Abstract
Comprehensive 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.
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Comprehensive 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.
https://doi.org/10.32942/X2XC96
Biodiversity, Bioinformatics, Ecology and Evolutionary Biology
spatially balanced sampling, monitoring network, Biodiversity Monitoring, generalized random tessellation stratified
Published: 2025-02-11 23:25
Last Updated: 2025-02-12 04:25
CC BY Attribution 4.0 International
Data and Code Availability Statement:
Code is archived at 10.5281/zenodo.14853263 and raw and interim data products are archived at 10.5281/zenodo.14853272.
Language:
English
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