I agree with Philipp but I would like to add that the way I think about the 
justification for the sliding of semilandmarks is that if one were smart enough 
to know exactly where the most meaningful locations are along some curve then 
one should just place the points along the curve and computationally treat them 
as fixed landmarks. However, if their exact positions are to some extend 
arbitrary (usually the case) although still along a defined curve then sliding 
makes sense to me as it minimizes the apparent differences among specimens (the 
sliding minimizes your measure of how much specimens differ from each other or, 
usually, the mean shape. 


_ _ _ _ _ _ _ _ _

F. James Rohlf, Distinguished Prof. Emeritus

Depts. of Anthropology and of Ecology & Evolution



From: mitte...@univie.ac.at <mitte...@univie.ac.at> 
Sent: Tuesday, November 6, 2018 9:09 AM
To: MORPHMET <morphmet@morphometrics.org>
Subject: [MORPHMET] Re: semilandmarks in biology


I agree only in part.


Whether or not semilandmarks "really are needed" may be hard to say beforehand. 
If the signal is known well enough before the study, even a single linear 
distance or distance ratio may suffice. In fact, most geometric morphometric 
studies are characterized by an oversampling of (anatomical) landmarks as an 
exploratory strategy: it allows for unexpected findings (and nice 


Furthermore, there is a fundamental difference between sliding semilandmarks 
and other outline methods, including EFA. When establishing correspondence of 
semilandmarks across individuals, the minBE sliding algorithm takes the 
anatomical landmarks (and their stronger biological homology) into account, 
while standard EFA and related techniques cannot easily combine point homology 
with curve or surface homology. Clearly, when point homology exists, it should 
be parameterized accordingly. If smooth curves or surfaces exists, they should 
also be parameterized, whether or not this makes the analysis slightly more 


Anyway, different landmarks often convey different biological signals and 
different homology criteria. For instance, Type I and Type II landmarks (sensu 
Bookstein 1991) differ fundamentally in their notion of homology. Whereas Type 
I landmarks are defined in terms of local anatomy or histology, a Type II 
landmark is a purely geometric construct, which may or may not coincide with 
notions of anatomical/developmental homology. ANY reasonable morphometric 
analysis must be interpreted in the light of the correspondence function 
employed, and the some holds true for semilandmarks. For this, of course, one 
needs to understand the basic properties of sliding landmarks, much as the 
basic properties of Procrustes alignment, etc.. For instance, both the sliding 
algorithm and Procrustes alignment introduce correlations between shape 
coordinates (hence their reduced degrees of freedom). This is one of the 
reasons why I have warned for many years and in many publications about the 
biological interpretation of raw correlations (e.g., summarized in Mitteroecker 
et al. 2012 Evol Biol). Interpretations in terms of morphological integration 
or modularity are even more difficult because in most studies these concepts 
are not operationalized. They are either described by vague and biologically 
trivial narratives, or they are themselves defined as patterns of correlations, 
which is circular and makes most "hypotheses" untestable.


The same criticism applies to the naive interpretation of PCA scree plots and 
derived statistics. An isotropic (circular) distribution of shape coordinates 
corresponds to no biological model or hypothesis whatsoever (e.g., Huttegger & 
Mitteroecker 2011, Bookstein & Mitteroecker 2014, and Bookstein 2015, all three 
in Evol Biol). Accordingly, a deviation from isometry does not itself inform 
about integration or modularity (in any reasonable biological sense).

The multivariate distribution of shape coordinates, including "dominant 
directions of variation," depend on many arbitrary factors, including the 
spacing, superimposition, and sliding of landmarks as well as on the number of 
landmarks relative to the number of cases. But all of this applies to both 
anatomical landmarks and sliding semilandmarks.


I don't understand how the fact that semilandmarks makes some of these issues 
more obvious is an argument against their use.











Am Dienstag, 6. November 2018 13:28:55 UTC+1 schrieb alcardini:

As a biologist, for me, the question about whether or not to use semilandmarks 
starts with whether I really need them and what they're actually measuring.

On this, among others, Klingenberg, O'Higgins and Oxnard have written some very 
important easy-to-read papers that everyone doing morphometrics should consider 
and carefully ponder. They can be found at: 

I've included there also an older criticism by O'Higgins on EFA and related 
methods. As semilandmarks, EFA and similar methods for the analysis of outlines 
measure curves (or surfaces) where landmarks might be few or missing: if 
semilandmarks are OK because where the points map is irrelevant, as long as 
they capture homologous curves or surfaces, the same applies for EFAs and 
related methods; however, the opposite is also true and, if there are problems 
with 'homology' in EFA etc., those problems are there also using semilandmarks 
as a trick to discretize curves and surfaces. 

Even with those problems, one could still have valid reasons to use 
semilandmarks but it should be honestly acknowledged that they are the best we 
can do (for now at least) in very difficult cases. Most of the studies I know 
(certainly a minority from a now huge literature) seem to only provide post-hoc 
justification of the putative importance of semilandmarks: there were few 'good 
landmarks'; I added semilandmarks and found something; therefore they work.


>From a mathematical point of view, I cannot say anything, as I am not a 
>mathematician. On this, although not specific to semilandmarks, a fundamental 
>reading for me is Bookstein, 2017, Evol Biol (also available for a few days, 
>as the other pdfs, at the link above). That paper is one of the most inspiring 
>I've ever read and it did inspire a small section of my recent Evol Biol paper 
>on false positives in some of the tests of modularity/integration using 
>Procrustes data. For analyses using sliding semilandmarks, the relevant 
>figures are Figs 4-5, that suggest how tricky things can be. If someone 
>worries that that's specific to my example data (and it could be!), the 
>experiment is trivial to repeat on anyone's own semilandmark data.

Taken from the data of the same paper, below you find a PCA of rodent 
hemimandibles (adults, within a species) using minBE slid semilandmarks or just 
9 'corresponding' landmarks. The advantage of semilandmarks, compared to the 9 
landmarks, is that they allow to capture a dominant direction of variation (PC1 
accounting for 14% of shape variance), whose positive extreme (magnified 3 
times) is shown with a really suggestive deformation grid diagram. In 
comparison, 9 landmarks do not suggest any dominant direction of variation 
(each PC explaining 9-5% of variance), the scatterplot is circular and the TPS 
shape diagram much harder to interpret.

What these two PCAs have in common is that they are both analyses of random 
noise (multivariate random normally distributed numbers added to a mean shape).


All the best








Dr. Andrea Cardini
Researcher, Dipartimento di Scienze Chimiche e Geologiche, Università di Modena 
e Reggio Emilia, Via Campi, 103 - 41125 Modena - Italy
tel. 0039 059 2058472
Adjunct Associate Professor, School of Anatomy, Physiology and Human Biology, 
The University of Western Australia, 35 Stirling Highway, Crawley WA 6009, 
E-mail address: alca...@gmail.com <javascript:> , andrea....@unimore.it 
WEBPAGE: https://sites.google.com/site/alcardini/home/main
FREE Yellow BOOK on Geometric Morphometrics: 

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