the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Leaf carbon and nitrogen stoichiometric variation along environmental gradients
Huiying Xu
Han Wang
I. Colin Prentice
Sandy P. Harrison
Abstract. Leaf stoichiometric traits are central to ecosystem function and biogeochemical cycling, yet no accepted theory predicts their variation along environmental gradients. Using data in the China Plant Trait Database version 2, we aimed to characterize variation in leaf carbon and nitrogen per unit mass (Cmass, Nmass) and their ratio, and to test an eco-evolutionary optimality model for Nmass. Community-mean trait values were related to climate variables by multiple linear regression. Climatic optima and tolerances of major genera were estimated; Pagel’s λ was used to quantify phylogenetic controls, and Bayesian phylogenetic linear mixed models to assess the contributions of climate, species identity and phylogeny. Optimality-based predictions of community-mean Nmass were compared to observed values. All traits showed strong phylogenetic signals. Climate explained only 18 % of C : N ratio variation among species but 45 % among communities, highlighting the role of taxonomic replacement in mediating community-level responses. Geographic distributions of deciduous taxa separated primarily by moisture, evergreens by temperature. Cmass increased with irradiance, but decreased with moisture and temperature. Nmass declined with all three variables. C : N ratio variations were dominated by Nmass. The coefficients relating Nmass to the ratio of maximum carboxylation capacity at 25 °C (Vcmax25) and leaf mass per area (Ma) were influenced by leaf area index. The optimality model captured 68 % and 53 % of variation between communities for Vcmax25 and Ma respectively, and 30 % for Nmass. We conclude that stoichiometric variations along climate gradients are achieved largely by environmental selection among species and clades with different characteristic trait values. Variations in leaf C : N ratio are mainly determined by Nmass, and optimality-based modelling shows useful predictive ability for community-mean Nmass. These findings should help to improve the representation of C : N coupling in ecosystem models.
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Huiying Xu et al.
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RC1: 'Comment on bg-2023-87', Anonymous Referee #1, 03 Jul 2023
In this manuscript, Xu et al demonstrate improving leaf C:N ratio representation in ESM by showing how environmental selection drives community leaf stoichiometry and individual plasticity plays a relatively small role.
The manuscript is very interesting and presents the problem and the authors approach well, but I have a question about the robustness of the analysis for Eco-Evolutionary Optimality as presented in the graphs. It looks to me like the main conclusions are affected by a low number of points with very high leverage. Can the results be presented to account for these outliers by log transforming the data or removing these points? A large part of the paper depends on accepting these analyses as robust. Correcting these may change some of the discussion.
- Figure 4. These relationships are look like they are affected by a minority of points with a high VcMax25/Ma ratio.
- Likewise in figure 5c, are the optimality predictions of Nmass skewed by the relatively low proportion of low Nmass species? It looks like the relationship would be very different without these points. Most of the species are between 2 and 2.25 with visually quite a different relationship.
Additionally, while informative and well written, a little extra detail in some places would no go amiss:
- A simple explanation of what exactly Pagels λ is – what is phylogentetic signal – would be useful to readers with a more biogeochemical background as one would expect from this journal
- Why specifically is the China plant trait database used? What advantage is this giving over other trait databases? Given that the rationale is improving models, would a global database be more suitable? Also, if I understand it correcvtly, the physical sampling methods described L76 – 89 are the direct collation of this database? This could be clearer.
Minor comments
L61 – this sentence is quite unclear, not sure if the reference temp of 25 C refers to Vcmax25 or both this and Ma
L102 – individuals of the same species? Or different species for community averages? If so, how were these determined?
L205 – are these fixed values the same across all LSMs? This is unclear to me from the text and from Figure 6.
Figure comments
Figure 1 – this figure is really hard to read, it needs to be larger or simpler.
Figure 2 – with 11 genera, this could be listed in the caption and reduce reliance on the SI
Figure 3 – caption should indicate what the *** mean
Citation: https://doi.org/10.5194/bg-2023-87-RC1 -
AC1: 'Reply on RC1', Huiying Xu, 12 Sep 2023
Dear reviewer,
We greatly appreciate the comments and constructive suggestions from you on our manuscript. These comments and suggestions have helped to significantly improve our manuscript in both scientific rigor and clarity. We have thoroughly revised our manuscript in order to address the reviewer’ concerns. The point-to-point response can be found in the supplement file.
We hope that you will find the revised manuscript an improvement. Thank you for your thoughtful consideration.
Yours faithfully,
Han Wang, on behalf of all authors
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RC2: 'Comment on bg-2023-87', Anonymous Referee #2, 22 Aug 2023
General comments
This manuscript with reference ID bg-2023-87 presents an optimality-based approach investigating the drivers of leaf trait variation along elevational gradients across China. To this end, the authors make use of data available from the China Plant trait database (version 2) to parameterize and test an eco-evolutionary optimality model for leaf nitrogen per unit mass (Nmass). Results obtained with a Bayesian phylogenetic linear mixed model suggest that variation in leaf stoichiometric traits are mainly controlled by species identity and phylogeny, thus indicating that accounting for community level responses and shifts in species turnover may allow for a more dynamic representation of ecosystem processes in Earth System models. Albeit the fact that this conclusion is not novel, the analysis appears to be sound and the manuscript is concise and very well written. Hence, I conclude that the article should be of great interest to the academic readership of the journal, and subject to minor amendments and modifications (see recommendations in the specific comments provided below), could be considered for publication.
Specific comments
The study by Xu and colleagues presents an interesting analysis investigating the drivers of leaf trait variation across environmental gradients. While the manuscript is generally well written and the findings are presented in a concise and informative way, I would suggest adding some further clarifications with regard to (i) statistical analysis, (ii) intra-specific trait variation, and (iii) parameters obtained from remote sensing. First, there appears to be a potential spatial bias in the analysis (L71-74) investigating trait variation across large spatial scales, such as the large-scale environmental gradients across China. For instance, multiple regression on distance matrices (MRM) could be applied to quantify the relative amount of trait variation in response to space and environmental factors in ecological data (Lichstein, 2006) and to relate phylogenetic or functional beta diversity to spatial and environmental distance (Swenson, 2014). Second, the lack of phenotypic plasticity in leaf stoichiometry (L237-239) and the associated conclusion that leaf stoichiometric traits might be mainly controlled by species identity and phylogeny without proper consideration of intraspecific trait variation (ITV) could be misleading as the mechanisms driving trait variation across environmental gradients have been reported to shift across large spatial gradients (Ackerly & Cornwell, 2007). Whereas, across larger spatial scales abiotic factors, such as temperature and precipitation, represent key determinants of ecosystem processes, at smaller spatial scales other biotic factors, such as competition among coexisting tree species, strongly affect ecosystem structure and functioning via the composition of the local species pool (Hofhansl, 2021). Hence, biotic factors can have equally strong impacts on trait expression as the dominant abiotic driver (Albert, 2010; Jung 2010, Violle 2012). As a result, an increasing number of studies documented the importance of ITV and thus it would be great to see a discussion on the potential of including ITV in optimization-based models, such as the one applied in this study. Third, the lack of a significant relationship of leaf stoichiometry with LAI and soil fertility (L252-253) both obtained from remote sensing estimates, and the controversial finding that nitrogen allocation to metabolic and structural components was related to leaf area index (L324-325), made me wonder if it would actually require data obtained from in-situ measurements (that match the spatial and temporal extend of the trait data) in order to identify these effects. Overall, I would appreciate a more thorough discussion on some of the topics indicated above and would therefore recommend revising the manuscript based on the findings presented in the scientific literature (see additional references to be considered below) and how these results could be used to improve the dynamic representation of plant tissue stoichiometry in Earth System models.
Technical corrections
L316: correct typo: “our EEO-based approach thus suggests …”
L613-615: Please add a description for the (i) labels “Cmass”, “Nmass”, “C:N ratio”; (ii) colour code (red-blue gradient); and (iii) test statistics used in respective panels of Figure 1 A/B/C.
Additional references to be considered
Ackerly, D. D., & Cornwell, W. K. (2007). A trait-based approach to community assembly: Partitioning of species trait values into within- and among-community components. Ecology Letters, 10, 135–145. https://doi.org/10.1111/j.1461-0248.2006.01006.x
Albert, C. H., Thuiller, W., Yoccoz, N. G., Soudant, A., Boucher, F., Saccone, P., & Lavorel, S. (2010). Intraspecific functional variability: Extent, structure and sources of variation. Journal of Ecology, 98, 604–613. https://doi.org/10.1111/j.1365-2745.2010.01651.x
Hofhansl, F, Chacón-Madrigal, E, Brännström, Å, Dieckmann, U, Franklin, O. Mechanisms driving plant functional trait variation in a tropical forest. Ecol Evol. 2021; 11: 3856–3870. https://doi.org/10.1002/ece3.7256
Jung, V., Violle, C., Mondy, C., Hoffmann, L., & Muller, S. (2010). Intraspecific variability and trait-based community assembly. Journal of Ecology, 98, 1134–1140. https://doi.org/10.1111/j.1365-2745.2010.01687.x
Lichstein, J. W. (2006). Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology, 188, 117–131. https://doi.org/10.1007/s11258-006-9126-3
Swenson, N. G. (2014). Functional and phylogenetic ecology in R (Vol. 535). New York: Springer. Available from: http://ndl.ethernet.edu.et/bitstream/123456789/39476/1/Nathan%20G.%20Swenson.pdf
Violle, C., Enquist, B. J., McGill, B. J., Jiang, L., Albert, C. H., Hulshof, C., Jung, V., & Messier, J. (2012). The return of the variance: Intraspecific variability in community ecology. Trends in Ecology & Evolution, 27, 244–252. https://doi.org/10.1016/j.tree.2011.11.014
Citation: https://doi.org/10.5194/bg-2023-87-RC2 -
AC2: 'Reply on RC2', Huiying Xu, 12 Sep 2023
Dear reviewer,
We greatly appreciate the comments and constructive suggestions from you on our manuscript. These comments and suggestions have helped to significantly improve our manuscript in both scientific rigor and clarity. We have thoroughly revised our manuscript in order to address the reviewer’ concerns. The point-to-point response can be found in the supplement file.
We hope that you will find the revised manuscript an improvement. Thank you for your thoughtful consideration.
Yours faithfully,
Han Wang, on behalf of all authors
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AC2: 'Reply on RC2', Huiying Xu, 12 Sep 2023
Huiying Xu et al.
Huiying Xu et al.
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