辽东山区日本落叶松一级枝条数量及密度预估模型

doi:10.13287/j.1001-9332.202408.006

摘要/Abstract

摘要： 枝条数量作为重要的枝条特征因子,对树冠结构特征、树木生长及木材质量具有重要的影响。本研究以辽宁省清原县大孤家林场日本落叶松人工林为研究对象,基于负二项分布模型构建包含水枝的日本落叶松一级枝条数量混合效应预估模型,又基于负指数模型构建包含水枝的日本落叶松一级枝条密度混合效应预估模型。结果表明: 对于一级枝条数量模型来说,考虑样木水平的混合效应模型可以有效地降低异方差性和自相关性,拟合效果优于传统模型。模拟结果表明,树木的冠长率越大,枝条的数量越多。将最优一级枝条数量基础模型的截距作为随机效应参数的混合效应模型被确定为最优模型,其R_a²=0.552,均方根误差为7.242。对于一级枝条密度来说,考虑样木水平的混合模型同样降低了模型的异方差性和自相关性,枝条的密度随着冠长率的增大而增大。将最优的一级枝条密度基础模型的截距与着枝深度作为随机效应参数的混合效应模型被确定为最优模型,其R_a²=0.792,均方根误差为4.447。本研究构建的日本落叶松枝条数量及密度模型为制定科学的森林经营方案以提高木材质量奠定了重要基础。

关键词: 日本落叶松, 枝条数量, 枝条密度, 混合效应

Abstract: As an important branch characteristic factor, the quantity of branches could influence crown structure, tree growth, and wood quality. Taking Larix kaempferi plantation in Dagujia Forest Farm, Qingyuan County, Liao-ning Province as the research object, we developed a mixed effect prediction model of the first-order branches quantity of L. kaempferi including sprouting branches based on the negative binomial distribution model, and a mixed effect prediction model of the first-order branches density of L. kaempferi including sprouting branches based on the negative exponential model. The results showed that the mixed effect model considering sample level as the random effect effectively decreased the heteroscedasticity and autocorrelation. The fitting goodness was better than the traditional model. The quantity of the first-order branches increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches quantity as the random effect parameter was determined as the optimal model, with R_a²=0.552 and the RMSE=7.242. As for the density of the first-order branches, the heteroscedasticity and autocorrelation were also reduced when the random effect was added. The density of the first-order increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches density model and branch depth as random effects was determined as the optimal model, with R_a²=0.792 and the RMSE=4.447. The model for branch quantity and density of L. kaempferi constructed would lay an important foundation for making scientific forest management plans and improving wood quality.

Key words: Larix kaempferi, branch quantity, branch density, mixed effect

倪铭岐, 高慧淋, 刘家腾, 佟艺玟, 邱瑜, 邢晖. 辽东山区日本落叶松一级枝条数量及密度预估模型[J]. 应用生态学报, 2024, 35(8): 2082-2090.

NI Mingqi, GAO Huilin, LIU Jiateng, TONG Yiwen, QIU Yu, XING Hui. Prediction model for the quantity and density of first-order branches of Larix kaempferi in eastern area of Liaoning Province, China[J]. Chinese Journal of Applied Ecology, 2024, 35(8): 2082-2090.

参考文献

[1] Ozanne CM, Anhuf D, Boulter SL, et al. Biodiversity meets the atmosphere: A global view of forest canopies. Science, 2003, 301: 183-186
[2] 佟艺玟, 陈东升, 冯健, 等. 基于线性分位数混合效应的辽东山区红松冠幅模型. 应用生态学报, 2022, 33(9): 2321-2330
[3] Crecente-Campo F, Marshall P, Lemay V, et al. A crown profile model for Pinus radiata D. Don in northwestern Spain. Forest Ecology and Management, 2009, 257: 2370-2379
[4] Chmura DJ, Rahman MS, Tjoelker MG. Crown structure and biomass allocation patterns modulate aboveground productivity in young loblolly pine and slash pine. Forest Ecology and Management, 2007, 243: 219-230
[5] 董灵波, 刘兆刚, 李凤日, 等. 基于线性混合效应模型的红松人工林一级枝条大小预测模型. 应用生态学报, 2013, 24(9): 2447-2456
[6] Hein S, MäKinen H, Yue C, et al. Modelling branch characteristics of Norway spruce from wide spacings in Germany. Forest Ecology and Management, 2007, 242: 155-164
[7] Nemec AFL, Goudie JW, Parish R. A gamma-Poisson model for vertical location and frequency of buds on lodgepole pine (Pinus contorta) leaders. Canadian Journal of Forest Research, 2010, 40: 2049-2058
[8] 王曼霖, 董利虎, 李凤日. 基于Poisson回归混合效应模型的长白落叶松一级枝数量模拟. 北京林业大学学报, 2017, 39(11): 45-55
[9] 郑杨, 董利虎, 李凤日. 黑龙江省红松人工林枝条分布数量模拟. 应用生态学报, 2016, 27(7): 2172-2180
[10] Ishii H, Mcdowell N. Age-related development of crown structure in coastal Douglas-fir trees. Forest Ecology and Management, 2002, 169: 257-270
[11] 郭孝玉. 长白落叶松人工林树冠结构及生长模型研究. 博士论文. 北京: 北京林业大学, 2013
[12] Weiskittel AR, Seymour RS, Hofmeyer PV, et al. Mode-lling primary branch frequency and size for five conifer species in Maine, USA. Forest Ecology and Management, 2010, 259: 1912-1921
[13] 刘家腾, 赵雪晗, 刘伟韬, 等. 基于边际回归的沈阳市绿化树种人工油松冠形模拟. 应用生态学报, 2022, 33(11): 2915-2922
[14] 苗铮. 人工长白落叶松枝条特征因子及其动态模拟. 博士论文. 哈尔滨: 东北林业大学, 2022
[15] 贾炜玮, 罗天泽, 李凤日. 基于抚育间伐效应的红松人工林枝条密度模型. 北京林业大学学报, 2021, 43(2): 10-21
[16] Beaulieu E, Schneider R, Berninger F, et al. Modeling jack pine branch characteristics in eastern Canada. Forest Ecology and Management, 2011, 262: 1748-1757
[17] Sattler DF, Comeau PG, Achim A. Branch models for white spruce (Picea glauca (Moench) Voss) in naturally regenerated stands. Forest Ecology and Management, 2014, 325: 74-89
[18] 牛小云, 孙晓梅, 陈东升, 等. 辽东山区不同林龄日本落叶松人工林土壤微生物、养分及酶活性. 应用生态学报, 2015, 26(9): 2663-2672
[19] 肖晨, 田栋元, 马榕, 等. 兴安落叶松天然林更新数量相容性预测模型. 应用生态学报, 2023, 34(9): 2345-2354
[20] 高超, 林红蕾, 胡海清, 等. 我国林火发生预测模型研究进展. 应用生态学报, 2020, 31(9): 3227-3240
[21] Eskelson BNI, Temesgen H, Barrett TM. Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods. Canadian Journal of Forest Research, 2009, 39: 1749-1765
[22] 王蒙, 李凤日. 基于抚育间伐效应的长白落叶松人工林单木直径生长模型. 南京林业大学学报, 2018, 42(3): 28-36
[23] Hellstrm JR, Rudholm N. Advertising as a signaling device: Simulated maximum likelihood estimation of a multiple random effects count data model. Economics Letters, 2008, 101: 227-229
[24] Fang ZX, Bailey RL. Nonlinear mixed effects modeling for slash pine dominant height growth following intensive silvicultural treatments. Forest Science, 2001, 47: 287-300
[25] 贾炜玮, 崔璨, 李凤日. 基于混合效应模型的人工红松节子属性. 应用生态学报, 2018, 29(1): 33-43
[26] Achim A, Gardiner B. Predicting the branching properties of Sitka spruce grown in great Britain. New Zealand Journal of Forestry Science, 2006, 36: 246-264
[27] 李泽霖, 贾炜玮, 郭昊天, 等. 三种针叶树种节子属性通用方程的构建. 应用生态学报, 2023, 34(11): 2907-2918
[28] Hasenauer H, Monserud RA. A crown ratio model for Austrian forests. Forest Ecology and Management, 1996, 84: 49-60
[29] 王烁, 董利虎, 李凤日. 人工长白落叶松枝条存活模型. 北京林业大学学报, 2018, 40(1): 57-66
[30] 刘泰瑞, 董威, 覃志杰, 等. 不同间伐强度对华北落叶松人工林竞争关系的影响. 森林与环境学报, 2019, 39(2): 153-158
[31] Contreras MA, Affleck D, Chung W, et al. Evaluating tree competition indices as predictors of basal area increment in western Montana forests. Forest Ecology and Management, 2011, 262: 1939-1949
[32] 潘磊, 王轶夫, 孙钊, 等. 长白落叶松树冠半径分布特征及其对竞争的相应. 林业科学研究, 2022, 35(3): 27-37
[33] Trincado G, Burkhart HE. A framework for modeling the dynamics of first-order branches and spatial distribution of knots in loblolly pine trees. Canadian Journal of Forest Research, 2009, 39: 566-579
[34] Makinen H. Growth, suppression, death, and self-pru-ning of branches of Scots pine in southern and central Finland. Canadian Journal of Forest Research, 1999, 29: 585-594
[35] 易达, 李凤日, 马爱云, 等. 基于混合效应模型和分位数回归的长白落叶松枝下高模型构建. 应用生态学报, 2023, 34(4): 1035-1042
[36] Valentine HT, Amateis RL, Gove JH, et al. Crown-rise and crown-length dynamics: Application to loblolly pine. Forestry, 2013, 86: 371-375
[37] Duchateau E, Auty D, Mothe F, et al. Models of knot and stem development in black spruce trees indicate a shift in allocation priority to branches when growth is limited. PeerJ, 2015, 3: e873
[38] Sprugel DG. When branch autonomy fails: Milton’s law of resource availability and allocation. Tree Physiology, 2002, 22: 1119-1124