34253748     DOI: 10.1038/s41598-021-93122-x

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  10 in total

1.  Prioritizing tiger conservation through landscape genetics and habitat linkages.

Authors:  Bibek Yumnam; Yadvendradev V Jhala; Qamar Qureshi; Jesus E Maldonado; Rajesh Gopal; Swati Saini; Y Srinivas; Robert C Fleischer
Journal:  PLoS One       Date:  2014-11-13       Impact factor: 3.240

Review 2.  Community structure in social and biological networks.

Authors:  M Girvan; M E J Newman
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-11       Impact factor: 11.205

Review 3.  Biodiversity conservation: challenges beyond 2010.

Authors:  Michael R W Rands; William M Adams; Leon Bennun; Stuart H M Butchart; Andrew Clements; David Coomes; Abigail Entwistle; Ian Hodge; Valerie Kapos; Jörn P W Scharlemann; William J Sutherland; Bhaskar Vira
Journal:  Science       Date:  2010-09-10       Impact factor: 47.728

4.  Link communities reveal multiscale complexity in networks.

Authors:  Yong-Yeol Ahn; James P Bagrow; Sune Lehmann
Journal:  Nature       Date:  2010-06-20       Impact factor: 49.962

5.  The emergent interactions that govern biodiversity change.

Authors:  James S Clark; C Lane Scher; Margaret Swift
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-06       Impact factor: 11.205

6.  Spatial Search by Quantum Walk is Optimal for Almost all Graphs.

Authors:  Shantanav Chakraborty; Leonardo Novo; Andris Ambainis; Yasser Omar
Journal:  Phys Rev Lett       Date:  2016-03-11       Impact factor: 9.161

7.  Optimal Quantum Spatial Search on Random Temporal Networks.

Authors:  Shantanav Chakraborty; Leonardo Novo; Serena Di Giorgio; Yasser Omar
Journal:  Phys Rev Lett       Date:  2017-11-28       Impact factor: 9.161

8.  When is an ecological network complex? Connectance drives degree distribution and emerging network properties.

Authors:  Timothée Poisot; Dominique Gravel
Journal:  PeerJ       Date:  2014-02-04       Impact factor: 2.984

9.  The many facets of community detection in complex networks.

Authors:  Michael T Schaub; Jean-Charles Delvenne; Martin Rosvall; Renaud Lambiotte
Journal:  Appl Netw Sci       Date:  2017-02-15

10.  Detecting multiple communities using quantum annealing on the D-Wave system.

Authors:  Christian F A Negre; Hayato Ushijima-Mwesigwa; Susan M Mniszewski
Journal:  PLoS One       Date:  2020-02-13       Impact factor: 3.240

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.