Community Ecology
Volume/Issue:
Volume 20: Issue 3
On the use of nestedness-based similarity functions (NBSF) to classify and/or order operational geographic units (OGUs)
Authors:
E. Feoli Department of Life Sciences University of Trieste, 34127, Italy

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P. Ganis Department of Life Sciences University of Trieste, 34127, Italy

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J. J. Ibáñez Research Centre on Desertification (Centro de Investigaciones sobre Desertificación, CIDE ,CSIC-UV), Km 405. Apdo. Oficial 46113, Valencia, Spain

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R. Pérez-Gómez Department of Topographic and Cartographic Engineering, Polytechnic University of Madrid (Departamento de Ingeniería Topográfica y Cartografía, Universidad Politécnica de Madrid, UPM), Camino de la Arboleda, s/n. Campus Sur UPM, Autovía de Valencia km. 7, E 28031 Madrid, Spain

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Pages:
223–229
Online Publication Date:
Dec 2019
Publication Date:
01 Dec 2019
Article Category:
Research Article
DOI:
https://doi.org/10.1556/168.2019.20.3.2
Keywords:
Impoverishment; Inclusion; Maximum similarity; Nestedness; Set theory
Open access

Abstract

In this paper, we want to support the idea of using a family of indices of similarity, that we call the Simpson's family indices or nestedness-based similarity functions (NBSF) for comparing operational geographic units (OGUs) (phytosociological relevés, animal traps, watersheds, administrative units, industrial areas, islands etc.). In these cases, similarity-dissimilarity depends, in addition to factors that induce replacement, also on factors that produce reduction or increment in the number of features within the same typology of OGUs (e.g., extent, reduction of fertility, anthropogenic pressure etc.). To keep into consideration this aspect, the indices are defined to be equal to 1 when the OGUs are completely nested. The results of the application to four simulated data sets prove that, when the data set does not show clear nested pattern, the use of NBSF produces results similar to the nestedness-free similarity functions, however since NBSF clearly detect nested situations, we should prefer their use in the circumstances where we think important to put in evidence nestedness. In conclusion, we support the idea of using both types of indices in order to improve the knowledge about the structure of any data set.

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Cover Community Ecology
Community Ecology An Interdisciplinary Journal Reporting Progress in Community and Population Studies
Print ISSN:
1585-8553
Online ISSN:
1588-2756

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Community Ecology
Language English
Size A4
Year of
Foundation		 2000
Volumes
per Year		 1
Issues
per Year		 3
Founder Akadémiai Kiadó
Founder's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
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Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher		 Chief Executive Officer, Akadémiai Kiadó
ISSN 1585-8553 (Print)
ISSN 1588-2756 (Online)

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