Early detection of nuisance species is crucial for the conservation and management of threatened ecosystems, reducing the risk of widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, oftentimes with minimal cost and effort. However, eDNA analyses have inherent errors that can complicate the integration of molecular survey methods into existing management frameworks. Therefore, it is crucial for eDNA studies to consider imperfect detections and estimate error rates accordingly. Detecting nuisance species in low abundance with minimal uncertainty is vital to increase the chance of containment and eradication. We developed a novel eDNA assay to detect a nuisance marine macroalga across its colonization front using surface seawater samples from Papahānaumokuākea Marine National Monument (PMNM), one of the world’s largest marine reserves. Chondria tumulosa, a cryptogenic red alga with invasive characteristics, has been documented forming dense mats that overgrow coral reefs and smother native flora and fauna in PMNM. We verified the eDNA assay using site-occupancy detection modeling from quantification polymerase chain reaction (qPCR) data, calibrated with visual estimates of benthic cover of C. tumulosa that ranged from < 1% to 95%. Results were subsequently validated with high-throughput sequencing of amplified eDNA and negative control samples. Overall, the probability of detecting C. tumulosa at occupied sites was at least 92% when multiple qPCR replicates were positive. Modeled false-positive inferences were 3% or less and false-negative errors were 11% or less. The developed assay is suitable for routine monitoring at shallow sites (less than 10 m), even when C. tumulosa abundance was less than 1%. Successful implementation of eDNA tools in conservation decision-making relies on balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay’s sensitivity to C. tumulosa, and we outline the necessary steps to infer ecological presence-absence from molecular detection data. By providing a reliable, cost-effective tool for detecting low-abundance species, eDNA analyses have the potential to enhance the surveillance of nuisance species and inform timely management interventions.