TraitAM, a global spore trait database for arbuscular mycorrhizal fungi.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
V Bala Chaudhary, Liam F Nokes, Jennifer B González, Peri O Cooper, Anne M Katula, Emma C Mares, Smriti Pehim Limbu, Jannetta N Robinson, Carlos A Aguilar-Trigueros
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引用次数: 0

Abstract

Knowledge regarding organismal traits supports a better understanding of the relationship between form and function and can be used to predict the consequences of environmental stressors on ecological and evolutionary processes. Most plants on Earth form symbioses with mycorrhizal fungi, but our ability to make trait-based inferences for these fungi is limited due to a lack of publicly available trait data. Here, we present TraitAM, a comprehensive database of multiple spore traits for all described species of the most common group of mycorrhizal fungi, the arbuscular mycorrhizal (AM) fungi (subphylum Glomeromycotina). Trait data for 344 species were mined from original species descriptions and used to calculate newly developed fungal trait metrics that can be employed to explore both intra- and inter-specific variation in traits. TraitAM also includes an updated phylogenetic tree that can be used to conduct phylogenetically-informed multivariate analyses of AM fungal traits. TraitAM will aid our further understanding of the biology, ecology, and evolution of these globally widespread, symbiotic fungi.

全球丛枝菌根真菌孢子性状数据库。
有关生物特征的知识有助于更好地理解形式与功能之间的关系,并可用于预测环境压力源对生态和进化过程的影响。地球上的大多数植物都与菌根真菌形成共生关系,但由于缺乏公开可用的性状数据,我们对这些真菌进行基于性状的推断的能力有限。在这里,我们提出了TraitAM,这是一个针对最常见的菌根真菌——丛枝菌根真菌(AM)真菌(小球菌属亚门)的所有已描述物种的多个孢子特征的综合数据库。从原始物种描述中挖掘了344个物种的性状数据,并用于计算新开发的真菌性状指标,这些指标可用于探索性状的种内和种间变异。TraitAM还包括一个更新的系统发育树,可用于对AM真菌性状进行系统发育信息的多变量分析。TraitAM将帮助我们进一步了解这些全球广泛分布的共生真菌的生物学,生态学和进化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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