Growth Equations in Forest Research: Mathematical Basis and Model Similarities

基础(线性代数) 树(集合论) 数学模型 功能(生物学) 简单(哲学) 微分方程 过程(计算) 数学 应用数学 计算机科学 生态学 数学分析 统计 生物 进化生物学 认识论 操作系统 哲学 几何学
作者
Christian Salas,Lauri Mehtätalo,Timothy G. Grégoire,Daniel P. Soto,Rodrigo Vargas‐Gaete
出处
期刊:Current forestry reports [Springer Nature]
卷期号:7 (4): 230-244 被引量:25
标识
DOI:10.1007/s40725-021-00145-8
摘要

Growth equations have been widely used in forest research, commonly to assess ecosystem-level behavior and forest management. Nevertheless, the large number of growth equations has obscured the growth-rate behavior of each of these equations and several different terms for referring to common phenomena. This review presents a unified mathematical treatment of growth-rates besides several well-known growth equations by giving their mathematical basis and representing their behavior using tree growth data as an example. We highlight the mathematical differences among several growth equations that can be better understood by using their differential equations forms rather than their integrated forms. Moreover, the assumed-and-claimed biological basis of these growth-rate models has been taken too seriously in forest research. The focus should be on using a plausible equation for the organism being modelled. We point out that more attention should be drawn to parameter estimation strategies and behavior analysis of the proposed models. Thus, it is difficult for a single model to capture all possible shapes and rates that such a complex biological process as tree growth can depict in nature. We pointed out misleading concepts attributed to some growth equations; however, the differences come from their mathematical properties rather than pure biological reasoning. Using the tree growth data, we depict those differences. Thus, comparisons of some functional forms (at least simple ones) must be carried out before selecting a function for drawing scientific findings.
最长约 10秒,即可获得该文献文件

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
王大帅哥完成签到,获得积分10
1秒前
桐桐应助sunjian采纳,获得10
1秒前
mocheer发布了新的文献求助10
1秒前
土豆晴发布了新的文献求助10
1秒前
2秒前
aabb完成签到 ,获得积分10
2秒前
2秒前
小豌豆完成签到,获得积分10
2秒前
2秒前
2秒前
大模型应助乐观的阿这采纳,获得10
2秒前
Leo_Sun发布了新的文献求助10
2秒前
深情安青应助丰富画笔采纳,获得10
2秒前
2秒前
2秒前
曹小妍完成签到,获得积分10
3秒前
yanglinxia完成签到,获得积分20
3秒前
小蘑菇应助ren采纳,获得10
4秒前
小鱼完成签到,获得积分10
4秒前
所所应助大佛老爷采纳,获得10
4秒前
量子星尘发布了新的文献求助10
4秒前
4秒前
4秒前
风清扬应助zzzz采纳,获得10
4秒前
同花顺完成签到,获得积分10
5秒前
5秒前
Xiaowen发布了新的文献求助10
6秒前
6秒前
小马甲应助彪壮的如柏采纳,获得10
6秒前
琪琪发布了新的文献求助10
7秒前
JamesPei应助梁某采纳,获得10
7秒前
7秒前
Melody发布了新的文献求助10
7秒前
milkmore完成签到,获得积分10
8秒前
ainiowo发布了新的文献求助10
8秒前
九点半上课了完成签到,获得积分10
8秒前
可乐发布了新的文献求助10
8秒前
懦弱的易绿完成签到,获得积分10
8秒前
9秒前
10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
Introduction to strong mixing conditions volume 1-3 5000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
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 5718762
求助须知:如何正确求助?哪些是违规求助? 5254117
关于积分的说明 15287024
捐赠科研通 4868786
什么是DOI,文献DOI怎么找? 2614471
邀请新用户注册赠送积分活动 1564338
关于科研通互助平台的介绍 1521791