Collaborative Research: A Comparative Phylogeographic Approach to Predicting Cryptic Diversity - The Inland Temperate Rainforest as a Model System

几个世纪以来，生物学家一直在进行旨在发现和描述物种的调查。然而，目前还不知道地球上有多少物种，因为逐个发现物种既耗时又昂贵。这种知识的缺乏阻碍了保护目标和其他对社会很重要的目标。为了满足这一需求，研究人员开发了一种方法来识别可能由几个独立物种组成的种群。由于这种方法是基于现有的生态和遗传数据，随着更多数据的收集，该技术将变得更加完善。这项研究将利用太平洋西北部生态系统的数据开发和测试这种方法，该生态系统支持许多生物多样性，最近发现了几种神秘物种。这项研究将开发一个预测框架，用于发现神秘生物多样性，可应用于生态系统的所有成员。在这两个阶段的框架中，研究人员将（i）从一组参考分类群中收集环境，分类，功能和遗传数据，并确定这些物种中哪些包含隐藏的多样性，（ii）进行统计分析，以确定具有隐藏多样性的分类群所共有的栖息地特征，（iii）收集和分析第二组物种的环境数据，以预测这些物种中的哪些包含隐蔽多样性，以及（iv）通过收集第二组分类群的遗传数据来测试这些预测。北美太平洋西北部的温带雨林将作为模型系统。太平洋沿岸和落基山脉内部段之间的同种种群或假定的姐妹种对的分离提出了关于潜在的隐蔽多样性的明确假设：无论是前更新世的vicariance，预测高隐蔽多样性，或后更新世的分散，预测缺乏隐蔽多样性。这些预测将通过收集24种地方性间断植物和动物的基因组规模数据进行测试，并应用近似贝叶斯计算来评估对每个假设的支持。然后，这组分类群将被用作训练集，用于从它们的发生数据和与每个相关联的气候变量中对神秘多样的物种进行分类。经典的多变量方法，如判别函数分析，以及较新的决策树方法（如随机森林）将进行评估。

Biologists have been conducting surveys aimed at discovering and describing species for centuries. However, it is not known how many species are on the Earth, because discovering species on a one-by-one basis is time consuming and expensive. This lack of knowledge is an impediment to conservation aims and other goals important to society. To address this need, the researchers have developed an approach to identifying groups of populations that are likely to consist of several independent species. Because this approach is based on existing ecological and genetic data, the technique will become more refined as additional data are collected. The proposed research will develop and test this approach using data from an ecosystem in the Pacific Northwest that supports much biodiversity, and where several cryptic species have recently been discovered.The proposed research will develop a predictive framework for the discovery of cryptic biodiversity that can be applied to all members of an ecosystem. In this two-phase framework, researchers will (i) gather environmental, taxonomic, functional, and genetic data from a reference set of taxa native a model ecosystem and identify which of these species contain cryptic diversity, (ii) conduct statistical analyses to identify habitat features shared by the taxa that harbor cryptic diversity, (iii) collect and analyze environmental data for a second set of species to make predictions about which of these species contain cryptic diversity, and (iv) test these predictions via collecting genetic data for the second set of taxa. The temperate rainforests of the Pacific Northwest of North America will serve as the model system. The disjunction of conspecific populations or putative sister-species pairs between Pacific coastal and interior Rocky Mountain segments presents clear hypotheses regarding potential cryptic diversity: either pre-Pleistocene vicariance, which predicts high cryptic diversity, or post- Pleistocene dispersal where which predicts a lack of cryptic diversity. These predictions will be tested by collection of genomic scale data for 24 endemic disjunct plants and animals, and application of Approximate Bayesian Computation to assess support for each hypothesis. This set of taxa will be then used as a training-set for classifying cryptically diverse species from their occurrence data and climatic variables associated with each. Classical multivariate approaches, such as Discriminant Function Analysis, as well as newer decision-tree approaches (such as RandomForest) will be assessed.