the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Mar 2021
08 Mar 2021
Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China
- 1Institute of Ecology, College of Urban and Environmental Sciences and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871
- 2State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093
- 3Institute of Ecology, Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
- Equal contribution
- 1Institute of Ecology, College of Urban and Environmental Sciences and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871
- 2State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093
- 3Institute of Ecology, Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
- Equal contribution
Abstract. Recent increases in atmospheric carbon dioxide (CO2) and temperature relieve the limitation of these two on terrestrial ecosystem productivity, while nutrient availability constrains the increasing plant photosynthesis more intensively. Nitrogen (N) and phosphorus (P) are critical for plant physiological activities and consequently regulates ecosystem productivity. Here, for the first time, we mapped N and P densities of leaves, woody stems, roots, litter and soil in forest, shrubland and grassland ecosystems across China, based on an intensive investigation in 4175 sites, covering species composition, biomass, and nutrient concentrations of different tissues of living plants, litter and soil. Forest, shrubland and grassland ecosystems in China stored 7665.62 × 106 Mg N, with 7434.53 × 106 Mg (96.99 %) fixed in soil (to a depth of one metre), and 32.39 × 106 Mg (0.42 %), 59.57 × 106 Mg (0.78 %), 124.21 × 106 Mg (1.62 %) and 14.92 × 106 Mg (0.19 %) in leaves, stems, roots and litter, respectively. The forest, shrubland and grassland ecosystems in China stored 3852.66 × 106 Mg P, with 3821.64 × 106 Mg (99.19 %) fixed in soil (to a depth of one metre), and 3.36 × 106 Mg (0.09 %), 14.06 × 106 Mg (0.36 %), 11.47 × 106 Mg (0.30 %) and 2.14 × 106 Mg (0.06 %) in leaves, stems, roots and litter, respectively. Our estimation showed that N pools were low in northern China except Changbai Mountains, Mount Tianshan and Mount Alta, while relatively higher values existed in eastern Qinghai-Tibetan Plateau and Yunnan. P densities in plant organs were higher towards the south and east part of China, while soil P density was higher towards the north and west part of China. The estimated N and P density datasets, Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China
(the pre-publication sharing link: https://datadryad.org/stash/share/78EBjhBqNoam2jOSoO1AXvbZtgIpCTi9eT-eGE7wyOk, are available from the Dryad Digital Repository (Zhang et al., 2020). These patterns of N and P densities could potentially improve existing earth system models and large-scale researches on ecosystem nutrients.
Yi-Wei Zhang et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2020-398', Anonymous Referee #1, 27 Mar 2021
General comment
Yi-Wei Zhang et al. presented a data analysis study for terrestrial ecosystem N and P pools over China. The data collection, model fitting, regional and pft level aggregation and analysis are well done. The presentation is smooth. Below are my major suggestions and specific comments.
- Root N, P and Soil N, P model fitting
Root and soil N, P models underperformed (e.g., R2 0.27~0.47), in comparison with models of other plant components (e.g., R2=0.56-0.81). I would suggest 1) trying more complex neural network models (more layers or more nodes within each layer) 2) trying different types of ML models (e.g., random forest, support vector regression) 3) including more explaining variables besides MAT, MAP, elevation, and PFT. For example, N/P deposition, land use history, soil order, GPP and so on.
- representativeness of data for regional extrapolation
It will be helpful to show 1) a map that includes the location of all data samples 2) MAT, MAP, elevation ranges for data samples, compared with those variables but across China. The purposes are to reveal whether the data samples are spatially representative and whether the data reasonably cover the full range of T,P,Elevation so that the spatial extrapolation is reliable (for each vegetation cover).
- N, P mass concentration
This analysis focused on area-based N,P concentrations (gN/m2 of land surface), which do not directly link to ecosystem N/P limitations. And given that the vegetation is not evenly distributed, it will be helpful to also present the mass-based N,P concentrations (e.g., gN/g tissue biomass or soil) that could directly reveal the strength of plant and soil N,P limitation.
- N:P stoichiometry
From an ecosystem N/P limitation perspective, the ratio of N and P within different plant tissues will be more informative than the individual concentrations. I would suggest also showing N:P stoichiometry, e.g., across pfts, leaf vs fine root.
Specific comments:
L54 independently or jointly
L63 allocated to plant
L167 since the model uses re-scaled predictors (eq. 3), it is important the make sure the training data could represent the full climate envelopes over China.
L226 what is site-averaged?
L238 density varied
L294 “soil N and P are stable” is not a convincing reason why soil models underperformed. In contrast, one would expect that stable N P pools shall be better modeled by long-term climatology, compared with e.g, seasonally changed leaf N/P concentrations.
L309 this section needs more quantitative evidence for drivers that are included in this study (e.g., T, P, elevation) and should consider including potential drivers that are discussed if spatial data are available (e.g., soil age, soil order).
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AC1: 'Reply on RC1', Yiwei Zhang, 15 Jun 2021
Dear reviewer,
Thank you very much for your constructive suggestions to our manuscript entitled Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China (essd-2020-398). We have revised the manuscript accordingly. Please find attached our point-to-point responses to each comment.
All the best,
Yi-Wei Zhang
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RC2: 'Comment on essd-2020-398', Enqing Hou, 31 Mar 2021
Zhang et al. mapped distributions of N and P pools in China terrestrial ecosystems, based on the most intensive field measurements in China ever, including all major (semi-)natural ecosystem types and ecosystem components. The study is generally well performed, and the manuscript is well written. I think the paper deserve a publication on Earth System Science Data and would be highly influential one after published. Before its publication, the authors may improve the manuscript by considering my comments and suggestions as follows.
Major comments
- I think the authors should justify their use of artificial neural network for mapping. This method is a complex one but necessarily be the best one. Did the authors test or use other methods such as random forest?
- Ideally, the authors may also show and discuss the relative importance of the predictors in predicting the nutrient densities. This will help readers to build a more mechanistic view of the patterns. Not sure whether neutral network can do this.
- While I agree with the authors’ argument that “the first time, we mapped N and P densities of leaves, woody stems, roots, litter and soil in forest, shrubland and grassland ecosystems across China”, there are some previous estimates of nutrient stocks in China, maybe only for one ecosystem component or one nutrient. I think a comparison of the authors’ estimates with previous estimates, e.g. Tian et al. (2010), would benefits the study. It will make the study well in context of previous studies, and will also show how the estimates are improved compared to previous estimates.
Tian, H., Chen, G., Zhang, C., Melillo, J.M. & Hall, C.A. (2010). Pattern and variation of C: N: P ratios in China’s soils: a synthesis of observational data. Biogeochemistry, 98, 139-151.
Minor comments
L18-19: “the limitation of these two” may be changed to “their limitations”.
L26-31: the numbers are unreadable. Mg is million gram? Given the use of 10^6, you may use bigger units (e.g., Tg).
L49: here you may also cite Sun, Y., Peng, S., Goll, D.S., Ciais, P., Guenet, B., Guimberteau, M. et al. (2017). Diagnosing phosphorus limitations in natural terrestrial ecosystems in carbon cycle models. Earth's Future, 5, 730-749.
L91-92: Not very clear. Du et al. (2020) showed either N or P limitation. If you mean ubiquitous limitation by N and P, you may refer to Elser et al. (2007), LeBauer and Treseder, K.K. (2008), Augusto et al. (2017), and more recently Hou et al. (2020). Similarly, L46-60 may cite more recent papers on the topic to reflect recent progresses in the field.
LeBauer, D.S. & Treseder, K.K. (2008). Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology, 89, 371-379.
Hou, E., Luo, Y., Kuang, Y., Chen, C., Lu, X., Jiang, L. et al. (2020). Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nature Communications, 11, 637.
Augusto, L., Achat, D.L., Jonard, M., Vidal, D. & Ringeval, B. (2017). Soil parent materialâa major driver of plant nutrient limitations in terrestrial ecosystems. Global Change Biology, 23, 3808-3824.
L100: “a high-resolution map” to “high-resolution maps”
L111-112: are all plots the same size for forests, shrublands, and grasslands?
L123-126: you may give references for the methods here.
Equation 1: should the sum symbol with “i = 0” to “n” added? n is the total number of plant species. Similar for Equation 2.
L259, the unit of 5?
L269-281: one digit after decimal is enough and would be easier to read.
L295: I can’t understand the reason. The reason may be expanded to be clear.
L303: “the predicted SDs” is confusing. You may mean “SDs of the predictions”
L313: remove “the”
L330: You may also cite the classic paper on this topic: Walker, T.W. & Syers, J.K. (1976). The fate of phosphorus during pedogenesis. Geoderma, 15, 1-19.
L346: not necessarily more accurate predictions, depends on whether the models are informed by measurements such as those used in this study. “could” may be changed to “may”.
Fig. 3 color legend in panel (a) may include colors only for leaf/stem/root, with colors for vegetation/soil moved to panel (c), because panel (a) and (b) do not have vegetation vs. soil.
Fig. 4: is there a reason for the slopes to be consistently higher than 1.0 across ecosystem components and nutrients? It seems to be a systematic bias in the models: overestimate when observed values are low and underestimate when observed values are high.
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AC2: 'Reply on RC2', Yiwei Zhang, 15 Jun 2021
Dear reviewer,
Thank you very much for your constructive suggestions to our manuscript entitled Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China (essd-2020-398). We have revised the manuscript accordingly. Please find attached our point-to-point responses to each comment.
All the best,
Yi-Wei Zhang
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EC1: 'Final reviewer report for essd-2020-398', David Carlson, 28 Jul 2021
On 14 July 2021 I recieved this comment, posted verbatim, from Dr Qing Zhu:
"The authors have addressed all my comments. They used a better statistical model as suggested, they also show and discuss N:P stoichiometry, mass-based concentrations, which help better understand the data in the context of nutrient limitations. I think the manuscript is appropriate for publication at present form."
- AC3: 'Reply on EC1', Yiwei Zhang, 30 Jul 2021
Yi-Wei Zhang et al.
Data sets
Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China Zhang, Y. W., Guo, Y. P., Tang, Z. Y., Feng, Y. H., Zhu, X. R., Xu, W. T., Bai, Y. F., Zhou, G. Y., Xie, Z. Q., and Fang, J. Y. https://doi.org/10.5061/dryad.6hdr7sqzx
Yi-Wei Zhang et al.
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