The goal of morphospace
is to enhance representation and heuristic
exploration of multivariate ordinations of shape data. This package can
handle the most common types of shape data working in integration with
other widely used R packages such as Morpho
(Schlager 2017),
geomorph
(Adams et al. 2021), shapes
(Dryden 2019), Momocs
(Bonhomme et al. 2014) and mvMORPH
(Clavel et al.2015), which cover
other more essential steps in the geometric morphometrics pipeline
(e.g. importation, normalization, statistical analysis, phylogenetic
modeling).
Below there is broad-strokes account of the morphospace
capacities;
for more specific guidance, refer to General
usage
and Worked
examples.
You can install the development and CRAN versions of morphospace
from
GitHub with:
# Development version
# install.packages("devtools")
devtools::install_github("millacarmona/morphospace")
# CRAN version
# install.packages("morphospace") #not yet ready
The basic idea behind morphospace
is to build empirical morphospaces
using multivariate ordination methods, then use the resulting ordination
as a reference frame in which elements representing different aspects of
morphometric variation are projected. These elements are added to both
graphic representations and objects as consecutive ‘layers’ and list
slots, respectively, using the %>%
pipe operator from magrittr
(Bache & Wickham 2022).
The starting point of the morphospace
workflow is a set of shapes
(i.e. morphometric data that is already free of variation due to
differences in orientation, position and scale). These are fed to the
mspace
function, which generates a morphospace using a variety of
multivariate methods related to Principal Component Analysis. This
general workflow is outlined below using the tails
data set from
Fasanelli et al. (2022), which contains tail shapes from 281 specimens
belonging to 13 species of the genus Tyrannus.
library(morphospace)
library(geomorph)
library(Morpho)
library(Momocs)
library(magrittr)
library(rgl)
# Load tail data
data("tails")
shapes <- tails$shapes
spp <- tails$data$species
wf <- tails$links
phy <- tails$tree
# Generate morphospace
mspace(shapes, links = wf, cex.ldm = 5)
The ordination produced by mspace
is used as a reference frame in
which scatter points, convex hulls / confidence ellipses, a phylogeny, a
set of morphometric axes or a landscape surface can be projected using
the proj_*
functions:
# Get mean shapes of each species
spp_shapes <- expected_shapes(shapes = tails$shapes, x = tails$data$species)
# Generate morphospace and project:
msp <- mspace(shapes = shapes, links = wf, cex.ldm = 5) %>%
# scatter points
proj_shapes(shapes = shapes, col = spp) %>%
# convex hulls enclosing groups
proj_groups(shapes = shapes, groups = spp, alpha = 0.5) %>%
# phylogenetic relationships
proj_phylogeny(shapes = spp_shapes, tree = phy, lwd = 1.5,
col.tips = match(phy$tip.label, levels(spp)))
Once the "mspace"
object has been created, the plot_mspace
function
can be used to either regenerate/modify the plot, add a legend, or to
combine morphometric axes with other non-shape variables to produce
‘hybrid’ morphospaces. For example, PC1 can be plotted against size to
explore allometric patterns.
# Plot PC1 against log-size, add legend
plot_mspace(msp, x = tails$sizes, axes = 1, nh = 6, nv = 6, cex.ldm = 4,
alpha.groups = 0.5, col.points = spp, col.groups = 1:nlevels(spp),
phylo = FALSE, xlab = "Log-size", legend = TRUE)
Or against taxonomic classification to assess patterns of intra- and/or interspecific variation.
# Plot PC1 against species classification
plot_mspace(msp, x = spp, axes = 1, nh = 6, nv = 6, cex.ldm = 4, boxplot.groups = TRUE,
alpha.groups = 0.5, col.points = spp, col.groups = 1:nlevels(spp),
phylo = FALSE, xlab = "Log-size", legend = TRUE)
Alternatively, ordination axes can be combined with a phylogenetic tree to create a phenogram:
# Plot vertical phenogram using PC1, add a legend
plot_mspace(msp, y = phy, axes = 1, nh = 6, nv = 6, cex.ldm = 4,
col.groups = 1:nlevels(spp), ylab = "Time", legend = TRUE)
morphospace
can also handle closed outlines (in the form of elliptic
Fourier coefficients) and 3D landmark data, as shown below briefly using
the shells
and shells3D
data sets:
# Load data
data("shells")
shapes <- shells$shapes
spp <- shells$data$species
# Generate morphospace
mspace(shapes, mag = 1, nh = 5, nv = 4, bg.model = "light gray") %>%
proj_shapes(shapes = shapes, col = spp) %>%
proj_groups(shapes = shapes, groups = spp, alpha = 0.5, ellipse = TRUE)
# Load data
data("shells3D")
shapes <- shells3D$shapes
spp <- shells3D$data$species
mesh_meanspec <- shells3D$mesh_meanspec
# Generate surface mesh template
meanspec_shape <- shapes[,,findMeanSpec(shapes)]
meanmesh <- tps3d(x = mesh_meanspec,
refmat = meanspec_shape,
tarmat = expected_shapes(shapes))
# Generate morphospace
mspace(shapes, mag = 1, bg.model = "gray", cex.ldm = 0, template = meanmesh,
adj_frame = c(0.9, 0.85)) %>%
proj_shapes(shapes = shapes, col = spp, pch = 16) %>%
proj_groups(shapes = shapes, groups = spp, alpha = 0.3)
#> Preparing for snapshot: rotate mean shape to the desired orientation
#> (don't close or minimize the rgl device).Press <Enter> in the console to continue:
#>
#> This can take a few seconds...
#> DONE.
Aside from working with these types of morphometric data, morphospace
provides functions to perform some useful shape operations, use TPS
interpolation of curves/meshes to improve visualizations, and supports a
variety of multivariate methods (bgPCA, phylogenetic PCA, PLS,
phylogenetic PLS) to produce ordinations. For these and other options
and details, go to General
usage
and Worked
examples.
-
Different behavior for
proj_shapes
(now replacesmspace$x
with the actual scores being projected) andproj_axis
(now adds one or more axes into anmspace$shape_axis
). -
New
ellipses_by_groups_2D
(usescar::ellipse
) function as an option forproj_groups
andplot_mspace
. -
Morphospaces without background shape models are now an option (for both
mspace
andplot_mspace
). -
plot_mspace
now regenerates the original mspace plot by default (proj_*
functions were modified such that all the relevant graphical parameters are inherited downstream toplot_mspace
), has further flexibility regarding hybrid morphospaces (plot_phenogram
has been updated) and allows adding a legend (and some various bugs were fixed as well). -
Univariate morphospaces and associated density distributions are now an option (all the
mspace
workflow functions have been modified accordingly, especiallyproj_shapes
andproj_groups
). -
consensus
andexpected_shapes
have been merged in a single function (the nameexpected_shapes
was retained as the former was clashing withape::consensus
), which can handle both factors and numerics. -
Both
detrend_shapes
andexpected_shapes
can now calculate phylogenetically-corrected coefficients for interspecific data sets (Revell 2009).
-
The structure of
"mspace"
objects has been reorganized and now contain 3 main slots:$ordination
(multivariate ordination details),$projected
(elements added usingproj_*
functions) and$plotinfo
(used for regeneration usingplot_mspace
). This has been complemented with aprint
method for the"mspace"
class. -
New
proj_landscape
function has been added to represent adaptive surfaces interpolated from functional or performance indices (although can be used for any numerical variable). -
proj_consensus
has been removed. -
New
extract_shapes
function for extracting synthetic shapes from"mspace"
objects (background shape models, shapes along ordination axes, or specific coordinates selected interactively). -
New
burnaby
function, implementing Burnaby’s approach for standardization of morphometric data by computing a shape subspace orthogonal to an arbitrary vector or variable -
New
phyalign_comp
function, implementing Phylogenetically aligned component analysis, which finds the linear combination of variables maximizing covariation between trait variation and phylogenetic structure (Collyer & Adams 2021). Still a work in progress. -
Several internal adjustments have been introduced to the
mspace
,proj_*
andplot_mspace
functions in order to improve visualization and make the workflow more flexible. -
Legends created using
plot_mspace
have been improved, and scale bars for interpreting landscapes have also been made available.
Significant changes aimed at enhancing integration with other GM/MV R packages and improving procedures involving phylogenetic data, as well as a couple of new features:
-
The internal behavior of the
mspace
workflow has been modified so objects containing multivariate ordinations produced bygeomorph
,Morpho
,mvMORPH
,Momocs
andphytools
can be now used as input. -
mspace
can now be fed with objects containing multivariate ordinations directly. This is implemented through an alternative combination of arguments for themspace
function (ord
+datype
as an alternative toshapes
+FUN
+...
). -
ax_transformation
(and by extensionproj_axis
) anddetrend_shapes
can now be fed with objects containing a linear model fitted using functions fromgeomorph
,RRPP
andmvMORPH
. Internal phylogenetic correction of linear coefficients indetrend_shapes
has been abandoned, relying now on the results of the (phylogenetic) linear model provided by the user. -
phy_prcomp
and the experimentalphyalign_comp
have been removed (users interested in these methods should refer tophytools::phyl.pca
,geomorph::gm.prcomp
and/ormvMORPH::mvgls.pca
). -
Estimation of ancestral shapes (performed internally by
expected_shapes
,detrend_shapes
,proj_phylogeny
,pls2b
,pls_shapes
,burnaby
andplot_mspace
) now relies onmvMORPH::mvgls
, which has the advantage of allowing estimation under evolutionary models other than Brownian motion. In addition, ancestral character estimation of discrete non-shape variables (attempted internally by the phylogenetic version ofpls2b
(and by extensionpls_shapes
) andplot_mspace
in certain situations) is performed under a simple Equal rates model viaape::ace
. -
Introduction of boxplots and violin plots for combining shape ordination axes with categorical variables via
plot_mspace
. -
Tip and node labels can now be included in phylomorphospaces, phenograms and hybrid phylomorphospaces.
A few relevant changes:
-
morphospace
can now generate Pareto rank ratio surfaces (Deakin et al. 2022), thanks to the code provided by Will Deakin.proj_landscape
has been modified accordingly and can now take either two or more functions or two or more functional metrics (argumentsFUN
andX
, respectively; see?proj_landscape
). -
new
proj_pfront
function for projecting Pareto fronts (i.e., the subset of shapes optimizing two or more antagonistic measures of performance). -
proj_landscape
can now accept surfaces created withMorphoscape
(Dickson et al. 2023) through the argumentobj
. -
options for controlling the aspect ratio of morphospaces have been included (argument
asp
inmspace
).
If you find any bugs please send me an email at pablomillac@gmail.com
.
Thanks!!
Adams D.C., Collyer M.L., Kaliontzopoulou A., & Baken E.K. (2021). geomorph: Software for geometric morphometric analyses. R package version 4.0.2. https://cran.r-project.org/package=geomorph.
Bache S.F., & Wickham H. (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3. https://CRAN.R-project.org/package=magrittr.
Bonhomme V., Picq S., Gaucherel C., & Claude J. (2014). Momocs: Outline Analysis Using R. Journal of Statistical Software, 56(13), 1-24. https://www.jstatsoft.org/v56/i13/.
Clavel, J., Escarguel, G., & Merceron, G. (2015). mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data. Methods in Ecology and Evolution, 6(11), 1311-1319. https://doi.org/10.1111/2041-210X.12420.
Collyer, M. L., & Adams, D. (2021). Phylogenetically aligned component analysis. Methods in Ecology and Evolution, 12(2), 359-372. https://doi.org/10.1111/2041-210X.13515.
Deakin, W. J., Anderson, P. S., den Boer, W., Smith, T. J., Hill, J. J., Rücklin, M., Donoghue, P. C. J. & Rayfield, E. J. (2022). Increasing morphological disparity and decreasing optimality for jaw speed and strength during the radiation of jawed vertebrates. Science Advances, 8(11), eabl3644. https://doi.org/10.1126/sciadv.abl3644.
Dickson, B. V., Pierce, S., & Greifer, N. 2023. Morphoscape: computation and visualization of adaptive landscapes. version 1.0.2. https://CRAN.R-project.org/package=Morphoscape
Dryden, I.L. (2019). shapes: statistical shape analysis. R package version 1.2.5. https://CRAN.R-project.org/package=shapes.
Fasanelli M.N., Milla Carmona P.S., Soto I.M., & Tuero, D.T. (2022). Allometry, sexual selection and evolutionary lines of least resistance shaped the evolution of exaggerated sexual traits within the genus Tyrannus. Journal of Evolutionary Biology, in press. https://doi.org/10.1111/jeb.14000.
Revell, L.J. (2009). Size-correction and principal components for interspecific comparative studies. Evolution, 63, 3258-3268 https://doi.org/10.1111/j.1558-5646.2009.00804.x.
Schlager S. (2017). Morpho and Rvcg - Shape Analysis in R. In Zheng G., Li S., Szekely G. (eds.), Statistical Shape and Deformation Analysis, 217-256. Academic Press. https://doi.org/10.1016/B978-0-12-810493-4.00011-0.