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本文引用的文献

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BIG DATA AND BIODIVERSITY. Filling in biodiversity threat gaps.大数据与生物多样性。填补生物多样性威胁缺口。
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Biodiversity scenarios neglect future land-use changes.生物多样性情景忽略了未来的土地利用变化。
Glob Chang Biol. 2016 Jul;22(7):2505-15. doi: 10.1111/gcb.13272. Epub 2016 Mar 31.
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Reliable ABC model choice via random forests.基于随机森林的可靠 ABC 模型选择。
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The evolution of phylogeographic data sets.系统发育地理数据集的演变。
Mol Ecol. 2015 Mar;24(6):1164-71. doi: 10.1111/mec.13108. Epub 2015 Mar 9.
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Historical Species Distribution Models Predict Species Limits in Western Plethodon Salamanders.历史物种分布模型预测西部 plethodon 蝾螈的物种界限。
Syst Biol. 2015 Nov;64(6):909-25. doi: 10.1093/sysbio/syu090. Epub 2014 Nov 19.
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A global model of the response of tropical and sub-tropical forest biodiversity to anthropogenic pressures.热带和亚热带森林生物多样性对人为压力响应的全球模型。
Proc Biol Sci. 2014 Oct 7;281(1792). doi: 10.1098/rspb.2014.1371.
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The biodiversity of species and their rates of extinction, distribution, and protection.物种的生物多样性及其灭绝率、分布和保护。
Science. 2014 May 30;344(6187):1246752. doi: 10.1126/science.1246752.
8
Cryptic diversity hides host and habitat specialization in a gorgonian-algal symbiosis.隐存多样性隐藏了在柳珊瑚-藻类共生关系中的宿主和生境特化现象。
Mol Ecol. 2014 Jul;23(13):3330-40. doi: 10.1111/mec.12808. Epub 2014 Jun 16.
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The geography and ecology of plant speciation: range overlap and niche divergence in sister species.植物物种形成的地理和生态:姊妹种的分布重叠和生态位分歧。
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10
Cryptic genetic diversity is paramount in small-bodied amphibians of the genus Euparkerella (Anura: Craugastoridae) endemic to the Brazilian Atlantic forest.对于巴西大西洋森林特有的尤帕克氏蛙属(无尾目:溪蟾科)小型两栖动物而言,隐秘遗传多样性至关重要。
PLoS One. 2013 Nov 1;8(11):e79504. doi: 10.1371/journal.pone.0079504. eCollection 2013.

利用预测性系统发育地理学识别隐秘多样性。

Identifying cryptic diversity with predictive phylogeography.

作者信息

Espíndola Anahí, Ruffley Megan, Smith Megan L, Carstens Bryan C, Tank David C, Sullivan Jack

机构信息

Department of Biological Sciences, University of Idaho, 875 Perimeter Drive MS 3051, Moscow, ID 83844-3051, USA

Biological Sciences, Institute for Bioinformatics and Evolutionary Studies (IBEST), 875 Perimeter Drive MS 3051, Moscow, ID 83844-3051, USA.

出版信息

Proc Biol Sci. 2016 Oct 26;283(1841). doi: 10.1098/rspb.2016.1529.

DOI:10.1098/rspb.2016.1529
PMID:27798300
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5095379/
Abstract

Identifying units of biological diversity is a major goal of organismal biology. An increasing literature has focused on the importance of cryptic diversity, defined as the presence of deeply diverged lineages within a single species. While most discoveries of cryptic lineages proceed on a taxon-by-taxon basis, rapid assessments of biodiversity are needed to inform conservation policy and decision-making. Here, we introduce a predictive framework for phylogeography that allows rapidly identifying cryptic diversity. Our approach proceeds by collecting environmental, taxonomic and genetic data from codistributed taxa with known phylogeographic histories. We define these taxa as a reference set, and categorize them as either harbouring or lacking cryptic diversity. We then build a random forest classifier that allows us to predict which other taxa endemic to the same biome are likely to contain cryptic diversity. We apply this framework to data from two sets of disjunct ecosystems known to harbour taxa with cryptic diversity: the mesic temperate forests of the Pacific Northwest of North America and the arid lands of Southwestern North America. The predictive approach presented here is accurate, with prediction accuracies placed between 65% and 98.79% depending of the ecosystem. This seems to indicate that our method can be successfully used to address ecosystem-level questions about cryptic diversity. Further, our application for the prediction of the cryptic/non-cryptic nature of unknown species is easily applicable and provides results that agree with recent discoveries from those systems. Our results demonstrate that the transition of phylogeography from a descriptive to a predictive discipline is possible and effective.

摘要

识别生物多样性的单位是生物生物学的一个主要目标。越来越多的文献关注隐秘多样性的重要性,隐秘多样性被定义为在单一物种内存在深度分化的谱系。虽然大多数隐秘谱系的发现是逐个分类群进行的,但需要对生物多样性进行快速评估,以为保护政策和决策提供信息。在这里,我们介绍了一种系统发育地理学的预测框架,该框架能够快速识别隐秘多样性。我们的方法是通过收集来自具有已知系统发育地理历史的同域分布分类群的环境、分类学和遗传数据来进行的。我们将这些分类群定义为一个参考集,并将它们分类为具有或缺乏隐秘多样性。然后,我们构建一个随机森林分类器,使我们能够预测同一生物群落中其他哪些特有分类群可能包含隐秘多样性。我们将这个框架应用于两组已知包含具有隐秘多样性分类群的间断生态系统的数据:北美太平洋西北部的中生温带森林和北美西南部的干旱地区。这里提出的预测方法是准确的,根据生态系统的不同,预测准确率在65%到98.79%之间。这似乎表明我们的方法可以成功地用于解决关于隐秘多样性的生态系统层面的问题。此外,我们对未知物种隐秘/非隐秘性质的预测应用很容易应用,并提供与那些系统最近的发现一致的结果。我们的结果表明,系统发育地理学从描述性学科向预测性学科的转变是可能且有效的。