Collaborative Research: Individual and Taxonomic Discrimination Through Laser Scan Analysis of Joint Congruence in Extant Hominoids

合作研究：通过激光扫描分析现存人科动物的关节一致性进行个体和分类歧视

• 批准号: 0452961
• 负责人: Eric Delson
• 金额: $ 8.39万
• 依托单位: American Museum Natural History
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-15 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0452961&HistoricalAwards=false
• 关键词: Collaborative; Research; Individual; Taxonomic; Discrimination

The allocation of fossil specimens to a taxon, and in some cases to an individual, is a frequent and important problem of paleontology. When multiple individuals of one (or more) species are recovered from a site, it is often vital to associate elements of a single individual in order to determine the number of individuals preserved, as well as examine proportions, functional adaptations and other factors. In this project, the PIs propose a relatively new approach to these problems, combining laser surface scans of opposing joint surfaces with advanced statistical analyses to examine ways to differentiate taxa and associate elements of unique individuals using a sample of extant hominoids where individuals are known. Previous studies using a laser scanner suffered from the lack of suitable computational power and statistical methodology. Delson and colleagues will build upon that work in combination with their own extensive familiarity with high-resolution scanning, geometric morphometric analysis and joint surfaces of extant and extinct catarrhine taxa. This work also has implications for analyses of forensic and archaeological accumulations of multiple-individual human remains.This study will focus on the bony components of two important joints: the elbow (humero-ulnar) and ankle (tibio-talar). Both of these joints are relatively "tight" showing a high degree of congruence where the proposed method has a higher chance of success. The elbow joint is "tighter" than the ankle, further allowing some evaluation of how tightly a joint articulates influences how well the method works. Finally, both of these joints are of functional interest among hominoids and are also well-represented in the human fossil record, the planned focus of later phases of research. Preliminary analysis of limited data has demonstrated that this basic approach can successfully discriminate between tibio-talar surfaces of different human individuals and among joints belonging to different hominoid species. The present study will collect additional data and concentrate on development of statistically powerful methods for their analysis.In addition to these questions, this work has broader impacts in that it will: 1) recruit Lehman College undergraduates from groups underrepresented in science to be research assistants; 2) enhance infrastructure through collaboration with other universities as well as British colleagues and by developing and freely distributing algorithms and software with which to analyze congruence patterns; 3) expand collaboration with computer scientists in the development of software and analytical methods; and 4) integrate research, teaching and wider dissemination of results by developing and freely distributing three-dimensional virtual models and visualizations of joint surfaces for use in teaching.

将化石标本分配给一个分类群，在某些情况下分配给一个个体，是古生物学中经常遇到的一个重要问题。当一个（或多个）物种的多个个体从一个地点被发现时，为了确定保存的个体数量，以及检查比例、功能适应和其他因素，将单个个体的元素联系起来通常是至关重要的。在这个项目中，pi提出了一种相对较新的方法来解决这些问题，将激光表面扫描与先进的统计分析相结合，使用已知个体的现存类人猿样本来研究区分独特个体的分类群和相关元素的方法。以前使用激光扫描仪的研究缺乏适当的计算能力和统计方法。Delson和他的同事们将在这项工作的基础上，结合他们自己对高分辨率扫描、几何形态计量学分析和现存和已灭绝的卡鼻鱼分类群的关节表面的广泛熟悉。这项工作也有意义的法医和考古多个人遗骸的积累分析。本研究将集中于两个重要关节的骨组成部分：肘关节（肱骨-尺骨）和踝关节（胫距骨）。这两个关节都相对“紧密”，显示出高度的一致性，所提出的方法有更高的成功机会。肘关节比踝关节更“紧”，从而进一步评估关节的紧密程度对方法效果的影响。最后，这两个关节在类人猿中都具有重要的功能，在人类化石记录中也有很好的代表性，这是后期研究的重点。对有限数据的初步分析表明，这种基本方法可以成功地区分不同人类个体的胫距面和属于不同类人猿物种的关节。本研究将收集更多的数据，并集中于发展强有力的统计分析方法。除了这些问题之外，这项工作还具有更广泛的影响，因为它将：1)从科学领域代表性不足的群体中招募雷曼学院的本科生担任研究助理；2)通过与其他大学和英国同事的合作，通过开发和自由分发用于分析一致性模式的算法和软件，加强基础设施；3)扩大与计算机科学家在开发软件和分析方法方面的合作；4)通过开发和自由发布用于教学的三维虚拟模型和关节面可视化，将研究、教学和更广泛的成果传播结合起来。