**Key Points:**

•Morphometrics is the quantitative study of form. There are many available morphometrics to study form and its change with ontogeny, evolution, taphonomy, etc.

•Principal Components Analysis (and its close cousing Principal Coordinates Analysis) are the real workhorse methods in morphometrics, summarizing multiple variables at once.

•Morphospaces are the expression of quantified shape into graphical form. There are both theoretical (mathematical) morphospaces, determined by a priori selection of the axis variables; and empirical morphospaces, derived directly from the morphometric analysis results.

•Our ability to reconstruct the function of extinct forms comes through multiple lines of evidence: analogies with the function in extant animals with similar forms; phylogenetic inferences such as the extant phylogenetic bracket method; biomechanics and other cases of modeling; trace fossil analyses; and correlations between phenomena.

•That said, not all form is functional. Some features at least start as "spandrels": the byproduct of the constructional development of the organism rather than actively-selected traits.

*"If you want to inspire confidence, give plenty of statistics. It does not matter that they should be accurate, or even intelligible, as long as there is enough of them."* -- *La poétique de l'espace*, Gaston Bachelard (1958)

**MORPHOMETRICS: INTRODUCATION TO THE ANALYSIS OF SHAPE**

We encourage everyone interested in this subject to examine the excellent "PalaeoMath" series of articles by Norm MacLeod. Furthermore, for those interested in exploring these techniques directly, please check out the following software packages:

- Wolfram's Mathematica
- PAST: Paleontological Statistics
- R (and see the instructions "R for Palaeontologists" by Mark Bell.)

Note that these notes are by no means comprehensive: you can take (literally) entirely courses on morphometrics, statistical analyses, and functional morphology.

We have seen the Raupian attempt to reduce morphological variation to a small number of parameters. In all but the simplest systems, such a method greatly oversimplifies morphology. Nevertheless, the need to describe morphology with quantitative rigor is fundamental to descriptive paleontology, and to the testing of hypotheses of functional morphology. In this lecture we consider morphology and function separately.

**Morphometrics**: The quantitative study of form, including both size and shape. Fundamental to the description of individual specimens and collections of specimens in order to: recognize the degree of distinctiveness of different morphs (which might be age stages, sexes, taxa, etc.); trace changes due to evolution, ontogeny, etc.; and to remove the disruptive effects of taphonomy by **retrodeformation** back to the original shape.

The key here is to develop and recognize **landmarks**: points on a body (often where bones or other body parts join together) which can be compared from one specimen to another.

But what do the data actually mean? In traditional morphometrics, data are measurements of the **distances between points** on a specimen. These points may be:

**Landmarks**: Discrete points marked by**homologous**features (right - red) like:- Foramina
- Intersections of sutures in bones
- Branching points of veins
- Other similar points

**Semilandmarks**: Arbitrary points on the exterior margin describe shape but may be non-homologous (right - blue) like:- points of maximum width
- the tips of snouts
*etc.*

One of the most standard of all **statistical analyses** is:

**Least-squares linear regression**: A cluster of bivariate data points are reduced to a line that minimizes the**residuals**- distances from the line to the data points - on the Y variable on the the square root of the sum of their squares. The regression line represents an estimate of the relationship between the variables. The more strongly correlated the variables, the closer the cluster of points will fall to the regression line. This standard regression (the one built into Excel, for instance) assumes that variable X is the known (**dependent variable**) that is being varied to produce a change in the**independent variable Y**.**Reduced Major Axis (RMA) regression**: Another bivariate plot, but where the line minimizes the residuals on BOTH X and Y. This results in a line which is invariant as to which variable is which axis, and doesn't require an*a priori*decision as to a "dependent" and an "independent" variable.

From Wikipedia

**Phylogenetic Considerations**: Biologists are fond of performing statistical studies, looking for meaningful correlations between measurable aspects of different parts of animals' anatomies. For instance, one might look at the ratio of the length and depth of birds' beaks vs. the length of their tarsals to see if larger birds have proportionately deeper beaks. Measurements of this sort can be subjected to the full range of statistical analyses. A fundamental assumption of such studies is that **all observations be made from independent members of the same underlying population**.

However, when samples are being taken from groups of populations nested in a hierarchical phylogeny, this assumption is violated. The example at right shows of how misleading this can be. Imagine a simple phylogeny with two sister clades, one black, the other red. When we make a bivariate plot of data taken from them, there appears to be a strong statistical relationship. But in fact, when we compare values only within each clade, we see very low correlation.

Fortunately, Joe Felsenstein of the University of Washington provided a first attempt at a solution in a statistical technique for adjusting data to account for the phylogeny of the taxa sampled, called **phylogenetically independent contrasts (PIC).** This method is based on the concept that although the taxa may be non-independent, the differences between measured values in them **are independent.** Statistical correlation techniques are, therefore, applied to pairwise contrasts in measurements from sister taxa. By applying it, meaningful correlation studies can be performed. Without it, they would be meaningless or misleading. **Applying this method absolutely requires a known phylogeny.** If hypotheses of phylogeny change, the results of correlation studies based on them must be revised, too.

Work in subsequent decades have recognized Felsenstein's method is just one of multiple alternative solutions with different models of evolutionary change. Many additional alternative statistics have been developed, depending on the model of evolution assumed in a given situation.

What if you are interested in examining many variables in the same system? You can do a series of bivariate plots, doing each possible pair of variables as its own graph. As the number of variables increases, though, this becomes difficult to interpret. One could to a trivariate plot (XYZ plot), but these can be difficult to interpret (you need to move the figure around to get the sense of the distribution of data), and only gets you one more variable. Thankfully, there is a good way of boiling down multiple variables into a more simplified format.

From Wikipedia

- The first principal component represents the primary source of variation, the second, the secondary, etc. Indeed, you can recognize HOW MUCH of the variation is explained by each component.
- There can be as many principal components as there are variables.
- The principal components, being mutually perpendicular, are not correlated with one another.

PCA need not be done only on morphometrics. For instance, ecologists and paleoecologists often use abundance data of species (where localities are the object of study and the abundance of each species is the variable) to find similarities and dissimilarities between localities.

A related technique is **Principle Coordinate Analysis**, which doesn't require that every cell in your measurement matrix is occupied.

This may be trivial for a bivariate plot, but discriminant function analysis also works for multivariate analyses in many dimensions, where data cannot be as easily visualized.

Thompson was fond of expressing morphological variation in terms of **"transformations"** - by which the shape of one species could be transformed into that of another by the application of regular simple transformations, reflected as deformed grids. (E.g. puffer and mola, right.) Although clever and not lacking merit, these reflected a level of subjectivity. Indeed, the technique was pioneered by the Renaissance artist Albrecht Dürer. They pointed out the need for an algorithmic approach to the quantitative analysis of shape, however, which were finally developed in the 1980s and 1990s. Here is a synopsis of what has emerged.

A. Traditional morphometrics: size information is preserved but shape is lost.

B. Landmark-based morphometrics: Shape information is preserved.

A.

- The data must reflect shape adequately.
- Comparable landmarks must be present on all specimens.
- The number of specimens required depends on the size of the difference you seek to study compared to the amount of variation in your group.

**Step 1**: Data acquisition. Can be through:

- Calipers, steel tape, etc. on the real specimen
- Digital measurements on images of the same

(Schematic only)

- Centroids calculated for all shapes
- Size and orientation normalized using least squares concept.

(Schematic only)

Schematic for eigenshape analysis.

For closed curves, **Elliptical Fourier analysis**: (Not pictured) In which the shape of the outline is approximated by the sum of the minimum number of ellipses needed to mimic its shape. This only works on outlines that are consistently convex.

Data acquisition: Depending on the size of the subject:

- Reflex microscopes
- Microscribe robotic arms
- Laser scanners
- CT scanners

A **morphospace** is an *n*-dimensional representation of possible form, shape, or structure of organisms. Each axis represents some variable describing the organism (either the raw variable, or a transformed component/coordinate). The point in morphospace represents an individual or an averaged representation of a taxon.

Raup's 3D morphospace for conical shells is a classic example of a morphospace in paleontology and zoology:

From Raup, D.M. 1966. Geometric analysis of shell coiling: general problems. *Journal of Paleontology* **40**: 1178-1190.

In this space, the axes represent the translation of the aperture (x), the expansion of the aperture (y), and the distance of the generating curve from the axis (z). The fields represent the distribution of known shells (of mollusks and brachiopods) within this morphospace.

Raup's morphospace is an example of a **theoretical** (or **mathematical**) **morphospace**. The axes are pre-determined paramaters, allowing for all possible ranges of morphological variability to be expressed by systematically varying the values. It may be that some of the space is not occupied at all. One advantage of this sort of morphospace is that it is set up "in advance" of any analysis, as it were. The axes are not contingent on the measurements you take, and you can free add to the plots with new specimens as you discover them. A disadvantage is that it requires you to develop the parameters in advance: in reality, you might not really know which measured variables really are the most significant (i.e., explain the most data range) in your sample.

In order to address this problem, many choose to use **empirical morphospaces**. In these the axes are derived from statistical analyses of the actual specimens (typically a PCA or Principal Coordinate analysis). Thus the axes of the morphospace are the most significant ones within the population of data you sampled, and the morphospace is likely to be more greatly occupied. A disadvantage, however, is that the axes are analysis-dependent; you can't just pop new measurements onto your current plot. Instead, you would need to rerun the entire analysis and replot the whole set of data.

Revisiting the issues of theoretical morphospace: why might it be that some parts of it are not occupied by known taxa? Several ideas have been proposed (and, as usual, are not mutually exclusive):

- Mechanical limitations: it might be that certain combinations of variables simply do not work (i.e., are too fragile; cannot actually be generated by the biological system; etc.)
- Nature simply hasn't had time to fill in those spots
- In order to get into those regions, evolution would have to move "downslope" on an
**adaptive landscape**- Also called
**fitness landscapes**, these are graphical metaphors proposed by leading New Synthesis proponent Sewell Wright. - Under this concept, there is a "landscape" of peaks (of high fitness) and valleys (low fitness). Populations can only evolve towards adaptive peaks: to do otherwise would mean that the population is moving towards reduced fitness.
- Selection (under ordinary circumstances) can only move towards local optima.
- But large mutational jumps might get you "over" a peak onto the slopes of an even steeper (more highly adaptive) one
- And the landscape itself changes as the environmental conditions change (i.e., what is favored by selection)

- Also called

Note that morphospaces might be used to explore the ecology in which an organism occupies as much as its shape.

**Analogies with Living Organisms**: the oldest method, using the "form follows function" concept. For instance, aquatic vertebrates today with slender snouts and needle-like teeth tend to feed on small fish, so finding a fossil animal with such jaws in aquatic deposits suggests it might have done the same.**Phylogenetic Inferences**: the Extant Phylogenetic Bracket and related methods can help us infer behavior that is inherited from common ancestry. Obviously if the known anatomy of the fossil form contradicts this inference we can determine that it likely lost that function in its evolution.**Biomechanics**: Organisms operate in the world of physics, and we can use our knowledge of mechanics (levers, pulleys, torsion, bending strength, compression, etc.) to find the likely forces capable of being exerted by (or on) a part of a living thing.- A simple example would be
**beam theory** - A more complex one is
**finite element analysis**(**FEA**) where a system is broken down into a large number of small parts whose properties can be modeled, then subjecting the model to likely loads and seeing how they behave:- Complex shapes like bridges, for which the equations are known but solutions are impossible, are broken down in to a series of
**finite elements**- simple shapes for which the equations can be solved. Together, these are the**finite element mesh**. The composite solution of equations for each element approximates a solution for the complex object being modeled.- Finite elements can be on dimensional rods, two dimensional polygons, or simple three dimensional polyhedrons.
- The more elements, the more computations are required but the smaller the error. Thus a compromise is sought. An advantage of the method is that the mesh can have more detail in important regions and less detail in unimportant ones.

**Nodes**: Corners and surfaces of finite elements, where they interface with adjacent elements.- The
**displacement**of nodes in a given element serves as the basis for the calculation of system equations for the entire element, which are represented as a matrix - Because adjacent elements share nodes, their matrices overlap. Through a series of matrix calculations, stress and strain experienced by each element can be calculated and mapped.
- These values can then be fed into secondary iterations of the process to simulate sequences of events such as fluid flow.

- Complex shapes like bridges, for which the equations are known but solutions are impossible, are broken down in to a series of

- A simple example would be

Additionally, **trace fossil data** might be used to see a record of behavior (especially locomotion and feeding) in the fossil record, so long as you can identify the trace maker.

The spandrels of San Marco

S.J.Gould and R. Lewontin challenged this idea in 1979, and took a critical look at the "pan-selectionist" appropach. They compared this to attempts to infer structural function to **spandrels**, sections of wall filling spaces between load-bearing components like arches and domes in classic Medieval churches. (See the spandrels of San Marco right.) In fact, spandrels are just space-fillers between functional elements; byproducts of how the arches and domes were built rather than designed features per se. (By the way, the features show are technically **NOT** spandrels (which are along the corners of arches), but their 3D equivalent pendentives (which are at the corners of domes). Nevertheless, we use the term **spandrel** for biological structures resulting as developmental byproducts.)

A biological comparison: the human chin - a functionless feature that is a developmental consequence of the reduction of the anterior tooth row.

The panda's thumb from Athro Limited

**Structural constraints**: By their basic anatomy and margin accretion growth strategy, adult mollusks**must**retain the skeleton they had as juveniles, even if they would be better off not having to carry it around.**Contingency (evolutionary heritage)**: Organisms must work with what they inherit. Indeed the variety of ways in which they have addressed biomechanical problems - each in some way suboptimal but all good enough - testifies to that fact. E.G.: rapid swimming in vertebrates and cephalopods. Rapid undulations of the body is more energetically efficient than cephalopod "jet-propulsion," in which they must expend energy hauling their reaction mass (water) around inside their mantles before expelling it; yet there squids are, the victims of their heritage.**Pleiotropy**: Some features may be suboptimal because they are coded in the same gene that codes for a highly beneficial trait. Consider aggression and genitalia in hyenas.**Structures may have more than one function**: In that case, their form represents a compromise between adaptive trends for each function. Consider wydah tails. The male must use this structure both to attract a mate, and in flight whereas the female only needs to fly with hers.**Limits to genetic diversity**: Just because a given change is adaptive doesn't mean that a species has the genetic material with which to effect it.

The result is that few structures are adaptively optimal, as in the classic panda's thumb. (Another structure constrained by evolutionary history.)

- Define and diagnose the adaptation. This enables you to identify creatures that may have it
- Employ phylogenetic information to determine whether the "adaptive feature" is actually a derived evolutionary state.
- Consider whether the feature is a "spandrel"-an inevitable artifact of other features.
- In a paleontological setting, propose a hypothesis to explain the structure's function. (Once the above issues have been addressed.) This can then be tested using:
- The comparative method.

- Physical models both simple and complex (Quetzalcoatlus ornithopter, right)

Finite element model of*Smilodon*bite force from McHenry et al. 2007. - Mathematical and computer models (e.g., McHenry et al. 2007).

Egg-shape morphospace from Deeming and Ruta, 2014 - Mapping onto theoretical morphospace, with reference to the distribution there of well-understood organisms.

- The comparative method.

Structures that appear functionally analogous are our starting point for the development of hypotheses about life-style. For example, numerous anatomical characters demonstrate that the extant **timber wolf** and the extinct native North American **dire wolf** (right) are closely related and generally similar. They differ mostly in that the dire wolf's skull and teeth were more robust - a little closer to what one sees in spotted hyaenas or other bone-crushing animals.

Thus, we speculate that the dire wolf was generally similar to the timber wolf, but better adapted to bone crushing. Because we are dealing with an organism and with biomechanical functions that are well understood, this contention can be evaluated with any of the methods listed above.

Alas, so do other, ecologically dissimilar reptiles, including vertical clingers and leapers like Boyd's forest dragon. In this light, speculations on *Eudibamus'* locomotion seem reckless.

Consider *Parasaurolophus*, a Cretaceous lambeosaurine ornithopod dinosaur. Like other ornithopods, it had a deep, laterally compressed torso and tail. The vertebral column of both was stiffened by ossified tendons. The comparison of the deep flat tail to those of swimming vertebrates, combined with the early 20th century conviction that large dinosaurs would have had trouble generating the energy to support their weight on land gave rise to reconstructions of *Parasaurolophus* as an aquatic creature. The idiosyncratic crest must, therefore, have been a snorkel.

This, at least, was a falsifiable hypothesis. More thoughtful biomechanical analyses of the vertebral column showed that the trunk and tail were inflexible from side to side. Furthermore, the tail vertebrae of actual aquatic reptiles like crocodilians generally had long lateral extensions to give axial muscles better leverage.

Worse, although the crest connected to the pharynx and nasal cavity though an elaborate system of passages, no specimen of the "snorkel" actually had a hole in the top to admit air. Eventually a revised interpretation of *Parasaurolophus* as a land animal emerged.

Still, its crest stimulates speculation, including.

- Species and breeding status signal
- According to Weishampel, 1997 resonating chambers in the nasal passages facilitated vocalizations. Weishampel modelled these passages in PVC pipe, creating an instrument that produced sound in a low register similar to that of living elephants.
- According to creationist publicist Duane Gish, holding chambers for reactive chemicals that, when exhaled, would explode, giving rise to legends of fire-breathing leviathans. (Cf. bombardier beetle)

Perceiving comparative similarities, like the perception of any pattern in a set of data, is a creative act. Ironically, of all paleontological methods, this one probably has the greatest tendency to push the limits of the proper Scientific Method. In the worst case, the result can be "Just so stories" - appealing speculation with no hope of ever being rigorously tested.

From Palaeos

- It requires a biological material to exceed its known strength. Thus, Farlow
*et. al*, 1995 demonstrated that an adult*Tyrannosaurus rex*could not withstand a fall from a standing position. - Requires muscles to exceed the known limits of their energy output.

To Syllabus.