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Reply to 'Dissimilarity measures affected by richness differences yield biased delimitations of biogeographic realms'.
Costello, Mark J; Tsai, Peter; Cheung, Alan Kwok Lun; Basher, Zeenatul; Chaudhary, Chhaya.
Afiliação
  • Costello MJ; Institute of Marine Science, University of Auckland, Auckland, 1142, New Zealand. m.costello@auckland.ac.nz.
  • Tsai P; Bioinformatics Institute, University of Auckland, Auckland, 1142, New Zealand. m.costello@auckland.ac.nz.
  • Cheung AKL; Bioinformatics Institute, University of Auckland, Auckland, 1142, New Zealand.
  • Basher Z; School of Environment, University of Auckland, Auckland, 1142, New Zealand.
  • Chaudhary C; Institute of Marine Science, University of Auckland, Auckland, 1142, New Zealand.
Nat Commun ; 9(1): 5085, 2018 11 30.
Article em En | MEDLINE | ID: mdl-30504796
ABSTRACT
Recently, we classified the oceans into 30 biogeographic realms based on species' endemicity. Castro-Insua et al. criticize the choices of dissimilarity coefficients and clustering approaches used in our paper, and reanalyse the data using alternative techniques. Here, we explain how the approaches used in our original paper yield results in line with existing biogeographical knowledge and are robust to alternative methods of analysis. We also repeat the analysis using several similarity coefficients and clustering algorithms, and a neural network theory method. Although each combination of methods produces outputs differing in detail, the overall pattern of realms is similar. The coarse nature of the present boundaries of the realms reflects the limited field data but may be improved with additional data and mapping to environmental variables.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise por Conglomerados / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise por Conglomerados / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article