Variance partitioning and spatial eigenvector analyses with large macroecological datasets
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2020
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Resumo
Macroecological 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.
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Palavras-chave
Macroecology, PCNM, Resample cells, Spatial analyses, Richness patterns, Sharks, Spatial autocorrelation
Citação
PAULA-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.