Implication of community-level ecophysiological parameterization to modelling ecosystem productivity: a case study across nine contrasting forest sites in eastern China

Minzhe Fang1,2(), Changjin Cheng1, Nianpeng He3,4, Guoxin Si3, Osbert Jianxin Sun1()

PDF
Journal of Forestry Research ›› 2023, Vol. 35 ›› Issue (1) : 7. DOI: 10.1007/s11676-023-01650-1

Implication of community-level ecophysiological parameterization to modelling ecosystem productivity: a case study across nine contrasting forest sites in eastern China

  • Minzhe Fang1,2(), Changjin Cheng1, Nianpeng He3,4, Guoxin Si3, Osbert Jianxin Sun1()
Author information +
History +

Abstract

Parameterization is a critical step in modelling ecosystem dynamics. However, assigning parameter values can be a technical challenge for structurally complex natural plant communities; uncertainties in model simulations often arise from inappropriate model parameterization. Here we compared five methods for defining community-level specific leaf area (SLA) and leaf C:N across nine contrasting forest sites along the North–South Transect of Eastern China, including biomass-weighted average for the entire plant community (AP_BW) and four simplified selective sampling (biomass-weighted average over five dominant tree species [5DT_BW], basal area weighted average over five dominant tree species [5DT_AW], biomass-weighted average over all tree species [AT_BW] and basal area weighted average over all tree species [AT_AW]). We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites, with deviations ranging from 28.0 to 73.3%. In addition, there were only slight deviations (< 10%) between the whole plant community sampling (AP_BW) predicted NPP and the four simplified selective sampling methods, and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site. The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling, and will support the choice of parameterization methods.

Keywords

Biome-BGC / Community traits / Forest Ecosystems / Model parameterization

Cite this article

Download citation ▾
Minzhe Fang, Changjin Cheng, Nianpeng He, Guoxin Si, Osbert Jianxin Sun. Implication of community-level ecophysiological parameterization to modelling ecosystem productivity: a case study across nine contrasting forest sites in eastern China. Journal of Forestry Research, 2023, 35(1): 7 https://doi.org/10.1007/s11676-023-01650-1

References

[1]
Beer C, Lucht W, Gerten D, Thonicke K, Schmullius C (2007) Effects of soil freezing and thawing on vegetation carbon density in Siberia: a modeling analysis with the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Global Biogeochem Cycles 21:629–639
[2]
Berzaghi F, Wright IJ, Kramer K, Oddou-Muratorio S, Bohn FJ, Reyer CP, Sabate S, Sanders TG, Hartig F (2020) Towards a new generation of trait-flexible vegetation models. Trends Ecol Evol 35:191–205
[3]
Borgy B, Violle C, Choler P, Garnier E, Kattge J, Loranger J, Amiaud B, Cellier P, Debarros G, Denelle P (2017) Sensitivity of community-level trait–environment relationships to data representativeness: a test for functional biogeography. Glob Ecol Biogeogr 26:729–739
[4]
Fisher RA, Muszala S, Verteinstein M, Lawrence P, Xu C, McDowell NG, Knox RG, Koven C, Holm J, Rogers BM, Spessa A, Lawrence D, Bonan G (2015) Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci Model Dev 8:3593–3619
[5]
Foley JA, Prentice IC, Ramankutty N, Levis S, Pollard D, Sitch S, Haxeltine A (1996) An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob Biogeochem Cycles 10:603–628
[6]
Golinkoff J (2010) Biome BGC version 4.2: theoretical framework of Biome-BGC. Terradynamic Simulation Group. University of Montana, USA. (https://www.ntsg.umt.edu/files/biome-bgc/Golinkoff_BiomeBGCv4.2_TheoreticalBasis_1_18_10.pdf; Last Accessed 1 Jul 2021)
[7]
He NP, Liu CC, Piao SL, Sack L, Xu L, Luo Y, He JS, Han XG, Zhou GS, Zhou XH, Lin Y, Yu Q, Liu SR, Sun W, Niu SL, Li SG, Zhang JH, Yu GR (2019) Ecosystem traits linking functional traits to macroecology. Trends Ecol Evol 34:200–210
[8]
He NP, Yan P, Liu CC, Xu L, Li MX, Meerbeek KV, Zhou GS, Zhou GY, Liu SR, Zhou XH, Li SG, Niu SL, Han XG, Buckley TN, Sack L, Yu GR (2022) Predicting ecosystem productivity based on plant community traits. Trends Plant Sci 28:45–53
[9]
Li XH, Sun JX (2018) Testing parameter sensitivities and uncertainty analysis of Biome-BGC model in simulating carbon and water fluxes in broadleaved-Korean pine forests. Chin J Plant Ecol 42:1131–1144
[10]
Li Y, Liu CC, Zhang JH, Yang H, Xu L, Wang QF, Sack L, Wu XQ, Hou JH, He NP (2018) Variation in leaf chlorophyll concentration from tropical to cold-temperate forests: association with gross primary productivity. Ecol Indic 85:383–389
[11]
Liu QY, Zhang TL, Du MX, Hao HL, Zhang QF, Sun R (2022) A better carbon-water flux simulation in multiple vegetation types by data assimilation. For Ecosyst 9:100013
[12]
Ren HG, Zhang L, Yan M, Tian X, Zheng XB (2022) Sensitivity analysis of Biome-BGCMuSo for gross and net primary productivity of typical forests in China. For Ecosyst 9:100011
[13]
Running SW, Hunt ER (1993) Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer JR, Field CB (eds) Scaling physiological processes. Academic Press, San Diego, pp 141–158
[14]
Running SW, Loveland TR, Pierce LL, Nemani RR, Hunt ER (1995) A remote sensing based vegetation classification logic for global land cover analysis. Remote Sens Environ 51:39–48
[15]
Sacks WJ, Schimel DS, Monson RK, Braswell BH (2006) Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado. Glol Change Biol 12:240–259
[16]
Sakschewski B, von Bloh W, Boit A, Rammig A, Kattge J, Poorter L, Pe?uelas J, Thonicke K (2015) Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model. Glob Change Biol 21(7):2711–2725
[17]
Scheiter S, Langan L, Higgins SI (2013) Next-generation dynamic global vegetation models: learning from community ecology. New Phytol 198:957–969
[18]
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res-Atmos 106:7183–7192
[19]
Thornton PE, Running SW (1999) An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric For Meteorol 93:211–228
[20]
Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen Needleleaf forests. Agric For Meteorol 113:185–222
[21]
Van Bodegom PM, Douma JC, Witte JPM, Ordonez JC, Bartholomeus RP, Aerts R (2012) Going beyond limitations of plant functional types when predicting global ecosystem-atmosphere fluxes: exploring the merits of traits-based approaches. Glob Ecol Biogeogr 21:625–636
[22]
Van Bodegom PM, Douma JC, Verheijen LM (2014) A fully traits-based approach to modeling global vegetation distribution. Proc Natl Aacd Sci USA 111:13733–13738
[23]
Violle C, Navas ML, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E (2007) Let the concept of trait be functional! Oikos 116:882–892
[24]
Wang RL, Yu GR, He NP, Wang QF, Zhao N, Xu ZW (2016) Latitudinal variation of leaf morphological traits from species to communities along a forest transect in eastern China. J Geogr Sci 26:15–26
[25]
Wang H, Prentice IC, Keenan TF, Davis TW, Wright IJ, Cornwell WK, Evans BJ, Peng CH (2017) Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3:734–741
[26]
White MA, Thornton PE, Running SW, Nemani RR (2000) Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interact 4:1–84
[27]
Zaehle S, Sitch S, Smith B, Hatterman F (2005) Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Glob Biogeochem Cycles 19:GB3020
[28]
Zhang JH, He NP, Liu CC, Xu L, Chen Z, Li Y, Wang RM, Yu GR, Sun W, Xiao CW, Chen HYH, Reich PB (2020) Variation and evolution of C: N ratio among different organs enable plants to adapt to N-limited environments. Glob Change Biol 26:2534–2543
PDF

Accesses

Citations

Detail

Sections
Recommended

/