Indeed one of my favourite examples where semilandmarks are really useful is a paper by Hublin, Gunz et al. (with apologies for the inaccurate ref. and mixed up order of authors) where they manage to classify as Neanderthal a piece of cranial vault found (I believe) in Belgium and possibly in the sea. With all the limits we mentioned, it's probably hard to do any better. Certainly it's not the only type of useful application but that kind of 'forensic' analysis, where the main aim is pure classification accuracy, is where I see (potentially) less problems. However, even with phenetics (in the original sense of the term), putting together all sorts of different characters and characters states, regardless of their evolutionary significance, one could probably get a very good and stable classification. Yet, to what extent that would be biologically meaningful is hard to say, and we abandoned phenetics for cladistics. With shape data, as implied by Jim's comment to my message, we don't yet have the same kind of understanding to be able to do the same (some kind of 'biologically meaningful superimposition). For now, in a way, it seems to me that it's as if we were aligning sequences using, say, a least square method, which molecular biologists would never use because they know much more about DNA evolution and can model that more accurately in the alignment. We can't. Thus, for me, semilandmarks are useful if (as in your example) they may provide information which is relevant and there's no other way to get. The limits will still be there but the pros may be more than the cons. Where I disagree is the general trend to believe that more is always better.
The field is different but I am very much sympathetic with what Hawkins said in his review of spatial data analysis: "This has led to a confusing literature and a proliferation of increasingly complicated analytical methods that are difficult to evaluate or even understand if you are not a statistician. A tendency to ignore assumptions of many of these complex methods does not help matters. It has also diverted attention away from epistemo- logical and conceptual issues of importance to our field, some of which I have tried to highlight. Although it is self-evident that statistics are an indispensible tool for evaluating data, when we focus too much on methods it is natural to add new layers of complexity as our view becomes narrower and narrower and we try to capture every nuance of our data. But biogeography and geographical ecology are not branches of theoretical statistics, and there does come a point at which analytical complexity begins to interfere with understanding." Journal of Biogeography (J. Biogeogr.) (2012) 39, 1–9 Good night Andrea On 06/11/2018, Mike Collyer <mlcoll...@gmail.com> wrote: > Andrea, > > I am intrigued by your initial comment about adding covariance that was > apparently absent. I tend to think of the problem from the other > perspective of not accounting for covariance that should be present. As a > thought experiment (that could probably be simulated, and maybe I am not > correct in my thinking), I like to think of two landmark configurations that > are the same in all regards except for one curve, where two groups have > distinctly different curves but maybe would not be obviously distinctively > different if an insufficient number of semi-landmarks (or none) were used to > characterize the curve. If one were to (maybe simulate this example and) > use one sparse representation of landmarks and one dense representation, > perform a cross-validation classification analysis, and calculate posterior > classification probabilities (let’s assume equal sample sizes and, > therefore, equal prior probabilities), I would expect that the posterior > probabilities of the dense landmark configuration would better assign > specimens to the appropriate process that generated them (i.e., their > correct groups). The posterior probabilities would be closer to 0 and 1 > because of the “added covariance”, as reflected by the squared generalized > Mahalanobis distances, based on the pooled within-group covariance. The > added covariance would be essential for the posterior probabilities, if the > sparse configurations produced similar generalized distances to group means, > and therefore, similar posterior probabilities for classification. > > I’m not sure adding covariance is an issue. To me it simply changes the > hypothetical (null) covariance structure, which Philipp mentioned should > probably not be assumed to be independent (isotropic). I think your example > might best highlight that a different multivariate normal distribution of > residuals is to be expected with a different configuration. > > Cheers! > Mike > > >> On Nov 6, 2018, at 12:34 PM, alcardini <alcard...@gmail.com> wrote: >> >> Yes, but doesn't that also add more covariance that wasn't there in >> the first place? >> Neither least squares nor minimum bending energy, that we minimize for >> sliding, are biological models: they will reduce variance but will do >> it in ways that are totally biologically arbitrary. >> >> In the examples I showed sliding led to the appearance of patterns >> from totally random data and that effect was much stronger than >> without sliding. >> I neither advocate sliding or not sliding. Semilandmarks are different >> from landmarks and more is not necessarily better. There are >> definitely some applications where I find them very useful but many >> more where they seem to be there just to make cool pictures. >> >> As Mike said, we've already had this discussion. Besides different >> views on what to measure and why, at that time I hadn't appreciated >> the problem with p/n and the potential strength of the patterns >> introduced by the covariance created by the superimposition (plus >> sliding!). >> >> Cheers >> >> Andrea >> >> On 06/11/2018, F. James Rohlf <f.james.ro...@stonybrook.edu >> <mailto:f.james.ro...@stonybrook.edu>> wrote: >>> 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 visualizations). >>> >>> >>> >>> 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 challenging. >>> >>> >>> >>> 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. >>> >>> >>> >>> Best, >>> >>> >>> >>> Philipp >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> 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: >>> https://preview.tinyurl.com/semilandmarks >>> >>> 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 >>> >>> >>> >>> Andrea >>> >>> >>> >>> 9 LANDMARKS PLUS 22 SLID SEMILANDMARKS >>> >>> >>> <https://groups.google.com/a/morphometrics.org/group/morphmet/attach/dcce33d95d952/oclbeaidoponnmni.jpeg?part=0.1.1&view=1&authuser=0> >>> >>> >>> 9 LANDMARKS >>> >>> >>> <https://groups.google.com/a/morphometrics.org/group/morphmet/attach/dcce33d95d952/pebddfgpogepigmi.jpeg?part=0.1.2&view=1&authuser=0> >>> >>> >>> -- >>> >>> 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, Australia >>> >>> E-mail address: alca...@gmail.com <http://gmail.com/> <javascript:> , >>> andrea....@unimore.it >>> <javascript:> >>> WEBPAGE: https://sites.google.com/site/alcardini/home/main >>> <https://sites.google.com/site/alcardini/home/main> >>> >>> FREE Yellow BOOK on Geometric Morphometrics: >>> https://tinyurl.com/2013-Yellow-Book >>> <https://tinyurl.com/2013-Yellow-Book> >>> >>> ESTIMATE YOUR GLOBAL FOOTPRINT: >>> http://www.footprintnetwork.org/en/index.php/GFN/page/calculators/ >>> <http://www.footprintnetwork.org/en/index.php/GFN/page/calculators/> >>> >>> -- >>> MORPHMET may be accessed via its webpage at http://www.morphometrics.org >>> <http://www.morphometrics.org/> >>> --- >>> You received this message because you are subscribed to the Google >>> Groups >>> "MORPHMET" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an >>> email to morphmet+unsubscr...@morphometrics.org >>> <mailto:morphmet+unsubscr...@morphometrics.org> >>> <mailto:morphmet+unsubscr...@morphometrics.org >>> <mailto:morphmet+unsubscr...@morphometrics.org>> . >>> >>> -- >>> MORPHMET may be accessed via its webpage at http://www.morphometrics.org >>> <http://www.morphometrics.org/> >>> --- >>> You received this message because you are subscribed to the Google >>> Groups >>> "MORPHMET" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an >>> email to morphmet+unsubscr...@morphometrics.org >>> <mailto:morphmet+unsubscr...@morphometrics.org>. >>> >> >> >> -- >> >> 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 <tel:0039%20059%202058472> >> >> Adjunct Associate Professor, School of Anatomy, Physiology and Human >> Biology, The University of Western Australia, 35 Stirling Highway, >> Crawley WA 6009, Australia >> >> E-mail address: alcard...@gmail.com <mailto:alcard...@gmail.com>, >> andrea.card...@unimore.it <mailto:andrea.card...@unimore.it> >> WEBPAGE: https://sites.google.com/site/alcardini/home/main >> <https://sites.google.com/site/alcardini/home/main> >> >> FREE Yellow BOOK on Geometric Morphometrics: >> http://www.italian-journal-of-mammalogy.it/public/journals/3/issue_241_complete_100.pdf >> <http://www.italian-journal-of-mammalogy.it/public/journals/3/issue_241_complete_100.pdf> >> >> ESTIMATE YOUR GLOBAL FOOTPRINT: >> http://www.footprintnetwork.org/en/index.php/GFN/page/calculators/ >> <http://www.footprintnetwork.org/en/index.php/GFN/page/calculators/> >> >> -- >> MORPHMET may be accessed via its webpage at http://www.morphometrics.org >> <http://www.morphometrics.org/> >> --- >> You received this message because you are subscribed to the Google Groups >> "MORPHMET" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to morphmet+unsubscr...@morphometrics.org >> <mailto:morphmet+unsubscr...@morphometrics.org>. > > -- 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, Australia E-mail address: alcard...@gmail.com, andrea.card...@unimore.it WEBPAGE: https://sites.google.com/site/alcardini/home/main FREE Yellow BOOK on Geometric Morphometrics: http://www.italian-journal-of-mammalogy.it/public/journals/3/issue_241_complete_100.pdf ESTIMATE YOUR GLOBAL FOOTPRINT: http://www.footprintnetwork.org/en/index.php/GFN/page/calculators/ -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org --- You received this message because you are subscribed to the Google Groups "MORPHMET" group. 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