Branch density model for <i>Pinus koraiensis</i> plantation based on thinning effects

Jia Weiwei; Luo Tianze; Li Fengri

doi:10.12171/j.1000-1522.20200057

Journal of Beijing Forestry University > 2021 > 43(2): 10-21. > DOI: 10.12171/j.1000-1522.20200057

Jia Weiwei, Luo Tianze, Li Fengri. Branch density model for Pinus koraiensis plantation based on thinning effects[J]. Journal of Beijing Forestry University, 2021, 43(2): 10-21. DOI: 10.12171/j.1000-1522.20200057

Citation:

PDF (989 KB)

Branch density model for Pinus koraiensis plantation based on thinning effects

School of Forestry, Key Laboratory of Sustainable Forest EcosystemManagement of Ministry of Education, Northeast Forestry University, Harbin 150040, Heilongjiang, China

More Information

Received Date: March 01, 2020
Revised Date: April 16, 2020
Available Online: January 07, 2021
Published Date: February 23, 2021

Graphical Abstract

Abstract

Abstract

Objective In order to analyze the influence of thinning on the number of branches for Pinus koraiensis plantation, this study constructed a biological mathematic model based on the thinning effect, and provided a theoretical basis for developing a scientific and reasonable thinning program.
Method Based on the data of 4 370 branches from 49 sample trees in Pinus koraiensis plantation in Linkou and Dongjingcheng Forestry Bureau of Heilongjiang Province of northeastern China, this study established a single-level nonlinear mixed effect model of branch density with thinning effects using nlme package of R. The converged models were then evaluated by adjusted coefficient of determination ( ${R}_{{\rm{a}}}^{2}$ ), Akaike information criterion AIC, Bayesian information criterion (BIC), log likelihood and likelihood ratio test (LRT).
Result When site index and tree size were similar, branch density increased with the increase of thinning intensity and crown length. When thinning intensity and tree size were similar, branch density increased with the increase of site index and crown length. However, when thinning density and site index were similar, branch density was negatively correlated with DBH. Nonlinear mixed effect model with plot effect had higher fitting precision than that with tree effect and corresponding fixed effect model. Finally, the nonlinear mixed model with five random coefficients, including DINC (depth into crown), lnRDINC (natural logarithm of relative depth into crown), RDINC² (square of relative depth into crown), DBH and TI/TA (thinning intensity over thinning age) was selected as the most optimized model for predicting branch density, whose ${R}_{{\rm{a}}}^{2}$ was 0.825 7 and RMSE was 2.171 4.
Conclusion The optimal nonlinear mixed effect model with thinning effect not only has higher precision, but also more accurately reflects the effect of thinning on tree branches than other models.
- thinning,
- branch density,
- nonlinear mixed effect model,
- Pinus koraiensis plantation

FullText(HTML)

References (30)

References

[1]	Weiskittel A R, Maguire D A, Monserud R A. Modeling crown structural responses to competing vegetation control, thinning, fertilization, and Swiss needle cast in coastal Douglas-fir of the Pacific Northwest, USA[J]. Forest Ecology and Management, 2007, 245: 96−109. doi: 10.1016/j.foreco.2007.04.002.
[2]	Li F R. Modeling crown profile of Larix olgensis trees[J]. Scientia Silvae Sinicae, 2004, 40(5): 16−24.
[3]	Fernandes P M, Rigolot E. The fire ecology and management of maritime pine (Pinus pinaster Ait)[J]. Forest Ecology and Management, 2007, 241(1−3): 1−13.
[4]	Keim R F. Attenuation of rainfall intensity by forest canopies[D]. Corvallis: Oregon State University, 2004.
[5]	Kucharik C J, Norman J M, Gower S T. Measurements of branch area and adjusting leaf area index indirect measurements[J]. Agricultural and Forest Meteorology, 1998, 91(1–2): 69−88.
[6]	Barbeito I, Pardos M, Calama R, et al. Effect of stand structure on Stone pine (Pinus pinea L.) regeneration dynamics[J]. Forestry, 2008, 81(5): 617−629. doi: 10.1093/forestry/cpn037
[7]	董希斌, 李耀翔, 姜立春. 间伐对兴安落叶松人工林林分结构的影响[J]. 东北林业大学学报, 2000, 28(1):16−18. doi: 10.3969/j.issn.1000-5382.2000.01.004. Dong X B, Li Y X, Jiang L C. The effects of thinning on stand structure for larch plantation[J]. Journal of Northeast Forestry University, 2000, 28(1): 16−18. doi: 10.3969/j.issn.1000-5382.2000.01.004.
[8]	潘辉, 张金文, 林顺德, 等. 不同间伐强度对巨尾桉林分生产力的影响研究[J]. 林业科学, 2003, 39(专刊 1): 106−111. Pan H, Zhang J W, Lin S D, et al. Effects of different thinning intensity on stand productivity of Eucalyptus grandis × E. urophylla[J]. Scientia Silvae Sinicae, 2003, 39(Spec. 1): 106−111.
[9]	李春明. 基于两层次线性混合效应模型的杉木林单木胸径生长量模型[J]. 林业科学, 2012, 48(3):66−73. doi: 10.11707/j.1001-7488.20120311. Li C M. Individual tree diameter increment model for Chinese fir plantaion based on two-level linear mixed effects models[J]. Scientia Silvae Sinicae, 2012, 48(3): 66−73. doi: 10.11707/j.1001-7488.20120311.
[10]	雷相东, 李永慈, 向玮. 基于混合模型的单木断面积生长模型[J]. 林业科学, 2009, 45(1):74−80. doi: 10.3321/j.issn:1001-7488.2009.01.014. Lei X D, Li Y C, Xiang W. Individual basal area growth model using multi-level linear mixed model with repeated measures[J]. Scientia Silvae Sinicae, 2009, 45(1): 74−80. doi: 10.3321/j.issn:1001-7488.2009.01.014.
[11]	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[J]. 南京林业大学学报(自然科学版), 2018, 42(3):28−36. Wang M, Li F R. Modelling individual tree diameter growth for Larix olgensis based on thinning effects[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2018, 42(3): 28−36.
[12]	雷相东, 陆元昌, 张会儒, 等. 抚育间伐对落叶松云冷杉混交林的影响[J]. 林业科学, 2005, 41(4):78−85. doi: 10.3321/j.issn:1001-7488.2005.04.014. Lei X D, Lu Y C, Zhang H R, et al. Effects of thinning on mixed stands of Larix olgensis, Abies nephrolepis and Picea jazoensis[J]. Scientia Silvae Sinicae, 2005, 41(4): 78−85. doi: 10.3321/j.issn:1001-7488.2005.04.014.
[13]	汤景明, 孙拥康, 冯骏, 等. 不同强度间伐对日本落叶松人工林生长及林下植物多样性的影响[J]. 中南林业科技大学学报, 2018, 38(6):90−93, 122. Tang J M, Sun Y K, Feng J, et al. Influence of thinning on the growth and the diversity of undergrowth of Larix kaempferi plantation forest[J]. Journal of Central South University of Forestry & Technology, 2018, 38(6): 90−93, 122.
[14]	Wang Z B, Yang H J, Wang D H. Response of height growth of regenerating trees in a Pinus tabulaeformis Carr. plantation to different thinning intensities[J]. Forest Ecology and Management, 2019, 444: 280−289. doi: 10.1016/j.foreco.2019.04.042.
[15]	Ishii H, McDowell N. Age-related development of crown structure in coastal Douglas-fir trees[J]. Forest Ecology and Management, 2002, 169(3): 257−270. doi: 10.1016/S0378-1127(01)00751-4.
[16]	Weiskittel A R, Seymour R S, Hofmeyer P V, et al. Modelling primary branch frequency and size for five conifer species in Maine, USA[J]. Forest Ecology and Management, 2010, 259(10): 1912−1921. doi: 10.1016/j.foreco.2010.01.052.
[17]	Hein S, MäKinen H. Modelling branch characteristics of Norway spruce from wide spacings in Germany[J]. Forest Ecology and Management, 2007, 242(2−3): 155−164.
[18]	Hein S, Weiskittel A R, Kohnle U. Branch characteristics of widely spaced Douglas-fir in south-western Germany: comparisons of modelling approaches and geographic regions[J]. Forest Ecology and Management, 2008, 256(5): 1064−1079.
[19]	郭孝玉. 长白落叶松人工林树冠结构及生长模型研究[D]. 北京: 北京林业大学, 2013. Guo X Y. Crown structure and growth modle for Larix algersis plantation[D]. Beijing: Beijing Forestry University, 2013.
[20]	苗铮, 董利虎, 李凤日, 等. 基于GLMM的人工林红松二级枝条分布数量模拟[J]. 南京林业大学学报(自然科学版), 2017, 41(4):121−128. Miao Z, Dong L H, Li F R, et al. Modelling the vertical variation in the number of second order branches of Pinus koraiensis plantation trees through GLMM[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2017, 41(4): 121−128.
[21]	王曼霖, 董利虎, 李凤日. 基于Possion回归混合效应模型的长白落叶松一级枝数量模拟[J]. 北京林业大学学报, 2017, 39(11):45−55. Wang M L, Dong L H, Li F R. First-order branch number simulation for Larix olgensis plantation through Poisson regression mixed effect model[J]. Journal of Beijing Forestry University, 2017, 39(11): 45−55.
[22]	Weiskittel A R, Maguire D A, Monserud R A. Response of branch growth and mortality to silvicultural treatments in coastal Douglas-fir plantations: implications for predicting tree growth[J]. Forest Ecology and Management, 2007, 251(3): 182−194.
[23]	王烁. 基于GLMM的人工长白落叶松枝条存活模型研究[D]. 哈尔滨: 东北林业大学, 2018. Wang S. Branch survival models of planted Larix olgensis tree based on generalized linear mixed model[D]. Harbin: Northeast Forestry University, 2018.
[24]	Andreassen K, Tomte S M. Basal area growth models for individual trees of Norway spruce, Scots pine, birch and other broadleaves in Norway[J]. Forest Ecology and Management, 2003, 180: 11−24. doi: 10.1016/S0378-1127(02)00560-1.
[25]	Fang Z, Bailey R L. Nonlinear mixed effects modeling for slash pine dominant height growth following intensive silvicultural treatments[J]. Forest Science, 2001, 47: 287−300.
[26]	祖笑锋, 倪成才, Gorden Nigh, 等. 基于混合效应模型及EBLUP预测美国黄松林分优势木树高生长过程[J]. 林业科学, 2015, 51(3):25−33. Zu X F, Ni C C, Gorden N, et al. Based on Mixed-Effects model and empirical best linear unbiased predictor to predict growth profile of dominant height[J]. Scientia Silvae Sinicae, 2015, 51(3): 25−33.
[27]	Weiskittel A R, Maguire D A, Monserud R A. Modeling crown structural responses to competing vegetation control, thinning, fertilization, and Swiss needle cast in coastal Douglas-fir of the Pacific Northwest, USA[J]. Forest Ecology and Management, 245(1–3): 96–109.
[28]	Thorpe H C, Astrup R, Trowbridge A, et al. Competition and tree crowns: a neighborhood analysis of three boreal tree species[J]. Forest Ecology & Management, 2010, 259(8): 1586−1596.
[29]	Sattler D F, Comeau P G, Achim A. Branch models for white spruce (Picea glauca (Moench) Voss) in naturally regenerated stands[J]. Forest Ecology & Management, 2014, 325: 74−89.
[30]	Sprugel D G. When branch autonomy fails: Milton’s law of resource availability and allocation[J]. Tree Phys, 2002, 22: 1119−1124. doi: 10.1093/treephys/22.15-16.1119.

[1]	Feng Yuan, Li Guixiang, He Liping, Bi Bo, Qin Yangping, Wang Faping, Hu Binxian, Yin Jiuming. Tree height curves of Pinus yunnanensis forest based on nonlinear mixed effects model[J]. Journal of Beijing Forestry University. DOI: 10.12171/j.1000-1522.20240063
[2]	Li Xinyu, Yeerjiang Baiketuerhan, Wang Juan, Zhang Xinna, Zhang Chunyu, Zhao Xiuhai. Relationship between tree height and DBH of Pinus koraiensis in northeastern China based on nonlinear mixed effects model[J]. Journal of Beijing Forestry University. DOI: 10.12171/j.1000-1522.20240321
[3]	Du Zhi, Chen Zhenxiong, Li Rui, Liu Ziwei, Huang Xin. Development of climate-sensitive nonlinear mixed-effects tree height-DBH model for Cunninghamia lanceolata[J]. Journal of Beijing Forestry University, 2023, 45(9): 52-61. DOI: 10.12171/j.1000-1522.20230052
[4]	Wang Longfeng, Xiao Weiwei, Wang Shuli. Changes of soil aggregate stability and carbon-nitrogen distribution after artificial management of natural secondary forests[J]. Journal of Beijing Forestry University, 2022, 44(7): 97-106. DOI: 10.12171/j.1000-1522.20210497
[5]	Jin Xiaojuan, Sun Yujun, Pan Lei. Prediction model of base diameter of primary branch for Larix olgensis based on mixed effects[J]. Journal of Beijing Forestry University, 2020, 42(10): 1-10. DOI: 10.12171/j.1000-1522.20200133
[6]	ZANG Hao, LEI Xiang-dong, ZHANG Hui-ru, LI Chun-ming, LU Jun. Nonlinear mixed-effects height-diameter model of Pinus koraiensis[J]. Journal of Beijing Forestry University, 2016, 38(6): 8-9. DOI: 10.13332/j.1000-1522.20160008
[7]	DONG Li-hu, LI Feng-ri, JIA Wei-wei.. Effects of tree competition on biomass and biomass models of Pinus koraiensis plantation.[J]. Journal of Beijing Forestry University, 2013, 35(6): 14-22.
[8]	DONG Li-hu, LI Feng-ri, JIA Wei-wei. Development of tree biomass model for Pinus koraiensis plantation[J]. Journal of Beijing Forestry University, 2012, 34(6): 16-22.
[9]	WANG Xiong-bin, YU Xin-xiao, XU Cheng-li, , GU Jian-cai, ZHOU Bin, FAN Min-rui, JIA Guo-dong, LV xi-zhi. Effects of thinning on edge effect of Larix principisrupprechtii plantation.[J]. Journal of Beijing Forestry University, 2009, 31(5): 29-34.
[10]	LI Chun-ming.. Simulating basal area growth of fir plantations using a nonlinear mixed modeling approach.[J]. Journal of Beijing Forestry University, 2009, 31(1): 44-49.

Cited By

Cited by

Periodical cited type(44)

1.	韩彦隆，魏亚娟，左小锋，左轶璆，康帅，童国利，李建媛，王永平. 吉兰泰荒漠绿洲过渡带土壤生态化学计量特征及养分恢复状况研究. 水土保持研究. 2025(02): 207-214+223 .
2.	罗婷，黄甫昭，李健星，陆芳，文淑均，阮枰臻，李先琨. 广西漓江流域喀斯特地区植被不同恢复阶段植物优势种叶片和土壤的生态化学计量特征. 植物资源与环境学报. 2024(02): 80-90 .
3.	任泽文，陈昕，陈玥，钟曲颖，余泽平，刘骏，杨清培，宋庆妮. 亚热带森林演替中优势种茎干-土壤碳氮磷生态化学计量的变化特征. 江西农业大学学报. 2024(02): 401-410 .
4.	侯贻菊，姚雾清，杨光能，崔迎春，周华. 黔竹笋期生长特性及配方施肥效应研究. 贵州林业科技. 2024(02): 19-24 .
5.	刘亚博，冯天骄，王平，卫伟. 黄土丘陵区典型小流域不同植被恢复方式土壤理化性质差异及其影响因素. 生态学报. 2024(15): 6652-6666 .
6.	史丽娟，吕海涛，张树梓，李联地，任启文，冯广. 白洋淀上游典型林分类型土壤理化性质及其化学计量特征. 土壤通报. 2024(04): 960-967 .
7.	武仁杰，邢玮，葛之葳，毛岭峰，彭思利. 4种林分凋落叶不同分解阶段化学计量特征. 浙江农林大学学报. 2023(01): 155-163 .
8.	孙阔，袁兴中，王晓锋，袁嘉，候春丽，魏丽景. 三峡水库消落带土壤养分含量及生态化学计量特征. 长江流域资源与环境. 2023(02): 403-414 .
9.	刘根，岳翰林，陈雨志，汪富资，李先宝，代智蓝，李德欢，李思雨，卫万荣. 3种修复措施对高原高速公路边坡土壤化学计量特征的影响. 草业科学. 2023(01): 71-78 .
10.	王艺伟，仇模升，孙彩丽. 植物反馈作用对火棘群落土壤养分、酶活性及化学计量特征的影响. 中南林业科技大学学报. 2023(03): 127-134 .
11.	梁楚欣，范弢，陈培云. 滇东石漠化坡地不同恢复模式下云南松林土壤碳氮磷化学计量特征及其影响因子. 浙江农林大学学报. 2023(03): 511-519 .
12.	白金珂，李笑雨，王力. 1980s与2020s青藏高原南部土壤质量变化. 应用生态学报. 2023(05): 1367-1374 .
13.	徐子涵，王磊，崔明，刘玉国，赵紫晴，李嘉豪. 南水北调水源区不同植被恢复模式的土壤化学计量特征. 南京林业大学学报(自然科学版). 2023(03): 173-181 .
14.	何芳远，苏权，陈坤铨，陈善栋，姜勇，罗明，梁士楚. 基于功能性状及系统发育的桂林喀斯特石山群落构建. 广西师范大学学报(自然科学版). 2023(03): 171-181 .
15.	吴丹，温晨，卫伟，张钦弟. 黄土高原小流域不同植物群落土壤生态化学计量的垂直变化特征. 广西植物. 2023(05): 923-935 .
16.	宁静，杨磊，曹建华，李亮. 基于文献计量分析的岩溶区植被恢复研究现状与热点. 中国岩溶. 2023(02): 321-336 .
17.	李雪梅，舒英格. 不同土地利用类型下土壤养分变化及生态化学计量特征分析. 中国农学通报. 2023(28): 62-69 .
18.	周士锋，魏亚娟，何磊，王项飞，刘美萍，刘澜波. 包头市南海湿地不同季节土壤养分分布特征. 北方园艺. 2023(20): 69-76 .
19.	侯贻菊，姚雾清，杨光能，崔迎春，周华，张喜. 黔竹秆形结构和地上生物量分配格局研究. 竹子学报. 2023(04): 51-57 .
20.	胡林安，邱江梅，李强. 云南岩溶断陷盆地植被演替土壤碳氮磷化学计量学特征. 中国岩溶. 2023(06): 1213-1223 .
21.	袁在翔，关庆伟，李俊杰，韩梦豪，金雪梅，陈霞. 不同植被恢复模式对紫金山森林土壤理化性质的影响. 东北林业大学学报. 2022(01): 52-57 .
22.	曹全恒，胡健，陈雪玲，孙梅玲，刘小龙，杨丽雪，周青平 . 川西北沙地植被恢复对土壤碳氮磷及生态化学计量特征的影响. 草地学报. 2022(03): 523-531 .
23.	蔡雅梅，冯民权，肖瑜. 人类活动对河岸带植被氮磷生态化学计量特征的影响——以汾河临汾段为例. 水土保持通报. 2022(01): 17-25 .
24.	孟海，王海燕，侯文宁，赵晗，宁一泓. 重庆笋溪河流域河岸带水体-土壤-植物的氮磷特征及影响因素. 水土保持学报. 2022(02): 275-282+291 .
25.	章润阳，钱前，刘坤平，梁月明，张伟，靳振江，潘复静. 喀斯特不同土地利用方式和恢复模式对土壤酶活性C∶N∶P比值的影响. 广西植物. 2022(06): 970-982 .
26.	王杰. 河岸带土壤氮磷时空分布及影响因素分析. 水土保持应用技术. 2022(04): 13-16 .
27.	孙渝雯，马赞文，陶贞，张乾柱，唐文魁，吴迪，钟庆祥，王振刚，丁健. 海南岛西南部土壤生物硅分布的时空差异及其驱动机制. 生态学报. 2022(17): 7092-7104 .
28.	张萌，沈雅飞，陈天，王丽君，曾立雄，孙鹏飞，肖文发，田耀武，程瑞梅. 宜阳县不同森林类型土壤化学计量特征. 陆地生态系统与保护学报. 2022(02): 1-8 .
29.	陈培云，范弢，何停，户红红. 滇东岩溶高原不同恢复阶段云南松林叶片-枯落物-土壤碳氮磷化学计量特征. 应用与环境生物学报. 2022(06): 1549-1556 .
30.	刘翔，张连凯，黄超，徐灿，马一奇，杨慧. 广西岩溶区芒果园土壤碳氮磷化学计量特征. 南方农业学报. 2022(12): 3346-3356 .
31.	李强. 土地利用方式对岩溶断陷盆地土壤细菌和真核生物群落结构的影响. 地球学报. 2021(03): 417-425 .
32.	陈云，李玉强，王旭洋，牛亚毅. 中国典型生态脆弱区生态化学计量学研究进展. 生态学报. 2021(10): 4213-4225 .
33.	蔡雅梅，冯民权. 汾河河岸带土壤氮、磷的时空分布规律及其影响因素研究. 水土保持学报. 2021(04): 222-229+236 .
34.	蔡国俊，锁盆春，张丽敏，符裕红，李安定. 黔南喀斯特峰丛洼地3种建群树种不同器官C、N、P化学计量特征. 贵州师范大学学报(自然科学版). 2021(05): 36-44 .
35.	魏亚娟，汪季，党晓宏，韩彦隆，高岩，李鹏，金山. 白刺灌丛沙堆演化过程中叶片C、N、P、K含量及其生态化学计量的变化特征. 中南林业科技大学学报. 2021(10): 102-110+139 .
36.	杨洪炳，肖以华，李明，许涵，史欣，郭晓敏. 典型城市森林旱季土壤团聚体稳定性与微生物胞外酶活性耦合关系. 生态环境学报. 2021(10): 1976-1989 .
37.	陈剑，王四海，杨卫，吴超. 外来入侵植物肿柄菊群落动态变化特征. 生态学杂志. 2020(02): 469-477 .
38.	郑鸾，龙翠玲. 茂兰喀斯特森林不同地形土壤生态化学计量特征. 南方农业学报. 2020(03): 545-551 .
39.	夏光辉，郭青霞，卢庆民，杜轶，康庆. 黄土丘陵区不同土地利用方式下土壤养分及生态化学计量特征. 水土保持通报. 2020(02): 140-147+153 .
40.	吴鹏，崔迎春，赵文君，侯贻菊，朱军，丁访军，杨文斌. 茂兰喀斯特区68种典型植物叶片化学计量特征. 生态学报. 2020(14): 5063-5080 .
41.	朱平宗，张光辉，杨文利，赵建民. 红壤区林地浅沟不同植被类型土壤生态化学计量特征. 水土保持研究. 2020(06): 60-65 .
42.	余杭，罗清虎，李松阳，林勇明，王道杰. 灾害干扰受损森林土壤的碳、氮、磷初期恢复特征与变异性. 山地学报. 2020(04): 532-541 .
43.	郭汝凤，刘鑫铭，李冠军，黄婷，吴承祯，林勇明，李键. 武夷山人工湿地系统植物生长期内土壤-植物碳氮磷变化特点. 应用与环境生物学报. 2020(02): 433-441 .
44.	闫丽娟，王海燕，李广，吴江琪. 黄土丘陵区4种典型植被对土壤养分及酶活性的影响. 水土保持学报. 2019(05): 190-196+204 .

Other cited types(42)

Get Citation

PDF

XML

Article views (1682) PDF downloads (109) Cited by(86)

Turn off MathJax

Article Contents

Abstract

References

Branch density model for Pinus koraiensis plantation based on thinning effects