Filling Monitoring Gaps for Data-deficient Species Using Annual Occupancy Predictions from Co-occurrence Models

This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Fragmented surveys and limited monitoring have excluded most invertebrates from conservation policy. We present a fill-in framework that uses species distribution models (SDMs) to reconstruct missing annual trends—not to extrapolate trends, but to fill them in. Instead of filtering data-sparse regions or years or relying on static environmental variables, we used co-occurrence patterns (COP) as variables, to capture year-to-year assemblage shifts. COP variables enabled annual prediction at all recorded sites from multisource, presence-only, sparse data. When applied to four rare native ladybugs across North America (2007–2021), COP models exceeded reliability benchmarks (Accuracy ≥ 0.70, AUC ≥ 0.70, Kappa ≥ 0.40, Brier ≤ 0.25) across standard 7:3 splits, cross-source and cross-period validations. Annual predictions were robust to temporal biases from variation in data volume and source composition. Multiple regression indicated negligible effects of those biases on reconstructed trends. Predicted decadal declines (9–31%) closely aligned with independent regional long-term monitoring, operationalizing IUCN Red List classification (from least concern to vulnerable) in the absence of standardized monitoring. By converting fragmented observations—primarily from citizen science—into reliable annual trend estimates, the fill-in approach extends extinction-risk assessment to data-deficient taxa long excluded from conservation frameworks. https://doi.org/10.32942/X2KS8V Biodiversity, Ecology and Evolutionary Biology, Entomology, Life Sciences Data Deficient, extinction risk, citizen science, Multi-source, Structural Bias, Temporal Bias, Adventive Species, Native Ladybug Decline Published: 2025-05-21 15:18 Last Updated: 2025-11-11 09:04 CC BY Attribution 4.0 International Conflict of interest statement: The authors declare no conflict of interest. Data and Code Availability Statement: A code package designed for ease of public use will be made available with official publication. Current version: https://figshare.com/s/36131cf2516dc300e80a?file=54689660 Language: English