the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Wood microclimate as a predictor of carbon dioxide fluxes from deadwood in tropical Australia
Elizabeth S. Duan
Luciana Chavez Rodriguez
Nicole Hemming-Schroeder
Baptiste Wijas
Habacuc Flores-Moreno
Alexander W. Cheesman
Lucas A. Cernusak
Michael J. Liddell
Paul Eggleton
Amy E. Zanne
Steven D. Allison
Abstract. Deadwood is an important yet understudied carbon pool in tropical ecosystems. Wood microclimate, as defined by wood moisture content and temperature, drives decomposer (microbial, termite) activities and deadwood degradation to CO2. Microclimate is strongly influenced by local climate, and thus, climate data could be used to predict CO2 fluxes from decaying wood. Given the increasing availability of gridded climate data, this link would allow the rapid estimation of deadwood-related CO2 fluxes from tropical ecosystems worldwide. In this study, we adapted a mechanistic fuel moisture model that uses weather variables (e.g. air temperature, precipitation, solar radiation) to characterize wood microclimate along a rainfall gradient in Queensland, Australia. We then developed a Bayesian statistical relationship between microclimate and CO2 flux from pine (Pinus radiata) blocks deployed at sites and combined this relationship with our microclimate simulations to predict CO2 fluxes from deadwood at 1-hour temporal resolution. We compared our pine-based simulations to moisture-CO2 relationships from stems of native tree species deployed at the wettest and driest sites. Finally, we integrated fluxes over time to estimate the amount of carbon entering the atmosphere and compared these estimates to measured mass loss in pines and native stems. Our statistical model showed a positive relationship between CO2 fluxes and wood microclimate variables. Comparing cumulative CO2 with wood mass loss, we observed that carbon from deadwood decomposition is mainly released as CO2 regardless of the precipitation regime. At the dry savanna, only about 19 % of the wood mass loss was released to CO2 within 48 months, compared to 86 % at the wet rainforest, suggesting longer residence times of deadwood compared to wetter sites. However, the amount of carbon released in-situ as CO2 is lower when wood blocks are attacked by termites, especially at drier sites. These results highlight the important but understudied role of termites in the breakdown of deadwood in dry climates. Additionally, mass loss-flux relationships of decaying native stems deviated from that of pine blocks. Our results indicate that wood microclimate variables are important in predicting CO2 fluxes from deadwood degradation, but are not sufficient, as other factors such as wood traits (wood quality, chemical composition, and stoichiometry) and biotic processes should be considered in future modeling efforts.
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Elizabeth S. Duan et al.
Status: open (until 25 Oct 2023)
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RC1: 'Comment on egusphere-2023-1952', Anonymous Referee #1, 29 Sep 2023
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The manuscript is very well written, clearly structured, generally very well illustrated, and covers an highly interesting topic. The presented results are original, novel, and based on an innovative approach. However, before publication can be recommended, the following should be addressed:
Â
Title, abstract, and elsewhere:
The terminology is somewhat unclear. The term ‘microclimate’ usually refers to the conditions (T, RH, prec) in the direct and closest environment of a wooden item. MC and T over time are usually named ‘material climate’, i.e. the conditions inside the material. The hierarchy is global climate – macro climate – meso climate – local climate – micro climate – materials climate. Suggest to adapt the terminology (see also numerous publications on ‘decay modelling of timber structures using this terminology in accordance with ISO standards, e.g. ISO 15686 series).General comment / Introduction:
During the last approx. 20 years. Research developed parallels in wood material science and forest ecology. Decay models were developed for timber structures in use (above ground and in soil contact) as well as for deadwood and debris. The intro would significantly improve if similarities and differences between the two approaches would be highlighted.
E.g. the hypothesis formulated in L 72-74 has been shown to be correct by different studies in the field of wood material science (e.g. lab tests with pine blocks performed at VTT, Finland, and calorimetry measurements at Lund University, Sweden).
L9 and 89: Has it been Radiata pine sapwood? Please clarify.
Figure 2: The colour scale is hard to interpret. Differences in colour between the test sites are hard to distinguish (even for me, and I am not colour blind).
L 94: Unclear what is meant with ‘treatments’. Is it the application of the mesh? Treatment of wood usually refers to a coating or impregnation with repellents and biocides.
L 128: What is meant with ‘intact wood’? Is it non-decayed wood? The wood MC will drastically differ between decayed and non-decayed wood – how has this been considered?
Section 2.2.2:
It stays unclear how the dimension of deadwood components can be considered.L 183 / 192: Unclear what fuel moisture sensor is referred to. Â What kind of sensor? Where installed?
Figure 6: Species codes should be explained in the main text, not only in the supplementary material.
Discussion, L 303 ff:
The discrepancy between measured MC and predicted FMC is especially prominent at high moisture levels. Isn’t it most likely that this is explained by the geometry of the wooden elements. The effect of capillary water uptake must have been a multiple in the small (more or less cuboid wood block) compared to ‘normal’ deadwood forming long cylinders. Should eventually be included in the discussion.
General comment / Discussion:
The link of the presented models/ simulations to the physiological needs of the decay organisms involved is somewhat lacking. Numerous decay models have been developed in Europe and Australia (e.g. at CSIRO) to describe the relationship between wood decay, climate, and a couple of other impact factors. How does all this relate to the findings of the recent study?
Citation: https://doi.org/10.5194/egusphere-2023-1952-RC1
Elizabeth S. Duan et al.
Data sets
WTF-Climate-Flux Elizabeth S. Duan, Luciana Chavez Rodriguez, Nicole Hemming-Schroeder, Baptiste Wijas, Habacuc Flores-Moreno, Alexander W. Cheesman, Lucas A. Cernusak, Michael J. Liddell, Paul Eggleton, Amy E. Zanne, and Steven D. Allison https://github.com/Zanne-Lab/WTF-Climate-Flux
Model code and software
WTF-Climate-Flux Elizabeth S. Duan, Luciana Chavez Rodriguez, Nicole Hemming-Schroeder, Baptiste Wijas, Habacuc Flores-Moreno, Alexander W. Cheesman, Lucas A. Cernusak, Michael J. Liddell, Paul Eggleton, Amy E. Zanne, and Steven D. Allison https://github.com/Zanne-Lab/WTF-Climate-Flux
Elizabeth S. Duan et al.
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