Naseem A, Aplop F and Aslam F, 2026. Multimodal deep learning for Chelonia mydas conservation genomics: Architectures, fusion approaches, and research directions. Agrobiological Records 25: 146-159. https://doi.org/10.47278/journal.abr/2026.054

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Modern deep learning approaches have enhanced predictive capability in conservation genomics beyond what is achievable with conventional statistical models. For Chelonia mydas (green sea turtle), DL is promising for genomic-enabled prediction of conservation-relevant traits (e.g., disease susceptibility, growth, hatchling success), but conventional unimodal approaches often underuse the rich ecological and health context surrounding genomic data. Multimodal deep learning (MMDL) addresses this limitation by integrating multiple information sources, including genomic sequences and variants, environmental drivers (sea-surface temperature, salinity, storm exposure), and phenotypic or health indicators, to increase predictive capacity. In this review, we introduce the core concepts of MMDL, outline widely used neural architectures (Multilayer Perceptron [MLP], Convolutional Neural Network [CNN], temporal RNNs/Transformers, autoencoders), and describe strategies for fusing heterogeneous modalities (early, intermediate, late fusion). We also summarize practical computational resources for implementing MMDL in turtle genomics. Finally, we surveyed applications and cross-domain evidence relevant to C. mydas, providing a meta-level view of when and why MMDL performs well, as well as the capacity of these techniques to handle multifaceted conservation challenges. In general, multimodal deep learning achieves superior prediction accuracy compared with single-modality deep learning and conventional machine learning approaches, albeit with increased computational demands, due to its ability to capture cross-modal relationships. In C. mydas, effective implementation relies on aligning model architectures and data fusion schemes with both the nature of the datasets and the targeted biological questions. Given its predictive edge over traditional approaches, MMDL is a valuable addition to the toolkit for genomic-driven conservation and long-term population resilience in C. mydas.