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dc.contributor.author Maldonado, C
dc.contributor.author Mora-Poblete, F
dc.contributor.author Echeverria, C
dc.contributor.author Baettig, R
dc.contributor.author Torres-Díaz, C
dc.contributor.author Contreras-Soto, RI
dc.contributor.author Heidari, P
dc.contributor.author Lobos, GA
dc.contributor.author do Amaral, AT
dc.date.accessioned 2024-01-17T15:53:54Z
dc.date.available 2024-01-17T15:53:54Z
dc.date.issued 2022
dc.identifier.uri https://repositorio.uoh.cl/handle/611/275
dc.description.abstract Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance-progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale.
dc.description.sponsorship ANID, FONDECYT
dc.relation.uri http://dx.doi.org/10.3390/rs14122898
dc.subject convolutional neural network
dc.subject multilayer perceptron
dc.subject population genetic structure
dc.subject remote sensing classification
dc.subject sugar gum
dc.title A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations
dc.type Artículo
uoh.revista REMOTE SENSING
dc.identifier.doi 10.3390/rs14122898
dc.citation.volume 14
dc.citation.issue 12
dc.identifier.orcid Contreras-Soto, Rodrigo/0000-0001-6468-9394
dc.identifier.orcid Heidari, Parviz/0000-0003-4716-0143
dc.identifier.orcid do Amaral, Antônio Teixeira/0000-0003-4831-7878
dc.identifier.orcid Lobos, Gustavo/0000-0002-0874-4309
dc.identifier.orcid Baettig, Ricardo/0000-0003-3891-4898
dc.identifier.orcid Echeverria, Cristian/0000-0001-6456-6431
dc.identifier.orcid Torres Diaz, Cristian/0000-0002-5741-5288
uoh.indizacion Web of Science

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