not-yet-known not-yet-known not-yet-known unknown 1) DNA metabarcoding has been successful for the rapid identification of species in ecological assemblages, including identifying interspecific interactions among species. However, advances in metabarcoding plants have been hampered due to a lack of universal gene regions that work across all taxa, limiting the applications of metagenomics in ecology more broadly. 2) To circumvent these limitations, we propose a spatio-temporal approach that combines multi-gene barcoding with existing plant occurrence databases, species distribution models, and phenological analyses to generate a shortened list of candidate species to increase metabarcoding accuracy. To validate the ecological accuracy of our methodological framework, we compared the results of the DNA metabarcoding from pollen loads of wild bumble bees to long-term field observations of bee-plant interactions, and visual pollen identification. 3) We show that DNA metabarcoding of the plant species included in bumble bee pollen loads was most accurate when combined with a candidate taxa list of plant species flowering in the area when the bumble bees were foraging, which improved the accuracy and taxonomic precision of 77.5% of samples. 4) With the recent proliferation of species occurrence and phenology data in tandem with advances in computing and software development, we believe that spatio-temporal filtering provides a simple approach for interpreting metagenomic studies globally. Additionally, we demonstrate that the Angiosperms 353 probes offer significant promise for metagenomics projects globally, including metabarcoding to reveal species interactions within complex communities. Further, our approach demonstrates that integrating DNA metabarcoding is most accurate and powerful when combined with local ecological data.