Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

被引:0
|
作者
Martinez, Neo D. [1 ,2 ,3 ]
机构
[1] Indiana Univ, Ctr Complex Networks & Syst, Sch Informat Comp & Engn, Bloomington, IN USA
[2] Pacific Ecoinformat & Computat Ecol Lab, Berkeley, CA USA
[3] Indiana Univ, Ctr Complex Networks & Syst, Sch Informat Comp & Engn, 919 E 10th St,Room 302, Bloomington, IN 47408 USA
来源
ECOLOGY AND EVOLUTION | 2023年 / 13卷 / 03期
基金
美国国家科学基金会;
关键词
computation; data science; ecology; ecosystem; environmental nucleic acids; evolution; networks; prediction; synthesis; theory; FOOD WEBS; MODEL; ROBUSTNESS; DIVERSITY; PHENOTYPE; DYNAMICS; SUCCESS; SCIENCE;
D O I
10.1002/ece3.9872
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.
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页数:8
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