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.