Articles | Volume 21, issue 14
https://doi.org/10.5194/bg-21-3321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-3321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2024
Research article |
| 23 Jul 2024
| 23 Jul 2024
Climate-based prediction of carbon fluxes from deadwood in Australia
Elizabeth S. Duan
Department of Biology, University of Washington, Seattle, Washington, USA
Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, USA
Luciana Chavez Rodriguez
CORRESPONDING AUTHOR
Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, USA
Nicole Hemming-Schroeder
Department of Earth System Science, University of California Irvine, Irvine, California, USA
Baptiste Wijas
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
Department of Biology, University of Miami, Miami, Florida, USA
Habacuc Flores-Moreno
Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
Alexander W. Cheesman
College of Science and Engineering, James Cook University, Cairns, Queensland, Australia
Lucas A. Cernusak
College of Science and Engineering, James Cook University, Cairns, Queensland, Australia
Michael J. Liddell
College of Science and Engineering, James Cook University, Cairns, Queensland, Australia
Centre for Tropical Environmental and Sustainability Science, James Cook University, Cairns, Queensland, Australia
Paul Eggleton
Life Sciences Department, The Natural History Museum, London, UK
Amy E. Zanne
Department of Biology, University of Miami, Miami, Florida, USA
Cary Institute of Ecosystem Studies, Millbrook, New York, USA
Steven D. Allison
Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, USA
Department of Earth System Science, University of California Irvine, Irvine, California, USA
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Cited articles
Alduchov, O. A. and Eskridge, R. E.: Improved Magnus Form Approximation of Saturation Vapor Pressure, J. Appl. Meteorol., 35, 601–609, https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2, 1996. a
A'Bear, A. D., Jones, T. H., Kandeler, E., and Boddy, L.: Interactive effects of temperature and soil moisture on fungal-mediated wood decomposition and extracellular enzyme activity, Soil Biol. Biochem., 70, 151–158, https://doi.org/10.1016/j.soilbio.2013.12.017, 2014. a
Barker, J.: Decomposition of Douglas-fir coarse woody debris in response to differing moisture content and initial heterotrophic colonization, Forest Ecol. Manag., 255, 598–604, https://doi.org/10.1016/j.foreco.2007.09.029, 2008. a
Bond-Lamberty, B., Wang, C., and Gower, S. T.: Annual carbon flux from woody debris for a boreal black spruce fire chronosequence: Carbon flux for Black Spruce woody debris, J. Geophys. Res.-Atmos., 107, WFX 1-1–WFX 1–10, https://doi.org/10.1029/2001JD000839, 2002. a
Brischke, C. and Rapp, A. O.: Dose–response relationships between wood moisture content, wood temperature and fungal decay determined for 23 European field test sites, Wood Sci. Technol., 42, 507–518, https://doi.org/10.1007/s00226-008-0191-8, 2008a. a, b, c, d
Brischke, C. and Rapp, A. O.: Influence of wood moisture content and wood temperature on fungal decay in the field: observations in different micro-climates, Wood Sci. Technol., 42, 663–677, https://doi.org/10.1007/s00226-008-0190-9, 2008b. a, b, c
Brischke, C., Bayerbach, R., and Otto Rapp, A.: Decay-influencing factors: A basis for service life prediction of wood and wood-based products, Wood Material Science & Engineering, 1, 91–107, https://doi.org/10.1080/17480270601019658, 2006. a, b
Bürkner, P.-C.: brms: An R Package for Bayesian Multilevel Models Using Stan, J. Stat. Softw., 80, 1–28, https://doi.org/10.18637/jss.v080.i01, 2017. a
Bürkner, P.-C.: Advanced Bayesian Multilevel Modeling with the R Package brms, R Journal, 10, 395, https://doi.org/10.32614/RJ-2018-017, 2018. a
Bürkner, P.-C.: Bayesian Item Response Modeling in R with brms and Stan, J. Stat. Softw., 100, 1–54, https://doi.org/10.18637/jss.v100.i05, 2021. a
Chambers, J. Q., Higuchi, N., Schimel, J. P., Ferreira, L. V., and Melack, J. M.: Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon, Oecologia, 122, 380–388, https://doi.org/10.1007/s004420050044, 2000. a, b, c
Cheesman, A. W., Cernusak, L. A., and Zanne, A. E.: Relative roles of termites and saprotrophic microbes as drivers of wood decay: A wood block test, Austral. Ecol., 43, 257–267, https://doi.org/10.1111/aec.12561, 2018. a, b, c, d
Clement, R. A., Flores-Moreno, H., Cernusak, L. A., Cheesman, A. W., Yatsko, A. R., Allison, S. D., Eggleton, P., and Zanne, A. E.: Assessing the Australian Termite Diversity Anomaly: How Habitat and Rainfall Affect Termite Assemblages, Front. Ecol. Evol., 9, 657444, https://doi.org/10.3389/fevo.2021.657444, 2021. a, b
Cornwell, W. K., Cornelissen, J. H. C., Allison, S. D., Bauhus, J., Eggleton, P., Preston, C. M., Scarff, F., Weedon, J. T., Wirth, C., and Zanne, A. E.: Plant traits and wood fates across the globe: rotted, burned, or consumed?, Glob. Change Biol., 15, 2431–2449, https://doi.org/10.1111/j.1365-2486.2009.01916.x, 2009. a, b, c, d, e
Dossa, G. G. O., Paudel, E., Wang, H., Cao, K., Schaefer, D., and Harrison, R. D.: Correct calculation of CO2 efflux using a closed-chamber linked to a non-dispersive infrared gas analyzer, Methods Ecol. Evol., 6, 1435–1442, https://doi.org/10.1111/2041-210X.12451, 2015. a
Dossa, G. G. O., Yang, Y.-Q., Hu, W., Paudel, E., Schaefer, D., Yang, Y.-P., Cao, K.-F., Xu, J.-C., Bushley, K. E., and Harrison, R. D.: Fungal succession in decomposing woody debris across a tropical forest disturbance gradient, Soil Biol. Biochem., 155, 108142, https://doi.org/10.1016/j.soilbio.2021.108142, 2021. a
Duan, E. S., Chavez Rodriguez, L., Flores-Moreno, H., Cheesman, A. W., Liddell, M. J., Zanne, A. E., and Allison, S. D.: WTF Climate dataset: 4 years of weather data from tropical Queensland, Australia, Zenodo [data set], https://doi.org/10.5281/ZENODO.7958670, 2023. a, b, c
Duan, E. S., Chavez Rodriguez, L., Hemming-Schroeder, N., Wijas, B., Flores-Moreno, H., Cheesman, A. W., Cernusak, L. A., Liddell, M. J., Eggleton, P., Zanne, A. E., and Allison, S. D.: Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.11623043, 2024. a
FAO: Global Forest Resources Assessment 2020, FAO, https://doi.org/10.4060/ca9825en, 2020. a
Floriancic, M. G., Allen, S. T., Meier, R., Truniger, L., Kirchner, J. W., and Molnar, P.: Potential for significant precipitation cycling by forest-floor litter and deadwood, Ecohydrology, 16, e2493, https://doi.org/10.1002/eco.2493, 2023. a
Forrester, J. A., Mladenoff, D. J., Gower, S. T., and Stoffel, J. L.: Interactions of temperature and moisture with respiration from coarse woody debris in experimental forest canopy gaps, Forest Ecol. Manag., 265, 124–132, https://doi.org/10.1016/j.foreco.2011.10.038, 2012. a, b
Francois, C., Ottle, C., and Prevot, L.: Analytical parameterization of canopy directional emissivity and directional radiance in the thermal infrared. Application on the retrieval of soil and foliage temperatures using two directional measurements, Int. J. Remote Sens., 18, 2587–2621, https://doi.org/10.1080/014311697217495, 1997. a, b, c
Gale, N.: The aftermath of tree death: coarse woody debris and the topography in four tropical rain forests, Can. J. Forest Res., 30, 1489–1493, https://doi.org/10.1139/x00-071, 2000. a, b
González, G., Gould, W. A., Hudak, A. T., and Hollingsworth, T. N.: Decay of Aspen (Populus tremuloides Michx.) Wood in Moist and Dry Boreal, Temperate, and Tropical Forest Fragments, Ambio, 37, 588–597, https://doi.org/10.1579/0044-7447-37.7.588, 2008. a, b
Green, M. B., Fraver, S., Lutz, D. A., Woodall, C. W., D'Amato, A. W., and Evans, D. M.: Does deadwood moisture vary jointly with surface soil water content?, Soil Sci. Soc. Am. J., 86, 1113–1121, https://doi.org/10.1002/saj2.20413, 2022. a, b, c
Griffiths, H. M., Ashton, L. A., Evans, T. A., Parr, C. L., and Eggleton, P.: Termites can decompose more than half of deadwood in tropical rainforest, Curr. Biol., 29, R118–R119, https://doi.org/10.1016/j.cub.2019.01.012, 2019. a
Gómez-Brandón, M., Ascher-Jenull, J., Bardelli, T., Fornasier, F., Fravolini, G., Arfaioli, P., Ceccherini, M. T., Pietramellara, G., Lamorski, K., Sławiński, C., Bertoldi, D., Egli, M., Cherubini, P., and Insam, H.: Physico-chemical and microbiological evidence of exposure effects on Picea abies – Coarse woody debris at different stages of decay, Forest Ecol. Manag., 391, 376–389, https://doi.org/10.1016/j.foreco.2017.02.033, 2017. a
Hagemann, U., Moroni, M. T., Gleißner, J., and Makeschin, F.: Disturbance history influences downed woody debris and soil respiration, Forest Ecol. Manag., 260, 1762–1772, https://doi.org/10.1016/j.foreco.2010.08.018, 2010. a
Hansson, E. F., Brischke, C., Meyer, L., Isaksson, T., Thelandersson, S., and Kavurmaci, D.: Durability of timber outdoor structures – modeling performance and climate impacts, New Zealand Timber Design Society, Auckland, New Zealand, 295–303, 2012. a
Herrmann, S. and Bauhus, J.: Effects of moisture, temperature and decomposition stage on respirational carbon loss from coarse woody debris (CWD) of important European tree species, Scand. J. Forest Res., 28, 346–357, https://doi.org/10.1080/02827581.2012.747622, 2013. a
Honjo, T., Lin, T.-P., and Seo, Y.: Sky view factor measurement by using a spherical camera, J. Agr. Meteorol., 75, 59–66, https://doi.org/10.2480/agrmet.D-18-00027, 2019. a
Hu, Z., Xu, C., McDowell, N. G., Johnson, D. J., Wang, M., Luo, Y., Zhou, X., and Huang, Z.: Linking microbial community composition to C loss rates during wood decomposition, Soil Biol. Biochem., 104, 108–116, https://doi.org/10.1016/j.soilbio.2016.10.017, 2017. a
Hu, Z., Michaletz, S. T., Johnson, D. J., McDowell, N. G., Huang, Z., Zhou, X., and Xu, C.: Traits drive global wood decomposition rates more than climate, Glob. Change Biol., 24, 5259–5269, https://doi.org/10.1111/gcb.14357, 2018. a, b, c
Jamali, H., Livesley, S. J., Hutley, L. B., Fest, B., and Arndt, S. K.: The relationships between termite mound
Jomura, M., Kominami, Y., Dannoura, M., and Kanazawa, Y.: Spatial variation in respiration from coarse woody debris in a temperate secondary broad-leaved forest in Japan, Forest Ecol. Manag., 255, 149–155, https://doi.org/10.1016/j.foreco.2007.09.002, 2008. a
Kahl, T., Baber, K., Otto, P., Wirth, C., and Bauhus, J.: Drivers of CO2 Emission Rates from Dead Wood Logs of 13 Tree Species in the Initial Decomposition Phase, Forests, 6, 2484–2504, https://doi.org/10.3390/f6072484, 2015. a
Kim, S., Han, S. H., Li, G., Roh, Y., Kim, H.-J., and Son, Y.: The initial effects of microclimate and invertebrate exclusion on multi-site variation in the mass loss of temperate pine and oak deadwoods, Sci. Rep.-UK, 11, 14840, https://doi.org/10.1038/s41598-021-94424-w, 2021. a, b
Kipping, L., Maurer, F., Gossner, M. M., Muszynski, S., Kahl, T., Kellner, H., Weiser, W. W., Jehmlich, N., and Noll, M.: Drivers of deadwood decay of 13 temperate tree species are similar between forest and grassland habitats, Frontiers in Forests and Global Change, 5, 1020737, https://doi.org/10.3389/ffgc.2022.1020737, 2022. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Koutsoyiannis, D.: Clausius–Clapeyron equation and saturation vapour pressure: simple theory reconciled with practice, Eur. J. Phys., 33, 295–305, https://doi.org/10.1088/0143-0807/33/2/295, 2012. a
Kumar, P., Chen, H. Y., Thomas, S. C., and Shahi, C.: Effects of coarse woody debris on plant and lichen species composition in boreal forests, J. Veg. Sci., 28, 389–400, https://doi.org/10.1111/jvs.12485, 2017. a
Law, S., Flores-Moreno, H., Cheesman, A. W., Clement, R., Rosenfield, M., Yatsko, A., Cernusak, L. A., Dalling, J. W., Canam, T., Iqsaysa, I. A., Duan, E. S., Allison, S. D., Eggleton, P., and Zanne, A. E.: Wood traits explain microbial but not termite-driven decay in Australian tropical rainforest and savanna, J. Ecol., 111, 982–993, https://doi.org/10.1111/1365-2745.14090, 2023. a, b, c, d, e, f, g, h, i
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Van Den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Liski, J., Palosuo, T., Peltoniemi, M., and Sievänen, R.: Carbon and decomposition model Yasso for forest soils, Ecol. Model., 189, 168–182, https://doi.org/10.1016/j.ecolmodel.2005.03.005, 2005. a
Liu, W. H., Bryant, D. M., Hutyra, L. R., Saleska, S. R., Hammond-Pyle, E., Curran, D., and Wofsy, S. C.: Woody Debris Contribution to the Carbon Budget of Selectively Logged and Maturing Mid-Latitude Forests, Oecologia, 148, 108–117, https://www.jstor.org/stable/20445889 (last access: 19 July 2024), 2006. a
Mackensen, J., Bauhus, J., and Webber, E.: Decomposition rates of coarse woody debris—A review with particular emphasis on Australian tree species, Aust. J. Bot., 51, 27, https://doi.org/10.1071/BT02014, 2003. a
Makowski, D., Ben-Shachar, M., and Lüdecke, D.: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework, Journal of Open Source Software, 4, 1541, https://doi.org/10.21105/joss.01541, 2019a. a
Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D.: Indices of Effect Existence and Significance in the Bayesian Framework, Front. Psychol., 10, 2767, https://doi.org/10.3389/fpsyg.2019.02767, 2019b. a
Matthews, S.: A process-based model of fine fuel moisture, Int. J. Wildland Fire, 15, 155–168, https://doi.org/10.1071/WF05063, 2006. a
Matthews, S.: Dead fuel moisture research: 1991–2012, Int. J. Wildland Fire, 23, 78, https://doi.org/10.1071/WF13005, 2014. a
Mitchard, E. T. A.: The tropical forest carbon cycle and climate change, Nature, 559, 527–534, https://doi.org/10.1038/s41586-018-0300-2, 2018. a
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.: Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria, T. ASABE, 58, 1763–1785, https://doi.org/10.13031/trans.58.10715, 2015. a
Musselman, K. N., Pomeroy, J. W., and Link, T. E.: Variability in shortwave irradiance caused by forest gaps: Measurements, modelling, and implications for snow energetics, Agr. Forest Meteorol., 207, 69–82, 2015. a
Nelson, R. M.: Prediction of diurnal change in 10-h fuel stick moisture content, Can. J. Forest Res., 30, 1071–1087, https://doi.org/10.1139/x00-032, 2000. a, b
Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., Huynh, P., Braithwaite, D., Updegraff, G., Hsu, K., Kuligowski, B., Logan, W. S., and Sorooshian, S.: The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data, Scientific Data, 6, 180296, https://doi.org/10.1038/sdata.2018.296, 2019. a
Olou, B. A., Yorou, N. S., Striegel, M., Bässler, C., and Krah, F.-S.: Effects of macroclimate and resource on the diversity of tropical wood-inhabiting fungi, Forest Ecol. Manag., 436, 79–87, https://doi.org/10.1016/j.foreco.2019.01.016, 2019. a
Pfeifer, M., Lefebvre, V., Turner, E., Cusack, J., Khoo, M., Chey, V. K., Peni, M., and Ewers, R. M.: Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, Environ. Res. Lett., 10, 044019, https://doi.org/10.1088/1748-9326/10/4/044019, 2015. a, b, c
Piaszczyk, W., Lasota, J., Błońska, E., and Foremnik, K.: How habitat moisture condition affects the decomposition of fine woody debris from different species, CATENA, 208, 105765, https://doi.org/10.1016/j.catena.2021.105765, 2022. a
Pichler, V., Homolák, M., Skierucha, W., Pichlerová, M., Ramírez, D., Gregor, J., and Jaloviar, P.: Variability of moisture in coarse woody debris from several ecologically important tree species of the Temperate Zone of Europe, Ecohydrology, 5, 424–434, https://doi.org/10.1002/eco.235, 2012. a, b
Pomeroy, J. W., Marks, D., Link, T., Ellis, C., Hardy, J., Rowlands, A., and Granger, R.: The impact of coniferous forest temperature on incoming longwave radiation to melting snow, Hydrol. Process., 23, 2513–2525, https://doi.org/10.1002/hyp.7325, 2009. a
Pouska, V., Macek, P., Zíbarová, L., and Ostrow, H.: How does the richness of wood-decaying fungi relate to wood microclimate?, Fungal Ecol., 27, 178–181, https://doi.org/10.1016/j.funeco.2016.06.006, 2017. a
Prata, A. J.: A new long-wave formula for estimating downward clear-sky radiation at the surface, Q. J. Roy. Meteor. Soc., 122, 1127–1151, https://doi.org/10.1002/qj.49712253306, 1996. a, b
Progar, R. A., Schowalter, T. D., Freitag, C. M., and Morrell, J. J.: Respiration from coarse woody debris as affected by moisture and saprotroph functional diversity in Western Oregon, Oecologia, 124, 426–431, https://doi.org/10.1007/PL00008868, 2000. a, b, c
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 19 July 2024), 2021. a
Raich, J. W., Russell, A. E., Kitayama, K., Parton, W. J., and Vitousek, P. M.: Temperature influences carbon accumulation in moist tropical forests, Ecology, 87, 76–87, https://doi.org/10.1890/05-0023, 2006. a
Read, Z., Fraver, S., Forrester, J. A., Wason, J., and Woodall, C. W.: Temporal trends in CO2 emissions from Picea rubens stumps: A chronosequence approach, Forest Ecol. Manag., 524, 120528, https://doi.org/10.1016/j.foreco.2022.120528, 2022. a
Risch, A. C., Page-Dumroese, D. S., Schweiger, A. K., Beattie, J. R., Curran, M. P., Finér, L., Hyslop, M. D., Liu, Y., Schütz, M., Terry, T. A., Wang, W., and Jurgensen, M. F.: Controls of Initial Wood Decomposition on and in Forest Soils Using Standard Material, Frontiers in Forests and Global Change, 5, 829810, https://doi.org/10.3389/ffgc.2022.829810, 2022. a, b
Rowland, L., Stahl, C., Bonal, D., Siebicke, L., Williams, M., and Meir, P.: The Response of Tropical Rainforest Dead Wood Respiration to Seasonal Drought, Ecosystems, 16, 1294–1309, https://doi.org/10.1007/s10021-013-9684-x, 2013. a, b
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007. a
Seibold, S., Rammer, W., Hothorn, T., Seidl, R., Ulyshen, M. D., Lorz, J., Cadotte, M. W., Lindenmayer, D. B., Adhikari, Y. P., Aragón, R., Bae, S., Baldrian, P., Barimani Varandi, H., Barlow, J., Bässler, C., Beauchêne, J., Berenguer, E., Bergamin, R. S., Birkemoe, T., Boros, G., Brandl, R., Brustel, H., Burton, P. J., Cakpo-Tossou, Y. T., Castro, J., Cateau, E., Cobb, T. P., Farwig, N., Fernández, R. D., Firn, J., Gan, K. S., González, G., Gossner, M. M., Habel, J. C., Hébert, C., Heibl, C., Heikkala, O., Hemp, A., Hemp, C., Hjältén, J., Hotes, S., Kouki, J., Lachat, T., Liu, J., Liu, Y., Luo, Y.-H., Macandog, D. M., Martina, P. E., Mukul, S. A., Nachin, B., Nisbet, K., O'Halloran, J., Oxbrough, A., Pandey, J. N., Pavlíček, T., Pawson, S. M., Rakotondranary, J. S., Ramanamanjato, J.-B., Rossi, L., Schmidl, J., Schulze, M., Seaton, S., Stone, M. J., Stork, N. E., Suran, B., Sverdrup-Thygeson, A., Thorn, S., Thyagarajan, G., Wardlaw, T. J., Weisser, W. W., Yoon, S., Zhang, N., and Müller, J.: The contribution of insects to global forest deadwood decomposition, Nature, 597, 77–81, https://doi.org/10.1038/s41586-021-03740-8, 2021. a
Shorohova, E. and Kapitsa, E.: The decomposition rate of non-stem components of coarse woody debris (CWD) in European boreal forests mainly depends on site moisture and tree species, Eur. J. For. Res., 135, 593–606, https://doi.org/10.1007/s10342-016-0957-8, 2016. a
Signorell, A., Aho, K., Alfons, A., Anderegg, N., Aragon, T., Arachchige, C., Arppe, A., Baddeley, A., Barton, K., Bolker, B., Borchers, H. W., Caeiro, F., Champely, S., Chessel, D., Chhay, L., Cooper, N., Cummins, C., Dewey, M., Doran, H. C., Dray, S., Dupont, C., Eddelbuettel, D., Ekstrom, C., Elff, M., Enos, J., Farebrother, R. W., Fox, J., Francois, R., Friendly, M., Galili, T., Gamer, M., Gastwirth, J. L., Gegzna, V., Gel, Y. R., Graber, S., Gross, J., Grothendieck, G., Jr, F. E. H., Heiberger, R., Hoehle, M., Hoffmann, C. W., Hojsgaard, S., Hothorn, T., Huerzeler, M., Hui, W. W., Hurd, P., Hyndman, R. J., Jackson, C., Kohl, M., Korpela, M., Kuhn, M., Labes, D., Leisch, F., Lemon, J., Li, D., Maechler, M., Magnusson, A., Mainwaring, B., Malter, D., Marsaglia, G., Marsaglia, J., Matei, A., Meyer, D., Miao, W., Millo, G., Min, Y., Mitchell, D., Mueller, F., Naepflin, M., Navarro, D., Nilsson, H., Nordhausen, K., Ogle, D., Ooi, H., Parsons, N., Pavoine, S., Plate, T., Prendergast, L., Rapold, R., Revelle, W., Rinker, T., Ripley, B. D., Rodriguez, C., Russell, N., Sabbe, N., Scherer, R., Seshan, V. E., Smithson, M., Snow, G., Soetaert, K., Stahel, W. A., Stephenson, A., Stevenson, M., Stubner, R., Templ, M., Lang, D. T., Therneau, T., Tille, Y., Torgo, L., Trapletti, A., Ulrich, J., Ushey, K., VanDerWal, J., Venables, B., Verzani, J., Iglesias, P. J. V., Warnes, G. R., Wellek, S., Wickham, H., Wilcox, R. R., Wolf, P., Wollschlaeger, D., Wood, J., Wu, Y., Yee, T., and Zeileis, A.: DescTools: Tools for Descriptive Statistics, R package version 0.99.23, https://cran.r-project.org/web/packages/DescTools/index.html (last access: 24 April 2024), 2023. a
Stackhouse, P.: NASA POWER | Prediction Of Worldwide Energy Resources, NASA Langley Res. Ctr., Hampton, VA, https://power.larc.nasa.gov/ (last access: 3 April 2023), 2006. a
Taylor, P. G., Cleveland, C. C., Wieder, W. R., Sullivan, B. W., Doughty, C. E., Dobrowski, S. Z., and Townsend, A. R.: Temperature and rainfall interact to control carbon cycling in tropical forests, Ecol. Lett., 20, 779–788, https://doi.org/10.1111/ele.12765, 2017. a
Thybring, E., Kymäläinen, M., and Rautkari, L.: Moisture in modified wood and its relevance for fungal decay, iForest, 11, 418–422, https://doi.org/10.3832/ifor2406-011, 2018. a
Thybring, E. E., Fredriksson, M., Zelinka, S. L., and Glass, S. V.: Water in Wood: A Review of Current Understanding and Knowledge Gaps, Forests, 13, 2051, https://doi.org/10.3390/f13122051, 2022. a, b
Ulyshen, M. D.: Wood decomposition as influenced by invertebrates, Biol. Rev. Camb. Philos., 91, 70–85, https://doi.org/10.1111/brv.12158, 2016. a, b
van Niekerk, P. B., Brischke, C., and Niklewski, J.: Estimating the Service Life of Timber Structures Concerning Risk and Influence of Fungal Decay – A Review of Existing Theory and Modelling Approaches, Forests, 12, 588, https://doi.org/10.3390/f12050588, 2021. a, b, c
Viitanen, H. A.: Modelling the Time Factor in the Development of Brown Rot Decay in Pine and Spruce Sapwood – The Effect of Critical Humidity and Temperature Conditions, Holzforschung, 51, 99–106, https://doi.org/10.1515/hfsg.1997.51.2.99, 1997. a, b, c
Wang, C., Bond-Lamberty, B., and Gower, S. T.: Environmental controls on carbon dioxide flux from black spruce coarse woody debris, Oecologia, 132, 374–381, https://doi.org/10.1007/s00442-002-0987-4, 2002. a
Wang, H., Wu, C., Liu, J., Chen, Q., Li, C., Shu, C., Zhang, Y., and Liu, Y.: Changes in soil microbial communities induced by warming and N deposition accelerate the CO2 emissions of coarse woody debris, J. Forestry Res., 34, 1051–1063, https://doi.org/10.1007/s11676-022-01544-8, 2023. a, b
Weedon, J. T., Cornwell, W. K., Cornelissen, J. H., Zanne, A. E., Wirth, C., and Coomes, D. A.: Global meta-analysis of wood decomposition rates: a role for trait variation among tree species?, Ecol. Lett., 12, 45–56, https://doi.org/10.1111/j.1461-0248.2008.01259.x, 2009. a
Woldendorp, G. and Keenan, R. J.: Coarse woody debris in Australian forest ecosystems: A review, Austral Ecol., 30, 834–843, https://doi.org/10.1111/j.1442-9993.2005.01526.x, 2005. a, b
Woodall, C., Evans, D., Fraver, S., Green, M., Lutz, D., and D'Amato, A.: Real-time monitoring of deadwood moisture in forests: lessons learned from an intensive case study, Can. J. Forest Res., 50, 1244–1252, https://doi.org/10.1139/cjfr-2020-0110, 2020. a
Wu, C., Zhang, Z., Shu, C., Mo, Q., Wang, H., Kong, F., Zhang, Y., Geoff Wang, G., and Liu, Y.: The response of coarse woody debris decomposition and microbial community to nutrient additions in a subtropical forest, Forest Ecol. Manag., 460, 117799, https://doi.org/10.1016/j.foreco.2019.117799, 2020. a
Wu, D., Pietsch, K. A., Staab, M., and Yu, M.: Wood species identity alters dominant factors driving fine wood decomposition along a tree diversity gradient in subtropical plantation forests, Biotropica, 53, 643–657, https://doi.org/10.1111/btp.12906, 2021. a
Yan, E., Wang, X., and Huang, J.: Concept and Classification of Coarse Woody Debris in Forest Ecosystems, Frontiers of Biology in China, 1, 76–84, https://doi.org/10.1007/s11515-005-0019-y, 2006. a
Yoon, T. K., Noh, N. J., Kim, S., Han, S., and Son, Y.: Coarse woody debris respiration of Japanese red pine forests in Korea: controlling factors and contribution to the ecosystem carbon cycle, Ecol. Res., 30, 723–734, https://doi.org/10.1007/s11284-015-1275-1, 2015. a, b, c
Zanne, A. E., Flores-Moreno, H., Powell, J. R., Cornwell, W. K., Dalling, J. W., Austin, A. T., Classen, A. T., Eggleton, P., Okada, K.-i., Parr, C. L., Adair, E. C., Adu-Bredu, S., Alam, M. A., Alvarez-Garzón, C., Apgaua, D., Aragón, R., Ardon, M., Arndt, S. K., Ashton, L. A., Barber, N. A., Beauchêne, J., Berg, M. P., Beringer, J., Boer, M. M., Bonet, J. A., Bunney, K., Burkhardt, T. J., Carvalho, D., Castillo-Figueroa, D., Cernusak, L. A., Cheesman, A. W., Cirne-Silva, T. M., Cleverly, J. R., Cornelissen, J. H. C., Curran, T. J., D'Angioli, A. M., Dallstream, C., Eisenhauer, N., Evouna Ondo, F., Fajardo, A., Fernandez, R. D., Ferrer, A., Fontes, M. A. L., Galatowitsch, M. L., González, G., Gottschall, F., Grace, P. R., Granda, E., Griffiths, H. M., Guerra Lara, M., Hasegawa, M., Hefting, M. M., Hinko-Najera, N., Hutley, L. B., Jones, J., Kahl, A., Karan, M., Keuskamp, J. A., Lardner, T., Liddell, M., Macfarlane, C., Macinnis-Ng, C., Mariano, R. F., Méndez, M. S., Meyer, W. S., Mori, A. S., Moura, A. S., Northwood, M., Ogaya, R., Oliveira, R. S., Orgiazzi, A., Pardo, J., Peguero, G., Penuelas, J., Perez, L. I., Posada, J. M., Prada, C. M., Přívětivý, T., Prober, S. M., Prunier, J., Quansah, G. W., Resco de Dios, V., Richter, R., Robertson, M. P., Rocha, L. F., Rúa, M. A., Sarmiento, C., Silberstein, R. P., Silva, M. C., Siqueira, F. F., Stillwagon, M. G., Stol, J., Taylor, M. K., Teste, F. P., Tng, D. Y. P., Tucker, D., Türke, M., Ulyshen, M. D., Valverde-Barrantes, O. J., van den Berg, E., van Logtestijn, R. S. P., Veen, G. F. C., Vogel, J. G., Wardlaw, T. J., Wiehl, G., Wirth, C., Woods, M. J., and Zalamea, P.-C.: Termite sensitivity to temperature affects global wood decay rates, Science, 377, 1440–1444, https://doi.org/10.1126/science.abo3856, 2022. a, b, c, d
Zhou, L., Dai, L.-m., Gu, H.-y., and Zhong, L.: Review on the decomposition and influence factors of coarse woody debris in forest ecosystem, J. Forestry Res., 18, 48–54, https://doi.org/10.1007/s11676-007-0009-9, 2007. a, b, c
Short summary
Understanding the link between climate and carbon fluxes is crucial for predicting how climate change will impact carbon sinks. We estimated carbon dioxide (CO2) fluxes from deadwood in tropical Australia using wood moisture content and temperature. Our model predicted that the majority of deadwood carbon is released as CO2, except when termite activity is detected. Future models should also incorporate wood traits, like species and chemical composition, to better predict fluxes.
Understanding the link between climate and carbon fluxes is crucial for predicting how climate...
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