Depth-dependent soil C-N-P stoichiometry in a mature subtropical broadleaf forest

Qiao, Yang; Wang, Jing; Liu, Heming; Huang, Kun; Yang, Qingsong; Lu, Ruiling; Yan, Liming; Wang, Xihua; Xia, Jianyang

doi:10.1016/j.geoderma.2020.114357

Cited by 55 publications

(31 citation statements)

References 57 publications

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“…1). This finding is in agreement with previous studies addressing vertical pattern of soil C:N:P stoichiometry in forest soils (Feng, Bao & Pang, 2017;Li et al, 2013;Qiao et al, 2020;Tian et al, 2010;Tischer, Potthast & Hamer, 2014). These results are maybe related to the fact that soil nutrients decreased with soil depth.…”

Section: Vertical Pattern Of Soil C:n:p Ecological Stoichiometrysupporting

confidence: 93%

Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations

Pang

Yang

et al. 2020

PeerJ

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Understanding ecological stoichiometric characteristics of soil nutrient elements, such as carbon (C), nitrogen (N) and phosphorus (P) is crucial to guide ecological restoration of plantations in ecologically vulnerable areas, such as alpine and subalpine regions. However, there has been only a few related studies, and thus whether and how different tree species would affect soil C:N:P ecological stoichiometry remains unclear. We compared soil C:N:P ecological stoichiometry of Pinus tabulaeformis, Larix kaempferi and Cercidiphyllum japonicum to primary shrubland in a subalpine region. We observed strong tree-specific and depth-dependent effects on soil C:N:P stoichiometry in subalpine plantations. In general, the C:N, C:P and N:P of topsoil (0–10 cm) are higher than subsoil (>10 cm) layer at 0–30 cm depth profiles. The differences in C:N, N:P and C:P at the topsoil across target tree species were significantly linked to standing litter stock, tree biomass/total aboveground biomass and Margalef’s index of plant community, respectively, whereas the observed variations of C:N, N:P and C:P ratio among soil profiles are closely related to differences in soil bulk density, soil moisture, the quantity and quality of aboveground litter inputs as well as underground fine root across plantations examined. Our results highlight that soil nutrients in plantation depend on litter quantity and quality of selected tree species as well as soil physical attributes. Therefore, matching site with trees is crucial to enhance ecological functioning in degraded regions resulting from human activity.

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Section: Vertical Pattern Of Soil C:n:p Ecological Stoichiometrysupporting

confidence: 93%

Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations

Pang

Yang

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…1). This finding is in agreement with previous studies addressing vertical pattern of soil C:N:P stoichiometry in forest soils (Feng et al 2017;Li et al 2013;Qiao et al 2020;Tian et al 2010;Tischer et al 2014). These results are maybe related to the fact that soil nutrients decreased with soil depth.…”

Section: Vertical Pattern Of Soil C:n:p Ecological Stoichiometrysupporting

confidence: 93%

Peer Review #1 of "Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations (v0.1)"

2020

View full text Add to dashboard Cite

Understanding ecological stoichiometric characteristics of soil nutrient elements, such as carbon (C), nitrogen (N) and phosphorus (P) is crucial to guide ecological restoration of plantations in ecologically vulnerable areas, such as alpine and subalpine regions. However, there has been only a few related studies, and thus whether and how different tree species would affect soil C:N:P ecological stoichiometry remains unclear. We compared soil C:N:P ecological stoichiometry of Pinus tabulaeformis, Larix kaempferi and Cercidiphyllum japonicum to primary shrubland in a subalpine region. We observed strong tree-specific and depth-dependent effects on soil C:N:P stoichiometry in subalpine plantations. In general, the C:N, C:P and N:P of topsoil (0-10 cm) are higher than subsoil (>10 cm) layer at 0-30 cm depth profiles. The differences in C:N, N:P and C:P at the topsoil across target tree species were significantly linked to standing litter stock, tree biomass/total aboveground biomass and Margalef's index of plant community, respectively, whereas the observed variations of C:N, N:P and C:P ratio among soil profiles are closely related to differences in soil bulk density, soil moisture, the quantity and quality of aboveground litter inputs as well as underground fine root across plantations examined. Our results highlight that soil nutrients in plantation depend on litter quantity and quality of selected tree species as well as soil physical attributes. Therefore, matching site with trees is crucial to enhance ecological functioning in degraded regions resulting from human activity.

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“…Another example is a long-term field warming experiment in the tallgrass prairie in the U.S. Great Plains, which has discovered numerous novel phenomena of carbon cycling in response to climate warming based on measurements of microbial functional genes (Xue et al, 2016), soil respiration (Luo, Wan, Hui, & Wallace, 2001), plant phenology (Sherry et al, 2007) and species composition (Shi et al, 2016), and ecosystem CO 2 exchanges (Niu, Sherry, Zhou, & Luo, 2013). In each of the seventy forest sites in the Forest Global Earth Observatory (Wang et al, 2019) and soil phosphorus (Qiao et al, 2020), while soil acidity is a key factor regulating soil carbon and nitrogen stocks (Qiao et al, 2020). In aquatic ecosystems, a large-scale comparative study of 69 boreal lakes has shown that surface water CO 2 concentration enhances phytoplankton production but not community composition (Vogt, St-Gelais, Bogard, Beisner, & del Giorgio, 2017).…”

Section: Changes In Community Structures and Ecosystem Functionsmentioning

confidence: 99%

Research challenges and opportunities for using big data in global change biology

Xia

Wang

Niu

2020

Global Change Biology

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Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new datadriven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes. K E Y W O R D S big data, Earth system model, global change biology, machine learning, model uncertainty | 6041 XIA et Al.

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Depth-dependent soil C-N-P stoichiometry in a mature subtropical broadleaf forest

Cited by 55 publications

References 57 publications

Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations

Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations

Peer Review #1 of "Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations (v0.1)"

Research challenges and opportunities for using big data in global change biology

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