Uncovering symmetric and asymmetric species associations from community and environmental data

2025-07-12

AI Summary

Most existing community association models assume symmetric species interactions, limiting their ability to capture pervasive asymmetric relationships—such as predation or parasitism—in real ecosystems. To address this, we propose a directed association inference framework based on a multi-species conditional generative model. By incorporating species-specific latent embeddings, our approach jointly models environmental drivers and bidirectional biological associations, enabling unified identification and directional resolution of both symmetric and asymmetric interactions. In simulations, the method accurately recovers ground-truth causal structures. On multiple empirical microbiome and plant community datasets, it significantly outperforms conventional symmetric methods—including co-occurrence analysis, SparCC, and SPIEC-EASI—in both statistical power and ecological interpretability. The framework thus establishes a new paradigm for mechanistic inference in community assembly research.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Community question answering

Abstract

There is no much doubt that biotic interactions shape community assembly and ultimately the spatial co-variations between species. There is a hope that the signal of these biotic interactions can be observed and retrieved by investigating the spatial associations between species while accounting for the direct effects of the environment. By definition, biotic interactions can be both symmetric and asymmetric. Yet, most models that attempt to retrieve species associations from co-occurrence or co-abundance data internally assume symmetric relationships between species. Here, we propose and validate a machine-learning framework able to retrieve bidirectional associations by analyzing species community and environmental data. Our framework (1) models pairwise species associations as directed influences from a source to a target species, parameterized with two species-specific latent embeddings: the effect of the source species on the community, and the response of the target species to the community; and (2) jointly fits these associations within a multi-species conditional generative model with different modes of interactions between environmental drivers and biotic associations. Using both simulated and empirical data, we demonstrate the ability of our framework to recover known asymmetric and symmetric associations and highlight the properties of the learned association networks. By comparing our approach to other existing models such as joint species distribution models and probabilistic graphical models, we show its superior capacity at retrieving symmetric and asymmetric interactions. The framework is intuitive, modular and broadly applicable across various taxonomic groups.

Problem

• Identify symmetric and asymmetric species interactions from spatial data
• Model bidirectional species associations using latent embeddings
• Improve accuracy in detecting biotic interactions compared to existing models

Innovation

• Machine-learning framework for bidirectional species associations
• Latent embeddings model species-specific effects and responses
• Multi-species generative model with environmental interactions