Contents

1. Load the data

Load 3D brain coordinates for the wholebrain and each region: telencephalon (tel), diencephalon (dien), mesencephalon (mes), cerebellum (cere), and medulla oblongata (medob). Load trait data. Load phylogenetic tree.

# Brain coordinate data
load(here::here('data/whole_brain_coords_S1.Rda')) # wholebrain
load(here::here('data/tel_coords_S1.Rda'))         # tel
load(here::here('data/dien_coords_S1.Rda'))        # dien
load(here::here('data/mes_coords_S1.Rda'))         # mes
load(here::here('data/cere_coords_S1.Rda'))        # cere
load(here::here('data/medob_coords_S1.Rda'))       # medob

# Trait data
load(here::here('data/lizard_trait_data.Rda'))

# Phylogenetic tree data
load(here::here('data/lizard_tree.Rda'))

2. Whole brain size and shape analysis

Part 1. Perform a generalize procrustes analysis (GPA) with gpagen on the coordinate data. GPA aligns all the species coordinates in the most optimal way (e.g., removing scale, orientation, and position) so that we can better compare the shapes of each species to each other.

library(geomorph)

wb.gpa <- gpagen(wholebrain, ProcD = T, verbose = T, print.progress = F)

Part 2. Create a geomorph data frame that contains the Procrustes coordinates and the trait data. This will be used in subsequent analyses.

wb.gdf <- geomorph.data.frame(wb.gpa, trait)

Part 3. Run a Phylogenetic ANOVA/regression (PGLS) with procD.pgls to test for correlations between ecological traits and whole brain size and shape.

Whole Brain Shape:
note: we included Csize as a covariate since shape often covaries with size. Refer to supplementary information for how much shape variation is explained by size.

pgls_wb.shape <- procD.pgls(coords ~ Csize + abslatitude + precip_seasonality + temp_seasonality + microhabitat + activity_time + foraging_mode, phy = lzrd_tree, SS.type = "II", data = wb.gdf, print.progress = F)

Whole Brain Size:

pgls_wb.size <- procD.pgls(Csize ~ abslatitude + precip_seasonality + temp_seasonality + microhabitat + activity_time + foraging_mode, phy = lzrd_tree, SS.type = "II", data = wb.gdf, print.progress = F)
Whole Brain Shape
Source df1 SS2 MS3 Rsq F Z p-value4
Csize 1 0.0002 0.0002 0.022 0.79 −0.29 0.606
abslatitude 1 0.0004 0.0004 0.040 1.45 1.00 0.164
precip_seasonality 1 0.0004 0.0004 0.038 1.40 0.90 0.187
temp_seasonality 1 0.0004 0.0004 0.043 1.58 1.20 0.121
microhabitat 2 0.0004 0.0002 0.037 0.68 −0.91 0.810
activity_time 2 0.0006 0.0003 0.060 1.10 0.42 0.341
foraging_mode 2 0.0011 0.0005 0.105 1.92 1.82 0.035
Residuals 18 0.0051 0.0003 0.491
Total 28 0.0104

1 df, degree of freedom.

2 SS, sum of squares.

3 MS, mean squares.

4 Significant values (p-value < 0.05) from permutation tests (1,000 permutation rounds) are bolded.

Whole Brain Size
Source df1 SS2 MS3 Rsq F Z p-value4
abslatitude 1 1,499,533 1,499,533 0.067 8.14 2.27 0.010
precip_seasonality 1 894,879 894,879 0.040 4.86 1.73 0.037
temp_seasonality 1 634,775 634,775 0.028 3.45 1.35 0.092
microhabitat 2 3,181,370 1,590,685 0.141 8.63 2.78 0.003
activity_time 2 3,196,026 1,598,013 0.142 8.67 2.73 0.001
foraging_mode 2 4,459,516 2,229,758 0.198 12.10 3.08 0.003
Residuals 19 3,500,391 184,231 0.156
Total 28 22,507,049

1 df, degree of freedom.

2 SS, sum of squares.

3 MS, mean squares.

4 Significant values (p-value < 0.05) from permutation tests (1,000 permutation rounds) are bolded.

Effect size of each ecological variable on Csize. In other words, the slope.

x
(Intercept) 25670.231936
abslatitude -282.523262
precip_seasonality -93.563890
temp_seasonality 0.931508
microhabitatScansorial 18999.847959
microhabitatTerrestrial 11975.208592
activity_timeDiurnal -11392.279470
activity_timeNocturnal 947.635953
foraging_modemixed -9009.089163
foraging_modesit_and_wait 3316.507502

Fig 1. Foraging Mode

Fig S1. Activity Time

Fig S2. Microhabitat

Part 4. To visualize shape variation, we performed a principle components analysis with phylogeny considered (phyloPCA) with gm.prcomp on the Procrustes shape coordinates and plotted them.

wb.pca <- gm.prcomp(wb.gpa$coords, phy = lzrd_tree, GLS = TRUE)
kable(summary(wb.pca))
## 
## Ordination type: Principal Component Analysis 
## Centering by GLS mean
## Oblique projection of GLS-centered residuals
## Number of observations: 29 
## Number of vectors 28 
## 
## Importance of Components:
##                               Comp1        Comp2        Comp3        Comp4
## Eigenvalues            0.0001316915 7.168048e-05 3.066885e-05 2.912548e-05
## Proportion of Variance 0.3544202540 1.929130e-01 8.253879e-02 7.838514e-02
## Cumulative Proportion  0.3544202540 5.473333e-01 6.298721e-01 7.082572e-01
##                               Comp5        Comp6        Comp7        Comp8
## Eigenvalues            1.792596e-05 1.396093e-05 1.180115e-05 9.329498e-06
## Proportion of Variance 4.824397e-02 3.757293e-02 3.176033e-02 2.510839e-02
## Cumulative Proportion  7.565012e-01 7.940741e-01 8.258344e-01 8.509428e-01
##                               Comp9       Comp10       Comp11       Comp12
## Eigenvalues            8.160733e-06 7.369369e-06 5.364527e-06 5.261816e-06
## Proportion of Variance 2.196290e-02 1.983311e-02 1.443750e-02 1.416108e-02
## Cumulative Proportion  8.729057e-01 8.927388e-01 9.071763e-01 9.213374e-01
##                              Comp13       Comp14       Comp15       Comp16
## Eigenvalues            4.636602e-06 3.913735e-06 3.312183e-06 2.516895e-06
## Proportion of Variance 1.247845e-02 1.053300e-02 8.914047e-03 6.773696e-03
## Cumulative Proportion  9.338159e-01 9.443489e-01 9.532629e-01 9.600366e-01
##                              Comp17       Comp18       Comp19       Comp20
## Eigenvalues            2.494481e-06 2.244242e-06 2.144861e-06 1.807060e-06
## Proportion of Variance 6.713373e-03 6.039909e-03 5.772445e-03 4.863325e-03
## Cumulative Proportion  9.667500e-01 9.727899e-01 9.785623e-01 9.834257e-01
##                              Comp21       Comp22       Comp23       Comp24
## Eigenvalues            1.358770e-06 1.084812e-06 1.063795e-06 8.240360e-07
## Proportion of Variance 3.656846e-03 2.919546e-03 2.862983e-03 2.217721e-03
## Cumulative Proportion  9.870825e-01 9.900020e-01 9.928650e-01 9.950827e-01
##                              Comp25       Comp26       Comp27       Comp28
## Eigenvalues            5.980015e-07 5.618345e-07 3.909081e-07 2.763531e-07
## Proportion of Variance 1.609396e-03 1.512060e-03 1.052048e-03 7.437467e-04
## Cumulative Proportion  9.966921e-01 9.982042e-01 9.992563e-01 1.000000e+00
## 
## 
## Dispersion (variance) of points, after projection:
##                                       Comp1       Comp2        Comp3
## Tips Dispersion                 0.009681169 0.007147906 1.424633e-03
## Proportion Tips Dispersion      0.276397409 0.204072738 4.067327e-02
## Cumulative Tips Dispersion      0.276397409 0.480470146 5.211434e-01
## Ancestors Dispersion            0.001433389 0.001439172 5.123635e-05
## Proportion Ancestors Dispersion 0.188462865 0.189223247 6.736587e-03
## Cumulative Ancestors Dispersion 0.188462865 0.377686112 3.844227e-01
##                                       Comp4        Comp5        Comp6
## Tips Dispersion                 0.004498675 0.0014740868 0.0014429243
## Proportion Tips Dispersion      0.128437181 0.0420851820 0.0411954938
## Cumulative Tips Dispersion      0.649580596 0.6916657778 0.7328612717
## Ancestors Dispersion            0.001551935 0.0002614725 0.0003820672
## Proportion Ancestors Dispersion 0.204049282 0.0343785602 0.0502344227
## Cumulative Ancestors Dispersion 0.588471980 0.6228505406 0.6730849633
##                                        Comp7        Comp8        Comp9
## Tips Dispersion                 0.0011558875 0.0009710775 0.0011683956
## Proportion Tips Dispersion      0.0330005911 0.0277242672 0.0333576989
## Cumulative Tips Dispersion      0.7658618628 0.7935861300 0.8269438289
## Ancestors Dispersion            0.0002525653 0.0002915638 0.0002873258
## Proportion Ancestors Dispersion 0.0332074365 0.0383349775 0.0377777747
## Cumulative Ancestors Dispersion 0.7062923997 0.7446273773 0.7824051520
##                                       Comp10       Comp11       Comp12
## Tips Dispersion                 0.0009704764 0.0005768218 5.462224e-04
## Proportion Tips Dispersion      0.0277071033 0.0164682644 1.559465e-02
## Cumulative Tips Dispersion      0.8546509322 0.8711191967 8.867138e-01
## Ancestors Dispersion            0.0003394359 0.0001042129 6.843176e-05
## Proportion Ancestors Dispersion 0.0446292407 0.0137019690 8.997449e-03
## Cumulative Ancestors Dispersion 0.8270343927 0.8407363617 8.497338e-01
##                                       Comp13       Comp14       Comp15
## Tips Dispersion                 0.0003536412 3.276284e-04 0.0004409799
## Proportion Tips Dispersion      0.0100964563 9.353793e-03 0.0125899767
## Cumulative Tips Dispersion      0.8968103057 9.061641e-01 0.9187540754
## Ancestors Dispersion            0.0000241316 3.234363e-05 0.0001142089
## Proportion Ancestors Dispersion 0.0031728374 4.252560e-03 0.0150162489
## Cumulative Ancestors Dispersion 0.8529066481 8.571592e-01 0.8721754566
##                                       Comp16       Comp17       Comp18
## Tips Dispersion                 0.0005190988 0.0002320877 2.611313e-04
## Proportion Tips Dispersion      0.0148202714 0.0066261044 7.455300e-03
## Cumulative Tips Dispersion      0.9335743468 0.9402004512 9.476558e-01
## Ancestors Dispersion            0.0002345241 0.0000232721 7.300627e-05
## Proportion Ancestors Dispersion 0.0308353727 0.0030598292 9.598908e-03
## Cumulative Ancestors Dispersion 0.9030108293 0.9060706584 9.156696e-01
##                                       Comp19       Comp20       Comp21
## Tips Dispersion                 1.932345e-04 3.043619e-04 2.052370e-04
## Proportion Tips Dispersion      5.516846e-03 8.689534e-03 5.859516e-03
## Cumulative Tips Dispersion      9.531726e-01 9.618621e-01 9.677216e-01
## Ancestors Dispersion            1.456516e-05 9.286695e-05 6.261602e-05
## Proportion Ancestors Dispersion 1.915035e-03 1.221020e-02 8.232792e-03
## Cumulative Ancestors Dispersion 9.175846e-01 9.297948e-01 9.380276e-01
##                                       Comp22       Comp23       Comp24
## Tips Dispersion                 0.0003165744 0.0001298523 1.528279e-04
## Proportion Tips Dispersion      0.0090382001 0.0037072839 4.363237e-03
## Cumulative Tips Dispersion      0.9767598484 0.9804671324 9.848304e-01
## Ancestors Dispersion            0.0001460516 0.0000252477 4.318368e-05
## Proportion Ancestors Dispersion 0.0192029540 0.0033195830 5.677816e-03
## Cumulative Ancestors Dispersion 0.9572305495 0.9605501325 9.662279e-01
##                                       Comp25       Comp26       Comp27
## Tips Dispersion                 1.169243e-04 1.042967e-04 1.120526e-04
## Proportion Tips Dispersion      3.338190e-03 2.977670e-03 3.199102e-03
## Cumulative Tips Dispersion      9.881686e-01 9.911462e-01 9.943453e-01
## Ancestors Dispersion            3.730216e-05 2.708299e-05 4.972226e-05
## Proportion Ancestors Dispersion 4.904510e-03 3.560887e-03 6.537512e-03
## Cumulative Ancestors Dispersion 9.711325e-01 9.746933e-01 9.812309e-01
##                                       Comp28
## Tips Dispersion                 0.0001980619
## Proportion Tips Dispersion      0.0056546686
## Cumulative Tips Dispersion      1.0000000000
## Ancestors Dispersion            0.0001427522
## Proportion Ancestors Dispersion 0.0187691414
## Cumulative Ancestors Dispersion 1.0000000000
Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 Comp8 Comp9 Comp10 Comp11 Comp12 Comp13 Comp14 Comp15 Comp16 Comp17 Comp18 Comp19 Comp20 Comp21 Comp22 Comp23 Comp24 Comp25 Comp26 Comp27 Comp28
Eigenvalues 0.0001317 0.0000717 0.0000307 0.0000291 0.0000179 0.0000140 0.0000118 0.0000093 0.0000082 0.0000074 0.0000054 0.0000053 0.0000046 0.0000039 0.0000033 0.0000025 0.0000025 0.0000022 0.0000021 0.0000018 0.0000014 0.0000011 0.0000011 0.0000008 0.0000006 0.0000006 0.0000004 0.0000003
Proportion of Variance 0.3544203 0.1929130 0.0825388 0.0783851 0.0482440 0.0375729 0.0317603 0.0251084 0.0219629 0.0198331 0.0144375 0.0141611 0.0124784 0.0105330 0.0089140 0.0067737 0.0067134 0.0060399 0.0057724 0.0048633 0.0036568 0.0029195 0.0028630 0.0022177 0.0016094 0.0015121 0.0010520 0.0007437
Cumulative Proportion 0.3544203 0.5473333 0.6298721 0.7082572 0.7565012 0.7940741 0.8258344 0.8509428 0.8729057 0.8927388 0.9071763 0.9213374 0.9338159 0.9443489 0.9532629 0.9600366 0.9667500 0.9727899 0.9785623 0.9834257 0.9870825 0.9900020 0.9928650 0.9950827 0.9966921 0.9982042 0.9992563 1.0000000
Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 Comp8 Comp9 Comp10 Comp11 Comp12 Comp13 Comp14 Comp15 Comp16 Comp17 Comp18 Comp19 Comp20 Comp21 Comp22 Comp23 Comp24 Comp25 Comp26 Comp27 Comp28
Tips Dispersion 0.0096812 0.0071479 0.0014246 0.0044987 0.0014741 0.0014429 0.0011559 0.0009711 0.0011684 0.0009705 0.0005768 0.0005462 0.0003536 0.0003276 0.0004410 0.0005191 0.0002321 0.0002611 0.0001932 0.0003044 0.0002052 0.0003166 0.0001299 0.0001528 0.0001169 0.0001043 0.0001121 0.0001981
Proportion Tips Dispersion 0.2763974 0.2040727 0.0406733 0.1284372 0.0420852 0.0411955 0.0330006 0.0277243 0.0333577 0.0277071 0.0164683 0.0155947 0.0100965 0.0093538 0.0125900 0.0148203 0.0066261 0.0074553 0.0055168 0.0086895 0.0058595 0.0090382 0.0037073 0.0043632 0.0033382 0.0029777 0.0031991 0.0056547
Cumulative Tips Dispersion 0.2763974 0.4804701 0.5211434 0.6495806 0.6916658 0.7328613 0.7658619 0.7935861 0.8269438 0.8546509 0.8711192 0.8867138 0.8968103 0.9061641 0.9187541 0.9335743 0.9402005 0.9476558 0.9531726 0.9618621 0.9677216 0.9767598 0.9804671 0.9848304 0.9881686 0.9911462 0.9943453 1.0000000
Ancestors Dispersion 0.0014334 0.0014392 0.0000512 0.0015519 0.0002615 0.0003821 0.0002526 0.0002916 0.0002873 0.0003394 0.0001042 0.0000684 0.0000241 0.0000323 0.0001142 0.0002345 0.0000233 0.0000730 0.0000146 0.0000929 0.0000626 0.0001461 0.0000252 0.0000432 0.0000373 0.0000271 0.0000497 0.0001428
Proportion Ancestors Dispersion 0.1884629 0.1892232 0.0067366 0.2040493 0.0343786 0.0502344 0.0332074 0.0383350 0.0377778 0.0446292 0.0137020 0.0089974 0.0031728 0.0042526 0.0150162 0.0308354 0.0030598 0.0095989 0.0019150 0.0122102 0.0082328 0.0192030 0.0033196 0.0056778 0.0049045 0.0035609 0.0065375 0.0187691
Cumulative Ancestors Dispersion 0.1884629 0.3776861 0.3844227 0.5884720 0.6228505 0.6730850 0.7062924 0.7446274 0.7824052 0.8270344 0.8407364 0.8497338 0.8529066 0.8571592 0.8721755 0.9030108 0.9060707 0.9156696 0.9175846 0.9297948 0.9380276 0.9572305 0.9605501 0.9662279 0.9711325 0.9746933 0.9812309 1.0000000

Regional Brain Size/Shape Association with Foraging Mode

We are interested in the whole brain. We are also interested in the telencephalon and diencephalon becasue they are associated with olfaction and vision, respectively. This will be important when we test for association between these regions directly and the foraging modes associated with those sensory modalities. GPA on coordinates for each brain region

library(geomorph)
c.gpa <- gpagen(cere, ProcD = T, verbose = T)
d.gpa <- gpagen(dien, ProcD = T, verbose = T)
md.gpa <- gpagen(medob, ProcD = T, verbose = T)
ms.gpa <- gpagen(mes, ProcD = T, verbose = T)
t.gpa <- gpagen(tel, ProcD = T, verbose = T)

Create geomorph dataframe for each brain region

library(geomorph)
c.gdf <- geomorph.data.frame(c.gpa, trait)
d.gdf <- geomorph.data.frame(d.gpa, trait)
md.gdf <- geomorph.data.frame(md.gpa, trait)
ms.gdf <- geomorph.data.frame(ms.gpa, trait)
t.gdf <- geomorph.data.frame(t.gpa, trait)

Compute Csize ratio for each brain region to the whole brain (they don’t all add up to one…probably because the regions share some coordinates)

c <- c.gpa$Csize/wb.gpa$Csize
d <- d.gpa$Csize/wb.gpa$Csize
md <- md.gpa$Csize/wb.gpa$Csize
ms <- ms.gpa$Csize/wb.gpa$Csize
t <- t.gpa$Csize/wb.gpa$Csize

Create a data frame containing all the rgion-to-whole-brain Csize ratios and the raw Csize values for each region and the trait values

prop <- cbind(c, d, md, ms, t, wb.gpa$Csize, c.gpa$Csize, d.gpa$Csize, md.gpa$Csize, ms.gpa$Csize, t.gpa$Csize, trait)

PGLS of regional brain shape and foraging mode RESULTS: No regional effect of foraging mode on shape

pgls_cshape <- procD.pgls(coords ~ Csize + foraging_mode, phy = lzrd_tree, data=c.gdf, iter=999, print.progress = FALSE)
pgls_dshape <- procD.pgls(coords ~ Csize + foraging_mode, phy = lzrd_tree, data=d.gdf, iter=999, print.progress = FALSE)
pgls_mdshape <- procD.pgls(coords ~ Csize + foraging_mode, phy = lzrd_tree, data=md.gdf, iter=999, print.progress = FALSE)
pgls_msshape <- procD.pgls(coords ~ Csize + foraging_mode, phy = lzrd_tree, data=ms.gdf, iter=999, print.progress = FALSE)
pgls_tshape <- procD.pgls(coords ~ Csize + foraging_mode, phy = lzrd_tree, data=t.gdf, iter=999, print.progress = FALSE)
Regional Brain Shape
Source df1 SS2 MS3 Rsq F Z p-value4
Telencephalon
Csize 1 0.0035 0.0035 0.282 10.85 3.40 0.001
foraging_mode 2 0.0009 0.0004 0.070 1.34 0.90 0.188
Residuals 25 0.0081 0.0003 0.649
Total 28 0.0125
Mesencephalon
Csize 1 0.0005 0.0005 0.038 1.07 0.42 0.336
foraging_mode 2 0.0009 0.0005 0.075 1.06 0.39 0.353
Residuals 25 0.0112 0.0004 0.887
Total 28 0.0126
Medulla Oblongata
Csize 1 0.0006 0.0006 0.101 3.01 2.22 0.015
foraging_mode 2 0.0004 0.0002 0.062 0.93 0.02 0.486
Residuals 25 0.0049 0.0002 0.837
Total 28 0.0058
Diencephalon
Csize 1 0.0006 0.0006 0.060 1.74 1.26 0.106
foraging_mode 2 0.0008 0.0004 0.077 1.11 0.39 0.349
Residuals 25 0.0092 0.0004 0.863
Total 28 0.0107
Cerebellum
Csize 1 0.0014 0.0014 0.073 2.15 1.51 0.057
foraging_mode 2 0.0014 0.0007 0.075 1.11 0.48 0.317
Residuals 25 0.0161 0.0006 0.851
Total 28 0.0189

1 df, degree of freedom.

2 SS, sum of squares.

3 MS, mean squares.

4 Significant values (p-value < 0.05) from permutation tests (1,000 permutation rounds) are bolded.

PGLS of regional brain size and foraging mode RESULTS: Affects on all regions except the mesencephalon

pgls_csize <- procD.pgls(prop$c ~ foraging_mode, phy = lzrd_tree, data=c.gdf, iter=999, print.progress = FALSE)
pgls_dsize <- procD.pgls(prop$d ~ foraging_mode, phy = lzrd_tree, data=d.gdf, iter=999, print.progress = FALSE)
pgls_mdsize <- procD.pgls(prop$md ~ foraging_mode, phy = lzrd_tree, data=md.gdf, iter=999, print.progress = FALSE)
pgls_mssize <- procD.pgls(prop$ms ~ foraging_mode, phy = lzrd_tree, data=ms.gdf, iter=999, print.progress = FALSE)
pgls_tsize <- procD.pgls(prop$t ~ foraging_mode, phy = lzrd_tree, data=t.gdf, iter=999, print.progress = FALSE)
Regional Brain Size
Source df1 SS2 MS3 Rsq F Z p-value4
Telencephalon
foraging_mode 2 0.0004 0.0002 0.483 12.15 3.14 0.001
Residuals 26 0.0004 0.0000 0.517
Total 28 0.0008
Mesencephalon
foraging_mode 2 0.0001 0.0000 0.089 1.27 0.61 0.279
Residuals 26 0.0006 0.0000 0.911
Total 28 0.0007
Medulla Oblongata
foraging_mode 2 0.0001 0.0000 0.412 9.12 3.29 0.001
Residuals 26 0.0001 0.0000 0.588
Total 28 0.0002
Diencephalon
foraging_mode 2 0.0000 0.0000 0.197 3.19 1.65 0.045
Residuals 26 0.0001 0.0000 0.803
Total 28 0.0001
Cerebellum
foraging_mode 2 0.0001 0.0000 0.210 3.46 1.72 0.044
Residuals 26 0.0002 0.0000 0.790
Total 28 0.0003

1 df, degree of freedom.

2 SS, sum of squares.

3 MS, mean squares.

4 Significant values (p-value < 0.05) from permutation tests (1,000 permutation rounds) are bolded.

Supplementary Material

Correlation matrix between continuous trait variables - included in manuscript

1. Phylogentic Signal

physignal(wb.gpa$coords, lzrd_tree)
## 
## Call:
## physignal(A = wb.gpa$coords, phy = lzrd_tree) 
## 
## 
## 
## Observed Phylogenetic Signal (K): 0.598
## 
## P-value: 0.001
## 
## Effect Size: 3.4198
## 
## Based on 1000 random permutations
physignal(wb.gpa$Csize, lzrd_tree)
## 
## Call:
## physignal(A = wb.gpa$Csize, phy = lzrd_tree) 
## 
## 
## 
## Observed Phylogenetic Signal (K): 0.5364
## 
## P-value: 0.232
## 
## Effect Size: 0.7312
## 
## Based on 1000 random permutations

2. Allometry of CSize and Shape - the results are in our shape pgls because we control for size

pgls_allom <- procD.pgls(coords ~ Csize, phy = lzrd_tree, data=wb.gdf, iter=999,
                    print.progress = FALSE)
Allometry
Source df1 SS2 MS3 Rsq F Z p-value4
Csize 1 0.0017 0.0017 0.162 5.24 3.08 0.001
Residuals 27 0.0087 0.0003 0.838
Total 28 0.0104

1 df, degree of freedom.

2 SS, sum of squares.

3 MS, mean squares.

4 Significant values (p-value < 0.05) from permutation tests (1,000 permutation rounds) are bolded.