Dear colleagues, My co-authors and I are pleased to share with you the publication of our package, 'sampley', and the associated paper, 'sampley: A Python package for sampling visual survey data'. The package's functionality is applicable to, and indeed was developed for, data collected during aerial and/or boat surveys for marine mammals. As such, it can assist marine mammal researchers by facilitating the processing of their survey data into samples that can be used in ecological modelling. The package is freely available at: https://pypi.org/project/sampley/ The full paper is freely available at: https://doi.org/10.1111/2041-210x.70320 Syme, J., Pendleton, D. E., Meyer-Gutbrod, E. L., Tupper, B., & Record, N. R. (2026). sampley: A Python package for sampling visual survey data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.70320 Abstract: Visual surveys are a common method of obtaining data for ecological studies, including studies that develop models (e.g. species distribution models). In the case of transect or non-transect line surveys, data must often be partitioned into spatiotemporal units (i.e. samples) before being modelled. This processing typically follows one of three approaches -the grid, segment and point approaches- each with its own variations. Currently, processing of visual survey data is done in custom scripts that can be time-consuming and technically demanding to develop, which hinders the development of ecological models. This issue is exacerbated if multiple approaches and/or variations are to be applied. Here, we present sampley, a user-friendly Python package for processing visual survey data into samples. It operates on various common formats and filetypes, ensuring its applicability to diverse datasets and compatibility with other software. It can process data by multiple variations of the grid, segment and point approaches. As this processing follows a straightforward sequence of stages and involves a limited number of functions, it is easy to learn and use. The combination of these traits will increase efficiency and reproducibility of survey data processing and allow researchers to readily trial different methods. In this paper, we provide a description of the package - the types of data that can be processed, the approaches and variations available and the stages involved in processing. An example application, based on a mock dataset consisting of survey tracks, sightings and in situ environmental data, is also provided with descriptions of each stage. Thank you, Jonathan Jonathan Syme PhD, BSc (Hons) jonathan_syme@outlook.com