Simulation and uncertainty analysis of natural regeneration for pine-oak forests.

doi:10.13287/j.1001-9332.202012.018

Abstract

Abstract: The complexity and uncertainty of forest regeneration is crucial for predicting forest ecosystem dynamics. A natural regeneration model of pine-oak forests in Qinling Mountains was constructed with competition, climate and topography factors using Bayesian statistics and global sensitivity analysis (GSA). The alternative models were based on Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models. According to the uncertainty of model parameter transfer, the analysis results were quantified, and the dominant factors of small probability events affecting forest regeneration were explained. The results showed that the ZINB model was the best one in the simulation of Pinus tabuliformis and Quercus aliena var. acuteserrata. Stand basal area, light interception, slope location and minimum temperature during growing season were the most critical factors affecting natural regeneration of P. tabuliformis, while stand basal area, cosine of aspect interacted with the natural logarithm of elevation, annual mean temperature, and precipitation of the warmest quarter were the most critical factors for Q. aliena var. acuteserrata. The contributions of various factors to the predictive uncertainty were: competition factor (25%) < climate factor (29%) < topography factor (46%) for the simulation of P. tabuliformis regeneration, and climate factor (12%) < competition factor (24%) < topography factor (64%) for the simulation of Q. aliena var. acuteserrata regeneration. The natural regeneration quantity of P. tabuliformis was positively correlated with mean annual temperature and minimum precipitation during growing season, and negatively correlated with the mean temperature in the driest quarter. The natural regeneration quantity of Q. aliena var. acuteserrata was positively correlated with mean annual temperature, minimum precipitation during growing season, precipitation of the warmest quarter, and negatively correlated with mean temperature of the driest quarter. The ZINB model based on Bayesian methods could effectively quantify the major factors driving forest regeneration and interpret the uncertainty propagated from parameters, which was useful for predicting forest regeneration.

Key words: climate sensitivity, Bayesian statistics, uncertainty, natural regeneration, Pinus tabu-liformis, Quercus aliena var. acuteserrata.

WANG Bin, TIAN Xiang-lin, LIAO Zi-yan, WANG Zhi-tao, GENG Sheng-lian, CAO Tian-jian. Simulation and uncertainty analysis of natural regeneration for pine-oak forests.[J]. Chinese Journal of Applied Ecology, 2020, 31(12): 4004-4016.

References

[1] 金永焕, 李敦求, 姜好相, 等. 长白山区次生林恢复过程中天然更新的动态. 南京林业大学学报: 自然科学版, 2005, 29(5): 65-68 [Jin Y-H, Li D-Q, Jiang H-X, et al. Quantitative dynamics on natural regeneration of secondary forest during the restoration period in Changbai Mountain Area. Journal of Nanjing Forestry University: Natural Sciences, 2005, 29(5): 65-68]
[2] 蔺菲, 郝占庆, 叶吉, 等. 苔藓植物对植物天然更新的影响. 生态学杂志, 2006, 25(4): 456-460 [Lin F, Hao Z-Q, Ye J, et al. Effect of bryophytes on plant natural regeneration. Chinese Journal of Ecology, 2006, 25(4): 456-460]
[3] Sansevero JBB, Prieto PV, de Moraes LFD, et al. Natural regeneration in plantations of native trees in lowland Brazilian Atlantic Forest: Community structure, diversity, and dispersal syndromes. Restoration Ecology, 2011, 19: 379-389
[4] Ferguson DE, Carlson CE. Predicting Regeneration Establishment with the Prognosis Model. Ogden, UT, USA: Intermountain Research Station, 1993
[5] Hynynen J, Ojansuu R, Hokka H, et al. Models for Predicting Stand Development in MELA System. Helsinki, Finland: Finnish Forest Research Institute, 2002
[6] 杨占彪. 六盘山自然保护区主要森林群天然更新与生态恢复评价研究. 硕士论文. 兰州: 兰州大学, 2009 [Yang Z-B. Studies on Regeneration and Assessment of Ecological Restoration of Major Forest Communities in Liupan Mountain Natural Reserve. Master Thesis. Lanzhou: Lanzhou University, 2009]
[7] 康冰, 王得祥, 崔宏安, 等. 秦岭山地油松群落更新特征及影响因子. 应用生态学报, 2011, 22(7): 1659-1667 [Kang B, Wang D-X, Cui H-A, et al. Regeneration characteristics and related affecting factors of Pinus tabulaeformis secondary forests in Qinling Mountains. Chinese Journal of Applied Ecology, 2011, 22(7): 1659-1667]
[8] 康冰, 王得祥, 李刚, 等. 秦岭山地锐齿栎次生林幼苗更新特征. 生态学报, 2012, 32(9): 2738-2747 [Kang B, Wang D-X, Li G, et al. Characteristics of seedlings regeneration in Quercus aliena var. acuteserrata secondary forests in Qinling Mountains. Acta Ecologica Sinica, 2012, 32(9): 2738-2747]
[9] 卢彦磊, 张文辉, 杨斌, 等. 秦岭中段不同坡向锐齿栎种子雨、土壤种子库与幼苗更新. 应用生态学报, 2019, 30(6): 1965-1973 [Lu Y-L, Zhang W-H, Yang B, et al. Seed rain, soil seed bank and seedling regeneration of Quercus aliena var. acureserrata in diffe-rent slope directions on the middle Qinling Mountains. Chinese Journal of Applied Ecology, 2019, 30(6): 1965-1973]
[10] Yu F, Wang DX, Shi XX, et al. Effects of environmental factors on tree seedling regeneration in a pine-oak mixed forest in the Qinling Mountains, China. Journal of Mountain Science, 2013, 10: 845-853
[11] 张仰渠, 张岂凡, 吴建恭, 等. 陕西森林. 北京: 中国林业出版社, 1989 [Zhang Y-Q, Zhang Q-F, Wu J-G, et al. Shanxi Forestry. Beijing: China Forestry Press, 1989]
[12] Vanclay JK. A growth model for north Queensland rainforest. Forest Ecology and Management, 1989, 27: 245-271
[13] Kolo H, Ankerst D, Knoke T. Predicting natural forest regeneration: A statistical model based on inventory data. European Journal of Forest Research, 2017, 136: 923-938
[14] Miina J, Heinonen J. Stochastic simulation of forest regeneration establishment using a multilevel multivariate model. New Forests, 2008, 54: 206-219
[15] Hasenauer, H, Kindermann G. Methods for assessing regeneration establishment and height growth in uneven-aged mixed species stands. Forestry, 2002, 75: 385-394
[16] Yaacob WFW, Lazim MA, Wah YB. A practical approach in modeling count data. Proceedings of Regional Conference on Statistical Sciences, 2010, 7: 176-183
[17] Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 1992, 34: 1-14
[18] Karazsia, BT, Van Dulmen MH. Regression models for count data: Illustrations using longitudinal predictors of childhood injury. Journal of Pediatric Psychology, 2008, 33: 1076-1084
[19] 李春明, 赵丽芳, 李利学. 基于混合效应模型和零膨胀模型方法的蒙古栎林分水平枯损模型. 林业科学, 2019, 55(11): 27-36 [Li C-M, Zhao L-F, Li L-X, et al. Modeling stand-level mortality of Mongolian oak (Quercus mongolica) based on mixed effect model and zero-inflated model methods. Scientia Silvae Sinicae, 2019, 55(11): 27-36]
[20] Zhang XQ, Lei YC, Cai DX, et al. Predicting tree recruitment with negative binomial mixture models. Forest Ecology and Management, 2012, 270: 209-215
[21] Flores O, Rossi V, Mortier F. Autocorrelation offsets zero-inflation in models of tropical saplings density. Ecological Modelling, 2009, 220: 1797-1809
[22] Dobrowoska D, Bolibok L. Is climate the key factor limi-ting the natural regeneration of silver fir beyond the northeastern border of its distribution range? Forest Ecology and Management, 2019, 439: 105-121
[23] Kemp KB, Higuera PE, Morgan P, et al. Climate will increasingly determine post-fire tree regeneration success in low-elevation forests, Northern Rockies, USA. Ecosphere, 2019, 10(1): e02568
[24] Miina J, Saksa T. Predicting regeneration establishment in Norway spruce plantations using a multivariate multilevel model. New Forests, 2006, 32: 265-283
[25] Vergarechea M, Calama R, Fortin M, et al. Climate-mediated regeneration occurrence in Mediterranean pine forests: A modeling approach. Forest Ecology and Management, 2019, 446: 10-19
[26] Lessard VC, McRoberts RE, Holdaway MR. Diameter growth models using minnesota forest inventory and analy-sis data. Forest Science, 2001, 47: 301-310
[27] Stahl G, Holm S, Gregoire TG, et al. Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway. Canadian Journal of Forest Research, 2011, 41: 96-107
[28] van Oijen M, Reyer C, Bohn FJ. Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe. Forest Ecology and Management, 2013, 289: 255-268
[29] van Oijen M, Rougier J, Smith R. Bayesian calibration of process-based forest models: Bridging the gap between models and data. Tree Physiology, 2005, 25: 915-927
[30] Dauby G, Stévart T, Droissart V, et al. ConR: An P package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecology and Evolution, 2017, 7: 11292-11303
[31] Fick SE, Hijmans RJ. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 2017, 37: 4302-4315
[32] Liao ZY, Zhang L, Nobis MP, et al. Climate change jointly with migration ability affect future range shifts of dominant fir species in Southwest China. Diversity and Distributions, 2020, 26: 352-367
[33] Xiang W, Lei XD, Zhang XQ. Modelling tree recruitment in relation to climate and competition in semi-natural Larix-Picea-Abies forests in northeast China. Forest Ecology and Management, 2016, 382: 100-109
[34] 刘胜. 黄土高原半干旱区人工林林分消光特性及辐射热量平衡研究. 硕士论文. 北京: 北京林业大学, 2006 [Liu S. Study on Extinction Characteristics and Heat Balance of Plantations in Semi-arid Region on Loess Plateau. Master Thesis. Beijing: Beijing Forestry University, 2006]
[35] 宋子炜, 郭小平, 赵廷宁, 等. 北京山区油松林光辐射特征及冠层结构参数. 浙江林学院学报, 2009, 26(1): 38-43 [Song Z-W, Guo X-P, Zhao T-N, et al. Light environment and canopy structure of a Pinus tabulaeformis community in the mountainous area of Beijing. Journal of Zhejiang Forestry College, 2009, 26(1): 38-43]
[36] 李秋元, 陈海滨. 锐齿栋叶面积叶干重研究初报. 西北林学院学报, 1993, 8(4): 24-29 [Li Q-Y, Chen H-B. Preliminary study on the leaf area and its dry weight of Quercus aliena var. acuteserrata. Journal of Northwest Forestry College, 1993, 8(4): 24-29]
[37] 陈存根, 彭鸿. 秦岭火地塘林区主要森林类型的现存量和生产力. 西北林学院学报, 1996, 11(suppl.): 92-102 [Chen C-G, Peng H. Standing crops and producti-vity of the major forest-types at the Huoditang Forest Region of the Qinling Mountains. Journal of Northwest Forestry College, 1996, 11(suppl.): 92-102]
[38] Hastings WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 1970, 57: 97-109
[39] Vrugt JA, terBraak CJF, Diks CGH, et al. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation, 2009, 10: 273-290
[40] Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Statistical Science, 1992, 7: 457-472
[41] Brooks S, Gelman A. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 1998, 7: 434-455
[42] Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in Practice. London: Chapman and Hall, 1996
[43] Simo NF, Wieler M, Feltenb F, et al. Bayesian analysis for determination and uncertainty assessment of strength and crack growth parameters of brittle materials. Journal of the European Ceramic Society, 2017, 37: 1769-1777
[44] Laloy E, Bielders CL. Modelling intercrop management impact on runoff and erosion in a continuous maize cropping system: Part I. Model description, global sensitivity analysis and Bayesian estimation of parameter identifia-bility. European Journal of Soil Science, 2009, 60: 1005-1021
[45] Eriksson O, Jauhiainen A, Sasane SM, et al. Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models. Bioinformatics, 2019, 35: 284-292
[46] Saltelli A. Making best use of model evaluations to compute sensitivity indices. Computer Physics Communication, 2002, 145: 280-297
[47] LeBauer DS, Wang D, Richter KT, et al. Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs, 2013, 83: 133-154
[48] McMahon SM, Dietze MC, Hersh MH, et al. A predictive framework to understand forest responses to global change. Annals of the New York Academy of Sciences, 2009, 1162: 221-236
[49] 秦立厚, 张茂震, 钟世红, 等. 森林生物量估算中模型不确定性分析. 生态学报, 2017, 37(23): 7912-7919 [Qin L-H, Zhang M-Z, Zhong S-H, et al. Model uncertainty in forest biomass estimation. Acta Ecologica Sinica, 2017, 37(23): 7912-7919]
[50] Vanlier J, Tiemann CA, Hilbers PAJ, et al. Parameter uncertainty in biochemical models described by ordinary differential equations. Mathematical Biosciences, 2013, 246: 305-314
[51] Hardtle W, von Oheimb G, Westphal C. The effects of light and soil conditions on the species richness of the ground vegetation of deciduous forests in northern Germany (Schleswig-Holstein). Forest Ecology and mana-gement, 2003, 182: 327-338
[52] Larsen DR, Metzger MA, Johnson PS. Oak regeneration and overstory density in the Missouri Ozarks. Canadian Journal of Forest Research, 1997, 27: 869-875
[53] Lynch TB, Nkouka J, Huebschmann MM, et al. Maxi-mum likelihood estimation for predicting the probability of obtaining variable shortleaf pine regeneration densities. Forest Science, 2003, 49: 577-584
[54] Ruan X, Cun-De Pan CD, Liu R, et al. Effects of climate warming on plant autotoxicity in forest evolution: A case simulation analysis for Picea schrenkiana regeneration. Ecology and Evolution, 2016, 6: 5854-5866