福建漳州人，南京林业大学生态与环境学院教授、博士生导师、高层次引进人才、中国生态学会生态模型专业委员会委员，中国林学会计算机应用专业委员会理事、Methods in Ecology and Evolution、Journal of Plant Ecology 、Statistics Innovation、《生物多样性》和《南京林业大学学报（自科版）》等期刊编委。研究方向为数量生态学和生物统计，发表论文60多篇。主持国家自然科学基金3项。近年来致力于在国内生态学界推广R语言和数量分析方法，开发有R程序包rdacca.hp、glmm.hp、gam.hp、phylolm.hp和spatialreg.hp包，利用“平均共享方差”方法获取典范排序分析（RDA, CCA和dbRDA）,广义混合效应模型(GLMM)和广义可加模型中、谱系广义线性回归和空间自回归模型单个解释变量的贡献，已经被广泛应用于生态学与环境科学数据分析，截止2026年1月，已经累计被使用2000多次。出版译著《数量生态学-R语言应用》（第一版、第二版）（高等教育出版社），该书成为国内高校生态学与R语言教学的基本教材，并获得第二十届中国输出引入版优秀图书奖，目前已经累计发行3万多册。并多次受邀国内各大高校与科研院所开展R语言的培训，为R语言和生态数量方法在国内的生态与环境科学界的普及做出重要的贡献。
Currently, I am a Principal Investigator (PI) in Quantitative Ecology at Nanjing Forestry University, China, having previously worked at the Institute of Botany, Chinese Academy of Sciences. I was also a visiting scholar in the laboratory of Professor Pierre Legendre at the University of Montreal, the founder of numerical ecology. I currently serve on the editorial boards of Methods in Ecology and Evolution, Journal of Plant Ecology, Statistics Innovation, Biodiversity Science, and the Journal of Nanjing Forestry University (Natural Sciences Edition).
My research focuses on advancing statistical methodologies to address key challenges in ecology, particularly the long-standing lack of quantitative frameworks for evaluating the overall and relative importance of individual predictors in multi-response and complex regression models. To address this issue, my colleagues and I established explicit mathematical links among commonality analysis, variation partitioning, and hierarchical partitioning, and further generalized these frameworks to accommodate any number of predictors or predictor groups. This unified framework is implemented in the R package rdacca.hp, which enables robust decomposition of explained variation in RDA, CCA, and dbRDA models.
This work was highlighted in the fourth issue of Methods in Ecology and Evolution in 2022 (https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13800) and has since received broad recognition, accumulating over 1050 citations on Google Scholar and 1000 citations on Web of Science as of January 2026.
Building upon the concept of average shared variance, we further extended this framework to a wide range of regression models commonly used in ecology. Specifically, we developed methods to decompose the Nakagawa marginal R² of Generalized Linear Mixed Models (GLMMs), the adjusted R² (explained deviance) of Generalized Additive Models (GAMs), Phylogenetic Linear and Generalized Linear Models (PGLMs), as well as spatial autoregressive regression models. These advances are implemented in a series of R packages, including glmm.hp, gam.hp, phylolm.hp, and the recently developed spatialreg.hp, providing unified and interpretable tools for assessing the relative importance of predictors across diverse modeling frameworks.
In addition to my research, I am committed to the dissemination of quantitative ecological methods. I have organized and led numerous workshops on statistical ecology and R programming across China, attracting thousands of participants from universities and research institutes. I also serve as an associate editor for Methods in Ecology and Evolution, Journal of Plant Ecology, and Statistics Innovation.
1.Jiangshan Lai*, Yan He, Mi Hou, Aiying Zhang, Gang Wang, Lingfeng Mao*, 2025.Evaluating the relative importance of phylogeny and predictors in Phylogenetic Generalized Linear Models using the phylolm.hp R package, Plant Diversity, 47, 709-717
2.Jiangshan Lai*, Bangken Ying, Yan He, Mi Hou, et al. 2026. Quantifying Spatial and Environmental Effects in Spatial Autoregressive Model with the spatialreg.hp R package. Journal of Plant Ecology 19：10.1093/jpe/rtaf220
近期代表性论文 (Main publications)：
1. Jiangshan Lai*, Yi Zou, Jinlong Zhang, Pedro R. Peres-Neto. 2022. Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package. Methods in Ecology and Evolution,13(4):782-788（截止2026年1月已经被引用1000多次，从2022年7月开始连续12次入选Clarivate 前0.1%热点论文, 被引次数位列2022年全球环境生态学科论文第8名）
2. Jiangshan Lai*, Yi Zou, Shuang Zhang, Xiaoguang Zhang. Lingfeng Mao.2022. glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models. Journal of Plant Ecology 15(6):1302-1307. （截止2026年1月被引400多次，入选 Clarivate 2023年5月开始10次入选前0.1%热点论文，被引次数位列2022年全球动植物学科论文第8名））
3.Gao, M., Ye, Y., Zheng, Y., & Lai, J*. 2025. A comprehensive analysis of R’s application in ecological research from 2008 to 2023. Journal of Plant Ecology, 18(1): rtaf010. https://doi.org/10.1093/jpe/rtaf010
4. Jiangshan Lai, Jing Tang, Tingyuan Li, Aiying Zhan, Lingfeng Mao. 2024. Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. Plant Diversity, 46(4): 542-546 https://www.sciencedirect.com/science/article/pii/S2468265924000854（截止2026年1月被引70多次，入选 Clarivate 2025年7月开始3次入选前0.1%热点论文）
5. Jiangshan Lai*, Weijie Zhu, Dongfang, Cui, Lingfeng Mao. 2023. Extension of the glmm.hp Package to Zero-Inflated Generalized Linear Mixed Models and Multiple Regression Journal of Plant Ecology, 16(6):rtad038,https://doi.org/10.1093/jpe/rtad038（截止2026年1月被引140多次，入选 Clarivate 2025年5月开始4次入选前0.1%热点论文）
6. Jiangshan Lai, Weijie Zhu, Dongfang Cui, Dayong Fan, Lingfeng Mao, The use of R in forestry research, Journal of Plant Ecology, 2023, rtad047, https://doi.org/10.1093/jpe/rtad047
7.Jiangshan Lai,Dongfang Cui, Weijie Zhu, Lingfeng Mao. 2023. The Use of R and R Packages in Biodiversity Conservation Research. Diversity 15, 12: 1202. https://doi.org/10.3390/d15121202
8.Yasi Liu, Xiangping Wang , Dayong Fan*, Jiangshan Lai*. 2022 The use of R in photosynthesis research. Functional Plant Biology. 49, 565–572.
9.Jiangshan Lai*, Christopher J. Lortie, Robert A. Muenchen, Jian Yang, Keping, Ma. 2019. Evaluating the popularity of R in ecology. Ecosphere. 10(1):e02567
10.Joel E. Cohen*#, Jiangshan Lai#, David A. Coomes and Robert B. Allen. 2016 Taylor’s law and related allometric power laws in New Zealand mountain beech forests: the roles of space, time and environment. Oikos. 125 (9): 1342-1357（共同一作）
11.Jiangshan Lai, David A. Coomes, Xiaojun, Du, Chang - fu Hsieh, I-Fang Sun, Wei-chun Chao, Xiangcheng Mi, Haibao Ren, Xugao, Wang, Zhanqing Hao,and Keping Ma* .2013. A general combined model to describe tree-diameter distributions within subtropical and temperate forest communities. Oikos. 122 (11): 1636-1642
12.Jiangshan Lai, Bo Yang, Dunmei Lin, Andrew J. Kerkhoff, Keping Ma *. 2013. The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression? PLoS ONE, 8: e77007
13.Jiangshan Lai, Xiangcheng Mi, Haibao Ren, Keping Ma*. 2009. Species-habitat associations change in a subtropical forest of China. Journal of Vegetation Science. 20(3):415-423.
1.Daniel Borcard, François Gillet, Pierre Legendre 著,（赖江山译）.2014. 数量生态学-R语言的应用，北京：高等教育出版社
2.Daniel Borcard, François Gillet, Pierre Legendre 著,（赖江山译）.2020. 数量生态学-R语言的应用（第二版），北京：高等教育出版社 (荣获第二十届引进版优秀图书奖)。
科研成果：
1.开发新方法解决“典范分析”中变量相对重要性评估的国际性难题。
典范分析（RDA、CCA和dbRDA）是生态多元统计核心方法，由于受到多元共线性的影响，解释变量相对重要性评估一直是尚未解决的国际性难题。本人将统计学中“层次分割”的理论应用于典范分析，并将层次分割与变差分解建立起数学联系，认为可以通过平均分配共同解释的组分与边际效应之和获得单个解释变量所分配的解释率，通过比较单个变量解释率来评估典范分析中共线性的解释变量相对重要性。该研究成果于2022年4月正式发表在生态方法学权威期刊Methods in Ecology and Evolution，本人为文章第一和唯一通讯作者。国际同行（审稿人）评价该论文为多变量模型的解读和模型筛选做出非常有价值的贡献。截至2025年9月，本论文谷歌学术搜索显示已经被引用1000多次， 2022年7月至今连续12次被科睿唯安列为生态环境领域全球前1%高引文章和前0.1%的热点文章,被引次数位列2022年全球30万篇环境生态学科论文第8名。
R语言已经成为国际上生态与环境科学研究中首先的数据分析工具，但国内生态学界在R语言教学基础薄弱，也缺乏相关的教材。申请人独自翻译了国际权威的教材“Numerical Ecology with R”第一版（2014）和第二版（2020），并由高等教育出版社出版，目前中文版累计发行3万多册，已经成为国内高校生态学科R语言教学的基本教材，并获得第二十届中国引入版优秀图书奖。申请人也多次受邀国内各大高校与科研院所开展R语言与数据分析的培训，累计授课学员数上万人次，同时也是中国科学院大学研究生课程“R语言及其在生态学上的应用”首席教师，被评为国科大2021年教学优秀岗位教师，为R语言在国内的生态学界的普及做出重要的贡献。2016年11月，应中国科学院加德满都科教中心和尼泊尔特里布文大学共同邀请，申请人赴尼泊尔主讲10天“基于R 语言统计分析方法”培训课程，为促进中尼科技交流做出贡献。
