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. Integration with linear models and ordinations generated with other packages (geomorph, Morpho, Momocs, mvMORPH, RRPP, phytools) is also possible. For these and other options and details, go to General usage and Worked examples.

• Different behavior for proj_shapes (now replaces mspace$x with the actual scores being projected) and proj_axis (now adds one or more axes into an mspace$shape_axis).

• New ellipses_by_groups_2D (uses car::ellipse) function as an option for proj_groups and plot_mspace.

• Morphospaces without background shape models are now an option (for both mspace and plot_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 to plot_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, especially proj_shapes and proj_groups).

• consensus and expected_shapes have been merged in a single function (the name expected_shapes was retained as the former was clashing with ape::consensus), which can handle both factors and numerics.

• Both detrend_shapes and expected_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 using proj_* functions) and $plotinfo (used for regeneration using plot_mspace). This has been complemented with a print 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_* and plot_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 by geomorph, Morpho, mvMORPH, Momocs and phytools 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 the mspace function (ord + datype as an alternative to shapes + FUN + ...).

• ax_transformation (and by extension proj_axis) and detrend_shapes can now be fed with objects containing a linear model fitted using functions from geomorph, RRPP and mvMORPH. Internal phylogenetic correction of linear coefficients in detrend_shapes has been abandoned, relying now on the results of the (phylogenetic) linear model provided by the user.

• phy_prcomp and the experimental phyalign_comp have been removed (users interested in these methods should refer to phytools::phyl.pca, geomorph::gm.prcomp and/or mvMORPH::mvgls.pca).

• Estimation of ancestral shapes (performed internally by expected_shapes, detrend_shapes, proj_phylogeny, pls2b, pls_shapes, burnaby and plot_mspace) now relies on mvMORPH::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 of pls2b (and by extension pls_shapes) and plot_mspace in certain situations) is performed under a simple Equal rates model via ape::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 (arguments FUN and X, 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 with Morphoscape (Dickson et al. 2023) through the argument obj.

• options for controlling the aspect ratio of morphospaces have been included (argument asp in mspace).

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. R package 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.