Individual-tree diameter growth model for rebollo oak (Quercus pyrenaica Willd.) coppices

断面积 数学 胸径 站点索引 混合模型 统计 树(集合论) 森林资源清查 变量 林业 竞赛(生物学) 森林经营 生态学 地理 生物 数学分析
作者
Patricia Adame,Jari Hynynen,Isabel Cañellas,Miren del Rı́o
出处
期刊:Forest Ecology and Management [Elsevier]
卷期号:255 (3-4): 1011-1022 被引量:105
标识
DOI:10.1016/j.foreco.2007.10.019
摘要

In this study, a distance-independent mixed model was developed for predicting the diameter growth of individual trees in Mediterranean oak (Quercus pyrenaica Willd.) coppices located in northwest Spain. The data used to build the model came from 41 permanent plots belonging to the Spanish National Forest Inventory with the dependent variable being 10-year diameter increment over bark for trees larger than 7.5 cm at breast height. The basic field data required for predictions had been divided into four main groups: size of the tree, stand variables, competition indices and biogeoclimatic variables. The most significant independent variables were the individual-tree diameter, the basal area of trees larger than the subject tree, dominant height, site index and biogeoclimatic stratum. The model was defined as a mixed linear model with random plot effect, achieving an efficiency of 44.38%. The accuracy of the model was tested against the modelling data and against independent data from the same stands. Mixed model calibration of diameter increment was carried out with the independent data using a different sample of complementary observations of the dependent variable. The calibrated model was an improvement on the trivial model, which assumes constancy in diameter increment for a short projection period, especially the pattern of residuals with respect to predicted diameter and the independent variables.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
clyhg完成签到,获得积分10
2秒前
11111111发布了新的文献求助10
3秒前
思源应助123采纳,获得10
3秒前
qiaoyun发布了新的文献求助10
3秒前
3秒前
Rijie发布了新的文献求助10
4秒前
森岛完成签到,获得积分10
4秒前
大方幻珊完成签到,获得积分10
5秒前
5秒前
星星落我怀发布了新的文献求助100
6秒前
111完成签到,获得积分10
6秒前
张宇发布了新的文献求助10
6秒前
monica完成签到,获得积分10
7秒前
此生不换完成签到,获得积分10
7秒前
张毓完成签到,获得积分10
7秒前
8秒前
Cyyyy发布了新的文献求助10
10秒前
huazwz应助封25采纳,获得20
11秒前
刚果红染液完成签到,获得积分10
11秒前
11秒前
11秒前
Mhj13810应助扭一扭泡一泡采纳,获得10
11秒前
姬会会发布了新的文献求助50
11秒前
张宇完成签到,获得积分10
13秒前
13秒前
14秒前
15秒前
16秒前
17秒前
xy发布了新的文献求助50
17秒前
nnc完成签到,获得积分10
17秒前
18秒前
18秒前
香蕉觅云应助嫩叠采纳,获得10
18秒前
汉堡包应助nini采纳,获得30
19秒前
20秒前
Jasper应助CXSCXD采纳,获得10
20秒前
绝迹天明发布了新的文献求助10
21秒前
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 3000
Les Mantodea de guyane 2500
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 2000
What is the Future of Psychotherapy in a Digital Age? 700
The Psychological Quest for Meaning 600
Zeolites: From Fundamentals to Emerging Applications 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5955779
求助须知:如何正确求助?哪些是违规求助? 7169325
关于积分的说明 15939745
捐赠科研通 5090764
什么是DOI,文献DOI怎么找? 2735901
邀请新用户注册赠送积分活动 1696705
关于科研通互助平台的介绍 1617378