Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga.

Early detection of nuisance species is crucial for managing threatened ecosystems and preventing widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, often at minimal cost and effort. However, eDNA analyses are prone to errors that can complicate their use in management frameworks. To address this, eDNA studies must consider imperfect detections and estimate error rates. Detecting nuisance species at low abundances with minimal uncertainty is vital for successful 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 is a cryptogenic red alga with invasive traits, 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 quantitative 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. False-positive rates were 3% or less and false-negative errors were 11% or less. The assay proved effective for routine monitoring at shallow sites (less than 10 m), even when C. tumulosa abundance was below 1%. Successful implementation of eDNA tools in conservation decision-making requires balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay's high sensitivity to C. tumulosa, and we outline steps to infer ecological presence-absence from molecular data. This reliable, cost-effective tool enhances the detection of low-abundance species, and supports timely management interventions.