Understanding the assembly of ecological communities is a core goal in ecology. Despite advancements in statistical models, disentangling the influences of biotic and abiotic constraints on communities remains challenging due to data complexity. We introduce the MrIML 2.0 R package (multi-response interpretable machine learning) which employs machine learning to approximate graphical network models (GGNs), revealing complex relationships in community structure, including asymmetric co-occurrence associations where one species influences another but not vice versa. Using the Tidymodels R architecture, we empower users to build models across algorithms and interpret them using interpretable machine learning (IML) approaches. Our method captures known interactions in simulated data and improves upon commonly used models by quantifying marginal relationships that capture non-linear biotic relationships and complex predictor interactions. We validate our approach on a range of datasets, highlighting the method’s efficacy in providing high-resolution insights into community dynamics and generating new hypotheses for ecological research.

The study of microbiomes across organisms and environments has become a prominent focus in molecular ecology. This perspective article explores methodological advancements, common challenges and future directions in the field. Key research areas include understanding the drivers of microbiome community assembly, linking microbiome composition to host genetics, exploring microbial functions, transience, and spatial partitioning, and disentangling non-bacterial components of the microbiome. Methodological advancements, such as quantifying absolute abundances, sequencing complete genomes, and utilizing novel statistical approaches, are also useful tools for understanding complex microbial diversity patterns. Our aims are to encourage robust practices in microbiome studies and inspire researchers to explore the next frontier of this rapidly changing field.