联合物种分布模型与生物群落层次框架在群落生态学中的理论、方法及应用

doi:10.17520/biods.2025364

生物多样性

联合物种分布模型与生物群落层次框架在群落生态学中的理论、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；5. School of Geography and the Environment, University of Oxford, Oxford OX1 3QR, United Kingdom; 6. 厦门大学环境与生态学院, 厦门361102, 中国；7. 江南大学生物工程学院，江苏无锡 221151, 中国; 7. 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 修回日期:2025-12-08
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); 国家自然科学基金项目(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 and the HMSC Framework in community ecology

Jiqi Gu¹, Jiangshan Lai^2,3, Ying Wang⁴, Haoran Wu⁵, Xue Zhang⁶, Xiaotong Song⁷, Xiaoming Shao^8*, Anru Lou^1*

1. College of Life Sciences, Key Laboratory for Biodiversity Science and Ecological Engineering of the Ministry of Education, Beijing Normal University, Beijing 100875

2. College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037

3. Research Center of Quantitative Ecology, Nanjing Forestry University, Nanjing 210037 School of Ecology and Environment, 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

6. College of Environment and Ecology, Xiamen University, Xiamen 361102, China

7. The School of Biotechnology, Jiangnan University, Wuxi 221151, China

8. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

Received:2025-09-09 Revised:2025-12-08
Contact: Xiaoming Shao, Anru Lou
Supported by:
Supported by Third Xinjiang Scientific Expedition Program(2022xjkk1201); National Natural Science Foundation of China(41771054); State-Sponsored Postgraduate Program of the 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框架（Hierarchical Modelling of Species Communities, HMSC）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、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，本文通过解析潜在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JSDM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；随着显卡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, 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 JSDM 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. 联合物种分布模型与生物群落层次框架在群落生态学中的理论、方法及应用. 生物多样性, DOI: 10.17520/biods.2025364.

Jiqi Gu, Jiangshan Lai, Ying Wang, Haoran Wu, Xue Zhang, Xiaotong Song, Xiaoming Shao, Anru Lou. Theoretical foundations, methodological advances, and applications of Joint Species Distribution Models and the HMSC Framework in community ecology. Biodiversity Science, DOI: 10.17520/biods.2025364.

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

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