联合物种分布模型与生物群落层次建模框架: 生态学理论、方法及应用

doi:10.17520/biods.2025364

生物多样性 ›› 2026, Vol. 34 ›› Issue (1): 25364. DOI: 10.17520/biods.2025364 cstr: 32101.14.biods.2025364

• 生态学数据分析方法专题 • 上一篇下一篇

联合物种分布模型与生物群落层次建模框架: 生态学理论、方法及应用

谷际岐¹, 赖江山²^,³, 王瑛⁴, 吴浩然⁵, 张雪⁶, 宋晓彤⁷, 邵小明⁸^,^*(), 娄安如¹^,^*()

1.北京师范大学生命科学学院, 生物多样性与生态工程教育部重点实验室, 北京 100875, 中国
2.南京林业大学生态与环境学院, 南京 210037, 中国
3.南京林业大学数量生态学研究中心, 南京 210037, 中国
4.首都师范大学生命科学学院, 北京 100048, 中国
6.厦门大学环境与生态学院, 福建厦门 361102, 中国
7.江南大学生物工程学院, 江苏无锡 221151, 中国
8.中国农业大学资源与环境学院, 北京 100193, 中国

收稿日期:2025-09-09 接受日期:2026-01-08 出版日期:2026-01-20 发布日期:2026-02-06
通讯作者: 邵小明,娄安如
基金资助:
第三次新疆综合科学考察项目(2022xjkk1201);国家自然科学基金(41771054);国家留学基金管理委员会国家公派研究生项目(202506040049)

Theoretical foundations, methodological advances, and applications of joint species distribution models with a focus on the HMSC framework in ecology

Jiqi Gu¹, Jiangshan Lai²^,³, Ying Wang⁴, Haoran Wu⁵, Xue Zhang⁶, Xiaotong Song⁷, Xiaoming Shao⁸^,^*(), Anru Lou¹^,^*()

1 Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing 100875, China
2 College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China
3 Research Center of Quantitative Ecology, Nanjing Forestry University, Nanjing 210037, China
4 College of Life Sciences, Capital Normal University, Beijing 100048, China
5 School of Geography and the Environment, University of Oxford, Oxford OX1 3QR, United Kingdom
5 School of Geography and the Environment, University of Oxford, Oxford OX1 3QR, United Kingdom
6 College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China
7 School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 221151, China
8 College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

Received:2025-09-09 Accepted:2026-01-08 Online:2026-01-20 Published:2026-02-06
Contact: Xiaoming Shao, Anru Lou
Supported by:
Third Xinjiang Scientific Expedition Program(2022xjkk1201);National Natural Science Foundation of China(41771054);Program of China Scholarship Council(202506040049)

摘要/Abstract

摘要：

理解环境过滤、生物相互作用与中性过程如何共同塑造物种分布与群落结构, 是现代群落生态学的核心问题。然而, 传统多样性指数、排序分析及单物种分布模型(single-species distribution models, SDMs)难以同时整合物种间关联、环境梯度、性状与谱系等多维信息, 导致对群落构建机制的解析能力受限。联合物种分布模型(joint species distribution models, JSDMs)特别是生物群落层次建模(hierarchical modelling of species communities, HMSC)框架的提出, 为群落尺度的机制推断提供了统一而灵活的贝叶斯工具。本文系统综述了HMSC的统计结构、数学原理与推断机制, 构建了一个从数据组织、模型设定、马尔可夫链蒙特卡洛(Markov Chain Monte Carlo, MCMC)估计、模型评估到生态解释与预测的完整分析流程。同时结合苔藓群落数据配套编写了《联合物种分布模型HMSC的应用分步教程》, 通过分步讲解与可运行R代码, 助力研究者快速掌握该方法的实操应用。在理论部分, 本文明确了HMSC如何在统一的贝叶斯层级框架下整合环境梯度、物种性状、系统发育关系以及空间结构, 从而分离环境过滤、生物过滤与扩散限制的统计信号。在方法层面, 本文通过解析潜变量模型的数学结构, 阐明了残差相关在生态解释中的边界, 为理解物种共现信号、区分环境效应与未观测因子提供了理论依据; 对比了HMSC与其他主流JSDMs工具及传统群落统计方法的优势及适用性。在应用层面, 综述了其在森林、湿地、草原、海洋、城市及微生物生态学中的应用进展, 展示了其在保护规划、入侵种风险评估、共现网络分析及情景预测中的广泛价值; 随着图形处理器加速与迁移学习与大规模高维数据框架的发展, HMSC可提升稀有物种生态位估计与分布预测, 使数十万物种的群落建模成为可能。综上, JSDMs及HMSC不仅在生态统计方法论上实现了从单物种预测到多物种‒多维信息整合的跨越, 更为生态理论检验、群落构建机制解析及保护决策制定提供了高效、可扩展且能量化不确定性的工具平台。

关键词: 群落生态学, 联合物种分布模型, 生物群落层次模型, 环境过滤, 中性过程, 贝叶斯模型

Abstract

Background: Understanding how environmental filtering, biotic interactions, and neutral processes jointly shape species distributions and community structure is a central question in modern community ecology. However, traditional diversity indices, ordination analyses, and single-species distribution models (SDMs) cannot simultaneously integrate species associations, environmental gradients, functional traits, and phylogenetic relationships, thereby limiting their ability to disentangle community assembly mechanisms.

Framework: Joint species distribution models (JSDMs), particularly the hierarchical modelling of species communities (HMSC) framework, offer a unified and flexible Bayesian tool for community-level mechanistic inference. This study provides a systematic review of the statistical structure, mathematical foundations, and inferential mechanisms of HMSC, and establishes a complete analytical workflow encompassing data organization, model specification, Markov Chain Monte Carlo (MCMC) estimation, model evaluation, ecological interpretation, and predictive applications. A step-by-step tutorial, Joint Species Distribution Modelling with HMSC, accompanies the review and illustrates the practical implementation of HMSC through bryophyte community data and fully reproducible R code.

Theory: In the theoretical component, we clarify how HMSC integrates environmental gradients, species traits, phylogenetic relationships, and spatial structure within a unified hierarchical Bayesian framework to distinguish statistical signals of environmental filtering, biotic filtering, and dispersal limitation. Methodologically, we dissect the mathematical structure of latent variable models, elucidate the boundaries of ecological interpretation for residual correlations, and provide a conceptual basis for differentiating species co-occurrence signals from environmental effects and unobserved factors. We further compare HMSC with other mainstream JSDMs implementations and traditional community analytical methods, highlighting their relative advantages and ecological applicability.

Applications: On the applied side, we synthesize the rapidly expanding use of JSDMs across forest, wetland, grassland, marine, urban, and microbial ecosystems, demonstrating their value in conservation planning, invasive species risk assessment, co-occurrence network analysis, and scenario-based forecasting. With the advancement of GPU-accelerated computation, migration learning, and high-dimensional modelling frameworks, HMSC greatly improves ecological niche estimation and distribution prediction for rare species and enables community modelling for tens of thousands of taxa. Overall, JSDMs and HMSC in particular represent a methodological shift from single-species prediction toward integrative, multi-species and multi-dimensional ecological modelling. They provide an efficient, scalable, and uncertainty-aware platform that strengthens ecological theory testing, enhances understanding of community assembly mechanisms, and supports biodiversity conservation and management decisions.

Key words: community ecology, joint species distribution models, hierarchical modelling of species communities, environmental filtering, neutral processes, Bayesian models

谷际岐, 赖江山, 王瑛, 吴浩然, 张雪, 宋晓彤, 邵小明, 娄安如 (2026) 联合物种分布模型与生物群落层次建模框架: 生态学理论、方法及应用. 生物多样性, 34, 25364. DOI: 10.17520/biods.2025364.

Jiqi Gu, Jiangshan Lai, Ying Wang, Haoran Wu, Xue Zhang, Xiaotong Song, Xiaoming Shao, Anru Lou (2026) Theoretical foundations, methodological advances, and applications of joint species distribution models with a focus on the HMSC framework in ecology. Biodiversity Science, 34, 25364. DOI: 10.17520/biods.2025364.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2025364

https://www.biodiversity-science.net/CN/Y2026/V34/I1/25364

图/表 6

图1 群落构建过程的多尺度框架及观测数据类型示意图。图中展示了群落构建过程所处的不同时间与空间尺度, 包括全球尺度、区域尺度与局域尺度。群落构建过程可分为物种形成、中性过程(扩散、生态漂变等)、生物过滤(物种间相互作用)和环境过滤(非生物因子选择)等环节。蓝色箭头表示机制作用方向, 绿色箭头表示不同群落构建过程作用的路径。

Fig. 1 Schematic diagram of the multi-scale framework of community assembly processes and corresponding types of observational data. The figure illustrates the different temporal and spatial scales at which community assembly processes occur, including global, regional, and local scales. Community assembly processes can be divided into speciation, neutral processes (dispersal, ecological drift, etc.), biotic filtering (species interactions), and environmental filtering (selection by abiotic factors). Blue arrows indicate the direction of mechanisms, and green arrows denote the pathways of different community assembly processes.

表1 生物群落层次建模框架中的索引及其范围

Table 1 Indices and their ranges in the HMSC framwork

索引及其范围 Index and range	含义说明 Description
i = 1,..., n	样方(采样单元) Sampling plots (Sampling units)
j = 1,..., n_s	物种 Species
k = 1,..., n_c	环境协变量 Environmental covariates
l = 1,..., n_t	物种性状 Species traits
h = 1,..., n_f	潜在因子 Latent factors
u = 1,..., nᵤ	层级单元 Hierarchical units
q = 1,..., d	空间坐标维度 Spatial coordinate dimensions
r = 1,..., nᵣ	随机效应 Random effects

表2 生物群落层次建模框架核心模型的数据矩阵及其维度

Table 2 Data matrices and their dimensions in the core model of HMSC framework

数据矩阵 Data matrix	数据维度 Data dimension	含义说明 Description
𝐘, 元素 $γ i j$ 𝐘, elements $γ i j$	$n × n s$	群落数据 Community data
𝐗, 元素 $x i k$ 𝐗, elements $x i k$	$n × n c$	环境数据 Environmental data
𝐓, 元素 $t j l$ 𝐓, elements $t j l$	$n s × n t$	物种性状数据 Species trait data
𝐂, 元素 $c j 1 j 2$ 𝐂, elements $c j 1 j 2$	$n s × n s$	系统发育数据 Phylogenetic data
𝚷, 元素 $π i u$ 𝚷, elements $π i u$	$n × n u$	研究设计 Study design
𝐒, 元素 $s u q$ 𝐒, elements $s u q$	$n u × d$	空间坐标 Spatial coordinates

表2 生物群落层次建模框架核心模型的数据矩阵及其维度

Table 2 Data matrices and their dimensions in the core model of HMSC framework

数据矩阵 Data matrix	数据维度 Data dimension	含义说明 Description
𝐘, 元素 $γ i j$ 𝐘, elements $γ i j$	$n × n s$	群落数据 Community data
𝐗, 元素 $x i k$ 𝐗, elements $x i k$	$n × n c$	环境数据 Environmental data
𝐓, 元素 $t j l$ 𝐓, elements $t j l$	$n s × n t$	物种性状数据 Species trait data
𝐂, 元素 $c j 1 j 2$ 𝐂, elements $c j 1 j 2$	$n s × n s$	系统发育数据 Phylogenetic data
𝚷, 元素 $π i u$ 𝚷, elements $π i u$	$n × n u$	研究设计 Study design
𝐒, 元素 $s u q$ 𝐒, elements $s u q$	$n u × d$	空间坐标 Spatial coordinates

表3 生物群落层次建模(HMSC)框架核心模型中的参数及其解释

Table 3 Parameters in the core model of the HMSC framework and their interpretations

类别 Category	参数 Parameter	类型 Type	含义 Description
固定效应 Fixed effect	L^F, 元素 $L i j F$ L^F, elements $L i j F$	$n × n s$ 矩阵 $n × n s$ matrix	固定效应的线性预测量 Linear predictor of fixed effects
固定效应 Fixed effect	B, 元素 $β k j$ B, elements $β k j$	$n c × n s$ 矩阵 $n c × n s$ matrix	物种生态位 Species ecological niches
固定效应 Fixed effect	M, 元素 $μ k j$ M, elements $μ k j$	$n c × n s$ 矩阵 $n c × n s$ matrix	基于性状的物种生态位期望值 Trait‐based expected values of species niches
固定效应 Fixed effect	ρ	标量 Scalar	物种生态位的系统发育信号 Phylogenetic signal in species niches
固定效应 Fixed effect	Γ, 元素 $γ k j$ Γ, elements $γ k j$	$n c × n t$ 矩阵 $n c × n t$ matrix	性状对生态位的影响 Effects of traits on species niches
固定效应 Fixed effect	V, 元素 $V k 1 k 2$ V, elements $V k 1 k 2$	$n c × n c$ 矩阵 $n c × n c$ matrix	物种生态位的残差协方差 Residual covariance of species niches
随机效应 Random effect	Lᴿ, 元素 $L i j R$ Lᴿ, elements $L i j R$	$n × n s$ 矩阵 $n × n s$ matrix	随机效应的线性预测量 Linear predictor of random effects
随机效应 Random effect	H, 元素 $η u h$ H, elements $η u h$	$n u × n f$ 矩阵 $n u × n f$ matrix	样地载荷 Site loadings
随机效应 Random effect	α, 元素 $α h$ α, elements $α h$	长度为 $n f$ 的向量 Vector of length $n f$	样地载荷的空间尺度 Spatial scale of site loadings
随机效应 Random effect	Λ, 元素 $λ h j$ Λ, elements $λ h j$	$n f × n s$ 矩阵 $n f × n s$ matrix	物种载荷 Species loadings
随机效应 Random effect	Ω, 元素 $Ω j 1 j 2$ Ω, elements $Ω j 1 j 2$	$n s × n s$ 矩阵 $n s × n s$ matrix	物种间的关联关系 Interspecific association matrix
随机效应 Random effect	Φ, 元素 $φ h j$ Φ, elements $φ h j$	$n f × n s$ 矩阵 $n f × n s$ matrix	物种载荷的局部收缩项 Local shrinkage parameters of species loadings
随机效应 Random effect	δ, 元素 $δ l$ δ, elements $δ l$	长度为 $n f$ 的向量 vector of length $n f$	物种载荷的全局收缩项 Global shrinkage parameters of species loadings
数据模型 Data model	L, 元素 $L i j$ L, elements $L i j$	$n × n s$ 矩阵 $n × n s$ matrix	线性预测量 Linear predictor
数据模型 Data model	Σ, 元素 $σ j 2$ Σ, elements $σ j 2$	$n s × n s$ 对角矩阵 $n s × n s$ diagonal matrix	残差方差 Residual variances

表3 生物群落层次建模(HMSC)框架核心模型中的参数及其解释

Table 3 Parameters in the core model of the HMSC framework and their interpretations

类别 Category	参数 Parameter	类型 Type	含义 Description
固定效应 Fixed effect	L^F, 元素 $L i j F$ L^F, elements $L i j F$	$n × n s$ 矩阵 $n × n s$ matrix	固定效应的线性预测量 Linear predictor of fixed effects
固定效应 Fixed effect	B, 元素 $β k j$ B, elements $β k j$	$n c × n s$ 矩阵 $n c × n s$ matrix	物种生态位 Species ecological niches
固定效应 Fixed effect	M, 元素 $μ k j$ M, elements $μ k j$	$n c × n s$ 矩阵 $n c × n s$ matrix	基于性状的物种生态位期望值 Trait‐based expected values of species niches
固定效应 Fixed effect	ρ	标量 Scalar	物种生态位的系统发育信号 Phylogenetic signal in species niches
固定效应 Fixed effect	Γ, 元素 $γ k j$ Γ, elements $γ k j$	$n c × n t$ 矩阵 $n c × n t$ matrix	性状对生态位的影响 Effects of traits on species niches
固定效应 Fixed effect	V, 元素 $V k 1 k 2$ V, elements $V k 1 k 2$	$n c × n c$ 矩阵 $n c × n c$ matrix	物种生态位的残差协方差 Residual covariance of species niches
随机效应 Random effect	Lᴿ, 元素 $L i j R$ Lᴿ, elements $L i j R$	$n × n s$ 矩阵 $n × n s$ matrix	随机效应的线性预测量 Linear predictor of random effects
随机效应 Random effect	H, 元素 $η u h$ H, elements $η u h$	$n u × n f$ 矩阵 $n u × n f$ matrix	样地载荷 Site loadings
随机效应 Random effect	α, 元素 $α h$ α, elements $α h$	长度为 $n f$ 的向量 Vector of length $n f$	样地载荷的空间尺度 Spatial scale of site loadings
随机效应 Random effect	Λ, 元素 $λ h j$ Λ, elements $λ h j$	$n f × n s$ 矩阵 $n f × n s$ matrix	物种载荷 Species loadings
随机效应 Random effect	Ω, 元素 $Ω j 1 j 2$ Ω, elements $Ω j 1 j 2$	$n s × n s$ 矩阵 $n s × n s$ matrix	物种间的关联关系 Interspecific association matrix
随机效应 Random effect	Φ, 元素 $φ h j$ Φ, elements $φ h j$	$n f × n s$ 矩阵 $n f × n s$ matrix	物种载荷的局部收缩项 Local shrinkage parameters of species loadings
随机效应 Random effect	δ, 元素 $δ l$ δ, elements $δ l$	长度为 $n f$ 的向量 vector of length $n f$	物种载荷的全局收缩项 Global shrinkage parameters of species loadings
数据模型 Data model	L, 元素 $L i j$ L, elements $L i j$	$n × n s$ 矩阵 $n × n s$ matrix	线性预测量 Linear predictor
数据模型 Data model	Σ, 元素 $σ j 2$ Σ, elements $σ j 2$	$n s × n s$ 对角矩阵 $n s × n s$ diagonal matrix	残差方差 Residual variances

图2 生物群落层次建模(HMSC)框架的完整分析流程与结果示意。图中依次展示了HMSC建模与推断的5个关键步骤: (1)模型构建与数据匹配, 将物种分布数据与环境因子、功能性状、系统发育和空间随机效应整合到统一的层级贝叶斯框架中; (2) MCMC收敛性检查, 通过迹线图、后验分布、有效样本量和潜在尺度缩减因子评估模型收敛与参数混合情况; (3)模型拟合度评估与比较, 利用均方根误差(RMSE)、曲线下面积(AUC)和R2等指标量化模型预测性能; (4)参数估计与生态解释, 包括环境响应参数、性状与系统发育效应、残差相关结构以及方差分解结果; (5)模型预测与应用, 展示物种对关键环境梯度的响应曲线及不确定性区间, 用于群落分布预测与情景分析。

Fig. 2 Schematic overview of the complete analytical workflow and outputs of hierarchical modelling of species communities (HMSC) framework. The figure illustrates five key steps of HMSC-based modelling and inference: (1) model construction and data integration, in which species distribution data are jointly modelled with environmental variables, functional traits, phylogenetic relationships, and spatial random effects within a unified hierarchical Bayesian framework; (2) MCMC convergence diagnostics, where trace plots, posterior distributions, effective sample size (ESS), and potential scale reduction factors (PSRF) are used to assess convergence and fitness parameter mixing; (3) model fit evaluation and comparison, in which predictive performance is quantified using root mean square error (RMSE), area under curve (AUC), and R²; (4) parameter estimation and ecological interpretation, including environmental response parameters, trait and phylogenetic effects, residual correlation structures, and variance partitioning; and (5) model prediction and application, illustrating species responses to key environmental gradients with associated uncertainty intervals for community-level prediction and scenario-based analyses.

图3 2011-2024年JSDMs发表文章数量变化

Fig. 3 Changes in the number of JSDMs publications from 2011 to 2024

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联合物种分布模型与生物群落层次建模框架: 生态学理论、方法及应用

Theoretical foundations, methodological advances, and applications of joint species distribution models with a focus on the HMSC framework in ecology

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