Factors affecting the productivity of sapling and adult trees in the warm temperate deciduous broad-leaved forest of Donglingshan, Beijing

doi:10.17520/biods.2024284

Abstract

Abstract:

Aim: Research on forest productivity is of great significance for improving forest ecosystem function. However, most studies focus on the productivity of total trees in forests, with relatively little research on the productivity of sapling trees and adult trees, and rarely discuss the effects of species diversity and community structure on the productivity of sapling trees and adult trees.

Methods: This study is based on tree inventory data collected from 20 ha Donglingshan warm temperate deciduous broad-leaf forest plot. Species richness, coefficient of variation in diameter at breast height (DBHcv), and soil nutrients were calculated. We utilized regression models and structural equation models to examine the effects of these factors on productivity of total trees, sapling trees, and adult trees.

Results: For productivity of total trees, initial biomass of total trees, total tree species richness, and soil nutrients had significant positive effects, while DBHcv had a significant negative effect. For productivity of sapling trees, initial biomass of sapling trees, sapling tree species richness, and DBHcv had significant positive effects, while total tree species richness and soil nutrients had no significant impact. As soil nutrients increased, sapling tree species richness had a significant positive effect on productivity. For productivity of adult trees, initial biomass of adult trees, total tree species richness, and soil nutrients had significant positive effects, while DBHcv had a significant negative effect and adult tree species richness had no significant effect. As soil nutrients increased, adult tree species richness had a significant negative effect on productivity. The effects of species diversity, community structure and soil nutrients on productivity of adult trees were similar to those on productivity of total trees, but different in sapling trees.

Conclusion: Our study demonstrates that the effects of species diversity, community structure, and soil nutrients on productivity of trees vary across different life history stages. Both the niche complementarity effect hypothesis and the vegetation quantity hypothesis can well explain the increase of forest productivity. This study provides a theoretical basis for the management practices of trees in different life history stages and will have important implications for forest tending in warm temperate secondary forests.

Key words: species diversity, community structure, soil nutrients, productivity, life history stages

Qingqing Du, Siyuan Ren, Nicole Tsz Shun Yuan, Yan Zhu. Factors affecting the productivity of sapling and adult trees in the warm temperate deciduous broad-leaved forest of Donglingshan, Beijing[J]. Biodiv Sci, 2024, 32(12): 24284.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2024284

https://www.biodiversity-science.net/EN/Y2024/V32/I12/24284

Figures/Tables 4

Table 1 Basic tree information of total trees, sapling trees, and adult trees in the Donglingshan plot

	生活型 Life form	胸径 DBH (cm)	物种数量 Species richness	个体数量 Number of individuals
幼树 Sapling trees	乔木 Canopy tree	1.0-10.0	19	22,173
	小乔木 Understory tree	1.0-5.0	4	315
	灌木 Shrub	1.0-2.0	23	4,632
成树 Adult trees	乔木 Canopy tree	≥ 10.0	19	8,852
	小乔木 Understory tree	≥ 5.0	6	142
	灌木 Shrub	≥ 2.0	25	11,526
总体树木 Total trees	乔木 Canopy tree	/	19	31,025
	小乔木 Understory tree	/	6	457
	灌木 Shrub	/	28	16,158

Fig. 1 Bivariate relationships between species richness of total trees, coefficient of variation of the DBH, initial biomass, and soil nutrients with productivity. All data are standardized before analysis, subtracting the mean and dividing by the standard deviation. Soil nutrients are represented by the PC1 axis of the principal component analysis of five soil physicochemical indicators (SOM, TN, TP, TK, SW). The solid lines represent a significant relationship, the dashed lines represent a non-significant relationship, and the gray area represents the 95% confidence interval of the model. Slope represents the estimated coefficient of bivariate regression; P represents significance of the whole regression model.

Fig. 2 The effects of tree species richness, coefficient of variation of the DBH, initial biomass, and soil nutrients on the productivity of total trees, sapling trees, and adult trees. AGBi_tot, Initial biomass of total trees; AGBi_sap, Initial biomass of sapling trees; AGBi_adu, Initial biomass of adult trees; SR_tot, Species richness of total trees; SR_sap, Species richness of sapling trees; SR_adu, Species richness of adult trees; DBHcv, Coefficient of variation of DBH; PC1, PC1 axis of soil nutrient principal component analysis. The circles and short lines represent the estimated values of standardized regression coefficients and the 95% confidence interval, respectively. A solid circle means the effect is significant, while a hollow circle is not. “:” indicates the interaction of two variables.

Fig. 3 Bayesian structural equation models about the impact of direct and indirect drivers on productivity of total trees, sapling trees, and adult trees. PC1, PC1 axis of soil nutrient principal component analysis; SR_tot, Species richness of total trees; SR_sap, Species richness of sapling trees; SR_adu, Species richness of adult trees; DBHcv, Coefficient of variation of DBH; AGBi_tot, Initial biomass of total trees; AGBi_sap, Initial biomass of sapling trees; AGBi_adu, Initial biomass of adult trees; P_tot: Productivity of total trees; P_sap: Productivity of sapling trees; P_adu: Productivity of adult trees. Solid red and blue lines represent significant positive and negative paths, respectively, while gray dashed lines represent non-significant paths. Black dots represent interaction effects. Numerical values adjacent to arrow lines represent standardized path coefficients, with arrow width proportional to the coefficient, and response variables’ R2 are provided. Arrows and black dots pointing towards other arrows represent interactive effects, demonstrating one variable mediating the relationship between two other variables. * P < 0.05; ** P < 0.01; *** P < 0.001. LOOIC (leave-one-out information criterion) assess predictive performance of the models.

References 51

[1]	Ali A (2019) Forest stand structure and functioning: Current knowledge and future challenges. Ecological Indicators, 98, 665-677.
[2]	Ali A, Lin SL, He JK, Kong FM, Yu JH, Jiang HS (2019) Big-sized trees overrule remaining trees’ attributes and species richness as determinants of aboveground biomass in tropical forests. Global Change Biology, 25, 2810-2824.
[3]	Bertness MD, Callaway R (1994) Positive interactions in communities. Trends in Ecology & Evolution, 9, 191-193.
[4]	Bourdier T, Cordonnier T, Kunstler G, Piedallu C, Lagarrigues G, Courbaud B (2016) Tree size inequality reduces forest productivity: An analysis combining inventory data for ten European species and a light competition model. PLoS ONE, 11, e0151852.
[5]	Brenes-Arguedas T, Roddy AB, Coley PD, Kursar TA (2011) Do differences in understory light contribute to species distributions along a tropical rainfall gradient? Oecologia, 166, 443-456. DOI PMID
[6]	Brown AJ, Payne CJ, White PS, Peet RK (2020) Shade tolerance and mycorrhizal type may influence sapling susceptibility to conspecific negative density dependence. Journal of Ecology, 108, 325-336.
[7]	Caspersen JP, Thürig E, Rigling A, Zimmermann NE (2018) Complementarity of gymnosperms and angiosperms along an altitudinal temperature gradient. Oikos, 127, 1787-1799.
[8]	Condit R (1995) Research in large, long-term tropical forest plots. Trends in Ecology & Evolution, 10, 18-22.
[9]	Coomes DA, Lines ER, Allen RB (2011) Moving on from metabolic scaling theory: Hierarchical models of tree growth and asymmetric competition for light. Journal of Ecology, 99, 748-756.
[10]	Dănescu A, Albrecht AT, Bauhus J (2016) Structural diversity promotes productivity of mixed, uneven-aged forests in southwestern Germany. Oecologia, 182, 319-333. DOI PMID
[11]	del Río M, Schütze G, Pretzsch H (2014) Temporal variation of competition and facilitation in mixed species forests in Central Europe. Plant Biology, 16, 166-176. DOI PMID
[12]	DeMalach N, Zaady E, Weiner J, Kadmon R (2016) Size asymmetry of resource competition and the structure of plant communities. Journal of Ecology, 104, 899-910.
[13]	Díaz S, Lavorel S, de Bello F, Quétier F, Grigulis K, Robson TM (2007) Incorporating plant functional diversity effects in ecosystem service assessments. Proceedings of the National Academy of Sciences, USA, 104, 20684-20689.
[14]	Feng YH, Schmid B, Loreau M, Forrester DI, Fei SL, Zhu JX, Tang ZY, Zhu JL, Hong PB, Ji CJ, Shi Y, Su HJ, Xiong XY, Xiao J, Wang SP, Fang JY (2022) Multispecies forest plantations outyield monocultures across a broad range of conditions. Science, 376, 865-868. DOI PMID
[15]	Fichtner A, Härdtle W, Li Y, Bruelheide H, Kunz M,von Oheimb G (2017) From competition to facilitation: How tree species respond to neighbourhood diversity. Ecology Letters, 20, 892-900. DOI PMID
[16]	Gamfeldt L, Snäll T, Bagchi R, Jonsson M, Gustafsson L, Kjellander P, Ruiz-Jaen MC, Fröberg M, Stendahl J, Philipson CD, Mikusiński G, Andersson E, Westerlund B, Andrén H, Moberg F, Moen J, Bengtsson J (2013) Higher levels of multiple ecosystem services are found in forests with more tree species. Nature Communications, 4, 1340. DOI PMID
[17]	Huang YY, Chen YX, Castro-Izaguirre N, Baruffol M, Brezzi M, Lang A, Li Y, Härdtle W, von Oheimb G, Yang XF, Liu XJ, Pei KQ, Both S, Yang B, Eichenberg D, Assmann T, Bauhus J, Behrens T, Buscot F, Chen XY, Chesters D, Ding BY, Durka W, Erfmeier A, Fang JY, Fischer M, Guo LD, Guo DL, Gutknecht JLM, He JS, He CL, Hector A, Hönig L, Hu RY, Klein AM, Kühn P, Liang Y, Li S, Michalski S, Scherer-Lorenzen M, Schmidt K, Scholten T, Schuldt A, Shi XZ, Tan MZ, Tang ZY, Trogisch S, Wang ZW, Welk E, Wirth C, Wubet T, Xiang WH, Yu MJ, Yu XD, Zhang JY, Zhang SR, Zhang NL, Zhou HZ, Zhu CD, Zhu L, Bruelheide H, Ma KP, Niklaus PA, Schmid B (2018) Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science, 362, 80-83. DOI PMID
[18]	LaManna JA, Mangan SA, Alonso A, Bourg NA, Brockelman WY, Bunyavejchewin S, Chang LW, Chiang JM, Chuyong GB, Clay K, Condit R, Cordell S, Davies SJ, Furniss TJ, Giardina CP, Nimal Gunatilleke IU, Savitri Gunatilleke CV, He FL, Howe RW, Hubbell SP, Hsieh CF, Inman-Narahari FM, Janík D, Johnson DJ, Kenfack D, Korte L, Král K, Larson AJ, Lutz JA, McMahon SM, McShea WJ, Memiaghe HR, Nathalang A, Novotny V, Ong PS, Orwig DA, Ostertag R, Parker GG, Phillips RP, Sack L, Sun IF, Sebastián Tello J, Thomas DW, Turner BL, Vela Díaz DM, Vrška T, Weiblen GD, Wolf A, Yap S, Myers JA (2017) Plant diversity increases with the strength of negative density dependence at the global scale. Science, 356, 1389-1392.
[19]	Liang JJ, Buongiorno J, Monserud RA, Kruger EL, Zhou M (2007) Effects of diversity of tree species and size on forest basal area growth, recruitment, and mortality. Forest Ecology and Management, 243, 116-127.
[20]	Liu HF, Li L, Sang WG (2011) Species composition and community structure of the Donglingshan forest dynamic plot in a warm temperate deciduous broad-leaved secondary forest, China. Biodiversity Science, 19, 232-242. (in Chinese with English abstract) DOI
	[刘海丰, 李亮, 桑卫国 (2011) 东灵山暖温带落叶阔叶次生林动态监测样地: 物种组成与群落结构. 生物多样性, 19, 232-242.] DOI
[21]	Liu QJ, Meng SW, Zhou H (2017) Tree Volume Tables of China. China Forestry Publishing House, Beijing. (in Chinese)
	[刘琪璟, 孟胜旺, 周华 (2017) 中国立木材积表. 中国林业出版社, 北京.]
[22]	Lohbeck M, Poorter L, Martínez-Ramos M, Bongers F (2015) Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology, 96, 1242-1252. PMID
[23]	Long JN, Shaw JD (2010) The influence of compositional and structural diversity on forest productivity. Forestry, 289, 121-128.
[24]	Loreau M, Hector A (2001) Partitioning selection and complementarity in biodiversity experiments. Nature, 412, 72-76.
[25]	Luo S, Phillips RP, Jo I, Fei SL, Liang JJ, Schmid B, Eisenhauer N (2023) Higher productivity in forests with mixed mycorrhizal strategies. Nature Communications, 14, 1377. DOI PMID
[26]	McCann KS (2000) The diversity-stability debate. Nature, 405, 228-233.
[27]	Mina M, Huber MO, Forrester DI, Thürig E, Rohner B (2018) Multiple factors modulate tree growth complementarity in Central European mixed forests. Journal of Ecology, 106, 1106-1119.
[28]	Ouyang S, Xiang WH, Wang XP, Xiao WF, Chen L, Li SG, Sun H, Deng XW, Forrester DI, Zeng LX, Lei PF, Lei XD, Gou MM, Peng CH (2019) Effects of stand age, richness and density on productivity in subtropical forests in China. Journal of Ecology, 107, 2266-2277. DOI
[29]	Paquette A, Messier C (2011) The effect of biodiversity on tree productivity: From temperate to boreal forests. Global Ecology and Biogeography, 20, 170-180.
[30]	Pinheiro J, Bates D,R Core Team (2022) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-160. https://CRAN.R-project.org/package=nlme. (acce- ssed on 2024-05-01)
[31]	Prado-Junior JA, Schiavini I, Vale VS, Arantes CS, van der Sande MT, Lohbeck M, Poorter L (2016) Conservative species drive biomass productivity in tropical dry forests. Journal of Ecology, 104, 817-827.
[32]	Quesada CA, Phillips OL, Schwarz M, Czimczik CI, Baker TR, Patiño S, Fyllas NM, Hodnett MG, Herrera R, Almeida S, Alvarez Dávila E, Arneth A, Arroyo L, Chao KJ, Dezzeo N, Erwin T, di Fiore A, Higuchi N, Honorio Coronado E, Jimenez EM, Killeen T, Lezama AT, Lloyd G, López-González G, Luizão FJ, Malhi Y, Monteagudo A, Neill DA, Núñez Vargas P, Paiva R, Peacock J, Peñuela MC, Peña Cruz A, Pitman N, Priante Filho N, Prieto A, Ramírez H, Rudas A, Salomão R, Santos AJB, Schmerler J, Silva N, Silveira M, Vásquez R, Vieira I, Terborgh J, Lloyd J (2012) Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences, 9, 2203-2246.
[33]	R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
[34]	Rosseel Y (2012) lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1-36.
[35]	Sohel MSI (2022) Systematic review and meta-analysis reveals functional traits and climate are good predictors of tropical tree water use. Trees, Forests and People, 8, 100226.
[36]	Sullivan MJP, Talbot J, Lewis SL, Phillips OL, Qie L, Begne SK, Chave J, Cuni-Sanchez A, Hubau W, Lopez-Gonzalez G, Miles L, Monteagudo-Mendoza A, Sonké B, Sunderland T, Ter Steege H, White LJT, Affum-Baffoe K, Aiba SI, de Almeida EC, Almeida de Oliveira E, Alvarez-Loayza P, Dávila EÁ, Andrade A, Aragão LEOC, Ashton P, Baker TR, Balinga M, Banin LF, Baraloto C, Bastin JF, Berry N, Bogaert J, Bonal D, Bongers F, Brienen R, Camargo JLC, Cerón C, Moscoso VC, Chezeaux E, Clark CJ, Pacheco ÁC, Comiskey JA, Valverde FC, Honorio Coronado EN, Dargie G, Davies SJ, De Canniere C, Marie Djuikouo K, Doucet JL, Erwin TL, Espejo JS, Ewango CEN, Fauset S, Feldpausch TR, Herrera R, Gilpin M, Gloor E, Hall JS, Harris DJ, Hart TB, Kartawinata K, Kho LK, Kitayama K, Laurance SGW, Laurance WF, Leal ME, Lovejoy T, Lovett JC, Lukasu FM, Makana JR, Malhi Y, Maracahipes L, Marimon BS, Junior BHM, Marshall AR, Morandi PS, Mukendi JT, Mukinzi J, Nilus R, Vargas PN, Pallqui Camacho NC, Pardo G, Peña-Claros M, Pétronelli P, Pickavance GC, Poulsen AD, Poulsen JR, Primack RB, Priyadi H, Quesada CA, Reitsma J, Réjou-Méchain M, Restrepo Z, Rutishauser E, Abu Salim K, Salomão RP, Samsoedin I, Sheil D, Sierra R, Silveira M, Ferry Slik JW, Steel L, Taedoumg H, Tan S, Terborgh JW, Thomas SC, Toledo M, Umunay PM, Gamarra LV, Vieira ICG, Vos VA, Wang O, Willcock S, Zemagho L (2017) Diversity and carbon storage across the tropical forest biome. Scientific Reports, 7, 39102. DOI PMID
[37]	Taylor AR, Gao BL, Chen HYH (2020) The effect of species diversity on tree growth varies during forest succession in the boreal forest of central Canada. Forest Ecology and Management, 455, 117641.
[38]	Tilman D, Lehman CL, Thomson KT (1997) Plant diversity and ecosystem productivity: Theoretical considerations. Proceedings of the National Academy of Sciences, USA, 94, 1857-1861.
[39]	Toïgo M, Vallet P, Perot T, Bontemps JD, Piedallu C, Courbaud B (2015) Overyielding in mixed forests decreases with site productivity. Journal of Ecology, 103, 502-512.
[40]	Urgoiti J, Messier C, Keeton WS, Reich PB, Gravel D, Paquette A (2022) No complementarity no gain—Net diversity effects on tree productivity occur once complementarity emerges during early stand development. Ecology Letters, 25, 851-862. DOI PMID
[41]	van der Plas F, Manning P, Allan E, Scherer-Lorenzen M, Verheyen K, Wirth C, Zavala MA, Hector A, Ampoorter E, Baeten L, Barbaro L, Bauhus J, Benavides R, Benneter A, Berthold F, Bonal D, Bouriaud O, Bruelheide H, Bussotti F, Carnol M, Castagneyrol B, Charbonnier Y, Coomes D, Coppi A, Bastias CC, Dawud SM, De Wandeler H, Domisch T, Finér L, Gessler A, Granier A, Grossiord C, Guyot V, Hättenschwiler S, Jactel H, Jaroszewicz B, Joly FX, Jucker T, Koricheva J, Milligan H, Müller S, Muys B, Nguyen D, Pollastrini M, Raulund-Rasmussen K, Selvi F, Stenlid J, Valladares F, Vesterdal L, Zielínski D, Fischer M (2016) Jack-of-all-trades effects drive biodiversity-ecosystem multifunctionality relationships in European forests. Nature Communications, 7, 11109. DOI PMID
[42]	van der Sande MT, Arets EJMM, Peña-Claros M, Hoosbeek MR, Cáceres-Siani Y, van der Hout P, Poorter L (2018) Soil fertility and species traits, but not diversity, drive productivity and biomass stocks in a Guyanese tropical rainforest. Functional Ecology, 32, 461-474.
[43]	Weiner J, Thomas SC (1986) Size variability and competition in plant monocultures. Oikos, 47, 211-222.
[44]	Williams LJ, Paquette A, Cavender-Bares J, Messier C, Reich PB (2017) Spatial complementarity in tree crowns explains overyielding in species mixtures. Nature Ecology & Evolution, 1, 63.
[45]	Yachi S, Loreau M (2007) Does complementary resource use enhance ecosystem functioning? A model of light competition in plant communities. Ecology Letters, 10, 54-62. PMID
[46]	Yang H, Li YL, Shen L, Kang XG, Yue G, Wang Y (2014) Spatial distribution patterns of seedling and sapling in a spruce-fir forest in the Changbai Mountains area in northeastern China. Acta Ecologica Sinica, 34, 7311-7319. (in Chinese with English abstract)
	[杨华, 李艳丽, 沈林, 亢新刚, 岳刚, 王妍 (2014) 长白山云冷杉林幼苗幼树空间分布格局及其更新特征. 生态学报, 34, 7311-7319.]
[47]	Yuan ZQ, Ali A, Wang SP, Gazol A, Freckleton R, Wang XG, Lin F, Ye J, Zhou L, Hao ZQ, Loreau M (2018) Abiotic and biotic determinants of coarse woody productivity in temperate mixed forests. Science of the Total Environment, 630, 422-431.
[48]	Yue QM, Hao MH, Li XY, Zhang CY, von Gadow K, Zhao XH (2020) Assessing biotic and abiotic effects on forest productivity in three temperate forests. Ecology and Evolution, 10, 7887-7900. DOI PMID
[49]	Zhang Y, Chen HYH (2015) Individual size inequality links forest diversity and above-ground biomass. Journal of Ecology, 103, 1245-1252.
[50]	Zheng LT, Chen HYH, Biswas SR, Bao DF, Fang XC, Abdullah M, Yan ER (2021) Diversity and identity of economics traits determine the extent of tree mixture effects on ecosystem productivity. Journal of Ecology, 109, 1898-1908.
[51]	Zhu Y, Comita LS, Hubbell SP, Ma KP (2015) Conspecific and phylogenetic density-dependent survival differs across life stages in a tropical forest. Journal of Ecology, 103, 957-966.