Mutualistic interactions are increasingly recognized as playing important roles in community assembly. We hypothesized that mutualisms can influence the accumulation of evolutionary history within communities through indirect interactions, which we investigated by quantifying the impact of mutualism gains and losses on phylogenetic structure in the Fabaceae family. Analyzing global distribution data, we find that legumes lacking mutualistic interactions exhibit reduced phylogenetic clustering, resulting in higher phylogenetic diversity in regions richer in non-mutualistic legumes. Moreover, the probability of a plant species being introduced to a new range is negatively related to phylogenetic distance to its nearest native relative, but this effect is weaker for species without mutualistic interactions. These findings highlight the significant role of mutualism in restricting the local distribution of evolutionary history at a global scale. Our study advances community assembly theory and underscores the importance of considering mutualism in the conservation and restoration of phylogenetic diversity.

Data on the three dimensional shape of organismal morphology is becoming increasingly availability, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order to meet the potential of this revolution we need new data analysis tools to deal with the complexity and heterogeneity of large-scale phenotypic data such as 3D shapes. In this study we explore the potential of generative Artificial Intelligence to help organise and extract meaning from complex 3D data. Specifically, we train a deep representational learning method known as DeepSDF on a dataset of 3D scans of the bills of 2,020 bird species. The model is designed to learn a continuous vector representation of 3D shapes, along with a 'decoder' function, that allows the transformation from this vector space to the original 3d morphological space. We find that approach successfully learns coherent representations: particular directions in latent space are associated with discernible morphological meaning (such as elongation, flattening, etc.). More importantly, learned latent vectors have ecological meaning as shown by their ability to predict the trophic niche of the bird each bill belongs to with a high degree of accuracy. Unlike existing 3D morphometric techniques, this method has very little requirements for human supervised tasks such as landmark placement, increasing it accessibility to labs with fewer labour resources. It has fewer strong assumptions than alternative dimension reduction techniques such as PCA. The computational requirements for training the model, while substantial, is still within the reasonable reach of most researchers, with a ~2000 shape model taking just over 2 days to train on only a single current generation consumer-level GPU. Once trained, 3D morphology predictions can be made from latent vectors very computationally cheaply.