Biological foundation modelsBiological foundation models (FMs) represent a paradigm shift in the life sciences. They enable powerful predictive capabilities on complex biological systems via transfer learningTransfer learning, leveraging pre-training on vast unlabeled biological datasets. Optimizing these models for diverse downstream applications requires a deep understanding of the factors influencing their performance. This chapter addresses the critical question: How does dataset biodiversity and sequence input length shape the transfer learningTransfer learning potential of biological foundationBiological foundation models models? Instead of presenting novel experimental data, this work synthesizes existing literature from bioinformatics, machine learning, and genomics to evaluate the distinct and combined impacts of these two crucial parameters. We review evidence suggesting that broader dataset biodiversity (encompassing more species, environments, and functional classes) generally enhances model generalization and robustness, allowing FMs to tackle unfamiliar biological contexts more effectively. However, we also discuss the potential for diminishing returns and the need for careful dataset curation and design. Simultaneously, we explore the significance of sequence input length, highlighting how the ability to process longer contexts (from kilobases to megabases) is vital for capturing long-range biological interactions such as distal gene regulationRegulation or protein structural dependencies which encode functional information, perhaps necessitating advanced model architectures. We further discuss the fundamental tradeoff between FM performance gains due to increased biodiversity, which mostly exists in short sequence lengths, and gains due to longer sequence contexts. By providing a conceptual framework and consolidating insights from prior studies, this chapter aims to clarify these relationships and their profound implications for fields ranging from synthetic biology design to biosecurityBiosecurity assessment. We conclude by emphasizing the need for strategic investment and collaborative standardization in data curation and model development to harness the full potential of biological FMs responsibly and effectively, offering insights relevant to researchers, policymakers, and defense stakeholders navigating this rapidly evolving landscape.