Dear Lea,

I see others have responded to your inquiry, already.  I thought I would add an 
additional perspective.

Your question about statistical significance requires asking a follow-up 
question.  What statistical methods would you intend to use to evaluate 
“significance”?  If you are worried about the number of landmarks, your concern 
suggests you might be using parametric test statistics frequently associated 
with MANOVA, like Wilks lambda or Pilai trace.  Indeed, when using these 
statistics and converting them to approximate F values, one must have many more 
specimens than landmarks (more error degrees of freedom than shape variables, 
to be more precise), if “significance” is to be inferred from probabilities 
associated with F-distributions.  Therefore, limiting the number of landmarks 
might be a goal.

When using resampling procedures to conduct ANOVA, using fewer landmarks can 
paradoxically decrease effect sizes, as an overly simplified definition of 
shape becomes implied.  We demonstrated this in our paper: Collyer, M.L., D.J. 
Sekora, and D.C. Adams. 2015. A method for analysis of phenotypic change for 
phenotypes described by high-dimensional data. Heredity. 115: 357-365.  This is 
consistent with Andrea’s comment about quality over quantity with the caveat 
that limited quantity precludes quality.  In other words, too few landmarks 
translates to limited ability to discern shape differences, because the shape 
compared is basic.  In the paper, we used two separate landmark configurations: 
one with few landmarks and the other with the same landmarks plus sliding 
semilandmarks between fixed points, on different populations of fish.  We found 
that adding the semilandmarks increased the effect size for population 
differences and sexual dimorphism.  But if we constrained our analyses to 
parametric MANOVA for our small samples, we would have to use the simpler 
landmark configurations and live with the results.

I do not wish to suggest that adding more landmarks is better.  Overkill is 
certainly a concern.  I would suggest though that statistical power would be 
for me less of a concern than a proper characterization of the shape I wish to 
compare among samples.  If I suspect curvature is important but am afraid to 
use (semi)landmarks that would allow me to assess the curvature differences 
among groups, opting instead to use just the endpoints of a structure because I 
am worried about statistical power, then I just allowed a statistical procedure 
to take me away from the biologically relevant question I sought to address.  
Andrea is correct that quality is better than quantity, but quantity can be a 
burden in either direction (too few or too many).  Additionally, statistical 
power will vary among statistical methods.  Reconsidering methods might be as 
important as reconsidering landmarks configurations.


> On May 4, 2017, at 5:19 AM, Lea Wolter <> wrote:
> Hello everyone,
> I am new in the field of geometric morphometrics and have a question for my 
> bachelor thesis.
> I am not sure how many landmarks I should use at most in regard to the sample 
> size. I have a sample of about 22 individuals per population or maybe a bit 
> less (using sternum and epigyne of spiders) with 5 populations. 
> I have read a paper in which they use 18 landmarks with an even lower sample 
> size (3 populations with 20 individuals, 1 with 10). But I have also heard 
> that I should use twice as much individuals per population as land marks... 
> Maybe there is some mathematical formula for it to know if it would be 
> statistically significant? Could you recommend some paper?
> Because of the symmetry of the epigyne I am now thinking of using just one 
> half of it for setting landmarks (so I get 5 instead of 9 landmarks). For the 
> sternum I thought about 7 or 9 landmarks, so at most I would also get 18 
> landmarks like in the paper. 
> I would also like to use two type specimens in the analysis, but I have just 
> this one individual per population... would it be totally nonesens in a 
> statistical point of view?
> Thanks very much for your help!
> Best regards
> Lea
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