Browsing by Author "Gladman, Justin T"

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    A digital collection of rare and endangered lemurs and other primates from the Duke Lemur Center.
    (PloS one, 2019-01) Yapuncich, Gabriel S; Kemp, Addison D; Griffith, Darbi M; Gladman, Justin T; Ehmke, Erin; Boyer, Doug M
    Scientific study of lemurs, a group of primates found only on Madagascar, is crucial for understanding primate evolution. Unfortunately, lemurs are among the most endangered animals in the world, so there is a strong impetus to maximize as much scientific data as possible from available physical specimens. MicroCT scanning efforts at Duke University have resulted in scans of more than 100 strepsirrhine cadavers representing 18 species from the Duke Lemur Center. An error study of the microCT scanner recovered less than 0.3% error at multiple resolution levels. Scans include specimen overviews and focused, high-resolution selections of complex anatomical regions (e.g., cranium, hands, feet). Scans have been uploaded to MorphoSource, an online digital repository for 3D data. As captive (but free ranging) individuals, these specimens have a wealth of associated information that is largely unavailable for wild populations, including detailed life history data. This digital collection maximizes the information obtained from rare and endangered animals with minimal degradation of the original specimens.
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    A new fully automated approach for aligning and comparing shapes.
    (Anatomical record (Hoboken, N.J. : 2007), 2015-01) Boyer, Doug M; Puente, Jesus; Gladman, Justin T; Glynn, Chris; Mukherjee, Sayan; Yapuncich, Gabriel S; Daubechies, Ingrid
    Three-dimensional geometric morphometric (3DGM) methods for placing landmarks on digitized bones have become increasingly sophisticated in the last 20 years, including greater degrees of automation. One aspect shared by all 3DGM methods is that the researcher must designate initial landmarks. Thus, researcher interpretations of homology and correspondence are required for and influence representations of shape. We present an algorithm allowing fully automatic placement of correspondence points on samples of 3D digital models representing bones of different individuals/species, which can then be input into standard 3DGM software and analyzed with dimension reduction techniques. We test this algorithm against several samples, primarily a dataset of 106 primate calcanei represented by 1,024 correspondence points per bone. Results of our automated analysis of these samples are compared to a published study using a traditional 3DGM approach with 27 landmarks on each bone. Data were analyzed with morphologika(2.5) and PAST. Our analyses returned strong correlations between principal component scores, similar variance partitioning among components, and similarities between the shape spaces generated by the automatic and traditional methods. While cluster analyses of both automatically generated and traditional datasets produced broadly similar patterns, there were also differences. Overall these results suggest to us that automatic quantifications can lead to shape spaces that are as meaningful as those based on observer landmarks, thereby presenting potential to save time in data collection, increase completeness of morphological quantification, eliminate observer error, and allow comparisons of shape diversity between different types of bones. We provide an R package for implementing this analysis.
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    Predicting euarchontan body mass: A comparison of tarsal and dental variables.
    (American journal of physical anthropology, 2015-07) Yapuncich, Gabriel S; Gladman, Justin T; Boyer, Doug M
    Multiple meaningful ecological characterizations of a species revolve around body mass. Because body mass cannot be directly measured in extinct taxa, reliable body mass predictors are needed. Many published body mass prediction equations rely on dental dimensions, but certain skeletal dimensions may have a more direct and consistent relationship with body mass. We seek to evaluate the reliability of prediction equations for inferring euarchontan body mass based on measurements of the articular facet areas of the astragalus and calcaneus.Surface areas of five astragalar facets (n = 217 specimens) and two calcaneal facets (n = 163) were measured. Separate ordinary least squares and multiple regression equations are presented for different levels of taxonomic inclusivity, and the reliability of each equation is evaluated with the coefficient of determination, standard error of the estimate, mean prediction error, and the prediction sum of squares statistic. We compare prediction errors to published prediction equations that utilize dental and/or tarsal measures. Finally, we examine the effects of taxonomically specific regressions and apply our equations to a diverse set of non-primates.Our results reveal that predictions based on facet areas are more reliable than most linear dental or tarsal predictors. Multivariate approaches are often better than univariate methods, but require more information (making them less useful for fragmentary fossils). While some taxonomically specific regressions improve predictive ability, this is not true for all primate groups.Among individual facets, the ectal and fibular facets of the astragalus and the calcaneal cuboid facet are the best body mass predictors. Since these facets have primarily concave curvature and scale with positive allometry relative to body mass, it appears that candidate skeletal proxies for body mass can be identified based on their curvature and scaling coefficients.