For the Western Indian Ocean region, there is a significant knowledge gap regarding marine turtle nesting on the continental coast of East Africa. Here we present results from a long-term (2000–2020) community-based monitoring programme in and around Watamu Marine National Park, Kenya, covering 30 km of coastline (c. 6% of the national total). Conservation actions effectively protected nesting turtles and resulted in a near-total cessation of illegal egg harvesting in Watamu Marine National Park. Collected data indicate this is an important marine turtle nesting index site in Kenya and the wider region. Green turtle Chelonia mydas nests were most common (95%), followed by olive ridley turtles Lepidochelys olivacea (4%), with occasional nests of hawksbill Eretmochelys imbricata and leatherback turtles Dermochelys coriacea. Clutches per season increased significantly over the 20-year monitoring period for green turtles (50%) and showed a positive trend for olive ridley turtles. Watamu remains an area at risk from human pressures such as coastal development. Clutch distribution along the Watamu Marine National Park beach has shifted over time, probably because of coastal development and disturbance. Illegal take of adults and eggs continues in areas north and south of the Watamu Marine National Park, possibly slowing rates of recovery. Clutches deemed at risk were moved to a safe location within the National Park, and hatching success was high. Continued conservation efforts, including wider engagement with stakeholders to reduce human pressures, are needed to ensure the perpetuation of this nesting site.

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

We agree with Lake and colleagues on their list of “key ingredients” for building human-like intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand engineering. We believe an approach centered on autonomous learning has the greatest chance of success as we scale toward real-world complexity, tackling domains for which ready-made formal models are not available. Here, we survey several important examples of the progress that has been made toward building autonomous agents with human-like abilities, and highlight some outstanding challenges.