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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。

相似文献

[1]
Identifying cryptic diversity with predictive phylogeography.

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[2]
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[3]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
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J R Soc N Z. 2022-5-26

[2]
Reduced representation approaches produce similar results to whole genome sequencing for some common phylogeographic analyses.

PLoS One. 2023

[3]
Hybrid zone analysis confirms cryptic species of banded newt and does not support competitive displacement since secondary contact.

Ecol Evol. 2023-8-30

[4]
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[5]
Environmentally driven phenotypic convergence and niche conservatism accompany speciation in hoary bats.

Sci Rep. 2022-12-19

[6]
Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation.

PLoS One. 2022

[7]
Evolutionary Genetics of Cacti: Research Biases, Advances and Prospects.

Genes (Basel). 2022-3-1

[8]
Genomic evidence of an ancient inland temperate rainforest in the Pacific Northwest of North America.

Mol Ecol. 2022-5

[9]
Using natural history to guide supervised machine learning for cryptic species delimitation with genetic data.

Front Zool. 2022-2-22

[10]
The Evolution of Comparative Phylogeography: Putting the Geography (and More) into Comparative Population Genomics.

Genome Biol Evol. 2022-1-4

本文引用的文献

[1]
BIG DATA AND BIODIVERSITY. Filling in biodiversity threat gaps.

Science. 2016-4-22

[2]
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PLoS One. 2013-11-1

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