Bridging biology and paleontology – a novel combined machine-learning approach to species delimitation

连接生物学和古生物学——一种新颖的组合机器学习方法来划分物种

• 批准号: 538733775
• 负责人: Dr. Thomas A. Neubauer
• 金额: --
• 依托单位: Bayerische Staatssammlung für Paläontologie und Geologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/538733775?language=en
• 关键词: Bridging; biology; paleontology; novel; combined

After more than 250 years after the definition of the first species by Linnaeus, there is still disagreement about how to define and delimit species. This discrepancy is especially evident when comparing fossil and living species, for which typically different types and amounts of information are available, which has led to a multitude of different species concepts over the centuries. To compare past, present and predicted rates of biodiversity turnover, reconstruct biogeographic patterns and infer evolutionary processes realistically, a standardized species classification system is needed that deals with fossils and living taxa equally. Recent developments in machine learning (ML) and image recognition provide a unique opportunity to jointly delimit fossil and recent species in a modern analytical framework. We will develop a novel ML approach that uses image data for recent species, for which species boundaries were established based on molecular data, to allow for a standardized and unified species delimitation of related fossil species. We will use Siamese Convolutional Neural Networks, which require relatively few images to learn similarities, can be applied to unlabeled data without re-training and can even deal with unknown classes. As model group we will use freshwater gastropods of the family Viviparidae, where the recent and fossil target groups encompass a comparable, high morphological plasticity that has caused taxonomic confusion in the past. In addition, we will i) assess the value of a ML-based species delimitation system versus traditional taxonomy by carrying out independent delimitations made by taxonomists, ii) test the limits of the species delimitation system with regard to different types and degrees of fossil preservation, iii) assess the level of detail that is required to reliably delimit fossil species, by feeding the system images of variable quality, including simple drawings from the literature, and finally iv) apply the newly inferred species boundaries to reconstruct accurate biodiversity patterns and estimate diversification processes for the fossil species group. Our new machine-learning-derived approach will be widely usable across different taxonomic groups and form an important starting point for making species comparable through space and time. A standardized species delimitation system that is applicable to extant and extinct species is imperative to compare pathways of turnover events and biodiversity crises throughout geological time and finally provide more realistic outlooks on the Anthropocene Biodiversity Crisis.

在Linneeus定义第一个物种250多年后，关于如何定义和界定物种仍然存在分歧。当比较化石和活物种时，这种差异尤其明显，对于这些物种，通常有不同类型和数量的信息，这导致了几个世纪以来许多不同的物种概念。为了比较过去、现在和预测的生物多样性周转速度，重建生物地理格局，并现实地推断进化过程，需要一个标准化的物种分类系统，将化石和活的类群平等地处理。机器学习(ML)和图像识别的最新发展为在现代分析框架中联合划分化石和现代物种提供了独特的机会。我们将开发一种新的ML方法，它使用最近物种的图像数据，其中物种边界是基于分子数据建立的，以允许对相关化石物种进行标准化和统一的物种划界。我们将使用暹罗卷积神经网络，它需要相对较少的图像来学习相似性，可以应用于未标记的数据而无需重新训练，甚至可以处理未知类别。作为模型组，我们将使用Viviparidae科的淡水腹足类，其中最近的和化石的目标类群包含了可比的、高形态可塑性，这在过去造成了分类上的混乱。此外，我们将：i)通过执行分类学家所做的独立定界来评估基于ML的物种定界系统相对于传统分类学的价值；ii)测试物种定界系统对于不同类型和化石保存程度的限制；iii)通过提供不同质量的系统图像(包括文献中的简单绘图)来评估可靠地定界化石物种所需的详细程度；以及iv)应用新推断的物种边界来重建准确的生物多样性格局并估计化石物种组的多样化过程。我们新的机器学习派生方法将广泛适用于不同的分类群体，并形成一个重要的起点，使物种在空间和时间上具有可比性。一个适用于现存和灭绝物种的标准化物种划界系统势在必行，以比较整个地质时期更替事件和生物多样性危机的途径，并最终对人类世生物多样性危机提供更现实的展望。