{"paper_id":"0cfb5aa7-9de8-4987-b086-19e46b16e009","body_text":"This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.\nYou must log in to post a comment.\nThere are no comments or no comments have been made public for this article.\nThis is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.\nAdd a Comment\nYou must log in to post a comment.\nComments\nThere are no comments or no comments have been made public for this article.\nComprehensive monitoring of biodiversity to direct conservation action is foundational to addressing the ongoing biodiversity crisis. As integrative monitoring programs increasingly come online in response to multilateral biodiversity agreements, establishing best practices for optimal design is critical. Selecting the appropriate algorithm for identifying sample sites is both necessary for robust inference from biodiversity data and largely neglected from broader monitoring network discussions. Here, we benchmark the performance of four common selection algorithms, outline the characteristics of the suite of algorithms suitable for ecological monitoring design, and offer recommendations for their best use. While all algorithms outperformed simple random samples, performance differences were negligible between algorithms. We recommend instead that practitioners choose algorithms based on feature availability, which varies greatly between algorithms.\nhttps://doi.org/10.32942/X2XC96\nBiodiversity, Bioinformatics, Ecology and Evolutionary Biology\nspatially balanced sampling, monitoring network, Biodiversity Monitoring, generalized random tessellation stratified\nPublished: 2025-02-11 23:25\nLast Updated: 2025-02-12 04:25\nCC BY Attribution 4.0 International\nData and Code Availability Statement:\nCode is archived at 10.5281/zenodo.14853263 and raw and interim data products are archived at 10.5281/zenodo.14853272.\nLanguage:\nEnglish","source_license":"CC-BY-4.0","license_restricted":false}