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. 2021 Dec 13;12(12):1115.
doi: 10.3390/insects12121115.

DBSCAN and GIE, Two Density-Based "Grid-Free" Methods for Finding Areas of Endemism: A Case Study of Flea Beetles (Coleoptera, Chrysomelidae) in the Afrotropical Region

Maurizio Biondi  1 , Paola D'Alessandro  1 , Walter De Simone  1 , Mattia Iannella  1
Affiliations

DBSCAN and GIE, Two Density-Based "Grid-Free" Methods for Finding Areas of Endemism: A Case Study of Flea Beetles (Coleoptera, Chrysomelidae) in the Afrotropical Region

Maurizio Biondi et al. Insects. .

Abstract

Areas of endemism (AoEs) are a central area of research in biogeography. Different methods have been proposed for their identification in the literature. In this paper, a "grid-free" method based on the "Density-based spatial clustering of applications with noise" (DBSCAN) is here used for the first time to locate areas of endemism for species belonging to the beetle tribe Chrysomelidae, Galerucinae, Alticini in the Afrotropical Region. The DBSCAN is compared with the "Geographic Interpolation of Endemism" (GIE), another "grid-free" method based on a kernel density approach. DBSCAN and GIE both return largely overlapping results, detecting the same geographical locations for the AoEs, but with different delimitations, surfaces, and number of detected sinendemisms. The consensus maps obtained by GIE are in general less clearly delimited than the maps obtained by DBSCAN, but nevertheless allow us to evaluate the core of the AoEs more precisely, representing of the percentage levels of the overlap of the centroids. DBSCAN, on the other hand, appears to be faster and more sensitive in identifying the AoEs. To facilitate implementing the delimitation of the AoEs through the procedure proposed by us, a new tool named "CLUENDA" (specifically developed is in GIS environment) is also made available.

Keywords: Afrotropical region; ArcGIS Pro; Chrysomelidae; DBSCAN; GIE; GIS analysis; Model Builder; areas of endemism; density-based clustering.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study area and occurrence localities used for analysis (see text).
Figure 2
Figure 2
Scheme of the CLUENDA toolbox, developed in ArcGIS Pro 2.8.
Figure 3
Figure 3
(a) Consensus maps of the areas of endemism detected by GIE. (b) Areas of endemism detected by GIE in Class 1 (100 km), (c) Class 2 (150 km) and (d) Class 3 (200 km).
Figure 4
Figure 4
Areas of endemism detected by DBSCAN using species with distribution range up to 100 km and distance between centroids of 100, 150, and 200 km.
Figure 5
Figure 5
Areas of endemism detected by DBSCAN using species with distribution range up to 300 km and distance between centroids to 100, 150, and 200 km.
Figure 6
Figure 6
Areas of endemism detected by DBSCAN using species with distribution range up to 500 km and distance between centroids to 100, 150, and 200 km.
Figure 7
Figure 7
Areas of endemism detected by DBSCAN using the distance between centroids to 100 km and species with distribution range up to 100, 300, and 500 km.
Figure 8
Figure 8
Areas of endemism detected by DBSCAN using the distance between centroids to 150 km and species with distribution range up to 100, 300, and 500 km.
Figure 9
Figure 9
Areas of endemism detected by DBSCAN using the distance between centroids to 200 km and species with distribution range up to 100, 300, and 500 km.
Figure 10
Figure 10
Box plots of the ratio “number of endemic species/total of species” for each of the AoEs identified by the DBSCAN analysis. Red spot = median.
See this image and copyright information in PMC

References

    1. Prado J.R., Brennand P.G.G., Godoy L.P., Libardi G.S., Abreu-Júnior E.F., Roth P.R.O., Chiquito E.A., Percequillo A.R. Species richness and areas of endemism of oryzomyine rodents (Cricetidae, Sigmodontinae) in South America: An NDM/VNDM approach. J. Biogeogr. 2015;42:540–551. doi: 10.1111/jbi.12424. - DOI
    1. DaSilva M.B., Pinto-da-Rocha R., de Carvalho C., Almeida E.A.B. Biogeografia Da América Do Sul: Padrões E Processos. Editora Roca; São Paulo, Brazil: 2011.
    1. Da Cardoso Silva J.M., de Cardoso Sousa M., Castelletti C.H. Areas of endemism for passerine birds in the Atlantic forest, South America. Glob. Ecol. Biogeogr. 2004;13:85–92. doi: 10.1111/j.1466-882X.2004.00077.x. - DOI
    1. Hurdu B.-I., Escalante T., Pușcaș M., Novikoff A., Bartha L., Zimmermann N.E. Exploring the different facets of plant endemism in the South-Eastern Carpathians: A manifold approach for the determination of biotic elements, centres and areas of endemism. Biol. J. Linn. Soc. 2016;119:649–672. doi: 10.1111/bij.12902. - DOI
    1. Brown J.H., Stevens G.C., Kaufman D.M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 1996;27:597–623. doi: 10.1146/annurev.ecolsys.27.1.597. - DOI

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