Global prediction of soil microbial growth rates and carbon use efficiency based on the metabolic theory of ecology

土壤碳 冻土带 环境科学 碳循环 生物群落 生物量(生态学) 生态学 陆地生态系统 生态系统 异养 微生物种群生物学 细菌生长 植物凋落物 土壤生态学 土壤有机质 土壤科学 土壤水分 生物 土壤生物多样性 遗传学 细菌
作者
Decai Gao,Edith Bai,Daniel Wasner,Frank Hagedorn
出处
期刊:Soil Biology & Biochemistry [Elsevier BV]
卷期号:190: 109315-109315 被引量:29
标识
DOI:10.1016/j.soilbio.2024.109315
摘要

Soil microbial growth rate and microbial carbon use efficiency (CUE) are critical parameters of soil microbial carbon metabolism, moderating soil organic carbon (SOC) dynamics. However, global patterns of soil microbial growth rate and microbial CUE are still unresolved. Here, we show that the metabolic theory of ecology (MTE) can be applied to model soil microbial growth rate at the global scale as a function of microbial biomass, temperature, and SOC contents. Rates of soil microbial growth were modeled at depths of 0–30 cm and calibrated against rates measured with the 18O-labeled H2O incubation method. The modeled soil microbial growth rates were strongly driven by temperature. They decreased with latitude and had greater seasonal variations in tundra and boreal forest compared to tropical biomes. Soil microbial CUE (0–30 cm) ranged from 0.25 to 0.63 among global biomes, averaging at 0.43. Modeled annual soil microbial growth rates followed the same global patterns and were on the same order of magnitude as other key ecosystem C fluxes such as net primary productivity, litterfall, and heterotrophic respiration. This indicates a strong functional linkage of aboveground and belowground communities at the global scale. Our MTE-based approach provides the first estimates of global patterns for soil microbial growth rate and microbial CUE and potentially provides a powerful mechanistic framework to incorporate soil microbes into Earth System Models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
今后应助活泼源智采纳,获得10
1秒前
2秒前
哇哈哈发布了新的文献求助10
3秒前
小爽完成签到,获得积分10
3秒前
简单妖妖发布了新的文献求助30
3秒前
6秒前
幽默的友灵完成签到,获得积分10
6秒前
hh发布了新的文献求助10
7秒前
8秒前
8秒前
8秒前
早早早完成签到,获得积分10
10秒前
fatcatty发布了新的文献求助10
12秒前
无私的丹完成签到,获得积分10
12秒前
14秒前
我太饿了发布了新的文献求助10
14秒前
光亮天真发布了新的文献求助10
15秒前
爱学习的憨憨鸭完成签到,获得积分10
16秒前
16秒前
FashionBoy应助暴走章鱼采纳,获得10
17秒前
6260完成签到,获得积分10
17秒前
17秒前
呆萌老丁完成签到,获得积分10
19秒前
19秒前
wgy完成签到 ,获得积分10
19秒前
哇哈哈完成签到,获得积分10
19秒前
活泼源智发布了新的文献求助10
20秒前
20秒前
zong240221完成签到,获得积分10
20秒前
里子完成签到 ,获得积分10
22秒前
xh发布了新的文献求助20
22秒前
椰汁完成签到 ,获得积分10
23秒前
科研通AI6.3应助巴哒采纳,获得10
23秒前
zong240221发布了新的文献求助10
23秒前
张锐斌完成签到,获得积分10
24秒前
TwinQ发布了新的文献求助10
24秒前
搜集达人应助自信的晓绿采纳,获得10
25秒前
忐忑的尔容完成签到,获得积分10
25秒前
星辰大海应助满当当采纳,获得10
26秒前
星岛完成签到,获得积分10
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing 36th edition 400
How to Design and Conduct an Experiment and Write a Lab Report: Your Complete Guide to the Scientific Method (Step-by-Step Study Skills) 333
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6363382
求助须知:如何正确求助?哪些是违规求助? 8177252
关于积分的说明 17232206
捐赠科研通 5418431
什么是DOI,文献DOI怎么找? 2867043
邀请新用户注册赠送积分活动 1844285
关于科研通互助平台的介绍 1691794