Using hierarchical stable isotope to reveal microbial food web structure and trophic transfer efficiency differences during lake melt season

Dang, Zhengzhu; Luo, Zhongxin; Wang, Shuo; Liao, Yinhao; Jiang, Zhuo; Zhu, Xianfang; Ji, Guodong

doi:10.1016/j.scitotenv.2022.156893

Cited by 3 publications

(2 citation statements)

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“…In aquatic ecosystems, the material and energy of microbial food webs flow from primary to higher trophic levels and finally enter the classic food web ( 5 ). Therefore, microbial food webs are a material and energy source in lake water ecosystems, and their structure and function are crucial for maintaining water ecological health ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

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Predation has a significant impact on the complexity and stability of microbial food webs in subalpine lakes

Guo,

Li,

Liu

et al. 2023

Microbiol Spectr

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Microbial food webs are drivers of material circulation and energy flow in lake ecosystems. The structural shifting of microbial food webs has important implications for the function of lake ecosystems. However, we continue to lack a clear understanding of the mechanisms underlying the complexity and stability of microbial food web structures. In this study, we investigated the diversity and structure of protist communities and bacterial communities by using DNA meta-barcoding to reveal the key factors affecting the complexity and stability of microbial food webs in subalpine lakes. Our analysis shows that significant seasonal variations occurred in protist taxonomic and functional communities (e.g., algivores, bacterivores, and phototrophs), which were mainly controlled by hydrological and nutrient parameters such as temperature, pH, and NH 4 + -N. Based on the network approach, the most complex food webs in the summer exhibited the lowest stability. We also found that the trophic interaction between bacterivorous protozoans and bacteria was the main factor significantly having influence on microbial food web complexity and stability ( P < 0.01). Moreover, biotic factors (i.e., bacterivorous protozoan composition) were better predictors of the relative abundance of several bacteria (Proteobacteria and Cyanobacteria) as prey than abiotic factors. Thus, these results suggest that predation is essential for the complexity and stability of the microbial food webs in lake ecosystems. Our findings suggest that understanding these complex interactions of microbial food webs is critical for ecosystem management and for predicting the ecological consequences of future climate changes in aquatic ecosystems. IMPORTANCE As an important part of microbial food webs, protists transfer organic carbon and nutrients to higher trophic levels in aquatic ecosystems. Protist predation often influences the abundance and composition of bacterial communities. However, we still do not understand whether and how predation affects the complexity and stability of microbial food webs. This study assessed the seasonal dynamic characteristics and driving factors of microbial food webs in terms of complexity and stability. Our findings have implications for future surveys to reveal the effects of climate and environmental changes.

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Section: Introductionmentioning

confidence: 99%

“…Previous studies of microbial food webs have mainly been conducted on oceans ( 2 , 30 ), lowland lakes ( 16 ), and rivers ( 22 ). Most alpine lakes are relatively closed ecosystems and particularly sensitive to climate change and human interference ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

Predation has a significant impact on the complexity and stability of microbial food webs in subalpine lakes

Guo,

Li,

Liu

et al. 2023

Microbiol Spectr

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Water quality drives the reconfiguration of riverine planktonic microbial food webs

Ren,

Shi,

Chen

et al. 2024

Environmental Research

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Network structure indicators predict ecological robustness in food webs

Tang,

Wang,

Zhou

2024

Ecological Research

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Food web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs.

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Using hierarchical stable isotope to reveal microbial food web structure and trophic transfer efficiency differences during lake melt season

Cited by 3 publications

References 54 publications

Predation has a significant impact on the complexity and stability of microbial food webs in subalpine lakes

Predation has a significant impact on the complexity and stability of microbial food webs in subalpine lakes

Water quality drives the reconfiguration of riverine planktonic microbial food webs

Network structure indicators predict ecological robustness in food webs

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