the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Variation of the interspecific forest mass-density relationship along gradients of leaf area and global radiation
Abstract. Stand mass scales as the -1/3 exponent of plant density for large compilations of plant communities on a continental or global scale, being the slope of the regression line in a log–log plot, where the intercept is a normalization constant reflecting the assumption of a constant rate of energy use by the species and environments involved. Here the normalization constant is replaced by a light absorption function, enabling to investigate how the interspecific mass–density relationship varies along spatial, largely latitudinal gradients of leaf area and the sum of global radiation over the growing season for relatively undisturbed forests. The test of the model for globally distributed forest communities shows the highest explained variance when both gradients are included in the light absorption function, meaning that the exponent is determined not only by the rate but also the sum of energy use over the growing season. The exponent of tree density converges to 1/2 instead of the expected 1/3 value based on the -1/3 exponent value in the bivariate biomass–density relationship. The 1/2 value corresponds with the so-called self-thinning rule that applies to the self-thinning line constructed as the upper boundary of mass–density points for monospecific even-aged plant stands, where gradients in energy use can be neglected. The results demonstrate the appropriateness of introducing a light absorption function in the bivariate mass–density relationship, suggesting a thermodynamic interpretation that may be of interest to other plants and even animals when gradients in energy use similarly affect the intercept and slope of the interspecific mass–density relationship.
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RC1: 'Comment on egusphere-2024-2753', Pablo C. Salazar, 18 Feb 2025
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General comments
The creation of an improved power law equation that incorporates available radiation and leaf metrics is necessary, given the advancement in technology to estimate leaf area index (either through field measurements or remote sensing techniques). This paper provides new insights into how we build mass-density relationships in forest ecology and includes new variables to improve our biomass models. However, the document requires substantial revision before it can be accepted. In particular, more details about data collection methods and the main objectives of the manuscript need to be clearly stated.
The Yoda power law discussion creates confusion, as it seems this can only be applicable in monospecific forest plantations with well-established designs. At some points in the introduction, the manuscript addresses this as a real limitation of the equation (e.g., Lines 47 or 57). However, in other sections, the text seems to dismiss these limitations simply because the logic behind the equation appears intuitive (Line 70). This may create the misconception that these equations could predict forest biomass globally. The manuscript later acknowledges its limitations at the end of the introduction when indicating that only "undisturbed stands" will be included in the model testing. Therefore, it would be wise to clearly distinguish between the actual application of the equation and the broader theoretical implications.
The objective of the manuscript is not clearly stated in either the introduction or the abstract. As I understand it, the objective is to develop a model for the mass-density relationship that uses a light absorption function instead of a normalization constant. This should be explicitly stated in the document for better clarity.
The final five paragraphs of the discussion focus on limitations and scope of the results. However, the potential applications of the results are missing from the last paragraph. It would be better to conclude the discussion with the manuscript's conclusions, though this may be challenging without a clear explanation of the objectives.
Minor Comments:
Line 123: Where did you obtain the leaf area index data? It would be surprising if it came from the Cannell database. This should be detailed in the materials and methods section as it is not a standard variable. In line 203, you mention obtaining it from the Cannell database, but could you elaborate on how this variable was measured? If obtained through remote sensing, this should be explicitly stated, as mixing estimated and field data could be a limitation of your work. After reviewing the supplementary material, the LAI values seem unusually high. Typically, these values range from 4 to 6, meaning the canopy cover is 4-6 times higher than the projected ground area. With your reported LAI values, it's surprising that any light reaches the soil. As someone with field experience measuring LAI, values above 3 indicate dense forest canopies. Your database shows no values under 1, suggesting your equation may only be applicable to very dense forests or forest canopies. Please explain how the LAI measurements were conducted.
Line 49: Should this have an additional reference, or is this information contained in (Weller, 1987a)?
Line 61: The manuscript mentions "leaf area," but it seems to refer to "total leaf area," as individual tree leaf area doesn't vary significantly. This distinction should be clarified in the introduction. While the authors later refer to leaf area index as a measure of total leaf area for their calculations, greater precision in terminology would be helpful.
Line 131: Is the first "and" necessary?
Line 159: Consider mentioning hillshade as a factor influencing seasonal global radiation.
Line 217: Since most of your data comes from broadleaved trees, this should be mentioned as a limitation.
Table 1: Consider adding a column at the beginning with equation names (e.g., "AEBD model" or "LEBD model").
Line 231 & 271: How can you explain the lower scaling exponent due to reduced leaf area if this variable isn't used in the equation? This appears to be an educated guess rather than a direct result from your model. Consider adding "Possibly" at the beginning of the sentence in Line 271.
Line 234: Is it valid to compare scaling exponents when the equations are fundamentally different?
Line 235: Figures should appear in the document in the order they are referenced. Asking readers to check figures 1c, 2c, and 3c makes the flow difficult to follow.
Line 287: A map showing plot locations (with different colors for each forest type) is necessary, especially given that you explain results based on geographic distribution.
Line 349: A "second alternative premise" is mentioned, but this doesn't appear to be an original objective of the paper. Again, the objectives should be clearly stated in the introduction. Furthermore, it's surprising that a proper latitudinal analysis isn't included in the methods or results, yet a new figure appears in the discussion.
Line 357: The location of dataset S1 from Deng et al. (2012) and its relationship to your equation in Table 1 needs clarification. The data, analysis, and reasoning behind Figure 4 are completely missing from the document. It's problematic that the largest figure in the manuscript appears without proper context or explanation.
Citation: https://doi.org/10.5194/egusphere-2024-2753-RC1
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