Variance partitioning and spatial eigenvector analyses with large macroecological datasets

Página do item simplificado
dc.creatorSouza, Laura Barreto de Paula
dc.creatorDiniz Filho, José Alexandre Felizola
dc.date.accessioned2022-12-29T11:11:21Z
dc.date.available2022-12-29T11:11:21Z
dc.date.issued2020
dc.description.abstractMacroecological data are usually structured in space, so taking into account spatial autocorrelation in regression and correlation analyses is essential for a better understanding of patterns and processes. Many methods are available to deal with spatial autocorrelation, but there are some difficulties when one is dealing with huge geographical extents and fine-scale data. So, we propose a relatively simple and fast computer-intensive approach to deal with Principal Coordinate of Neighbor Matrices (PNCM)/ Moran’s Eigenvector Mapping (MEM) analyses for large datasets, using global richness pattern of sharks as a model. We performed a variance partitioning approach by regressing species richness against environmental variables and spatial eigenvectors derived from PCNM. Due to the large number of ocean grid cells (> 9000), we ran the analyses 1000 timesby randomly subsampling each time 50 to 4500 cells and compared the distribution of the variance partitioning components, as well as the slopes of the environmental variables. We also estimated Moran’s I coefficients for regression residuals to check if spatial eigenvectors took into account spatial autocorrelation. Comparing statistics of analyses with different sample sizes, we note that although the environmental component increases linearly, other components (unique space and shared) of the most important variables stabilize with about 1000 cells, whereas all other smaller effects tend to stabilize between 2500 and 3000 cells. Besides that, PCNM eigenvectors were able to control spatial autocorrelation very well. We showed that shark richness patterns are strongly and positively correlated with temperature range, according to the well-known pattern of distribution for the taxon, and strong negatively correlated with oxygen supplies, which are higher in polar zones where ice acts as a barrier to sharks. Our approach clearly shows that it is possible to perform a robust evaluation of global patterns of diversity using eigenvector approaches based on a resampling strategy and allows effective computation of the variance partitioning even when dealing with large datasets.pt_BR
dc.identifier.citationPAULA-SOUZA, Laura Barreto de; DINIZ FILHO, Jose Alexandre F. Variance partitioning and spatial eigenvector analyses with large macroecological datasets. Frontiers of Biogeography, Westlake Village, v. 12, n. 4, e47295, 2020.pt_BR
dc.identifier.doihttps://doi.org/10.21425/F5FBG47295
dc.identifier.issne- 1948-6596
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/21682
dc.language.isoengpt_BR
dc.publisher.countryEstados unidospt_BR
dc.publisher.departmentInstituto de Ciências Biológicas - ICB (RG)pt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMacroecologypt_BR
dc.subjectPCNMpt_BR
dc.subjectResample cellspt_BR
dc.subjectSpatial analysespt_BR
dc.subjectRichness patternspt_BR
dc.subjectSharkspt_BR
dc.subjectSpatial autocorrelationpt_BR
dc.titleVariance partitioning and spatial eigenvector analyses with large macroecological datasetspt_BR
dc.typeArtigopt_BR

Arquivos

Pacote Original
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
Artigo - Laura Barreto de Paula Souza - 2020.pdf
Tamanho:
1.23 MB
Formato:
Adobe Portable Document Format
Descrição:
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar

Coleções

ICB - Artigos publicados em periódicos