Disentangling drivers of mudflat intertidal DOM chemodiversity using ecological models

Microorganisms consume and transform dissolved organic matter (DOM) into various forms. However, it remains unclear whether the ecological patterns and drivers of DOM chemodiversity are analogous to those of microbial communities. Here, a large-scale investigation is conducted along the Chinese coasts to resolve the intrinsic linkages among the complex intertidal DOM pools, microbial communities and environmental heterogeneity. The abundance of DOM molecular formulae best fits log-normal distribution and follows Taylor's Law. Distance-decay relationships are observed for labile molecular formulae, while latitudinal diversity gradients are noted for recalcitrant molecular formulae. Latitudinal patterns are also observed for DOM molecular features. Negative cohesion, bacterial diversity, and molecular traits are the main drivers of DOM chemodiversity. Stochasticity analyses demonstrate that determinism dominantly shapes the DOM compositional variations. This study unveils the intrinsic mechanisms underlying the intertidal DOM chemodiversity and microbial communities from ecological perspectives, deepening our understanding of microbially driven chemical ecology.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-08-05
DOI
https://doi.org/10.1038/s41467-024-50841-9
Citations
27
Primary Topic
Microbial Community Ecology and Physiology
Type
article
Field-Weighted Citation Impact
12.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Disentangling drivers of mudflat intertidal DOM chemodiversity using ecological models

Qichao Tu, Kai Ma, Jiayin Zhou, Linlin Wang et al.
27 citations
Nature Communications
Microbial Community Ecology and Physiology
12.39
article

Disentangling drivers of mudflat intertidal DOM chemodiversity using ecological models

Qichao Tu, Kai Ma, Jiayin Zhou, Linlin Wang, Mengqi Wang, Wen Song, Yueyue Li, Xia Liu, Xiaofan Gong
article en
27 citations

Abstract

Microorganisms consume and transform dissolved organic matter (DOM) into various forms. However, it remains unclear whether the ecological patterns and drivers of DOM chemodiversity are analogous to those of microbial communities. Here, a large-scale investigation is conducted along the Chinese coasts to resolve the intrinsic linkages among the complex intertidal DOM pools, microbial communities and environmental heterogeneity. The abundance of DOM molecular formulae best fits log-normal distribution and follows Taylor's Law. Distance-decay relationships are observed for labile molecular formulae, while latitudinal diversity gradients are noted for recalcitrant molecular formulae. Latitudinal patterns are also observed for DOM molecular features. Negative cohesion, bacterial diversity, and molecular traits are the main drivers of DOM chemodiversity. Stochasticity analyses demonstrate that determinism dominantly shapes the DOM compositional variations. This study unveils the intrinsic mechanisms underlying the intertidal DOM chemodiversity and microbial communities from ecological perspectives, deepening our understanding of microbially driven chemical ecology.

Nature CommunicationsVol. 15(1)
Shandong University (CN), Marine Biology Institute of Shandong Province (CN), Ocean University of China (CN), Shandong University of Science and Technology (CN)
Shandong University, National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 1%
Microbial Community Ecology and Physiology
12.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.