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Table 1 Comparison of autoregressive models with different W for assessing global patterns of spatial autocorrelations

From: An autoregressive model for global vertebrate richness rankings: long-distance dispersers may have stronger spatial structures

W Mammals Amphibians Birds
  ρ AIC C R 2 Moran'sI ρ AIC C R 2 Moran'sI ρ AIC C R 2 Moran'sI
Distance matrix             
a = 1 0.118 7,443.883 0.914 0.184 0.126 8,034.76 0.784 0.206 0.117 8,693.137 0.862 0.153
a = 1.1 0.17 7,414.297 0.916 0.213 0.182 7,972.835 0.797 0.238 0.169 8,664.672 0.866 0.18
a = 1.2 0.242 7,382.141 0.919 0.243 0.261 7,906.554 0.81 0.27 0.241 8,631.682 0.87 0.208
a = 1.3 0.341 7,347.771 0.921 0.274 0.369 7,836.91 0.822 0.304 0.341 8,594.431 0.874 0.237
a = 1.4 0.476 7,311.783 0.924 0.306 0.516 7,765.194 0.834 0.338 0.478 8,553.433 0.879 0.268
a = 1.5 0.658 7,274.965 0.927 0.338 0.714 7,692.925 0.846 0.372 0.662 8,509.428 0.884 0.299
Binary matrix             
 Class = 2 0.003 9,385.66 0.441 −0.031 0.003 9,273.898 0.274 −0.021 0.004 10,170.65 0.442 0.016
 Class = 4 0.003 9,385.66 0.441 −0.031 0.003 9,273.898 0.274 −0.021 0.004 10,170.65 0.442 0.016
 Class = 6 0.003 9,305.439 0.483 0.011 0.003 9,211.798 0.316 0.03 0.003 10,094.25 0.481 0.045
 Class = 8 0.003 9,305.439 0.483 0.011 0.003 9,211.798 0.316 0.03 0.003 10,094.25 0.481 0.045
 Class = 10 0.003 9,305.439 0.483 0.011 0.003 9,211.798 0.316 0.03 0.003 10,094.25 0.481 0.045
  1. ρ, autoregressive parameter; AICc, modified Akaike information criterion; R2, the proportion of explained variation; a, the parameter in Equation 5.