Sci Total Environ 2022 Dec 10;851Pt 1:158143. doi: 10.1016/j.scitotenv.2022.158143.

Developing an effective marine eDNA monitoring: eDNA detection at pre-outbreak densities of corallivorous seastar (Acanthaster cf. solaris).

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Outbreaks of the corallivorous Crown-of-Thorns Seastar (CoTS) Acanthaster cf. solaris contribute significantly to coral reef loss. Control of outbreaks is hampered because standard monitoring techniques do not detect outbreaks at early (low density) stages, thus preventing early intervention. We previously demonstrated that eDNA monitoring can detect CoTS at intermediate densities. Here, we test whether detection probability can be improved by (i) targeted site selection or collection at specific times and (ii) moving from an average eDNA copy number approach (based on the limit of quantification) to a presence/absence approach (based on the limit of detection). Using a dataset collected over three years and multiple reef sites, we demonstrated that adding water residence age, sea surface level and temperature into generalized linear models explained low amounts of variance of eDNA copy numbers. Site specific CoTS density, by contrast, was a significant predictor for eDNA copy numbers. Bayesian multi-scale occupancy modelling of the presence/absence data demonstrated that the probability of sample capture (θ) on most reefs with intermediate or high CoTS densities was >0.8. Thus, confirming CoTS presence on these reefs would only require 2-3 samples. Sample capture decreased with decreasing CoTS density. Collecting ten filters was sufficient to reliably (based on the lower 95 % Credibility Interval) detect CoTS below nominal outbreak levels (3 Ind. ha-1). Copy number-based estimates may be more relevant to quantify CoTS at higher densities. Although water residence age did contribute little to our models, sites with higher residence times may serve as sentinel sites accumulating eDNA. The approach based on presence or absence of eDNA facilitates eDNA monitoring to detect CoTS densities below outbreak thresholds and we continue to further develop this method for quantification.

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