[MORPHMET] Digital morphology data survey results

[Christy Anna Hipsley]

Dear Morphmeters,

The digital morphology data survey posted here 2 weeks ago is now closed, 
and we thank you immensely for your overwhelming (117!) responses. 

For a graphical summary of those answers, please click here 

. 

Next week I will present these results together with a literature survey on 
the impacts of digital morphology in systematic research at the BioSyst.EU 
 
meeting in Gothenburg, Sweden. After that we will be working to put this 
information into a broader context with recommendations on how we can 
practically move forward as a community to ethically source and disseminate 
our data, based largely on your feedback. At first glance it's clear that 
issues like author acknowledgement, access, resources, and institutional 
policies will play major roles in this endeavour.  


As a simple illustration, below is a word cloud based on your responses to 
the question *"What do you see as the biggest obstacles to obtaining 
digital morphology data?"*.


We will continue to post our findings here and again we thank everyone for 
their time and feedback.

Best wishes,
Christy & Emma


Dear Morphmet Community, 
  
I kindly ask your help in completing a short survey on trends in digital 
morphology data generation and use as part of a perspectives piece on the 
“Ethics of data sharing in the age of digital imaging”, which I will 
present at this year’s BioSyst EU meeting in Gothenburg, Sweden (
http://www.conferencemanager.se/BiosystEU2017/speakers-and-symposia.html). 
  
While many recommendations have been made on best practices in this field, 
we as a community are still far from a consensus on how our data should be 
managed, both on the side of the people generating it and the people 
requesting it. 
  
Your responses are anonymous, and I will post the results on Morphmet at 
the end of the survey. The questions are focused on 3D digital morphology 
data (CT, laser scanning, etc), but please feel free to answer if you also 
deal with 2D images. 
  
To reach the survey, go here: https://goo.gl/forms/igFKPc376kVn5eI13 
  
The site will remain active for the next 2 weeks, until Friday, 11 August. 
  
Almost every question has the option to enter text, so please give as much 
information and opinions as possible to help us represent your concerns! 
  
Thank you for your time, 
Christy Hipsley & Emma Sherratt 

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Re: [MORPHMET] eliminating the effect of population differences

[David Katz]

Well, that took longer than I'd hoped... Our multivariate mixed model
approach to 3DGM is now available in early view. Using a global human
sample, we treat population history and structure as a random effect in
order to quantify the (fixed) effect of the transition to agriculture on
skull shape and form.

http://www.pnas.org/content/early/2017/07/18/1702586114.full

I hope you find it interesting.

David



On Fri, Jan 20, 2017 at 8:50 AM, David Katz  wrote:

> Elahe and Ari,
>
> If your dependent observations have more than a few dimensions, such as is
> typical with landmark data or even a collections of linear measurements,
> then I think the common covariance matrix recommendation from Dr. Rohlf is
> the more standard approach. A typical strategy for removing group structure
> is to perform your analysis on the pooled within group covariance matrix.
> Chs.11 and 12 of Zelditch et al's *Geometric Morphometrics for Biologists*
> provides an overview and citations, as well as the equation for
> partitioning overall covariance into within and between group components.
>
> However, Dr. Belk is correct that, in principle, a mixed model suits your
> needs. The mixed model provides a way to partition random deviances
> attributable to population history and structure from common (fixed)
> effects of developmental stage and sex. You will have to think about how
> you want to model conditionality of sexual dimorphism with respect to
> stage. For example, if dimorphism does not arise until later stages of
> development in your subject species, it does not make much sense to model a
> single dimorphism coefficient across the entire sample.
>
> To make the model go, you will need data with which to estimate pairwise
> evolutionary relationships among the groups in your sample. The computation
> of random effects depends upon it. Stone et al (
> http://rstb.royalsocietypublishing.org/content/366/1569/1410) describe
> some of the challenges of estimating relatedness where there is gene flow
> among the sample clusters (i.e., where your groups are populations rather
> than non-reticulating species). A paper from me+colleagues, linked to
> below, provides an overly simple but (we think) reasonable solution to this.
>
> This leaves one more potential problem: dimensionality. If the dependent
> observation is univariate (centroid size, the first shape principal
> component, etc.) or very low dimensional, the MCMCglmm package (see
> http://onlinelibrary.wiley.com/doi/10./j.1420-9101.2009.01915.x/full)
> could suit your mixed model needs. However, if your data is truly
> multivariate (GM or anything more than a handful of linear measurements), I
> would recommend looking at the BSFG package. We used it to estimate sex and
> climate effects on the human cranium, using a subset of populations from
> the Howells linear craniometric collection. You can find that paper on my
> ResearchGate page (ResearchGate profile
> ). You will also find
> a poster where we extend the application to cranial and mandibular 3D
> landmark data. In the next few months, we should have a paper out that
> combines high-dimensional mixed model analysis with 3DGM in a more
> satisfying way than was managed in the poster.
>
> Unfortunately, unless the developers have finished updating the BSFG
> package, it is not quite plug and play. It takes some time to figure out.
> You need familiarity with Bayesian analysis, and with Matlab. However, the
> outputs are worth the effort. For example, with GM data, your fixed effect
> posterior will estimate shape contrasts for sex and developmental stage
> effects for the whole shape configuration, rather than on synthetic,
> orthogonal subsets (PCs).
>
> Conclusion: if your data is multidimensional and you don't think you can
> get going with BSFG, for the time being, the common covariance matrix
> approach is probably your best option.
>
> David
>
> On Thu, Jan 19, 2017 at 5:24 PM, Mark Belk  wrote:
>
> What you describe – samples from multiple populations – is best considered
> as a random effect in a typical generalized linear model format.  You have
> randomly sampled some populations from all of those that might be
> available.  If I understand your data correctly, to evaluate allometry, use
> a mixed model approach where some trait measurement is the response
> variable and some measure of body size would be the predictor variable,
> then population would be included as a random effect in the model.  This
> structure has the advantage of accounting for and adjusting for covariation
> among populations before the fixed effect is evaluated.  Appropriately
> crafted mixed models can rigorously account for a range of complicated
> covariance structures within the context of one model.  Several examples of
> the use of mixed models in ecology and evolution can be found in the
> literature.
>
> Hope that helps,
>
>
>
> Mark
>
>
>
> Mark C. Belk, Professor of